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首页> 外文期刊>Applied Vegetation Science >Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification
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Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification

机译：使用物种分布建模，基于图像的纹理分析和基于对象的分类来绘制山地植被图

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Objective: The objective of this study was to map vegetation composition across a 24 000 ha watershed.Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California. USA.Methods: Automated image segmentation was used to delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified using a decision tree and data sources extracted from individual species distribution models. Landsat spectral data, and life from cover estimates derived from aerial image-based texture variables.Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a kappa-value of 0.69. Species distribution models inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions. based solely oil predictive distribution model results. proved to be more accurate than mapping based on Landsat data, and equivalent in accuracy to mapping based oil all image data sources.Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes. and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework.
机译：目的：这项研究的目的是在24000公顷的流域上绘制植被组成图。位置：该研究是在加利福尼亚内华达山脉的西坡上进行的。美国：方法：使用自动图像分割来描绘代表相貌和结构相似的植被斑块的图像对象。使用决策树对图像对象进行分类，并从各个物种分布模型中提取数据源。 Landsat光谱数据以及来自基于航空影像的纹理变量的覆盖率估计值的生命。结果：总共绘制了12个植物群落的图，总体准确性为75％，kappa值为0.69。与仅从图像数据得出的地图相比，物种分布模型输入的地图精度提高了约15％。自动绘制现有植被分布图。仅基于石油的预测分布模型结果。事实证明，这种方法比基于Landsat数据的映射更为准确，并且与基于油类的所有图像数据源进行映射的准确性相当。结论：结果突出了在复杂地形中绘制植被群落时，地形，深层和生物气候变量的重要性。测绘误差源于植被类别之间缺乏光谱可分辨性。在静态分布模型框架内无法解释土地使用历史和干扰的混杂影响。

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