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| Research on Forest Fire Monitoring Based on Multi-Source Satellite
Remote Sensing Images |
| YIN Jun-yue1, HE Rui-rui2, ZHAO Feng-jun3*, YE Jiang-xia1* |
1. College of Forestry，Southwest Forestry University，Kunming 650224，China
2. Forestry and Grassland Administration of Bayingoleng Mongol Autonomous Prefecture Xinjiang Bazhou “Three Norths” Protection Forest Construction Management Office，Bayingoleng 841009，China
3. Institute of Forest Ecology and Nature Conservation，Chinese Academy of Forestry，Key Laboratory of Forest Conservation，State Forestry and Grassland Administration，Beijing 100091，China
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Abstract At present, remote sensing forest fire monitoring mainly focuses on the accuracy of fire point detection by polar-orbiting satellites. At the same time, there is less research on remote sensing monitoring and identification of fire points, smoke characteristics and other comprehensive fire information based on multi-source remote sensing images. The forest fire of May 9, 2020, in Anning City, Yunnan Province, was studied based on the Gaofen-6 wide-field (GF-6 WFV) data and the FY-3D polar-orbiting meteorological satellite medium-resolution spectrometer (FY-3D MERSI) data for smoke, burned areas extraction and fire point identification. Firstly, Based on GF-6 WFV data, six spectral feature indices were selected to identify fire smoke and fire trails by maximum likelihood, support vector machine, and random forest classification methods, and evaluated for accuracy. Then, Based on the 1 km mid-infrared channel data of FY-3D MERSI, the potential fire point identification algorithm is improved, and the basic principles of FY-3C VIRR and MODIS fire point detection are combined with dynamic threshold and context detection method to identify fire points. Then the identification results are optimized by combining the far-infrared channel with 250 m resolution. Finally, the information on smoke, fire points and fire trails extracted from the two kinds of data were combined to explore and analyze the monitoring capability of GF-6 WFV and FY-3D MERSI for forest fires. The results show that the smoke and burned areas can be effectively identified by five feature indices and eight bands of GF-6 WVF data, and the random forest classification is the most effective among the three classification methods, with an overall classification accuracy and Kappa coefficient of 97.20% and 0.955. The improved fire point recognition algorithm for FY-3D MERSI data can effectively improve the recognition accuracy of fire points. Combining the mid-infrared -and far-infrared channels to detect fires can improve the fire detection accuracy from kilometer to 100 meter level. The combined GF-6 and FY-3D MERSI data can effectively extract smoke, burned areas and fire point information from the fire site, and the use of multi-source data can carry out forest fire monitoring and early warning in multiple directions, which is of great significance to improve the capacity of satellite remote sensing forest fire monitoring.
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Received: 2022-08-29
Accepted: 2022-11-16
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Corresponding Authors:
ZHAO Feng-jun, YE Jiang-xia
E-mail: zhaofengjun1219@163.com; yjx125@163.com
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[1] HE Rui-rui，ZHAO Feng-jun，ZENG Yu-ting，et al(何瑞瑞，赵凤君，曾玉婷，等). World Forestry Research(世界林业研究)，2022，35(2)：59.
[2] Li X L，Song W G，Lian L P，et al. Remote Sensing，2015，7(4)：4473.
[3] QIN Xian-lin，LI Xiao-tong，LIU Shu-chao，et al(覃先林，李晓彤，刘树超，等). Journal of Remote Sensing(遥感学报)，2020，24(5)：511.
[4] Pereira Jose M C. IEEE Transactions on Geoscience and Remote Sensing，1999，37(1)：217.
[5] Giglio L，Kendall J D，Justice C O. International Journal of Remote Sensing，1999，20(10)：1947.
[6] ZHANG Jie，ZHANG Wen-yu，FENG Jian-dong，et al(张 婕，张文煜，冯建东，等). Remote Sensing Technology and Application(遥感技术与应用)，2016，31(5)：886.
[7] FENG Huo-lang，ZHANG Gui，TAN San-qing，et al(冯豁朗，张 贵，谭三清，等). Journal of Central South University of Forestry & Technology(中南林业科技大学学报)，2021，41(8)：75.
[8] RAO Yue-ming，WANG Chuan，HUANG Hua-guo(饶月明，王 川，黄华国). Journal of Remote Sensing(遥感学报)，2020，24(5)：12.
[9] GAO Zhong-liang，WEI Jian-heng，LONG Teng-teng，et al(高仲亮, 魏建珩, 龙腾腾, 等). Fire Science and Technology(消防科学与技术)，2020，39(9)：1285.
[10] DENG Zhong-jian，LI Xiao-na，ZHOU Ru-liang，et al(邓忠坚，李晓娜，周汝良，等). Journal of Southwest Forestry University(西南林业大学学报)，2016，36(4)：132.
[11] ZHENG Wei，SHAO Jia-li，WANG Meng，et al(郑 伟，邵佳丽，王 萌，等). Journal of Natural Disasters(自然灾害学报)，2013，22(3)：54.
[12] HUANG Jian-wen，LI Zeng-yuan，CHEN Er-xue，et al(黄建文，李增元，陈尔学，等). Journal of Remote Sensing(遥感学报)，2021，25(2)：539.
[13] ZHI Jun-jun，DONG Ya，LU Li-can，et al(支俊俊，董 娅，鲁李灿，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2021，37(18)：48.
[14] LIU Qian，QIN Xian-lin，HU Xin-yu，et al(刘 倩，覃先林，胡心雨，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2021，41(8)：2536.
[15] YIN Zhen-zhen，CHEN Fang，LIN Zheng-yang，et al(殷针针，陈 方，林政阳，等). Remote Sensing Technology and Application(遥感技术与应用)，2020，35(5)：1099.
[16] Wang N，Wu H，Nerry F，et al. IEEE Transactions on Geoscience and Remote Sensing，2011，49(4)：1291.
[17] Galve J M，Coll C，Caselles V，et al. IEEE Transactions on Geoscience & Remote Sensing，2008，46(5)：1547.
[18] GUO Jie，ZHANG Yue-wei，ZHAO Wen-hua，et al(郭 捷，张月维，赵文化，等). Forest Fire Prevention(森林防火)，2015，(1)：4.
[19] Lin Z，Chen F，Niu Z ，et al. Remote Sensing of Environment，2018，211：376.
[20] ZHENG Wei，CHEN Jie，TANG Shi-hao，et al(郑 伟，陈 洁，唐世浩，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报)，2020，39(1)：120.
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