北疆牧区积雪图像分类与雪深反演模型的研究

利用NOAA/AVHRR晴空气象条件下的资料, 建立积雪监测反演模型, 动态监测雪灾期间大范围的积雪空间分布状况, 对牧区雪灾综合评价及防灾救灾具有重要的意义. 使用北疆地区1996-97年2次雪灾期间的4个时相的晴空NOAA卫星数据及20个地面气象台站观测资料, 研究了云与雪的判识及图像去云处理方法, 提出了云层覆盖下图像缺值插补处理的一种新算法; 采用线性混合光谱分解方法, 研究了基于像元的积雪覆盖率及积雪空间分类算法, 模拟出北疆地区积雪深度遥感地学反演模型. 研究结果可为牧区雪情动态监测、分析与综合评价系统的建设提供科学依据.

Snow Classification and Monitoring Models in the Pastoral Areas of the Northern Xinjiang

Establishing snow depth monitoring models using NOAA/AVHRR data received in the sunshiny weather condition, and dynamically monitoring the spatial distribution of snow on large scale during disaster taking place, have an important significance for comprehensively estimating and preventing the disaster caused by snow in pastoral areas. In this paper, four NOAA satellite digital images under sunshiny condition during two snow disasters from 1996 to 1997 and corresponding ground observation in 20 climate stations in the North of Xinjiang are used to study the approaches of identifying snow and clouds, and cutting off the image blocks with cloud. Meanwhile, a new method, coupled with geographic information system technology, is put forward to solve the problem of absent data for the image blocks with cloud. Using linear mixture spectrum disassembling method, the snow coverage rate and snow spatial classification are studied, and the remotely sensed monitoring models are simulated in the North of Xinjiang. Results indicated that: 1) The image cells covered by cloud may be identified and cut off from NOAA images of channels 1 and 2, and the spectral data of the cells covered by cloud can be restored using the image difference between channels 3 and 4 and GIS technology. Accordingly, the snow and cloud in the visible and near-infrared images can be identified. 2) The snow coverage rate based on image cells can be calculated by linear mixture spectrum disassembling method to improve the precision in snow classification. 3) Snow depth can be simulated by use of NOAA satellite data of channels 1 and 2. The study results will provide scientific basis for the construction of dynamically monitoring and analyzing, as well as integrated evaluating the condition of snow disaster in the pastoral areas.