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快速提取森林冰雪受灾范围,有利于准确掌握森林受灾情况,为此类灾害性气候事件防灾减灾、森林资源管理和生态保护提供科学依据。本文利用2001-2007年NDVI数据,提取灾前植被NDVI参考值和正常波动范围,结合2008年NDVI数据提取冰雪冻灾范围。该方法弥补了基于单一时相的传统方法(NDVI差值法)忽略植被指数正常波动的问题,分像元提取植被NDVI正常波动范围,使提取结果更加客观合理。与传统方法提取结果对比,省级尺度的验证结果相同(即森林受灾率均为34.72%,而实地调查森林受灾率35.3%),但2种方法在县市行政单元提取的森林受灾率相差较大。NDVI阈值法提取的森林冰雪受灾范围主要分布于湖南省南部地区,北部地区分布相对较少,而传统方法提取结果主要分布于湖南省北部地区,南部地区分布相对较少。根据实地考察资料显示,相比于传统方法,NDVI阈值法提取结果与实际森林冰雪冻灾空间分布信息更接近,精度更高,更适合于区域大尺度提取森林冰雪受灾范围。
Rapid extraction of the affected area of snow and ice in forest will help to accurately grasp the situation of forest disaster and provide a scientific basis for disaster prevention and reduction, forest resource management and ecological protection for such catastrophic climate events. In this paper, the NDVI data of 2001-2007 were used to extract the reference values of NDVI and normal fluctuation range of pre-disaster vegetation, and the 2008 NDVI data were used to extract the scope of ice and snow disaster. The method compensates for the problem that normal NDVI based on single time phase ignores the normal fluctuation of vegetation index, and the sub-pixel extraction normal fluctuation range of vegetation NDVI makes the extraction result more objective and reasonable. Compared with the results of traditional methods, the verification results at the provincial scale are the same (that is, the forest disaster rate is 34.72%, while the field survey forest disaster rate is 35.3%). However, the difference between the two methods is that Big. The range of snow and ice damage extracted by the NDVI threshold method is mainly distributed in the southern part of Hunan Province. The distribution in the northern part is relatively small while the traditional method is mainly distributed in the northern part of Hunan Province, while the distribution in the southern part is relatively small. According to the field survey data, compared with the traditional methods, the NDVI threshold extraction method is more close to the spatial distribution information of the actual forest frost and snowfall, with higher accuracy and more suitable for large-scale extraction of snow and ice damage in the region.