@article{ref1,
title="Mapping fire severity in southwest China using the combination of sentinel 2 and gf series satellite images",
journal="Sensors (Basel)",
year="2023",
author="Zhang, Xiyu and Fan, Jianrong and Zhou, Jun and Gui, Linhua and Bi, Yongqing",
volume="23",
number="5",
pages="e2492-e2492",
abstract="Fire severity mapping can capture heterogeneous fire severity patterns over large spatial extents. Although numerous remote sensing approaches have been established, regional-scale fire severity mapping at fine spatial scales (<5 m) from high-resolution satellite images is challenging. The fire severity of a vast forest fire that occurred in Southwest China was mapped at 2 m spatial resolution by random forest models using Sentinel 2 and GF series remote sensing images. This study demonstrated that using the combination of Sentinel 2 and GF series satellite images showed some improvement (from 85% to 91%) in global classification accuracy compared to using only Sentinel 2 images. The classification accuracy of unburnt, moderate, and high severity classes was significantly higher (>85%) than the accuracy of low severity classes in both cases. Adding high-resolution GF series images to the training dataset reduced the probability of low severity being under-predicted and improved the accuracy of the low severity class from 54.55% to 72.73%. RdNBR was the most important feature, and the red edge bands of Sentinel 2 images had relatively high importance. Additional studies are needed to explore the sensitivity of different spatial scales satellite images for mapping fire severity at fine spatial scales across various ecosystems.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23052492",
url="http://dx.doi.org/10.3390/s23052492"
}