土地覆盖变化是土地分析与评价和生态环境变化预测的重要科学基础, 通过精确的土地覆盖分类方法 获取高精度的土地覆盖图是研究煤田火区生态环境变化的必要手段。本文以最大似然法、光谱角度法、面向对象 分类法和基于复合分区的分层分类法进行乌达煤田火区土地覆盖分类的方法研究。研究结果表明, 基于复合分区的 分层分类方法分类精度较高, 总体分类精度为92.97%, kappa 系数为0.9155。该方法通过基于地表热辐射特征、热 异常状况、地貌类型, 以及对生态系统扰动状况等的划分, 减少了地物信息的混淆度, 即通过提高单一地物的分类 精度来提高总体分类精度, 突出位于戈壁区的煤田火区土地覆盖的地带性和规律性特征, 增加土地覆盖类型的可 分性, 使得土地覆盖的分类方法具有针对性, 有效提高了分类精度。

Land cover change is an important scientific issue for the land evaluation and eco-environmental change forecasting. It is the necessary means to study the eco-environmental changes in coal fire zone by acquiring high precise land cover map through accurate classification approaches. In this paper, Maximum likelihood classification (MLC), Spectral Angle Mapping (SAM), object-oriented classification (OOC) and the multi-level classification based on compound subregion (MCBCS) approaches are used to classify land cover in the Wuda coal fire area. The results show that the multi-level classification based on compound subregion method leads to the highest accuracy up to 92.97% and the Kappa Coefficient is 0.9155. This method segments the study area based on the thermal characteristic, thermal anomalies, landscape and the disturbing to ecosystems. It reduces the confusion among different landcover types, emphasizes the zonal and regularity of land cover of the coal fire area in Gobi, and increases the separability of land cover. The multi-level classification based on compound subregion increases the whole accuracy by the accuracy improvement of single land cover.