Evaluation of Terrestrial Laser Scanner and Structure from Motion photogrammetry techniques for quantifying soil surface roughness parameters over agricultural soils

Martinez‐Agirre, Alex; Álvarez‐Mozos, Jesús; Milenković, Milutin; Pfeifer, Norbert; Giménez, Rafael; Valle, José Manuel; Rodrı́guez, Ana Valdeolmillos

doi:10.1002/esp.4758

Cited by 18 publications

(12 citation statements)

References 63 publications

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“…The root mean square (RMS) of height deviations of the snow depth is calculated from the three dimensional plane fitted to the UAV-derived snow depth using the methodology detailed in Martinez-Agirre et al (2020). An additional measure of the two-dimensional local variability (n−) is provided through the calculation of variograms constructed using the Scikit GStat library Variography (Mälicke and Schneider, 2021) using 15,000 randomly sampled points from the UAV-derived acquisitions for the west and east portions of the lake.…”

Section: Spatial Statisticsmentioning

confidence: 99%

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Gunn

Jones

Rangel

2021

Front. Remote Sens.

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The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.

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Section: Spatial Statisticsmentioning

confidence: 99%

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Gunn

Jones

Rangel

2021

Front. Remote Sens.

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“…While we reviewed the uncertainties of certain input data (e.g., DEM, SOC, and soil texture), measures for other complex input‐data (e.g., BD, erosional resistance, and hydraulic surface roughness) are still missing. The gap could be filled by utilizing geostatistical or machine learning approaches (e.g., Emadi et al, 2020; McBratney et al, 2003), or new measuring techniques using remote‐sensing data (Martinez‐Agirre et al, 2020). At the same time, data and scripts of such studies should be freely accessible in spirit of reproducibility, to facilitate exchange and progress in modelling (Kuhwald et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Are compacted tramlines underestimated features in soil erosion modeling? A catchment‐scale analysis using a process‐based soil erosion model

et al. 2021

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Process‐based model ling has long been used as a well‐suited tool to simulate soil erosion. Despite a large number of scientific articles that emphasize the role of compacted tramlines for soil erosion and surficial nutrient transport, no study exists that integrates these artificial structures into process‐based modelling at catchment scales. Thus, this study aims to quantify the effects of tramline compaction on soil erosion dynamics within a catchment. For this reason, high‐resolution spatial data (1 × 1 m) have been incorporated into the model EROSION 3D, which was used, calibrated, and tested for a soil erosion event with measured discharge and soil loss data. To quantify the contribution of tramlines to soil erosion, two different model parameterizations have been used: the tramline parameterization (TLP) and the common model parameterization (CP) that disregards the tramlines. The results reveal that: (i) the parameterization of tramlines improved model outcomes; (ii) tramlines significantly contribute to overall soil loss and sediment entrance into the channel network; (iii) the bulk density of tramlines is the most important driver of increases in soil erosion and runoff. Moreover, our investigation suggests that the impact of compacted tramlines have long been underestimated in soil erosion modelling and can assist in the assessment of surficial flow path connectivity. Thus, the integration of tramlines into soil erosion modeling is important for the implementation of adequate soil conservation and water protection measures.

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“…Nowadays, a rapid and low-cost solution is the structure-from-motion (SfM) photogrammetry technique paired with multi-stereo view (MSV) algorithms (hereafter SfM). This has been used in different studies in agriculture: to analyse microtopography on surfaces managed with conservation agricultural management (Tarolli et al, 2019); to monitor the bank erosion in drainage network (Prosdocimi et al, 2015); to measure the surface roughness of cultivated terrain surfaces (Martinez-Agirre et al, 2020;Snapir et al, 2014); and to estimate soil loss by erosion (Vinci et al, 2017). SfM may be used through different acquisition platforms that permit analysis at different spatial levels, from plot to field scale (Dong et al, 2017;Jay et al, 2015;Nguyen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Mapping potential surface ponding in agriculture using UAV‐SfM

Straffelini

Cucchiaro

Tarolli

2021

Earth Surf Processes Landf

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Among the environmental problems that could affect agriculture, one of the most critical is ponding. This may be defined as water storage on the surface in concavities and depressions due to soil saturation. Stagnant water can seriously affect crops and the management of agricultural landscapes. It is mainly caused by prolonged rainfall events, soil type, or wrong mechanization practices, which cause soil compaction. To better understand this problem and thus provide adequate solutions to reduce the related risk, high-resolution topographic information could be strategically important because it offers an accurate representation of the surface morphology. In the last decades, new remote sensing techniques provide interesting opportunities to understand the processes on the Earth's surface based on geomorphic signatures. Among these, Uncrewed Aerial Vehicles (UAVs), combined with the structure-from-motion (SfM) photogrammetry technique, represent a solid, low-cost, rapid, and flexible solution for geomorphological analysis. This study aims to present a new approach to detect the potential areas exposed to water stagnation at the farm scale. The high-resolution digital elevation model (DEM) from UAV-SfM data is used to do this. The potential water depth was calculated in the DEM using the relative elevation attribute algorithm. The detection of more pronounced concavities and convexities allowed an estimation and mapping of the potential ponding conditions. The results were assessed by observations and field measurements and are promising, showing a Cohen's k(X) accuracy of 0.683 for the planimetric extent of the ponding phenomena and a Pearson's r xy coefficient of 0.971 for the estimation of pond water depth. The proposed workflow provides a useful indication to stakeholders for better agricultural management in lowland landscapes.

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Evaluation of Terrestrial Laser Scanner and Structure from Motion photogrammetry techniques for quantifying soil surface roughness parameters over agricultural soils

Cited by 18 publications

References 63 publications

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Are compacted tramlines underestimated features in soil erosion modeling? A catchment‐scale analysis using a process‐based soil erosion model

Mapping potential surface ponding in agriculture using UAV‐SfM

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