A smartphone camera for the structure from motion reconstruction for measuring soil surface variations and soil loss due to erosion

Vinci, Alessandra; Todisco, Francesca; Brigante, Raffaella; Mannocchi, Francesco; Radicioni, Fabio

doi:10.2166/nh.2017.075

Cited by 36 publications

(30 citation statements)

References 40 publications

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“…In the present investigation, the circumstance that this margin of error was known and relatively low assured the reliability of the direct measurement and, as consequence, the correct evaluation of the soil loss measurement errors by TS, UAV, and UAV + TS. The low measurement errors (ranging from −6% to 6% for P 3 and from −8% to 13% for P 2 ) were close to those (ranging from −6.4% to 16.8%) obtained by Di Stefano, Ferro, Palmeri, Pampalone, and Agnello () and higher than that (−2%) obtained by Vinci et al (). Di Stefano, Ferro, Palmeri, Pampalone, and Agnello () measured artificial rills using a single terrestrial survey for applying SfM photogrammetry, whereas Vinci et al () measured interrill soil loss by difference of DEMs (DoDs) resulting from two terrestrial surveys conducted before and after a low‐intensity erosive event.…”

Section: Discussionsupporting

confidence: 86%

“…For evaluating the accuracy of the photogrammetric project, RMSE on independent check points (CPs) has to be calculated. This RMSE value is also useful for establishing the minimum level of detection when geomorphic change is determined by difference of models (3D point clouds, DEMs; Prosdocimi, Calligaro, Sofia, Dalla Fontana, & Tarolli, 2015;Balaguer-Puig, Marqués-Mateu, Lerma, & Ibáñez-Asensio, 2017;Hänsel, Schindewolf, Eltner, Kaiser, & Schmidt, 2016;Vinci, Todisco, Brigante, Mannocchi, & Radicioni, 2017).…”

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confidence: 99%

“…The authors demonstrated that the measurements by 3D models were affected by low errors. Vinci et al (2017) measured plot soil surface variation and soil loss due to erosion using both a smartphone camera for the SfM terrestrial photogrammetry and TLS. Considering the weight of the sediment stored in the tank downstream of the plot as reference, the authors found that TLS underestimated soil loss whereas SfM detected soil loss correctly.…”

mentioning

confidence: 99%

“…The low measurement errors (ranging from −6% to 6% for P 3 and from −8% to 13% for P 2 ) were close to those (ranging from −6.4% to 16.8%) obtained by DiStefano, Ferro, Palmeri, Pampalone, and Agnello (2017) and higher than that (−2%) obtained byVinci et al (2017) Di Stefano, Ferro, Palmeri, Pampalone, and Agnello (2017). measured artificial rills using a single terrestrial survey for applying SfM photogrammetry, whereasVinci et al (2017) measured interrill soil loss by difference of DEMs (DoDs) resulting from two terrestrial surveys conducted before and after a low-intensity erosive event.…”

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confidence: 99%

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An automatic approach for rill network extraction to measure rill erosion by terrestrial and low‐cost unmanned aerial vehicle photogrammetry

Stefano

Palmeri

Pampalone

2019

Hydrological Processes

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For an erosion event (October 2016) occurred at the Sparacia experimental area (Southern Italy), both terrestrial and low‐altitude aerial surveys were carried out by consumer grade camera and quadcopter (low‐cost unmanned aerial vehicle [UAV]) to measure rill erosion on two plots with steepness of 22% and 26%. Applying the structure from motion (SfM) technique, the three‐dimensional digital terrain models (3D‐DTMs) and the quasi three‐dimensional models (2.5D‐digital elevation model [DEM]) were obtained by the two surveys. Furthermore, 3D‐DTM and DEM were built using the available aerial photographs (166) and adding 40 terrestrial photographs. For the first time, the convergence index was applied to high‐resolution rill data for extracting the rill network, and a subsequent separation into contributing and non‐contributing rills was carried out. The comparison among the three surveys (terrestrial, UAV, and UAV + terrestrial) was developed using two morphometric parameters of the rill network (drainage density and drainage frequency). Moreover, using as reference the weight of sediment stored on the tanks located downstream of the plots, the reliability of soil loss measurement by 3D models was tested. For both contributing and non‐contributing rills, the morphometric parameters were higher for the terrestrial than for UAV and UAV + terrestrial surveys. For both plots, SfM always provided reliable soil loss measurements, which were affected by errors ranging from −8% to 13%. Although the applied technique used a low‐cost UAV and a consumer grade camera, the obtained results demonstrated that a reliable estimate of rill erosion can be obtained in an area of interest.

