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PREFACEEngineers are the creative technocrats who apply the theoretical classroom knowledge to build and develop the practical realities in the form of machines, buildings, software, tools, and what not. For the purpose of serving the true aim and objective of this noble profession, it is very essential for the students to engage in the practical learning and its do’s and don’ts while still in the learning phase, and hence the norm of Practical Training was introduced in the curriculum and its concept was laid.

I was fortunate enough to have been selected in ISRO, the pioneer in the field of Space Research and Technology, which itself is the “GODFATHER” to my own branch, for my Summer Internship.

The knowledge that I had gained in the past 2 years was altogether put to use in the project assigned to me and with the expert guidance and supervision of my mentor Er. Sagar S. Salunkhe and the team of inspiring scientists, I was able to successfully complete my Training Project.

The duration of my Internship was 30 days beginning on 1st June, 2015 and was concluded on 3rd July, 2015.

The experience of the Internship was motivational and inspiring making me realize what responsibilities lay ahead for us engineers and also how to accomplish and fulfill each with the zeal and dedication we saw at ISRO.

3rd July, 2015

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ACKNOWLEDGEMENT

I am very grateful to Dr. A. T. Jeyaseelan General Manager, RRSC-W, NRSC/ISRO, Jodhpur for providing us with this opportunity to carry out our summer training in this esteemed and pioneering institution.

I am also thankful to our project mentor Er. Sagar S. Salunkhe, Scientist/Engineer ‘SD’, RRSC-W, NRSC/ISRO, Jodhpur who extended his full support to my project and helped me complete it successfully with flying colours. His guidance and expert supervision taught us the rightful method of Time Management to complete the work in time and his vision of approach taught us inventive apprehension.

I am also deeply thankful to the team of hardworking scientists of the organization- Dr. A. K. Bera, Shri Ashish Jain, Shri Hansraj Meena, Shri Gaurav Kumar, Shri Amanpreet Singh, Shri Sushil Rehpade and Shri Shashikant Sharma, who gave me several knowledgeable presentations and brushed up my knowledge about the various processes and aspects of the organization and the fundamentals of the technologies less known to me.

Lastly, I would like to convey my heartfelt gratitude to my family members and friends for giving me their full support during the project duration.

Shadaab

(Summer trainee, 2015)

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ABSTRACTEnvironmental issues are of vital importance for human life on Earth. A question is how can 3 dimensional information of the topography of the Earth surface help us in understanding our vulnerable environment and secure a more sustainable management and use of our environmental resources? What are the areas in which digital elevation models are essential tools easing the struggle towards a better ability to perceive and analyze the physical, biological, chemical and cultural character of the Earth’s surface? This project tries to answer some of these questions by giving a presentation of the fundamentals and the representative applications and possible business opportunities with respect to digital elevation models. Digital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEMs are used to determine terrain attributes such as elevation at any point, slope and aspect. Terrain features like drainage basins and channel networks can also be identified from the DEMs. DEMs are widely used in hydrologic and geologic analyses, hazard monitoring, natural resources exploration, agricultural management etc. Hydrologic applications of the DEM include groundwater modeling, estimation of the volume of proposed reservoirs, determining landslide probability, flood prone area mapping etc.

DEM is generated from the elevation information from several points, which may be regular or irregular over the space. In the initial days, DEMs were used to be developed from the contours mapped in the topographic maps or stereoscopic areal images. With the advancement of technology, today high resolution DEMs for a large part of the globe is available from the radars onboard the space shuttle.

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CONTENTS

TITLE PAGE NO

1. INDIAN SPACE RESEARCH ORGANISATION v

2. NATIONAL REMOTE SENSING CENTRE vii

3. REGIONAL REMOTE SENSING CENTRE ix

4. REMOTE SENSING x

5. GEOREAPHIC INFORMATION SYSTEM xii

6. DIGITAL ELEVATION MODEL (DEM) xiii

7. DIGITAL ELEVATION MODEL and its DERIVATIVES xxvii

8. REFERENCES xliii

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1. INDIAN SPACE RESEARCH ORGANISATIONA. S. Kiran Kumar took charge as the Chairman of Indian Space Research Organization and Space Commission, and also as the Secretary of Department of Space on 12, January, 2015. Mr. Kiran Kumar is a highly accomplished space scientist and engineer with a distinguished career spanning over four decades in ISRO in the satellite payload and applications domains.

The corporate headquarters of the Indian Space Research Organization (ISRO) is located in Bangalore, but, activities related to satellites, launch vehicles, and applications are carried out at numerous centers throughout the country. The development of the sensors and payloads is the responsibility of ISRO's Space Application Center (SAC) in Ahmedabad. ISRO Satellite Center (ISAC) in Bangalore is responsible for the design, development, assembly, and testing of satellites. Vikram Sarabhai Space Center (VSSC), at Tiruvananthapuram, is responsible for launch vehicles. Liquid propulsion modules, including cryogenic engines, are developed at the Liquid Propulsion Systems Center located near Tiruvananthapuram. Satellite launching takes place from Sriharikota, north of Madras, referred to as SHAR. Hassan, near Bangalore, is where the Master Control facilities for satellite station keeping are located. The reception and processing facilities for remote sensing data are available at National Remote Sensing Centre (NRSA), in Hyderabad.

Antrix Corporation Limited (Antrix), the commercial arm of Indian Space Research Organisation (ISRO), from 1999 onwards - till date, has successfully launched 40 satellites of foreign customers from 19 countries, using ISRO's Polar Satellite Launch Vehicle (PSLV) and space projects including: (i)Establishment of ground stations for reception of data from Indian Remote Sensing (IRS) satellites along with processing facilities at 20 locations outside India; (ii) Building two contemporary communication satellites for European customers, and one communication satellite for Indian strategic user; (iii) providing tracking support for over 70 spacecraft missions of foreign customers; (iv) provisioning of satellite transponder capacity from Indian communication satellites for telecommunication, TV broadcasting, Direct-To-Home (DTH) services and VSAT applications; (v) launching of 40 foreign satellites on-board ISRO's PSLV; (vi) establishment of ground terminals for tele-education, tele-medicine, disaster mitigation and Village Resource Centres; and (vii) consultancy services to domestic and foreign clients.

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Today, the main purpose of building launch vehicles is to carry satellites, unmanned spacecraft and humans to space. ISRO is now building heavier and more complex communication, weather and remote sensing satellites capable of offering more services. Besides, it is developing an independent navigation satellite system called Indian Regional Navigation Satellite System. It will be capable of providing highly accurate position, speed, direction, and time information to vehicles travelling on land, sea and in the air.

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2. NATIONAL REMOTE SENSING CENTREIn a large country with growing population, challenges in managing natural resources and sustaining its environment are manifold. The need for spatial data and a further need to update the information frequently are of profound value. Realizing this, the Department of Science and Technology of the Government of India lost no time in moving towards establishing an institution that would in future become the leading center for remote sensing activities in the country.

