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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.

Le moustique Aedes (Stegomyia) albopictus (Skuse) (Diptera : Culicidae) est une espèce particulièrement invasive, qui démontre de remarquables capacités d'adaptation à des conditions climatiques multiples. Le suivi de son aire de diffusion répond à une préoccupation importante de santé publique, puisque cette espèce présente, en plus d'une forte capacité de nuisance diurne, la capacité de transmettre les virus de la dengue, du chikungunya, et du Zika. Sa forte adaptation aux milieux anthropisés, et son caractère hautement invasif justifient une politique de surveillance appropriée. Dans cette étude publiée dans la revue International Journal of Environnemental Research and Public Health sous le titre " A rainfall- and temperature-driven abundance model for Aedes albopictus populations ", la dynamique de population du moustique " tigre " Aedes Albopictus a pu être modélisée pour la première fois en climat tempéré au travers d'une approche mécaniste. La dynamique du modèle est pilotée par les variables météorologiques de température et de précipitation. Cette approche a été validée par comparaison avec des données entomologiques relevées sur quatre années dans la région de Nice (coefficient de pearson 0,73-0,93). La validation satisfaisante du modèle en climat tempéré s'explique principalement par la prise en compte de la diapause, une période défavorable au développement du moustique, et pendant laquelle seuls les oeufs des moustiques survivent. Dans un second temps, ce modèle a été spatialisé et implémenté à La Réunion en 2015 dans le cadre du projet ALBORUN, avec pour objectif le développement d'un outil opérationnel à destination du service de lutte anti-vectorielle (LAV) de l'Agence de Santé Océan Indien (ARS OI). Les prédictions du modèle ont montré un bon accord avec les observations de terrain, ce qui a conduit à la construction d'un outil opérationnel de cartographie des densités de moustique intégrant les données de stations météorologiques in situ. Cet outil est actuellement utilisé en routine par les services de LAV à La Réunion. La généricité de ce modèle mécaniste permet aussi d'envisager son application sur l'ensemble des aires du territoire métropolitain où l'espèce Aedes albopictus est installée. C'est l'objet du projet ARBOCARTO, financé par la Direction Générale de la Santé (DGS) et les Agences Régionales de Santé (ARS Auvergne-Rhône-Alpes, Occitanie et Nouvelle- Aquitaine). Dans cette nouvelle étape, l'outil est à nouveau paramétré pour servir de démonstrateur sur les trois sites pilotes de Montpellier, Grenoble, et Bordeaux. L'intérêt des sorties cartographiques des densités vectorielles d'Aedes albopictus est en cours d'évaluation, pour répondre à terme à une double intégration : (i) comme support décisionnel par les ARS mentionnées, et ii) comme support sur le terrain par les Ententes Interdépartementales de Démoustication (EID). Les possibilités d'adaptation du modèle aux différentes aires de distribution reposent en grande partie sur l'étude de sensibilité réalisée dans l'étude publiée. Celle-ci a permis d'identifier en particulier un paramètre clef dans sa contextualisation géographique : la variable dite de " capacité de charge de l'environnement ". Cette variable reflète la capacité de l'environnement à offrir des conditions favorables à la présence de gîtes de ponte pour les moustiques. Dans les projets ALBORUN et ARBOCARTO, le paramétrage du modèle a pu être affiné en établissant une valeur de capacité de charge à partir de données issues de la littérature et des échanges réalisés avec les acteurs de terrain de la démoustication. L'estimation de cette valeur de " capacité de charge de l'environnement " bénéficie de l'intégration de données géographiques complémentaires pour caractériser l'environnement en termes d'occupation (p.ex. densité de bâti) et d'usage du sol (p.ex. hotspot type cimetière). Une meilleure estimation de la capacité de charge est également attendue avec l'intégration d'informations issues des données de télédétection, en particulier avec l'estimation de la densité de végétation dans la maille élémentaire du modèle. Des développements méthodologiques portant sur une meilleure intégration des données de télédétection, typologie urbaine en particulier, sont également en cours dans le cadre du projet APUREZA (" Analyses des relations entre paysages urbains dengue et Zika ", financement TOSCA CNES 2017-2020). Enfin, une nouvelle perspective d'application est envisagée en Asie du Sud-Est avec le projet ECOMORE II (ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation), financé par l'Agence Française de Développement.