Abstract: This experiment focuses on the fragile zone in Mu Us Desert where farming, forestry, animal husbandry interlacing ecologically in desertification research as the object. It studies the optimal computer automatic classification of Mu Us Desert based on CBERS and TM remote sensing image data type, and investigates the evolution process of Mu Us Desert (20002013) combined with the evolution of GIS spatial analysis and landscape index quantitative. The experiment shows that, through the maximum likelihood supervised classification method, the highest overall precision of sand type obtained is above 86.21%, which is the ideal means of desertification land classification. A nonlinear relationship exists between environment changes and shifting sands, semishifting sands, semifixed sands，fixed sands. Those four types of sand plaques present to be instability, in which shifting and semishifting sands change significantly in position, going on gradually from southwest to northwest and south. Succession between patches will still occur frequently in a period of time in the future, while the percentage of the four types of sands accounts from 82.29% to 75.07% of the study area, proving the ecological environment is getting better by the governance of Mu Us Desert.

Key words: Mu Us Desert, Desertification, Sand land information extraction, Image classification method, Landscape pattern

摘要： 以沙漠化研究中农、 林、 牧生态交错脆弱带毛乌素沙地为研究对象, 基于C B E R S和 TM 遥感影像数据, 对毛乌素沙地类型区提取的最优计算机自动分类方法进行了研究, 并结合 G I S空间分析和景观指数定量研究了2 0 0 0、 2 0 0 6、 2 0 1 3年间毛乌素沙地的演化过程。实验结果表明, 通过计算机自动监督分类中的最大似然法解译获取的沙地类型区得到的总体精度最高, 在8 6. 2 1%以上, 是荒漠化土地分类的理想手段; 流动沙地、 半流动沙地、 半固定沙地和固定沙地与环境变化之间存在非线性的响应关系。目前4种沙地类型斑块均呈现不稳定状态, 其中流动型沙地和半流动性沙地的空间位置变化较为显著, 呈现由西南部逐步向西北部和南部推移或转移的趋势, 斑块之间的演替在今后的一段时间内仍将频繁发生, 但4种类型沙地的面积之和占研究区总面积的比例由8 2. 2 9%降至7 5. 0 7%,证明通过治理, 毛乌素沙地的生态环境总体转好。

关键词: 毛乌素沙地, 荒漠化, 遥感信息提取, 监督分类, 景观格局