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International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.

International audience ; The forest decline in tropical areas is one of the largest global environmental threats as the growth of both global population and its needs have put an increasing pressure on these ecosystems. Efforts are ongoing to reduce tropical deforestation rates. Earth observations are increasingly used to monitor deforestation over the whole equatorial area. Change detection methods are mainly applied to satellite optical images which face limitations in humid tropical areas. For instance, due to frequent cloud cover in the tropics, there are often long delays in the detection of deforestation events. Recently, detection methods applied to Synthetic Aperture Radar (SAR) have been developed to address the limitations related to cloud cover. In this study, we present an application of a recently developed change detection method for monitoring forest cover loss from SAR time-series data in tropical zone. The method is based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 ground range detected (GRD) dual polarization (VV, VH) images forming a dataset of 60 images to monitor forest cover loss in a legal forest concession of the Democratic Republic of Congo during the 2018-2020 period. A cross-threshold recombination was then conducted on the computed maps. Evaluated against reference forest cut maps, an overall accuracy up to 91% and a precision up to 75% in forest clear cut detection was obtained. Our results show that more than 60% of forest disturbances were detected before the PlanetScope-based estimated date of cut, which may suggest the capacity of our method to detect forest degradation.