Doctoral Dissertation Research: An Investigation of the Scale Effects of DEM-based Fuzzy k-means Landform Classifications

BCS-0425273 John P WilsonDeng Yongxin University of Southern加州本项目重点研究了模糊k-均值地貌分类中空间尺度的三个连通分量：分类的空间分辨率、属性计算的空间分辨率以及邻域窗口大小对计算属性的影响。 当使用这种分类方法根据地形属性和生物物理特性之间存在的遗传联系来描绘连续的生物物理模式时，这些尺度组成部分具有潜在的重要影响。 该项目包括三个实验。 第一个实验比较了10个分类在10个分类分辨率范围从10到200米。 第二个实验将不同的属性分辨率（范围从10到200米）到一个统一的分类分辨率（10米），然后比较这些结果。 实验三比较了使用不同大小邻域窗口计算地形属性的分类效果。 该项目将使用三种方法来比较模糊k-means分类结果。 第一种方法根据中心属性的相对差异计算两个类中心的属性距离;第二种方法分析两个分类去模糊化后硬类重叠的程度;第三种方法检查一个隶属度分布减去另一个隶属度分布得到的剩余隶属度表面。 实验结果将显示如何敏感的模糊k-均值地貌分类是选定的空间尺度;如何分类可能会定义不同的景观格局在不同的空间尺度;如何测试的地形属性可能会有不同的反应规模的变化;以及是否有规模阈值或自然边界存在于研究区域。 该项目的科学价值在于，模糊k-均值分类可以潜在地使用广泛可用的数字高程模型（DEM）来描述环境特征的地理变化，这通常是非常难以测量的。 在这个建模过程中，对空间尺度问题的关注是中心，因为地形表面和环境在不同的空间尺度范围内变化，并且环境特征的描绘依赖于空间尺度的适当选择。 研究结果也将有助于提高我们对地理学、地理信息科学（GIS）和环境建模中尺度问题的认识。 作为博士论文研究改进奖，该奖项还将为计划在未来独立追求这些和类似研究问题的年轻学者提供支持。

BCS-0425273John P WilsonYongxin DengUniversity of Southern California This project focuses on three connected components of spatial scales in fuzzy k-means landform classifications: the spatial resolution of the classification, the spatial resolution of the attribute calculation, and the effects of size of neighborhood window on calculated attributes. These scale components have potentially important impacts when using this classification method to delineate continuous biophysical patterns based on the genetic linkages existing between terrain attributes and biophysical properties. The project includes three experiments. The first experiment compares 10 classifications performed at 10 classification resolutions ranging from 10 to 200 m. The second experiment incorporates different attribute resolutions (ranging from 10 to 200 m) into a uniform classification resolution (10 m), and then compares these results. The third experiment compares the classification effects of using different sizes of neighborhood windows to calculate terrain attributes. The project will use three methods to compare the fuzzy k-means classification results. The first calculates the attribute distance of two class centers based on the relative differences of center attributes; the second analyzes the extent of hard class overlaps between two classifications after they are defuzzified; and the third examines the residual membership surfaces obtained by subtracting one membership distribution from another. The experimental results will show how sensitive fuzzy k-means landform classifications are to the selected spatial scales; how the classifications may define different landscape patterns at different spatial scales; how the tested terrain attributes may respond differently to scale changes; and whether or not there are scale thresholds or natural boundaries present in the study area. The scientific value of this project lies in the fact that fuzzy k-means classification can potentially use widely available digital elevation models (DEM) to depict the geographic variations of environmental characteristics, which are often extremely difficult to measure. The focus on spatial scale issues is central in this modeling process because both the terrain surface and the environment vary at a diverse range of spatial scales, and the delineation of environmental characteristics relies on appropriate selection of spatial scales. The research results will also help to advance our knowledge of scale issues in geography, Geographic Information Science (GIS), and environmental modeling. As a Doctoral Dissertation Research Improvement award, this award will also provide support to a young scholar that plans to pursue these and similar research questions independently in the future.