Supporting Sustainable Water Management in Ontario … RTK Cross-Section Data ... Figure 22...

Supporting Sustainable Water Management in Ontario

Through Innovation

Prepared By:

Ganaraska Region Conservation Authority

March 2014

Supporting Sustainable Water Management in Ontario Through Innovation

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Contents

Acknowledgements............................................................................................................................7

Executive Summary ............................................................................................................................7

1.0 Introduction ...........................................................................................................................8

1.1 New Mapping Technologies .................................................................................................8

1.2 Elevation Data .....................................................................................................................9

1.3 Elevation Data and Hydrology ..............................................................................................9

1.4 Elevation Data and Engineering ...........................................................................................9

1.5 Technological Trends ......................................................................................................... 10

1.6 Innovative Approaches ...................................................................................................... 10

2.0 Flood Line Mapping ............................................................................................................... 11

2.1 Introduction ...................................................................................................................... 11

2.2 Ontario Floodplain Management Policies ........................................................................... 12

2.2.1 The Planning Act ........................................................................................................ 12

2.2.2 The Conservation Authorities Act ............................................................................... 12

2.2.3 Flood Hazard Criteria Zones ........................................................................................ 12

2.3 Flood Line Mapping in Ontario ........................................................................................... 15

2.3.1 Flood Damage Reduction Program ............................................................................. 15

2.3.2 Moving Forward ........................................................................................................ 15

2.4 Flood Line Mapping Data Requirements ............................................................................. 16

2.4.1 Overland Topography ................................................................................................ 17

2.4.2 Hydrology .................................................................................................................. 17

2.4.3 Hydraulics .................................................................................................................. 18

2.4.4 Orthophotography ..................................................................................................... 18

2.5 Summary .......................................................................................................................... 19

3.0 Elevation Data Acquisition .................................................................................................... 20

3.1 RTK GNSS Survey ............................................................................................................... 20

3.1.1 Precise Point Positioning (PPP) ................................................................................... 21

3.2 LIDAR ................................................................................................................................ 22


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3.3 Pixel Autocorrelation......................................................................................................... 24

3.4 3D Digitizing ...................................................................................................................... 26

3.5 SONAR .............................................................................................................................. 27

3.6 Summary .......................................................................................................................... 28

4.0 Modeling Procedures ............................................................................................................ 29

4.1 The Elevation Model ......................................................................................................... 29

4.1.1 Traditional Approach ................................................................................................. 30

4.1.2 The Fabric Approach .................................................................................................. 31

4.2 The Flood Event Model ...................................................................................................... 32

4.2.1 Hydrology .................................................................................................................. 32

4.2.2 Hydraulics .................................................................................................................. 33

4.3 3D Data Fusion .................................................................................................................. 33

4.4 Summary .......................................................................................................................... 34

5.0 Case Study: Ops No. 1 Drain/Jennings Creek .......................................................................... 35

5.1 Introduction ...................................................................................................................... 35

5.1.1 Objective ................................................................................................................... 35

5.1.2 Watercourse Context and Description ........................................................................ 35

5.1.3 Background Information ............................................................................................ 37

5.1.4 Modeling Approach ................................................................................................... 38

5.2 Rainfall ............................................................................................................................. 38

5.2.1 Rainfall Data .............................................................................................................. 38

5.2.2 Design Storms ............................................................................................................ 40

5.2.3 Regional Storms ......................................................................................................... 41

5.3 Parameters ....................................................................................................................... 41

5.3.1 Overview ................................................................................................................... 41

5.3.2 DEM .......................................................................................................................... 41

5.3.3 Cross-sections ............................................................................................................ 42

5.3.4 Culvert and Road Crossings ........................................................................................ 44

5.3.5 Expansion/Contraction Coefficients ............................................................................ 44

5.3.6 Manning n values ....................................................................................................... 44

5.3.7 Ineffective Flow Elevations ......................................................................................... 45

5.3.8 Building Obstructions ................................................................................................. 45


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5.3.9 Boundary Conditions .................................................................................................. 45

5.3.10 Flows ......................................................................................................................... 45

5.4 Hydraulic Model ................................................................................................................ 46

5.4.1 Schematic .................................................................................................................. 46

5.4.2 Sensitivity Analysis..................................................................................................... 47

5.5 Model Results ................................................................................................................... 48

5.5.1 Comparing LIDAR and PAC Model Data Input .............................................................. 48

5.6 Conclusions and Recommendations ................................................................................... 53

6.0 Case Study: Fused SWM Pond Fused DEM.............................................................................. 54

6.1 Introduction ...................................................................................................................... 54

6.2 Background ....................................................................................................................... 54

6.3 Data .................................................................................................................................. 56

6.3.1 LIDAR ........................................................................................................................ 56

6.3.2 RTK GPS Survey .......................................................................................................... 56

6.3.3 Software List .............................................................................................................. 56

6.4 Procedures ........................................................................................................................ 57

6.4.1 Data Preparation ....................................................................................................... 57

6.4.2 3D Data Fusion........................................................................................................... 57

6.4.3 Engineering Data Requirements ................................................................................. 58

6.5 Engineering Data Uses ....................................................................................................... 59

6.6 Summary .......................................................................................................................... 60

7.0 Assessing and Reporting Accuracy ......................................................................................... 61

7.1 Introduction ...................................................................................................................... 61

7.2 Data Accuracy and Flood Line Mapping .............................................................................. 61

7.2.1 Hydrologic and Hydraulic Analysis .............................................................................. 62

7.2.2 Elevation Modeling .................................................................................................... 62

7.3 Data Accuracy ................................................................................................................... 62

7.4 Data Acquisition Quality Assurance ................................................................................... 63

7.4.1 LIDAR ........................................................................................................................ 63

7.4.2 RTK GPS ..................................................................................................................... 63

7.4.3 3D Digitizing .............................................................................................................. 64

7.4.4 Summary ................................................................................................................... 64


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7.5 RTK GPS ............................................................................................................................ 64

7.5.1 Project Design ............................................................................................................ 64

7.5.1.1 Precise Point Positioning ........................................................................................ 64

7.5.1.2 RTK GPS Accuracy Assessment ................................................................................ 65

7.5.1.3 RTK Augmentation Data ......................................................................................... 65

7.5.1.4 RTK Cross-Section Data ........................................................................................... 66

7.6 LIDAR ................................................................................................................................ 66

7.6.1 Project Design ............................................................................................................ 66

7.6.1.1 Point Classification ................................................................................................. 67

7.6.2 Results ....................................................................................................................... 67

7.6.2.1 LIDAR Quality Control – Vegetation ........................................................................ 67

7.6.2.2 LIDAR Quality Control – Elevation ........................................................................... 68

7.7 Orthoimagery.................................................................................................................... 69

7.7.1 Project Design ............................................................................................................ 69

7.7.1.1 Flight Plan .............................................................................................................. 69

7.7.1.2 Data Deliverables ................................................................................................... 70

7.7.2 Results ....................................................................................................................... 70

7.7.2.1 Qualitative Assessment .......................................................................................... 70

7.7.2.2 Quantitative Assessment ........................................................................................ 71

7.8 Final DEM ......................................................................................................................... 71

7.8.1 Project Design ............................................................................................................ 71

7.8.2 Results ....................................................................................................................... 72

7.9 Summary .......................................................................................................................... 72

8.0 Cost Comparison ................................................................................................................... 73

8.1 Traditional Approaches ..................................................................................................... 73

8.1.1 Costs Associated with traditional approaches ............................................................. 74

8.2 New Approaches ............................................................................................................... 74

8.2.1 Costs Associated with new approaches ....................................................................... 74

8.3 Summary .......................................................................................................................... 75

9.0 Recommendations ................................................................................................................ 76

9.1 The Fabric Approach .......................................................................................................... 76

9.2 Recommendations for Next Steps ...................................................................................... 77


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9.2.1 Data Management ..................................................................................................... 77

9.2.2 Data Acquisition ........................................................................................................ 77

9.2.3 Flood Line Mapping .................................................................................................... 77

9.3 Summary .......................................................................................................................... 78

Appendices

Appendix A GIS Floodplain Mapping – Processing Engineering Data Models into a Floodplain GIS Analysis

Appendix B Peer Review of Remote Sensing and GIS Data Support for Flood Line Mapping – Byersville Creek

Appendix C Peer Review of Remote Sensing and GIS Data Support for Flood Line Mapping – Ops No. 1 Drain/Jennings Creek

Appendix D “Small Town Upgrades Storm Water Management” – ArcNews Winter 2012-13

Appendix E “Mitigating Flood Risk in the Town of Cobourg” – ArcNorth News Fall 2012

List of Tables

Table 1 IDF parameters in the City of Kawartha Lakes' engineering standards ......................................... 39

Table 2 IDF parameters re-calculated by GRCA .......................................................................................... 39

Table 3 Rainfall depths from Lindsay AES station (24 years of data) ......................................................... 40

Table 4 Flood lines results for Timmins, 100 yr Chicago and Region storm events.................................... 50

Table 5 Floodplain surface area comparisons for respective flood event models and DEM data ............. 51

List of Figures

Figure 1 - Printed flood line map (2003) ..................................................................................................... 11

Figure 2 Flood hazard criteria zones in Ontario .......................................................................................... 13

Figure 3 One zone floodplain concept ........................................................................................................ 14

Figure 4 Two zone floodway - floodfringe concept .................................................................................... 14

Figure 5 FDRP map example (1986) ............................................................................................................ 15


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Figure 6 Digital floodplain disseminated using WebGIS service (2013)...................................................... 16

Figure 7 Oblique view of 3D floodplain analysis (2013).............................................................................. 17

Figure 8 Hydraulic cross-section example .................................................................................................. 18

Figure 9 Orthophoto with floodplain overlain ............................................................................................ 19

Figure 10 RTK GPS rover unit with data logger ........................................................................................... 20

Figure 11 Quasar - the absolute reference for modern GNSS .................................................................... 22

Figure 12 LIDAR mission .............................................................................................................................. 23

Figure 13 LIDAR bathymetric mission ......................................................................................................... 24

Figure 14 Colourized pixel autocorrelated point cloud .............................................................................. 25

Figure 15 Pixel autocorrelated point cloud colourized by feature classification ........................................ 26

Figure 16 3D digitizing workstation ............................................................................................................ 27

Figure 17 SONAR data 3D visualization ....................................................................................................... 28

Figure 18 Oblique view of hillshaded DEM with derived contours overlain .............................................. 30

Figure 19 Fabric approach example: Stormwater management pond ....................................................... 32

Figure 20 3D data fusion example: RTK-augmented 3D cross-sections with RTK points (red) .................. 34

Figure 21 Orthophoto with catchment data visualization of study area .................................................... 36

Figure 22 RTK-augmented cross-sections showing RTK points (red) .......................................................... 43

Figure 23 HEC-RAS schematic ..................................................................................................................... 46

Figure 24 Cross-section example #1: Cut from LIDAR DEM ........................................................................ 49

Figure 25 Cross-section example #1: Cut from PAC DEM ........................................................................... 49

Figure 26 Cross-section example #2: Cut from LIDAR DEM ........................................................................ 49

Figure 27 Cross-section example #2: Cut from PAC DEM ........................................................................... 50

Figure 28 PAC DEM hillshade draped over orthophoto data...................................................................... 52

Figure 29 PAC DEM slope raster ................................................................................................................. 52

Figure 30 LIDAR DEM slope raster .............................................................................................................. 53

Figure 31 Stormwater managment pond ................................................................................................... 55

Figure 32 Engineering drawing of SWM pond ............................................................................................ 56

Figure 33 DEM with RTK-derived TIN for in-pond bathymetric 3D representation ................................... 57

Figure 34 RTK-augmented fused DEM ........................................................................................................ 58

Figure 35 Oblique view of 3D contours derived from fused DEM (not to scale - vertical exaggeration) ... 59

Figure 36 Contours of interest draped over orthophoto ............................................................................ 60


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Acknowledgements

Supporting Sustainable Water Management in Ontario Through Innovation is intended to serve as a

summation of innovative and novel geospatial analytical techniques developed over a three-year period

at the Ganaraska Region Conservation Authority (GRCA) from 2011 to 2013.

Supporting Sustainable Water Management in Ontario Through Innovation was written by Ian Jeffrey,

B.A. (Hons), GIS-AS, GIS / Remote Sensing Specialist of the Ganaraska Region Conservation Authority

(GRCA) with technical assistance from Mark Peacock, P. Eng., Director, Watershed Services (GRCA), and

Jessica Mueller, PhD, GIS/Engineering Technician (GRCA).

Financial support for Supporting Sustainable Water Management in Ontario Through Innovation was

provided by the Ontario Ministry of the Environment (MOE) as part of the Showcasing Water Innovation

program, Town of Cobourg, Municipality of Port Hope, County of Northumberland, Ontario Ministry of

Natural Resources, University of Guelph, and the GRCA.

Executive Summary

Technological advancements in geospatial data capture and computational power have enabled for

geospatial data to be captured and modeled at unprecedented levels of accuracy and precision. This

accuracy and level of detail allows for new and innovative approaches to many areas of scientific

inquiry, with flood line mapping being one such application which requires high levels of accuracy given

the legal and economic implications of flood events. In Ontario, conservation authorities are tasked with

floodplain management in close partnership with municipalities. The findings documented in this report

serve as lessons learned in addressing traditional floodplain management standards using innovative

technologies and modeling techniques.

Correct citation of this document: Ganaraska Region Conservation Authority. 2014. Supporting Sustainable Water Management in Ontario Through Innovation. Ganaraska Region Conservation Authority. Port Hope, Ontario.


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1.0 Introduction

Since the advent of the digital age, computers have enabled for a host of scientific exploration and

analysis previously thought to be impossible. Allowing for the collection, manipulation, analysis, and

dissemination of vast and complex collections of data, digital technology has become the foundation

upon which most scientific inquiry rests today.

Water management has benefited greatly from the computer-based analysis given the complexity of

analysis required in this area of science. Water resource engineering analyses such as flood line mapping

bring together multiple sets of data which are combined together in complex models in efforts to

quantify the effects of various flood events. The data required as input for these models can be quite

large in size and detail, with digital computation alleviating much of the tedious intermediary steps en

route to producing meaningful results. Technologies used to produce the data, as well as organize and

model it, have undergone rapid development alongside improvements in computer processors. Namely,

remote sensing involves observing and recording details of an object from afar. Examples include aerial

photography as well as satellite RADAR scanning, both of which produce large datasets that are required

for a host of environmental analyses, flood line mapping being one.

Remote sensing data and analysis, combined together in a geographic information system (GIS), allow

for the collection of otherwise unobservable information into a catalogued digital arrangement ready

for user interpretation and application. As evidenced in this report, flood line mapping in Ontario has

much to gain from moving into the digital age.

1.1 New Mapping Technologies

Data acquisition and modeling techniques represent new mapping technologies that are capable of

providing the analysis necessary for flood line delineation. Remote sensing uses different types of light,

or electromagnetic radiation (EMR), to detect and record information pertaining to an object without

interacting through touch – hence, “remote”. By tying this remotely sensed information to the Earth’s

surface – via a process known as “georeferencing” – a detailed picture of natural phenomena, otherwise

out-of-scope for the human eye, emerges in plain view as a digital representation of the “lay of the

land”.

Light can be used not only to observe the properties of an object remotely using spectroscopy, but can

also inversely deduce the distance of an object from a light source by measuring the time which it takes

for a pulse of light to reach an object, reflect off of it, and return to the sensor location. This basic

principle is at the core of such remote sensing technologies as light detection and ranging (LIDAR) and

interferometric synthetic aperture RADAR (IFSAR), two widely-used techniques for acquiring 3D data

representing the Earth’s surface, commonly known as topographic data or, in even simpler terms,

elevation data.

In addition to LIDAR and IFSAR, elevation data can also be derived using stereoscopic principles by way

of digital photogrammetry. Within the world of photogrammetry, data can be generated using


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automated routines that triangulate pixel-by-pixel with highly-accurate locational information pertaining

to a camera’s location aboard an aerial vehicle, producing millions, or billions, of discrete 3D points for

each ground pixel in an aerial photo. A second method of generating elevation data using

photogrammetry, involves the manual 3D digitizing of features-of-interest in the form of spot heights or

breaklines.

Real-time kinematic (RTK) GPS systems capable of providing centimetre-level data in the field are

valuable not only for capturing land form features but also checking other datasets being used in a flood

line mapping study. The latter benefit provides practitioners with the ability to conduct quality control

checking on datasets purchased from vendors or created in-house.

1.2 Elevation Data

Regardless of the technique of data capture, there exists a widespread need for high-quality, accurate

elevation data in the modern world. Given the detailed technical requirements of many applications, it is

of the utmost importance to properly understand the accuracy and precision of the elevation data in its

raw form as well as its applied derivative form. The widespread misinterpretation of elevation data

specifications is alarming, particularly in the environmental sector. In Canada, regulatory considerations,

such as floodplain management and natural disaster mitigation, call for the quality and validity of the

underlying elevation data to be of the highest level in the effort to protect people and property.

1.3 Elevation Data and Hydrology

Modeling the Earth’s surface has many applications in environmental study. Generating a highly-

accurate 3D representation of a given study area can shed light on many different areas of inquiry, from

surface water modeling to archaeological study. Each different application determines the type of data

and processing techniques employed to derive a 3D model.

Commonly known as a digital elevation model (DEM), a 3D representation of an area of the Earth acts as

the fundamental dataset for many types of environmental study. For instance, hydrologic modeling is

conducted on a bare earth DEM, modeling terrain over which the water will travel. In offering a

seamless 3D digital model of a given area, from a single watershed to an entire continent, a network

model can be established to assist in understanding of how water behaves and reacts across locations

and under different weather event conditions.

1.4 Elevation Data and Engineering

3D mapping has long been a part of various engineering analyses, commonly referred to as computer-

aided design, or CAD. During the evolution of geospatial data analysis, the CAD environment has

changed to allow for drawings to be georeferenced and not standalone unto themselves. This has aided

in many civil engineering exercises, particularly those which require the analysis of large areas or the

interaction between two adjacent areas. For all intents and purposes, a cohesive understanding of the

physical makeup of a given study area can be considered all-in-one.


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Water Resource Engineering is one scientific discipline that relies upon elevation data for much of its

analysis. By combining hydrologic and hydraulic information with engineering principles, Water

Resource Engineering strives to quantify the behaviour of water across many different scenarios, from

water distribution networks to the natural interplay between sub-catchments of a watershed in various

different weather events. Given the mathematical complexity of water resource engineering analysis,

the elevation data input for engineering models must be thoroughly understood in order to ensure the

scientific validity of results.

1.5 Technological Trends

Technological advancement has changed the way in which elevation data is acquired, processed, and

used. Advancements in sensor technology as well as computer processing power has enabled the

processing of large, detailed volumes of data offering new capabilities in the way elevation data is

acquired and used.

Much is to be gained from how much technology has evolved but a proper understanding of practical

applications of these advancements is required. It is important to be reminded that the “latest and

greatest” is rarely the full solution to a technical problem. For instance, there are still applications that

are best served by RADAR which newer technologies like LIDAR fail to address. Careful consideration still

needs to be paid to data acquisition and modeling procedures despite new products being marketed as

“the answer”. The important understanding to be maintained is that each data acquisition or modeling

technique is but one amongst many that reside in the user’s “toolbox”. Each “job” calls for a different

“tools” for different reasons. Thus, it is very important to pay careful consideration to each option at the

project planning stage as projects can be made much easier in the long run if the right tools are applied

in the right way.

1.6 Innovative Approaches

It is the purpose of this paper to explore and apply modern elevation data acquisition and modeling

techniques to provide solutions to real-world water management scenarios. New data capture

technologies such as RTK GPS survey, LIDAR, and digital photogrammetry were applied in an effort to

provide innovative spatial data support for geospatial and engineering analyses. The road to the

application of these methods is documented in this report, as well as illustrated in multiple case studies.

Findings are presented in addition to recommendations for next steps in furthering this work.


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2.0 Flood Line Mapping

2.1 Introduction

The management of flood susceptible areas begins with the identification of which areas should be

classified as such. Flood line mapping is a multi-disciplinary analysis with the objective of understanding

how areas may be flood prone under certain flood events. Flood lines produced are required to be of

the highest accuracy as they are at the very core of efforts in protecting people and property. In Ontario,

floodplain management is dealt with in a regulatory manner, falling under the mandate of the Ontario

Ministry of Natural Resources (OMNR) and Ontario’s municipalities. At the local level, Conservation

Authorities and OMNR district offices implement programs that address flooding as well as other natural

hazards.

Figure 1 - Printed flood line map (GRCA/Queen’s Printer, 2007)


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2.2 Ontario Floodplain Management Policies

In terms of Provincial legislation, flood line mapping falls under two Acts: the Conservation Authorities

Act and the Planning Act.

2.2.1 The Planning Act

As defined in the Ontario Technical Guide: River & Stream Systems: Flooding Hazard Limit, the Ontario

Provincial government’s role in the planning and management of flood risk areas is to “protect society,

including all levels of government, from being forced to bear unreasonable social and economic burdens

due to unwise individual choices” (Ont. Technical Guide, 2002). This broad concept was originally

realized in the form of a Provincial Policy Statement (May 1996) issued under the authority of the

Planning Act which put forth that “the Province’s long-term economic prosperity, environmental health

and social well-being depend on reducing the potential for public cost and risk to Ontario’s residents by

directing development away from areas where there is a risk to public health and safety or a risk of

property damage” (PPS, 1996).

2.2.2 The Conservation Authorities Act

Flood line mapping also falls under the Conservation Authorities Act, rendering Ontario Conservation

Authorities responsible for floodplain management at the watershed scale through most of southern

Ontario and where they exist in northern Ontario (P&P, 2009).

