Evaluation of Four Satellite-Derived Fire Products in the Fire-Prone, Cloudy, and Mountainous Area Over Subtropical China

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Jiao, Miao, Quan, Xingwen, Yao, Jinsong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial satellites; Climate change; Cloudy and mountainous area; Clustering; Detection; Earth; End users; fire; Fire detection; Fire Disturbance Climate Change Initiative (Fire_CCI51); Fires; Forest fires; Forest management; Forestry; Imaging radiometers; Imaging techniques; Infrared imaging; Infrared radiometers; Landsat; Landsat satellites; MCD14ML; MCD64A1; Measurement; MODIS; Mountainous areas; Mountains; Performance evaluation; Radiometers; Radiometry; Random sampling; Records; Remote sensing; Satellites; Spectroradiometers; Statistical sampling; VNP14DL; China
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3188259

In the absence of historical fire records, end users intend to adopt free satellite-derived fire products, including global burn area (BA) and active fire (AF) products, to understand the historical fire dynamics for better forest management. Previous literature evaluated the accuracy of these fire products in regions with different environments, but no study evaluated the performance of these fire products in fire-prone, cloudy, and mountainous areas. This study contributed to filling this gap, through the first evaluation of four broadly used fire products: Moderate Resolution Imaging Spectroradiometer (MODIS)-based MCD64A1, MCD14ML, Visible Infrared Imaging Radiometer Suite (VIIRS)-based VNP14DLIMGTDL_NRT (hereafter simplified as VNP14DL), and European Space Agency (ESA) Fire Disturbance Climate Change Initiative (Fire_CCI51) over subtropical China. Two methods were applied to this end: the spatiotemporal clustering algorithm based on official historical fire records and the density-based random sampling and estimation method from the Landsat 8 fire scenes. The results show that both the AF and BA products show poor fire detection ability in this area (with all the <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>-Score < 0.5). Among them, the VNP14DL performed best, followed by MCD14ML, Fire_CCI51, and MCD64A1. The MCD14ML had the best detection capability for small fires (<50 hectares). AF products have an overall higher fire detection ability than BA products. These findings provide insights for the improvement of fire detection algorithms for these fire products in the fire-prone, cloudy, and mountainous area over subtropical China.