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Precise Extraction of Damaged Forest Range Caused by Ice-snow Frozen Disaster Based on the NDVI Threshold Method

Date: 2018-06-08      View counts: 5683    


WANG Xuecheng, YANG Fei1, GAO Xing, et al
Journal of Geo-information Science
The damaged vegetation detection
Paper Keyword
forests; snow disaster; NDVI threshold; information extraction; Hunan province
Extraction of damaged forest range caused by ice- snow frozen disaster is good for knowing the relevant regional disaster information in time, and it can provide scientific support for disaster prevention and protection of forest resources and ecosystem. We extract pre- disaster plant NDVI reference value and the threshold of normal change with the time series data of 2001- 2008. We attained the results of the spatial distribution of Hunan forest disturbed by ice-snow frozen disaster with NDVI data in 2008. The NDVI threshold method can make up for the defect of traditional method based on single-temporal images, which doesn’t take the normal change of vegetation index into consideration. The NDVI threshold method helps extracting different normal change threshold for each pixel, causing the results extracted by NDVI threshold method is more objective and reasonable. Contrasted with the results extracted by the traditional method, the percentage of damaged forest according to two methods have significant difference at county level, although the rates forest disasters with two methods are all 34.74%(the real rate of forest disaster is 35.3%)at provincial level. The forest snow disaster is mainly distributed in southern Hunan province and less in northern Hunan province using the NDVI threshold method, but the results using traditional method is contrary compared to the NDVI threshold method. According to field survey data, the spatial distribution of forest snow disaster using the method of NDVI threshold is more closed to real results compared to traditional method and its extracting accuracy is higher. Therefore, the NDVI threshold method is more suitable for extracting the spatial distribution information of forest snow disaster at large regional scale.
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