Consequent to the approval of the Union Cabinet, the National Remote Sensing Centre (NRSC) was registered as a Society on 2nd September 1974, with clearly stated objectives in the broader domain of undertaking and facilitating remote sensing activities in the country. On January 15, 1975, Wg.Cdv.K.R.Rao AVSM (retd) took over as Director, NRSC. An airborne Geophysical survey was started as the first technical activity. The early seventies can be deemed as our organization formative phase. During the early years, the focus was aerial photography. A HS-748 aircraft was procured. However, it was being felt that the absence of an Indian Earth Station to receive satellite data would be a serious handicap in our efforts of resources survey. NRSC's Governing Body, in its first meeting in August 1975 under the chairmanship of the then Prime Minister Mrs. Indira Gandhi, approved the establishment of an earth station in India under the auspices of NRSC and entering into a MoU with the USA towards receiving earth resources data directly from the Land-sat series of satellite launched by NASA. Land was allotted by the Govt. of Andhra Pradesh near Shadnagar, about 60 km south of Hyderabad for this purpose.

The station was declared operational from 1st January 1980, just one and a half years from concept to completion. Standard output data products were generated for the first time in the country with proper and systematic radiometric and geometric corrections. A data bank along with microfiche browse facility was also established. Computer listings giving cloud cover, data quality, etc. were made available to the user community in the country. With that, NRSC established the first operational satellite data reception facility in the country. Photo Processing Lab and Cart The launch of India first operational remote sensing satellite IRS-1A on 17th March, 1988 initiated NRSC's operational phase. Entire ground segment for reception, processing and dissemination of IRS-1A was established. Microwave link facility from Shadnagar

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to Balanagar and a dedicated line to Bangalore for telemetry data were also established. Data archival and quick look real time system was integrated with the data acquisition systems. Digital browse facility of data products was fully established for operational use. With the sales figure of data crossing 10,000 mark, NRSC turned towards International marketing.

Subsequently, facilities were upgraded to receive data from SPOT, SAR data from ERS, Metsat, MODIS data from AQUA and Terra satellites. The IRS-1A initiated national missions in application and the continuance of the application projects necessitated the launch of subsequent satellites in the IRS series like IRS1B, IRS-1C, IRS-1D, IRS-P3, IRS-P4 and IRS-P6ographic facility were also commissioned.

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3. REGIONAL REMOTE SENSING CENTREFive Regional Remote Sensing Service Centres (RRSSCs) established under National Natural

Resources Management System (NNRMS) by Department of Science at Bangalore, Jodhpur,

Kharagpur (recently relocated to Kolkata), Dehradun and Nagpur have been integrated with

NRSC and renamed as Regional Remote Sensing Centres (RRSCs) South, West, East, North and

Central respectively on December 2, 2009.

RRSCs support various remote sensing tasks specific to their regions as well as at the national

level. RRSCs are carrying out application projects encompassing all the fields of natural

resources like agriculture and soils, water resources, forestry, oceanography, geology,

environment and urban planning.

Apart from executing application projects, RRSCs are involved in software development,

customisation and packaging specific to user requirements and conducting regular training

programmes for users in Remote Sensing Application, digital techniques, GIS and theme based

applications.RRSC also provides expert advice / consultancy towards promotion of technology

in the country.

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4..REMOTE SENSINGRemote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information.

Remote sensors collect data by detecting the energy that is reflected from Earth. These sensors can be on satellites or mounted on aircraft. Remote sensors can be either passive or active. Passive sensors respond to external stimuli. They record natural energy that is reflected or emitted from the Earth's surface. The most common source of radiation detected by passive sensors is reflected sunlight. In contrast, active sensors use internal stimuli to collect data about Earth. For example, a laser-beam remote sensing system projects a laser onto the surface of Earth and measures the time that it takes for the laser to reflect back to its sensor.

Remote sensing has a wide range of applications in many different fields:

Coastal applications: Monitor shoreline changes, track sediment transport, and map coastal features. Data can be used for coastal mapping and erosion prevention.

Ocean applications: Monitor ocean circulation and current systems, measure ocean temperature and wave heights, and track sea ice. Data can be used to better understand the oceans and how to best manage ocean resources.

Hazard assessment: Track hurricanes, earthquakes, erosion, and flooding. Data can be used to assess the impacts of a natural disaster and create preparedness strategies to be used before and after a hazardous event.

Natural resource management: Monitor land use, map wetlands, and chart wildlife habitats. Data can be used to minimize the damage that urban growth has on the environment and help decide how to best protect natural resources.

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The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions.

Spatial resolutionThe size of a pixel that is recorded in a raster image – typically pixels may correspond to square areas ranging in side length from 1 to 1,000 metres (3.3 to 3,280.8 ft).Spectral resolutionThe wavelength width of the different frequency bands recorded – usually, this is related to the number of frequency bands recorded by the platform. Current Land-sat collection is that of seven bands, including several in the infra-red spectrum, ranging from a spectral resolution of 0.07 to 2.1 μm. The Hyperion sensor on Earth Observing-1 resolves 220 bands from 0.4 to 2.5 μm, with a spectral resolution of 0.10 to 0.11 μm per band.

Radiometric resolutionThe number of different intensities of radiation the sensor is able to distinguish. Typically, this ranges from 8 to 14 bits, corresponding to 256 levels of the gray scale and up to 16,384 intensities or "shades" of colour, in each band. It also depends on the instrument noise.Temporal resolutionThe frequency of flyovers by the satellite or plane, and is only relevant in time-series studies or those requiring an averaged or mosaic image as in deforesting monitoring. This was first used by the intelligence community where repeated coverage revealed changes in infrastructure, the deployment of units or the modification/introduction of equipment. Cloud cover over a given area or object makes it necessary to repeat the collection of said location.

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5. GEOGRAPHIC INFORMATION SYSTEM

A geographic information system (GIS) is a computer system for capturing, storing, checking, and displaying data related to positions on Earth’s surface. GIS can show many different kinds of data on one map. This enables people to more easily see, analyze, and understand patterns and relationships. GIS can use any information that includes location. The location can be expressed in many different ways, such as latitude and longitude, address, or ZIP code. Many different types of information can be compared and contrasted using GIS. The system can include data about people, such as population, income, or education level. It can include information about the land, such as the location of streams, different kinds of vegetation, and different kinds of soil. It can include information about the sites of factories, farms, and schools, or storm drains, roads, and electric power lines.

Putting information into GIS is called data capture. Data that are already in digital form, such as images taken by satellites and most tables, can simply be uploaded into GIS. Maps must be scanned, or converted into digital information. GIS technology allows all these different types of information, no matter their source or original format, to be overlaid on top of one another on a single map. GIS must make the information from all the various maps and sources align, so they fit together. One reason this is necessary is because maps have different scales. A scale is the relationship between the distance on a map and the actual distance on Earth. GIS combines the information from different sources in such a way that it all has the same scale.

Once all of the desired data have been entered into a GIS system, they can be combined to produce a wide variety of individual maps, depending on which data layers are included. For instance, using GIS technology, many kinds of information can be shown about a single city.