2.2.3 Flood Hazard Criteria Zones

Flood hazard analysis criteria is defined by historical regional storm events or, in the absence of a

regional storm on record, the 100 year flood. In Ontario, three zones exist where regional flood criteria

are defined. In south-central and south-western Ontario, the Hurricane Hazel 1954 flood event applies

(Zone 1). Hurricane Hazel hit the north shore of Lake Ontario and its rain caused immediate widespread

devastation in the form of mass flooding. In northern Ontario, the Timmins Storm of 1961 is the regional

storm event where a significant rainfall event caused widespread destruction in the Timmins area (Zone

3). The regional flood event for eastern Ontario is defined by the 100-year flood event (Zone 2).


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Figure 2 Flood hazard criteria zones in Ontario (Queen’s Printer, 2002)

Further to the regional storm events, floodplains can be defined as one- or two-zone. One-zone means

that no development can occur in the floodplain whereas two-zone allows for some development to

occur in areas specified as ‘flood fringe’ and not in core areas identified as the ‘floodway’.


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Figure 3 One zone floodplain concept (Queen’s Printer, 2002)

Figure 4 Two zone floodway - floodfringe concept (Queen’s Printer, 2002)


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2.3 Flood Line Mapping in Ontario

2.3.1 Flood Damage Reduction Program

The Flood Damage Reduction Program (FDRP) was a Government of Canada national initiative with the

stated objective to “discourage flood vulnerable development”. In the face of escalating costs associated

with dealing with flood damage recovery, the FDRP program commenced in 1975 as a cost-shared

program between the federal and provincial governments. Core to this work was the establishment of

flood line mapping across Canada. Many, though not all, communities established municipal zoning

based on the findings of the FDRP program.

Figure 5 FDRP map example (1986) (GRCA, 2013)

2.3.2 Moving Forward

Since the FDRP program, there has been limited coordinated flood line mapping conducted on a national

or provincial scale in Ontario. In 2013, recent major flood events in Winnipeg, Toronto, and Calgary,

have put the issue in the front page news with the stated fact that Canada is the only G8 country that

does not have a national flood hazard program. With FDRP maps being out-of-date and a product of


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older technology, there exist large data gaps in terms of understanding and locating flood lines across

Canada. The Insurance Bureau of Canada has called for a national coordinated flood line mapping

strategy as there currently is no overland flood insurance offered in Canada. The IRB has categorically

stated in 2013 that until there are reliable flood line maps, there will continue to be no overland flood

insurance available to Canadians.

Figure 6 Digital floodplain disseminated using WebGIS service (2013) (FEMA, accessed Nov. 2013)

Further to the pronounced information gaps, the steadily increasing frequency of flood events has

added to the urgency of the matter. Climate change has brought flood-related damages to the fore,

surpassing fire-related damages for the first time in Canadian history. Mapping and understanding the

effects of climate change on flood events is only increasing in importance, with climate change

adaptation the key focus.

2.4 Flood Line Mapping Data Requirements

In order to produce flood line maps, there are multiple data requirements, mostly spatial by nature. This

section serves to define, in general terms, what data is required to produce a flood line map that meets

commonly accepted standards. In Section 3, the ways such data can be acquired are explored at a more

technical level.


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Figure 7 Oblique view of 3D floodplain analysis (2013) (GRCA, 2013)

2.4.1 Overland Topography

The largest, and perhaps most expensive, flood line mapping data requirement is accurate

representation of the topography for the area of undertaking. The focus here is to think not of the water

itself, but the paths over which it will flow given the local terrain. Following the law of gravity, surface

water will flow from higher points to lower, and delineating flood hazard areas is largely a function of

determining where the water will flow and how it will get there. The accuracy of the topographic data is

absolutely crucial in obtaining meaningful results at the end of the flood line mapping process. A

difference of 20 – 30 centimetres vertically can mean the difference in hundreds of metres horizontally

in terms of floodplain delineation. It is therefore imperative that topographic representation is accurate

and the error associated is well understood.

2.4.2 Hydrology

Hydrology can be defined as the study of the movement, distribution, and quantity of water. This report

deals with the hydrologic modeling of surface water in addressing natural hazard issues. Water resource

engineering applies these hydrologic models to real-world scenarios to provide practical solutions.

In addition to detailed elevation data, information required to develop a hydrologic model include soil

types, forest cover, groundwater, land use, infiltration rates, and soil moisture conditions. Input

parameters associated with these types of information are calibrated within a hydrologic model which


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can then be used to understand the runoff characteristics of catchments under specified weather

conditions which may lead to flooding.

2.4.3 Hydraulics

Derivative data components from topography are stream channel profiles and cross-sections. Cross-

sections represent a bisection of a stream channel, perpendicular to the route of flow. The information

provided by cross-sections is fundamental to generating stream flow characteristics when used as input

in the most commonly used water resource engineering models.

Figure 8 Hydraulic cross-section example (HEC-RAS website, accessed Jan. 2014)

2.4.4 Orthophotography

After flood lines are produced, they need to be communicated. Overlaying flood lines on orthophotos is

one of the most effective ways of answering the question that is the reason that a flood line mapping

project was conducted: “am I in the floodplain, or not?”. Through the scientific processes involved in

creating flood lines, the reliability of the flood lines themselves are carefully calculated and understood,

therefore the way in which they are expressed must be subject to the same level of scrutiny.

Orthophotography must be flown to meet a required specification in order to properly display and

ultimately put the flood lines to use.


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Figure 9 Orthophoto with floodplain overlain (GRCA, 2013)

2.5 Summary

Flood line mapping is an integral tool which a society can utilize to protect its people, property, and

economic prosperity. Natural disasters such as flooding can lead to widespread property damage and

even loss of life. With that being said, the accuracy of the flood lines produced is of the utmost

importance. As is outlined above, the information requirements for a flood line mapping project begin

with elevation data but also include a host of engineering parameters required for the hydrologic and

hydraulic modeling. Given the multidisciplinary nature of the process, effective communication is

required to ensure the continuity of understanding as different data components are acquired,

processed, and modeled.


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3.0 Elevation Data Acquisition

The acquisition of elevation data has increased in both accuracy and efficiency, enabling the capture of

high-quality elevation data at lower costs than previously possible. From GPS to photogrammetry,

advancements in sensor and computing capabilities have opened the door for a host of environmental

and local government applications.

The intent of this section is to provide brief overviews of the elevation data acquisition techniques that

are the focus of this paper.

3.1 RTK GNSS Survey

The real-time kinematic global navigation satellite system (RTK GNSS) is a useful, in-the-field technology

that can be implemented to capture as well as check elevation data. RTK GNSS uses a technique known

as Differential GNSS which implements real-time correctional communications to maximize accuracy

and precision on the fly. Essentially, the RTK GNSS system is divided into two main components: the

base station and the rover. The base station is a continually-operating GNSS receiver that is measuring

its own location constantly relative to the GNSS satellite constellation and ground-based network. The

base station communicates with the rover unit to provide it with correctional data based on the high

accuracy of its known location. Alternatives to running a base station include subscribing to a GNSS base

network which follows the same processes.

Figure 10 RTK GPS rover unit with data logger (Ashtech, 2013)

The RTK rover unit consists of a RTK antenna fixed upon a survey pole of known height and connected to

a data logger. The RTK rover antenna communicates with the RTK base station as well as GNSS satellites


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to deduce its location to centimetre-level accuracy. To ensure proper communication with GNSS

satellites, the RTK rover antenna must have enough open sky above it for the communication transfer.

In areas of dense vegetation or other obstructions this communication can be compromised, and the

desired levels of accuracy may not be achievable. Therefore, with careful planning, the RTK GNSS

technology can greatly speed up a geospatial data acquisition while controlling for accuracy, ensuring

the data collected meets or exceeds project requirements.

Geospatial product derivatives from RTK GNSS capture include spot heights as well as 3D breakline

features. Though effective as a data capture tool, RTK GNSS also serves as an effective tool for quality

control. Capable of achieving accuracy levels of +/- 0.01 – 0.02 m, a simple RTK GNSS survey can reveal a

thorough understanding of the accuracy of a data product of lesser accuracy such as a LIDAR-derived

DEM or a total station survey.

3.1.1 Precise Point Positioning (PPP)

Natural Resources Canada (NRCan) offers an online application for GNSS data post-processing that

allows users to submit observation data over the internet and recover, using precise GNSS orbit and

clock information, enhanced positioning precisions. In practice, an RTK GNSS antenna is set atop a tripod

over a firmly-anchored point and set to conduct a static survey over a few hours. The readings recorded

during this time period are obtained from the unit and uploaded to the NRCan PPP web service. Usually

in short order, the service returns a report that details the position of the surveyed point to an accuracy

within millimetres. This technique is very useful in establishing ground control points (GCPs) which can

be used to check equipment performance, such as benchmarks in a total station survey.

The extreme level of accuracy offered by the PPP process is made possible by an astronomic technique

called Very Long Baseline Interferometry (VLBI). A wonder of modern astrophysics, the VLBI technique

allows for very accurate measurements of the distance of astronomical objects from Earth. In the case of

the PPP system, quasars serve as beacons that are so far away from Earth (billions of light years away)

that they are as close to a truly static monument as is currently possible by way of human technology.

The core challenge in these efforts is to achieve a static benchmark of known location while operating in

a wholly dynamic environment. From plate tectonics to the slow rebounding of the Canadian shield from

ice age glaciers, it takes a constellation of quasars billions of light years from Earth to achieve what can

effectively be considered to be “absolute accuracy”. (http://webapp.geod.nrcan.gc.ca/geod/)


22

Figure 11 Quasar - the absolute reference for modern GNSS (ESO/M. Kornmesser, 2013)

3.2 LIDAR

LIDAR – or Light Detection and Ranging – is an efficient means of acquiring highly-accurate elevation

data for a given area. Whereas RTK GNSS can be used to capture targeted and discrete features, LIDAR is

essentially a scan of the Earth that results in a large dataset that can be used to derive elevation data

products.

A remote sensing technique, the LIDAR system is comprised of a laser scanner mounted in a fixed- or

rotating-wing aircraft alongside an inertial measurement unit (IMU) linked to an atomic clock and

survey-grade GNSS unit. In brief, the laser scanner scans the Earth from the belly of an aircraft, while the

IMU records orientation of the aircraft in the sky and the GNSS simultaneously measures the aircraft

location relative to a geodetic datum. The scanner measures the time it takes for each laser scan pulse

emitted to return to the sensor. Given that the speed of light can be considered infinite due to the close

relative proximity of the scanner to the Earth, a direct inference of time can be determined as a function

of distance. What is retrieved from the aircraft upon mission completion is an irregularly-spaced mass of

points known as a point cloud.

One key advantage of LIDAR is that since it is the distance (as an inverse of time) that is recorded,

multiple points in a small area can be teased apart to determine exactly what objects were captured.

This works by the simple assumption that laser pulses that take longer to return to the sensor represent

objects that are further away. When applying this logic to a LIDAR acquisition, the furthest away objects

are typically ground features. Multiple returns can be recorded during LIDAR acquisitions but, generally

speaking, “First Return” points are non-ground, and “Last Return” points can be assumed to be ground.


23

Figure 12 LIDAR mission (geospatialworld.net, Accessed Nov. 2013)

One limitation of LIDAR is its inability to capture information with regards to water surfaces or

inundated areas. There exist LIDAR techniques that are capable of penetrating water to capture

bathymetric data but unless specified in the project requirements, it should be assumed that a given

LIDAR dataset does not include meaningful information for water features. It is common practice to

deliver a processed DEM as part of a LIDAR deliverables package which includes data for water features

but this is commonly the result of post-processing techniques aimed at filling data gaps such as those

that are returned for a LIDAR mission in areas of inundation. An example of this is a processing

technique called “hydro flattening” which blindly interpolates across water features from one shore or

bank to the other. Careful consideration should be paid to this fact as it is a common misconception in

the industry that DEMs delivered as part of a LIDAR acquisition are a perfect representation of the study

area. Furthermore, the specifications the LIDAR was required to meet are often incorrectly applied to

the raster DEM derivative. As is detailed later in this report, stream channel cross-sections are a key data

input for flood line engineering models and the amount of error introduced by cutting cross-sections

from a DEM that was not created for this purpose can greatly skew results produced.


24

Figure 13 LIDAR bathymetric mission (SHOALS, accessed 2013)

3.3 Pixel Autocorrelation

Pixel autocorrelation (PAC) comes under many guises such as DTM Extraction, Multi-Ray Matching,

among others, but can be defined as the process by which elevation data can be generated through the

digital processing of overlapping aerial image stereo pairs. A digital photogrammetric method, PAC is an

elevation data acquisition technique that is capable of producing a high-quality point cloud

representation of a given area similar to that which can be produced by LIDAR. The use of PAC has seen

rapid emergence in recent years primarily due to the advancement of computational processing

capacities offered by computer processing unit (CPU) and graphics processing unit (GPU) speed and

capacity. The horse power required to conduct a PAC analysis is very intensive as each pixel in one

stereo image is matched to what is determined to be the spatially-coincident pixel in the other stereo

image, establishing an aerotriangulation model by which the pixels location in 3D space can be

determined. Given that billions of pixels need to be processed in this way to produce a point cloud of a

given area, the processing is quite extensive and can take days to complete with even the most

advanced computer.


25

Figure 14 Colourized pixel autocorrelated point cloud (GRCA, 2013)

After a point cloud is produced using PAC, it needs to be filtered and classified. The key difference

between PAC and LIDAR is that a LIDAR-derived point cloud typically features Return information for

each individual point which enables ground/non-ground point delineation. With PAC, the resultant point

cloud is produced using the aerial imagery as captured, which means that if the ground can’t be seen,

then no information can be derived. To remedy this, the raw point cloud is run through multiple stages

of filter algorithms designed to differentiate between ground, building, and vegetation, tagging each

point with one or more of these categories. Fortunately, there exist techniques such as RTK GNSS which

can fill in data gaps where no ground data was produced. A manual inspection is necessary – as is the

case with all data acquisition techniques – in determining if what was targeted was indeed captured and

meets data requirements.


26

Figure 15 Pixel autocorrelated point cloud colourized by feature classification (GRCA, 2013)

3.4 3D Digitizing

A second digital photogrammetric data acquisition technique is commonly referred to simply as “3D

digitizing”. Like PAC, this technique involves extracting high-quality elevation data from overlapping

stereo image pairs but through manual digitizing in a 3D-viewing environment. The theory behind the

process is nothing new, in fact, it was the common source for mass elevation datasets before the advent

of digital sensors. Stereoplotters were large analog machines operated using four limbs to view

hardcopy aerial stereo pairs in 3D for data capture. The process was quite cumbersome and migrating it

to the digital environment greatly improved the accuracy and efficiency of the process on the whole.

Typical features targeted for 3D digitizing include spot heights and breaklines. Breaklines are particularly

useful in tying-in elevation models for particular uses. For example, in hydrologic modeling,

discontinuities in terrain can result in breakages in flow or unique routing characteristics in a given area.

By capturing hydrologic breaklines in 3D, an elevation dataset can be augmented by fusing the

breaklines into the elevation model.


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Figure 16 3D digitizing workstation (OMNR, 2012)

3.5 SONAR

SONAR – or Sound Navigation and Ranging – is an age-old technique initially used to aid the navigation

of sea vessels and, during wartime, detect and avoid enemy advances. Though the core principals of

SONAR have gone unchanged, technological advancements in the actual sensors coupled with RTK GNSS

technology have revolutionized the way SONAR can be used.

In terms of elevation data capture, the SONAR unit operates quite similarly to LIDAR. A SONAR scanner

can be mounted underneath a vessel and used to scan the sea floor below. Instead of lasers, SONAR

uses sound waves emitted and measures the amount of time they take to bounce off an object and

return to the sensor to deduce the objects distance from the sensor. When linked to an RTK GNSS unit,

the data representation captured relative to the vessel can be georeferenced, transforming it into

bathymetric data.

Modern SONAR units come in varying sizes designed to achieve different types of SONAR surveys. With a

thorough understanding of the data being captured, bathymetric datasets captured using SONAR can be

fused together with terrestrial elevation data to produce a seamless representation of the terrain

beneath all water and vegetation. This capability is of particular importance in water resource

engineering as the quality of data representation of terrestrial topography as well as in-channel

geomorphology directly impacts analytical results.


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Figure 17 SONAR data 3D visualization (wordlesstech.com, accessed Nov. 2013)

3.6 Summary

Given the high levels of accuracy offered by the above elevation data acquisition techniques, it is

important to maintain the understanding that no one data acquisition technique stands out as the best.

As will be evidenced later in this report, all elevation data acquisition techniques should be considered

tools which can be used to satisfy defined data needs. Each individual tool must be thoroughly

understood in terms of its strengths and weaknesses, and should be used appropriately and wisely.


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4.0 Modeling Procedures

The term “model”, when applied in a digital context, implies a nonphysical abstraction of a natural

system. For the purpose of this paper, two main types of models will be considered: the elevation model

and the flood event model. By definition these two types of models are quite different but are brought

together as one through the flood line mapping process.

Firstly, the elevation model is a three-dimensional georeferenced digital representation of a given area

on the surface of the Earth. There are many different intended uses an elevation model may be

designed to fulfill and considering the purpose of the model at the earliest stages of project planning can

avoid many headaches at subsequent stages. As outlined in Section 3.0, there exist many different forms

of data acquisition techniques and selection of the correction combination of sources impacts the

success of achieving the elevation model that is being produced.

Secondly, the flood event model is a general term used, for the purpose of this report, to describe flood

events in their various forms for the purpose of water resource engineering analysis. These are complex

weather- and environment-related phenomenon that seek to produce concrete results from a host of

complex variables and parameters.

The elevation model and flood event model are inextricably linked during a flood line mapping exercise.

Careful attention to detail is required at each step along the way from producing the elevation model

through to calibrating the event model. The purpose of this section is to take a look at what data is being

captured for what purpose in efforts to produce flood line mapping.

4.1 The Elevation Model

An elevation model can come in many forms – a raster DEM, vector contours, or simply a collection of

spot heights. This paper considers the term “elevation model” as referring to a digital 3D representation

of an area of the Earth’s surface. Modern geospatial data technologies allow for discrete 3D points and

breaklines to be stored and manipulated at known levels of absolute accuracy. This elevation model can

be manipulated and disseminated to fit a specified use via an approach this paper terms, “The Fabric

Approach”.


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Figure 18 Oblique view of hillshaded DEM with derived contours overlain (GRCA, 2013)

4.1.1 Traditional Approach

Before the advent of digital mapping, GIS, or GNSS, standalone surveys were commonly used to

represent a local topography. Though surveying techniques were employed to very high standards, the

resultant survey was at best a three-dimensional survey linked to a local survey monument. The intent

here is not to undermine the effectiveness of standalone analog surveys but in terms of reusability, and

transferability, the traditional hardcopy survey did not perform well. Furthermore, the absolute

accuracy of the survey was only as good as the local survey monument to which it was tied. In the

absence of continual checks, many survey benchmark networks could have been moved by frost/thaw

or development. Overall reliability always needed to be questioned depending on currency and accuracy

of the information.

When compared to modern practice, arguably the greatest limitation with the traditional approach was

the challenge presented when more than one survey or topographic map needed to be considered at

one time. There existed no alternative but to conduct an intensive manual comparison of multiple

topographic maps simultaneously, leaving room for subjectivity in the analysis.


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4.1.2 The Fabric Approach

In the digital world, fusion of data is commonplace. As described in Section 3.0, multiple acquisition

techniques can be combined to produce a product enhanced by the interplay of information offered by

each part. For instance, the effect of a proposed development can be weighed against current

conditions in a given area, allowing for anticipated hydrologic and hydraulic effects to be analyzed by

water resource engineers and accepted for consideration by decision makers. Nothing works better at a

boardroom table than a highly-accurate and detailed model of what it is that is being considered.

Modern GIS modeling techniques allow elevation models to be produced at unprecedented levels of

accuracy and precision. Key to producing an elevation model – as with any product – is to properly

define its intended use and the steps required to produce it. Gone are the days when a raster DEM

would be purchased and used for all purposes. Elevation data can now be stored in its discrete and

absolute form as a point cloud with vector breaklines, for which accuracy and reliability can be defined

in terms of absolutes. A DEM is an interpolated product meaning that in order to produce it,

approximations need to be made. There are many options for interpolation methods but they all result

in a blended raster product that is really a gradient more than an absolute dataset. In addition to this, a

DEM once produced is much like an orthophoto in that it is a snapshot in time. Immediately after it is

produced, it is essentially obsolete. Though its usefulness can be realized for some time after its genesis,

the shelf-life of a DEM will inevitably expire.

An alternate way of approaching long-term elevation data management will be put forth here under the

name “The Fabric Approach”. If elevation is maintained in point clouds and breaklines, it can be allowed

to evolve over time alongside the area it is designed to represent. Points and breaklines are discrete

datasets with defined absolute accuracy levels and can be removed or added to an elevation model at

any time. Therefore, when it comes time for the data to be consumed for analysis, it is in its most up-to-

date form. The raster DEM is the most common form of elevation data used for many different types of

analysis. With the Fabric Approach, the ever-changing core elevation model can be exported to a raster

DEM or contour dataset at any time as an up-to-date topographic dataset ready to be consumed.

In addition to the improvements to data accuracy and currency outlined above, the Fabric Approach also

works from a project management standpoint in the cost savings it can afford. Instead of purchasing a

one-off DEM from a vendor every so often, funds can be selectively allocated to areas where they are

most needed – i.e. areas of development, change – with the data acquired being fed directly into the

elevation model for instant updating. This leaves room for areas of high concern, such as floodplains, to

receive the highest amount of funding support in acquiring data that in most cases is the most expensive

given the commonly low-lying and densely-vegetated conditions. For example, LIDAR of the highest

accuracy is cost prohibitive for most when considering capturing elevation data for a given area but

could be within the realm of fiscal possibility if only those areas where it is required are targeted for

collection. The new LIDAR acquisition can then be fused into the long-term elevation model and

exported to a seamless DEM or contour dataset for the entire area as long as careful consideration is

paid to reporting the boundaries of the different levels of accuracy to ensure proper use of the resultant

data.


