Airborne LiDAR Data Acquisition for Forestry Applications

Mischa Hey – WSI (Corvallis, OR)

WSI Services Corvallis, OR

Airborne Mapping: • Light Detection and Ranging (LiDAR)

• Thermal Infrared Imagery

• 4-Band Multi-Spectral Imagery

• Bathymetric/Sonar

• Geodetic Survey

Analysis: • Forest Inventory and Vegetation Analysis

• Automated Feature Extraction.

• Water Quality Modeling.

• Fish & Wildlife Habitat Assessments.

Forest Research Affiliations

• Oregon State University – USFS PNW, Forest Sciences Laboratory, Corvallis.

• University of Washington – Precision Forestry Cooperative.

• USFS Rocky Mountain Research Station Moscow, ID.

• US Forest Service Research Lab, Portland and Seattle.

• Panther Creek LiDAR Research (BLM, EPA, Private Industry)

Presentation Outline

• LiDAR Concepts • Forest Applications

- Timber inventory, terrain mapping, roads, stream networks.

• Flight timing • Acquisition specifications • Processing needs • Product development

Heading (from IMU)

Lat/Long/EL (from GPS)

Roll (from IMU)

Pitch (from IMU)

Range and Intensity Scan Angle

GPS Base

Airborne Light Detection and Ranging (LiDAR)

Airborne LiDAR Terms

• Laser Wavelength • Field-of-View (FOV) • Pulse Density-

- Emitted pulses from sensor per unit area

• Return Density- - Pulses returning to sensor per unit area

• Discrete Return- - Individual return from emitted pulse

• Full Waveform- - Digitized entire wave returning to sensor

• Back-Scatter Intensity - Reflected energy from pulse

Acquisition Control

• Airborne instrumentation - GPS accuracy (PDOP, constellations) - IMU accuracy (drift, line length) - Base length < 13miles

• Ground control network

- Monument occupation - Control point distribution - Considerations: access,

security, sky visibility

Relative Accuracy (Line-to-Line Calibration)

Individual flight-line swaths are spatially integrated. Good relative accuracy is essential for vegetation analysis. Relative accuracy is good QC measure.

Absolute Accuracy

Point classification

Calibrated Points

Ground Classified Points

Full Feature Classification

Automated algorithms Manual interpretation

Accurate classification is essential for model development.

False vegetation from offset

When to go and why…

Acquisition Timing

Timing Considerations

• Leaf-on vs Leaf-off - Leaf-on => better canopy surface, spectral

info from intensity - Leaf-off => increased canopy penetration,

better ground model, hardwood/conifer distinction

• Climate and Other Factors - Snow, clouds, fog, smoke - Think broad patterns not specific days

• Find the balance: Leaf off, low flow, over

2,500 ft in PNW is tricky….

The important numbers…

Acquisition Specifications

Acquisition Specifications

• Side-lap and FOV - Decreases shadowing - Consistent point distribution

• Point density - Dictates resolution of information

available - Higher density => increased ground

returns, increased canopy detail (8 pts/sqm)

- Ground return density can be 1/10th the native density a heavily forested environment.

Flight Line 1 Flight Line 2

Point Density: More Points are Better

40pts/sqm 20pts/sqm 10pts/sqm 4pts/sqm 2pt/sqm

Real world examples

LiDAR Survey Settings & Specifications Sensor Leica ALS60

Survey Altitude (AGL) 800 m Target Pulse Rate 106 kHz

Sensor Configuration Single Pulse in Air (SPiA) Laser Pulse Diameter 19 cm

Field of View 28⁰ GPS Baselines ≤13 nm

GPS PDOP ≤3.0 GPS Satellite Constellation ≥6

Maximum Returns 4

Intensity

ALS 60 LiDAR sensor

install

8-bit Resolution/Density Average 8 pulses/m2

Accuracy RMSEZ ≤ 15 cm

Absolute Accuracy

Relative Accuracy

Sample 1,301 points 86 surfaces

Average -0.001 m 0.034 m

Median 0.000 mt 0.034 m

RMSE 0.023 m 0.033 m

1σ 0.024 m 0.016 ft

2σ 0.046 m 0.031 m

Classification Point Density

First-Return 10.75 points/m2

Ground Classified 6.03 points/m2

Contracted Delivered

Treating the data right…

Processing Considerations

Processing Considerations

• LiDAR classification - Ground, vegetation, building, utilities - High, medium, low vegetation - Water surface, bridges/culverts

Intensity Normalization

Line to Line Inconsistency

Streaking is Occurring Throughout Image

• Receiver auto-gain-control (AGC) • Laser power emission variations • Atmospheric transmissivity • Laser Angle of incidence

Raw Intensities

Normalized Intensity

What should you buy…

Products

Products - Basic • Point Cloud

- Classified and Calibrated Points (LAS)

• Surface models - Bare earth DEM, Canopy DSM, Canopy height

nDSM - Contours (requires smoothing tolerance)

• Intensity Image (normalized) • Report and Metadata!

Products - Advanced

• Feature extraction - Road networks - Stream networks - Hydro breaklines - Building footprints

• Feature analysis - Stand delineation and characterization - Individual tree inventory and attribution

Network extraction

Mapping yesterday’s tomorrow today…

The Future

Full Waveform

• Discrete Return: Capture only the exact time of the peaks of independently-recognized return pulses.

• Most current systems record up to 4 returns. However, new systems are starting to have more dynamic return recording.

• Full Waveform (FWF): the entire return signal is measured, allowing capture of subtle deviations in the shape of the reflected as compared to the shape of the outbound laser pulse

Full Waveform Green LiDAR point cloud highlighting 7 returns digitized from 1 outgoing pulse using Riegl’s online waveform processing

Full-Waveform Considerations Advantages

• Detection of pulse stretching (return pulse wider than laser pulse) indicating:

• Low vegetation on ground, indicating need to adjust point elevation downward

• Improved classification by using combination of return pulse width and spatial context

• Indication of biomass by evaluating area contained under the pulse shape.

Considerations

• Massive storage requirements often

require subsampling or switching drives during flight.

• More difficult to perform accurate geo-correction of the continuous wave-form.

• Limited software tools.

Discrete Returns

Multi-wavelength LiDAR • Applications:

o Forestry: Potential for species

delineation using return intensity information.

o Stream/Riparian: shallow water bathymetric data for surface water modeling, wetlands, and habitat assessment.

NIR laser

Green laser

Green laser NIR laser

Summary…

• Not all LiDAR is created equal. - High density, high accuracy are

paramount.

• Consider all desired applications. - Get the most from your data.

• Talk to your vendors and outside experts.

- Find a trusted source and be specific about your goals

• Cost and quality are tightly correlated. - Cheaper data is cheaper for a reason.

2007 LiDAR 8 pulses/sq m

2005 LiDAR 2-3 pulses/sq m

Mischa Hey WSI- Corvallis, OR

mhey@wsidata.com www.wsidata.com