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.
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