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Figure 19 Fabric approach example: Stormwater management pond (GRCA, 2013)

4.2 The Flood Event Model

Flood event models can be used to simulate a natural phenomenon in an effort to produce quantifiable

effects. For the purpose of flood line mapping, various different flood events can be considered by water

resource engineers in assessing the effects such events may have on a local area. In terms of elevation

data, the DEM (or “mesh” as it is sometimes referred to in the engineering world) is a fundamental input

dataset for hydrologic models. It provides the overland topographic representation which goes a long

way towards conceptualizing how water will behave when routed over a given terrain. After event

models are run for a given scenario, the resultant hydraulic information is then laid over a DEM for the

actual delineation of the floodplain itself. So, the DEM is a key dataset from start to finish in any flood

line mapping project and flood event model input/output requirements are of the utmost importance

when planning a flood line mapping project.

4.2.1 Hydrology

Hydrologic modeling aims to estimate flow characteristics such as response to weather events,

frequency distribution of high flows, attenuation effects of storm water management infrastructure, and

the effects of a watershed as a whole. Hydrologic models can be single event – modeling a standalone

rainfall event – or continuous – modeling long sequences of stream flow data over a long term.


33

4.2.2 Hydraulics

Hydraulic modeling typically takes the results of a hydrologic model and combines it with stream

channel landscape information to produce flood elevations which is then applied to the DEM to show

the actual flood lines over the floodplain.

4.3 3D Data Fusion

The interdependent nature of elevation model and flood event model offers opportunity to streamline

workflow by creating data that can be used for GIS and engineering analyses. A typical work flow

involves producing the elevation model data and making it workable for engineering models. The

hydrology and hydraulic engineering models are run and the output of these models is then combined

with the initial elevation model to produce a flood line map. Given this symbiotic relationship, elevation

and flood event model data is becoming increasingly interchangeable with technological advancements.

Input data for hydrologic and hydraulic models can now be the same. Traditionally, separate datasets

were used for each modeling process, leaving room for slight differences between the datasets that

could potentially impact findings. Overland topography can now be captured and fused with in-channel

hydraulic data which enables for technical continuity between GIS, remote sensing, surveying, and

engineering that was previously not possible.

Further to the idea of combining data resources, the benefit of RTK GPS survey cannot be overstated.

For the first time, centimetre-level data can be captured in the field providing the ability for quality

control to be conducted in almost any area. By using RTK GPS in this manner, data products can be

assessed for quality for the specific areas where they are being applied which can be used to ensure

compliance with data acquisition standards.


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Figure 20 3D data fusion example: RTK-augmented 3D cross-sections with RTK points (red) (GRCA, 2013)

4.4 Summary

Adherence to standards and overall defensibility of results are both much more feasible because of RTK

GPS technology. With the 2009 Imagery and Elevation Acquisition Guidelines, the levels of required

accuracy for different flood hazard scenarios were established which clearly describes what level of

accuracy elevation data needs to be captured at in order to satisfy technical flood line mapping

requirements. This may serve as an effective starting point for flood line mapping projects, grounding

the overall analysis in a concrete risk assessment and proceeding to the modeling selection from there.

The level of risk needs to be defined in specific terms which then allows the definition of the level of

accuracy required in a given flood hazard scenario. The level of detail required then can be used to

select the appropriate flood event model. This process allows floodplain management to be based on

both need and science, thus maximizing overall validity and defensibility of results while achieving best

use of financial resources.


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5.0 Case Study: Ops No. 1

Drain/Jennings Creek

5.1 Introduction

5.1.1 Objective

The Ops#1 Drain/Jennings Creek flood plain study is being conducted to assist the City of Kawartha

Lakes in generating accurate and defensible hydraulic and hydrologic models. The results of the

hydraulic modeling work will provide regulatory floodlines within community of Lindsay. Since the

acquisition of LiDAR data can be cost intensive a more cost effective method of obtaining terrain data

was applied. A digital elevation model (DEM) was derived using pixel autocorrelation for generating

elevation data and compared to results obtained from the LiDAR generated DEM.

5.1.2 Watercourse Context and Description

Urban drainage from Lindsay Square and other lands adjacent Kent Street drain to a small ditch to the

south and adjacent to Commerce Road, forming the upstream channel of what eventually becomes

Jennings Creek. From Commerce Road the channel turns west and crosses McLaughlin Road picking up

drainage from commercial properties and residential development. From here the watercourse

continues west to Greenfield Road. At Greenfield Road the channel becomes the OPS # 1 Drain, flowing

under Highway 35 into rural lands to the west. The drain turns north through agricultural lands, running

parallel to Hwy 35 before it re-crosses Highway 35 moving in an easterly direction. It becomes Jennings

Creek once it passes under Angeline Street within the northern part of the older built area of the

community of Lindsay. The Ops #1 Drain/Jennings Creek discharges to the Scugog River and eventually

to Sturgeon Lake.

As a result of the amalgamation of OPS Township and the Town of Lindsay, the City of Kawartha Lakes

has municipal jurisdiction over the entire drain and its watershed. Originally the drain was constructed

to improve the drainage of agricultural lands by serving as the discharge point for agricultural tile

drainage systems and local surface drainage systems. In addition, it removes excess urban storm water

collected by roadside ditches, residential lots, industrial lands, commercial lands and any other

properties along its path.

The majority of the watershed west of the Community of Lindsay is rural farmland and wetlands, with

pockets of small villages and rural subdivisions. The watershed has a size of 1675 hectares. The Ops #1

drain/Jennings Creek main channel is about 7.6 km long. Ops Drain is exceedingly flat, with an average

slope of 0.1%. Jennings Creek is steeper, with a 1% slope.


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Figure 21 Orthophoto with catchment data visualization of study area (KRCA, 2013)


37

5.1.3 Background Information

OPS #1 Drain and Jennings Creek are a vital component of the local infrastructure and are facing

pressure from continued growth and future urban expansion. Both watercourses are subject to flooding

due to urbanizing and its associated land use changes. Existing flooding concerns may be amplified by

future growth and need to be addressed in order to manage flood water flow within the drainage area.

In particular, flooding has been experienced at Commerce Road close to the South Mall Entrance,

Highway 7/35, McLaughlin Road, west of Highway 35 adjacent to the airport and at Jennings Creek in

the vicinity of the Victoria Recreation Transportation Corridor. Flooding issues appear to result from

increased runoff due to change of land use from agricultural to mixed residential and commercial uses.

Because of the historical flooding problems and increased development pressures in the upstream area,

numerous studies have been carried out in the past to attempt to understand and reduce future

flooding.

The engineering firm Aquafor Beech was retained by the City of Kawartha Lakes to carry out the Ops# 1

Drain Functional Storm Water Management Study, a draft copy of which was written in November 2001.

The firm calculated runoff results for the Ops #1 Drain catchment upstream of Hwy 35 to determine the

floodplain. The consulting company utilized a steady state modeling approach in HEC-RAS to assess the

capacity of the drain. The hydrology undertaken used the model Visual Otthymo (VO2). Since input of

hydrographs into HEC-RAS was not an option at the time of the study, Aquafor Beech recommended an

unsteady model approach be undertaken to refine flows to obtain more accurate peak flow rates and

water surface elevations. This recommendation was based on the concern that the storage

characteristics of this very flat watershed were not being appropriately considered.

Following this work, the engineering consulting firm AECOM, formerly Totten Sims Hubicki, extended

the Aquafor Beech study downstream to include Jennings Creek. Their draft report, OPS #1 Drain

Floodplain Mapping Update, was produced in April 2010. This firm was retained by the City of Kawartha

Lakes to identify and investigate stormwater management opportunities in both the upstream

developed areas and downstream underdeveloped areas. Additionally they were to assess existing

flooding issues and flooding impacts of anticipated future development. They replaced the VO2/HEC-RAS

simulation with an unsteady model employing the EXTRAN module of SWMM 4.4 to assess the storage

characteristics of the flood plain. While their results differed from the steady flow assessment carried

out by Aquafor Beech, the changes and their impacts on flood plain extents were not significant. This

was attributed to the fact that the underlying mapping products used for both studies were not refined

enough to translate modeled elevations and cross-section survey data used in the analysis into an

accurate representation of the flood plain.

An additional study by Greck and Associates, Ops 1 Drain Flood Hazard Management Guidelines, was

finalized in July 2011. The study reviewed Aecom’s floodlines to develop guidelines to address flood

hazards. Six separate management areas were created, and unique flood hazard reduction measures

suggested for each. In addition, the study re-examined the dynamics of flooding using an unsteady flood

flow hydraulic analysis (XP STORM program), which is similar to the EXTRAN module. This analysis

confirmed that flood elevations derived by usual standard methods (such as steady-state flow analysis)


38

will not sufficiently capture the complex hydrology and hydraulics found within the Ops #1

Drain/Jennings Creek. The study also concluded that the mapping information was not detailed enough

to portray the routing characteristics of the floodplain.

Another study used for reference information is the March 2004 Totten Sims Hubicki Associates’ City of

Kawartha Lakes Community of Lindsay Storm Sewer Servicing Study. The firm was hired to analyze the

minor storm system within Lindsay town limits to determine areas of surplus capacity and to determine

capital project upgrades. The chief item used from this project is minor drainage area mapping.

5.1.4 Modeling Approach

As noted above, in past studies, the dynamics of flooding has been assessed using standard steady flow

hydrologic methods as input to steady-state HEC-RAS models. However, this approach does not account

for attenuation and backwater effects from undersized culverts and massive shallow flood storage

areas.

Thus the modeling of unsteady flow using a dynamic wave (instead of kinematic) is the preferred

method for Ops Drain/Jennings Creek. This approach will ensure that the discharge will vary in space

and attenuate as it moves downstream to account for the time and volume dynamics of complex

floodplain storage. As agreed by the Technical Committee in June 2013, this will be the ultimate analysis

carried out for the study. For the draft report, only a steady-state hydraulic analysis has been carried

out, using peak flows calculated using the dynamic routing capabilities of a PCSWMM hydrology model.

It is expected that taking such an approach would result in the establishment of more realistic peak

flows and associated flood lines along the Ops #1 drain/Jennings Creek. Additional to the work noted

above, comparisons of all results will be undertaken to evaluate the change in flood plain elevations and

extents. The following chart shows how results will be compared.

This approach was peer-reviewed by Greck and Associates Limited in August 2013 and was found to be

acceptable, as documented in the separate report titled Peer Review Services for Terms of Reference of

Hydrologic and Hydraulic Assessments, Final Report.

5.2 Rainfall

5.2.1 Rainfall Data

Rainfall Intensity–Duration–Frequency (IDF) values and curves are used to define the amount of rainfall

that will be input into a model. IDF values provide estimates of the extreme rainfall intensity for any

given duration corresponding to different return periods. Rainfall volumes are taken from the

Atmospheric Environment Services (AES) gauge in Lindsay. Other rainfall stations, such as Peterborough

(AES) and Ontario Ministry of Transportation (MTO) were considered, however earlier reports utilized

the Lindsay station precipitation data. The Technical committee for the Ops #1 Drain/Jennings Creek

decided to carry on with the use of the Lindsay station as the values for the Lindsay station are very

similar to other local stations values. Additionally, use of the Lindsay Filtration Plant values provides for


39

continuity as much of the infrastructure in the community has been designed using this curve. Finally, it

was felt that this gauge proved the most representative data for the study area.

The Ontario Ministry of Natural Resources (MNR) technical manuals provide a rainfall reduction table for

the Timmins storm. For drainage areas larger than 25 km2, an areal reduction is applied to the Timmins

point rainfall based on 24 hr isohyets as shown in Table D-5 of the MNR manual. The Technical Advisory

committee concluded that given the size of the catchment no areal reduction factors would be used.

Rainfall intensity is calculated by the formula

I = a/(t+b)c, where

I in mm/hr

T in minutes

The City of Kawartha Lakes engineering design standards state the relevant IDF parameters for the

gauge are:

Table 1 IDF parameters in the City of Kawartha Lakes' engineering standards

Return Period (yr) A B C

2 628.107 5.273 0.78

5 820.229 6.011 .768

10 915.845 6.006 .757

25 1041.821 6.023 .748

50 1139.702 6.023 .743

100 1230.783 6.023 .738

Through the course of this study it was discovered that when the a, b, and c parameters listed above

were input into the hydrology models, the corresponding total rainfall volumes generated for a 12-

hour storm were overestimated by 11% to 25%. As a result, a, b, and c parameters which are listed

below in Table 2 were re-created; these values provided rainfall depths within 1% of measured

volumes. These are the values used throughout the study.

Table 2.2: IDF Parameters re-calculated by GRCA

Table 2 IDF parameters re-calculated by GRCA

Return Period (yr) A B C

2 808.299 7.413 0.835

5 1248.097 9.76 0.857

10 1486.792 10.44 0.859

25 1917.848 11.842 0.873


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50 2142.007 12.182 0.872

100 2465.522 12.897 0.879

Table 3 Rainfall depths from Lindsay AES station (24 years of data)

Return Period (yr) 6-hour (mm) 12-hour (mm) 24-hour (mm)

2 36.6 39.8 43.6

5 50.8 53.2 56.4

10 60.2 62.2 64.8

25 72.1 73.4 75.4

50 80.9 81.8 83.3

100 89.7 90.1 91.2

5.2.2 Design Storms

Three different elements are reviewed regarding rainfall to generate return period events: the total

volume of rain, the storm duration, and the rainfall distribution. Rainfall distribution is the specific

apportionment of rain over time, or the shape of the storm being considered. The relative importance of

these factors varies with the characteristics of a catchment. It is accepted practice to test different

design storms to determine the most conservative response of a hydrologic system. It is the intent of

this study to use the most conservative of commonly used approaches to ensure the most appropriate

protection for the community of Lindsay.

In order to determine conservative catchment response generated by different rainfall storm events

initially, a variety of rainfall durations (6- and 12-hours) for 2-100 year return periods are tested.

Additionally, in order to determine the critical design storm creating the highest peak discharges,

different sets of rainfall distribution are tested. The following discusses the rainfall distributions

evaluated in this study.

The Soil Conservation Service type II (SCS) distribution is a rainfall distribution curve which represents

high-intensity rainfall rates that are generally associated based on 24-hr rainfall. For more than a

century, the Natural Resources Conservation Service (US) has continued working on the development of

empirical formulas to improve the Soil Conservation (SCS) method for predicting storm runoff from

design storm events. The SCS method (1973) presents the 24-hr Type I, IA, II, and IIA rainfall time

distributions for runoff predictions. The Type II curve is applied to much of the United States, Puerto

Rico, and the Virgin Islands. Generally, other distributions are recommended for coastal area of the

country. Due to the general usage, this distribution is generally tested in hydrology studies undertaken

in Southern Ontario. The bulk of the rainfall occurs in the second half of the storm.

Environment Canada has developed a design storm for southern Ontario. When compared to the SCS

distribution, the majority of the rainfall in the Atmospheric Environment Service (A.E.S.) storm occurs at

the beginning of the storm. The Southern Ontario 30% curve is used in this study.


41

The Chicago storm distribution is one of the commonly used distributions for the design and analysis of

storm sewer systems within urban areas. The distribution of rainfall is generally in the centre of the

storm and the peak of storm is quite intense. Some investigators consider that this distribution yields

unrealistically “peaky” hyetographs, especially when a small time step is used.

The 2010 Aecom report concluded that the runoff from a 6-hour SCS storm is the most critical for the

Ops Drain/Jennings Creek catchment. However, it was discovered that Aecom had used an incorrect

hyetograph shape for the storm distribution. The peak rainfall intensity is exaggerated by 165%. Due to

this fact, this report carries out a fresh analysis of a variety of storm events (i.e. 6- and 12-hour Chicago,

SCS, and AES storms) for 2-100 year return periods, using the design storm hyetographs as determined

by both the MTO and MNR.

The worst case storm (the duration and distribution producing the highest discharges at key nodes) is

selected as the critical event for the watershed.

5.2.3 Regional Storms

The Timmins storm with a total rainfall of 193mm is applied to the Ops #1 Drain/Jennings Creek as the

Regional storm event. The full storm is defined by Chart 1.04 of the MTO Drainage Manual. Antecedent

moisture content (AMC) condition II, referred to as AMC (II), was applied. For a conservative estimate,

and to be consistent with previous studies, saturated ground conditions reflected by AMC (III) were also

applied. An aerial reduction factor was not applied to the Regional model as previously discussed in

section 2.1.

5.3 Parameters

5.3.1 Overview

Ops #1 Drain/Jennings Creek were originally modeled by AECOM. Unfortunately, this information has

not been detailed enough to adequately represent the complex hydrology within the Ops #1

Drain/Jennings Creek. For this study base information for its watershed was updated. This information

included newly acquired LiDAR imagery, updated Arc Hydro watershed boundaries, and field surveys.

Ganaraska Conservation Region Authority staff modified the AECOM model and found significant

differences in model results. In addition, an identical model was created to explore the effectiveness of

a pixel-autocorrelation DEM that has been generated from orthoimagery. This DEM is generated by

comparing the different X,Y coordinates to their neighbours to determine elevation variations. A suite of

tools is being applied to carry out filtering algorithms that help generate a terrain surface.

5.3.2 DEM

In order to generate a highly accurate digital elevation model (DEM) for the study area, two points per

square meter LiDAR data was acquired. ArcGIS version 10.1 computer software programs translated the

collected data points as a Triangulated Irregular Network (TIN) in order to isolate ground elevation

points from the full dataset. This resulting data was converted to a 0.5 m raster digital elevation model


42

(DEM), which serves to obtain elevation information. LiDAR data was used in conjunction with Real Time

Kinematic (RTK) Global Positioning System (GPS) survey data of culvert locations and invert elevations to

create a drainage network.

The validity of the DEM was analyzed in June 2013 as detailed in the report titled, Peer Review of

Remote Sensing and GIS Data Support for Flood Line Mapping – Ops No. 1 Drain/Jennings Creek. It is in

compliance with the 2009 Ontario Imagery and Elevation Acquisition Guidelines.

As an alternative to LiDAR acquired data the generation of a Pixel autocorrelated DEM was explored.

Alongside LIDAR, a full suite of aerial imagery deliverables were also captured. By way of a digital

photogrammetric data acquisition technique called pixel autocorrelation (PAC), a second DEM was

generated at the same 0.5 m ground resolution. A comparative analysis was setup to assess the ability of

the PAC method in the creation of a DEM to support flood line mapping. Within the comparison, RTK

GPS in-channel information was kept constant, with DEM-derived overland topography being extracted

for two separate HEC-RAS models. The resultant flood lines produced from these models were

compared.

5.3.3 Cross-sections

The cross-section geometric data used in hydraulic modeling was extracted from the DEM using HEC-

GeoRAS. The use of HEC-GeoRAS ensures spatial reference of geometry data when imported into HEC-

RAS. Cross-sections were cut in the LiDAR-derived DEM. Since LiDAR does not return laser points for any

ground below the water surface it is necessary to supplement these areas with surveyed data to create

accurate river geometry. Bathymetric survey points were taken in-channel up to the top of bank

throughout the project area. The surveyed data was fused into the cross-sections generated by HEC-

GeoRAS. Data sources generated by different entities were placed into the same projection and datum

for consistency in processing. Stream crossings have been identified and located by reviewing the most

recent aerial orthophotography in conjunction with field reconnaissance and information utilized by

previous reports.

As per HEC-RAS requirements, all cross-sections are oriented looking downstream. The initial cross-

section is at the mouth of Jenning’s Creek where it joins the Scugog River; cross-section nomenclature

reflects the distance in meters relative to the initial cross-section.

Left overbank, main channel, and right overbank downstream lengths were measured from the GIS. As

per HEC-RAS recommendations, the overbank distances are measured from each overbank centroid.


43

Figure 22 RTK-augmented cross-sections showing RTK points (red) (GRCA, 2013)

Hec-GeoRAS Preprocessing

Before the cross-sections can be transferred to Hec-RAS it is required to develop an import file with a

defined 3-D stream network and 3-D cross sections, which entails the digitizing of 2-D polylines defining

stream centerline and cross-sections, followed by the extraction of terrain elevation data from the DEM.

As mentioned above LiDAR data cannot capture bathymetric information if water is present in the

channel. The Update Elevation option in GeoHec-RAS is intended to incorporate bathymetric

information into the XSCutline 3D feature class that has been previously extracted from the land

surface. Bathymetric data was collected using RTK GPS survey. Surveyed data has been downloaded

from the GPS device and was converted into a point feature class. Elevations on the 3-D cross-section

feature class were replaced by using the surveyed point elevations feature class. In order to capture the

survey points in question a bounding feature class is digitized. The elevation values within the bounding

features are removed and replaced with the bathymetry point data elevations by snapping the point to

the closest point on the cross section. The updated cross section profile feature class was then exported

for import to RAS.

Transitioning from Geo-RAS to RAS

The import of river and cross-sections is carried out by using the ‘Import GIS Import File’ tab within the

Geometric Data Editor. The stream system is represented as multi point line.

Cross-Section Cut Lines are georeferenced derived from their location along the river. Their surface line

is represented by a set of points which are defined by a series of X, Y, Z coordinates for each point in the

cross-section. The coordinates are being transformed to station and elevation points within HEC-RAS.


44

This is followed by inputting additional geometric data that is needed to represent the true conditions in

the system, such as culvert data, roughness coefficients (Manning’s n), deck information, location of

bank stations, levees, ineffective flow areas and blocked obstructions.

This procedure was done for both, LiDAR and pixel autocorrelated derived cross-sections.

