Combined Analysis of Optical and SAR Remote Sensing Data

8/14/2019 Combined Analysis of Optical and SAR Remote Sensing Data

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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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E. Lehmann et al.: Combined analysis of SAR and optical data for forest mapping and monitoring

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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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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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.

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-5

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water

bare ground

agriculture (crops)

forest (dense)


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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)


F/NF accuracy: F/NF accuracy: 93.6%

89.7%

F/NF accuracy: F/NF accuracy: 90.7%

84.8%


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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.

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CV1

CV2

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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

Documents

Combined Analysis of Optical and SAR Remote Sensing Data