Spatial Dimensions of Tower Karst and Cockpit Karst: A Case Study of Guilin, China

Tower karst (fenglin) and cockpit karst (fengcong) are two globally important representative styles of tropical karst. Previously proposed sequential and parallel development models are preliminary, and geomorphological studies to date do not provide enough satisfactory evidence to delineate the spatial and temporal relation between the two landscapes. This unclear interpretation of tower-cockpit relationships not only obscures understanding of the process-form dynamics of these tropical karst landforms, but also confuses their definition. Moreover, previous technological limitations, as well as the fragmental nature of the karst landscapes, has limited incorporation of geologic and other data into broad geospatial frameworks based on geographic information science (GIS) and remote sensing (RS), with such data being spatially and temporally disparate. This study incorporates various data sources to address the fenglin-fengcong relationship, particularly the recently postulated edge effect , which has not been examined in detail previously and which may hinge upon the interaction of multiple environmental variables, including geomorphology, vegetation and hydrology. To address these issues, this research combines geographic, geologic and hydrologic data, using GIS and RS technologies to test quantitatively the edge effect hypothesis. Specifically, there are four inter-related objectives of this study. The first is to develop a method to effectively differentiate fenglin and fengcong. The second is to extract optimally the vegetation information from satellite imagery, and investigate the correlation between tropical karst topography and its vegetation. The third is to combine the regional hydrologic data and solute transport models to estimate geochemicals control of fenglin and fengcong. The fourth one, perhaps the most important, is to test the edge effect hypothesis using the results from the other three objectives. There are several significant conclusions. First, DEM data are very useful for extracting profiles of complex surface landforms from satellite imagery. Second, the vegetation distribution varies between tower karst and cockpit karst and the differences correlate with topographic characteristics. The under-representation of vegetation on the south-southwest aspect of tower karst is remarkable, and its overall distribution is both less abundant and dispersed than in cockpit karst. Third, the edge effect exists in the Guilin area, with variable intensity and extension in different dimensions. In summary, the major contributions of the study include the following. First, the study has developed a method to classify fenglin-fengcong tropical karst effectively, even with the presence of shadows that would otherwise hinder traditional classification. Second, the study showed a variance of vegetation vitality within aspects of fenglin that might relate to its geomorphic difference from fengcong. Third, the study combined groundwater and solute transport models to estimate bicarbonate distributions, representing a novel systematic and quantitative approach to tropical karst studies

Automatic Extraction of Number of Lanes from Aerial Images for Transportation Applications

Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions