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Soil Degradation Mapping in Drylands Using Unmanned Aerial Vehicle (UAV) Data

Krenz, Juliane and Greenwood, Philip and Kuhn , Nikolaus J.. (2019) Soil Degradation Mapping in Drylands Using Unmanned Aerial Vehicle (UAV) Data. Soil Systems, 3 (2). p. 33.

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Official URL: https://edoc.unibas.ch/71141/

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Abstract

Arid and semi-arid landscapes often show a patchwork of bare and vegetated spaces. Their heterogeneous patterns can be of natural origin, but may also indicate soil degradation. This study investigates the use of unmanned aerial vehicle (UAV) imagery to identify the degradation status of soils, based on the hypothesis that vegetation cover can be used as a proxy for estimating the soils’ health status. To assess the quality of the UAV-derived products, we compare a conventional field-derived map (FM) with two modelled maps based on (i) vegetation cover (RGB map), and (ii) vegetation cover, topographic information, and a flow accumulation analysis (RGB+DEM map). All methods were able to identify areas of soil degradation but differed in the extent of classified soil degradation, with the RGB map classifying the least amount as degraded. The RGB+DEM map classified 12% more as degraded than the FM, due to the wider perspective of the UAV compared to conventional field mapping. Overall, conventional UAVs provide a valuable tool for soil mapping in heterogeneous landscapes where manual field sampling is very time consuming. Additionally, the UAVs’ planform view from a bird’s-eye perspective can overcome the limited view from the surveyors’ (ground-based) vantage point.
Faculties and Departments:05 Faculty of Science > Departement Umweltwissenschaften > Geowissenschaften > Physiogeographie und Umweltwandel (Kuhn)
UniBasel Contributors:Krenz, Juliane and Greenwood, Philip and Kuhn, Nikolaus J.
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:MDPI
ISSN:2571-8789
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:17 Aug 2020 13:31
Deposited On:17 Aug 2020 13:31

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