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Inter-comparison of optical and SAR-based forest disturbance warning systems in the Amazon shows the potential of combined SAR-optical monitoring

Authors :
Doblas Prieto, Juan
Lima, Lucas
Mermoz, Stephane
Bouvet, Alexandre
Reiche, Johannes
Watanabe, Manabu
Sant Anna, Sidnei
Shimabukuro, Yosio
Doblas Prieto, Juan
Lima, Lucas
Mermoz, Stephane
Bouvet, Alexandre
Reiche, Johannes
Watanabe, Manabu
Sant Anna, Sidnei
Shimabukuro, Yosio
Source :
ISSN: 0143-1161
Publication Year :
2023

Abstract

More than half a decade after the launch of the Sentinel-1A C-band SAR satellite, several near real-time forest disturbances detection systems based on backscattering time series analysis have been developed and made operational. Every system has its own particular approach to change detection. Here, we have compared the performance of the main SAR-based near real-time operational forest disturbance detection systems produced by research agencies (INPE, in Brazil, CESBIO, in France, JAXA, in Japan, and Wageningen University, in the Netherlands), and compared them to the state-of-the-art optical algorithm, University of Maryland’s GLAD-S2. We implemented an innovative validation protocol, specially conceived to encompass all the analysed systems, which measured every system’s accuracy and detection speed in four different areas of the Amazon basin. The results indicated that, when parametrized equally, all the Sentinel-1 SAR methods outperformed the reference optical method in terms of sample-count F1-Score, having comparable results among them. The GLAD-S2 optical method showed superior results in terms of user’s accuracy (UA), issuing no false detections, but had a lower producer accuracy (PA, 84.88%) when compared to the Sentinel-1 SAR-based systems (PA∼90%). Wageningen University’s system, RADD, proved to be relatively faster, especially in heavily clouded regions, where RADD warnings were issued 41 days before optical ones, and the one that better performs on small disturbed patches (

Details

Database :
OAIster
Journal :
ISSN: 0143-1161
Notes :
application/pdf, International Journal of Remote Sensing 44 (2023) 1, ISSN: 0143-1161, ISSN: 0143-1161, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1430717366
Document Type :
Electronic Resource