论文部分内容阅读
乌达矿区的煤火自燃造成了严重的环境、经济和安全灾害，对该地区的土地覆被变化研究有助于评估煤火灾害的影响程度和范围，而Landsat8卫星影像为煤火区的土地覆被分类探测与研究提供了可能。依据乌达地区的地形、地貌和地表辐射特征划分5个子区域，基于通用单决策树模型，利用光谱特征分析、高程、坡度和热红外信息对每个子区域分别构建5种不同参数的决策树模型。相比通用单决策树模型以及其他4种普通分类方法，因减少了土地覆被的混淆度，多子区决策树模型土地覆被分类的整体精度和Kappa系数更高，分别达到87.63%和0.86，尤其是建筑物和煤灰的分类精度有较为明显的提升。“,”Coal fires burning caused serious environmental, economic and safety catastrophe in Wuda district, North China. The land cover change research helped to evaluate the extent of coal fire damage. The image data of Landsat8 satellite offered the possibility of detecting and studying land cover/use in coal fire area. Five subregions were divided from one Wuda image based on topographic, landform and land surface radiation characteristics. Corresponding to each subregion, five different decision tree models with different parameters were respectively constructed based on a general sole decision tree for the whole research area, which was built by spectral characteristics analysis, height, slope and infrared information. By contrasting with a general sole decision tree and other four common classification methods applied to the whole area, land cover accuracy of multi-subregions decision tree classification approach derived higher overall accuracy (87.63%) and Kappa coefficient (0.86) because subregions decreased land-cover confusions. In particular, the accuracy of building and coal ash classification mapping showed a marked increase.