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Maps can be produced that relate such information as average income, book sales, and voting patterns. Any GIS data layer can be added or subtracted to the same map. People working in many different fields use GIS technology. Many businesses use GIS to help them determine where to locate a new store. Biologists use GIS to track animal migration patterns. City officials use GIS to help plan their response in the case of a natural disaster such as an earthquake or hurricane. GIS maps can show these officials what neighborhoods are most in danger, where to locate shelters, and what routes people should take to reach safety. Scientists use GIS to compare population growth to resources such as drinking water, or to try to determine a region’s future needs for public services like parking, roads, and electricity. There is no limit to the kind of information that can be analyzed using GIS technology.

6. Digital Elevation ModelDigital Elevation Model (DEM) is the digital representation of the land surface elevation with respect to any reference datum. DEMs are used to determine terrain attributes such as elevation at any point, slope and aspect. Terrain features like drainage basins and channel networks can also be identified from the DEMs. DEMs are widely used in hydrologic and geologic analyses, hazard monitoring, natural resources exploration, agricultural management etc. Hydrologic applications of the DEM include groundwater modeling, estimation of the volume of proposed reservoirs, determining landslide probability, flood prone area mapping etc.

DEM is generated from the elevation information from several points, which may be regular or irregular over the space. In the initial days, DEMs were used to be developed from the contours mapped in the topographic maps or stereoscopic areal images. With the advancement of technology, today high resolution DEMs for a large part of the globe is available from the radars onboard the space shuttle.

Definition of a DEM

A DEM is defined as "any digital representation of the continuous variation of relief over space," (Burrough, 1986), where relief refers to the height of earth’s surface with respect to the datum considered. It can also be considered as regularly spaced grids of the elevation information, used for the continuous spatial representation of any terrain.

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Differences between DEM, DSM & DTMDigital Terrain Model (DTM) and Digital Surface Model (DSM) are often used as synonyms of the DEM.

Digital Elevation Model (DEM)

A Digital Elevation Model (DEM) is a digital cartographic/geographic dataset of elevations in xyz coordinates. The terrain elevations for ground positions are sampled at regularly spaced horizontal intervals. Technically a DEM contains only the elevation information of the surface, free of vegetation, buildings and other non-ground objects with reference to a datum such as Mean Sea Level (MSL). In simple terms, a digital elevation model (DEM) is a digital model or 3D representation of a terrain's surface — commonly for a planet (including Earth), moon, or asteroid — created from terrain elevation data.

Digital Terrain Model (DTM)A digital terrain model is a topographic model of the bare earth –terrain relief - that can be manipulated by computer programs. The data files contain the spatial elevation data of the terrain in a digital format which usually presented as a rectangular grid. Vegetation,buildings and other man-made (artificial) features are removed digitally - leaving just the underlying terrain.

DTM model is mostly related as raster data type (opposed to vector data type), stored usually as a rectangular equal-spaced grid, with space (resolution) of between 50 and 500 meters mostly presented in cartesian coordinate system – i.e. x, y, z (there are DTMs presented in geographic coordinate system – i.e. angular coordinates of latitude and longitude). For several applications a higher resolution is required (as high as 1 meter spacing). A DTM can be used to guide automatic machinery in the construction of a physical model or even in computer games, where is describes the relief map.

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Figure1. Example of a (a) DEM and (b) DTM

Digital Surface Model (DSM)

DSM's measure the height values of the first surface on the ground. This includes terrain features, buildings, vegetation and power lines etc. DSM's therefore provide a topographic model of the earth's surface. DSM's can be used to create 3D fly-through, support location-based systems and augmented simulated environments.

Types of Digital Elevation ModelsDEMs are generated by using the elevation information from several points spaced at regular or irregular intervals. The elevation information may be obtained from different sources like field survey, topographic contours etc. DEMs use different structures to acquire or store the elevation information from various sources. Three main type of structures used are the following.

a) Regular square grids

b) Triangulated irregular networks (TIN)

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c) Contours

Figure2. Different types of DEMs (a) Gridded DEM (b) TIN DEM (c) Contour-based DEM

a) Gridded structure

Gridded DEM (GDEM) consists of regularly placed, uniform grids with the elevation information of each grid. The GDEM thus gives a readily usable dataset that represents the elevation of surface as a function of geographic location at regularly spaced horizontal (square) grids. Since the GDEM data is stored in the form of a simple matrix, values can be accessed easily without having to resort to a graphical index and interpolation procedures.

Figure 3. Gridded DEM

Accuracy of the GDEM and the size of the data depend on the grid size. Use of smaller grid size increases the accuracy. However it increases the data size, and hence results in computational difficulties when large area is to be analyzed. On the other hand, use of larger grid size may lead to the omission of many important abrupt changes at sub-grid scale.

Some of the applications of the GDEMs include automatic delineation of drainage networks and catchment areas, development of terrain characteristics, soil moisture estimation and automated extraction of parameters for hydrological or hydraulic modeling.

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(b) TIN structure

TIN is a more robust way of storing the spatially varying information. It uses irregular sampling points connected through non-overlapping triangles. The vertices of the triangles match with the surface elevation of the sampling point and the triangles (facets) represent the planes connecting the points.

Location of the sampling points, and hence irregularity in the triangles are based on the irregularity of the terrain. TIN uses a dense network of triangles in a rough terrain to capture the abrupt changes, and a sparse network in a smooth terrain. The resulting TIN data size is generally much less than the gridded DEM.

Figure4. TIN DEM

TIN is created by running an algorithm over a raster to capture the nodes required for the triangles. Even though several methods exist, the Delaunay triangulation method is the most preferred one for generating TIN. TIN for Krishna basin in India created using USGS DEM data (http://www.usgs.gov) is shown in Fig.5. It can be observed from this figure that the topographical variations are depicted with the use of large triangles where change in slope is small. Small triangles of different shapes and sizes are used at locations where the fluctuations in slope are high.

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Figure 5. TIN for Krishna basin created from USGS DEM data

Due to its capability to capture the topographic irregularity, without significant increase in the data size, for hydrologic modeling under certain circumstances, TIN DEM has been considered to be better than the GDEM by some researchers. For example, in gridded DEM-based watershed delineation, flow is considered to vary in directions with 450 increments. Using TIN, flow paths can be computed along the steepest lines of descent of the TIN facets.

(c) Contour-based structure

Contours represent points having equal heights/ elevations with respect to a particular datum such as Mean Sea Level (MSL). In the contour-based structure, the contour lines are traced from the topographic maps and are stored with their location (x, y) and elevation information.

These digital contours are used to generate polygons, and each polygon is tagged with the elevation information from the bounding contour.

Figure 6. Contour-based DEM

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Contour-based DEM is often advantageous over the gridded structure in hydrological and geomorphological analyses as it can easily show the water flow paths. Generally the orthogonals to the contours are the water flow paths.

Major drawback of contour based structure is that the digitized contours give vertices only along the contour. Infinite number of points are available along the contour lines, whereas not many sampling points are available between the contours. Therefore, accuracy of DEM depends on the contour interval. Smaller the contour interval, the better would be the resulting DEM. If the contour interval of the source map is large, the surface model created from it is generally poor, especially along drainages, ridge lines and in rocky topography.

