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9 October 2018 Insights into burned areas detection from Sentinel-1 data and locally adaptive algorithms
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Worldwide, about 2.1 PgC are released every year from biomass burning. Due to its importance for climate modelling, several products were developed to map burned areas (BA) at global levels. Most of these products are based on medium or low-resolution optical sensors which are rather insensitive to small size fires. Moreover, frequent cloud cover and smog may hinder BA detection using optical sensors. To mitigate such shortcomings, BA may be derived from high resolution radar backscatter time-series. This study analyses the results of a locally adaptive BA detection algorithm based on data acquired by the ESA’s C-band synthetic aperture radar (SAR) Sentinel-1 A and B satellites. Sentinel-1 time series were analysed to understand the backscatter coefficient variation over burned and unburned areas. In addition, the analysis was extended to areas where the detection algorithm was affected by commission and omission errors. The study was carried out at 17 sites globally distributed. The analysis revealed shortcomings of the proposed algorithm particularly over areas where fire does not significantly decrease the cross-polarized backscatter. Over such areas, the backscatter change in the co-polarized channel could provide additional information that may overcome the lack of pre to post fire dynamic range of the VH polarization. In fact, for more than 50% of the study sites the change in VV polarization was peaked over undetected burned areas (omission errors). Furthermore, the VV polarization may help reducing commission errors as similar backscatter changes over burned and unburned areas was observed over less tiles (47%) when compared to the VH polarization (70%).
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Miguel A. Belenguer-Plomer, Mihai A. Tanase, Angel Fernandez-Carrillo, and Emilio Chuvieco "Insights into burned areas detection from Sentinel-1 data and locally adaptive algorithms", Proc. SPIE 10788, Active and Passive Microwave Remote Sensing for Environmental Monitoring II, 107880G (9 October 2018);

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