基于遥感影像的露天煤矿区土地特征信息及分类研究

Land characteristic information and classification in opencast coal mine based on remote sensing images

该研究利用Landsat TM数据，以安太堡大型露天煤矿为例，在对地物光谱特征深入分析的基础上，设计了大型露天矿区土地剧烈扰动下不同地物特征提取模型，提取了安太堡露天矿区植被高覆盖区、植被低覆盖区、剥离堆垫区、采煤运煤区及边坡区等不同地物信息。采用归一化植被指数（NDVI）提取植被低覆盖区与植被高覆盖区信息，采用（TM4-TM5）〉O提取植被高覆盖区信息并与NDVI进行了比较，采用TM4〈40提取采煤运煤区信息，采用TM4／TM7在0．99～1．01范围来提取边坡区信息，并统计计算了各类地物所占面积和分布情况。对研究区TM影像进行主成分分析，剥离堆垫区、采煤运煤区和边坡区等反映矿区扰动特征的信息主要由第1主成分反映，植被低覆盖区和高覆盖区等反映矿区植被覆盖特征的信息主要由第2主成分反映，两个主成分的贡献率达到97．16％，并利用扰动特征和植被特征对研究区地物进行了分类。该技术与方法为露天矿地物变化动态监测以及土地复垦与生态重建均提供了准确数据支持。

Using Landsat TM data, based on the spectral character analysis of main land-use types, different feature models under strong land disturbance in Antaibao opencast coal mine were set up. Vegetation higher cover area, vegetation lower cover area, denudation and cumuli area, excavation and transport area and slope area in Antaibao were extracted. The information of areas with higher vegetation cover and lower vegetation cover were detected by NDVI （normalized difference vegetation index）. The information of higher vegetation-cover area was obtained by （TM4-TM5）〉 0, and compared with the vegetation cover area of NDVI. The information of excavation and transport area was obtained by TM4〈40. The information of sloping area in range of 0. 99-1.01 by TM4/TM7 was also obtained. Principal components analysis was applied for analyzing TM image. The characteristic information of denudation and cumuli area, excavation and transport area, and slope area is highlighted in the first principal components. The information of higher and lower vegetation-cover area is highlighted in the second principal component. Results show that the contribution rate of two principal components in the front achieves 97.16%. And object feature was classified by using characteristic of disturbance and vegetation. This technology and method can provide accurate data support for the dynamic monitoring of feature changing in opencast coal mine and the rebuilding and reclamation of land.