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An evolving Remote Sensing tool -Bob Champoux -ICESAT, NASA LIght Detection And Ranging Introduction to LIDAR -POB Magazine

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An evolving Remote Sensing tool

-Bob Champoux

-ICESAT, NASA

LIght Detection And Ranging

Introduction to LIDAR

-POB Magazine

The basics (of Photo Interpretation)….

-Lixin Wang

What about elevations?

Aerial Photo Contours from Photos

-Eastern Topographics

Contours developed manually…

LIDAR Points

Each black dot is a LIDAR point

•At each point, the elevation has

been sampled.

Start counting!

-GIS Dept. Clinton County, Ohio

Seeing more than contours….

-GIS Dept. Clinton County, Ohio

Active Imagery…

Three Components:

•Laser Scanner

•Up to 400,000 pulses/second

•Near infrared

•Global Positioning System

•Positions the scanner

•Inertial Navigation System

•Orientation of the scanner-Andersen

-Andersen

How LIDAR works

The process:

•LIDAR sensor measures time from

when pulse sent to when received.

•This translates to distance

from sensor to object.

•GPS knows location of sensor.

•Coordinates (X,Y,Z)

are assigned to each pulse.

•Result is a "cloud of points"

each point has its own coordinates & height.

How LIDAR works

•So, not only does each dot

(sample point) have an elevation.

•Each dot has X and Y coords

or, Latitude and Longitude

•The collected data are

hundreds of thousands,

sometimes, millions of points.

•Each with its own:

•Latitude

•Longitude

•Elevation

-GIS Dept. Clinton County, Ohio

How many points?

• Points equals resolution.

• Dependent on these variables:

• Number of pulses.

• Flying height.

• Flying speed.

• Scan angle.

-N. Carolina State University

Guesses on file size?

– Megabytes

– Gigabytes

– Terabytes

-Seagate

How LIDAR works

LIDAR image of Durham, NH

-DeLorme

-GIS Dept. Clinton County, Ohio

-Natural Hazards Centre, NZ

Triangulated Irregular Network Digital Elevation Model

Modeling with LIDAR

Modeling with LIDAR

Contours can be derived

-Digital Mapping Australia

Modeling with LIDAR

Oblique view of Wilmington, OH

-GIS Dept. Clinton County, Ohio

-DeLorme

Modeling with LIDAR

LIDAR image showing all points LIDAR image after removal of trees

Digital Elevation Models can be created

Modeling with LIDAR

Develop Watersheds

-Instituto Agrario di S. Michele all’Adige

Map Slopes and Aspect

-Free Geography Tools-ESRI

Map Slopes

Colors relate to

Aspect (Direction)

Modeling with LIDAR

Profile View…

-GIS Dept. Clinton County, Ohio

Modeling Forests

Canopy Height Model

Instead of focusing on the

ground…– Measure the heights of the trees.

– Canopy height is a critical parameter

for ecosystem modeling

-Digital Mapping Australia

Modeling Forests

Estimate stand volume and

biomass by:– Identify and measure

the individual tree crowns.

– Individual tree volume and biomass

are strongly correlated to the tree’s

crown area.

– Can look at distribution of volume

across the forest.

– Aids in forest management

-Lefsky

Other Forest Applications:

•Vertical and Horizontal

distribution of:•Individual trees

•Branches

•From this:•Canopy density

•Species Identification

and Classification

•Stand Structure

•Successional state

•Forest health

•Habitats

•With time: Change detection

Modeling Forests

-Spencer B. Gross

Modeling Individual Trees

-Stephen F. Austin State University

• With a very high resolution– Individual trees can be mapped.

-Lefsky

LIDAR points

Modeling Individual Trees

Species can be identified of individual trees

Modeling Individual Trees

Can develop:•Crown base height

•Individual tree crowns

•3D tree models

Subcanopy•Volumes, distribution, etc.

The forest floor:•Amount of light reaching the ground.

(By studying canopy interception).

•Woody debris

•Fuel loading

Modeling urban areas

-TerrascanHouston, TX

-OpenTopography

Modeling urban areas

Uses:•Urban Planning

•Transportation

•Emergency Response

•Drainage Patterns

•Flooding

•Flooding Response

•Amount of Impervious

Surface

•Vegetation

Image above:

New Orleans, LA-Terrascan

Two types of LIDAR sensors

• Discrete– Individual points

– Small footprint (5-30 cm)

– More common sensor

-University of Washington

Two types of LIDAR sensors

-CSIRO

• Waveform– Continuous vertical return

– Better for forest biometrics

– Less cost

-ASPRS

Two types of LIDAR sensors

• Waveform– Previously:

– Large footprint (10’s of m)

– Short range

-CSIRO

LIDAR Limitations...

Bathymetric and shoreline work is being done

with LIDAR that transmits at two wavelengths:

•Infrared to detect surface

•Green (provides for max. penetration)

-Terrascan

• Water– Because of using NIR:

– Water absorbs signal

– Same issue with:

– Asphalt

– Clouds

– Fog, smoke, haze

– Rain

• Dense Vegetation– May not reach ground

– Light may scatter and reflect

before returning to sensor (False elevations are the result)

Why LIDAR now?

• History– 1970’s Development began (NASA)

– 1980’s GPS incorporated

– 1990’s Commercial LIDAR

– 2000’s Maturing of technologies:

• GPS positional accuracy increases

cm level accuracy

• INS orientation accuracy increases

Pitch/roll accuracy ≈ 0.005º

– What’s to come:

• More incorporation of LIDAR with other technologies

– Digital camera commonly flown with LIDAR

• Use of signal intensity

• Multiple pulses in the air

-GPS satellite

-Fugro EarthData

LIDAR equipment

-Wire Services-McKim & Creed

LIDAR unit in action

-ICESAT, NASA

Pretty exciting, huh?

LIDAR:

-Provides the Elevation

-Intensity can be measured

-DN values

Accuracies?

• Dependent on:– Laser scanner

• Range errors

– GPS

• Position errors

– IMU

• Orientation errors

• For Discreet LIDAR:– 15-30 cm Vertical

– 1m Horizontal

-USGS

Accuracies?

Other factors affecting LIDAR accuracy:– Steep slopes

– Recording of scanner angle

– Atmospheric effects

-N. Carolina State University

References

• Baltsavais, E.P. (1999). Airborne Laser Scanning: Basic Relations and

Formulas. ISPRS Journal of Photogrammetry and Remote Sensing 54

(2/3), 199-214.

• Flood, M. and Satalich, J. (3/28/01) LIDAR 101. POB.

• Dubayah, R. and Drake, J. LIDAR Remote Sensing for Forestry

Applications, College Park, MD, University of Maryland, Department of

Geography.

• Kraus, K. and Pfeifer, N. (1998) Determination of Terrain Models in

Wooded Areas with Airborne Laser Scanning Data. ISPRS Journal of

Photogrammetry and Remote Sensing 53 (4), 193-203.

• Lefsky, M., Cohen, W., Parker, G. and Harding, D. (2002) LIDAR Remote

Sensing for Ecosystem Studies. BioScience 52 (1), 19-30

• CLICK—USGS Center for LIDAR Information Coordination