利用多信息源提高半干旱地区TM影像的森林类型制图精度:以北京西部山区为例

以地处半干旱地区的北京西部山区为例,利用研究区森林类型的季相特征、已有的少部分林相图、Google Earth免费影像数据等信息选择不同坡向的相同森林类型做训练样本,通过加入其他辅助数据(海拔和坡向数据),来提高Landsat TM影像的森林类型分类精度,同时对比了基于像元和面向对象方法提取森林类型的效果。结果表明:1)就半干旱山区的森林类型划分来说,TM影像的TM4、TM5、TM4-TM2及辅助数据DEM和坡向可作为TM影像森林类型划分的最佳数据源。2)单独加入海拔信息,阔叶林的提取精度提高23%,针叶林和混交林的分类精度只提高了4%~5%;单独加入坡向信息,阔叶林的提取精度只提高21%,但是针叶林和混交林的分类精度则分别提高了13%、18%,显著优于单独加入海拔信息的效果。同时加入海拔信息和坡向信息,至少可以准确区分出约70%以上的针叶林、阔叶林和混交林。3)就本研究区而言,坡向比海拔更有效地辅助提高森林分类精度。4)就混淆矩阵数据而言,面向对象的分类方法比基于像元分类结果总体精度低3%,Kappa系数低4%,但面向对象的分类结果更加符合研究区实际情况。该研究对中分辨率影像应用于半干旱山区森林类型划分具有一定的借鉴意义。

Improving the Forest Type Mapping Accuracy in Semiarid Mountainous Areas Based on TM Images—Take the West Mountain of Beijing as an Example

Since forest is an important indicator of global climate change, the way to extract forest changing should be top priority in forest management and utilization. Especially, the extraction of sub-categories of forest vegetation has always been a difficult point in remote sensing image classification. Therefore, it is important to find a suitable method for forest type mapping, especially in regions with diverse climatic conditions and complex terrain. The present study discussed various methods that could be used to improve the accuracy of forest type classification using Landsat Thematic Mapper (TM) imagery data, taking a semiarid mountainous area in Beijing, China as an example. All classification results were compared with confusion matrices and Kappa statistics. The results showed that: 1) The combination of a digital elevation model (DEM), aspect data, TM4 and TM5, and a synthetic band (TM4-TM2) comprised an optimal dataset when using pixel-based classification. 2) Elevation alone could increase the accuracy by 23% in broad-leaved forest, whereas by 4%-5% in coniferous and mixed forest. Meanwhile, aspect alone could increase the accuracy by 21% in broad-leaved forest, whereas by 13% in coniferous forest and 18% in mixed forest, respectively. Aspect can provide more valuable information for forest mapping than elevation. 3) According to the confusion matrices, the accuracy of pixel-based classifications was slightly higher than that of object-based classification. 4) However, the latter seemed to consist with field investigations better. Our findings implied that integrating distributional characteristics of forests in semiarid regions with Landsat TM imagery could improve the accuracy of forest stand mapping at a regional scale.