Evaluation of Multi-temporal and Multipolarization ASAR for Boreal Forests
in Hinton
David G. Goodenough
Hao Chen
Andrew Dyk
Tian Han
Pacific Forestry Centre
Natural Resources Canada
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© May 16, 2005
Project Objectives



Radarsat-2 has a significant improvements in capability compared
with Radarsat-1: better spatial resolution and fully polarimetric.
Envisat ASAR alternating polarization (AP) mode can help to
study the advanced capabilities of Radarsat-2 and make progress
towards defining methods for potential use of multi-polarization Cband SAR data for forest applications.
Objectives:
• Develop a forest land-cover classifier for multitemporal and
multi-polarization ASAR APP data; focus on clear-cut,
reforestation, coniferous, deciduous, and mixed wood in
northern forested areas in Canada; determine effectiveness of
C-band ASAR APP data for boreal forest mapping and change
detection.
• Perform data fusion analysis with multitemporal ASAR and
Landsat ETM+ data and determine improvements of fused
data over single sensor data sets.
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Radarsat-2, ASAR, and Convair-580


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Radarsat-2, operating at 5.405 GHZ, will be able to image at
spatial resolutions ranging from 3 to 100 meters with nominal
swath widths ranging from 10 to 500 kilometers. In addition,
Radarsat-2 will offer multi-polarization, a capability that aids in
identifying a wide variety of surface features and targets.
Envisat ASAR is a C-band sensor with dual-polarization and ability
to acquire data with broad swath coverage, range of incidence
angles, polarization, and modes of operation. This study uses
Envisat ASAR data for multitemporal SAR classification.
The Convair 580 C-band SAR can provide quad-polarization data
(HH/HV/VH/VV), all in one scene, for polarimetric or for dual
polarization combination analyses. Convair 580 data will help the
study of Radarsat-2 data for forest applications and transformation
of the technology to the end users of Radarsat-2 in the remote
sensing service industry.
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Study Site in Hinton, AB
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Landsat TM channel 5, 4, and 3 are shown as red,
green, and blue channels. The white rectangles
indicate the 29 township-ranges.
Image Acquisitions
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Image acquisitions
Satellite
Envisat
Envisat
Envisat
Envisat
Landsat

Sensor
ASAR
ASAR
ASAR
ASAR
ETM+
Collection date
1/10/2004
2/14/2004
7/3/2004
8/7/2004
8/23/2002
Polarizations
HV/HH
VV/VH
HV/HH
VV/VH
N/A
Beam
IS7
IS7
IS7
IS7
N/A
Product Type
ASA_APP_1P
ASA_APP_1P
ASA_APP_1P
ASA_APP_1P
Scene based
Image information

ASAR Alternating Polarization Precision (APP):
Channels: Two co-registered image channels corresponding to one of HV/HH,
VV/VH, and HH/VV
Spatial resolution: Approximately 30 m
Pixel spacing: 12.5 m
Beam mode: IS 7 (42.5 – 45.5D)
 Landsat ETM+:
Channels: 6 Spectral channels
Spatial resolution: Approximately 30 m
Cloud coverage: 0%
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SAFORAH Data Grid
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http://www.saforah.org

Image locations
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ASAR APP metadata
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ASAR Data Preprocessing
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Single dual-pol. ASAR APP image:
Radiometric calibration: Power
Speckle filtering (Lee adaptive): 5x5

_
R (t )  I (t )  W (t )  I (t )  (1  w(t ))
where the weighting function W is given by
W (t )  1  C / C (t )
2
u
2
I
_
( Cu
 u /u
Noise variation coefficient)
A. Lopes, R. Touzi and E. Nezry, "Adaptive speckle filters and Scene heterogeneity",
IEEE Transaction on Geoscience and Remote Sensing, Vol. 28, No. 6, pp. 992-1000,
Nov. 1990.

