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Use of airborne laser scanning (LIDAR) as a tool
for forest measurement and monitoring:
use and potential
Steve Reutebuch
Bob McGaughey
Hans-Erik Andersen
Demetrios Gatziolis
Resource Monitoring & Assessment Program
Vegetation Monitoring & Remote Sensing Team
USDA Forest Service
PNW Research Station
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
LIDAR—what is it?
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Light detection and ranging (LIDAR)
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Uses laser light to measure distance
Different detection approaches
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Time of flight
Phase difference
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Hundreds of applications
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In natural resources, 3 LIDAR types are
widely available
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Widely available LIDAR
 Terrestrial
laser scanning
(TLS)
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Primarily used in engineering
Some use in forestry research
scanning plots or individual
trees and logs
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Widely available LIDAR
 NASA
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IceSAT satellite LIDAR
Global- and continental-scale
forest canopy height and
biomass estimates
 70 m diameter footprint
 175 meters spacing
Difficult to remove topographic
effects on canopy heights
Operational 2003-2009
IceSAT-2 launch 2016 ???
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Widely available LIDAR
 Airborne
laser scanning
(ALS)
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Routinely flown commercially
over large areas
Large vendor pool
Mature mission specs &
deliverables
Mature software to process data
Many state and federal partners
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
ALS LIDAR data uses

Topographic mapping of bare earth
surface—primary use
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Engineering
Flood risk mapping
Hydrologic modeling
Geologic mapping
Landslide mapping
Infrastructure mapping—still developing
 Vegetation measurement and mapping—
still developing, with operational uses

USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
National review of ALS LIDAR data
needs
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USGS National Digital Elevation Program:
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Enhanced Elevation Data Requirements Study
 Funded: USGS, FEMA, NRCS, NGA (DOD)
 FY10-12: Conduct study
 FY13:
Initiate enhanced elevation data collection
 Primary use: update bare earth surface models
 USGS study recognizes many other uses
130,000 sq miles of data with ARRA funds
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
USGS recognized uses of LIDAR
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
2010 State LIDAR efforts
8
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8
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states have statewide LIDAR programs
North Carolina, Louisiana, New Jersey, Maryland,
Delaware, Pennsylvania, Ohio, Iowa
states have program initiatives
Florida, Texas, New York, Oregon, Washington,
Minnesota, South Carolina, Mississippi
 Many
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more projects areas have been flown
~25% of the conterminous US already has LIDAR
collected
Unknown amount of private forest coverage
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
2010 Oregon LIDAR Consortium
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
2010 Puget Sound LIDAR
Consortium
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Not all LIDAR data are the same

Things that affect LIDAR data for forest
measurements:
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Mission specs (pulse rate, scan pattern, flying
height, airspeed, pulse diameter, etc.)
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Time of year (leaf-off, leaf-on, snow free, etc.)
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LIDAR sensor and data processing
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Experience of LIDAR vendor
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Not all LIDAR data are the same
Therefore, don’t expect to get same results
when models from one LIDAR dataset are
applied to other datasets, even in the same
forest type!!!
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
LIDAR used in forest
measurement
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When only partial LIDAR coverage of an area is
possible:
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Sampling within a multi-stage framework
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Statistical framework has been developed
and tested by several researchers
PNW LIDAR trials in Alaska:
 Hans Andersen, PI
 Kenai Peninsula
 Interior Alaska
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Example: Multi-level sampling to support forest
inventory in remote northern regions
Wall-to-wall lowresolution coverage w/
LANDSAT TM, SPOT, etc.
Remote sensing
Subsampling high res.
satellite imagery
Subsampling with high
res. LIDAR, aerial photos
Field plots
USDA Forest Service PNW Research Station
Measurements of trees,
shrubs, moss, soils,
down wood.
RMA Vegetation Monitoring and Remote Sensing Team
PNW-RMA (Anchorage) is carrying out a project to test a
multi-level approach for biomass estimation in the Tok
(1,911 sq km)
Multi-level approach
will use:
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USDA Forest Service PNW Research Station
Satellite imagery
(Landsat, SPOT,
PALSAR, Quickbird)
27 High-density LIDAR
strip samples
Field plot data (80 plots)
RMA Vegetation Monitoring and Remote Sensing Team
LIDAR used in forest
measurement
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When “wall-to-wall” LIDAR coverage is
available 2 types of measurements can be
made:
1. Forest layers computed solely from the LIDAR
2. Inventory layers predicted from regression
models or imputation methods using LIDAR and
well measured ground plots
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
1– Layers computed solely from the
LIDAR point cloud—obvious ones
Canopy surface model
 Bare earth model
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USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
3-ftbare
canopy
surface
3-ft
earth
modelmodel
1:12,000 aerial photo
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Layers computed solely from the
LIDAR point cloud—obvious ones
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Bare earth model
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Canopy surface model
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Canopy height model
(Canopy surface minus ground surface)
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
3-ft resolution canopy height model
Buildings
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Layers computed solely from the
LIDAR point cloud—obvious ones
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Bare earth model
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Canopy surface model
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Canopy height model
 Canopy
cover model
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
% Canopy Cover (0.1 acre pixels)
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Layers computed solely from the
LIDAR point cloud—obvious ones
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Bare earth model
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Canopy surface model
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Canopy height model
 Canopy
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cover model
Intensity image from 1st returns
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
1.5-ft resolution intensity image
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Layers computed solely from the
LIDAR point cloud—not so obvious
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Variance, standard deviation, skewness,
kurtosis, etc. of the canopy
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Mean, min, max, percentile heights of the
canopy
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Density of the canopy
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Forest / non-forest mask
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Standard Deviation of Canopy Height
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
LIDAR used in forest
measurement

