Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 444006
Title Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation
Author(s) Reiche, J.; Souza, C.; Hoekman, D.H.; Verbesselt, J.; Haimwant, P.; Herold, M.
Source IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (2013)5. - ISSN 1939-1404 - p. 2159 - 2173.
DOI https://doi.org/10.1109/JSTARS.2013.2245101
Department(s) Laboratory of Geo-information Science and Remote Sensing
Earth System Science
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2013
Keyword(s) brazilian amazonia - sar - imagery - classification - emissions - countries - accuracy - band
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 sub-pixel fraction information. After independently processing SAR and optical input data streams the extracted SAR and optical sub-pixel 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% formapping 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.
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