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Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability.

Authors :
Millard, K.
Darling, S.
Pelletier, N.
Schultz, S.
Source :
Remote Sensing of Environment. Dec2022, Vol. 283, pN.PAG-N.PAG. 1p.
Publication Year :
2022

Abstract

Peatlands throughout the boreal forest are expected to experience changes in precipitation, evapotranspiration and temperature due to climate change. Correspondingly, changes in hydrologic regimes could lead to increased drought and occurrence of wildfire. Fire management agencies require information about near-real time wildfire vulnerability in boreal peatlands. Remote sensing tools (e.g., NDVI, NDII) used to monitor changing wildfire vulnerability focus on monitoring changes in vascular vegetation and are not necessarily applicable to moss-dominated peatlands. We use time series analysis of Sentinel-1 SAR backscatter data to compare the trends in peatlands that have burned to unburned peatlands and show that the Theil-Sen slopes of seasonally decomposed SAR backscatter reflects prolonged drought conditions that can lead to burning. Seasonally decomposed Sentinel-2 NDVI and NDII were also tested but no statistical differences were found between burned and unburned peatlands. Overall, we found that 6 months prior to a wildfire the slope of seasonally decomposed Sentinel-1 VV SAR backscatter was significantly different in burned and unburned peatlands, and can be used to spatially identify fire vulnerability and identify fire-prone areas. • Remote sensing tools to monitor wildfire vulnerability focus on vascular vegetation. • Surface wetness conditions are more applicable to moss-dominated peatlands. • We use SAR time series trends to compare burned to unburned peatlands. • Theil-Sen slope of seasonally decomposed SAR backscatter reflects prolonged drought. • 6 months prior to wildfire, slopes were different in burned and unburned peatlands. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00344257
Volume :
283
Database :
Academic Search Index
Journal :
Remote Sensing of Environment
Publication Type :
Academic Journal
Accession number :
160043737
Full Text :
https://doi.org/10.1016/j.rse.2022.113329