New deFOREST Pipeline Fuses Satellite Data for Advanced Deforestation Detection

Researchers have developed a new deforestation detection pipeline called deFOREST that fuses optical and radar satellite data for enhanced sensing. The system constructs anomaly maps from optical data using a discrete Karhunen-Loéve expansion, quantifying anomalies without requiring prior knowledge of data distribution. These optical anomaly maps are then combined with radar data and classified using a Hidden Markov Model. Tested in the Amazon forest with Sentinel-1 and Sentinel-2 data, the deFOREST approach demonstrated superior accuracy compared to existing hybrid methods, particularly in regions with sparse optical data due to cloud cover. AI

RANK_REASON The cluster contains an academic paper detailing a new methodology for deforestation detection using satellite data. [lever_c_demoted from research: ic=1 ai=0.7]

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• Amazon
• deFOREST
• hidden Markov model
• Julio Castrillon PhD
• Karhunen-Loéve expansions and their applications
• Sentinel-1
• Sentinel-2

• paper
• other

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