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Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Besides, the characteristics of the river crossing bridges may deteriorate due to flood induced scouring around bridge piles or due to flood loads. Thus, in this study high-resolution topography of the study region was acquired during two consecutive years by unmanned aerial vehicle (UAV) based surveys using Structure-from-Motion (SfM) processing. Following the extraction of digital elevation models (DEM), repeat data that were obtained at each UAV survey were compared using Geomorphic Change Detection (GCD) to calculate volumes of deposition and erosion via DEM of difference (DoD) algorithm. Thus, detailed high-resolution maps of the river channels can be rapidly and efficiently generated by low cost UAV based measurement methods in order for continuous tracking of stream channel morphology for the rivers sensitive to floods.

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Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Besides, the characteristics of the river crossing bridges may deteriorate due to flood induced scouring around bridge piles or due to flood loads. Thus, in this study high-resolution topography of the study region was acquired during two consecutive years by unmanned aerial vehicle (UAV) based surveys using Structure-from-Motion (SfM) processing. Following the extraction of digital elevation models (DEM), repeat data that were obtained at each UAV survey were compared using Geomorphic Change Detection (GCD) to calculate volumes of deposition and erosion via DEM of difference (DoD) algorithm. Thus, detailed high-resolution maps of the river channels can be rapidly and efficiently generated by low cost UAV based measurement methods in order for continuous tracking of stream channel morphology for the rivers sensitive to floods. [ABSTRACT FROM AUTHOR]

Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns in three consecutive summers (2015-2017) at a sub-Arctic mining site where production was temporarily suspended. The aim was to monitor a 0.5 km2 tailings impoundment and measure potential subsidence of tailings. SfM photogrammetry was used to produce yearly topographical models of the tailings surface, which allowed the amount of surface displacement between years to be tracked. Ground checkpoints surveyed in stable areas of the impoundment were utilized in assessing the vertical accuracy of the models. Observed surface displacements were linked to a combination of erosion, tailings settlement, and possible compaction of the peat layer underlying the tailings. The accuracy obtained indicated that UAV-assisted monitoring of tailings impoundments is sufficiently accurate for supporting impoundment management operations and for tracking surface displacements in the decimeter range. [ABSTRACT FROM AUTHOR]

Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns in three consecutive summers (2015–2017) at a sub-Arctic mining site where production was temporarily suspended. The aim was to monitor a 0.5 km2 tailings impoundment and measure potential subsidence of tailings. SfM photogrammetry was used to produce yearly topographical models of the tailings surface, which allowed the amount of surface displacement between years to be tracked. Ground checkpoints surveyed in stable areas of the impoundment were utilized in assessing the vertical accuracy of the models. Observed surface displacements were linked to a combination of erosion, tailings settlement, and possible compaction of the peat layer underlying the tailings. The accuracy obtained indicated that UAV-assisted monitoring of tailings impoundments is sufficiently accurate for supporting impoundment management operations and for tracking surface displacements in the decimeter range.

• UAV-SfM soil loss measurements were validated by direct sediment collection. • Photogrammetric precision estimates defined spatially variable levels of detection. • DEMs of difference illustrated the diffuse erosion patterns through time. • Accurate 3-D soil surface models were constructed from UAV-SfM data. Sheet erosion is common on agricultural lands, and understanding the dynamics of the erosive process as well as the quantification of soil loss is important for both soil scientists and managers. However, measuring rates of soil loss from sheet erosion has proved difficult due to requiring the detection of relatively small surface changes over extended areas. Consequently, such measurements have relied on the use of erosion plots, which have limited spatial coverage and have high operating costs. For measuring the larger erosion rates characteristic of rill and gully erosion, structure-from-motion (SfM) photogrammetry has been demonstrated to be a valuable tool. Here, we demonstrate the first direct validation of UAV-SfM measurements of sheet erosion using sediment collection data collected from erosion plots. Three erosion plots (12 m × 4 m) located at Lavras, Brazil, with bare soil exposed to natural rainfall from which event sediment and runoff was monitored, were mapped during two hydrological years (2016 and 2017), using a UAV equipped with a RGB camera. DEMs of difference (DoD) were calculated to detect spatial changes in the soil surface topography over time and to quantify the volumes of sediments lost or gained. Precision maps were generated to enable precision estimates for both DEMs to be propagated into the DoD as spatially variable vertical uncertainties. The point clouds generated from SfM gave mean errors of ~2.4 mm horizontally (xy) and ~1.9 mm vertically (z) on control and independent check points, and the level of detection (LoD) along the plots ranged from 1.4 mm to 7.4 mm. The soil loss values obtained by SfM were significantly (p < 0.001) correlated (r2 = 95.55%) with those derived from the sediment collection. These results open up the possibility to use SfM for erosion studies where channelized erosion is not the principal mechanism, offering a cost-effective method for gaining new insights into sheet, and interrill, erosion processes. [ABSTRACT FROM AUTHOR]