Review of Imported Data

Hec-RAS has only capabilities to compute 500 elevation points for each cross-section. Therefore it is

necessary to reduce to number of points that make up the line. Hec-RAS provides a set of algorithms to

reduce elevation points according to the users input. This was carried out using a near and collinear

filter that removed points in nearly straight lines. This procedure ensures that the profile of the cross-

section is maintained. For those areas where the floodline exceeded the length of the cross-sections

these were extended to capture the entire flood plain.

Additional data input

Before the Hec-RAS model can be run it is necessary to input the hydraulic structure data, levees,

ineffective flow areas, block obstructions data and flow data with boundary conditions.

5.3.4 Culvert and Road Crossings

Cross-sections are cut at culvert crossings, bridges and other restricting structures to accurately

represent channel flow. All culvert crossings are represented by two upstream and two downstream

bounding cross sections. Representative deck elevations were extracted from the DEM.

All culverts were field surveyed to ensure accuracy. Invert elevations, height/width dimensions, length,

channel bottom, were surveyed using either total station or GPS survey equipment. All relevant data

was noted and photographed.

5.3.5 Expansion/Contraction Coefficients

The model uses the HEC-RAS recommendations of 0.1 and 0.3 for contraction and expansion coefficients

at all normal cross sections. At culvert crossings, the values were increased to 0.6 and 0.8, respectively.

No bridges were coded in the model.

5.3.6 Manning n values

Manning’s n values for channel, left and right overbanks were based on recommended values in Table 3-

1 of the HEC-RAS River Analysis System Technical Manual. The main channel n values range from .035 to

.04 and the overbank n values range from .016 to 0.1, and were chosen based on air photo and survey

notes/photos. The length was determined by performing measurements in GIS.


45

5.3.7 Ineffective Flow Elevations

Ineffective flow areas were introduced at all culvert crossings, following HEC-RAS recommendations.

The upstream bounding cross-section had its ineffective flow elevations equal to the top deck

elevations, at locations immediately to the left and right of the culvert opening. For the downstream

bounding cross-section, the ineffective flow elevations were set at a point midway between the deck

and the culvert obvert elevation.

5.3.8 Building Obstructions

Where buildings are located within or between the cross-sections, ground elevations were artificially

increased by a minimum of 5m to replicate obstruction to flow. The effect of a building can be felt

upstream and downstream of a cross-section. A 1:1 contraction effect was used for a cross-section

upstream of a building; the actual building width is reduced at a 1:1 ratio from each end of the building

face. For instance, if a 30m building is 5m downstream of a cross-section, the representative building

width in the cross-section is 20m wide. A 4:1 expansion effect was used for a cross-section downstream

of a building. For instance, if a 30m building is 8m upstream of a cross-section, the representative

building width in the cross-section is 26m wide.

5.3.9 Boundary Conditions

For the draft report, only subcritical analyses will be carried out. The downstream boundary condition is

the starting water surface elevation. The starting water surface elevation is the controlled Sturgeon

Lake water level of 247.76m. It is noted that Sturgeon Lake is at a point greater than 1 kilometer

downstream of the Jennings Creek outlet to the Scugog River.

5.3.10 Flows

The input flows to the HEC-RAS model are derived from the September 2013 Floodplain Mapping Study

Hydrology Report-Draft. For this draft report, only the 100-year and Timmins flows were evaluated. The

final report will contain the full range of storm events, from the 2-year to the Regional event.


46

5.4 Hydraulic Model

5.4.1 Schematic

The information gathered in the preceding section was used to build a HEC-RAS model of the

watercourses, as shown schematically in Figure 23 below.

Figure 23 HEC-RAS schematic (GRCA, 2013)


47

5.4.2 Sensitivity Analysis

The model will be tested for sensitivity for the manning’s n, starting water surface elevation, and

reduction in tributary area. Flow through a forest is more turbulent and experiences more friction than

in a concrete pipe.

120% manning’s n

The manning’s number indicates the friction factor in a cross-section. The higher the number, the

rougher is the surface against which water flows. For instance, a smooth concrete pipe has a manning’s

n of 0.013 whereas a forest has a manning’s n value of 0.1.

By increasing the manning’s numbers by 20%, the flow is being subjected to a watershed with higher

friction forces acting upon it. It was found that overall there is little impact to the calculated water

surface elevations. Although 58% of the cross-sections experienced a change in water surface

elevations, 70% of those were less than 3cm. The largest changes in elevation were in the deep Jennings

Creek watercourse, where there is little impact.

80% manning’s n

By decreasing the manning’s numbers by 20%, the flow is being subjected to a watershed with lower

friction forces acting upon it. It was found that overall there is little impact to the calculated water

surface elevations. Although 48% of the cross-sections experienced a change in water surface

elevations, 68% of those were less than 3cm. Again, the largest changes in elevation were in the deep

Jennings Creek watercourse, where there is little impact.

Starting water surface elevation

The model was modified using different starting water surface elevations. As previously mentioned in

Section 2.9, the base model uses the controlled Sturgeon Lake water level of 247.76m. An alternate

water surface elevation of 248.4m was also used, which is the recorded 100-year Sturgeon Lake level.

It was discovered that the initial water surface elevation has little effect on the flood elevations. The

program defaulted to critical depth for the initial water surface elevation. It appears that this is due to

the fact that Sturgeon Lake is at too great a distance downstream of the watercourse outlet at the

Scugog River. An attempt was made to find a more valid recorded water level for the Scugog River in

the vicinity of the outlet. Municipal sewage and water treatment plants are located on the river. It was

hoped that City staff would have a record of average water levels in the river that could be input to the

model, but Kawartha Conservation was informed that no such recordings are kept by the City.


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5.5 Model Results

5.5.1 Comparing LIDAR and PAC Model Data Input

Base DEMs

Both DEMs were produced using data acquired in the same time period in Fall 2012. The vendor-

delivered LIDAR DEM was produced at 0.5 m resolution covering the entire study area.

For the purpose of determining if pixel autocorrelation is capable of supporting flood line mapping in

semi-rural areas, a DEM was produce using the aerial image stereo pairs captured during LIDAR

acquisition. The stereo pairs alongside the onboard GPS/IMU navigational data were processed using

Inpho Match-T software and filtered using Inpho DTMaster to remove non-ground points. PAC extracts

elevation values only for what is visible in an aerial image. Therefore, elevation points produced need to

be filtered and classified for ground and non-ground points. For flood line mapping, the non-ground

points are removed, leaving only ground points from which a bare earth DEM is derived.

Both DEMs were used to create channel cross-section data for input into the hydraulic model HEC-RAS.

RTK GPS survey data was then substituted in for DEM data that existed in channel as neither LIDAR nor

PAC are capable of achieving meaningful spatial data in watercourses.

Cross-sections

The hydraulic model created for this draft report includes 125 sections. All cross-sections were cut from

the LiDAR as well as from the pixel autocorrelated terrain model and input into two separate Hec-RAS

models with identical geometries and flows. Both models were run with the Regional, Timmins and 100

yr storm events. Figures 5.1 to 5.4 show the differences in elevation for two cross-sections. The profiles

extracted from the pixel autocorrelated DEM are coarser in nature and may need smoothing but

generally do not differ from the LiDAR extracted profiles. The coarser shape may be a result of the

filtering algorithm used, which can be adjusted to increase accuracy in computing elevation.


49

Figure 24 Cross-section example #1: Cut from LIDAR DEM

Figure 25 Cross-section example #1: Cut from PAC DEM

Figure 26 Cross-section example #2: Cut from LIDAR DEM

0 100 200 300 400 500268

269

270

271

272

273

274

275

FloodlineMappingCKL_KRCA1 Plan: Ex-modified 26/11/2013 River = Ops Drain Reach = A RS = 7604.08

Station (m)

Ele

vatio

n (m

)

Legend

Ground

Bank Sta

.016 .04

.016

0 100 200 300 400 500268

269

270

271

272

273

274

275

FloodlineMappingCKL_KRCA1 Plan: Pac 26/11/2013 River = Ops Drain Reach = A RS = 7604.08

Station (m)

Ele

vatio

n (m

)

Legend

Ground

Bank Sta

.016 .04

.016

0 50 100 150 200 250248

250

252

254

256

258

260

FloodlineMappingCKL_KRCA1 Plan: Ex-modified 26/11/2013 River = Ops Drain Reach = A RS = 273.7271

Station (m)

Ele

vatio

n (m

)

Legend

Ground

Bank Sta

.03 .035 .05


50

Figure 27 Cross-section example #2: Cut from PAC DEM

Obstructions

As previously mentioned in section 3.8, the hydraulic model includes building obstructions in the cross-

sections. HEC-RAS does not include the area represented by the building obstructions when calculating

the area and volume available for flow in each cross-section.

Flow Input

Floodlines results for the Timmins, 100 yr Chicago and the Regional storm event as listed in table 5.1

were compared.

Table 4 Flood lines results for Timmins, 100 yr Chicago and Region storm events

Node 100 yr

m3/s

Timmins

m3/s

Regional event

m3/s

Start of System 24.4 8.3 24.4

Commerce Rd culvert 18.0 8.2 18.0

former train track culvert 15.7 8.1 15.7

McLachlin Rd Culvert 15.0 8.2 15.0

Ballfield Culvert 21.2 12.7 21.2

Greenfield Rd culvert 18.8 12.9 18.8

0 50 100 150 200 250248

250

252

254

256

258

260

FloodlineMappingCKL_KRCA1 Plan: Pac 26/11/2013 River = Ops Drain Reach = A RS = 273.7271

Station (m)

Ele

vatio

n (m

)

Legend

Ground

Bank Sta

.03 .035

.05


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Hwy 7 Culvert 25.6 18.2 25.6

W of Hwy 7 at 90-degree bend in Drain 24.7 18.1 24.7

North of 90-degree bend 20.3 17.9 20.3

between Dew Drop Inn Rd & Hwy 7 33.2 40.3 40.3

D/S of Hwy 7 31.0 38.3 38.3

North of Hwy 7 past 90-degree bend 22.8 29.0 29.0

Colborne St Culvert 19.8 27.6 27.6

D/S of Colborne St 15.6 24.1 24.1

D/S of private driveway 17.1 24.0 24.0

U/S of Airport 40.2 63.2 63.2

D/S of Hwy 35 (near airport) 40.2 63.2 63.2

Angeline St Culvert 52.9 73.0 73.0

D/S of Angeline St 60.3 84.4 84.4

William St culvert 61.0 85.2 85.2

Flood Line Output

A visual comparison of the resulting differing floodlines are shown in figures 5.1 and 5.2

The flood lines produced for the three flood events were exported from RAS Mapper for comparison in

ArcGIS. Upon the initial visual inspection it is apparent that the results obtained from both methods are

quite similar. Only in the regional event flood lines is there any marked visible difference.

Flood line areas show the differences to be 1.01% for the 100 year flood event, 1.20% for the regional

flood event event, and 1.02% for the Timmins flood event.

Table 5 Floodplain surface area comparisons for respective flood event models and DEM data

Flood Event LIDAR PAC

100 year 1639065.6 m² 1654955 m²

Regional 1463807.3 m² 1768603.3 m²

Timmins 1707774.1 m² 1746209.4 m²


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Figure 28 PAC DEM hillshade draped over orthophoto data (GRCA, 2013)

Figure 29 PAC DEM slope raster (GRCA, 2013)


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Figure 30 LIDAR DEM slope raster (GRCA, 2013)

5.6 Conclusions and Recommendations

This study concludes that pixel autocorrelation is a valuable tool that is capable of producing elevation

data to support water resource engineering practices. The results indicate that the accuracy of the data

produced via the PAC method needs to be well understood and used appropriately in producing flood

line mapping. Like any other form of remote sensing, PAC has some limitations, namely its inability to

produce bare earth data where no bare earth can be seen in an aerial image acquisition. However, when

combined with other forms of elevation data capture such as RTK GPS survey, it is possible to produce a

data product of sufficient quality for flood line mapping.

This study recommends further research into the PAC process and subsequent fusion of data in effort to

produce elevation data. Refining the algorithms used to automate these processes could potentially

improve the quality of data produced. Furthermore, further exploration of the uses of RTK GPS

technology in these efforts is also recommended. This study explored one form of data fusion in

combined RTK GPS survey data with topographic cross-sections and other approaches to combining

separate datasets into enhanced combined products are recommended.

In summation, this study offers a cost-effective alternative in flood line mapping which is worth

consideration in addressing the large scale data gaps that exist across Ontario.


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6.0 Case Study: Fused SWM Pond

Fused DEM

6.1 Introduction

Geographic Information Systems (GIS) have evolved greatly in their effectiveness in providing data support for various engineering analyses. Advancements in remote sensing, survey-grade GPS, and 3D data modeling have offered new means of capturing field data and combining it with other datasets of comparable quality to produce better products. Water resources engineering relies on various forms of geospatial data for input into models that depict

how water behaves over land and over time. The digital elevation model (DEM) serves as one piece of

input data for these models. Hydrologic DEMs feature embedded watercourse data at the medium to

small scale and Hydraulic DEMs have bathymetric data fused as well. Between both of these concepts, a

new possibility exists to further blend GIS with engineering in utilizing 3D cross-section to inform DEMs

and vice versa. Furthermore, the introduction of water resources-specific structures, such as stormwater

management ponds, into a DEM can offer new ways of understanding how water interacts with

municipal infrastructure during certain flood events. With a seamless 3D representation of terrain and

stormwater structures the full interrelatedness of flood management systems can be better analyzed.

6.2 Background

A stormwater management (SWM) pond in Cobourg, Ontario, which was built in the 1980’s to help

attenuate for surface water flow in rainfall events and treat stormwater quality. The area of concern

features both commercial and residential infrastructure embedded in an urban setting. Recently, the

Town of Cobourg asked the Ganaraska Region Conservation Authority (GRCA) to determine if the pond

met current standards. In 2012, it was determined that the functionality of the pond may not meet

current standards.


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Figure 31 Stormwater managment pond (GRCA, 2013)

In response to this request it was established that a survey was needed of the existing pond and its surrounding area. The GRCA was tasked by the Town of Cobourg to conduct the Engineering review of this structure. An emerging spatial data processing technique called “3D data fusion” was identified as the best approach to provide the high levels of accuracy and representation required for the engineering review. This technique effectively brings together, or fuses, multiple elevation datasets into one cohesive dataset with synergistic results. For input into the 3D data fusion model, RTK GPS survey was identified as the ideal approach in

capturing SWM pond features to centimetre-level accuracy. This data was used to provide the in-pond

3D representation. To provide the surrounding terrain context, a LIDAR acquisition from 2006 was

identified to be at sufficient accuracy and precision levels for fusing with the RTK data.


56

Figure 32 Engineering drawing of SWM pond (GRCA, 2013)

6.3 Data

6.3.1 LIDAR

A LIDAR acquisition was obtained for Cobourg, Ontario in 2006. The deliverables from this mission

included all raw data with all points captured classified as ground or non-ground. For the purpose of this

exercise, all non-ground points were removed.

6.3.2 RTK GPS Survey

In the Summer of 2012, an RTK GPS survey of the SWM pond was conducted. Features targeted included

water’s-edge, berm outlines, bathymetric points, and surrounding topo points captured at random.

6.3.3 Software List

The following software was used in this project: ESRI ArcGIS 10.0

Inpho DTMaster 5.4

Microsoft Excel 2007


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6.4 Procedures

6.4.1 Data Preparation

LIDAR data was brought into the GIS environment by converting the proprietary format to a 3D LAS masspoint dataset. This dataset was then interpolated into a 0.5 m resolution GRID raster dataset to achieve representation of the area surrounding the SWM pond. The RTK GPS survey data was downloaded off of the field data logger and brough into the GIS

environment as point and polyline shapefiles. Small edits were made to automated breaklines captured

in the field and all points were classified as “TOPO” or “BATHY”.

Figure 33 DEM with RTK-derived TIN for in-pond bathymetric 3D representation (GRCA, 2013)

6.4.2 3D Data Fusion

A File Geodatabase containing a Feature Dataset was created in ArcCatalog to house all data to be fused together. A Terrain Dataset was then created in the Feature Dataset with bathymetric survey points as ‘masspoints’ and shore/berm features as hard breaklines.


58

The Terrain was then built and converted to a 0.5 GRID raster dataset. The resultant raster was then clipped to the boundary of the shore breakline, effectively maintaining only the pond 3D respresentation. This pond raster was then fused with the LIDAR raster to produce an “Enhanced DEM” featuring LIDAR-

derived data for the overland portion with RTK GPS survey enforcing the proper SWM pond

representation in the model.

Figure 34 RTK-augmented fused DEM (GRCA, 2013)

6.4.3 Engineering Data Requirements

The Enhanced DEM was converted to contours at 0.10 m intervals. Contours were edited to remove any

contour ‘ringlets’ thus maintaining only the ‘true’ contour information. Area was calculated for each

contour and the data layer was exported to Excel spreadsheet for use by the Engineering department.


59

Figure 35 Oblique view of 3D contours derived from fused DEM (not to scale - vertical exaggeration) (GRCA, 2013)

6.5 Engineering Data Uses

Contour data derived from the Enhanced DEM provided a more accurate and representative model from which Engineers could consider the effectiveness of the SWM pond. By increasing the quality of the contours derived, the validity of the Engineering SWM pond analysis was also increased. As shown in Figure 5, the Engineer-specified contours of interest were delineated and draped over highly-accurate orthoimagery of the area to produce an effective visual tool for communication of the findings. More specifically, the extent of road inundation can be seen by identifying the spill area to the northeast of the SWM pond. Though not included on this specific map, an ownership parcel fabric could be added to determine which properties would be flooded during different conditions. This level of detail and representation of real features is an extremely effective tool for engineers, planners, and can be understood by the expert and the layperson alike. In terms of Engineering work, the 3D data fusion approach allowed – for the first time – for the SWM

pond to be considered within the context of its surrounding terrain. This revealed some concerns in

terms of the capacity of the pond and its effect on adjacent properties in the event of an extreme flood.

By considering the SWM pond and surrounding terrain together, structural changes to the pond were recommended in order to protect adjacent properties during an extreme flood event.


60

Additional findings by the GRCA Engineering department included confirmation of proper functionality

of the SWM pond in terms of storm water quality. It was also determined that due to accumulated

sediment there was a need for the pond to be cleaned out.

Figure 36 Contours of interest draped over orthophoto (GRCA, 2013)

6.6 Summary

With modern terrain modeling technologies and techniques, it is now possible to consider multiple

standalone surveys in one cohesive context. Particularly in natural hazard areas, this approach is far

more effective than considering multiple surveys in a disjointed fashion. The data fusion approach not

only allows for the fusing of different datasets to produce one ‘enhanced’ dataset that’s easy to work

with, it also inherently reduces a complex scenario into a much more understandable and

communicable data model.


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7.0 Assessing and Reporting

Accuracy

7.1 Introduction

Flood line mapping must based on elevation data of a known level of accuracy that meets project

requirements. This data can be acquired through a number of means using remote sensing, GIS, and

surveying techniques. Given the complexity of the engineering computer models used to simulate

various flood events, even a small amount of error can have wide-ranging effects on the final results.

Elevation data can be processed for use as input data for various water resource engineering models.

Results from the engineering models are then represented on a DEM to produce the actual flood lines,

identifying the area of inundation for a specific flood event. Once the flood lines are produced it is then

overlain on orthoimagery to provide a visual representation of a simulated flood event.

This Section outlines a project workflow that considers LIDAR, orthoimagery, and RTK survey used as

spatial data in support of flood line mapping. The peer review process is divided into two stages: 1)

project design, and 2) project results. “Project design” follows a quality assurance process in that the

project planning was done is such a way that the deliverables are ensured to meet specifications and

requirements. “Project results” refers to the quality control of all deliverables, ensuring that all data

components received actually meet the specifications under which they were acquired.

Each of the remote sensing and GIS data components will be considered for these two stages of peer

review.

7.2 Data Accuracy and Flood Line Mapping

The creation of a flood line map is a multidisciplinary approach that combines GIS, remote sensing,

engineering, and surveying. Conceptual geospatial and engineering models are relied upon to

determine the effects of various different flood events in a given study area. Accuracy and precision

must be thoroughly understood and communicated when extracting real world information from such

digital models.

In short, floodplain mapping is the exercise of determining the spatial and temporal dynamics of

specified flood events. The process can be divided into two main components: 1) hydrologic and

hydraulic analysis, and 2) elevation modeling. The former employs water resources engineering

techniques while the latter is a remote sensing practice. GIS and surveying are used for both

components to properly georeference and manage the information being captured, modeled, and

generated.


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7.2.1 Hydrologic and Hydraulic Analysis

Water resource engineers construct flood simulation models for watercourse reaches in a given area of

concern. Software developed by the U.S. Army Corps of Engineers is currently an industry standard for

conducting the hydrologic and hydraulic analysis portion of flood line mapping. Hydrologic Engineering

Center-Hydrologic Modeling System (HEC-HMS) flood hydrologic model transforms storm rainfall input

into stream flow discharge output by considering drainage area, slope, length of the longest flow path,

and land cover and soil type. Alternative softwares used for hydrologic modeling include Storm Water

Management Model (SWMM) developed by the U.S. Environmental Protection Agency (EPA) and

OTTHYMO by Clarifica, among others. Regardless of the software, a flood hydrograph is used to estimate

the peak flow for each cross-section of the river reach. The peak flow represents the maximum flow or

discharge of water that the reach will experience during the storm event being considered.

The output of the hydrologic model is then entered as input data for the Hydrologic Engineering Center-

River Analysis System (HEC-RAS) flood hydraulic model which then takes the maximum flood discharge

at each cross-section along a streamcourse reach and gives an output of the water surface elevation at

that location as a steady-state analysis. The resultant information is then overlain upon a digital

elevation model (DEM) to produce a 3D flood line representing the inundation area for the specified

flood event.

