Classification and Deforestation Monitoring Using Sentinel-1 C-SAR Images in a Temperate Exploited Pine Forest - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2023

Classification and Deforestation Monitoring Using Sentinel-1 C-SAR Images in a Temperate Exploited Pine Forest

Abstract

Earth Observation data is often used for land cover classification or change monitoring. It is rarely used for both goals in a single algorithm. The multi-change Cumulative Sum (CuSum) algorithm proposed in this study allows both classification and change monitoring in a single algorithm using Sentinel-1 C-SAR time series. The multi-change CuSum approach allowed to classify pixels belonging to the fused non-forest vegetation and bare soil classes apart from the pixels belonging to new cuts. The distinction of each class is better made using the two polarizations: VV is more accurate for detecting non-forest vegetation (Kappa coefficient of 0.62) and VH for detecting new cuts (Kappa coefficient of 0.65). The algorithm showed an accuracy up to 0.82.
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Dates and versions

hal-04099628 , version 1 (17-05-2023)

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B. Ygorra, Frédéric Frappart, Wigneron J.-P., Christophe Moisy, B Pillot, et al.. Classification and Deforestation Monitoring Using Sentinel-1 C-SAR Images in a Temperate Exploited Pine Forest. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia. pp.691-694, ⟨10.1109/IGARSS46834.2022.9884389⟩. ⟨hal-04099628⟩
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