Detection of gullies in Fort Riley military installation using LiDAR derived high resolution DEM. (June 2018)

• Record Type: Journal Article
• Authors: Rijal, Santosh; Wang, Guangxing; Woodford, Philip B.; Howard, Heidi R.; Hutchinson, J.M. Shawn; Hutchinson, Stacy; Schoof, Justin; Oyana, Tonny J.; Li, Ruopu; Park, Logan O.
• Is Part Of: Journal of terramechanics. Volume 77(2018)
• Page Start: 15
• Page End: 22
• Publication Date: 2018-06
• Subjects: DEM -- Gully -- Land degradation -- LiDAR -- Military training; Trafficability -- Periodicals; Praticabilité (Routes) -- Périodiques; Trafficability; Periodicals; 629.222
• DOI: 10.1016/j.jterra.2018.02.001
• Languages: English
• ISSNs: 0022-4898

Abstract

Highlights: Military training induced gullies were detected by LiDAR derived DEM in Fort Riley. A method integrating the differences from mean elevation and curvature was proposed. WorldView-2 images and field measured gully data were used for accuracy assessment. 78–86% of gullies measured in the image and field were correctly predicted. Most of the gullies were distributed in the central and west parts of the area. Abstract: Intensive use of military vehicles in military installations create conditions favorable for gully formation. Gullies impede the access of vehicle, restrict the continuation of training, and lead to significant damage to vehicle and risk the life of soldiers. Therefore, it is critical to correctly identify the locations of gullies for continuous training mission. In this study, Fort Riley (FR) military installation was chosen as the study area. LiDAR derived 1 m resolution digital elevation model (DEM) acquired on 2010 was used to map the gullies. A procedure that measures local topographic position, i.e., difference from mean elevation (DFME) along with its integration to the land surface having high surface curvature values was employed. Two high spatial resolution WorldView-2 images of 2010 and field gully data collected in 2010 were utilized for accuracy assessment. Results showed that: (1) A total of 237 small and 166 large gullies were detected and most of them dominated the central west and northwest parts of the installation; (2) Based on the visual interpretation in the WorldView-2 images, there was no statistically significant difference between the detected and observed numbers of gullies; (3) Gullies measured in the field were well detected with an overall accuracy of 78%.