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Combined Analysis of Optical and SAR Remote
Sensing Data for Forest Mapping and Monitoring
E. Lehmann, Z.-S. Zhou, P. Caccetta, A. HeldCSIRO, Division of Mathematics, Informatics and Statistics, Australia
A. Mitchell, A. Milne, K. Lowell
Cooperative Research Centre for Spatial Information (CRC-SI), AustraliaS. McNeillLandcare Research, New Zealand
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Presentation Outline
Background
forest/non-forest (F/NF) mapping and monitoring
motivation
Data and Study Area
study area
optical and synthetic aperture radar (SAR) datasets
data pre-processing
Combined SAROptical Forest Classification
canonical variate analysis (CVA)
maximum-likelihood classification (MLC)
band information (variable selection) classification results
Conclusion
summary of main outcomes
strategies for non-coincident data processing
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Background
Assess and take advantage of the complementarity and inter-
operability of synthetic aperture radar (SAR) and optical sensors
for forest mapping and monitoring
Motivation
technological advances in synthetic aperture radar (not cloud-
affected) complement the existing optical datasets
GEO-FCT: Forest Carbon Tracking task of the Group on Earth
Observations (in support of global forest carbon estimation)
Australias response to GEO-FCT: International Forest Carbon
Initiative (IFCI) to increase forest monitoring capacity
further development of the National Carbon Accounting System
(NCAS) developed by CSIRO & partners: continental Landsat-
based forest monitoring system
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Data and Study Area
Pilot study area
north-eastern Tasmania
calibration site defined as part of
Australias GEO-FCT demonstrator
project under IFCI
66km x 50km area
main land covers:
dry & wet eucalypt forest
non-eucalypt forest
rainforest
plantations / deforestation
agriculture & urban areas
significant topographic variations
(elevation: 80m to 1500m)
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E. Lehmann et al.: Combined analysis of SAR and optical data for forest mapping and monitoring
RADARSAT-2 (VV,VH,VV-VH)
Datasets
PALSAR (HH,HV,HH-HV)
ALOS-PALSAR
fine-beam dual polarisa-
tion (HH and HV), L-band
(23.6cm)
ascending orbit (34.3 off-
nadir)
pre-processed to 25m
pixel size
acquired Sept./Oct. 2009
RADARSAT-2
wide-beam dual polarisa-
tion (VV and VH), C-band
(5.6cm)
ascending orbit (42.2 off-
nadir)
pre-processed to 25m
pixel size
acquired in Sept. 2009
Landsat TM
6 spectral bands (thermal
band omitted), 25m pixel
size
from the NCAS archive of
MSS/TM/ETM+ imagery
acquired Jan. 2009
(~18% fill-in)
Landsat TM (bands 5,4,2)
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Data Pre-Processing
SAR terrain illumination correction
Correct for illumination differences on forward/backward facing slopes
[Zhou et al., Terrain slope correction and precise registration of SAR data for forest mapping
and monitoring, ISRSE 2011, Sydney]
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E. Lehmann et al.: Combined analysis of SAR and optical data for forest mapping and monitoring
Data Pre-Processing
Assessment of the dataset co-registration: using cross-
correlation of image features (sub-pixel results)
PALSARLandsat: 0.56 pixel accuracy, 96% orresiduals below 1.5 pixels (127 features)
RADARSATLandsat: 1.05 pixel accuracy, 74%
of residuals below 1.5 pixels (90 features)
(HH,HV,Landsat Band 5)
SAR HV (greyscale-inverted)
Landsat band 5
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SAROptical F/NF Classification
Step 1: definition of spectral classes using
Canonical Variate Analysis (CVA)
268 training sites selected for theclassification, representing a broad
range of landcover types over the
study area
Analyses carried out for:
1. Landsat data (6 bands)
2. PALSAR data (2 bands)
3. RADARSAT data (2 bands)
4. combined SARoptical data
(10 bands, concatenation) Plot of training sites in CV1-CV2 space forLandsat data (4 out of 7 sub-classes
shown). Colour legend: forest sites, non-
forest sites, cleared/immature plantations.
0 5 10 15 20 25 30 35
-5
0
5
10
15
CV1
C
V2
169
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water
bare ground
agriculture (crops)
forest (dense)
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SAROptical F/NF Classification
Definition of spectral classes using CVA
The number of selected sub-classes reflects the ability of each
dataset to discriminate between different land covers
(*) mix of forest and non-forest sites.
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Step 2: Maximum-likelihood classification (using CVA-based classes)
SAROptical F/NF Classification
F/NF accuracy: F/NF accuracy: 93.6%
89.7%
F/NF accuracy: F/NF accuracy: 90.7%
84.8%
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SAROptical F/NF Classification
Band information
variable selection: percentage of
total F/NF information provided by
the optical and SAR bands (and
their combinations)
contrasts between various sub-
classes (clusters) of forest and
non-forest sites
Plot of selected training sites in CV1-CV2 space for
SARoptical data. Colour legend: forest sites, non-
forest sites, cleared/growing plantations.
0 5 10 15 20 25
25
30
35
CV1
CV2
159
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Multi-Temporal SAROptical Processing
assume that the datasets are not coincident temporally
consider independent forest probability maps from each dataset
refinement of the single-date forest classifications using a Bayesian
Conditional Probability Network (CPN): spatial-temporal model
Landsat
prob. image 1972
2010
LandsatLandsat
prob. image
CPN
forest map1972
2010
forest map
Landsat
prob. image
2010
Landsat
SAR prob.
image 2008
CPN
forest map1972
2010
forest map
Landsat
prob. image
Landsat
prob. image
Landsat time series (NCAS) SARLandsat time series
1972
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Conclusion
Summary
SAR and optical sensors provide complementary information for
forest mapping and monitoring
jointly considering PALSAR, RADARSAT-2 and Landsat data
improves the forest/non-forest classification significantly:
overall, L-band SAR data allows more separation than C-band
with SAR, the cross-polarisation (HV or VH) provides most of the
discrimination information
significant variation exists in the respective contribution of the SAR and
optical bands towards the separation of specific sub-classes of forest
and non-forest sites
strategies for dealing with non-coincident datasets: use of a multi-temporal approach (e.g. conditional probability network)
check for atypical spectral signatures in the maximum-likelihood
classification
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Contact Us
Phone: 1300 363 400 or +61 3 9545 2176Email: [email protected] Web: www.csiro.au
Thank you...
CSIRO Mathematics, Informatics & StatisticsEric A. Lehmann, Research Scientist
Phone: +61 (0)8 9333 6123
Email: [email protected]
Web: http://www.csiro.au/org/CMIS.html