A Spatio-Temporal Brightness Temperature Prediction Method for Forest Fire Detection with MODIS Data: A Case Study in San Diego
文献类型：期刊论文
| 作者 | Gong, Adu1,4,5; Li, Jing2,3; Chen, Yanling1,4,5 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 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. |
| WOS关键词 | LAND-SURFACE TEMPERATURE ; DETECTION ALGORITHM ; RADIATIVE POWER ; PRODUCT ; CLASSIFICATION |
| 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研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000682343300001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 5.Beijing Normal Univ, Beijing Key Lab Environm Remote Sensing & Digital, Beijing 100875, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。