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

机译:使用无人机(UAV)数据的旱地土壤退化映射

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摘要

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.
机译:干旱和半干旱的景观往往展示了裸露和植被的空间的拼凑而成。它们的异质图案可以是天然来源的,但也可能表明土壤退化。本研究研究了无人驾驶飞行器(UAV)图像的使用,以确定土壤的降解状态,基于植被覆盖可以用作估算土壤健康状况的代理。为了评估无人机衍生产品的质量,我们将传统的现场衍生地图(FM)与基于(i)植被盖(RGB地图)的两个建模地图进行比较,(ii)植被覆盖,地形信息和a流量累积分析(RGB + DEM地图)。所有方法都能够识别土壤退化的区域,但在分类的土壤降解程度上有所不同,RGB地图分类最少的量降解。由于与传统的现场映射相比,RGB + DEM地图比FM分为降级的12%,而不是FM。总的来说,传统的无人机为在异构景观中的土壤映射提供了一个有价值的工具,其中手动场采样非常耗时。此外,从鸟瞰角度的无人机的普通型视图可以克服测量师(基于地面)的有利位置的有限视图。

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