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Forest Discrimination Analysis of Combined
Landsat and ALOS-PALSAR Data
E. Lehmann, P. Caccetta, Z.-S. Zhou, A. Held CSIRO, Division of Mathematics, Informatics and Statistics, Australia
A. Mitchell, I. Tapley, A. Milne, K. Lowell Cooperative Research Centre for Spatial Information (CRC-SI), Australia
S. McNeill Landcare Research, New Zealand
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Presentation Outline
• Background
• forest mapping and monitoring (forest/non-forest, F/NF)
• motivation
• Data and Study Area
• study area
• PALSAR and Landsat TM datasets
• data pre-processing
• Combined SAR–Optical 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
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Background
Assess and take advantage of the complementarity and inter-
operability of radar 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)
• Australia’s 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: continental Landsat-based forest
monitoring system
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Data and Study Area
Pilot study area
• north-eastern Tasmania
• calibration site defined as part of
Australia’s GEO-FCT demonstrator
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)
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Datasets
Landsat TM
• 6 spectral bands (thermal band
omitted), 25m pixel size
• from the NCAS archive of
MSS/TM/ETM+ imagery
• acquired Jan./Feb. 2008
PALSAR (HH,HV,HH-HV) Landsat TM (bands 5,4,2)
ALOS-PALSAR
• fine-beam dual polarisation (HH
and HV), L-band (~24cm)
• ascending orbit (34.3 off-nadir)
• pre-processed to 25m pixel size
• acquired in Sept./Oct. 2008
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
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]
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Data Pre-Processing
Assessment of SAR–Landsat co-registration
• feature cross-correlation using 299 GCPs
• ~0.6 pixel combined RMS error (25m pixel size)
• 98% of residuals below 1.5 pixels
SAR HV (greyscale-inverted)
Landsat band 5
(HH,HV,Landsat Band 5)
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
SAR–Optical F/NF Classification
Step 1: definition of spectral classes using
Canonical Variate Analysis (CVA)
230 training sites selected for the classification,
representing a broad range of landcover types
over the study area
Analyses are carried out for:
1. Landsat data only (6 bands)
2. PALSAR data only (2 bands)
3. combined SAR–Landsat data (8 bands, concatenated)
Canonical roots (measure of separability):
1. Landsat only:
18.9 8.6 3.2 1.7 1.1 0.5
2. PALSAR only: 24.0 2.9
3. combined SAR–optical data:
28.9 12.6 7.7 3.3 1.8 1.3 1.1 0.4
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CVA results -- 2008_ago_sk55__AOI_pwr_fil2_trsites_all3
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Plot of training sites in CV1-CV2 space for
combined PALSAR–Landsat data (4 out of 6
sub-classes shown). Colour legend: forest
sites, non-forest sites, cleared/growing
plantations.
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Step 2: Maximum-Likelihood Classification (MLC)
... using the spectral classes defined by CVA
MLC example in the Ben Lomond region: alpine heathland (shrubs)
SAR–Optical F/NF Classification
PALSAR (HH/HV/HH-HV) Landsat (bands 5/4/2) TASVEG reference
SAR F/NF classification Landsat F/NF classification SAR–Landsat classification
5km
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Bands F vs. NF Contrast 1 Contrast 2 Contrast 3
HH 16.3% 23.7% 31.7% 3.9%
HV 67.8% 50.8% 24.0% 0.0%
HH+HV 68.0% 54.7% 39.1% 4.3%
TM (6 bands) 59.2% 73.6% 41.0% 98.6%
TM + HH 68.1% 82.2% 84.5% 100.0%
TM + HV 99.8% 98.9% 79.9% 98.9%
TM+HH+HV 100.0% 100.0% 100.0% 100.0%
SAR–Optical F/NF Classification
0 5 10 15 20 25
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CV1C
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Band information
• variable selection: percentage of
total F/NF information provided by
the Landsat and PALSAR bands
(and their combinations)
• contrasts between various sub-
classes (clusters) of forest and
non-forest sites
Plot of training sites in CV1-CV2 space (excluding
sites from the ‘water’ class) for SAR–optical data.
Colour legend: forest sites, non-forest sites,
cleared/growing plantations.
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Multi-Temporal SAR–Optical Processing
• assume that the datasets are not coincident temporally
• separate 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
Landsat
Landsat
prob. image
CPN
forest map 1972
2010
forest map
Landsat
prob. image
2010
Landsat
SAR prob.
image 2008
CPN
forest map 1972
2010
forest map
Landsat
prob. image
Landsat
prob. image
Landsat time series (NCAS) SAR–Landsat time series
1972
E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data
Conclusion
Summary
• SAR and optical sensors are inter-operable and provide
complementary information for forest mapping and monitoring
• jointly considering PALSAR and Landsat data improves the
forest/non-forest classification significantly:
◦ adding SAR bands (HH+HV) to the optical data provides one additional
dimension for classification
◦ in PALSAR, the HV polarisation provides most of the discrimination
information
◦ significant variation in the respective contribution of the PALSAR and
Landsat 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
Contact Us
Phone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au
Thank you...
CSIRO Mathematics, Informatics & Statistics Eric A. Lehmann, Research Scientist
Phone: +61 (0)8 9333 6123
Email: [email protected]
Web: http://www.csiro.au/org/CMIS.html