When “wall-to-wall” coverage is available 2
types of measurements can be made:
1. Forest layers computed solely from the LIDAR
2. Inventory layers predicted from regression
models or imputation methods using LIDAR and
well measured ground plots
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
WARNINGS !!!
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Can’t get species information from the
LIDAR data
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In some cases, can get:
 Deciduous vs non-deciduous
 Live crowns vs dead crowns
 Can’t
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get understory, down wood, etc.
Not all LIDAR is the same:

Changes in LIDAR sensors, sensor settings,
and flight parameters can change results
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
MORE WARNINGS !!!!!
Most difficult part of a LIDAR project is:
Getting good ground plot data:
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1. Matched with regards to geographic position
to an accuracy ~ equal to the LIDAR
horizontal accuracy (~+/- 1m)
2. Matched with regard to the primary element
being measured—large enough to minimize
plot edge effect, but small enough to
characterize tree size differences within plots
(~0.1 – 0.2 ac circular plot)
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
MORE WARNINGS !!!!! (cont.)
Most difficult part of a LIDAR project is:
Getting good ground plot data:
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3. Matched in time of measurement--generally
within 1-2 yrs of LIDAR acquisition
4. Matched in what’s measured by the LIDAR
and on the plot—all stems that make up a
significant portion of the above ground
canopy—generally down to a 7-10 cm DBH
lower limit, including all species
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Examples of layers predicted
from regression models
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Sherman Pass Scenic Byway
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Colville National Forest
100,000 acres flown in 2008
74 1/10th acre plots used to develop
LIDAR inventory regressions measured in
2008
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Sherman Pass LIDAR Project
Ground Plots
Forest cover minimum: 10ft ht &
2% cover in 66ft pixel
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Regression models
Lorey’s BA-weighted Height ft
[LHT_ft] = 21.4980 + [ElevP90] * 0.7242
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Regression models
Lorey’s BA-weighted Height ft
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Regression models
Live Basal Area sqft/ac
[LBA_sqftac] = sqr ( -5.0579 + [ElevSD] * -0.4280 + [ElevP95] * 0.2307 + [PC1stRtsCC] * 0.1039) + 2.809
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Regression models
Live Basal Area sqft/ac
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Red areas have LIDAR predictor values
>+/-10% beyond the range of the ground plots
Greater than +/- 10% beyond ground plot LIDAR Metrics
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Example ArcGIS Calculations
 Any of the LIDAR layers can be used in GIS to
calculate combinations of forest structure
variables
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Live Basal Area > 200 sqft/ac
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Canopy Cover 80%+ and Height 100ft+
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Current limitations on using
existing LIDAR data
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No coordination within natural resource
organizations at any level for:
1.
LIDAR specifications necessary for forest
measurements
2.
Ground plot measurements when large,
multi-agency LIDAR acquisitions occur
Missed opportunity to leverage existing LIDAR
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Possible problems with use of
FIA plots for LIDAR projects
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Plots not georeferenced well enough
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Not enough plots measured in area within
1-2 years of LIDAR acquisition
 Plot
layout not well designed for use with
high-resolution remote sensing data
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
Future for LIDAR in forest
measurement?
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Faster, cheaper, better LIDAR data, but
doesn’t solve ground plot problems
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Multi-temporal LIDAR datasets for change
analysis
 Multispectral
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LIDAR for species classification
New satellite-based systems for sampling
 Beyond
LIDAR—other 3D sensors (IFSAR,etc.)
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team
LIDAR software DEMO Thurs
2009 Savannah River DOE Site LIDAR Project
USDA Forest Service PNW Research Station
RMA Vegetation Monitoring and Remote Sensing Team