Sentinel-1 Based Cusum Capabilities As A Forest / Non-Forest Mask In Tropical Areas

• Published in: IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 6228 - 6230
• Main Authors: Ygorra, Bertrand, Frappart, Frederic, Wigneron, Jean-Pierre, Catry, Thibault, Pillot, Benjamin, Riazanoff, Serge
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.07.2023
• Subjects: C-band SAR; CuSum; European Space Agency; Forest/Non-Forest mask; Forestry; Optical imaging; Optical sensors; Radar polarimetry; Satellite constellations; Sentinel-1; Time series analysis; tropical forest; vegetation cover change
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS52108.2023.10282143

Tropical forests are vulnerable to deforestation. This phenomenon led to the development of a wide number of forest monitoring systems based on remotely sensed data. These systems face multiple issues in tropical areas, one of them being the need of a good forest / non-forest map to use as reference for the monitoring. Several of these maps are available online, most of them being based on optical remote sensing data, which is known to be subject to limitations in tropical areas due to high cloud cover. Sentinel-1 C-band Synthetic Aperture Radar (SAR) dense time series of images has already been used for forest / non-forest mapping through combination with X-band SAR images. In this study, a change detection algorithm was applied on timeseries of Sentinel-1 images in the Parà State, Brazil. The Cumulative Sum (CuSum) algorithm was used to assess all non-forest areas as the hypothesis found by preliminary results was that these areas were more varying in terms of backscatter coefficient than undisturbed forests. The validation was made by comparison with a forest / non-forest map derived from Global Forest Watch data. The algorithm detected 78.0 % of the non-forested areas in the study zone of 6,855 km² with a 0.785 F1-score value.