Sources of digital elevation dataElevation information for a DEM may be acquired through filed surveys, from topographic contours, aerial photographs or satellite imageries using the photogrammetric techniques. Recently radar interferometric techniques and Laser altimetry have also been used to generate a DEM.

Field surveys give the point elevation information at various locations. The points can be selected based on the topographic variations. Contours are the lines joining points of equal elevation. Therefore, contours give elevation at infinite numbers of points, however only along the lines.

A digital elevation model can be generated from the points or contours using various interpolation techniques like linear interpolation, kriging, TIN etc. Accuracy of the resulting DEM depends on the density of data points available depicting the contour interval, and precision of the input data.

On the other hand, photogrammetric techniques provides continuous elevation data using pairs of stereo photographs or imageries taken by instruments onboard an aircraft or space shuttle. Radar interferometry uses a pair of radar images for the same location, from two different points. The difference observed between the two images is used to interpret the height of the location. Lidar altimetry also uses a similar principle to generate the elevation information.

Today very fine resolution DEMs at near global scale are readily available from various sources. The following are some of the sources of global elevation data set.

GTOPO30 NOAA GLOBE project SRTM ASTER Global Digital Elevation Model Lidar DEM

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GTOPO30 is the global elevation data set published by the United State Geological Survey (USGS). Spatial resolution of the data is 30 arc second (approximately 1 Kilometer). The data for the selected areas can be downloaded from the following website.

http://www1.gsi.go.jp/geowww/globalmap-gsi/gtopo30/gtopo30.html

The Global Land One-km Base Elevation Project (GLOBE) generates a global DEM of 3 arc second (approximately 1 kilometer) spatial resolution. Data from several sources will be combined to generate the DEM. The GLOBE DEM can be obtained from the NOAA National Geophysical Data Centre.

Shuttle Radar Topographic Mission (SRTM) was a mission to generate the topographic data of most of the land surface (56°S to 60°N) of the Earth, which was jointly run by the National Geospatial-Intelligence Centre (NGA) and the National Aeronautics and Space Administration (NASA). In this mission, stereo images were acquired using the Interferometric Synthetic Aperture Radar (IFSAR) instruments onboard the space shuttle Endeavour, and the DEM of the globe was generated using the radar interferometric techniques. The SRTM digital elevation data for the world is available at 3 arc seconds (approximately 90 m) spatial resolution from the website of the CGIAR Consortium for Spatial Information website: http://srtm.csi.cgiar.org/. For the United States and Australia, 30m resolution data is also available.

ASTER Global Digital Elevation Model (GDEM) was generated from the stereo pair images collected by the Advanced Space Borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the sun-synchronous Terra satellite. The data was released jointly by the Ministry of Economy, Trade, and Industry (METI) of Japan and the United States National Aeronautics and Space Administration (NASA). ASTER instruments consisted of three separate instruments to operate in the Visible and Near Infrared (VNIR), Shortwave Infrared (SWIR), and the Thermal Infrared (TIR) bands. ASTER GDEMs are generated using the stereo pair images collected using the ASTER instruments, covering 99% of the Earth’s land mass (ranging between latitudes 83oN and 83oS). ASTER GDEM is available at 30m spatial resolution in the GeoTIFF format. The ASTER GDEM is being freely distributed by METI (Japan) and NASA (USA) through the Earth Remote Sensing Data Analysis Center (ERSDAC) and the NASA Land Processes Distributed Active Archive Center (LP DAAC) (https://lpdaac.usgs.gov/lpdaac/products/aster_products_table).

Light Detection and Ranging (LIDAR) sensors operate on the same principle as that of laser equipment. Pulses are sent from a laser onboard an aircraft and the scattered pulses are recorded. The time lapse for the returning pulses is used to determine the two-way distance to the object. LIDAR uses a sharp beam with high energy and hence high resolution can be achieved. It also enables DEM generation of a large area within a short period of time with minimum human dependence. The disadvantage of procuring high resolution LIDAR data is the expense involved in data collection.

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Figure. 7. Lidar DEM at 5m resolution for the downtown area of Austin

APPLICATIONS OF DEMGeographical information technology and digital image processing have become a rapidly expanding field in recent years with particular significance in the treatment of geo- and image information for scientific, commercial and operational applications. For most applications in these three domains, digital elevation models (DEMs) are an important, integral part. In the following sections the various applications and application areas of DEMs is described in more detail. It is decided to consider these as a selection of representative activities in the domains of

• Scientific applications

• Commercial applications

• Industrial applications

• Operational applications

• Military applications

where DEMs play a significant role in the improvement of analysis result, product development and decision making. Thus, DEM are an asset in a variety of both commercial and public business and

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management fields within telecommunications, navigation, energy, disaster management, transportation, weather forecast, remote sensing, geodesy, land cover classification, civil engineering and many more. The wide range of different applications in which DEMs will be useful reflect the overall importance of the availability of global, consistent, high quality digital elevation models.

SCIENTIFIC APPLICATIONS

Exact information about the Earths surface is of fundamental importance in all geosciences. The topography exaggerate control over range of Earth surface processes (evaporation, water flow, mass movement, forest fires) important for the energy exchange between the physical climate system in the atmosphere and the biogeochemical cycles at the Earth surface. Ecology investigates the dependencies between all life forms and their environment such as soil, water, climate and landscape. Hydrology needs the knowledge about the relief to model the movement of water, glaciers and ice. Geomorphology describes the relief recognizing form-building processes. Climatology investigates fluxes of temperature, moisture, air particles all influenced by topography.

• In weather forecast and climate modeling, models of conversion processes between the ground and the atmosphere as well as of movements in the lower atmospheric strata also rely on uniform and global DEMs. Relationship between the topography and the shape of the land surface with a variety of state variables of geo-processes like evaporation, runoff, soil moisture, influencing the climate in local and global-scale. Another area of application is a global land cover classification. Precise mapping and classification of the Earth's surface at a global scale is the most important prerequisite for large-scale modeling of geo-processes. In numerous studies, it was demonstrated that radar images are suitable for documentation and classification of natural vegetation and agricultural areas. In remote sensing DEMs are used together with GIS to correct images or retrieve thematic information with respect to sensor geometry and local relief to produce geocoded products. Thus, for the synergic use of different sensor systems (and GIS), digital elevation model are a prerequisite for geocoding satellite images and correcting terrain effects in radar scenes. To summarize, the science community, for example, employs DEMs for research on

• Climate impact studies

• Water and wildlife management

• Geological and hydrological modeling

• Geographic information technology

• Geomorphology and landscape analysis

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• Mapping purposes and

• Educational programs

COMMERCIAL APPLICATIONS

Commercial applications are more marked and business oriented applications related to sale and distribution of DEM and DEM products. From this point of view, two main market sectors are interested in digital terrain models. One sector employs basic DEM products where data are preprocessed and geocoded but have no application associated with them. The other sector encompasses the value-adding services, which couple DEMs with a specific application. The following overview for both sectors addresses questions like Is there a market for digital elevation models? Do the users know the available products? Are there any competitors on the market? Commercial providers offer DEMs or DEM -associated products, which are of interests for issues in

