Quantifying the extent of soil erosion at a fine spatial resolution can be time consuming and costly; however, proximal remote sensing approaches to collect topographic data present an emerging alternative for quantifying soil volumes lost via erosion. Herein we compare terrestrial laser scanning (TLS), and both unmanned aerial vehicle (UAV) and ground photography (GP) structurefrom-motion (SfM) derived topography. We compare the cost-effectiveness and accuracy of both SfM techniques to TLS for erosion gully surveying in upland landscapes, treating TLS as a benchmark. Further, we quantify volumetric soil loss estimates from upland gullies using digital surface models derived by each technique and subtracted from an interpolated pre-erosion surface. Soil loss estimates from UAV and GP SfM reconstructions were comparable to those from TLS, whereby the slopes of the relationship between all three techniques were not significantly different from 1:1 line. Only for the TLS to GP comparison was the intercept significantly different from zero, showing that GP is more capable of measuring the volumes of very small erosion features. In terms of costeffectiveness in data collection and processing time, both UAV and GP were comparable with the TLS on a per-site basis (13.4 and 8.2 person-hours versus 13.4 for TLS); however, GP was less suitable for surveying larger areas (127 person-hours per ha À1 versus 4.5 for UAV and 3.9 for TLS). Annual repeat surveys using GP were capable of detecting mean vertical erosion change on peaty soils. These first published estimates of whole gully erosion rates (0.077 m a À1 ) suggest that combined erosion rates on gully floors and walls are around three times the value of previous estimates, which largely characterize wind and rainsplash erosion of gully walls.
Peatlands are important reserves of terrestrial carbon and biodiversity, and given that many peatlands across the UK and Europe exist in a degraded state, their conservation is a major area of concern and a focus of considerable research. Aerial surveys are valuable tools for habitat mapping and conservation and provide useful insights into their condition. We investigate how SfM photogrammetry-derived topography and habitat classes may be used to construct an estimate of carbon loss from erosion features in a remote blanket bog habitat. An autonomous, unmanned, aerial, fixed-wing remote sensing platform (Quest UAV 300™) collected imagery over Moor House, in the Upper Teesdale National Nature Reserve, a site with a high degree of peatland erosion. The images were used to generate point clouds into orthomosaics and digital surface models using SfM photogrammetry techniques, georeferenced and subsequently used to classify vegetation and peatland features. A classification of peatbog feature types was developed using a random forest classification model trained on field survey data and applied to UAV-captured products including the orthomosaic, digital surface model and derived surfaces such as topographic index, slope and aspect maps. Using the area classified as eroded peat and the derived digital surface model, we estimated a loss of 438 tonnes of carbon from a single gully. The UAV system was relatively straightforward to deploy in such a remote and unimproved area. SfM photogrammetry, imagery and random forest modelling obtained classification accuracies of between 42% and 100%, and was able to discern between bare peat, saturated bog and sphagnum habitats. This paper shows what can be achieved with low-cost UAVs equipped with consumer grade camera equipment and relatively straightforward ground control, and demonstrates their potential for the carbon and peatland conservation research community.
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