Improved burn severity estimation by using Land Surface Phenology metrics and red edge information estimated from Landsat
نویسندگان
چکیده
Global wildfire activities are expected to increase substantially in the near future. Existing techniques for spaceborne burn severity estimation often rely on bi-temporal spectral indices, which related in-situ data. However, due cloud coverage and limited revisit frequency, combination with date of field surveys, it is a challenge find suitable phenologically comparable pre- -post-fire images. To overcome these issues improve accuracy estimations by incorporating ecologically relevant information, we investigated capability using Land Surface Phenology (LSP) metrics red edge information. We examined well-researched Jasper fire (September 2000, Black Hills, USA) dense time series Landsat-5 -7 generated synthesized bands through recently proposed harmonization technique computed several vegetation indices. Additionally, derived various bi-annual LSP from same used linear regression between composite index (CBI) ground truth data indices measure performance each approach, intercompared estimated maps. found added value both information into metrics. Among NDVI NDVIre1n performed best, latter being overall winner. This was observed metrics, wherein best Value Peak Season Green Mean Although correlation CBI point measurements similar LSP-based maps show more robustness regard clouds shadows, altitude gradients pre-processing uncertainty. The results not only sensors native like Sentinel-2 but also suggest that back-casting Landsat archive combined an based approach may existing maps, especially frequently clouded regions.
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ژورنال
عنوان ژورنال: International journal of applied earth observation and geoinformation
سال: 2022
ISSN: ['1872-826X', '1569-8432']
DOI: https://doi.org/10.1016/j.jag.2022.103126