Textures: Mean, Standard deviation, Entropy (11x11 window size)
Orthorectification: 12.5 m pixel resampling
ASAR APP data fusion (HV/HH/VV/VH):
Image fusion from a pair of two winter images: 35 days apart
Image fusion from a pair of two summer images: 35 days apart
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LOGIT non-Gaussian classification
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To determine the effectiveness of the C-band Envisat ASAR data
for forest typing, structure recognition, and disturbance detection,
a hierarchical logistic classifier, LOGIT, was developed.
LOGIT is a hierarchical logistic non-parametric classification
program:
SAR: Poisson distribution.
ETM+: Gaussian distribution
Standard Gaussian Distribution
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Poisson Distribution Curves
1
All Classes
3
2
Class Number
Class Name
1
Coniferous
5
39
Clouds
1,5,6,11,18,19,
26,30,34,40
"Barren Classes"
4
Mixed wood
5
1,5,18,19,
30,34,40
6,11,26
6
Regeneration
11
Clearcut
18
Scrub coniferous
19
Scrub deciduous
26
Mining area
30
Water
34
Deciduous
6
39
7
6
Regeneration
10
11
11
Clear-cut
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1,5,18,19,
34,40
13
12
26
Mining
40
Cloud Shadows
1,5,18,
19,34
15
14
1,5
18,19,34
16
17
34
Deciduous
18,19
Cloud
Shadow
9
30
Water
11,26
20
40
8
18
Scrub Coniferous
18
1
Conifers
19
5
M ixed Wood
21
19
Scrub Deciduous
Example of a Logistic Tree Built
from Landsat ETM+ Training
ASAR Textures
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Textures were used to extract additional information from original
SAR image channels by looking at the statistics and value changes
in small regions.
Different window sizes and grey levels were tested. The window size
11x11 and 32 grey levels were selected. The resulting texture
images tend to have interesting patterns and formations that
signified additional information for LOGIT classification.
Textures used in this study:
Mean
Standard Deviation
Entropy
mean 
x
n
2
n x   x 
2
SD 
nn  1
2
N 1
P
i, j
( ln Pi , j )
i , j 0
Hall-Beyer, Mryka, “GLCM TEXTURE: A TUTORIAL” 2004, Department of Geography University of Calgary
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Training for LOGIT Classification
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Training identified from Quickbird and aerial photos;
Examples of training samples from Quickbird:
Clear-cut
Low
Regeneration
Exposed Land
Dense
Coniferous
8 training classes were selected. Since the dominant forest in the
study area was coniferous, two forest classes were defined.
Coniferous contains at least 80% conifer and Mixed Wood contains
all other forest types. Other classes were Clear-cut, Low
regeneration (< 2 meters high), High regeneration (> 2 meters high),
Scrub, Water, and Exposed land.
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LOGIT Classification on Fused ASAR APP (1)
HV/HH (W)
VV/VH (W)
HV/HH/VV/VH (W)
HV/HH/VV/VH (S)
HV/HH/VV/VH (W)
Class Label
Accuracy
Accuracy
Accuracy
Accuracy
Accuracy
Coniferous
77.2%
66.3%
79.2%
78.2%
33.2%
Mixed wood
Not included
Not included
Not included
Not included
68.9%
Low regeneration
49.3%
28.5%
60.5%
36.8%
53.0%
High regeneration
Not included
Not included
Not included
Not included
32.8%
Clear-cut
69.0%
85.7%
86.8%
60.2%
86.7%
Scrub
65.4%
43.4%
82.9%
81.7%
83.0%
Water
81.0%
74.5%
89.6%
93.6%
89.6%
Exposed Land
54.1%
50.1%
90.1%
93.9%
90.2%
Average
66.0%
58.1%
81.5%
74.1%
67.2%
Overall
73.9%
66.0%
79.6%
73.9%
44.5%
W – Winter data set
S – Summer data set
 Without Mixed Wood and High Regeneration:
The classification accuracies of the summer HV/HH/VV/VH image were lower than the winter HV/HH/VV/VH data.
Adding in Mixed Wood and High Regeneration:
Classification accuracies were greatly reduced. An example was given in the table (HV/HV/VV/VH).
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Confusion Matrix of HV/HH/VV/VH (Winter )
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Adding Mixed Wood and High Regeneration to the classification
of the winter HV/HH/VV/VH data set.
Classification accuracy:
Average : 67.2%
Overall: 44.5%
26% of Coniferous was misclassified as Mixed wood, and
23.7% of Coniferous as High regeneration.
Low classification accuracies also occurred for the two
regeneration classes: High Regeneration and Low
Regeneration
Class
Coniferous
Mixed Wood
High Regen.
Low Regen.
Clearcut
Scrub
Exposed Land
Water
Coniferous
33.2
12.9
21.8
3.6
0.0
0.3
0.0
0.0
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Mixed Wood High Regen. Low Regen.
26.5
23.7
10.8
68.9
12.4
4.9
26.0
32.8
13.9
9.1
13.3
53.0
0.0
0.0
2.5
0.0
0.2
6.4
0.0
0.0
1.4
0.0
0.0
2.1
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Clearcut Scrub Water
0.0
5.6
0
0.0
0.9
0
0.0
5.5
0
5.3
10.9
1.2
86.7
6.1
1.3
4.7
83.0
0.2
2.9
0.8
89.6
3.6
2.5
1.7
Exposed Land
0.1
0.0
0.0
3.4
3.4
5.2
5.3
90.2
LOGIT Classification on Fused ASAR APP (2)
Multi-Pol. (W) + (S)
Landsat-7 (S)
Multi-Pol. (W) + L-7
Multi-Pol. (S) + L-7
Class Label
Accuracy
Accuracy
Accuracy
Accuracy
Coniferous
54.1%
90.8%
88.1%
92.2%
Mixed wood
69.9%
80.0%
82.1%
76.0%
Low regeneration
40.3%
82.8%
94.8%
89.6%
High regeneration
39.0%
84.5%
85.8%
75.8%
Clear-cut
94.6%
97.0%
97.8%
97.6%
Scrub
92.8%
85.1%
91.9%
88.5%
Water
94.0%
95.1%
99.1%
99.7%
Exposed Land
96.1%
95.6%
99.5%
99.2%
Average
72.6%
88.9%
92.4%
89.8%
Overall
59.4%
89.9%
89.8%
90.4%
W – Winter data set
S – Summer data set
The Landsat-only classification had high average and overall accuracies. The Landsat +ASAR (W) data achieved
92.4% for the average, 3.5% higher than ETM+. The fused Landsat +ASAR (S) classification was at the same level as
the Landsat +ASAR (W) image. The fused ASAR imagery, with both summer and winter images had the average
accuracy of 72.6% and overall accuracy of 59.4%. If Landsat or other optical images are not available, this data
combination should be the best alternative.
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Confusion Matrix of HV/HH/VV/VH
(Winter + Summer)

