Burn Severity Assessment Using Sentinel-1 SAR in the Southeast Peruvian Amazon, a Case Study of Madre de Dios

Burn Severity Assessment Using Sentinel-1 SAR in the Southeast Peruvian Amazon, a Case Study of Madre de Dios

Fire is one of the significant drivers of vegetation loss and threat to Amazonian landscapes. It is estimated that fires cause about 30% of deforested areas, so the severity level is an important factor in determining the rate of vegetation recovery. Therefore, the application of remote sensing to d...

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Bibliographic Details
Published in:Fire (Basel, Switzerland) Vol. 5; no. 4; p. 94
Main Authors: Alarcon-Aguirre, Gabriel, Miranda Fidhel, Reynaldo Fabrizzio, Ramos Enciso, Dalmiro, Canahuire-Robles, Rembrandt, Rodriguez-Achata, Liset, Garate-Quispe, Jorge
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-07-2022
Subjects:
absolute and relative predictors
Accuracy
Algorithms
Amazon
Backscattering
burn ratio
Burns
Burns and scalds
Clouds
Deforestation
Electric fields
Fires
Forest degradation
Low level
Physiological aspects
Polarization
Radar
radar forest degradation index
Radar imaging
Remote sensing
Satellites
Sensors
Topography
Vegetation
Germany
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Summary:Fire is one of the significant drivers of vegetation loss and threat to Amazonian landscapes. It is estimated that fires cause about 30% of deforested areas, so the severity level is an important factor in determining the rate of vegetation recovery. Therefore, the application of remote sensing to detect fires and their severity is fundamental. Radar imagery has an advantage over optical imagery because radar can penetrate clouds, smoke, and rain and can see at night. This research presents algorithms for mapping the severity level of burns based on change detection from Sentinel-1 backscatter data in the southeastern Peruvian Amazon. Absolute, relative, and Radar Forest Degradation Index (RDFI) predictors were used through singular polarization length (dB) patterns (Vertical, Vertical-VV and Horizontal, Horizontal-HH) of vegetation and burned areas. The Composite Burn Index (CBI) determined the algorithms’ accuracy. The burn severity ratios used were estimated to be approximately 40% at the high level, 43% at the moderate level, and 17% at the low level. The validation dataset covers 384 locations representing the main areas affected by fires, showing the absolute and relative predictors of cross-polarization (k = 0.734) and RDFI (k = 0.799) as the most concordant in determining burn severity. Overall, the research determines that Sentinel-1 cross-polarized (VH) data has adequate accuracy for detecting and quantifying burns.
ISSN:2571-6255
2571-6255
DOI:10.3390/fire5040094