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.
Highlights
Arid and semi-arid landscapes often show a heterogeneous pattern of bare and vegetated spaces [1,2,3,4]
Assuming all bare soil areas of the field-derived map (FM) The are indicative of soil degradation in the unmanned aerial vehicle (UAV)-derived digital RGB map, the extent of degradation mapped in the field was 5% lower than that mapped using UAV imagery
The results show that UAV imagery and data products generated with them can contribute to identifying soil degradation with similar accuracy to conventional field mapping
Summary
Arid and semi-arid landscapes often show a heterogeneous pattern of bare and vegetated spaces [1,2,3,4]. Most common vegetation patterns are banded [5,6] or spotted [1,7] This patchwork of vegetation is reflected in heterogeneous chemical, structural, and textural soil properties [8,9], which introduce spatial variations in factors such as infiltration capacity [10,11,12], soil nutrient content [8,13] and soil erodibility [12,14,15]. The spatial heterogeneity of dryland landscapes indicates that underlying soil types are inherently variable. This variability is often not depicted on soil maps. Most common soil maps for South Africa are large scale, varying between 1:1 and 1:5 million and including the Soil and Terrain (SOTER)
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