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

doi:10.6046/zrzyyg.2021142

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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.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021142 OR https://www.gtzyyg.com/EN/Y2022/V34/I3/88

Tab.1 Number of hotspots from different sources

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

Tab.3 Accuracy test results of a single land type(%)

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

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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