• Telecommunication

• Air traffic routing and navigation

• Planning and construction

• Geological exploration

• Hydrological and meteorological services

• Geokoding of remote sensing and

• Market of multimedia applications and computer games

INDUSTRIAL APPLICATIONS

For industrial applications digital elevation models are used for development of market-oriented product technology, improved services and to increase the economic outcome of the industrial production. Such applications are found within e.g. the Telecom, Telematics, Avionics, Mining, Mineral exploration, Tourism and Engineering industry. Telecom industry The telecommunication sector encompasses manufacturers, wireless service providers and operators. About 60% of all DEM sales in Europe are to the telecommunication industry. Topographic data are used for

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• macro cell planning (area grid cells ranging from 3-5km with elevated base transceiver stations)

• micro cell planning (mainly applied in central parts of large cities)

• clutter data (land use information in the telecommunication industry) Typically the databases of 100m grid elevation data are used for macro cell planning while detailed 5-10 m elevation data are used for micro cell planning. Tools are developed for modeling Multi-transmitter communication networks and for aid in positioning of radio towers. Such tools strongly rely on the availability of recent DEMs and land use information supplied by value adding companies. Actual digital elevation models are required for determining optimal locations for transmitting masts for all forms of terrestrial radio signal propagation. Based on e.g. the SAR data of the SRTM, DEMs will be calculated that have the advantage of showing the existing situation "as is", with all human-made and natural objects that might impede radio wave transmission. Avionics industry By combining land use data and terrain height information with airport databases, value-adding software companies are able to provide avionics industry with an important tool for collision avoidance systems, ground proximity warning and flight management systems. Flight simulators for crew training will have a realistic background and together with real-time in-flight GPS positioning information air traffic will become safer.

Telematics industry

The reliability of GPS (Global Positioning System) navigation applications, e.g., for automobiles and aircraft, depends on actual and precise data, too. The topographic data of SRTM are captured and processed in order to maintain compatibility with the global co-ordinate system defined for GPS. Hence, clear improvements in GPS navigation applications are expected to progress current development. Terrestrial data acquisition often depends on political decisions, in many cases resulting in abrupt resolution changes in areas where state borders meet. The resolution of SRTM DEMs will remain constant on a global scale. Great commercial expectations are associated with the expanding market of car navigation systems and digital road map. Here the most important information for the user remains the horizontal plane, nonetheless, terrain height is used by the mapping industry to estimate elevation values to road segments. New updated road data however are registered with an elevation value by using GPS in moving vehicles. Updated basic data can considerably enhance the efficiency of transportation network planning. Computer-based scenarios ease analyses of necessary excavations and bridges, which are primary cost factors in the construction of transportation infrastructure. A realistic simulation of air corridors to airports with actual data is another DEM application. Mining and oil industry Exploration experts are possibly the most experienced users of remote sensing data and digital elevation models. By analyzing optical radar images they determine promising regions of potential mineral deposits around the world. More and more, a combination of remote sensing data, especially

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DEMs, with gravity maps the identification of oil spills on satellite imagery and other phenomena and combinations leads the prospecting companies to successful explorations. In addition to exploration activities digital elevation models are also used for monitoring the exploration consequences. The problems of “subsidence in mining regions”, for example, is addressed by Atlantis Scientific Inc. in cooperation with Shell Oil.

Tourism industry

With the widespread availability of personal computers in private households, the valueadding sector increasingly turns to expanding private market. A great potential exists in the areas of tourist and leisure maps, digital color-coded satellite maps and digital elevation models. An example is the CD rom “Opplev Norge” provided by the Norwegian Mapping Authority. Although still only using 2D maps. It should be combined with a digital elevation model combined with a fly-through software that enables the user to virtually cruise over the terrain surface of Norway. Other applications and companies can be found in the tourist section. Portier System combines 2D map data, satellite images and orthophotos with digital elevation models with tourist information to yield a tourist information system for Norway or any specific region. Further, the users are able to inform themselves interactively about their potential vacation destination. The user can extract 3D visualizations and 3D fly-through animations of scenic viewpoints and specific scenic regions of Norwegian nature. They can also extract information on infrastructure, accommodations, leisure activities, opening hours of restaurants and More.

OPERATIONAL APPLICATIONS

Geoinformation is a substantial part of the need of modern society for communication and information technologies. This data is increasingly used throughout all levels in administration, management and planning. Geoinformation is the basis for regional planning and its availability the prerequisite for decision making processes in order to locate infrastructure development and investment. Operational applications include applications where DEM is used to improve management and planning of natural resources and within areas of regional planning, environmental protection, hazard reduction, military and other security-relevant applications, insurance issues, health services, agriculture, forestry, and soil conservation. Operational applications are mostly related to state and governmental services and management operations. DEMs may ultimately replace printed maps as the standard means of portraying landforms. Finally, operational users need DEMs for

• generating and updating geoinformation for governmental issues

• administering assistance in areas inflicted by disasters

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• airline operation safety, and

• security relevant activities (risk and hazard)

Below follows some examples of operational applications.

Reconnaissance for mineral and water resources A successful reconnaissance for mineral and water resources is often dependent on the opportunities of a synoptic view of many geospatial information sources. Geophysical and geological prospecting has to be done in several steps by increasing degrees of intensity mapping and prospecting . Remote sensing information and digital elevation models deliver basic information on geologic structures. These information sources are especially important in remote areas where coverage by topographic maps is limited. An example is finding ground water resources in Africa where drinking water resources are scarce. German Remotes Sensing Center aims at extracting ground water relevant information from radar images. Information on hydrological patterns, lineaments, morphological structures, tectonic and sedimentological anomalies are to be extracted and to be merged with optical satellite data and available topographic and geological maps. Remote sensing techniques are being utilized to search for promising bearing grounds and to designate locations of wells. Highresolution digital elevation models would deliver much needed additional information for the interpretation of the ground water relevant structures and catchment areas, e.g. for wadis. Aircraft guidance systems – flight simulations Current investigations in the field of aircraft guidance systems focus on the improvements of operational safety and quality. Today aircraft crews have to handle increasingly complex situations. The simulated topography renders additional information and ascertains safety, particular in critical situation like in darkness and under adverse weather conditions. The detailed simulation of landscape based on accurate digital elevation model is important for training purposes but, more so, guiding the aircraft when approaching an unsupported airstrip, be it times of military actions or when attempting to reach an undeveloped area affected by natural hazards or other catastrophic events. Only if the integrated terrain data are reliable can they be introduced safely to such critical applications. Consequently, high accuracy and high resolution are of great importance. Forest planning and management Resent years there has been an increasing focus on a sustainable management of natural resources like forest. Tools have been developed e.g. at SINTEF applied mathematics to create plans for a sustainable management of forest during a longer time span. By introducing digital terrain models different criteria concerning the topography of the forest area can be considered in the planning process. Examples are calculation of slope gradient and its influence on erosion process due to clear cutting of hill slopes; aspect computation and the effect of solar insolation on forest growth and derivation of surface curvature related to soil moisture conditions. Planning of breakwater constructions Breakwaters are important to protect harbors for damages caused by large ocean waves e.g. along the Norwegian coastline. Exact location of the breakwaters is important to give maximum protection due to e.g. prevailing wind directions. Digital models of the seafloor bottom and the coastal zone can be used to produce realistic computer simulations of the optimal location of

