Colloquium

Object-Based Classification of Gullies using UAV-Derived Digital Elevation Models; A case study for Navarre, Spain

Organisator Laboratory of Geo-information Science and Remote Sensing
Datum

di 18 augustus 2015 09:00 tot 09:30

Locatie Gaia, building number 101
Droevendaalsesteeg 3
101
6708 PB Wageningen
+31 317 48 16 00
Zaal/kamer 1
By David Scholte-Albers (The Netherlands)

Abstract
Land degradation is worldwide an increasing problem, and with rainfall patterns becoming more irregular, soils become more vulnerable to erosion during periods of heavy rain. Not all water can infiltrate the soil and this causes overland runoff. This can lead to the formation of rills and gullies. Gullies are the most severe form of land degradation and lead to permanently unusable land. Obtaining a good overview of their locations can lead to a better understanding of the processes involved in the formation of these processes. This study uses digital elevation models (DEMs) and orthophotos of two catchments in Navarre, Spain. These DEMs and orthophotos were created with photogrammetry from photographs taken by a digital camera on board of an unmanned aerial vehicle (UAV). In the first part of this study, the accuracy of gully features in the DEM is assessed. This is done by comparing real time kinematic (RTK) measurements of gullies taken in the field with DEM values for the same locations. The second part of the study focusses on deriving land surface parameters (LSPs) suitable for describing the location of the gully networks in the catchments. Object-based image analysis (OBIA) is used for classifying the gullies and the same rule set is tested for the same areas one year later.
Results show that the depth of gullies is not accurate in the DEMs, mostly because of dense vegetation covering the gullies. Despite the low accuracy, with the slope and profile curvature as most suitable LSPs, the gullies can be classified in both catchments. Both classification maps give a representation of the extent of the gully network. The same classification rule set leads also to good results for the data of one year later. The created rule set is therefore a powerful automated tool for locating erosion features in mountainous landscapes and it contributes to a better understanding and mapping of gully erosion.

Keywords: Digital Elevation Model; Gully; Land Surface Parameter; Object-based Image Analysis; Real Time Kinematic; Unmanned Aerial Vehicle