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毛乌素沙地植被覆盖变化的遥感分析 被引量:5

Remote Sensing Analysis of Vegetation Cover Change of Mu Us Sandland
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摘要 为了防止毛乌素沙地沙漠化、改善生态环境质量,近10年国家在该区域实施了一系列生态环境建设工程。这些工程的主要目标就是恢复退化植被,提高植被覆盖率。本研究基于2000年、2005年、2010年三期的Landsat数据,利用eCognition软件中面向对象分类技术和地理信息系统的叠加分析功能,分析了2000~2010年间毛乌素沙地植被覆盖变化情况。同时,通过MODIS-NDVI数据计算分析了植被覆盖度的变化趋势。研究得出:1)该区域草地大幅度增加,尤其是2005年到2010年之间,增加更加明显;林地基本稳定;旱地由于退耕还林等政策有所减少;2)近10年来毛乌素沙地植被覆盖度呈逐步增加趋势,西北部和东南部增加尤为明显。产生上述变化的自然因素和人为因素的贡献率需要进一步研究。 To prevent the land desertification and improve the ecological environmental quality , a series of ecological environment con-struction projects have been implemented for Mu Us Sandland by the country during the nearly 10 years.One of the main effects of these projects is to restore the degradation vegetation and increase the vegetation coverage .This study analyze the vegetation cover change of Mu Us Sandland from the year 2000 to 2010 by using the object -oriented classification technique of eCongnition software and the overlay analysis function of geographic information system , based on Landsat data of the three stages which respectively are 2000, 2005 and 2010.Meanwhile, the variation trend of vegetation coverage is calculated and analyzed with the MODIS -NDVI data. The conclusions are:(1) the grassland area of Mu Us Sandland increases significantly , especially the period between 2005 and 2010;the area of forest land is basically stable;the area of dry land decrease because of some policies , such as the policy converting farm-land into forest and so on;(2)the vegetation coverage of Mu Us Sandland present a gradually increasing thrend of Mu Us Sandland in the nearly 10 years, and which of the northwest and southeast area are relatively obvious .The further research of contribution ratio of natural and human factors leading to the above changes is needed .
出处 《测绘与空间地理信息》 2014年第4期58-61,65,共5页 Geomatics & Spatial Information Technology
基金 中国科学院重点部署项目(KZZD-EW-04-04-01) 国家自然科学基金(41171400 41361080)资助
关键词 植被类型 植被覆盖度 面向对象分类技术 eCogniton 毛乌素沙地 vegetation types vegetation coverage object-oriented classification technique eCogniton Mu Us sandland
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