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

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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.