A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego

doi:10.3390/rs13152900

	A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego
	Gong, Adu 1,2,3; Li, Jing 4,5; Chen, Yanling 1,2,3
刊名	REMOTE SENSING
	2021-08-01
卷号	13 期号:15 页码:16
关键词	brightness temperature prediction spatio-temporal information contextual MODIS
DOI	10.3390/rs13152900
通讯作者	Li, Jing(lij.18b@igsnrr.ac.cn)
英文摘要	Early detection of forest fire is helpful for monitoring the spread of fire promptly, minimizing the loss of forests, wild animals, human life, and economy. The performance of brightness temperature (BT) prediction determines the accuracy of fire detection. Great efforts have been made on BT prediction model building, but there still remains some uncertainty. Based on the widely used contextual BT prediction model (CM) and temporal-contextual BT prediction model (TCM), we proposed a spatio-temporal contextual BT prediction model (STCM), which involves historical images to contrast the BT correlation matrix between the pixel to be predicted and its background pixels within a dynamic window, and the spatial distance factor was introduced to modify the BT correlation matrix. We applied the STCM to a fire-prone area in San Diego, California, US, and compared it with CM and TCM. We found that the average RMSE of STCM was 12.54% and 9.12% lower than that of CM and TCM, and the standard deviation of RMSE calculated by STCM was reduced by 12.04% and 15.57% compared with CM and TCM, respectively. In addition, the bias of STCM was concentrated around zero and the range of bias of STCM was 88.7% and 15.3% lower than that of CM and TCM, respectively. The results demonstrated that the STCM can be used to obtain the highest BT prediction accuracy and most robust performance, followed by TCM, and CM performed worst. Our research on the BT prediction of potential fire pixels is helpful for improving the fire detection accuracy and is potentially useful for the prediction of other environmental variables with high spatial and temporal autocorrelation. However, the requirement of high-quality continuous data will limit the application of STCM in cloudy and rainy areas.
资助项目	National Key Research and Development Program of China[2017YFB0504102] ; National Key Research and Development Program of China[2017YFC1502402] ; National Key Research and Development Program of China[2019YFE01277002] ; National Key Research and Development Program of China[2017YFC1502704-01] ; National Natural Science Foundation of China[41671412]
WOS关键词	LAND-SURFACE TEMPERATURE ; DETECTION ALGORITHM ; RADIATIVE POWER ; PRODUCT ; CLASSIFICATION
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	MDPI
WOS记录号	WOS:000682343300001
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164693]
专题	中国科学院地理科学与资源研究所
通讯作者	Li, Jing
作者单位	1.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Gong, Adu,Li, Jing,Chen, Yanling. A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego[J]. REMOTE SENSING,2021,13(15):16.
APA	Gong, Adu,Li, Jing,&Chen, Yanling.(2021).A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego.REMOTE SENSING,13(15),16.
MLA	Gong, Adu,et al."A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego".REMOTE SENSING 13.15(2021):16.