The progress of operational forest fire monitoring with infrared remote sensing

• Published in: Journal of forestry research Vol. 28; no. 2; pp. 215 - 229
• Main Authors: Hua, Lizhong, Shao, Guofan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2017; Springer; Springer Nature B.V; College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361021, China%Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA
• Subjects: Algorithms; Biomedical and Life Sciences; Case studies; Climate change; Comparative studies; Detectors; Earth resources technology satellites; Environmental impact; Environmental monitoring; EOS-MODIS; Equipment and supplies; fire detection; Fire extinction; Forest & brush fires; Forest fire detection; Forest fires; Forest management; Forestry; Forests; Global climate; Global temperature changes; humans; Infrared imaging; Landsat; Landsat satellites; Life Sciences; Masks; moderate resolution imaging spectroradiometer; MODIS; monitoring; Monitoring methods; Regional development; Remote monitoring; Remote sensing; Review Article; Spectral methods; Wildfires; Wildlife habitats; Wildlife management; 全球气候变化; 内部收益率; 森林火灾监测; 火灾探测系统; 监测工作; 空间信息; 红外遥感; Wildfire; Landsat 8 OLI; Thermal infrared; VIIRS; Review; MODIS; Remote sensing
• ISSN: 1007-662X; 1993-0607
• DOI: 10.1007/s11676-016-0361-8

Forest wildfires pose significant and growing threats to human safety, wildlife habitat, regional economies and global climate change. It is crucial that forest fires be subject to timely and accurate monitoring by forest fire managers and other stake-holders. Measurement by spaceborne equipment has become a practical and appealing method to monitor the occurrence and development of forest wildfires. Here we present an overview of the principles and case studies of forest fire monitoring（FFM） with satelliteand drone-mounted infrared remote sensing（IRRS）. This review includes four types of FFM-relevant IRRS algorithms： bi-spectral methods, fixed threshold methods, spatial contextual methods, and multi-temporal methods. The spatial contextual methods are presented in detail since they can be applied easily with commonly available satellite IRRS data, including MODIS, VIIRS, and Landsat 8 OLI. This review also evaluates typical cases of FFM using NOAAAVHRR, EOS-MODIS, S-NPP VIIRS, Landsat 8 OLI,MSG-SEVIRI, and drone infrared data. To better implement IRRS applications in FFM, it is important to develop accurate forest masks, carry out systematic comparative studies of various forest fire detection systems（known as forest fire products）, and improve methods for assessing the accuracy of forest fire detection. Medium-resolution IRRS data are effective for landscape-scale FFM, and the VIIRS 375 m contextual algorithm and RST-FIRES algorithm are helpful for closely tracking forest fires（including small and shortlived fires） and forest-fire early warning.