LOGIT classification on the Multi-Pol. (W)+(S) data set:
Winter: HV, HH, VV, VH, + Means
Summer: HV, HH, VV, VH, + Means

Classification accuracy:
72.6% Average

59.4% Overall
Coniferous classification was improved from 33.2% (a single winter
multi-pol. data set) to 54.1%. But, classification accuracies on High
Regeneration and Low Regeneration are still low.
Class
Coniferous
Mixed Wood
High Regen.
Low Regen.
Clearcut
Scrub
Exposed Land
Water
Coniferous
54.1
10.1
26.9
8.7
0.0
0.1
0.0
0.0
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Mixed Wood High Regen.
11.9
20.6
69.9
13.8
19.5
39.0
11.3
9.2
0.0
0.0
0.0
0.4
0.0
0.0
0.0
0.0
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Low Regen.
10.3
4.9
9.4
40.3
1.7
3.6
0.2
2.6
Clearcut
0.0
0.0
0.1
8.9
94.6
2.4
2.1
0.0
Scrub
2.8
1.2
4.9
17.7
2.9
92.8
1.0
0.0
Water
0.0
0.0
0.0
0.0
0.3
0.2
94.0
0.9
Exposed Land
0.2
0.0
0.1
3.9
0.5
0.6
2.7
96.1
LOGIT Classification Image
ASAR-ETM+ Classification Image overlaid
on 20020823 Landsat-7 ETM+ Image
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Convair 580 quad polarization Data
Future Study for Radarsat-2
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We have acquired Convair 580 images over Hinton in September
2004.
• Conv580 data: Simultaneous C-Band in four polarization
combinations, HH/HV/VV/VH
• Conv580 data resolution: 6 – 10 meters.
This will allow us to select the combination that is best for forest
applications.
Compare LOGIT classification results between Convair 580 quad
polarization data and fused ASAR APP data.
Perform Convair 580 polarimetric data decomposition:
• Entropy/ decomposition (Ref. Cloude and Pottier, TGRS
1997) and three-component scattering decomposition (Ref.
Freeman and Durden, TGRS 1998)
• Wishart classification
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Samples of Convair 580 Images over Hinton
Township 48 Range 22
~ 9.66 x 9.66 KM2
Township 50 Range 23
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~ 9.66 x 9.66 KM2
Conclusions
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To prepare for Radarsat-2, Envisat ASAR APP and Landsat ETM+ were
acquired over Hinton. Image preprocessing and data fusion from
multitemporal ASAR images were performed.
A hierarchical logistic classification program, LOGIT, was implemented.
LOGIT provided the non-parametric method of classification on remotely
sensed data.
LOGIT was applied on ASAR APP, ETM+, and various fused image sets.
The classification results revealed that
• Landsat ETM+: High classification accuracy (Average 88.9%)
• Fused ASAR and ETM+: Average 92.4%; that is 3.5% higher than
ETM+ average classification accuracy
• Multitemporal and multi-polarization ASAR data set (winter and
summer): Average 72.6%
• Single-date dual-polarization ASAR: Average 66% (HV winter) that is
better than other single-date dual-polarization acquisitions.
More classification experiments on fused data sets, different band
combinations, and multitemporal classification comparisons are under way.
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Acknowledgements
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We acknowledge the European Space Agency for
providing Envisat ASAR data to Canadian Forest
Service under the Category-1 Project 2481.
We thank Tarin Resource Services Ltd. of Calgary for
providing online search of orthorectified aerial photos.
We appreciate the assistance of Steven Carey and
Lionel Cai, co-op students at the Pacific Forestry
Centre, Canadian Forest Service.
We are most grateful for financial support from Natural
Resources Canada, the Canadian Space Agency, and
the Natural Sciences and Engineering Research
Council.
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