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利用多源遥感数据信息,开展了青藏公路走廊所在区域的积雪空间分布特征调查,对重点路段进行了积雪灾害评估。充分考虑了地形和山地积雪特征,采用多种去云过程和步骤相结合,完成逐日积雪云量消除。利用无云MODIS逐日积雪面积数据统计年均积雪日数,在大尺度范围内提取公路积雪信息,判断整个线路中重点积雪路段。利用Landsat影像的高空间分辨率特性,采用归一化差值积雪指数阈值法识别重点积雪路段各里程处积雪和非积雪区域,有效提取了公路重点积雪路段积雪空间分布信息,更加精确地对公路积雪灾害进行监测和评估。基于影像时空信息对云覆盖像元下地表温度信息进行了重建,并对代表性问题进行分析验证,采用重建的MODIS逐日地表温度计算年均地表温度,对青藏公路走廊沿线重点积雪路段的积雪灾害信息进行判断提取。分析结果表明:评价方法可以有效地对青藏公路走廊沿线积雪分布进行监测与提取,重点积雪区域主要分布在昆仑山、五道梁、风火山、唐古拉山,其中部分路段积雪量较大且易发生道路结冰现象。
Using the multi-source remote sensing data, the spatial distribution of snow cover in the area where the Qinghai-Tibet Highway corridor is located was investigated, and snow damage assessment was carried out for the key sections. Full account of the terrain and mountain snow characteristics, the use of a variety of clouds to the process and steps to complete daily snow cover cloud eliminate. Using the snow-free MODIS day-by-day snow area data to calculate the average number of snow days, the snow information of the highway is extracted within the large-scale range to determine the key snow sections in the entire line. Using the feature of high spatial resolution of Landsat image, the normalized difference snow index threshold method is used to identify the snow-covered and non-snow-covered areas of the key snow-covered sections and effectively extract the snow-covered spatial distribution information of the heavy snow sections of the road , More accurate monitoring and assessment of snow-covered highway. Based on the spatial and temporal information of the image, the ground surface temperature information under the cloud-covered pixels is reconstructed, and the representative problems are analyzed and verified. The average surface temperature of the reconstructed MODIS day-to-day surface temperature is calculated. Snow disaster information to judge extraction. The analysis results show that the evaluation method can effectively monitor and extract the snow distribution along the Qinghai-Tibet Highway corridor. The main snow cover areas are mainly Kunlun Mountains, Wudaoliang, Fenghuoshan and Tanggula Mountains, And prone to road icing phenomenon.