1. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Forest Structure Estimation in the Canadian
Boreal forest
Michael L. Benson Leland E.Pierce Kathleen M. Bergen
Kamal Sarabandi Kailai Zhang Caitlin E. Ryan
The University of Michigan, Radiation Lab & School of Natural Resources and
the Environment
Ann Arbor, MI 48109-2122 USA
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

2. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Goal: Accurate estimation of Forest Structure parameters
using measured SAR, LIDAR, and Optical data.
Motivation: Forest Structure is important ecologically for global
climate estimation as well as biodiversity and other
topics.
This Talk: Use a set of simulators for each sensing modality as
well as real remotely sensed data and presents an
inversion algorithm capable of accurate forest
parameter retrieval requiring a minimal amount of
ancillary / ground truth data.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

3. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Outline
1. Introduction
2. Background
3. Approach
4. Forward Models & Database Generation
Forest Geometrical Model
Optical Model
SAR Model
LIDAR Model
5. Application to BOREAS Remotely Sensed Data sets
6. Classification Algorithm
7. Results
8. Conclusions
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

4. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Introduction
One possible mode of operation for DesDyni is to use LIDAR
shots in a region in combination with the contiguous maps
produced by SAR to better estimate forest structures
everywhere.
This talk explores one way of classifying the a large
observation area and determining underlying forest height and
biomass characteristics from areas where both SAR and
LIDAR are available to areas where only SAR is available.
We’ve previously presented results from our proof of concept
(IGARSS ’09) using only simulated data and a small sample of
real data (IGARSS ’ 10) we now present a working novel
multi-step classification algorithm.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

5. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Approach & High level algorithm
Use simulators to estimate OPTICAL, LIDAR and SAR
measurements from 3D forest descriptions
Generate many pine and spruce tree stands with a variety of
canopy heights and biomasses to generate a stand databse
Co-register OPTICAL, SAR, and LIDAR measurements in a
single image
Compare each image pixel to the database and find the most
similar database stand
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

6. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
BOREAS Southern Study Area
The SSA is approximately
11,700 square kilometers
centered on 53.87299◦ N
latitude and 105.2875 ◦ W
longitude.
A a confluence of multi-modal
remotely sensed data exists from
1994 - 1996.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

7. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Boreas Southern Study Area
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

8. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
BOREAS Southern Study Area: SAR & LiDAR Coverage
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

9. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Algorithm Overview
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

10. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Fractal Tree Model
Model developed in late 1990’s.
Fractal pseudo-random trees.
Use Lindenmayer System:
string-rewriting rules are used to
generate realistic branching
structures, with needles and
leaves.
Each species of tree has its own
set of rules so it looks realistic.
Both coniferous and deciduous
trees can be modeled.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

11. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Fractal Forest Model
Forest Attributes:
Biomass
Tree Species
Tree Attributes:
Height
Crown Diameter
Height to live crown
Trunk Diameter
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

12. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
SSA Jack Pine Stand
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

13. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
SSA Black Spruce Stand
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

14. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Optical Model
Use measured reflectance values for each canopy constituent:
branches, trunks, leaves, needles, ground.
Fractal geometry used with Pov-Ray ray-tracing code to
generate realistic 7-channel optical dataset.
Rays are traced for many bounces
Sensor is placed far above the forest, looking down at a 45◦
angle.
Values are averaged over one pixel to produce the simulated
data.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

15. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
SAR Model
Use Foldy’s approximation to obtain the mean field in a
vertically-layered approximation to the canopy.
Coherent simulation of each scattering mechanism: direct
crown, direct ground, trunk-ground, crown-ground,
crown-ground-crown,
Fully-polarimetric.
Use at L band (1.25GHz)
All simulations at 20, 45, and 80 degrees incidence angle, 100
looks.
Interpolated polynomial best fit to allow for incidence angle
flexibility.
Validated at L band with measured SAR data (from BOREAS
and Raco, MI).
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

16. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
LIDAR Model
Divide volume of stand into cubes.
Each cube analyzed for what fraction
of light is intercepted by the
vegetation (cylinders and disks).
Use vertical rays to estimate number
of intersections per cube.
Radiative Transfer from cube-to-cube
to produce time-trace of LIDAR signal.
Horizontal Gaussian pulse weighting
across the stand, with a vertical
Gaussian as well to obtain vertical
resolution.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

17. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Radiative Transfer for one cube
Given power propagating from above
and below: quantify how much
transmitted and reflected in each
direction.
Update the power propagating to next
cubes.
Can use measurements from literature
to determine value for %reflected for
branches: 10%.
Transmission through open areas is
assumed 100%.
Leaf transmission is assumed to be
50%. Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

18. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Database Overview
Generated 4707 jack pine stands
Generated 4364 black spruce
stands
All stands had a minimum of 10
types of trees and up to 2000
tree instances in an area of 625
m2
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

19. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Digital Elevation Model
The BOREAS project generated
a DEM in the 8th hydrological
project with 100m resolution
A higher resolution DEM was
required for accurate
orthorectification of the AirSAR
images
We created a 1315km by
1390km km DEM by
reprojecting and mosaicing
numerous DEMs from CDED.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

20. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
SAR: AirSAR
Numerous AirSAR images exist
in the Boreas SSA
For this study, we selected two
high resolution images with
6.66m range resolution and
9.26m azimuth resolution
These images were orthorectified
using a DEM from CDED to a
sub-pixel accuracy of 6m
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

21. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
LiDAR: Scanning Lidar Imager of Canopies by Echo
Recovery (SLICER)
37 Slicer flight paths were
conducted in the BOREAS
study areas in July 1996 yielding
a total of 834,277 LiDAR
waveforms
For each measurement, we
extracted the power at canopy
height and the power ratio
between the canopy height and
the ground return
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

22. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
LiDAR: SLICER
Based on the location of each
measurement, a weighted
average for both parameters was
derived for each
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

23. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Optical: LandSAT7
We used level 2T orthorectified
Landsat data acquired in July
1994
Images were atmospherically
corrected, cleaned of clouds and
cloud shadows and reprojected
into a single mosaic
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

24. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Ground Truth
Three data products from the BOREAS project were used as
ground truth for this study:
Forest Species (Jack Pine or Black Spruce)
Forest Biomass
Forest Canopy Height
Each ground truth data product was reprojected to 10m
resolution cells (as needed)
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

25. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Ground Truth - Tree Representations
DBHjp = 0.0066h3 − 0.1404h2 +
Stem mapped measurements 1.8672h − 1.9917
were recorded in the Jack Pine
stands as well as the Black CHgtjp = 0.0001h4 − 0.0001h3 −
Spruce stand. 0.0205h2 + 0.4788h − 0.7479
Using these measurements, we
have developed allometric DBHbs =
equations to generate tree a −0.0073h3 + 0.1708h2 + 0.2413h
given species’ height to live
crown and diameter at breast CHgtbs =
height as a function of the −0.0531h2 + 1.452h − 1.6152
desired tree height.
R 2 = 0.9564, 0.8555, 0.9442, 0.7133
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

26. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Algorithm Overview
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

27. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Level 0 Classification: Supervised Maximum Likilhood
Classification
A simple two class classification
scheme was used: Trees and
other.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

28. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Level 1 Classification: Database Comparison
Each pixel containing AirSAR, SLICER, and LandSAT data as
well as ground truth data was examined
Real remotely sensed values were compared to the 9000+
simulated stands in our database
The stand that most likely resembled the pixel under
examination was selected and that stand’s biomass and mean
canopy height were assigned to the pixel
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

29. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Error Function Measure
The error used is the weighted RMS error over the features:
1. 1.1 LIDAR mean power
1.2 LIDAR peak power / LIDAR ground power
1.3 SAR VV
1.4 SAR HH
2. Optical Ch. 6
3. Optical NDVI
4. SAR VH
VV
5. SAR HH
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

30. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Introduction to Results
Compare previous proof of concept to this study.
Note that the proof of concept additionally used C-band SAR
and IfSAR
Note that the proof of concept used the same forward models
to generate our database as well as for the inversion and
classification.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

31. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Proof of Concept Results: Height
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

32. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Proof of Concept Results: Biomass
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

33. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Classification Results
Classified 9071 pixels
Species retrieval was 76.94% accurate.
Height retrieval was 50.48% accurate with an RMS error of
5.3m (to 7.3m).
Biomass retrieval was 51.38% accurate with an RMS error of
155.53 Ton/Ha.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

34. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Classification Results (2)
If we know the target canopy will be small, under 13m, we can
achieve even better results:
Species retrieval was 76.94% accurate.
Height retrieval was 67.16% accurate with an RMS error of
4.37m.
Biomass retrieval was 50.03% accurate with an RMS error of
106.3 Ton/Ha.
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da

35. Motivation
Introduction
Models
Application to BOREAS Data
Classification Algorithm
Results and Conclusions
Conclusions and Future Work
We coregistered remotely sensed data from three different
sensors collected over a two year period.
We generated a database with over 9,000 stands that
resemble those found in the BOREAS SSA.
We created and implemented a multistep classification process
which correctly identified the predominant tree species and
was over 50% accurate in identifying the canopy height and
biomass
Future work includes introducing a recursive element to the
L1 classification
Future work includes the introduction of a multi-step error
function (used to select the most similar database stand)
Benson, et al. IGARSS 2011 Forest Structure Estimation using SAR, LiDAR, and Optical da