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breakwaters before construction. It is well known that seafloor topography and coast morphology have significant effects on the direction and size of ocean waves at shallow water. Mass movement and hazard prediction Rock fall and avalanches is responsible for large costs due to reparation of infrastructure and buildings in addition to the loss of life for more catastrophic events. An important task for the public community is to predict and prevent such hazardous events. Digital terrain models combined with geological, vegetation and soil information is important to detect risk areas in order to prevent building constructions and where to start specific protection actions. Such information is also of interest for insurance companies in determination of high-risk areas with larger potential for damages and thus exposed for higher insurance fees. Hydrology Flooding risk assessment The modeling of river catchment areas necessitates high-precision DEMs that are homogeneous and not confined to areas of the respective water authorities. Only the combination of exact topographic data, situational information, data on precipitation, water retention and storage capacities enables precise statements as to the duration and extent of floods caused by rivers. Aside from such extreme situations, a continuous monitoring of hydrological phenomena is useful in agriculture, for example, in helping making decisions on the need for irrigation. In coastal zones, DEMs can be used to assess in advance the dangers in areas exposed to potential inundation, and help governments in their task of maintaining open shipping routes. Disaster management (prevention, relief, assessment) Disaster management is often impeded by lacking, incorrect or simply imprecise information about the situation on the ground. Up-to-date and precise data are imperative in assessing potential risks (posed by floods, for example), in employing relief personnel effectively, in disaster aid (e.g., locating adequate spots for dropping of relief supplies) and in analyzing damages and changes.

6. DIGITAL ELEVATION MODEL and its DERIVATIVES

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With the Surface tools; also known as the digital elevation model derivatives, one can quantify and visualize a terrain landform.

Starting with a raster elevation surface as input, with these tools, one can gain information by producing a new dataset that identifies a specific pattern within an original dataset. One can derive patterns that were not readily apparent in the original surface, such as contours, angle of slope, steepest downslope direction (Aspect), shaded relief (Hill-shade), and View-shed.

Each surface tool provides insight into a surface that can be used as an end in it or as input into additional analysis.

Tool Description

Aspect Derives aspect from a raster surface. The aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors.

Contour Creates a line feature class of contours (isolines) from a raster surface.

Contour List Creates a feature class of selected contour values from a raster surface.

Contour with Barriers

Creates contours from a raster surface. The inclusion of barrier features will allow one to independently generate contours on either side of a barrier.

Curvature Calculates the curvature of a raster surface, optionally including profile and plan curvature.

Cut Fill Calculates the volume change between two surfaces. This is typically used for cut and fill operations.

Hill-shade Creates a shaded relief from a surface raster by considering the illumination source angle and shadows.

Observer Points

Identifies which observer points are visible from each raster surface location.

Slope Identifies the slope (gradient, or rate of maximum change in z-value) from each cell of a raster surface.

View-shed Determines the raster surface locations visible to a set of observer features.

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1. Aspect

Aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. It can be thought of as the slope direction. The values of each cell in the output raster indicate the compass direction that the surface faces at that location. It is measured clockwise in degrees from 0 (due north) to 360 (again due north), coming full circle. Flat areas having no downslope direction are given a value of -1. The value of each cell in an aspect dataset indicates the direction the cell's slope faces.

Aspect Directions

Conceptually, the Aspect tool fits a plane to the z-values of a 3 x 3 cell neighborhood around the processing or center cell. The direction the plane faces is the aspect for the processing cell.The following diagram shows an input elevation dataset and the output aspect raster.

Applications

With the Aspect tool, one can do the following:

Find all north-facing slopes on a mountain as part of a search for the best slopes for ski runs.

Calculate the solar illumination for each location in a region as part of a study to determine the diversity of life at each site.

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Find all southerly slopes in a mountainous region to identify locations where the snow is likely to melt first as part of a study to identify those residential locations likely to be hit by runoff first.

Identify areas of flat land to find an area for a plane to land in an emergency.

The Aspect algorithm

A moving 3 x 3 window visits each cell in the input raster, and for each cell in the center of the window, an aspect value is calculated using an algorithm that incorporates the values of the cell's eight neighbors. The cells are identified as letters a to i, with e representing the cell for which the aspect is being calculated.

Surface window

The rate of change in the x direction for cell e is calculated with the following algorithm:

[dz/dx] = ((c + 2f + i) - (a + 2d + g)) / 8

The rate of change in the y direction for cell e is calculated with the following algorithm:

[dz/dy] = ((g + 2h + i) - (a + 2b + c)) / 8

Taking the rate of change in both the x and y direction for cell e, aspect is calculated using:

aspect = 57.29578 * atan2 ([dz/dy] -[dz/dx])

The aspect value is then converted to compass direction values (0-360 degrees), according to the following rule:

if aspect < 0

cell = 90.0 - aspect

else if aspect > 90.0

cell = 360.0 - aspect + 90.0

else

cell = 90.0 – aspect

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2. Slope

For each cell, the Slope tool calculates the maximum rate of change in value from that cell to its neighbors. Basically, the maximum change in elevation over the distance between the cell and its eight neighbors identifies the steepest downhill descent from the cell.

Conceptually, the tool fits a plane to the z-values of a 3 x 3 cell neighborhood around the processing or center cell. The slope value of this plane is calculated using the average maximum technique (see References). The direction the plane faces is the aspect for the processing cell. The lower the slope value, the flatter the terrain; the higher the slope value, the steeper the terrain.

If there is a cell location in the neighborhood with a NoData z-value, the z-value of the center cell will be assigned to the location. At the edge of the raster, at least three cells (outside the raster's extent) will contain NoData as their z-values. These cells will be assigned the center cell's z-value. The result is a flattening of the 3 x 3 plane fitted to these edge cells, which usually leads to a reduction in the slope.

The output slope raster can be calculated in two types of units, degrees or percent (percent rise). The percent rise can be better understood if you consider it as the rise divided by the run, multiplied by 100. Consider triangle B below. When the angle is 45 degrees, the rise is equal to the run, and the percent rise is 100 percent. As the slope angle approaches vertical (90 degrees), as in triangle C, the percent rise begins to approach infinity.

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The Slope tool is most frequently run on an elevation dataset, as the following diagrams show. Steeper slopes are shaded red on the output slope raster.

The tool can also be used with other types of continuous data, such as population, to identify sharp changes in value.

The Slope algorithm

The rates of change (delta) of the surface in the horizontal (dz/dx) and vertical (dz/dy) directions from the center cell determine the slope. The basic algorithm used to calculate the slope is:

slope_radians = ATAN ( √ ([dz/dx]2 + [dz/dy]2) )

Slope is commonly measured in units of degrees, which uses the algorithm:

slope_degrees = ATAN ( √ ([dz/dx]2 + [dz/dy]2) ) * 57.29578

The slope algorithm can also be interpreted as:

slope_degrees = ATAN (rise_run) * 57.29578

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where:

rise_run = √ ([dz/dx]2 + [dz/dy]2]

The values of the center cell and its eight neighbors determine the horizontal and vertical deltas. The neighbors are identified as letters from a to i, with e representing the cell for which the aspect is being calculated.

