| This dissertation addresses several input data issues that are fundamental to terrain-based environmental analysis and classification. It starts with a literature review of terrain analysis, and then reports a series of experiments on DEM data quality, spatial resolution, and data organization.; Chapter 2 suggests that uncertainty in terrain-based environmental modeling is related to (1) landscape subdivision and process representation, (2) representation of spatial continua, and (3) the "equifinality" phenomena. It uses soil erosion modeling, soil mapping, and TOPMODEL to explain these issues respectively. Data quality and spatial scales are highlighted as enduring challenges.; Chapter 3 reports three experiments in a case study area. These experiments examined differences, quality, and impacts of DEM data sources in a spatially-explicit fashion. The results demonstrate the possibility that DEMs from different sources incorporate large differences both in point elevations and in terrain shape, and that these differences have strong impacts on calculated terrain attributes. Chapter 4 describes correlations and differences between terrain attributes of varying resolutions. A spatial sampling/resampling procedure ensures that the description is based on sufficient spatial differentiation. A fuzzy k-means landform classification assists to define how terrain attributes respond to changes in DEM resolution across landform classes. Distinguishable responses are observed from one attribute to another, and from one landform class to another in the selected study area.; Chapters 5 and 6 recognize geographic space, attribute space, and attribute space dimensions as conceptual steps in describing the biophysical environment. A case study (Chapter 5) shows that fuzzy k-means landform classification is very sensitive to the selection of terrain attributes. Chapter 6 further suggests the possibility of defining attribute space in a continuously adjustable way and shows that fuzzy k-means landform classification is also very sensitive to attribute weights. These results challenge the current approach of using tacit expert knowledge to organize input data.; Although these chapters standalone as individual research contributions, they collectively emphasize the importance of space (e.g. distribution), place (e.g. landscape location), and situation (e.g. application purpose) in the study of our biophysical environment. |
