BUDD: Multi-modal Bayesian Updating Deforestation Detections

The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-1 backscatter and interferometric coherence with...

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Bibliographic Details
Main Authors Durieux, Alice M. S, Ren, Christopher X, Calef, Matthew T, Chartrand, Rick, Warren, Michael S
Format Journal Article
LanguageEnglish
Published 28.01.2020
Subjects
Statistics - Applications
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Summary:The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-1 backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.
DOI:10.48550/arxiv.2001.10661