Surface scannng window

The rate of change in the x direction for cell e is calculated with the following algorithm:

[dz/dx] = ((c + 2f + i) - (a + 2d + g) / (8 * x-cell size)

The rate of change in the y direction for cell e is calculated with the following algorithm:

[dz/dy] = ((g + 2h + i) - (a + 2b + c)) / (8 * y-cell size)

3. Contour

Contours are lines that connect locations of equal value in a raster dataset that represents continuous phenomena such as elevation, temperature, precipitation, pollution, or atmospheric pressure. The line features connect cells of a constant value in the input. Contour lines are often generally referred to as isolines but can also have specific terms depending on what is being measured. Some examples are isobars for pressure, isotherms for temperature, and isohyets for precipitation.

The distribution of the contour lines shows how values change across a surface. Where there is little change in a value, the lines are spaced farther apart. Where the values rise or fall rapidly, the lines are closer together.

The contour creation tools, Contour, Contour List and Contour with Barriers, are used to create a polyline feature dataset from an input raster.

Why create contours?

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By following the polyline of a particular contour, you can identify which locations have the same value. Contours are also a useful surface representation, because they allow you to simultaneously visualize flat and steep areas (distance between contours) and ridges and valleys (converging and diverging polylines).

The example below shows an input elevation dataset and the output contour dataset. The areas where the contours are closer together indicate the steeper locations. They correspond with the areas of higher elevation (in white on the input elevation dataset).

The contour attribute table contains an elevation attribute for each contour polyline.

Contour quality

The contour tools produce engineering-quality contours, representing an exact interpretation of the raster surface. Overall contour accuracy depends on how well the data used to create the input raster represents the actual surface. The size of the raster cells used affects the appearance of the output contours. A large cell size may result in coarse, blocky contours.

Occasionally, engineering-quality contours may cross, appear to intersect, or form an unclosed branching line. Crossing contours can occur in saddle regions that lie exactly on a contour interval. In other cases, the contours may pass so close to one another that they appear to intersect. Branching contours can occur in cases of intersecting ridges that fall exactly on a contour interval. These are all valid engineering-quality interpretations of the surface that cartographers typically modify for aesthetic purposes.

Controlling contour quality

Occasionally, contours may be created that have square or blocky outlines, appearing to follow raster cell boundaries. This can occur when the raster values are integers and they fall exactly on a contour. This is not a problem, merely an exact contouring of the data.

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If you want smoother contours, some ways to overcome this condition include smoothing the source data or adjusting the base contour.

Smoothing the data

The easiest smoothing approach would be to preprocess the input raster with the Focal Statistics tool, using the Mean statistic.

Another method is to slightly adjust z-values so a contour will no longer pass exactly through the cell centers of the raster. Again, the Focal Statistics tool is used, but instead with a custom weighted kernel file and the Sum statistic. The structure of the kernel file is:

3 3

.005 .005 .005

.005 .960 .005

.005 .005 .005

The accuracy of the contour will not be significantly affected because the z-value adjustment is small and heavily weighted in favor of the center raster cell.

Adjusting the base contour-

Adjusting the base contour involves offsetting the base contour such that the contours no longer pass exactly through the cell centers. The offset can be very small; values as small as 0.0001 have been effective.

4. Hill-shade

The Hill-shade tool obtains the hypothetical illumination of a surface by determining illumination values for each cell in a raster. It does this by setting a position for a hypothetical light source and calculating the illumination values of each cell in relation to neighboring cells. It can greatly enhance the visualization of a surface for analysis or graphical display, especially when using transparency.

By default, shadow and light are shades of gray associated with integers from 0 to 255 (increasing from black to white).

The hill-shade parameters-

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The primary factor when creating a hillshade map for any particular location is the location of the sun in the sky.

Azimuth

The azimuth is the angular direction of the sun, measured from north in clockwise degrees from 0 to 360. An azimuth of 90º is east. The default azimuth is 315º (NW).

Default sun azimuth (direction) for hill-shade is 315º

Alttitude

The altitude is the slope or angle of the illumination source above the horizon. The units are in degrees, from 0 (on the horizon) to 90 (overhead). The default is 45 degrees.

5. Cut-fillA cut-and-fill operation is a procedure in which the elevation of a landform surface is modified by the removal or addition of surface material. The Cut Fill tool summarizes the areas and volumes of change from a cut-and-fill operation. By taking surfaces of a given location at two different time periods, it identifies regions of surface material removal, surface material addition, and areas where the surface has not changed.

Applications

With the Cut Fill tool, you can do the following:

Identify regions of sediment erosion and deposition in a river valley. Calculate the volumes and areas of surface material to be removed and areas to be filled to level

a site for building construction.

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Identify areas that become frequently inundated with surface material during a mudslide in a study to locate safe areas of stable land for building homes.

Display

When the Cut Fill tool is executed, by default, a specialized renderer is applied to it that highlights the locations of cut and of fill. The determinant is the attribute table of the output raster and considers positive volume to be where material was cut (removed) and negative volume where material was filled (added).

Using Cut Fill for river morphology

Using river morphology as an example to track the amount and location of erosion and deposition in a river valley, a series of cross sections need to be taken through the valley and surveyed on a regular basis to identify regions of sediment erosion and deposition.

The following graphics show a side profile of one of the cross-sections in the surface that has experienced changes where material has been removed from some areas and added to others.

The first graphic shows the surface in its original state

The second graphic shows the surface after a period of time where erosional and depositional forces have acted on it:

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The third graphic shows how the Cut Fill tool identifies the areas where material has been removed (cut) and where it has been gained (filled):

Calculations

The output raster retains several properties of the change in its attribute table.

Edge-connected areas identified

First, from the upper-right corner, a sequential value is given to each unique edge-connected area of cut, fill, or no change.Different types of connectivity are demonstrated in the following graphic:

Volume calculation

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For each of the cut/fill regions, the volume is calculated. For a single cell, the formula for the volume is:

Vol = (cell_area) * ΔZ

where:

ΔZ = ZBefore - ZAfter

For example, a particular cell has an initial z-value of 235 and a cell size of 10 meters. If the location is excavated by 3 metres, the volume will be:

Vol = (10m * 10m) * (235m - 232m)

= 100m2 * 3m

= 300m3

Area calculation

For each of the cut/fill regions, the area is also calculated. This is simply the number of cells in the region (Count) multiplied by the cell size of the raster.

Attribute table

An example of the attribute table for the output raster is the following:

Object ID Value Count Volume Area

0 1 55819 0.000 258107056

1 2 707 -137415060.250 3269168

2 3 65 -114913516.625 300560

3 4 810 1235057106.000 3745440

Positive values for volume indicate areas that have had material cut (removed), and negative volume values are for areas that have had material filled (added).

