Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil

Article Article scientifique Article Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil eng 2020 author Nazarova Tatiana 801539 author Martin Pascal author Giuliani Gregory 0000-0002-1825-8865 94488 1829 10.3390/rs12111829 2072-4292 https://www.mdpi.com/2072-4292/12/11/1829 Forests play major roles in climate regulation, ecosystem services, carbon storage, biodiversity, terrain stabilization, and water retention, as well as in the economy of numerous countries. Nevertheless, deforestation and forest degradation are rampant in many parts of the world. In particular, the Amazonian rainforest faces the constant threats posed by logging, mining, and burning for agricultural expansion. In Brazil, the “Sete de Setembro Indigenous Land”, a protected area located in a lowland tropical forest region at the border between the Mato Grosso and Rondônia states, is subject to illegal deforestation and therefore necessitates effective vegetation monitoring tools. Optical satellite imagery, while extensively used for landcover assessment and monitoring, is vulnerable to high cloud cover percentages, as these can preclude analysis and strongly limit the temporal resolution. We propose a cloud computing-based coupled detection strategy using (i) cloud and cloud shadow/vegetation detection systems with Sentinel-2 data analyzed on the Google Earth Engine with deep neural network classification models, with (ii) a classification error correction and vegetation loss and gain analysis tool that dynamically compares and updates the classification in a time series. The initial results demonstrate that such a detection system can constitute a powerful monitoring tool to assist in the prevention, early warning, and assessment of deforestation and forest degradation in cloudy tropical regions. Owing to the integrated cloud detection system, the temporal resolution is significantly improved. The limitations of the model in its present state include classification issues during the forest fire period, and a lack of distinction between natural vegetation loss and anthropogenic deforestation. Two possible solutions to the latter problem are proposed, namely, the mapping of known agricultural and bare areas and its subsequent removal from the analyzed data, or the inclusion of radar data, which would allow a large amount of finetuning of the detection processes. Sentinel-2 Google Earth engine Landcover Cloud cover Cloud shadow Vegetation Analysis-ready data Deep learning Rainforest Deforestation Dewey 333.7-333.9 18 Institut des sciences de l'environnement 0DB false 673 Envirospace Lab 859 Global Resource Information Database (GRID) 941 ISE Pôle Informatique environnementale 938 ISE Pôle Sciences 1057 TIGERS 970b71ee-b344-4440-bc52-c9af263975d0 Living Earth Lab Remote Sensing 12 11 2bb5e259-d07e-421c-8d84-7f96e48db5b7 Article (Published version) Public Nazarova.pdf 8510775 application/pdf CC BY openAccess Centres et instituts / Institut des sciences de l'environnement

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Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil	Remote Sensing			2020	374	291

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Nazarova, Tatiana