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Section: Discussionsupporting

confidence: 86%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An automatic approach for rill network extraction to measure rill erosion by terrestrial and low‐cost unmanned aerial vehicle photogrammetry

Stefano

Palmeri

Pampalone

2019

Hydrological Processes

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“…Guo et al () report very similar results from photogrammetric observations and runoff and sediment collections, although they use bar scales instead of GCPs to define the CRS; they do not assess DEM errors and do not apply any technique for discriminating actual changes in DoDs either. Vinci et al () also conclude that SfM estimates the measured soil loss correctly, but they apply a soil‐dependent procedure to estimate runoff and soil loss that needs a previous calibration phase and shows great differences between SfM and terrestrial laser scanning soil loss estimations.…”

Section: Discussionmentioning

confidence: 99%

Quantifying small‐magnitude soil erosion: Geomorphic change detection at plot scale

et al. 2017

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Soil erosion is a big concern in bare soils from burnt areas and agricultural lands that lack a vegetation cover. In those unprotected soils, intense rain episodes, typical in Mediterranean climate, cause severe soil erosion processes that have been well studied previously using a number of procedures, such as the geomorphic change detection (GCD) method. This method uses digital elevation models (DEMs) of the soil surface and determines the morphological changes in terms of both erosion and deposition by DEMs of difference (DoDs). However, some types of soil erosion, such as diffuse and sheet erosion, may have a small magnitude, at a millimetre scale, and their determination requires methods adapted to that scale. In this paper, we analyse the suitability of the GCD method to account for small magnitude soil erosion. We present a laboratory procedure and setup to represent and quantify sediment budget in small experimental soil plots through differences of DEMs obtained from images using photogrammetric structure from motion. This study explores several key aspects of the technique: establishment of a common reference system for DEMs; determination of errors in the generation of DEMs; selection of appropriate criteria to obtain reliable changes in DoDs; error propagation using Monte Carlo simulation; and validation of the procedure by comparing the results with actual sediments collected during the experiment. Results showed an overestimation of 13% in accumulated soil loss and confirmed that GCD approach with structure from motion‐based DoDs is a suitable method to quantify small‐magnitude erosion events.

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SfM‐MVS Photogrammetry for Splash Erosion Monitoring under Natural Rainfall

Laburda

Krása

Zumr

et al. 2021

Earth Surf Processes Landf

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An understanding of splash erosion is the basis to describe the impact of rain characteristics on soil disturbance. In typical splash cup experiments, splashed soil is collected, filtered, and weighed. As a way to collect additional data, our experiments have been supplemented by a photogrammetric approach. A total of three soils were tested across three sites, one in the Czech Republic and two in Austria, all equipped with rain gauges and disdrometers to measure rainfall parameters. The structure from motion multiview stereo (SfM-MVS) photogrammetric method was used to measure the raindrops impact on the soil surface. The images were processed using Agisoft PhotoScan, resulting in orthophotos and digital elevation models (DEMs) with a resolution of 0.1 mm/pix. The surface statistics included the mean surface height (whose standard deviation was used as a measure of surface roughness), slope, and other parameters. These parameters were evaluated depending on soil

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A smartphone camera for the structure from motion reconstruction for measuring soil surface variations and soil loss due to erosion

Cited by 36 publications

References 40 publications

An automatic approach for rill network extraction to measure rill erosion by terrestrial and low‐cost unmanned aerial vehicle photogrammetry

An automatic approach for rill network extraction to measure rill erosion by terrestrial and low‐cost unmanned aerial vehicle photogrammetry

Quantifying small‐magnitude soil erosion: Geomorphic change detection at plot scale

SfM‐MVS Photogrammetry for Splash Erosion Monitoring under Natural Rainfall

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