6. Using View-shed and Observer Points for visibility analysis

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A view-shed identifies the cells in an input raster that can be seen from one or more observation locations. Each cell in the output raster receives a value that indicates how many observer points can be seen from each location. If you have only one observer point, each cell that can see that observer point is given a value of 1. All cells that cannot see the observer point are given a value of 0. The observer points feature class can contain points or lines. The nodes and vertices of lines will be used as observation points.

Why calculate viewshed?

The viewshed analysis tools are useful when you want to know how visible objects might be—for example, from which locations on the landscape will the water towers be visible if they are placed in a particular location, or what will the view be from a road?

In the example below, the view-shed from an observation tower is identified. The elevation raster displays the height of the land (lighter shades represent higher elevations), and the observation tower is marked as a green triangle. The height of the observation tower can be specified in the analysis. Cells in green are visible from the observation tower, while cells in red are not.

Using layer transparency, you can display a hill-shade raster underneath your elevation raster and incorporate the output from view-shed analysis for visualizing the relationship between visibility and terrain.

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Not only can you determine which cells can be seen from the observation tower, but if you have several observation points, you can also determine which observers can see each observed location. Knowing which observer can see which locations can affect decision making. For example, in a visual quality study for siting a landfill, if it is determined that the proposed landfill can only be seen from dirt roads and not from the primary and secondary roads, it may be deemed a favorable location.

Tools for view-shed and observer points analysis

There are two tools available for visibility analysis, View-shed and Observer Points. They can both be used to generate an output view-shed raster. The output from Observer Points additionally identifies exactly which observer points are visible from each raster surface location.

Viewshed tool

The Viewshed tool creates a raster, recording the number of times each area can be seen from the input point or polyline observer feature locations. This value is recorded in the VALUE item in the table of the output raster. All cell locations assigned NoData on the input raster are assigned NoData on the output raster.

When polyline input is used, every node and vertex along each input arc is processed as an individual observation point. The values in the VALUE item of the output raster give the number of nodes and vertices visible to each cell.

When a SPOT attribute item does not exist in the input observer features table, bilinear interpolation is used to determine the elevation of each observation point. If the nearest raster cell to an observation point or vertex has a No-Data value, the tool will be unable to determine its elevation. In this case, the observation point will be excluded from the view-shed analysis.

Intervening No- Data cells between the point of observation and other cells are evaluated as invisible and do not obscure visibility.

Observer Points tool

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The Observer Points tool stores the binary-encoded information about which observation points can see each raster cell. This information is stored in the VALUE item.

To display all the regions of the raster that can be seen only by observer 3, open the output raster attribute table and select the row where observer 3 (OBS3) equals 1 and all other observers equal 0. The regions of the raster that can be seen only by observer 3 will be highlighted on the map.

To use linear data with Observer Points, convert the line feature class with the Feature Vertices To Points tool in the Data Management > Features toolset. There is a limit of 16 points.

When a SPOT attribute item does not exist in the feature attribute table, bilinear interpolation is used to determine the elevation of each observation point. If the nearest input raster cell to an observation point has the NoData value, the tool will be unable to determine its elevation. In this case, the observation point will be excluded from the visibility analysis.

Intervening NoData cells between the point of observation and other cells are evaluated as invisible and do not obscure visibility.

Observer Points details

Observer Points stores the binary-encoded information about which observation points can see each raster cell. This information is stored in the VALUE item.

To display all the regions of the raster that can be seen only by observer 3, open the output raster attribute table and select the row where observer 3 (OBS3) equals 1 and all other observers equal 0. The regions of the raster that can be seen only by observer 3 will be highlighted on the map.

Raster OBSn items

In addition to the standard items VALUE and COUNT in the raster attribute table, new items will be created corresponding to each observer in the input point dataset. The items are OBS1...OBSn, where n is the number of observers. They are defined as follows:

ITEM NAME WIDTH OUTPUT TYPE N.DEC

OBSn 2 2 B -

These items record the visibility of each cell by every input point observer features observer. For example, every raster cell that can be seen by observer 8 (featurename# = 8) will contain a value of 1 in

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the item named OBS8. Cells that cannot be seen by the observation point are assigned a value of 0. Cell locations assigned NoData on the input raster are assigned NoData on the output raster.

One can use the OBSn items to identify those raster cells that can be seen from a specific observation point. This is slightly different from the previous case where a selection was made based on the VALUE parameter. In this case, cells that can be seen by observers 1 and 8 may also be seen by other observers (in which case, they each would have a different value).

For example, to display all areas that can be seen by observation points 1 and 8, open the raster attribute table and select the row where both observer 1 (OBS1) and observer 8 (OBS8) equal 1 and all other observers equal 0.

Quantifying visual quality

The information in the tool output can also be used to perform an analysis of visual quality. For example, you can determine the visual quality of all locations on a surface by positioning an observation point at each significant visual feature within the input raster's extent. Such points might include the city dump, auto salvage yard, local parks, and each of the power transmission towers in the region.

After running Observer Points, use the OBSn item in the output raster's table to select those cell locations that can see each visual feature. Use any of a variety of the ArcGIS Spatial Analyst functions to accumulate positive or negative scores, depending on each observation point's visual quality and weight. After all observation points have been considered, those cell locations with the best scores will have the best visual quality.

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References

Li, Z., Zhu, Q. and Gold, C. (2005): Digital terrain modeling: principles and methodology|. CRC Press. Boca Raton.

Peckham, Robert J., Jordan, Gyozo (Eds.)(2007): Development and Applications in a Policy Support Environment Series: Lecture Notes in Geo-information and Cartography. Heidelberg.

Podobnikar (2008). "Methods for visual quality assessment of a digital terrain model". S.A.P.I.EN.S 1 (2).

Adrian W. G., Nicholas C. K., Peter M. P. (2007): Mobile radio network design in the VHF and UHF bands: a practical approach. West Sussex.

"Landslide Glossary USGS". Paula Messina. "Terrain Analysis Home Page". Spatial Analysis and Remote Sensing Lab at

Hunter College. Retrieved 2007-02-16. Wilson, J.P.; Gallant, J.C. (2000). "Chapter 1" (PDF). In Wilson, J.P., and Gallant, J.C. (Eds.).

Terrain Analysis: Principles and Applications. New York: Wiley. pp. 1–27. ISBN 0-471-32188-5. Retrieved 2007-02-16.

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Hirt, C.; Filmer, M.S.; Featherstone, W.E. (2010). "Comparison and validation of recent freely-available ASTER-GDEM ver1, SRTM ver4.1 and GEODATA DEM-9S ver3 digital elevation models over Australia.". Australian Journal of Earth Sciences 57 (3): 337–347. doi:10.1080/08120091003677553. Retrieved May 5, 2012.

Rexer, M.; Hirt, C. (2014). "Comparison of free high-resolution digital elevation data sets (ASTER GDEM2, SRTM v2.1/v4.1) and validation against accurate heights from the Australian National Gravity Database." (PDF). Australian Journal of Earth Sciences 61 (2). doi:10.1080/08120099.2014.884983. Retrieved April 24, 2014.

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