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

• Published in: PloS one Vol. 11; no. 11; p. e0165699
• Main Authors: Penizek, Vit, Zadorova, Tereza, Kodesova, Radka, Vanek, Ales
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.11.2016; Public Library of Science (PLoS)
• Subjects: Agricultural land; Agriculture; Biology and Life Sciences; Colluvial soils; Curvature; Czech Republic; Derivatives; Derivatives (Financial instruments); Digital mapping; Earth Sciences; Ecology and Environmental Sciences; Erosion; Evaluation; Food; Geography; Influence; Life sciences; Mapping; Mathematical models; Measuring instruments; Models, Theoretical; Natural resources; Neural networks; Organic carbon; Organic soils; Physical Sciences; Predictions; Raster; Real estate development; Regression analysis; Research and Analysis Methods; Sedimentation; Soil - chemistry; Soil carbon; Soil dynamics; Soil erosion; Soil mapping; Soil maps; Soil profiles; Soil properties; Soil sciences; Soils; Spatial data; Studies; Surveys and Questionnaires; Terrain; Topography; Czech Republic
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0165699

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.