DocumentCode :
742083
Title :
Feature Level Fusion of Multi-Temporal ALOS PALSAR and Landsat Data for Mapping and Monitoring of Tropical Deforestation and Forest Degradation
Author :
Reiche, Johannes ; Souzax, Carlos M. ; Hoekman, Dirk H. ; Verbesselt, Jan ; Persaud, Haimwant ; Herold, Martin
Author_Institution :
Centre of Geo-Inf., Wageningen Univ., Wageningen, Netherlands
Volume :
6
Issue :
5
fYear :
2013
Firstpage :
2159
Lastpage :
2173
Abstract :
Many tropical countries suffer from persistent cloud cover inhibiting spatially consistent reporting of deforestation and forest degradation for REDD+. Data gaps remain even when compositing Landsat-like optical satellite imagery over one or two years. Instead, medium resolution SAR is capable of providing reliable deforestation information but shows limited capacity to identify forest degradation. This paper describes an innovative approach for feature fusion of multi-temporal and medium-resolution SAR and optical subpixel fraction information. After independently processing SAR and optical input data streams the extracted SAR and optical subpixel fraction features are fused using a decision tree classifier. ALOS PALSAR Fine Bean Dual and Landsat imagery of 2007 and 2010 acquired over the main mining district in central Guyana have been used for a proof-of-concept demonstration observing overall accuracies of 88% and 89.3% for mapping forest land cover and detecting deforestation and forest degradation, respectively. Deforestation and degradation rates of 0.1% and 0.08% are reported for the observation period. Data gaps due to mainly clouds and Landsat ETM+ SLC-off that remained after compositing a set of single-period Landsat scenes, but also due to SAR layover and shadow could be reduced from 7.9% to negligible 0.01% while maintaining the desired thematic detail of detecting deforestation and degradation. The paper demonstrates the increase of both spatial completeness and thematic detail when applying the methodology, compared with potential Landsat-only or PALSAR-only approaches for a heavy cloud contaminated tropical environment. It indicates the potential for providing the required accuracy of activity data for REDD+ MRV.
Keywords :
geophysical image processing; image classification; image fusion; remote sensing by radar; synthetic aperture radar; vegetation; ALOS PALSAR Fine Bean Dual imagery; Landsat data; Landsat-like optical satellite imagery; central Guyana; cloud cover; decision tree classifier; deforestation information; feature level fusion; forest degradation; forest land cover mapping; heavy cloud contaminated tropical environment; medium-resolution SAR; multitemporal ALOS PALSAR; optical sub-pixel fraction information; single-period Landsat scenes; tropical deforestation mapping; tropical deforestation monitoring; Degradation; Earth; Optical imaging; Optical sensors; Remote sensing; Satellites; Synthetic aperture radar; ALOS PALSAR; Guyana; Landsat sub-pixel fraction; REDD; SAR-optical fusion; change detection; deforestation; forest degradation; tropical forest;
fLanguage :
English
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher :
ieee
ISSN :
1939-1404
Type :
jour
DOI :
10.1109/JSTARS.2013.2245101
Filename :
6463466
Link To Document :
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