7.2.2 Elevation Modeling

The DEM is a fundamental dataset for floodplain mapping at both the beginning and final steps. Firstly,

hydrologic information such as slope and overland elevation data are derived from the DEM for input

into the engineering HEC-RAS model that determines water surface elevations. Hydraulic information

can also be extracted from a DEM which is of particular importance for providing the finer precision and

detail required for mapping sub-100 year events.

The process of converting the outputs of engineering models into useable information relies solely on

the elevation model being used. High levels of horizontal and vertical accuracy are crucial in generating a

flood line that closely represents the area of inundation during a given flood event. For example, a

difference of mere centimetres in the vertical could mean the difference of kilometers in floodplain

area, particularly in the case of areas consisting of predominantly low-lying flat terrain. When

considering that people and property are at risk during a flood event, the delineation of the elevation

model of the highest quality is crucial.

7.3 Data Accuracy

Standard requirements for assessing and reporting the accuracy of flood line mapping data has evolved

over the years, with the focus shifting from hardcopy analog maps to digital data. Key in this transition is

the move from the use of scale to the use of more absolute accuracy values. For example, FDRP

standards from the 1980’s the focus lies with measuring a position as scaled off a hardcopy map. In

regions such as the U.S. where flood line mapping is digital, accuracy is assessed at an absolute, scale-

free value.


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In Ontario, official digital flood line mapping standards which do not currently exist. However, what does

exist are data acquisition standards which are used to acquire the data which can support a flood line

mapping project. This section takes a look at how these standards can be applied in a flood line mapping

context before offering a brief account of examples from other countries, namely the U.S. and Australia,

where more developed digital data accuracy reporting standards exist.

7.4 Data Acquisition Quality Assurance

Given that flood plain mapping is often conducted for large areas, remote sensing is the optimal data

acquisition technique in that it can be used to provide elevation data at the high levels of accuracy and

precision required for flood plain mapping. LIDAR is one such data capture technique that provides

extremely dense and accurate terrestrial representation over large areas. Pixel autocorrelation – a

photogrammetric technique of extracting elevation data from aerial imagery – can provide a more cost

effective alternative in acquiring LIDAR-like point cloud information.

To further ‘tie in’ the elevation representations derived from remotely sensed datasets, manual editing

of topographical features is a must. This can be achieved in-lab digitizing using digital photogrammetry

or in-field surveying using RTK GPS with each method yielding comparable results. It is imperative that

the elevation model represents reality as accurately as possible.

7.4.1 LIDAR

LIDAR is a remote sensing data capture technique that can provide highly accurate and precise elevation

data for a floodplain mapping area of interest. A LIDAR sensor can be mounted on a fixed-wing or rotary

aircraft in an effort to capture a point cloud representation of a given area. This point cloud is then

filtered and classified to delineate ground from other features. For the purposes of floodplain mapping,

it is the bare earth ground representation that is required. This delineated bare earth point cloud

dataset can then be turned into a TIN or raster DEM for use. Filtering and classification algorithms must

be customized to accommodate the inherently unique combinations of vegetative cover types and

terrain undulations found in the study area.

LIDAR datasets can be quite large and require a quite substantial amount of computer processing

power, particularly for the filtering and classification stage. Once ground points have been extracted

from the raw point cloud, this dataset can be made much more manageable through current data

modeling techniques.

7.4.2 RTK GPS

RTK GPS allows for data to be captured at centimetre-level accuracy in the field. The data captured can

then be imported into the GIS lab as 3D breaklines and topographic points. Using RTK GPS technology

bridges the gap between lab and field in a new and effective way as an in-the-field digitization tool.

In terms of cross-sections being used as input for hydrologic models, accurate and precise field

surveying is a must.


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7.4.3 3D Digitizing

3D digitizing is the manual capturing of features in a 3D stereoscopic environment. Overlapping aerial

imagery stereo pairs are georeferenced in a digital photogrammetric software to enable a

photogrammetrist to visualize and manipulate the scene in 3D. With current software packages, data

can be created at centimetre-level accuracy by a skilled photogrammetrist. The photogrammetric

approach can be particularly valuable given the birds-eye-view afforded by viewing a large area in 3D.

Furthermore, emergent features can be detected that may not be apparent when in the field.

7.4.4 Summary

As with all data acquisition techniques listed above, the key concept is to understand the strengths and

limitations afforded by the different data capture technologies. It must be maintained that all digital

spatial data contains some level of error. To remedy this, it is important to thoroughly understand all

forms of error in order for it to be effectively managed, communicated, and ultimately interpreted by an

end user.

7.5 RTK GPS

As with any engineering exercise, flood line mapping involves understanding and managing error. In

order to understand error of the elevation products to be used to generate flood lines, a means to

properly quantify and measure it must be established. RTK GPS technology allows for topographic field

data to be captured in an accurate and efficient manner. The centimetre-level accuracy offered by RTK

GPS is within the allowable error threshold for flood line mapping, thus making RTK GPS survey the ideal

form of data capture for generating topographic control data.

7.5.1 Project Design

In order to rely upon RTK GPS surveyed data as control data it must first be tested for its own accuracy.

To achieve this, a dataset of higher accuracy must be used for comparison.

7.5.1.1 Precise Point Positioning

Natural Resources Canada offers a web service which offers the capability of refining the accuracy of a

surveyed point to millimetre-level by way of a program called Precise Point Positioning (PPP). Commonly

used to establish survey monuments of the highest accuracy, PPP is an efficient and practical way of

planning and executing any survey job, such as flood line mapping, where the highest level of accuracy is

required. PPP applies post-processing corrections to a static survey which works to refine the location of

the survey using ultra-precise geophysical data acquired by Earth observation satellites during the time

the static survey was conducted. In short order, the web service returns a report which describes the

corrections applied to the point and its refined georeferenced location with accompanying statistical

information. This is where the accuracy assessment of a flood line mapping project begins, at +/- 0.001

m accuracy.


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7.5.1.2 RTK GPS Accuracy Assessment

Topographic PPP points can be established and re-shot using the RTK GPS equipment to be used for

capturing field data, resulting in a solid assessment of the accuracy of the equipment. The statistics used

to communicate error in 3D space are the Circular Map Accuracy Standard (CMAS) for horizontal

accuracy, and Linear Map Accuracy Standard (LMAS) for vertical accuracy (see Figure 1). Both are based

on the root mean squared error (RMSE) statistic.

(Imagery and Elevation Acquisition Guidelines, Queen’s Printer, 2009)

7.5.1.3 RTK Augmentation Data

In addition to capturing data for control purposes, RTK survey can also be used for capturing features

that can be used as augmentation data when producing elevation models. It is important to note that

data captured for augmentation purpose should not be used for the quality control of elevation

products as it would produce skewed results which would compromise the overall integrity of the

accuracy assessments.

The augmentation RTK survey dataset, herein referred to as the RTK AUG dataset, consists of actual

features to be used in conjunction with LIDAR for elevation modeling. Such features targeted in this

stage can include cross-sections for import into HEC-RAS models as standalone datasets or for fusing

with other data for interpolation in a raster. With modern geospatial modeling and data capture

technologies, it is possible to combine different datasets for collective interpolation into “enhanced”

DEMs.

Features targeted can be represented in the GIS lab as linear breaklines or standalone topo points.

Generally speaking, breaklines represent a spatial discontinuity in a given area of terrain (ex. top of bank

for river reach, ditch, shoreline, etc.). Breaklines are particularly important in “tying in” the elevation

model. By including breaklines, known linear features are “enforced” in the DEM at the interpolation

stage.

The practice of capturing breaklines with RTK GPS is similar to the photogrammetric practice of using 3D

stereo images to interpret and digitize breaklines. RTK technology enables this to be done in the field

but should not be considered a replacement for photogrammetry. There are certain features that are

only made evident by the unique perspective afforded by 3D nadir-view stereoscopic interpretation. RTK


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GPS is most effective in capturing larger scale features such as hydraulic cross-sections and top-of-bank

features that are best interpreted in the field, ideally in the presence of a water resources analyst.

7.5.1.4 RTK Cross-Section Data

Depending on the methods used to acquire cross-section data, the accuracy may already be understood.

RTK GPS is an effective means of acquiring cross-section data for smaller rivers and stream and, as

demonstrated in the results of Section 7.2.2, the accuracy of this data is already known. If the

watercourse is too large for RTK GPS to be used, many SONAR units can be tied into an RTK network

through either being retro-fitted with an RTK GPS receiver, or having one already built in.

7.6 LIDAR

For the purpose of capturing highly-accurate topographic data for a large area, LIDAR is a very efficient

and effective tool. There are many different aspects to laser mapping so this document will only focus

on using terrestrial LIDAR designed to meet Ontario floodplain mapping specifications. In this context,

LIDAR is flown by either a fixed-wing or rotary aircraft for a given area of study. LIDAR differs from

traditional remote sensing data acquisition techniques in that it is technically more accurate in the

vertical than in the horizontal (the opposite is more commonly the case, traditionally). A GPS base

station network is installed in the area of interest for the onboard GPS to connect with in order to

accurately position the aircraft in the sky. Alongside the laser scanner and onboard GPS is the inertial

measurement unit (IMU) which acts as a highly-accurate digital gyroscope, further orienting the aircraft

in terms of roll, pitch, and yaw (or omega, phi, and kappa). The flight dynamics information captured by

the IMU complements the GPS information to accurately determine the orientation of the LIDAR sensor

aboard the aircraft relative to the earth.

Once the LIDAR acquisition mission is flown, the data from the laser scanner is post-processed with

orientation and differential GPS data to produce a point cloud. A point cloud is an unevenly dispersed

mass of points each with x, y, and z values used to orient each point in 3D space. The point cloud is then

classified for ground and non-ground features via a combination of computer filtering algorithms and

manual checking. Filtering and manual checking is conducted in an iterative process until all that

remains is the bare earth masspoint representation – also known as a digital terrain model (DTM).


For the purpose of flood line mapping, specifications for LIDAR data acquisition must be extremely

accurate and precise. LIDAR emits a laser pulse that bounces off an object and returns to the sensor.

Since at this scale the speed of light can be – for practical purposes – considered to be instantaneous,

distance becomes a function of time which is measured at extreme precision.

Point density is a project planning consideration that can directly affect the cost, causing it to rise with

an increase to the number points per metre. Generally, two points per metre is an acceptable level for

flood line mapping.


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LIDAR equipment can be set to return a pulse one or more times. What this means is the last pulse to

return is the furthest away (distance being a function of time) so it can be assumed in most cases that

the last return represents bare earth. Depending on the non-ground features in the area, it may be

desirable to capture multiple returns which can assist in differentiating between non-ground features,

such as buildings from trees, and so on. For the purpose of flood line mapping, two or three returns

should suffice in getting to the desired bare earth representation.

7.6.1.1 Point Classification

After the last return pulse data is separated, the remaining point cloud is then classified into feature

categories. Common classification categories are as follows:

1. Processed, but unclassified

2. Bare-earth ground

3. Low Vegetation

4. Medium Vegetation

5. High Vegetation

6. Buildings

7. Bridges and other man-made structures

8. Noise (low or high, manually identified, if needed)

Additional classes can be added to the classification scheme should there be a need for it. The industry-

standard format for LIDAR data is the LAS file. It is the most efficient and readable format that exists at

present.

7.6.2 Results

For LIDAR quality control data, two separate – but not necessarily mutually-exclusive – RTK survey

datasets must be created. One dataset is for assessing the effectiveness of the remote sensing data

capture technique being employed (for the purpose of this section, LIDAR) in capturing accurate ground

representation in areas of different vegetative cover, which will herein be referred to as the “RTK VEG

dataset”. The second is for assessing the actual accuracy of the elevation products produced, which will

herein be referred to as the “RTK ELEV dataset”. Technical specifications for both surveys are identical in

terms of accuracy, thus presenting the opportunity for the actual field work to be streamlined into one

broad survey effort.

Once the processed LIDAR is delivered it must undergo a quality control (QC) process to ensure its

overall quality and compliance with project design specifications. The RTK QC dataset can be used as

control data for the LIDAR QC stage.

7.6.2.1 LIDAR Quality Control Vegetation

The first QC objective is to assess the effectiveness of LIDAR in capturing ground representation under

different land cover conditions. Since the DEM is the product being produced for the flood line mapping,

the final LIDAR point cloud which represents bare earth is interpolated into a Test DEM. This Test DEM is


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then checked for accuracy using the RTK QC dataset, extracting values from the Test DEM raster cells

coincident with the RTK survey points. Circular Map Accuracy Standard (CMAS) and Linear Map Accuracy

Standard (LMAS) values are then calculated for each category of land cover (at 90% confidence or

greater), to statistically represent accuracy in the horizontal and vertical, respectively.

The raw LIDAR acquisition must be checked for quality and compliance with the Ontario Imagery and

Elevation Data Acquisition Guidelines (2009) for its effectiveness in representing the natural world. Of

primary concern, is to assess the effectiveness of LIDAR in capturing bare earth representation across all

land cover scenarios in the area of interest. The 2009 guidelines identify five main vegetative land cover

categories:

1. Bare earth and low grass (e.g., roadways, ploughed fields, lawns, and golf courses)

2. High grass, weeds, and crops (e.g., hay fields, cornfields, and wheat fields)

3. Brush lands and low trees (e.g., young alders)

4. Forested, fully covered by trees (e.g., hardwoods, evergreens, mixed forests)

5. Urban areas (e.g., high, and/or dense manmade structures)

RTK GPS survey data must be captured in areas that apply to the five main land cover types to produce

the RTK VEG dataset, thus enabling a quantitative assessment of the LIDAR deliverables. To maximize

statistical integrity, twenty survey points should be captured for each land cover category as randomly

dispersed as possible. It should be noted that in the absence of one or more of the land cover types, it is

not necessary to capture more points for the existing land cover types. Twenty points per vegetative

cover category present in the study area will suffice.

7.6.2.2 LIDAR Quality Control Elevation

The second QA/QC dataset, the RTK ELEV dataset, is to test for the different levels of accuracy required

for flood line mapping. In the Ontario Imagery and Elevation Data Acquisition Guidelines (2009), four

levels of accuracy/detail are given:

Level 1 - Densely populated urban areas within the Regulated Flood Line or areas with very high

flood or other land form based risk are governed by Provincial Regulations and require detailed

information for land use planning and emergency management. These areas shall be mapped to an

accuracy of +/- 0.125 metre CMAS and LMAS.

Level 2 - Densely to moderately populated urban areas that may or may not fall within the

Regulated Flood Line likely may require less detail, but are critical to many potential funding

partners for a wide variety of business needs. These areas shall be mapped to an accuracy of +/-

0.25 metre CMAS and LMAS.


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Level 3 - Moderately to sparsely populated areas that are primarily surrounded by agricultural

and/or forested lands. These areas shall be mapped to an accuracy of +/- 0.5 metre CMAS and

LMAS.

Level 4 - Sparsely populated rural areas or areas with very low flood or other land form based risk.

These areas shall be mapped to an accuracy of +/- 1.0 metre CMAS and LMAS.

As with the vegetative cover assessment, a minimum of twenty ground control points must be used for

the CMAS and LMAS calculations for each accuracy level present in the study area.

Opportunity exists to streamline the RTK survey process by collecting points in a way that can be used

for both the vegetative cover and accuracy levels analyses. These collected points can be stored as two

separate datasets or one single dataset with two attribute fields.

7.7 Orthoimagery

Orthoimagery is an important component of floodplain mapping in terms of representation and

communication of results. Flood lines are overlain on georeferenced orthoimage data to determine

which properties and features lay within the flood hazard area for the given events. Given the potential

significance of an existing or proposed development being within a flood hazard area, the quality of the

orthoimagery must be at a level deemed acceptable for proper representation of flood lines.


For the purpose of flood line mapping, orthoimagery must be acquired at high quality. Various variables

must be taken into account, from the actual acquisition flight to the orthorectification of the captured

images.

7.7.1.1 Flight Plan

In terms of the actual mission flight, it is important to define the flying height to ensure proper

photogrammetric integrity. The time of flight is important primarily for controlling seasonal conditions

and shadow. Ortho missions can be flown in leaf-off or leaf-on depending on the use of the data. For the

purpose of flood line mapping, leaf-off is desired due to the visual exposure of ground features. Also,

snow-free is desired to further improve the interpretation of ground features. This leaves spring

(March/April) or fall (October/November) as ideal flying times in central Ontario for capturing leaf-off

and snow-free orthoimagery. Sun angle is important to consider in avoiding excessively-long shadows

from trees and buildings. An angle of at least 30° or greater is widely accepted as the optimal balance in

minimizing shadow while maximizing flight time duration.

To ensure success at the orthorectification stage, it is important to specify side and front photo overlap

parameters. In practice, overlap represents how the photo outlines overlap on the ground at nadir angle

during the flight in their raw form. For frame based sensors it is recommended that forward overlap be

a minimum of 65% and lateral a minimum of 20%, with both not exceeding 75% and 25%, respectively.


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For pushbroom sensors, forward overlap is not applicable given the scanning nature of the equipment.

Lateral overlap for pushbroom sensors should be at least 20% but no more than 25%.

Ground sample distance (GSD) is another important specification that is a function of flight planning. By

definition, GSD is the minimum size of data captured on the ground. This applies to the raw data but

determines the spatial (pixel) resolution of the final orthoimage. The orthorectification process requires

some additional sampling area to achieve proper spatial resolution. Traditionally, when using frame

based sensors a 4:5 ratio is employed (ex. GSD of 16cm in a raw image confidently produces

orthoimagery at a spatial resolution of 20 cm). It is therefore recommended that GSD and spatial

resolution are defined at the project design stage.

7.7.1.2 Data Deliverables

Aerial imagery is most commonly delivered in GeoTIFF uncompressed format or MrSID compressed

format. Other formats exist and may be more desirable given the technical capabilities of the purchaser.

The number of bands should be specified to ensure the proper deliverable is produced. For flood line

mapping, red-green-blue bands (RGB) will suffice as this represents the entire visible portion of the

electromagnetic spectrum. Additional spectral bands, such as near-infrared (invisible to the human eye),

may be offered but are not necessary for flood line mapping.

7.7.2 Results

Orthoimagery should be manually-inspected and assessed both qualitatively and quantitatively.

7.7.2.1 Qualitative Assessment

Visual inspection should be conducted for each tile delivered. Potential errors in orthoimage data

include:

Shadow

Clouds

Blurring

Colour imbalance

Sun glint

Pixel saturation

Relief displacement (features appear to lean away from nadir angle)

Aberrations unspecified

Any tiles found to include errors should be immediately reported to the vendor. There is potential for

such errors to be remedied by repeating the orthorectification stage but in some cases new imagery

may need to be captured altogether.


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7.7.2.2 Quantitative Assessment

Spatial accuracy of orthoimagery can be assessed using RTK survey of ground-identifiable features called

ground control points (GCPs). This means simply taking a RTK GPS survey of a feature at a road

intersection or parking lot that is easily identified in an orthoimage. The RTK GPS measurement can then

be loaded into a GIS along with the orthoimage to assess its horizontal accuracy. If multiple GCPs are

captured it is then possible to produce a statistical report on the horizontal accuracy of the orthoimage

data using the CMAS statistic.

7.8 Final DEM

Assessing and reporting DEM accuracy is a challenging task due to the fact that it is an interpolated

product. When data is expressed as a gradient, results must inherently be expressed in terms of levels of

confidence. When data is reported to be at +/- X m at a confidence level of 90%, in English that means

that of 100 points sampled, only 90 will be accurate to X m. Conversely, this also means that 10% of

points sampled will be outside of that statistical range. This is the nature of an interpolated dataset

whereas a vector dataset of discrete values can be assessed to an absolute value, assessing the accuracy

of a raster DEMs is a little more complex.

According to the Ontario Imagery and Elevation Acquisition Guidelines, the four levels of accuracy must

be assessed across the five different vegetation types. Using RTK GPS survey, points can be captured “at

a higher level of accuracy” in these areas and used in a GIS lab to determine the accuracy of the DEM

product.

Complications often arise when considering the accuracy of a DEM. For instance, if the DEM is reported

to be at +/- X m at a 90% level of confidence, what then is the accuracy of a clipped subsection version

of the DEM? Statistically-speaking, the original accuracy level does not apply as the distribution of points

has been altered with a smaller sample having been taken. Further to this, what if the error in a DEM is

not randomly-distributed – as is often the case – and the area that was clipped out is the very area

where the optimal level of accuracy was not achieved? Such consideration is imperative in the case of

flood line mapping as floodplains are often low-lying, densely vegetated areas – the very types of

conditions that prove troublesome when attempting to capture highly-accurate elevation data using

LIDAR, PAC, or any other means of data acquisition. Therefore, it is very important to determine the

spatial extents of the DEM, or subsection of DEM, that will be used and ensure that the satisfactory

amount of control data points are featured across the relevant types of vegetative cover.


Regardless of the production method employed, the final product must meet the accuracy specification

defined by the project engineer in accordance with the Ontario Imagery and Elevation Data Acquisition

Guidelines (2009). The project study area may feature one accuracy level or multiple accuracy levels.

This should be determined before the RTK GPS surveying is conducted to ensure test data for the final

DEM products.


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Concurrent to accuracy, spatial resolution (pixel size) of the DEM must be taken into account in that it

will determine the precision at which the flood lines are generated. This, too, is to the discretion of the

project engineer. Currently, half-metre resolution is the industry norm.

Manual inspection of the DEM should occur in the same fashion as at the orthoimagery QC stage. With

flood line mapping, some non-ground features may be maintained as they may affect surface water flow

(ex. Flood-retention walls, etc.). It is possible that such features may be targeted for removal by the

DEM vendor and consideration should be given to the matter by the project engineer.

