Understanding Elements of Remote Sensing Importance of Mapping ...

Nebraska GIS Symposium ‐ 2013 © 2013 Cornerstone Mapping, Inc.

Understanding Elements of Remote Sensing

Importance of Mapping Standards


Talking Points

Talk about current technology

Review remote sensing principles

Discuss importance of mapping standards


In the Past: Two flights for two products

Enables one flight for RGBN

Technology Trends

CIRRGB

Film Cameras

Digital Cameras


Moving into era of higher spatial resolution

Technology Trends

Higher image overlap

• Ultra dense surface models

• Less building lean

More detail than LiDAR

• 5‐cm engineering design

• 3‐inch high value mapping

• 6‐inch urban

• 1‐foot rural


Technology TrendsSensor Fusion

3D Modeling & Obliques

• Color + LiDAR

• 4‐band + Thermal

• LiDAR + Color, Thermal, Oblique

• Thermal + Hyperspectral

oblique

4‐Band


Spatial

Temporal

Spectral

Types of Image Resolution

Radiometric

Pixel Size 3‐inch, 6‐inch, 1‐meter

“Bands” blue, green, red, NIR, thermal

Shades of Gray 8‐bit, 12‐bit

Image Frequency monthly, annually, every few years


Types of Data Accuracy

Lines up in the right area, but not with good repeatability

Seamless, but with a horizontal‐vertical bias

Uniform accuracy and precision throughout dataset

Precision

Accuracy

Consistency

In context of remote sensing


What is Infrared Aerial Photography?

400 500 600 700 nm 1100

Color Image

Color Infrared Image

Infrared Imagery is

RED in color

Blue RedGreen Near InfraredNear Infrared


RGB = 1, 2, 3

Red

Green

Blue

NIRNIR

2

1

3

4

Layer / Band

Red

Green

Blue

NIRNIR

CIR = 4, 1, 2

Displayed As

Band Combinations

Color

Shift

Red car will ‘look’ greenin CIR mode


Features of Near‐Infrared

400 500 600 700 nm 1100

ChlorophyllAbsorption

ChlorophyllAbsorption

Biomass Indicator

Refle

ctan

ce

Plant Tissue Structure

Affects the NIR reflectanceThus creating a “signature”

Blue RedGreen Near InfraredNear Infrared


Near‐Infrared Detects Vegetation

Synthetic Turf“looks real”

Living Vegetation = Red

Color‐Infrared (CIR)Color


Applications for CIR

• Impervious Surface Area

• Land Cover – Land Use

• Wetland Delineation

• Crop Analyses (precision agriculture)

• Invasive Species (river management)


Color & CIR

Color

CIR

Vegetation = Red

Water = different signature

Wetland Mapping


Camera Sensitivity

End ProductMost people use 8‐bit imagery28 = 256 values (0‐255 DN)

Data AcquisitionQuality sensors acquire data at 12‐bits or higher212 = 4096 values (0‐4095 DN)

It is common to optimize 12‐bit data for 8‐bit output

• Enhance detail in shadows

• Reduce effects of haze

• Atmospheric corrections


Histogram Assessment

NIR Red Green

Typical Bell Curve

Standard Deviations


Histogram Stretching

Original

Standard Deviation Stretch

Min‐Max Stretch


Band Combinations

2

1

3

4

Layer / Band Band Combo

Not Used

Not Used

CIR: 4, 1, 2

Mix as Purple• Low vegetation vigor• Non‐vegetation

For Display Purposes Only‐ Does not affect pixel values ‐

False Color: 1, 4, 1

Vegetation as GreenGreenNIRNIR

Red Red

BlueRed


Band Ratios

NDVI – Normalized Difference Vegetation Index

Red NIRNIR

Red NIRNIR

‐

+NDVI =

‐1 ‐0.5 0 0.5 1Data Range

Indicates Non‐Vegetative Features


NDVI

Enhance crop vigor differences within a crop

Research Plots

Real‐World Field


Mapping Standards

Why they are so Important !


“It's unwise to pay too much, but it's worse to pay too little.

When you pay too much, you lose a little money ‐ that's all.

When you pay too little, you sometimes lose everything…”

Business Economics

‐ John Ruskin


Accuracy = Higher Cost

If project pricing is all over the board

Look at the mapping accuracy being delivered

Unusually low cost often translates into below mapping standards


Understanding Accuracy Standards

ASPRS

NSSDA

NMAS Era of hard copy, fixed‐scale mapping

Adapted NMAS to account for the dynamic scaling of digital data

Scale‐independent statistical measure


Understanding Accuracy Standards

ASPRS for 6‐inch GSD orthophotography

RMSe X or Y Radial RMSe

Class III

Class II

Class I

Map Scale1”=100’

1.0‐ft 1.4‐ft

2.0 2.8

3.0 4.2


Controlling Error

Total Error BudgetRandom Error

0 20 40 60 80 100%

Systematic Error

Sensor Calibration

Position (GPS)Orientation (IMU) Data Processing Interpretation


Accuracy = Higher Cost

Aerial Photography

Accurate map that always line up in your GIS

New data layers can be generated with confidence

Future data layers will also overlay with accuracy

Has no coordinatesSimply, a pretty picture Photogrammetry

• Ground Control

• Airborne GPS/IMU

• Aerial Triangulation

• Camera Calibration

• Expertise


Data fusion will only be successful if the spatial accuracy is consistent

Data Accuracy

Avoid having to say . . .• Wish it was better

• Its always been that way

The “low cost approach” can cost more in the long run


Real Cost of Lower Quality Data

• Re‐do a project from scratch?

• Extensive editing to cleanup data?

• Poor classification results?

What are down stream costs of poor accuracy ?

Quality of your base map (and other layers)

will directly affect your GIS applications


Don’t “copy & paste” requirements from another proposal

How to procure imagery

• Take the time to understand your needs

• Pretty picture vs. accuracy

Identify your needs

Determine your budget

Gather information from vendors


Bundling Services can be Counter Productive

Bundling Services

Often a Better Value to use Multiple Vendors

• Adds a middle‐man for communications

• Sometimes more overhead and project cost

• Get to choose best vendor per deliverable

GISObliqueLiDAR

Ortho


Request Samples

Review Sample Products

Evaluate the Imagery

Highlights / Shadows

Color / Tone Balance

Undesirable Characteristics


Multispectral Technology has Numerous Applications

Cornerstone MappingAaron Schepers8118 S 23rd CT

Lincoln, NE 68512(402) 450‐3194

[email protected]

Don’t Forget about Mapping Standards!

Understanding Elements of Remote Sensing Importance of Mapping ...

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