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8 October 2019 Evaluation of backscatter coefficient temporal indices for burned area mapping
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Fire has a vast influence on the climatic balance, and the Global Climate Observing System (GCOS) considers it an Essential Climate Variable (ECV). Remote sensing data is a powerful source of information for burned area detection and thus for estimating greenhouse gases (GHGs) emissions from fires. Currently, most burned area products are based on optical images. However, cloud cover independent Synthetic Aperture Radar (SAR) datasets are increasingly exploited for burned area mapping. This study assessed temporal indices based on temporal backscatter coefficient to understand their suitability for burned area detection. The analysis was carried out using the random forests machine learning classifier, which provides a rank for each independent variable used as input. Depending on land cover type, soil moisture, and topographic conditions, remarkable differences were observed between the temporal backscatter based indices.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miguel A. Belenguer-Plomer, Emilio Chuvieco, and Mihai A. Tanase "Evaluation of backscatter coefficient temporal indices for burned area mapping", Proc. SPIE 11154, Active and Passive Microwave Remote Sensing for Environmental Monitoring III, 111540D (8 October 2019);

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