Parallel identification and filling of depressions in raster digital elevation models. Issue 6 (3rd June 2017)

• Record Type: Journal Article
• Authors: Zhou, Guiyun; Liu, Xiaoli; Fu, Suhua; Sun, Zhongxuan
• Is Part Of: International journal of geographical information science. Volume 31:Issue 6(2017)
• Page Start: 1061
• Page End: 1078
• Publication Date: 2017-06-03
• Subjects: Parallel computing -- depression identification -- depression filling -- DEM; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2016.1262954
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: With the increasing sizes of digital elevation models (DEMs), there is a growing need to design parallel schemes for existing sequential algorithms that identify and fill depressions in raster DEMs. The Priority-Flood algorithm is the fastest sequential algorithm in the literature for depression identification and filling of raster DEMs, but it has had no parallel implementation since it was proposed approximately a decade ago. A parallel Priority-Flood algorithm based on the fastest sequential variant is proposed in this study. The algorithm partitions a DEM into stripes, processes each stripe using the sequential variant in many rounds, and progressively identifies more slope cells that are misidentified as depression cells in previous rounds. Both Open Multi-Processing (OpenMP)- and Message Passing Interface (MPI)-based implementations are presented. The speed-up ratios of the OpenMP-based implementation over the sequential algorithm are greater than four for all tested DEMs with eight computing threads. The mean speed-up ratio of our MPI-based implementation is greater than eight over TauDEM, which is a widely used MPI-based library for hydrologic information extraction. The speed-up ratios of our MPI-based implementation generally become larger with more computing nodes. This study shows that the Priority-Flood algorithm can be implemented in parallel, which makes it an ideal algorithm for depression identification and filling on both single computers and computer clusters.