7.8.2 Results

As outlined in Section 3.2.1, there exist four levels of accuracy for elevation data supporting flood line

mapping. Using the QC RTK Accuracy described in the same section, each DEM should be assessed for

CMAS and LMAS to ensure the data meets the engineering-specified level of accuracy.

After accuracy is determined, it must be reported in an understandable manner. This is commonly

referred to as metadata. Metadata is data about data. It describes all aspects of a dataset from geodetic

datum to how it was created. All input data should be thoroughly described. In the event of a DEM

featuring different accuracy levels for different areas, it is recommended that a spatial file or

cartographic map be included to ensure these aspects of the dataset are clearly communicated. The

effectiveness of a data product is determined by how well others can use it, whether it is for the current

project or an unanticipated purpose in the future.

7.9 Summary

Flood line mapping is an interdisciplinary collaboration between engineering, remote sensing, GIS, and

surveying. The success of any flood line mapping project depends upon effective communication and

cooperation between the aforementioned disciplines.

From a remote sensing and GIS point of view, much can be gained from the high levels of precision and

accuracy afforded by modern data acquisition techniques. Recent technological advancements have

pushed the boundaries of environmental modeling, enabling data to live longer and scientific

exploration that was previously unfeasible or cost prohibitive for most organizations. Remote sensing

can provide users with topographic data of the highest quality for large areas with relative ease and at

increasingly low costs.


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8.0 Cost Comparison

When comparing costs of new mapping technologies with old, it is important to understand the context

within which they are, or were, applied. For instance, accuracy standards have advanced significantly in

recent decades, effectively ruling out some traditional survey methods due to their ineffectiveness in

achieving the current standards. Furthermore, some technologies – though effective and efficient – are

featured at higher costs due to lateral expenses due to the need for an aircraft to fly them.

This section attempts to tease out the cost efficiencies gained from modern mapping technology.

Additionally, it presents a new approach to the way in which the data is used and maintained.

8.1 Traditional Approaches

Traditional approaches to mapping included procedures that can be described, in modern terms, as

labour-intensive and did not include much in the way of technology. From manual photogrammetry to

theodolite surveying, mapping was a very tedious and time-consuming effort that was only able to

achieve limited levels of accuracy when compared with today’s standards. With that being said,

surveying and analog photogrammetry served the needs of the industry for a very long time. A local

survey of a work site with a total station and level can feature very high levels of accuracy and precision.

The shortcoming, when compared to modern capabilities, mostly lies in the inability to georeference

and make the data compatible with adjacent datasets or other data.

Site surveys involved tying into an established geodetic framework established by the federal

government with equal, if not, greater technical challenges. A site survey would be referenced to a

concrete monument and ‘closed’ in order to understand the relative error of the survey unto itself. This

effectively ruled out the re-use of local survey for blending with surveys of adjacent areas or an area

topographic dataset. Pouring over contour maps with prolonged analysis was the industry standard in

gaining a scientific understanding of the behaviour of the land or water in any given area in the face of

development or a natural disaster such as a flood. Furthermore, the manual element to this type of

work injected a substantial amount of subjectivity into final mapping products. For instance,

photogrammetric interpretation was – and still is to a degree – a subjective process. In collecting a

widespread point dataset, consistency would have been a large challenge in ensuring that the dataset

for an area as expansive as the Ontario landmass was standard across the board. Modern methods of

acquiring 3D point data for large area – such as LIDAR or PAC – are easier to assess for accuracy and

replicable results.

With the introduction of GNSS, GIS, and digital data capture technologies, the mapping industry

underwent a transformation nothing short of a revolution but with it came the need to redefine

standards and expectations.


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8.1.1 Costs Associated with traditional approaches

Traditional approaches such as theodolite, level, and non-GNSS enabled total station survey featured

labour costs proportionate to the amount of human effort required in their application. Compounded

with these relative inefficiencies is the fact that the data produced was far less useable in other contexts

than that for which the data was captured. The natural world is dynamic and tying topographic data to a

fixed monument renders it standalone and prone to quick expiry. That is not to say that the total station

survey is not still useful – it very much is – but when compared to modern GNSS-enabled data capture

technologies, traditional survey methods do not offer the same data quality and currency when

transferring to other applications.

It should also be noted that current data products were simply not feasible to produce using traditional

methods. For example, when considering a modern tree-top biomass LIDAR survey, it is difficult to

imagine a similar product being created using traditional methods. This discontinuity is prevalent

throughout comparisons between old and new approaches. The present day absolute levels of accuracy

employed and volume of data capture, were simply impossible before the advent of GNSS technology.

8.2 New Approaches

A common theme of this report is the ability afforded by modern technology to measure spatial

accuracy at extremely high levels of confidence. This has the double-edged effect of enabling not only

the capture of high quality data but also the ability to analyze and scrutinize the accuracy of such data

with scientific confidence. With technologies such as Precise Point Positioning and RTK GPS survey, a

data steward can have a thorough understanding of not only the data itself but appropriate uses of the

data when applying it to real world problems. Current GIS mapping technologies operate in absolute

accuracies at any scale. Much work is needed to understand this as well as how to apply new

technologies to such complex uses as creating and displaying a flood line.

8.2.1 Costs Associated with new approaches

Remote sensing technologies such as LIDAR and IFSAR are expensive not due to labour per se but more

due to the operational costs of the necessary equipment. In any LIDAR acquisition mission the bulk of

the costs associated is a direct result of the flying of aircraft used to mount the sensor. Sensors

themselves can be quite expensive but, generally speaking, pay for themselves once one considers how

much better the data captured is relative to data captured using traditional techniques. Furthermore,

data can generally be used for many more applications once acquired. For instance, one LIDAR

acquisition can yield data for municipal planning, floodplain delineation, surface water analysis,

development site selection, and more. Traditionally, a different mapping project would have to be

conducted for each of the aforementioned uses.

A LIDAR project flown in Ontario in 2012 at Level 1 accuracy (+/- 12.5 m) covering 4 km² of semi-urban

area at $1,050 per square kilometre, for a total project cost of $4,200. It should be noted that this

included only part of the data required for flood line mapping and did not include any orthoimagery

capture, ground survey, or photogrammetric processing. When considering a project to cover the total


75

area of Ontario (1,076,395 km²), the cost would be $1,130,214,750. Again, this is for just part of the

process and does not include the other data requirements outlined in Section 3 of this report.

Furthermore, established the necessary ground control for the aircraft flying in the area increases in

difficulty and cost as the latitude increases. Also, land cover challenges add further difficulty to capturing

data as widespread RTK GNSS ground survey in remote areas would be require incalculable costs in

communications infrastructure.

An alternative project scenario involving the Ontario Imagery Strategy could potentially result in lower

project costs. First off, the Strategy data specification established meets flood line mapping standards.

Digital photogrammetry could be used to derive overland topography from the aerial image stereo pairs.

Accuracies obtained through automated digital photogrammetric techniques such as pixel

autocorrelation would have to be optimized and explored to determine the feasibility of this approach.

For further exploration on this topic, please refer to Section 9 of this report.

8.3 Summary

Comparing traditional and novel mapping technologies is difficult and one should always consider the

disconnect in technological capabilities as well as in the standards and expectations. Generally speaking,

the majority of costs associated with mapping projects have transferred from labour to automated data

acquisition such as those offered by remote sensing techniques. The invention of computer processing

has been widely regarded as a “disruptive technology” in that it literally revolutionized how geospatial

data capture and analysis are done. From satellite data capture to real-time survey-grade GNSS

surveying, the industry has undergone a fundamental change in how projects are planned and executed.

The new tools may be expensive but the reduction in human hours spent acquiring data at a small

fraction of the quality and usability more than compensates for the gains offered by new mapping

technologies.


76

9.0 Recommendations

Flood line mapping is a multi-disciplinary initiative that covers a wide range of technical and policy

considerations. This report addresses the underlying spatial data that is required for flood line mapping

and offers multiple examples of how it can and has been applied. The recommendations outlined in this

section are areas of geospatial mapping and analysis in most urgent need of further research.

Out-of-scope for this section are the engineering models that consume the geospatial data inputs while

also relying upon geospatial data for further analysis and communication of results. Given the findings of

this report in terms of accuracy and precision, re-visiting these engineering models is a worthwhile

endeavour. There is a need to ensure that the gains offered by technological advancements in digital

mapping are fully realized in innovative modeling approaches.

9.1 The Fabric Approach

In an effort to effectively balance the unprecedented capabilities of new mapping technology with the

heightened levels of standards, an innovative approach to the management of the data is required.

Termed “The Fabric Approach”, a new perspective is required when approaching the long-term

management of elevation data which is expected to serve a host of new and exciting uses.

DEMs and contours maps are still the required elevation data formats for most analytical uses. They are

traditional in nature but are what most technical processes require. The idea behind the Fabric

Approach is to take a step back to the source data for DEMs and contour maps. New mapping

technology acquires elevation data in point cloud format which are then fused with correctional

breaklines and interpolated to produce DEMs and, from the DEMs, contours. The contour can be seen as

a derivative of the DEM which is a derivative of the native point cloud with breaklines. Current

approaches to geospatial data management commonly involve purchasing an “updated” DEM from a

vendor that is delivered as a raster product. Like an orthophoto, this raster DEM product is a snapshot in

time, obsolete as soon as any changes to the area topography occur. Purchasing a DEM can be an

expensive endeavour depending on the level of accuracy required but is even more costly once the rate

at which it needs to be update is considered. Updating a DEM generally means putting another plane in

the sky which is always costly. Unmanned Aerial Vehicle (UAV) – also known simply as “drone” –

technology offers a cheaper alternative to operating an airplane but the approach is still the same –

purchase a new DEM for an entire area every X number of months or years.

The Fabric Approach offers a different type of data management which can lower costs as well as

increase the currency of existing data holdings. Given the unprecedented level of understanding with

regards to data accuracy, the vector format is the preferred type of data due to its discrete and finite

nature. Discrete points and lines can be measured for absolute accuracy whereas raster data can only be

described in terms of relative accuracy or an estimate of the general accuracy within a statistical level of

confidence. If collections of 3D points and lines can be stored with well-known level of accuracy then

current GIS modeling capabilities offer the ability to alter this data selectively. For example, if a DTM


77

exists for a municipality and a new subdivision is slated for development, there is no longer a need to re-

acquire a DEM for the area. Instead, only the areas of change need be targeted with the “new

acquisition” being limited in spatial extent enabling municipal resources to be focused only in that area.

Once the area of change is captured it can then be fused with existing data for the surrounding area

producing a data product that can then be exported to an updated DEM. Further to this, breaklines can

now be captured by ways of digital photogrammetry or in-the-field RTK GPS survey for incorporation

into the elevation fabric as needed. Data management could be now focused on the ongoing

maintenance of this Elevation Fabric instead of expensive one-off DEM products which expire as soon as

anything changes.

9.2 Recommendations for Next Steps

9.2.1 Data Management

Through the completion of the work documented in this report, the Fabric Approach outlined above was

conceived but only applied on a limited scale. It is therefore recommended that the Fabric Approach be

tested on a larger scale such as a municipality or region. A full-cycle data management pilot study could

exemplify this approach for widespread adoption across Ontario. Given the number of large scale

topographic data gaps in Ontario, this approach could go a long way in terms of providing data to those

who need it.

9.2.2 Data Acquisition

Data acquisition techniques have evolved greatly in recent years and will undoubtedly continue to do so

for into the future. It is recommended that innovative data capture techniques – such as pixel

autocorrelation and UAV technology in general – be not only explored but applied in practical uses. As

evidenced in Section 5 of this report, the quality of data achieved via pixel autocorrelation is comparable

to LIDAR at only a small fraction of the cost.

The Ontario Imagery Strategy – a multi-year province-wide aerial image data acquisition program with

costs sharing across public and private sectors – effectively provides data required for pixel

autocorrelation and 3D digitizing alongside orthophotos of flood line mapping standard. Efforts in

optimizing and standardizing the processes undertaken to produce the best possible data from this

multi-year all-Ontario program could piggy-back on this already successful program that serves all levels

of government.

9.2.3 Flood Line Mapping

In 2013, there is a renewed emphasis on overland flood insurance in Canada and the flood line mapping

required to support it. To fly the entire country with LIDAR is currently wildly cost prohibitive and

exploring alternate sources of data acquisition is therefore a worthwhile venture. The Fabric Approach

described above could serve as a data management framework within which optimal data acquisition

techniques could be implemented, appropriate to scientific inquiry and associated budgetary realities.


78

The need for updated flood line mapping is urgent and widespread across Ontario with new mapping

technologies as the only practical option for moving it all forward.

9.3 Summary

The topic of flood line mapping is in urgent need of revisiting by all levels of government due to its high

importance and potential economic impact. The underlying spatial data which is the topic of this report

is at the fundamental level of this scientific inquiry but should be considered in the broader context

given its far-reaching political and economic implications. Furthermore, need for revisiting flood line

mapping is not an Ontario-specific issue but is of national importance. Adding to the state of flood line

mapping is the increasing concern surrounding climate change and the strategic role flood line mapping

can play in adapting to projected scenarios. The increased frequency of significant rainfall events

coupled with the multiple freeze-thaw events further exacerbates the issue of flood mitigation and how

strategies can be developed at the municipal level with provincial oversight. The role of the

Conservation Authority in Ontario is an important one as it offers an effective and practical bridge

between these two levels of government as has been evidenced many times in recent history, with the

Source Water Protection program being one such example.

Given the state of flood line mapping across Canada and the rapidly increasing strain-effect flood events

have on the Canadian economy, a national mapping strategy which harnesses the new technologies

outlined in this report could be a hugely beneficial asset to disaster management in Canada.

GIS Floodplain Mapping

Processing Engineering Data Models into a Floodplain GIS Analysis

Prepared By:

Cody Brown – GIS Technician


December 2013


Page 2 of 17

Table of Contents

1. Overview

2. Convert Cross Section Survey Data into an Engineering Format

2.1. Set up for the file structure.

2.2. Format the surveyed cross section points.

2.3. Convert the surveyed cross section points to a line that represents the cross section channel.

2.4. Interpolate the DEM to create the out of channel portion of the cross section.

2.5. Merge the cross sections together.

2.6. Convert the cross sections vertices to points and remove overlaps.

2.7. Create the excel output.

2.8. Create the shapefile output.

3. Engineering Modelling

4. Converting the Engineering Data Model Result into a GIS Format

4.1. Set up the file structure.

4.2. Format the Excel Future Flows and Velocities into a CSV File

4.3. Export Future Flows and Velocities Table to a CSV

4.4. Format the Cross Sections to the Floodplain

4.5. Join the Cross Sections to the CSV

5. Setting up the Two Zone Analysis

5.1. Format and split the cross sections.

5.2. Create the channel polygons

6. Producing a Two Zone Analysis

6.1. Depth Raster Analysis

6.2. Velocity Raster Analysis

6.3. Depth / Velocity Product Raster Analysis

6.4. Depth on Roads Analysis (Ingress / Egress)

6.5. Storage Areas and Volumes Analysis

6.6. Ongoing Production of a Floodplain


Page 3 of 17

1. Overview

The goal of this methodology is to take raw real time kinematic (RTK) GPS and total station cross section survey

data and process it into a format that can be used by engineering modeling software and then used in a GIS

floodplain analysis. This survey information is used in conjunction with Lidar digital elevation models to create

cross section that best represent the channel of a stream. The output is then analysed within an engineering

floodplain model. Once the engineering modelling had been completed it is then processed into a visual

floodplain two zone analysis.

This report uses GIS terminology frequently and may be unclear to a reader inexperienced with GIS. The software

used in this methodology is ESRI ArcGIS Desktop Advanced 10.1 SP1 (Build 3143). The ArcGIS Extensions 3D and

Spatial analyst are also required. Specific ArcGIS Desktop tools are hyperlinked for additional information like so:

‘ArcGIS Tool’.

http://resources.arcgis.com/en/help/main/10.1/index.html


Page 4 of 17

2. Convert Cross Section Survey Data into an Engineering Format

2.1. Set up for the file structure.

The data need to begin this process is the surveyed RTK GPS and total station cross section point shapefile

and the Lidar digital elevation model (DEM). Gather this data into a working directory. Within this working

directory create an ESRI File Geodatabase.

In the Geodatabase create three feature datasets: RawData, TemporaryData and CrossSections. Ensure all

three are of the same horizontal (XY) and vertical (Z) projection. Import the surveyed points into the RawData

dataset.

2.2. Format the surveyed cross section points.

Open ArcMap and add the surveyed points to the data frame. The surveyed points should have three fields,

one denoting the cross section it is a part of, another field designating the survey point type and one field

indicating the surveyed elevation.

Each group of cross sections should contain at least five points with point designations of: TB (top of bank),

SL (shoreline), CCL (creek centerline), SL and TB. Each group can also have points such as BOB (bottom of

bank; in between TB and SL) and BATH (bathymetric; in between SL and CCL). Manually go through the

surveyed points to ensure that each cross section has this. Add a data source field to the attribute table of

the points and populate it with text denoting it as a surveyed point.

2.3. Convert the surveyed cross section points to a line that represents the cross section channel.

Use the tool Points to Line on the surveyed points. Select the cross section field as the Line Field. This will

create a separate line for each group of points that makes up a cross section. If you have a field that denotes

the right to left makeup of the points (all cross sections should go left to right down the stream) put that

field in the Sort Field. Save the result in the TemporaryData dataset.

If there is no sort field in the points to line process, each cross section must be manually checked to ensure

that the converted cross section lines go from left to right in the correct order. Edit the vertices of the line if

they are incorrect.

http://resources.arcgis.com/en/help/main/10.1/index.html#//00170000003s000000


Page 5 of 17

2.4. Interpolate the DEM to create the out of channel portion of the cross section.

Create a new line feature class in the TemporaryData dataset and begin editing it. For each cross section

create a new line to extend the cross section at each end of the channel segment. Follow as closely as

parallel to the channel cross section as possible, but do not intersect cross section lines. Extend each cross

section to the approximate floodplain. Extending each line further is more beneficial than not extending it

enough.

These newly created line extensions are the out of channel cross sections. These lines now need to be given

elevations from the DEM. Use the 3D Analyst tool Interpolate Shape on the newly created cross section

extensions. Use the following parameters for this tool.

Input Surface will be the DEM

Input Feature Class will be the out of channel extensions

Output Feature Class will be a 3D line feature saves in the TemporaryData dataset

Sample Distance will be 0.5 metres

Z Factor will be 1

Method can be any of the options depending the needs of the project. Natural Neighbour is the

standard option.

Vertices Only should be set to densify

Pyramid Level Resolution should be left a 0

The result is the out of channel portion of the cross section now populated with elevations from the DEM.

2.5. Merge the cross sections together.

Manually merge all a three portions of each cross section together. The channels portions attributes should

be kept.

2.6. Convert the cross sections vertices to points and remove overlaps.

Convert the lines back into points using the Feature Vertices to Points tool. This tool requires the advanced

license of ArcGIS Desktop. Select by location any points from the result that intersect themselves. From this

selection remove any points that match identically with the surveyed points. Delete the leftover selected

points. Select any points that match identically with the surveyed points and delete them. Add a data source

field and populate it with DEM. The result is out of channel cross section points

http://resources.arcgis.com/en/help/main/10.1/index.html#//00q90000006m000000

http://resources.arcgis.com/en/help/main/10.1/index.html#//00170000003p000000


Page 6 of 17

2.7. Create the excel output.

Merge the surveyed points and out of channel cross section points together. Add an X, Y and Z field and

populate accordingly. Arrange the attribute table so that each cross section of points are grouped and

arranged going from left to right. Paste the attribute table fields: X, Y, Z, Cross Section and Data Source into

excel.

2.8. Create the shapefile output.

Convert the previous result to a line with cross sections as the grouping field. Ensure each cross section goes

from left to right down stream as this is integral for the engineering model.

3. Engineering Modelling

The excel worksheet and shapefile outputs created in the previous steps can now be delivered to the engineers

for modelling a floodplain analysis.

4. Converting the Engineering Data Model Result into a GIS Format

The engineers should produce three outputs from the analysis: a future flows and velocities table in an excel

worksheet, cross sections in a shapefile format and a floodplain in a shapefile format. The future flows and

velocities excel spread sheet needs formatted to be converted back into a useable GIS format.

4.1. Set up the file structure.

Gather the data from the Engineering Model Data Output into the working directory. Within the working

directory create a File Geodatabase. This Geodatabase should be different from the one used in previous

steps so that it can be analyzed if errors are present in the final output. In this Geodatabase create two

feature datasets: CrossSections and FloodArea. Ensure they are off all the same horizontal (XY) and vertical (Z)

projection. Import the cross sections into the CrossSections dataset and the floodplain into the FloodArea

dataset.


Page 7 of 17

4.2. Format the Excel Future Flows and Velocities into a CSV File

The excel workbook delivered from the engineering model needs to be formatted correctly to be joined with

a spatial feature. Most of this includes formatting and can be done several different ways. The file format

should look something like this:

Format the header by removing rows 1, 2 and 4. Each program derives this output a little differently, so

interpretation may be needed to adjust this based on the sheet. In essence, the sheet needs the first line as

the headers of each column.

Format each header label so that it has no spaces or special charters (. @ # $ % ^ & * - =) within it. Normal

characters are A to Z or 0 to 9. All the empty rows also need to be removed. The easiest way to do this is to

sort the ‘River Sta’ column from largest to smallest. This will automatically remove all empty rows.

All culvert rows need to be removed from the sheet if they do not have associated values in the rest of the

columns. In this example they all appear at the top of the sheet after sorting it, if they do not they need to be

manually found.

Ensure that any columns that have no value in them are replaced with a 0. If left blank it will result in errors

within the processing later on. The final sheet should looks like this:


Page 8 of 17

Each storm (profile column) needs to be separated from each other. The easiest way to do this is create a

new worksheet for each different storm, sort by the profile column to arrange the sheet’s rows by each

storm. Each year’s storm data can then be copied into another excel sheet.

