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Hierarchical segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California

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REMOTE SENSING 2014年第8期6卷 7276-7302页

作者： Liau, Yan-Ting Univ Oklahoma Ctr Spatial Anal Norman OK 73019 USA

Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu's method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop.

关键词： object-based image analysis image segmentation optimized for delineation of tree density very high resolution imagery species association labeling the Z values of Moran's I

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