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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (3) : 88-96     DOI: 10.6046/zrzyyg.2021142
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Methods for the application of topography and NDVI in re-identification of remote sensing-based monitoring of forest fires
CHEN Yanying1(), MA Xincheng2(), XU Yanping3, WANG Ying4, WANG Yanbo5
1. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
2. CCCC Highway Consultants Co., Ltd., Beijing 100010, China
3. Kaizhou District Release Center of Emergency and Early Warning Information in Chongqing, Chongqing 405400, China
4. Jiangjin Modern Agricultural Meteorological Trial Station in Chongqing City, Chongqing 402260, China
5. Chaoyang Teachers College, Chaoyang 122000, China
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Abstract  

The indicative significance of normalized vegetation index (NDVI) and terrain factors in land classification can be applied to specific scenarios. This study extracted the land classification information of Chongqing using the AQUA/MODIS NDVI and terrain indices (height and slope) of 2002—2020 and accordingly divided the land in Chongqing into seven types, i.e., forest land, grassland, orchard, dry fields, paddy fields, waters, and residential and building land, with the former three types being economic forest land. Based on the characteristics of broken terrain caused by the staggered distribution of agricultural, forest, and grassland, as well as the need for fire prevention in Chongqing, this study categorized the economic forest land and dry fields as concern areas of forest fires and categorized paddy fields, waters, and residential and building land as unconcerned areas of forest fires. The hotspots monitored using AQUA/MODIS in 2002—2020, FY3-C/VIRR in 2014—2020, and FY3-D/MERSI in 2019—2020 individually were re-identified based on the classification results of the concern areas of forest fires. The results are as follows. The extraction accuracy of individual land types (except for orchard and dry fields) was over 64%, and that of the concern areas of forest fires was over 86%. Based on the classification results of concern areas of forest fires, the forest fire points monitored using the remote sensing techniques were re-identified. The re-identification results showed that the 46.27%, 26.47%, and 11.76% of forest fire points monitored using AQUA/MODIS, FY3-C/VIRR, and FY3-D/MERSI, respectively were in unconcerned areas of forest fires. The forest fires monitored using remote sensing techniques on May 1-2, 2021 were re-identified, and 71.4%and 81.08% of forest fire points monitored using FY3-C/VIRR and both AQUA/MODIS and TERRA/MODIS, respectively were in unconcerned areas of forest fires. Therefore, extracting land classification information in complex terrain areas using NDVI and terrain indices and applying the extraction results to the re-identification of forest fires monitored using remote sensing techniques can effectively reduce the interference to forest fire monitoring in complex terrain areas, thereby minimizing the input of manpower and properties for the verification of hotspots.

Keywords NDVI      terrain index      forest fire      remote sensing and monitoring      re-identification     
ZTFLH:  TP79  
Corresponding Authors: MA Xincheng     E-mail: chenyanying1618@163.com;maxincheng@hpdi.com.cn
Issue Date: 21 September 2022
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Yanying CHEN
Xincheng MA
Yanping XU
Ying WANG
Yanbo WANG
Cite this article:   
Yanying CHEN,Xincheng MA,Yanping XU, et al. Methods for the application of topography and NDVI in re-identification of remote sensing-based monitoring of forest fires[J]. Remote Sensing for Natural Resources, 2022, 34(3): 88-96.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021142     OR     https://www.gtzyyg.com/EN/Y2022/V34/I3/88
数据来源 AQUA/MODIS FY3-C/VIRR FY3-D/MERSI
热点数量/个 2 159 34 28
Tab.1  Number of hotspots from different sources
土地类型 林地 果园 草地 旱地 水田 建筑用地 水体
样地数/个 206 272 243 233 295 149 196
Tab.2  Number of plots collected by different land types
Fig.1  Monthly NDVI trend lines and slope/height values of different land types
Fig.2  Water and building extraction results
Fig.3  Agricultural land extraction results
Fig.4  Grassland and forestland extraction results and classification of forest fire concern areas
分类结果 样地类型及各类型所占比例
建筑用地 水体 水田 旱地 果园 草地 林地
建筑用地 65.90 19.18 8.93 5.36 26.45 0.00 0.18
水体 26.22 76.32 0.20 0.00 0.11 0.00 0.01
水田 1.87 0.85 80.53 36.65 1.26 0.00 0.17
旱地 1.44 0.87 7.04 47.22 7.61 2.87 15.80
果园 1.22 0.30 3.06 8.55 40.61 0.28 0.59
草地 0.00 0.00 0.00 0.00 0.00 64.16 3.31
林地 3.34 2.48 0.24 2.23 23.95 32.69 79.96
Tab.3  Accuracy test results of a single land type(%)
关注区 建筑用地、水
体、水田面积比
旱地、果园、草
地、林地面积比
林火非关注区 94.59 13.99
林火关注区 5.41 86.01
Tab.4  Classification accuracy test results of forest fire concern areas(%)
Fig 5  High temperature point distribution of FY3-C/VIRR, FY3-D/MERSI and AQUA/MODIS
热点种类 FY3-C/VIRR FY3-D/MERSI AQUA/MODIS
林火非关注区热点占比/% 26.47 11.76 46.27
林火关注区热点占比/% 73.53 70.59 53.73
Tab.5  High temperature statistics in the two types of forest fire concern areas
Fig.6  Distribution of high temperature points by FY3-C/VIRR, FY3-D/MERSI, AQUA/MODIS after re-identification
Fig.7  High temperature spots by TERRA(AQUA)/MODIS, FY3-C/VIRR during May 1—2, 2021
Fig.8  High temperature spots of re-identification by TERRA(AQUA)/MODIS and FY3-C/VIRR during May 1—2, 2021
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