Estimation of biomass burning emissions by integrating ICESat-2, Landsat 8, and Sentinel-1 data

Abstract

Anthropogenic carbon emissions directly contribute to global warming, which has induced severe environmental concerns like extreme droughts and devastating fires. To evaluate the effects of fires on carbon cycling and climate change, it is crucial to accurately estimate the amount of carbon released during fires. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission offers spaceborne light detection and ranging (LiDAR) measurements of global forests, providing excellent opportunities for monitoring forest dynamics and carbon emissions. The ICESat-2 Land and vegetation height product (ATL08), Landsat 8 data, and Sentinel-1 data were integrated to estimate biomass burning emissions with a three-phase hierarchy model. In Phase 1, the least absolute shrinkage and selection operator (LASSO) regression was used to establish the relationship between ATL08 LiDAR metrics and reference aboveground biomass (AGB), R2 0.77 and RMSE 56.67 Mg ha−1. In Phase 2, the LASSO regression predicted AGB for all ATL08 segments. A Random Forest Regression model was trained with Landsat 8 reflectance data and derived vegetation indices, Sentinel-1 data, and terrain variables as predictors and with the LASSO predicted AGB as the response variable, R2 0.71 and RMSE 45.91 Mg ha−1. Wall-to-wall maps of pre-fire and post-fire AGB were produced with the Random Forest model. In Phase 3, the difference between the pre-fire and the post-fire AGB was converted to carbon emissions. The estimated biomass burning emissions of the 2018 Carr fire group, the 2018 Camp fire, and the 2019 Walker fire in California are 3.54 (± 0.067) Tg C, 1.66 (± 0.041) Tg C, and 0.45 (± 0.022) Tg C, respectively. Forests have the highest biomass burning emissions with an average emission of 30.09 Mg ha−1, approximately twice and four times the average emissions from shrubs and grasses. This study highlights the merit of integrating spaceborne remote sensing data, including LiDAR data, optical data, and radar data, for estimating biomass burning emissions, opening an avenue for accurate forest biomass and carbon dynamics monitoring, as well as climate change mitigation.