Authors: Wenwu Tang*, University of North Carolina at Charlotte, Minrui Zheng, University of North Carolina at Charlotte, Zachery Slocum, University of North Carolina at Charlotte, Jianxin Yang, University of North Carolina at Charlotte, Craig Allan, University of North Carolina at Charlotte
Topics: Cyberinfrastructure, Spatial Analysis & Modeling, Geographic Information Science and Systems
Keywords: Cyberinfrastructure, High-performance computing, Microtopographic Analysis, Big Data
Session Type: Paper
Start / End Time: 5:00 PM / 6:40 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded
Microtopographic analysis is essential in the study of watershed-level spatial processes such as runoff and greenhouse gas emission. As high-resolution DEM data derived from, e.g., LiDAR technology are increasingly available, the processing and analysis of these data for microtopographic study represents a big data-driven challenge. Thus, in this study, we developed a cyberinfrastructure-enabled approach for the resolution of the big data challenge facing high-resolution microtopographic analysis. Parallel computing algorithms were implemented to harness high-performance computing power for the acceleration of the analysis steps required by the extraction of microtopographic features. The study area is a tidal wetland in South Carolina. Our experimental results indicate that the cyberinfrastructure-enabled approach can reveal the fine-scale microtopographic features efficiently.