Forest Discrimination Analysis of Combined Landsat … · E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data Background Assess and take advantage

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


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


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)


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


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]


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)


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.


Step 2: Maximum-Likelihood Classification (MLC)

... using the spectral classes defined by CVA

MLC example in the Ben Lomond region: alpine heathland (shrubs)


PALSAR (HH/HV/HH-HV) Landsat (bands 5/4/2) TASVEG reference

SAR F/NF classification Landsat F/NF classification SAR–Landsat classification

5km


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%


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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.


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


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

Documents

Forest Discrimination Analysis of Combined Landsat … · E. Lehmann et al.: Forest Discrimination Analysis of Combined Landsat and ALOS-PALSAR Data Background Assess and take advantage