A comparison of LiDAR-based DEMs and USGS-sourced DEMs in terrain analysis for knowledge-based digital soil mapping

Shi, Xun; Girod, L.; Long, Robert; DeKett, Roger; Philippe, Jessica; Burke, Tom

doi:10.1016/j.geoderma.2011.11.020

Cited by 30 publications

(12 citation statements)

References 42 publications

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“…It is important to note, however, that our different resolution DEMs (i.e.,5,10,15,20,25,30,35,40,45 and 50 m) were derived from the same LiDAR data and thus have the same vertical accuracy. It has been well established that LiDAR provides significantly higher elevation accuracy relative to traditional DEMs (e.g., USGS-sourced DEMs) (Shi et al, 2012;Vaze et al, 2010), however, the effects of DEM vertical accuracy on soil-landscape modeling have been shown to be less pronounced with coarser resolution DEM derived terrain attributes (Thompson et al, 2001). As a general rule, Thompson et al (2001) postulate that to properly characterize local topography the vertical precision must increase as the horizontal resolution increases such that the average change in elevation between grid points is greater than that of their vertical precision.…”

Section: Scale Dependency Of Soil-topography Relationshipsmentioning

confidence: 99%

Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent

Maynard

Johnson

2014

Geoderma

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Quantifying the spatial distribution of soil properties is essential for ecological and environmental modeling at the landscape scale. Terrain attributes are among the primary covariates in soil-landscape models due to their control on energy and mass fluxes, which in turn control the spatial distribution of soil properties and processes. While numerous studies have demonstrated the importance of terrain attributes for predicting landscape-scale soil variability, considerable uncertainty exists as to the scale-dependency of light detection and ranging (LiDAR) derived terrain attributes on the accuracy of soil-landscape model predictions. Thirty five pedons were sampled by genetic horizon in a 2300 ha forested watershed and three soil properties (clay, sum of bases, and total carbon), representing dominant pedogenic processes within the watershed were analyzed. Soil properties were used as dependent variables and terrain attributes, calculated from LiDAR derived DEMs of various grid resolutions (ranging from 5 to 50 m) and neighborhood extents (ranging from 15 to 350 m), were used as predictor variables in ordinary least-squares (OLS) regression models. Results from this study show that model predictions exhibit a strong scale-dependency, with percent clay, sum of bases, and total carbon having the highest R 2 -adj and lowest root mean square error (RMSE) at coarse neighborhood extents (i.e., 150 to 300 m) both between soil variables and across soil depths. Furthermore, in certain instances grid resolution was also shown to affect soil-terrain correlations, although to a lesser degree than neighborhood extent. In many cases fine to moderate scale grid resolutions (i.e.,b 30 m) more accurately represented terrain features, resulting in higher correlations to soil properties at fixed neighborhood extents relative to course grid resolutions. Additionally, these results show that fine scale topographic information (i.e., 1 to 5 m) does not necessarily provide a stronger predictor of soil spatial variability relative to moderate scale information. This study provides a robust framework for investigating pedogeomorphological processes on a landscape scale through examination of the scale dependency of modeled terrain attributes in quantitative soil-landscape modeling.Published by Elsevier B.V.

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Section: Scale Dependency Of Soil-topography Relationshipsmentioning

confidence: 99%

Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent

Maynard

Johnson

2014

Geoderma

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“…On the other hand, an increase of DEM grid size shifts the topographic index towards higher values due to greater upslope contributing area and smaller slope [32] [33] [34] [35]. The general expectation that DEMs with highest resolution deliver the best results is not always valid [30] [36] [37]. Some studies showed that the prediction accuracy increases with increasing raster resolution at a pixel range of hundreds to tens of meters [38] [39] [40], but this trend is often not significant when the pixel size ranges from a few meters to 1 meter or even sub-meter resolution.…”

Section: Introductionmentioning

confidence: 99%

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

et al. 2016

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The development of a soil cover is a dynamic process. Soil cover can be altered within a few decades, which requires updating of the legacy soil maps. Soil erosion is one of the most important processes quickly altering soil cover on agriculture land. Colluvial soils develop in concave parts of the landscape as a consequence of sedimentation of eroded material. Colluvial soils are recognised as important soil units because they are a vast sink of soil organic carbon. Terrain derivatives became an important tool in digital soil mapping and are among the most popular auxiliary data used for quantitative spatial prediction. Prediction success rates are often directly dependent on raster resolution. In our study, we tested how raster resolution (1, 2, 3, 5, 10, 20 and 30 meters) influences spatial prediction of colluvial soils. Terrain derivatives (altitude, slope, plane curvature, topographic position index, LS factor and convergence index) were calculated for the given raster resolutions. Four models were applied (boosted tree, neural network, random forest and Classification/Regression Tree) to spatially predict the soil cover over a 77 ha large study plot. Models training and validation was based on 111 soil profiles surveyed on a regular sampling grid. Moreover, the predicted real extent and shape of the colluvial soil area was examined. In general, no clear trend in the accuracy prediction was found without the given raster resolution range. Higher maximum prediction accuracy for colluvial soil, compared to prediction accuracy of total soil cover of the study plot, can be explained by the choice of terrain derivatives that were best for Colluvial soils differentiation from other soil units. Regarding the character of the predicted Colluvial soils area, maps of 2 to 10 m resolution provided reasonable delineation of the colluvial soil as part of the cover over the study area.

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“…However, their posting limits geomorphological mapping to landforms of the uppermost microscale 1 and larger. Geomorphological studies on a regional scale are still often based on DEMs derived from contour lines that have been digitized from topographic maps (e.g., Ruszkiczay-Rüdiger et al, 2009;Shi et al, 2012;Le Bris and Paul, 2013;Thomas et al, 2014). Although the posting of these data may indicate adequate resolution, a microscaleoriented morphological analysis based on these DEMs may produce questionable results because the topographic variability in areas between contour lines is inevitably underestimated and the contour lines themselves are generalized (Weibel, 1990;Reuter et al, 2008;Gallay, 2009;Florinsky, 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments — A case study from SE Tibet, China

2015

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A comparison of LiDAR-based DEMs and USGS-sourced DEMs in terrain analysis for knowledge-based digital soil mapping

Cited by 30 publications

References 42 publications

Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent

Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent

Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region

Evaluation of TanDEM-X elevation data for geomorphological mapping and interpretation in high mountain environments — A case study from SE Tibet, China

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