High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data

Abstract

Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10 were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO2 as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO2 emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO2 emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO2 emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion.

Optimized anchor-free network for dense rotating object detection in remote sensing images

“…45 Overall, the OBB annotation method can accurately describe nondirectional objects in images. [46][47][48] Based on the above discussion, a rotating object detection algorithm based on YOLOX is proposed, 49 which is optimized through a dual attention mechanism (DAM) module and a gated multiscale feature adaptive fusion module. Our algorithm can accurately extract objects in dense scenes of high-resolution remote sensing images and to some extent, reduce the effect of external factors.…”