大理苍山东西坡植被的垂直分布格局

  目的  分析云南省大理苍山东西坡植被的垂直分布格局变化特征，为有效保护苍山生态环境和物种多样性提供参考依据。  方法  以大理苍山为研究区域，基于高分二号(GF-2)高分辨率遥感影像，结合大理苍山完整的山地植被垂直地带性分布规律，辅以纹理特征和数字高程模型(DEM)数据，采用面向对象的多层次图像分割法，通过构建地形约束因子参与分类过程，准确选择样本，高精度提取研究区域的植被信息，并分析苍山东西坡植被的垂直分布格局。  结果  ①引入辅助信息的面向对象分类法提取的苍山各植被类型连续且效果好，分类总体精度为95.3%，Kappa系数为0.946 6。②苍山东西坡现状植被垂直分布格局明显，各自具有6个垂直分布带，并随着海拔高程的增大，植被分布类型趋同性增大，但东西坡垂直带谱内的优势植被类型相比也存在部分差异。  结论  相较于传统主观性强的分类方法，引入垂直带谱信息的地形约束因子进行分类，可以有效地提高山地植被分类的精度。基于面向对象的多层次分割法适用于苍山植被信息的精确提取。图5表3参20

Vertical distribution pattern of vegetation on the east and west slopes of Cangshan Mountain in Dali

  Objective  The purpose is to analyze the change characteristics of vertical distribution pattern of vegetation on the east and west slopes of Cangshan Mountain in Dali, Yunnan Province, so as to provide reference for effective protection of ecological environment and species diversity of Cangshan Mountain.   Method  Taking Cangshan Mountain in Dali as the study area, based on GF-2 high-resolution remote sensing images, combined with the complete vertical zonal distribution law of mountain vegetation in Cangshan Mountain, and supplemented by texture features and digital elevation model (DEM) data, the object-oriented multi-level image segmentation method was adopted by constructing terrain constraint factors to participate in the classification process, accurately select samples, and extract vegetation information in the study area with high precision, and analyze the vertical distribution pattern of vegetation on the east and west slopes of Cangshan Mountain.   Result  (1) The vegetation types extracted by the object-oriented classification method with auxiliary information were continuous and effective, and the overall classification accuracy was 95.3%, and Kappa coefficient was 0.946 6. (2) The current vegetation vertical distribution pattern on the east and west slopes of Cangshan Mountain is obvious, each of which has six vertical distribution zones. With the increase of altitude, the convergence of vegetation distribution types increases, but there are some differences between the dominant vegetation types in the spectrum of the vertical bands on the east and west slopes.  Conclusion  Compared with the traditional subjective classification method, the terrain constraint factor with vertical band spectrum information can effectively improve the accuracy of mountain vegetation classification, which fully shows that the object-oriented multi-level segmentation method is suitable for the accurate extraction of vegetation information in Cangshan Mountain. [Ch, 5 fig. 3 tab. 20 ref.]