Jun-Hak Lee

Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery

This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the “salt-and-pepper effect” and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Geocoding police collision report data from California: a comprehensive approach.

BackgroundCollision geocoding is the process of assigning geographic descriptors, usually latitude and longitude coordinates, to a traffic collision record. On California police reports, relative collision location is recorded using a highway postmile marker or a street intersection. The objective of this study was to create a geocoded database of all police-reported, fatal and severe injury collisions in the California Statewide Integrated Traffic Records System (SWITRS) for years 1997-2006 for use by public agencies.ResultsGeocoding was completed with a multi-step process. First, pre-processing was performed using a scripting language to clean and standardize street name information. A state highway network with postmile values was then created using a custom tool written in Visual Basic for Applications (VBA) in ArcGIS software. Custom VBA functionality was also used to incorporate the offset direction and distance. Intersection and address geocoding was performed using ArcGIS, StreetMap Pro 2003 digital street network, and Google Earth Pro. A total of 142,007 fatal and severe injury collisions were identified in SWITRS. The geocoding match rate was 99.8% for postmile-coded collisions and 86% for intersection-coded collisions. The overall match rate was 91%.ConclusionsThe availability of geocoded collision data will be beneficial to clinicians, researchers, policymakers, and practitioners in the fields of traffic safety and public health. Potential uses of the data include studies of collision clustering on the highway system, examinations of the associations between collision occurrence and a variety of variables on environmental and social characteristics, including housing and personal demographics, alcohol outlets, schools, and parks. The ability to build maps may be useful in research planning and conduct and in the delivery of information to both technical and non-technical audiences.

An improved topographic mapping technique from airborne lidar: application in a forested hillside

We developed a robust method to reconstruct a digital terrain model (DTM) by classifying raw light detection and ranging (lidar) points into ground and non-ground points with the help of the Progressive Terrain Fragmentation (PTF) method. PTF applies iterative steps for searching terrain points by approximating terrain surfaces using the triangulated irregular network (TIN) model constructed from ground return points. Instead of using absolute slope or offset distance, PTF uses orthogonal distance and relative angle between a triangular plane and a node. Due to this characteristic, PTF was able to classify raw lidar points into ground and non-ground points on a heterogeneous steep forested area with a small number of parameters. We tested this approach by using a lidar data set covering a part of the Angelo Coast Range Reserve on the South Fork of the Eel River in Mendocino County, California, USA. We used systematically positioned 16 reference plots to determine the optimal parameter that can be used to separate ground and non-ground points from raw lidar point clouds. We tested at different admissible hillslope angles (15° to 20°), and the minimum total error (1.6%) was acquired at the angle value of 18°. Because classifying raw lidar points into ground and non-ground points is the basis for other types of analyses, we expect that our study will provide more accurate terrain approximation and contribute to improving the extraction of other forest biophysical parameters.

Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization

Abstract Coupled 1D-2D modelling is a widely used approach to predict water movement in complicated surface and subsurface drainage systems in urban or peri-urban areas. In this study, a hybrid parallel code, H12, is developed for 1D-2D coupled urban flood modelling. Hybrid-1D-2D, or H12, enables street-resolving hyper-resolution simulation over a large area by combining Open Multi-Processing (OpenMP) and Message Passing Interface (MPI) parallelization. Variable grid sizing is adopted for detailed geometric representation of urban surfaces as well as efficient computation. To assess the capability of H12, simulation experiments were carried for the Johnson Creek Catchment (∼40 km2) in Arlington, Texas. The LiDAR-derived digital elevation model (DEM) and detailed land cover map at 1-m resolution are used to represent the terrain and urban features in flood modelling. Hybrid parallelization achieves up to a 79-fold reduction in simulation time compared to the serial run and is more efficient than either OpenMP or MPI alone especially in hyper-resolution simulations.