4.3. Export Future Flows and Velocities Table to a CSV

Check with the attribute table of the cross sections to ensure that the RiverSta column will match will the

station number on the cross sections. There shouldn’t be an issue with this as the both are engineering

model outputs.

Save the sheet as a CSV file in the working directory. Microsoft Excel prompts that CSV’s cannot contain

multiple worksheets. Therefore the active sheet must be the worksheet saved as a CSV.

A separate CSV must be saved for each storm. Each storms data can also be append each into one sheet,

with different headers, all matching the cross section number. This can result in less work later on.

4.4. Format the Cross Sections to the Floodplain

Open ArcMap and add the floodplain and cross sections to the data frame. Each cross section must reach or

exceed the boundary of the engineering floodplain. These cross sections are depicted as they were modelled

and do not always extend to the floodplain. They must reach the floodplain boundary for the TIN analysis to

be completed correctly. This step is similar to the editing work done in step 2.4 but, this time a defined

floodplain exists.


Page 9 of 17

Start editing the Cross Section layer. The Extend command in the Advanced Editing toolbar of ArcMap will

speed up this process.

This command takes each line and extends its end to the nearest selected border. While the extend tool does

help, it only works in some areas. Extending some lines will result in intersections with other cross sections.

Cross Sections cannot intersect each other must be manually editing to ensure that they do not.

It is recommended to run the Intersect Tool after editing to ensure that the Cross Sections do not intersect.

Cross Sections before Edit Cross Sections after Edit


Page 10 of 17

4.5. Join the Cross Sections to the CSV

Add the exported CSV’s to the data frame. Join the cross sections attribute table to the CSV based on the

River Station or M Profile. Ensure that all records join. Culverts will not match up as they were deleted in the

excel document if they did not have attributes. Delete the culvert cross sections without attributes.

Export the joined cross sections to the CrossSections data set. A join and export may be needed to separate

each year of each storm to a different copy. All storms can also be combined in excel before exporting to a

CSV. This is done by adding a separate header for the different depth and velocity values for each storm and

then appending them to the appropriate cross section row.

5. Setting up the Two Zone Analysis

The Velocity portion of the Two Zone Analysis involves running the velocity of the storms down the cross

sections to show the velocity of flowing water. The velocity produced by the engineering models defines three

different areas on each cross section: channel left, channel and channel right. Three zones must be created for

each of these zones within the floodplain to accurately show velocity.

5.1. Format and split the cross sections.

Import a second copy of the cross sections into the CrossSections data set. These cross sections will be

temporary and not used for any analysis. Also create three new polygon feature classes in the ‘FloodArea’

data set: ChannelLeft, Channel and ChannelRight. These polygon features do not need any attributes. Using

the instructions in step 5.5, join these Cross Sections to the CSV.

Each cross section must be split into three. Use the channel station left and channel station right to define

the two splits along the cross section. Cross sections are drawn left to right down the river. The first split will

be the channel station left value along the cross section.

Take the right most part of the split cross section and split it based on a value of channel station right minus

channel station left. The cross section will now be in three parts. Do this for every cross section.


Page 11 of 17

5.2. Create the channel polygons

Start editing the three new polygon channel areas. Create a polygon for channel left that will extend to the

floodplain then follow the first split up the cross sections. The result should be one polygon. The Channel

polygon will be the interior splits of the cross sections. The channel can also be modified in between cross

sections to better follow the centerline on the stream. The Channel Right polygon will fill from the Channel

polygon to the floodplain.

There should be no gap between the three polygons. To account for raster to polygon issues a 0.5 meter

outside buffer should be added to the channel polygon.

Cross Section Channels:


Page 12 of 17

6. Producing a Two Zone Analysis

6.1. Depth Raster Analysis

This analysis will show the inundation in the floodplain based on the cross section depth surface versus a

DEM. Ensure that you have ArcGIS 3D Analyst and Spatial Analyst extensions before beginning these analysis

steps.

a. Use the 3D analyst tool Create a TIN on the Cross Sections to properly delineate a depth Triangular

Irregular Network (TIN) between the cross sections. Use the following parameters for this tool:

In Features will be the cross sections and select the appropriate depth field as the Height Field.

Ensure to select the Constrain Delaunay checkbox so that the cross section depths are absolute.

TIN’s should be saved in a separate folder within your working directory. Each storm depth TIN

should have a unique name.

b. Convert the TIN to a Raster. This allows for analysis with the DEM. This raster shows the depth of the

water based on the cross sections down the stream. Use the following parameters for this tool:

Input TIN should be the TIN that was just created.

Output Raster should be a GRID saved within the File Geodatabase.

Data Type must be set to float. Otherwise the result will not be precise enough.

Method should be set to linear, but natural neighbour is also acceptable. These two methods will

produce slightly different results.

Sample Distance should be set to cell size and the value after it should match that of the DEM.

This is so that the output DEM has the same cell size as the DEM.

Z Factor is left at 1 as the depths are already accurate.

The Geoprocessing Environments also need to be set for this process. The Output Coordinates

need to be set. The Processing Extend Snap Raster value needs to be set to the DEM. The Raster

Analysis Cell Size must also be set to the value of the DEM.

c. Minus the depth raster from the DEM. This raster math function minuses the depth from the DEM

showing the inundated areas. Before this step can be completed, ensure that the DEM is projected to

the same projection as your cross sections. Use the following parameters for this tool:

First Input Raster will be the result of the TIN to Raster.

Second Input Raster will be the DEM.

Output Raster should be a GRID saved within your File Geodatabase.




d. When creating the TIN’s, extra area outside of the floodplain may have been included. This results in

sinks and flooding of area’s that may not be affected by the storms water depth. To resolve this issue

the DEM needs to be clipped to the floodplain. Use the Extract by Mask tool specifying the floodplain

as the mask. Use the following parameters for this tool:

http://resources.arcgis.com/en/help/main/10.1/index.html#//00q90000001v000000

http://resources.arcgis.com/en/help/main/10.1/index.html#//00q900000077000000

http://resources.arcgis.com/en/help/main/10.1/index.html#//001w0000003s000000

http://resources.arcgis.com/en/help/main/10.1/index.html#//00q90000004r000000

http://resources.arcgis.com/en/help/main/10.1/index.html#//009z0000002n000000


Page 13 of 17

Input Raster will be the result of the minus process.

Input Mask Data will be the floodplain.

Output Raster will be the final depth raster product for this storm. It should also be stored as a

GRID within your File Geodatabase.




The resulting Depth Raster now should show the inundated areas based on the storm. The result

should be inspected for sinks and if any are found, the floodplain may need to be adjusted for the

Depth Raster analysis

Depth Raster:


Page 14 of 17

6.2. Velocity Raster Analysis

The process for Velocity Raster Analysis is similar to the Depth Raster Analysis and will draw on several of the

same steps.

a. Three TIN’s need to be created from the Cross Sections: Velocity Left, Velocity Channel and Velocity

Right. Each TIN should be created the same as in the Depth Analysis, except set the ‘Height Field’ to

the appropriate velocity field for the channel.

b. Each raster also has to be converted from a TIN to Raster. Use the same process as the Depth Raster

Analysis for all three TIN’s. Each should be named differently.

c. The three Velocity Rasters now need to be clipped to their respective channel area. Use the Extract by

Mask tool on each Velocity Raster. Use the following parameters for this tool:

Input Raster will be the result of the one of the three Velocity Rasters.

Input Mask Data will be the respective Channel polygon based on the Velocity Raster that you are

masking.

Output Raster should also be stored as a GRID within your File Geodatabase.




d. The three Velocity Rasters now need to be merged together to produce a cohesive velocity profile of

the creek. Use the Mosaic to New Raster tool. Use the following parameters for this tool:

Input Raster’s should be all three masked Velocity Rasters.

Output Raster will be your final depth velocity product for this storm. It should also be stored as a

GRID within your File Geodatabase.

Pixel Type should be set to 32 Bit Float to ensure that all values are accurate.

Cell Size should be set to that of the DEM.

Mosaic Operator should be set to mean so that all the values that overlap are averages. Other

values for this variable are acceptable and will produce slightly different results.




http://resources.arcgis.com/en/help/main/10.1/index.html#//001700000098000000


Page 15 of 17

Velocity Raster:


Page 16 of 17

6.3. Depth / Velocity Product Raster Analysis

The Depth / Velocity Product raster is a raster that combines the two previous analyses into a product that

defines different hazard zones within the floodplain.

a. As the Depth / Velocity Raster is a product of the Depth Raster and Velocity Raster is has to be

combined from these two to be created. Use the Times tool to create it.

First Input Raster will be the final Depth Analysis Raster.

Second Input Raster will be the final Velocity Analysis Raster.

Output Raster should be a GRID saved within your file Geodatabase.

Depth / Velocity Product Raster

http://resources.arcgis.com/en/help/main/10.1/index.html#/Times/00q90000008s000000/


Page 17 of 17

6.4. Depth on Roads Analysis (Ingress / Egress)

This portion won’t be done; I haven’t done it yet, for this report.

6.5. Storage Areas and Volumes Analysis


6.6. Ongoing Production of a Floodplain


Peer Review of Remote Sensing and GIS

Data Support for Flood Line Mapping –

Byersville Creek

Prepared By:


June 2013

Peer Review of Remote Sensing and GIS Data Support for Flood Line Mapping – Byersville Creek

1

Table of Contents

1.0 Executive Summary .......................................................................................................................... 3

2.0 Report Structure ............................................................................................................................... 3

3.0 Statement of Peer Review Work ..................................................................................................... 4

4.0 PHASE ONE: Quality Assurance Assessment ................................................................................... 5

4.1 RTK GPS ......................................................................................................................................... 5

4.2 LIDAR ............................................................................................................................................. 5

4.3 Orthoimagery ............................................................................................................................... 6

5.0 PHASE TWO: Quality Control - Procedures ..................................................................................... 7

6.0 Quality Control - Results .................................................................................................................. 8

6.1 Data Completeness ...................................................................................................................... 8

6.2 RTK GPS ......................................................................................................................................... 8

6.3 LIDAR ............................................................................................................................................. 8

6.4 DEM............................................................................................................................................. 10

6.5 Orthoimagery ............................................................................................................................. 10

6.5.1 Spatial Accuracy ................................................................................................................. 10

6.5.2 Visual Inspection ................................................................................................................ 10

7.0 Conclusions ..................................................................................................................................... 11


2

Table of Figures

Table 1 Completeness of deliverables .......................................................................................................... 8

Table 2 RTK accuracy relative to CBN monument ........................................................................................ 8

Table 3 Detailed results of vegetative cover accuracy assessment ............... Error! Bookmark not defined.

Figure 1 Project area ..................................................................................................................................... 9

Figure 2 Accuracy of elevation data across vegetative cover categories ...... Error! Bookmark not defined.


3

1.0 Executive Summary

In 2012, the Otonabee Region Conservation Authority (ORCA) began a flood line mapping analysis for a

small urban creek (Byersville Creek) within the City of Peterborough. It was determined during the

project pre-planning stage that there existed pronounced data gaps, particularly with regards to

topographic data, which needed to be addressed in order to ensure successful completion of the

project. As a result, ORCA joined the Kawartha Region Conservation Authority (KRCA) in acquiring Light

Detection And Ranging (LIDAR) data covering the area of undertaking.

The intended use of the LIDAR acquisition was for it to be processed into a digital elevation model (DEM)

of a level of quality sufficient to meet the spatial accuracy specification of +/- 0.125 m as defined in a

Letter of Intent issued to the vendor by ORCA. In addition to LIDAR, orthoimagery provided by the City

of Peterborough as well as data acquired using Real-Time Kinematic Global Navigation Satellite System

survey (herein referred to as RTK GPS), were also identified as meeting the required level of accuracy.

The Ganaraska Region Conservation Authority (GRCA) was asked to provide additional engineering and

remote sensing/GIS support. This document communicates the results of quality assurance (QA) and

quality control (QC) peer review analyses conducted by the GRCA with regards to the main remote

sensing and GIS data products used as support for the flood line mapping analysis.

2.0 Report Structure

This peer review has been divided into two distinct phases (as defined in the project terms of reference):

Quality Assurance (Phase One): dealing with project design and undertaken at the initiation of the

project thereby addressing goals a and b of the peer review which are:

a. Ensure consistency with standards and generally accepted approaches (project design)

b. Validate the work plan/planned deliverables

Quality Control (Phase Two): addressing project results and undertaken throughout the project work

addressing peer review goal a and c which are:

a. Ensure consistency with standards and generally accepted approaches (product)

c. Ensure scientifically defensible results


4

3.0 Statement of Peer Review Work

The preparation of flood line mapping is based on accurate elevation and position data. This can be

acquired through a number of means including LIDAR, aerial photography, and RTK GPS survey.

Additionally, elevation data can be processed into a DEM that is then used to extract data for the

creation of HEC-RAS cross-sections. Finally, results of flood line modeling must be represented on maps

and orthophotography that match the accuracy of the acquired elevation and position data. The peer

reviewer will consider the following questions regarding the creation and deliverables of LIDAR,

orthophotography, RTK GPS survey, and DEM products.

Quality of Data

Do the products meet the deliverable requirements of the proposal?

Do the products delivered meet industry and provincial standards?

Do the quality assurance tests undertaken in the peer review show satisfactory results?

Does the accuracy of the areas tested meet those required by the proposal?

Has the consultant delivered a complete dataset with all required accompanying reports and

information?

Have the clear explanations of all steps of the product delivery been provided and can future

users of the data clearly understand the data’s limitations and quality?

Can the products provided be used to appropriately provide information to and portray results

of the flood plain analysis being prepared?


5

4.0 PHASE ONE: Quality Assurance Assessment

4.1 RTK GPS

RTK GPS survey equipment was procured by KRCA through a Request For Proposal (RFP) process.

Quotations were collection from four vendor companies. Upon completion of the procurement process,

one Trimble GEO XH 6000 3.5G CM was purchased by KRCA (and lent to ORCA) with a Can-Net control

network subscription for real-time positional corrections (for achieving the required levels of accuracy).

Concurrently, the same process was conducted for procurement of a total station resulting in the

purchase of one SOKKIA CX-105 total station with prism.

Upon review of procurement documents made available by KRCA, it has been determined that the

project design for the RTK GPS portion of the floodplain mapping project was conducted consistent with

standards and generally accepted approaches.

4.2 LIDAR

The City of Kawartha Lakes issued an RFP for a combined acquisition of LIDAR and orthoimagery data

which was extended by ORCA to include the acquisition of LIDAR (without orthoimagery products) for

the Byersville Creek project area within the City of Peterborough. It was made explicit by ORCA through

the Letter of Intent that LIDAR be obtained for the study area “at an accuracy of +/ 12.5 cm”.

Specifications for the LIDAR acquisition described in detail within the City of Kawartha Lakes vendor

agreement are consistent with the Ontario Imagery and Elevation Data Acquisition Guidelines (2009)

and generally accepted approaches. Pulse return was required to be captured at a level of two discrete

returns, sufficient to delineate ground from non-ground features. Requiring the LIDAR data to be at a

nominal pulse spacing (NPS) of “no greater than 1 metre” while maintaining a spatial distribution that is

“uniform and free from clustering” ensures the data will be delivered at a quality that would allow for a

DEM to be created of flood line mapping standard.

Climate conditions are controlled for in the LIDAR collection specifications. The atmosphere is required

to “cloud and fog free between aircraft and ground” which would maximize atmospheric penetration of

both the emitting laser pulse as well as the subsequent returns. The mission is required to fly during

leaf-off conditions, which further increases the probability of penetration for the emitting laser pulses,

particularly in reaching bare earth. Finally, to maximize scientific validity of the data in accordance with

the stated goals of the project, the LIDAR data is required to be acquired in the absence of “unusual

flooding or inundations”.

The format of the LIDAR data delivered is required to be “fully compliant with LAS v1.2 or v1.3 format”

which is the current leading standard. The projection and datum of data delivered is stated as “6-degree

Universal Transverse Mercator Projection… Zone 17N” on “NAD83-CSRS (Canadian Spatial Reference

System)” which is the accepted standard for the project area. The vertical datum of “Canadian Geodetic

Vertical Datum 1928 (CGVD28)” alongside the stated Geoid model “Canadian Gravimetric Geoid 2000

(CGG2000)” are both with commonly accepted modern standards for the project area.


6

The products to be delivered were to be comprised of classified point cloud datasets for the project area

in its entirety.

4.3 Orthoimagery

The City of Peterborough provided orthoimagery captured in 2010 for the project area in its entirety at

0.10 m resolution.


7

5.0 PHASE TWO: Quality Control - Procedures

Given the high accuracy levels afforded by modern geospatial data acquisition techniques, assessing and

managing error can become quite complex. Essentially, this process started with the end goal of

generating topographic data that meets the accuracy requirement stated by ORCA to be +/- 0.125 m.

The Final DEM, to be used for flood line mapping, required data of a higher known accuracy to be used

as control data in the accuracy assessment. For control, survey data captured using RTK GPS technology

qualified as it offers roughly +/- 0.02 – 0.04 m accuracy. To verify the accuracy of the RTK GPS survey

data itself, data of a still higher level of accuracy was required. A Canadian Base Network (CBN)

monument was located in City of Kawartha Lakes which was verified by Natural Resources Canada –

Geodetic Survey Division to be of +/- 0.001 m accuracy in the horizontal, and +/- 0.006 m in the vertical.

The level of accuracy offered by the CBN monument effectively designated it as the end of the line for

the purpose of assessing accuracy for the flood line mapping project.

The first step of the QC analysis was to ensure completeness of data delivered for the project. This

involved the manual checking of LIDAR point clouds, orthoimagery data, and the RTK GPS survey data.

Each data component was opened and checked manually to ensure all were delivered in a workable

format.

After completeness was verified, a static RTK GPS survey of the CBN monument was conducted to

quantify the level of accuracy achieved by the RTK GPS equipment in a working environment. Upon

assessment of the CBN monument survey – which verified the data to be within acceptable error – an

RTK GPS field survey was conducted to assess the overall accuracy of data delivered across the project

area. A common challenge for any remote sensing data capture technique is achieving effective

penetration of vegetative canopy in effort to capture bare earth representation. The Ontario Imagery

and Elevation Acquisition Guidelines (Ontario, 2009) define five exclusive vegetative canopy categories

for which accuracy is to be assessed; bare earth, high grass, brush lands, forested, and urban. After RTK

GPS data was captured for all vegetative categories, photo-identifiable features were also captured for

assessing the accuracy of the orthoimagery data.

Once all required data was captured it was brought into the GRCA GIS lab for analysis. Statistical

accuracy assessments were conducted and are reported in the Results section (Section 6.0) of this

report.


8

6.0 Quality Control - Results

6.1 Data Completeness

A classified LIDAR point cloud captured in Fall 2012 was received by ORCA in compliance with the

vendor agreement. All data was determined to be present and accounted for.

RTK GPS survey data was acquired by ORCA field crew in Spring 2013 which was subsequently checked

and verified for completeness.

An orthoimagery data acquisition captured in 2010 was acquired from the City of Peterborough,

providing full project area coverage at 0.10 m resolution.

Data Component Status

1 Classified LIDAR point cloud Complete

2 RTK GPS survey* Complete

3 Orthoimagery** Complete NOTE: LIDAR data provided by Aero-Photo (1961) Inc., with * acquired by ORCA, and ** provided by the City of Peterborough.

Table 1 - Completeness of project data

6.2 RTK GPS

RTK GPS survey data was identified as suitable by the project team for control data for testing DEM and

orthoimagery products due to its high levels of accuracy and efficiency in terms of data capture. In order

to be used as control data, the RTK GPS equipment had to first be assessed for its own accuracy.

With RTK GPS being of centimeter-level accuracy, data used to compare it against had to be of the

highest level possible, required to exceed the level of accuracy of the data being tested by a factor of

three. It was determined that a First-Order Canadian Base Network (CBN) monument located in the City

of Kawartha Lakes met the requirements. Natural Resources Canada Geodetic Survey Division

established the monument at horizontal accuracies of +/- 0.001 m in both latitude and longitude, and

+/- 0.006 m in the vertical (all at 68% confidence level). Coordinates for the monument were expressed

using the NAD83 (CSRS) horizontal datum and CGVD28 vertical datum (1997 epoch).

The RTK GPS accuracy survey consisted of twelve points captured on the monument, with the error

averaged between the twelve separate readings. Results indicated the average root-mean-square error

(RMSE) to be +/- 0.006 m in latitude, +/- 0.0131 m in longitude, and +/- 0.0109 m in the vertical.

Northing Easting Elevation

0.006 m 0.0131 m 0.0109 m Table 2 - RMSE error of RTK GPS relative to Canadian Base Network (CBN) monument

6.3 LIDAR

The LIDAR data was delivered by the vendor in the LAS format specified in the Agreement. The classified

point cloud data met all requirements in terms of classification categories.


9

The point density of two points per square metre was tested across the acquisition with all areas

meeting the required specification.

In the absence of quality control targets specific to LIDAR installed prior to the flying of the mission,

accuracy of the LIDAR acquisition had to be assessed by creating Test DEMs from points classified by

ground which were then compared against RTK GPS survey data (see Section 6.4).

Figure 1 - Project area (Aero-Photo, 2012)


10

6.4 DEM

The delivered LIDAR point cloud data was used to create a digital elevation model (DEM) to be used as

the fundamental dataset for the flood line mapping. Therefore, the accuracy of the DEM needed to be

determined with RTK GPS survey data as the control for the statistical comparison.

