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地形及NDVI在林火遥感监测二次识别中应用的方法探讨

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文章导航 > 自然资源遥感 > 2022 > 34(3): 88-96

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陈艳英, 马鑫程, 徐彦平, 王颖, 汪艳波. 2022. 地形及NDVI在林火遥感监测二次识别中应用的方法探讨. 自然资源遥感, 34(3): 88-96. doi: 10.6046/zrzyyg.2021142
引用本文: 陈艳英, 马鑫程, 徐彦平, 王颖, 汪艳波. 2022. 地形及NDVI在林火遥感监测二次识别中应用的方法探讨. 自然资源遥感, 34(3): 88-96. doi: 10.6046/zrzyyg.2021142
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CHEN Yanying, MA Xincheng, XU Yanping, WANG Ying, WANG Yanbo. 2022. Methods for the application of topography and NDVI in re-identification of remote sensing-based monitoring of forest fires. Remote Sensing for Natural Resources, 34(3): 88-96. doi: 10.6046/zrzyyg.2021142
Citation: CHEN Yanying, MA Xincheng, XU Yanping, WANG Ying, WANG Yanbo. 2022. Methods for the application of topography and NDVI in re-identification of remote sensing-based monitoring of forest fires. Remote Sensing for Natural Resources, 34(3): 88-96. doi: 10.6046/zrzyyg.2021142
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地形及NDVI在林火遥感监测二次识别中应用的方法探讨

  • 1.重庆市气象科学研究所,重庆 401147；

  • 2.中交公路规划设计院有限公司,北京 100010；

  • 3.重庆市开州区突发事件预警信息发布中心,重庆 405400；

  • 4.重庆市江津现代农业气象试验站,重庆 402260；

  • 5.朝阳师范高等专科学校,朝阳 122000

  • 基金项目:

    重庆市技术创新与应用发展专项“基于多源遥感的林火监测方法研究”(cstc2019jscx-msxmX0289)；重庆市气象部门业务技术攻关项目“基于多源卫星数据的重庆林火遥感监测方法探究”(YWJSGG-202102)；重庆市气象局2022年业务技术攻关项目“应用遥感产品改进重庆市森林火险气象条件预报技术研究”(YWJSGG-202204)；中国气象局省所科技创新发展专项“重庆丘陵山地多源卫星遥感数据应用关键技术研究”(SSCX2020CQ)

详细信息
    作者简介: 陈艳英(1974-),女,硕士,主要从事遥感及地理信息系统在地表监测及反演中的应用研究。Email: chenyanying1618@163.com。

  • 中图分类号: TP79

Methods for the application of topography and NDVI in re-identification of remote sensing-based monitoring of forest fires

  • 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

    摘要
  • 挖掘归一化植被指数(normalized difference vegetation index,NDVI)及地形因素在地表分类中具有指示意义,其结果可很好地应用于特定场景。利用2002—2020年AQUA/MODIS NDVI及地形指数(高程及坡度)提取重庆地表分类信息,将重庆地表分为林地、草地、果园、旱地、水田、水体和建筑用地7类。基于重庆地形破碎致使农、林、草用地交错分布的特征及防火需求,将林地、草地、果园、旱地划分为林火关注区,水田、水体、建筑用地划分为非林火关注区。利用林火关注区分级结果对2002—2020年AQUA/MODIS监测热点、2014—2020年FY3-C/VIRR监测热点、2019—2020年FY3-D/MERSI监测热点进行二次识别。结果表明,单项地类提取结果,除旱地和果园等经济林区外,其余各地类提取精度在64%以上; 林火关注分区精度在86%以上。利用林火关注分区结果对遥感监测林火点进行二次识别,发现AQUA/MODIS监测的林火点中,46.27%的点在非林火关注区,FY3-C/VIRR和FY3-D/MERSI监测的林火点中,分别有26.47% 和11.76%的点在非林火关注区。对2021年5月1—2日林火遥感监测结果进行二次识别,AQUA/MODIS和TERRA/MODIS监测结果中,有81.08%的点落在非林火关注区,FY3-C/VIRR监测结果有71.4%落在非林火关注区。利用NDVI及地形指数提取复杂地形区域地表分类信息并应用于林火遥感监测二次识别,可有效降低复杂地形区域林火监测干扰信息,降低热点核实人力物力投入。
    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.
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出版历程
收稿日期:  2021-05-11
修回日期:  2022-09-15
刊出日期:  2022-09-21

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