基于遥感和GIS的土地利用分类方法及其在土地退化程度分析中的应用——以陕西横山雷龙湾地区为例

基于陕西省横山县雷龙湾地区的遥感ETM＋影像,分别采用最大似然法和光谱角制图分类（SAM）方法进行了该区的土地利用类型分类。其中,光谱角制图分类利用最小噪声分离（MNF）及像元纯净指数（PPI）方法提取研究区的地类终端端元,在此基础上绘制土地利用类型图。最大似然法对水体及耕地的分类精度较高,而其它地类精度稍差,沙地有较大漏分;而基于像元纯净指数的光谱角制图法分类对沙地、水体分类效果较好,对建筑用地和非建筑用地区分度较高,但林地和草地有一定混分。根据研究区反照率影像、NDVI以及湿度图像构造了一个土地退化程度指数模型,并选择一定的系数对研究区的土地退化程度进行了分类,利用GIS软件成图输出。利用光谱角制图分类的纯净像元样本进行比较分析,发现土地退化程度指数与土地利用的相关性较强,表明该方法可以较好地反映当地的土地退化程度情况。

Remote sensing and GIS based comparison of land-use classification methods and data mining for degree of land degradation: a case study in Leilongwan Area, Hengshan County, Shannxi Province

This study employed the maximum likelihood classifier and spectral angle mapping (SAM) method to classify land-use types in the Lilongwan area, Hengshan County, Shannxi province, using Landsat ETM+ remote sensing data. The SAM adopted the minimum noise fraction rotation (MNF) and pixel purity index (PPI) to extract terminal elements of land-use types and based on that to develop the land-use map. The maximum likelihood classifier achieved higher accuracy on water and arable land than the other land-use categories with omission errors occurred on the sandy land category. As the SAM achieved better classification results for sandy land, urban and built-up land and water categories, certain level of confusion existed between woodland and grassland categories. We then established a model of land degradation degree index (DDI) using Albedo, NDVI and wetness from remote sensing data and selected parameters of the model to obtain an improved classification result. We employed GIS technology to produce the map for degree of land degradation. The comparison analysis between SAM classification and PPI samples showed a good correlation between DDI and land-use types. This study indicates that the methodologies developed in this study can be used to reveal the degree of land degradation information of the study area.