分层分类和多指标结合的西北农牧交错带植被信息提取

doi:10.12118/j.issn.1000-6060.2019.02.13

摘要/Abstract

摘要： 参照《中国植被》中的植被分类体系，结合野外考察结果，建立了适合中国西北农牧交错带的植被分类体系。以覆盖研究区的多幅Landsat影像为基础，按“分层分类，逐层验证”的思路，实现了对研究区植被信息的提取。提取时，先利用完全约束的最小二乘模型对遥感影像进行混合像元分解方法，将整个研究区划分为植被区和非植被区；在植被区，基于光谱特征、纹理特征和地形特征，构建CART决策树，获得了乔木林、灌丛和草原等7种主要植被型组；在植被型组内，基于不同植被类型NDVI的季节差异特征，构建NDVI差值比值指数 (NDVI_DR)，将乔木林和灌丛区分为常绿和落叶植被型，使用温度植被干旱指数(TVDI)，将草原进一步区分为荒漠草原、典型草原和草甸草原3种类型，从而得到各个植被型的空间分布范围。经验证，最终分类的总体精度能达到79.51%，kappa系数为0.773。采用的分类方法充分利用了遥感数据既有的光谱信息和纹理信息，同时辅以地形信息。实践结果表明，分层分类和多种指标相结合的方法可以有效实现对影像跨幅的、以复杂镶嵌结构为主要特征的农牧交错带植被信息提取，精度较高，技术可行。

关键词: 农牧交错区, 植被信息提取, CART决策树, 谱间关系法, 差值比值指数(NDVI_DR), 温度植被干旱指数(TVDI)

Abstract: Based on Chinese vegetation classification criteria described in the book “Chinese Vegetation”, the vegetation classification system suitable for the farming-pastoral ectones in Northwestern China was established with the field investigation. Using the Landsat images, the terrain data and field data of the study area, the vegetation information in the study area was refined according to the implementation strategy as "hierarchical classification and layer by layer verification". During the extraction process, the mixed pixels decomposition of preprocessed remotely sensed images was performed by using the fully constrained least-squares model, and the vegetation fraction of the study area was obtained. We classified the study area into vegetation area where the vegetation fraction is larger than 5% and non-vegetation area where the vegetation fraction is less than 5%. In the vegetation area, it was further classified into 7 main vegetation type groups which include the tree group, shrub group and grassland group using the CART decision tree based on the spectral characteristic, texture characteristic (Mean) and terrain characteristic (Digital Elevation Model, DEM). Each vegetation type group was again further classified into different sub types based on the refined indexes. The tree group and shrub group were categorized into evergreen vegetation type and deciduous vegetation type based on the NDVI difference ratio index which was established using the seasonal variations of their NDVIs of different plants. The grassland type group was categorized into desert grassland, the typical grassland and meadow grassland using the temperature vegetation dryness index (TVDI). After this step, the spatial distribution of each vegetation type was obtained. It was proved that the overall accuracy of the final classification can reach 79.51% and the kappa coefficient is 0.773. The classification method used in this study makes full use of the spectral information and texture information of the remotely sensed images, and cooperates with terrain information. Experimental result shows that the method of using hierarchical classification and multiple indexes could extract the vegetation information efficiently from the images of the farming-pastoral ectones with high accuracy. The classification result in this area provides the basic data for the further research on the relationship between surface hydrothermal process and land cover change, especially vegetation cover change. Meanwhile, it provides reference for the conservation of vegetation area and ecological environment construction in this area.

Key words: the farming-pastoral ectone, vegetation information extraction, LSMA, CART decision tree, NDVI difference ratio (NDVI_DR), temperature vegetation dryness index (TVDI）

中图分类号:

何鸿杰, 穆亚超, 魏宝成, 杜婷, 薛晓玉, 颉耀文. 分层分类和多指标结合的西北农牧交错带植被信息提取[J]. 干旱区地理, 2019, 42(2): 322-340.

He Hongjie, Mu Yachao, Wei baocheng, Du Ting, Xue Xiaoyu, Xie Yaowen. Vegetation information extraction in farming-pastoral ectones in northwest China using hierarchical classification and multiple indices[J]. Arid Land Geography, 2019, 42(2): 322-340.

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