Mapping Terrain and Forest Information with
Airborne Lidar Data
Instructor: Qi Chen
Naval Postgraduate School Lidar Workshop
Monterey, CA
5/24/2007
Applications of LIDAR
• Digital Elevation Model (DEM)
LIDAR can penetrate through the canopy
Applications of LIDAR
• Digital Elevation Model (DEM)
LIDAR can produce very accurate DEM
USGS DEM
LIDAR DEM
Applications of LIDAR
• DEM:
– Shoreline and Beach
Volume Changes
– Flood Risk Analysis
– Water-Flow Issues
– Subsidence Issues
– Riparian Studies
– Emergency Response
– Hydrology
– Geomorphology
– Geology
– …
Applications of LIDAR
• Buildings, powerlines, railroads, highways, etc.
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–
–
–
–
Urban Development
Telecommunication Planning
Transportation Mapping
Infrastructure planning/maintenance
Military application
Applications of LIDAR
• Forests
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–
–
–
–
–
Forest Management
Fire behavior modeling
Habitat Mapping
Biometeorology
Climate change
Carbon Cycle Modeling
LIDAR for Tree Structural Information
Measuring Canopy Structure with LIDAR
• Step 1: Laser Point Classification
– Classify canopy and ground returns
– Generating bare earth
(Chen et al., 2007)
• Step 2: Tree Isolation
– Delineate individual tree crowns
– Calculate crown area, tree height
(Chen et al., 2006)
• Step 3: Canopy Parameter Extraction
– Estimate basal area, stem volume, etc.
(Chen et al., in press)
(Chen, 2007)
Filtering with Morphological Opening
Opened
Original Points
Points
Object Returns
Ground Returns
A
B
C
Methods
window size=3
• Small window sizes cannot
remove large objects such as
buildings
• Large window sizes will
cut/flatten terrain features
window size=7
Data
ISPRS (International Society for Photogrammetry and Remote Sensing)
Commission III/WG3 benchmark dataset
Vaihingen/Enz test field / Stuttgart
Filtering in Urban Areas
Stuttgart, Germany
DEM
(filtered)
Digital Surface Model
DEM
(truth)
Filtering in Forest Areas
Vaihingen/Enz
test field
DEM
(filtered)
Digital Surface Model
DEM
(truth)
Comparison
The algorithm for
TerraScan
The method with the lowest error is highlighted with red color
for each sample
Millions and Billions of Points
• If density is 1 point/m2, there is 1 million points / km2
points
raster
Interpolation Methods
• Inverse Distance Weighting (IDW)
• Natural Neighbors
• Kriging
• Splines
• TIN
•…
Forward and Inverse Methods for Interpolation
ri,j = f(p1, p2 ,...pn )
where,
pi : the value for point i
ri,j : the value for grid node at row i and column j
pi = g(ru,l , ru,r , rl,l , ru,r )
Much faster!!! (~30 times
faster than TIN in ArcGIS)
Measuring Canopy Structure with LIDAR
• Step 1: Laser Pulses Classification
– Classify canopy and ground returns
• Step 2: Tree Isolation
– Delineate individual tree crowns
– Calculate crown area, tree Height
• Step 3: Canopy Parameter Extraction
– Estimate basal area, stem volume, etc.
(a) CMM, α =0.1
(c) CHM, lower-limit (α =0.1)
(b) CHM, fitted curve
(d) CHM, local maxima
Watershed Segmentation
A CSM
Revert the CSM
Build a dam
Tree Crown Delineation Map
(Chen et al., 2006)
Measuring Canopy Structure with LIDAR
• Step 1: Laser Pulses Classification
– Classify canopy and ground returns
• Step 2: Tree Isolation
• Delineate individual tree crowns
– Calculate crown area, tree Height
• Step 3: Canopy Parameter Extraction
– Estimate basal area, stem volume, etc.
What are Basal Area and Stem Volume?
• Stem volume:
A function of basal area and tree height
• Basal Area:
The cross-section area of stem at the
breast height (4.5feet)
Conventional Methods
+
• Canopy
structure
parameters
(Y)
Regress
Y=f(X)
Prediction
• Height metrics (X):
Max, min, mean,
standard deviation,
coefficient of
variance
Trees are Mis-segmented!
Under-segmentation
Ground
truth
Delineated
segment
Over-segmentation
Wrongly delineated
Prediction when Trees are Mis-segmented
Assume that regression model developed from field data has the form:
Y= a*Hb, where Y is the canopy structure parameter,
H is the maximum laser point height, a and b are constants
a*Hb
a*Hb a*Hb a*Hb
a*Hb
A Model Predicting the Total Value Correctly?
f(x0)
=
f(x1) + f(x2) + f(x3) + f(x4)
1. The sum rule:
x0=x1+x2+x3+x4 crown area canopy geometric volume
2. The proportional rule:
Y=f(x)=αx, where α is a constant
Basal Area/Stem Volume vs. Canopy Geometric Volume
1. DBH∝M3/8 and H∝M1/4 (West et al., 1999;
Enquist, 2002), where M is plant mass, and
2. M∝G, where G is canopy geometric volume
Basal area B∝G3/4
Stem volume V∝G
We don’t need to isolate individual trees
for estimating stem volume!
Data
Each plot:
0.13ha in area and
20m in radius
313 trees in total:
• 181 trees are correctly
segmented;
•132 trees are missegmented, which
corresponding to 110
segments,
• 67 pairs are formed for
mis-segmented trees
Models
Basal Area: B
Stem Volume: V
Hx: height
Ca: crown area
G: canopy geometric volume
Model Evaluation
• AIC (Akaike’s information criterion)
AICc = nlog(
2
ˆ
(y
y)
∑
n
2K(K +1)
) + 2K +
n -K -1
The smaller the AIC, the better the model
Results-Basal Area
-1220
-310
Correctly segmented trees
2D Graph 2
Mis-segmented trees
-1240
-320
-1260
-330
-1300
-340
-1320
-1340
-350
-1360
-360
-1380
-1400
-370
B.1
B.2
B.3
B.4
Model
B.5
B.6
B.7
AICc
AICc
-1280
Results-Stem Volume
-380
-10
Correctly segmented trees
Mis-segmented trees
-400
-20
-420
AICc
-460
-40
-480
-50
-500
-60
-520
-70
-540
-560
-80
V.1
V.2
V.3
V.4
Model
V.5
V.6
AICc
-30
-440
Estimating Individual Tree Structural Parameters
(Chen et al., 2007)
Tiffs (Toolbox for Lidar Data Filtering and Forest Studies)
(Chen, 2007)
GLAS (Geoscience Laser Altimeter System)
Ground-based LIDAR
Thank you!