A GIS add-in for automated measurement of sand dune migration using LiDAR-derived multitemporal and high-resolution digital elevation models

Abstract

Desert environment modeling involving sand dune migration is important for better understanding of Earth and planetary surface processes. This paper presents a geographic information system (GIS) add-in for automated measurement of dune migration directions and migration rates using multi temporal, high-resolution digital elevation models (DEM) derived from light detection and ranging (LiDAR) data or other sources. Using the angle of repose (AOR) as a sensitive movement indicator of barchan (crescent-shaped) and transverse dunes, an add-in toolbar for ArcGIS was developed using the Python programming language to automatically detect, measure, and store dune migration direction and migration rate at random point locations on dune slip face centerlines. Pseudo-code and application samples from the White Sands dune field in New Mexico, USA, are also presented. The free add-in can be used by the geoscience community for studying coastal and desert sand dunes. 1

The Imprint of Vegetation on Desert Dune Dynamics

“…We make use of repeat aerial LIDAR topographic data collected in September 2009 and June 2010, to construct digital elevation models (DEMs) with horizontal and vertical resolutions of 1 and 0.1 m, respectively (see section 4). The dunes at White Sands have characteristic lengths of ∼100 m, heights of several meters, and migration rates of several meters per year, so structure and dynamics are well resolved with this data set (Barchyn & Hugenholtz, 2015;Pelletier, 2015;Xia & Dong, 2016). We assume that the rates of dune migration for the 2009-2010 windy season are typical of the migration that would occur in any other given year.…”

Factors Influencing Movement of the Manila Dunes and Its Impact on Establishing Non-Native Species

“…High-resolution DSM derived through UAV and Lidar are widely used for detecting sand dune migrations [30,31], elevation changes [5,32], and coastal erosion [5]. However, it is challenging using two different sensors (for example, UAV and Lidar) for estimating dune migrations due to varying spatial and spectral resolutions, which need intensive geospatial processing for co-registration, resampling, and preprocessing.…”

A New Method for Automated Measurement of Sand Dune Migration Based on Multi-Temporal LiDAR-Derived Digital Elevation Models

“…Using LiDAR data collected for a 2.4 km by 9 km area in the White Sands Dune Field (WSDF) in New Mexico, USA on 24 January 2009 and 6 June 2010, Dong [44] demonstrated that sand dune migration rates and directions in large dune fields can be automatically detected and measured using multi-temporal LiDAR-derived DEMs at thousands of locations in minutes. A PSTP software tool was also developed by Xia and Dong [47] for the automated measurement of sand dune migration rates and directions.…”

“…Using LiDAR data collected for a 2.4 km by 9 km area in the Whi Dune Field (WSDF) in New Mexico, USA on 24 January 2009 and 6 June 2010, D demonstrated that sand dune migration rates and directions in large dune field automatically detected and measured using multi-temporal LiDAR-derived D thousands of locations in minutes. A PSTP software tool was also developed by Dong [47] for the automated measurement of sand dune migration rates and dire A major limitation of the PSTP method developed by Dong [44] is that it obvious dune slipfaces that reach the angle of repose (30° to 34°) in both DEMs. quirement can be difficult to meet if one or both DEMs are created from data c when sand dune slipfaces do not reach the angle of repose due to seasonal winds few slipfaces reached the angle of repose in the DEM derived from LiDAR coll WSDF on 26 September 2009 due to the influence of reverse winds or crosswinds 1 shows three west-east profiles of a dune in WSDF extracted from LiDAR-derive of 24 January 2009; 26 September 2009; and 6 June 2010.…”