In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling. 相似文献
Objective: An increasing number of motorcycle taxis have been involved in traffic crashes in many developing countries. This study examines the characteristics of both motorcycle taxi drivers and nonoccupational motorcyclists, investigates the risks they pose to road safety, and provides recommendations to minimize their risks.
Methods: Based on the data collected from a questionnaire survey of 867 motorcycle taxi drivers and 2,029 nonoccupational motorcyclists in Maoming, South China, comparisons were made to analyze differences of personal attributes, attitudes toward road safety, and self-reported behavior of the 2 groups.
Results: Results of the chi-square tests show that not only motorcycle taxi drivers but also nonoccupational motorcyclists in Maoming held poor attitudes toward road safety and both groups reported unsafe driving behavior. There is much room for improving local road safety education among all motorcyclists in Maoming. Yet, motorcycle taxi drivers were more likely to pose road safety risks than nonoccupational motorcyclists under some circumstances, such as speeding late at night or early in the morning, not requiring passengers to wear helmets, and running a red light. The results of the binary logistic regression model show that possessing a vehicle license for a motorcycle or not was the common significant predictor for unsafe driving behavior of motorcycle taxi drivers and nonoccupational motorcyclists. Therefore, enforcement against all motorcyclists not showing vehicle licenses for their motorcycles should be stepped up.
Conclusion: Motorcycle safety is largely poor in Maoming. Therefore, efforts to improve motorcycle safety should be strengthened by targeting not only motorcycle taxi drivers but also nonoccupational motorcyclists. 相似文献