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

Abstract

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.

Mapping and assessment of burned areas in Rizal, Palawan using SAR burned and vegetation indices

“…Radar Forest Degradation Index (RFDI) RFDI measures the strength of the doublebounce term, is computed using the ratio between the power of the HH and HV polarizations 24 .…”

Assessment of Burn Severity and Monitoring of the Wildfire Recovery Process in Mongolia

“…Many studies from the wildfire severity study field were reviewed. The studies were carried out using different satellite images, including optical [7][8][9][10], thermal [11,12], lidar [13], and synthetic aperture radar (SAR) [11,14,15] satellite images. Most of them used optical satellite images obtained from MODIS data, Sentinel-2, Landsat series images, and KOMPSAT-3A [16], which uses Shortwave Infrared (SWIR) bands for the calculation.…”

“…Other issues and different phenomena occur in different areas within different natural zones around the world. We collected and reviewed a few studies from different study areas, including Siberia, Russia [23,25,26], Indonesia [27], Canada [28,29], Australia [18,[30][31][32], Spain [33], Portugal [13], the Mediterranean [7,[34][35][36][37], Turkey [1,4], Greece [2,3], China [10,[38][39][40][41], California and Alaska [42][43][44], the US [45][46][47][48][49], Peru [14], Iran [50], Bolivia [51], the Amazon of Brazil [52] and India [53]. The wildfire studies from each country had their own characteristics.…”

Short-Term Effects and Vegetation Response after a Megafire in a Mediterranean Area

“…Depending on the severity, size, and frequency of fires; the type of vegetation; and other site-specific factors, the consequences can be twofold. Sporadic and low-severity fires can contribute to renewing vegetation and promoting structural complexity and biodiversity of ecosystems, but too frequent, large, and severe fires can cause long-term consequences such as shifts in ecological succession, permanent changes in plant community composition and diversity, ecosystem services loss, changes in soil properties, soil erosion, and runoff [1,3,17,18]. Moreover, frequent and large wildfires can, directly and indirectly, threaten human lives, compromise economic activities and natural resources and contribute to carbon emissions into the atmosphere [17,19,20].…”