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Tropical forests play a crucial role in sustaining life on Earth, however, they have been experiencing a significant reduction in vegetation cover due to activities like degradation and deforestation. One recent initiative to monitor these areas is the ForestEyes project, which combines citizen science and machine learning to track changes in tropical forests. The project currently uses images from optical sensors to create citizen science campaigns. However, the presence of clouds is a limiting factor in optical images and can prevent the detection of deforested areas. Thus, this article presents a preliminary evaluation of SAR images from the C-SAR/Sentinel-1 and PALSAR-2/ALOS-2 sensors, exploring their viability for citizen science campaigns in the context of the ForestEyes project. Experimental results indicate that PALSAR-2 data is more effective, as it has proved particularly advantageous in identifying deforested areas.
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