In compliance with the Ontario Imagery and Elevation Acquisition Guidelines (2009), an RTK GPS survey

was conducted for different vegetative cover types found in the study area. The intended goal of this

exercise is to determine if the remote sensing method (in this case, LIDAR) was successful in acquiring

accurate representation of the bare earth terrain in all vegetative cover conditions. The five vegetative

cover categories are as follows:

1. Bare-earth and low grass

2. High grass, weeds, and crops

3. Brush lands and low trees

4. Forested, fully covered by trees

5. Urban areas

The required level of accuracy of the DEM was identified as +/- 0.125 m. The Byersville DEM was found

to meet the accuracy requirements across all vegetative cover categories with a vertical accuracy of +/-

0.0967 m RMSE, indicating that the LIDAR acquisition mission was successful in penetrating all forms of

vegetation in the study area.

6.5 Orthoimagery

QC work for the orthoimagery data component was divided into two phases: spatial accuracy and visual

inspection. The spatial accuracy component was conducted to ensure proper representation of the flood

lines produced by this project, and the visual inspection was conducted to ensure the images

themselves are of sufficient quality, free from general errors commonly associated with digital aerial

photography such as the presence of clouds, blurring, poor editing, relief displacement, saturation, and

general aberrations and anomalies.

6.5.1 Spatial Accuracy

Ground control points (GCP) were not installed prior to the orthoimagery acquisition so photo-

identifiable structures were surveyed with RTK GPS equipment. These RTK GPS readings were used to

match the structures in the orthophotos. At 1:100 scale, the results of the accuracy assessment

indicated the horizontal accuracy to be +/- 0.118 m RMSE in longitude, and +/- 0.233 m RMSE in

latitude. This was deemed satisfactory by the project team for flood line representation.

6.5.2 Visual Inspection

All orthoimagery for the project area was visually inspected on a tile-by-tile basis. No noticeable errors

or anomalies were found.


11

7.0 Conclusions

Based on the results described in Section 6, the remote sensing, GIS, and survey data components were

found the meet the specification required to adequately support flood line mapping of Byersville Creek.

Peer Review of Remote Sensing and GIS

Data Support for Flood Line Mapping – Ops

No. 1 Drain/Jennings Creek

Prepared By:


June 2013

Peer Review of Remote Sensing and GIS Data Support for Flood Line Mapping – Ops No. 1 Drain/Jennings Creek

1

Table of Contents

1.0 Executive Summary .......................................................................................................................... 3

2.0 Report Structure ............................................................................................................................... 3

3.0 Statement of Peer Review Work ..................................................................................................... 4

4.0 PHASE ONE: Quality Assurance Assessment ................................................................................... 5

4.1 RTK GPS ......................................................................................................................................... 5

4.2 LIDAR ............................................................................................................................................. 5

4.3 Orthoimagery ............................................................................................................................... 6

4.4 DEM............................................................................................................................................... 6

5.0 PHASE TWO: Quality Control - Procedures ..................................................................................... 7

6.0 Quality Control - Results .................................................................................................................. 8

6.1 Data Completeness ...................................................................................................................... 8

6.2 RTK GPS ......................................................................................................................................... 8

6.3 LIDAR ............................................................................................................................................. 9

6.4 DEM............................................................................................................................................. 11

6.5 Orthoimagery ............................................................................................................................. 12

6.5.1 Spatial Accuracy ................................................................................................................. 12

6.5.2 Visual Inspection ................................................................................................................ 12

7.0 Conclusions ..................................................................................................................................... 13

8.0 Recommendations ......................................................................................................................... 13


2

Table of Figures

Table 1 - Completeness of deliverables from vendor ................................................................................... 8

Table 2 - Detailed results of DEM accuracy assessment using RTK GPS survey control points, LMAS (90%

confidence level) ......................................................................................................................................... 12

Figure 1 - Project area (Aero-Photo, 2012) ................................................................................................. 10

Figure 2 - Areas found not to meet required point density specifications ................................................. 11


3

1.0 Executive Summary

In 2012, the Kawartha Region Conservation Authority (KRCA) began a flood line mapping analysis for a

small urban creek (Ops No. 1 Drain/Jennings Creek) within the City of Kawartha Lakes. It was determined

during the project pre-planning stage that there existed pronounced data gaps, particularly with regards

to topographic data, which needed to be addressed in order to ensure successful completion of the

project. As a result, KRCA acquired Light Detection And Ranging (LIDAR) and aerial imagery data covering

the area of undertaking.

The intended use of the LIDAR data acquisition was for it to be processed into a digital elevation model

(DEM) of a level of quality sufficient to meet Level 2 vertical accuracy specification of +/- 0.25 m LMAS at

90% confidence in areas of dense to moderate population (Ontario Imagery and Elevation Acquisition

Guidelines, 2009), as defined in the vendor agreement. In addition to LIDAR, orthoimagery and data

acquired using Real-Time Kinematic Global Navigation Satellite System survey (herein referred to as RTK

GPS), were also identified as meeting the required level of accuracy.

The Ganaraska Region Conservation Authority (GRCA) was enlisted to provide additional engineering

and remote sensing/GIS support. This document describes the results of quality assurance (QA) and

quality control (QC) peer review analyses conducted by the GRCA with regards to main remote sensing

and GIS data products used as support for the flood line mapping analysis.

2.0 Report Structure

This peer review has been divided into two distinct phases (as defined in the project terms of reference):

Quality Assurance (Phase One): dealing with project design and undertaken at the initiation of the

project thereby addressing goals a and b of the peer review which are:

a. Ensure consistency with standards and generally accepted approaches (project design)

b. Validate the work plan/planned deliverables

Quality Control (Phase Two): addressing project results and undertaken throughout the project work

addressing peer review goal a and c which are:

a. Ensure consistency with standards and generally accepted approaches (product)

c. Ensure scientifically defensible results


4

3.0 Statement of Peer Review Work

The preparation of flood line mapping is based on accurate elevation and position data. This can be

acquired through a number of means including LIDAR, aerial photography, and RTK GPS survey.

Additionally, elevation data can be processed into a DEM that is then used to extract data for the

creation of HEC-RAS cross-sections. Finally, results of flood line modeling must be represented on maps

and orthophotography that match the accuracy of the acquired elevation and position data. The peer

reviewer will consider the following questions regarding the creation and deliverables of LIDAR,

orthophotography, RTK GPS survey, and DEM products. Note: The following questions deal with the

Phase 2 peer review. Details of the Phase One RFP process should be included in the peer review record.

Quality of Data

Do the products meet the deliverable requirements of the proposal?

Do the products delivered meet industry and provincial standards?

Do the quality assurance tests undertaken in the peer review show satisfactory results?

Does the accuracy of the areas tested meet those required by the proposal?

Has the consultant delivered a complete dataset with all required accompanying reports and

information?

Have clear explanations of all steps of the product delivery been provided and can future users

of the data clearly understand the data’s limitations and quality?

Can the products provided be used to appropriately provide information to and portray results

of the flood plain analysis being prepared?


5

4.0 PHASE ONE: Quality Assurance Assessment

4.1 RTK GPS

RTK GPS survey equipment was procured through a Request For Proposal (RFP) process. Quotations

were collection from four vendor companies. Upon completion of the procurement process, one

Trimble GEO XH 6000 3.5G CM was purchased with a Can-Net control network subscription for real-time

positional corrections (for achieving the required levels of accuracy).

Concurrently, the same process was conducted for procurement of a total station resulting in the

purchase of one SOKKIA CX-105 total station with prism.

Upon review of procurement documents made available by KRCA, it has been determined that the

project design for the RTK GPS portion of the floodplain mapping project was conducted consistent with

standards and generally accepted approaches.

4.2 LIDAR

The City of Kawartha Lakes issued an RFP for a combined acquisition of LIDAR and orthoimagery data. It

was made explicit through the Statement of Requirement within the RFP that the intended use of the

LIDAR data would be “delineating flood lines” within “flood damage and urban centres”.

Specifications for the LIDAR acquisition are described in detail consistent with the Ontario Imagery and

Elevation Data Acquisition Guidelines (2009) and generally accepted approaches. Pulse return was

required to be captured at a level of two discrete returns, sufficient to delineate ground from non-

ground features. Requiring the LIDAR data to be at a nominal pulse spacing (NPS) of “no greater than 1

metre” while maintaining a spatial distribution that is “uniform and free from clustering” ensures the

data will be delivered at a quality that would allow for a DEM to be created of flood line mapping

standard.

Climate conditions are controlled for in the LIDAR collection specifications. Atmospheric conditions are

required to be “cloud and fog free between aircraft and ground” which would maximize penetration of

both the emitting laser pulse as well as the subsequent returns. The mission is required to fly during

leaf-off conditions, which further increases the probability of penetration for the emitting laser pulses,

particularly in reaching bare earth. Finally, to maximize scientific validity of the data in accordance with

the stated goals of the project, the LIDAR data is required to be acquired in the absence of “unusual

flooding or inundations”.

The format of the LIDAR data delivered is required to be “fully compliant with LAS v1.2 or v1.3 format”

which is the current leading standard. The projection and datum of data delivered is stated as “6-degree

Universal Transverse Mercator Projection… Zone 17N” on “NAD83-CSRS (Canadian Spatial Reference

System)” which is the accepted standard for the project area. The vertical datum of “Canadian Geodetic

Vertical Datum 1928 (CGVD28)” alongside the stated Geoid model “Canadian Gravimetric Geoid 2000

(CGG2000)” are both with commonly accepted modern standards for the project area.


6

The delivered products listed in the RFP represent the full suite of data commonly associated with a

standard aerial LIDAR mission of this nature. This includes metadata, raw point cloud, classified point

cloud, bare earth DEM, building footprints, and orthoimagery.

4.3 Orthoimagery

The RFP issued for the LIDAR acquisition contained within it an orthoimagery acquisition proposal. LIDAR

and aerial imagery are often flown in the same mission.

The RFP orthoimagery specifications contained with the RFP focus on spatial resolution, spectral

information, area coverage, colour balance, and image quality. Ground sample distance was specified to

be of “80% of the final ortho-photo size” which is should provide for optimal results after

orthorectification.

Spectral bands were required to be captured with “no visible colour shifting or colour offsets that result

in degraded quality”. Colour balance was further ensured with specifying that steps be made to ensure

“the imagery is consistently radiometrically balanced throughout the project area”.

Positional accuracy for the orthoimagery is controlled for the “required CMAS accuracy requirements”.

This refers to the Level 2 horizontal accuracy requirement of a CMAS of 0.25 m (90% confidence level),

which is in compliance with flood line mapping standards in Ontario.

4.4 DEM

The RFP specifies that a bare earth raster DEM be delivered by the vendor at Level 2 (+/- 0.25 m LMAS)

mapping accuracy in “densely to moderately populated urban areas”.


7

5.0 PHASE TWO: Quality Control - Procedures

Given the high accuracy levels afforded by modern geospatial data acquisition techniques, assessing and

managing error can become quite complex. Essentially, this process started with the end goal of

generating topographic data that meets the Level 2 +/- 0.25 m LMAS (90% confidence level) project

accuracy requirements and working back from that in terms of assessing each source, or input, dataset

used during the production of the final data products.

The project DEM to be used for flood line mapping, required data of a higher known accuracy to be used

as control data in the accuracy assessment. For control, survey data captured using RTK GPS technology

qualified as it offers approximately +/- 0.02 – 0.04 m accuracy. To verify the accuracy of the RTK GPS

survey data itself, data of a still higher level of accuracy was required. A Canadian Base Network (CBN)

monument was located in close proximity to the project area which was verified by Natural Resources

Canada – Geodetic Survey Division to be of +/- 0.001 m accuracy in the horizontal, and +/- 0.006 m in

the vertical. The level of accuracy offered by the CBN monument effectively designated it as the end of

the line for the purpose of assessing accuracy for the flood line mapping project.

The first step of the QC analysis was to ensure completeness of data delivered for the project. This

involved the manual checking of raster DEM, LIDAR point clouds, orthoimagery data, and the RTK GPS

survey data. Each data component was opened and checked manually to ensure all were delivered in a

workable format.

After completeness was verified, an RTK GPS survey of the CBN monument was conducted to quantify

the level of accuracy achieved by the RTK GPS equipment in a working environment. Upon assessment

of the CBN monument survey – which verified the data to be within acceptable error – an RTK GPS field

survey was conducted for the project area. A common challenge for any remote sensing data capture

technique is achieving effective penetration of vegetative canopy in effort to capture bare earth

representation. The Ontario Imagery and Elevation Acquisition Guidelines (Ontario, 2009) define five

exclusive vegetative canopy categories for which accuracy to be assessed; bare earth, high grass, brush

lands, forested, and urban. After RTK GPS data was captured for all vegetative categories, photo-

identifiable features were also captured for the accuracy assessment of the orthoimagery data.

Once all required data was captured it was brought into the GRCA GIS lab for analysis. Statistical

accuracy assessments were conducted and are reported in the Results (Section 6.0) of this report.


8

6.0 Quality Control - Results

6.1 Data Completeness

LIDAR and aerial imagery data were captured in Fall 2012 was received by KRCA in compliance with the

vendor agreement. All data was determined to be present and accounted for.

RTK GPS survey data was acquired by KRCA field crew in Spring 2013 and was subsequently checked and

verified for completeness.

Data Component Status

1 Flight plans, flight reports and photo index Complete

2 16 cm resolution aerial digital imagery in RGB, NIR and Panchromatic bands for the project zone

Complete

3 Raw LIDAR data acquired at 2 pts/m² density over the project zone (in LAS v1.2 format)

Complete

4 Processed GPS/IMU data for both photo and LIDAR acquisitions Complete

5 Ground Control Survey report Complete

6 Aerial triangulation report (with computed coordinates, residuals and achieved accuracy levels)

Complete

7 Stereo pairs and orientation parameters Complete

8 Classified LIDAR data according to the RFP specifications (in LAS v1.2 format) Complete

9 New DEM over the project zone based on LIDAR data Complete

10 20 cm resolution digital orthophotography in RGBI bands (in GeoTIFF and MrSID formats)

Complete

11 All metadata Complete

12 QA/QC Reports and Final Project Reports Complete

13 RTK GPS Survey* Complete NOTE: All data components delivered by Aero-Photo (1961) Inc., except * acquired by KRCA.

Table 1 - Completeness of deliverables from vendor

6.2 RTK GPS

RTK GPS survey data was identified as suitable for control data for testing DEM and orthoimagery

products due to its high levels of accuracy and efficiency in terms of data capture. In order to be used as

control data, the RTK GPS equipment had to first be assessed for its own accuracy.

With RTK GPS being of centimeter-level accuracy, data used to compare it against had to be of the

highest level possible, required to exceed the level of accuracy of the data being tested by a factor of

three. It was determined that a First-Order Canadian Base Network (CBN) monument located in the City

of Peterborough met the requirements. Natural Resources Canada Geodetic Survey Division established

the monument at horizontal accuracies of +/- 0.001 m in both latitude and longitude, and +/- 0.006 m in

the vertical (all at 68% confidence level). Coordinates for the monument were expressed using the

NAD83 (CSRS) horizontal datum and CGVD28 vertical datum (1997 epoch).


9

The RTK GPS accuracy survey consisted of twelve points captured on the monument, with the error

averaged between the twelve separate readings. Results indicated the accuracy of the RTK GPS unit to

be +/- 0.0133 m CMAS and +/- 0.0179 m LMAS (both at 90% confidence level).

6.3 LIDAR

The point density requirement of two points per square metre was tested across the acquisition area.

The results revealed that a portion of regions in the area of acquisition were found to be below the

required point density value. Upon visual inspection, a spatial pattern emerged which matched closely

to a flight line derived during the data capture mission. It should also be noted that due to technical

limitations, only LIDAR points the lie over 750 m from the project boundary were assessed for point

density.

In the absence of quality control targets specific to LIDAR installed prior to the flying of the mission,

accuracy of the LIDAR acquisition had to be assessed by creating test DEMs from points classified by

ground which were then compared against RTK GPS survey data (see Section 6.4).


10

Figure 1 - Project area (Aero-Photo, 2012)


11

Figure 2 - Areas found not to meet required point density specifications

6.4 DEM

The vendor-delivered digital elevation model (DEM) was specified to meet the Level 2 mapping accuracy

standard of +/- 0.25 m LMAS 90% confidence level). The accuracy of the DEM needed to be determined

with RTK GPS survey data as the control for the statistical comparison.

In compliance with the Ontario Imagery and Elevation Acquisition Guidelines (2009), an RTK GPS survey

was conducted for different vegetative cover types found in the study area. The intended goal of this

exercise is to determine if the methods used to produce the DEM were successful in acquiring accurate

representation of the bare earth terrain in all vegetative cover conditions. The five vegetative cover

categories are as follows:

1. Bare-earth and low grass

2. High grass, weeds, and crops

3. Brush lands and low trees

4. Forested, fully covered by trees

5. Urban areas


12

The required level of accuracy of the DEM was identified as Level 2 +/- 0.25 m LMAS (90% confidence

level) in areas of dense to moderate population.

The vendor-delivered DEM was found to meet the Level 2 accuracy requirements as specified in the

vendor agreement. The accuracy was found to be +/- 0.122 m LMAS (90% confidence level) in areas of

dense and moderate population. Further accuracy checks were performed in areas where cross-sections

are to be extracted from the DEM. These areas registered an accuracy level of +/- 0.232 m LMAS (90%

confidence level) with the slight increase in error likely attributed to dense vegetation cover.

KRCA GIS staff produced a bare earth raster DEM using the LIDAR point data. This DEM was evaluated

using the same control data as the vendor-DEM with the KRCA-DEM yielding +/- 0.121 m LMAS in areas

of dense to moderate population and +/- 0.218 m LMAS near cross-sections (both at 90% confidence

level).

Bare earth DEM RMSE LMAS

Dense to moderate population (vendor-delivered DEM) 0.074 m 0.122 m

Near cross-sections (vendor-delivered DEM) 0.141 m 0.232 m

Dense to moderate population (KRCA-produced DEM) 0.074 m 0.121 m

Near cross-sections (KRCA-produced DEM) 0.133 m 0.218 m

Table 2 - Detailed results of DEM accuracy assessment using RTK GPS survey control points, LMAS (90% confidence level)

6.5 Orthoimagery

QC work for the orthoimagery data component was divided into two phases: spatial accuracy and visual

inspection. The spatial accuracy component was conducted to ensure proper representation of the flood

lines produced by this project, and the visual inspection conducted to ensure the images themselves are

of sufficient quality, free from general errors commonly associated with digital aerial photography such

as the presence of clouds, blurring, poor editing, relief displacement, saturation, and general aberrations

and anomalies.

6.5.1 Spatial Accuracy

Ground control points (GCP) were not installed prior to the orthoimagery acquisition so photo-

identifiable structures were surveyed with RTK GPS equipment. These RTK GPS readings were used to

match the structures in the orthophotos. At a 1:200 scale, the results of the accuracy assessment

indicated the horizontal accuracy to be +/- 0.294 m CMAS (90% confidence level). This was deemed

satisfactory by the project team for flood line representation.

6.5.2 Visual Inspection

All orthoimagery for the project area was visually inspected on a tile-by-tile basis. No pronounced errors

or anomalies were found.


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7.0 Conclusions

Aside from the point density issues, the results described in Section 6 indicate that the remote sensing,

GIS, and survey data components were found the meet the specification required to adequately support

flood line mapping of Ops No. 1 Drain/Jennings Creek.

Additional investigation should be paid to the LIDAR data with regards to point density. Point density

analysis of the remaining areas of acquisition should be conducted to identify potential cascading effects

of the point density issues should the source of the error be the performance of the sensor or common

weather conditions, etc.

For the purpose of this project, only the LIDAR points classified as “GROUND” will be used, therefore,

further assessment would be needed to determine the actual effect of the delivered point density on

the scientific validity of the project.

8.0 Recommendations

When considering the findings of this peer review, it is important to maintain the linkage between the

QA/QC results and the project itself. By assuming this contextual perspective, it is then possible to

reduce the purpose of the remote sensing and GIS data support to two main issues:

1. Is there enough data to provide for the requirements of the engineering modeling?

2. Is there enough data to properly represent the boundary of the flood line?

Addressing these two issues is, foremost, a spatial exercise. Essentially, if data is lacking in areas that are

not within, or adjacent to, the floodplain areas, then there may actually be no issue for the purpose of

this project. Problems could arise in terms of the common practice of reusing the data acquired for this

project in other applications, and this should be kept in mind. However, within the scope of this project,

it could be that the identified data gaps have no effect and determining whether or not this is the case

would be a worthwhile first step in addressing any potential issues.

In the event that the remote sensing and GIS data should prove insufficient in addressing either, or both,

main issues, there exist alternative data options that could work to provide supplementary information.

It is, therefore, recommended that the project team consider the following options:

A. Additional Survey

The first alternative is to use RTK GPS survey to capture data in the areas identified as lacking. This

approach requires that only small areas are affected and that RTK network coverage exists in the areas

targeted for re-capture. This approach could fit well within the existing project processes, resulting in an

increase in human effort rather than an additional data purchase.


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B. Pixel Autocorrelation

The second alternative is to use the raw aerial image stereo pairs to extract elevation data by means of

pixel autocorrelation (also known as image matching or, simply, DTM extraction). This would be the

ideal approach should larger areas be found lacking high quality topographic representation. The

resultant data from pixel autocorrelation is a point cloud similar to that produced from LIDAR. This

alternative would require access to a remote sensing/GIS lab with the capability of conducting pixel

autocorrelation which is quite computationally-intensive.

C. Combination of A and B

It could also be the case that the optimal solution be a combination of both RTK GPS survey (small areas)

and pixel autocorrelation (large areas). Both these technologies, in addition to LIDAR, produce data at

high levels of accuracy that allow for them to be combined using a technique called 3D data fusion. This

approach involves producing a fused product which is then exported as one cohesive Enhanced DEM

which is effectively a synergistic representation of all data combined.

Supporting Sustainable Water Management in Ontario … RTK Cross-Section Data ... Figure 22...

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