Classification and Deforestation Monitoring Using Sentinel-1 C-SAR Images in a Temperate Exploited Pine Forest
Language
en
Communication dans un congrès
This item was published in
IEEE International Symposium on Geoscience and Remote Sensing IGARSS, IEEE International Symposium on Geoscience and Remote Sensing IGARSS, IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022-07-17, Kuala Lumpur. 2023p. 691-694
IEEE
English 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 ...Read more >
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.Read less <
English Keywords
CuSum
Sentinel-1
C-SAR
vegetation cover change
temperate forest
deforestation
classification
Origin
Hal imported