Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region
Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
27846230
PubMed Central
PMC5112918
DOI
10.1371/journal.pone.0165699
PII: PONE-D-16-02227
Knihovny.cz E-zdroje
- MeSH
- průzkumy a dotazníky MeSH
- půda chemie MeSH
- teoretické modely * MeSH
- zeměpis MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika MeSH
- Názvy látek
- půda MeSH
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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Heung B, Bulmer CE, Schmidt MG. Predictive soil parent material mapping at a regional-scale: A Random Forest approach. Geoderma. 2014;214–215: 141–154. 10.1016/j.geoderma.2013.09.016 DOI
Lacoste M, Lemercier B, Walter C. Regional mapping of soil parent material by machine learning based on point data. Geomorphology. 2011;133: 90–99. 10.1016/j.geomorph.2011.06.026 DOI
Adhikari K, Minasny B, Greve MBMH, Greve MBMH. Constructing a soil class map of Denmark based on the FAO legend using digital techniques. Geoderma. The Authors; 2014;214–215: 101–113. 10.1016/j.geoderma.2013.09.023 DOI
Debella-Gilo M, Etzelmüller B. Spatial prediction of soil classes using digital terrain analysis and multinomial logistic regression modeling integrated in GIS: Examples from Vestfold County, Norway. CATENA. 2009;77: 8–18. 10.1016/j.catena.2008.12.001 DOI
Kempen B, Brus DJ, Heuvelink GBM, Stoorvogel JJ. Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach. Geoderma. 2009;151: 311–326. 10.1016/j.geoderma.2009.04.023 DOI
Arrouays D. GlobalSoilMap: Basis of the global spatial soil information system. Taylor and Francis; 2014.
Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, et al. SoilGrids1km—global soil information based on automated mapping. PLoS One. Public Library of Science; 2014;9: e105992 10.1371/journal.pone.0105992 PubMed DOI PMC
Sulaeman Y, Minasny B, McBratney AB, Sarwani M, Sutandi A. Harmonizing legacy soil data for digital soil mapping in Indonesia. Geoderma. 2013;192: 77–85. 10.1016/j.geoderma.2012.08.005 DOI
Kerry R, Goovaerts P, Rawlins BG, Marchant BP. Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma. 2012;170: 347–358. 10.1016/j.geoderma.2011.10.007 PubMed DOI PMC
Feder F. Soil map update: Procedure and problems encountered for the island of Réunion. CATENA. 2013;110: 215–224. 10.1016/j.catena.2013.06.019 DOI
Park S, McSweeney K, Lowery B. Identification of the spatial distribution of soils using a process-based terrain characterization. Geoderma. 2001;103: 249–272. 10.1016/S0016-7061(01)00042-8 DOI
Zádorová T, Penížek V, Šefrna L, Drábek O, Mihaljevič M, Volf Š, et al. Identification of Neolithic to Modern erosion–sedimentation phases using geochemical approach in a loess covered sub-catchment of South Moravia, Czech Republic. Geoderma. 2013;195–196: 56–69. 10.1016/j.geoderma.2012.11.012 DOI
Świtoniak M. Use of soil profile truncation to estimate influence of accelerated erosion on soil cover transformation in young morainic landscapes, North-Eastern Poland. CATENA. 2014;116: 173–184. 10.1016/j.catena.2013.12.015 DOI
Świtoniak M, Mroczek P, Bednarek R. Luvisols or Cambisols? Micromorphological study of soil truncation in young morainic landscapes—Case study: Brodnica and Chełmno Lake Districts (North Poland). CATENA. 2016;137: 583–595. 10.1016/j.catena.2014.09.005 DOI
Zádorová T, Penížek V, Šefrna L, Rohošková M, Borůvka L. Spatial delineation of organic carbon-rich Colluvial soils in Chernozem regions by Terrain analysis and fuzzy classification. CATENA. 2011;85: 22–33. 10.1016/j.catena.2010.11.006 DOI
Zádorová T, Penížek V, Vašát R, Žížala D, Chuman T, Vaněk A. Colluvial soils as a soil organic carbon pool in different soil regions. Geoderma. Elsevier; 2015;253–254: 122–134. 10.1016/j.geoderma.2015.04.012 DOI
Fridland V. Pattern of the Soil Cover. Akademy of Sciences of the USSR; 1972.
Sun X-L, Wu Y-J, Lou Y-L, Wang H-L, Zhang C, Zhao Y-G, et al. Updating digital soil maps with new data: a case study of soil organic matter in Jiangsu, China. Eur J Soil Sci. 2015;66: 1012–1022. 10.1111/ejss.12295 DOI
Kempen B, Brus DJ, Stoorvogel JJ, Heuvelink GBM, de Vries F. Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps. Soil Sci Soc Am J. The Soil Science Society of America, Inc.; 2012;76: 2097 10.2136/sssaj2011.0424 DOI
Jenny H. Factors of Soil Formation. McGraw-Hill, New York; 1941.
McBratney A, Mendonça Santos M, Minasny B. On digital soil mapping [Internet]. Geoderma. 2003. 10.1016/S0016-7061(03)00223-4 DOI
Florinsky I, Eilers R, Manning, Fuller L. Prediction of soil properties by digital terrain modelling. Environ Model Softw. 2002;17: 295–311. 10.1016/S1364-8152(01)00067-6 DOI
Cavazzi S, Corstanje R, Mayr T, Hannam J, Fealy R. Are fine resolution digital elevation models always the best choice in digital soil mapping? Geoderma. Elsevier B.V.; 2013;195–196: 111–121. 10.1016/j.geoderma.2012.11.020 DOI
Mulder VL, de Bruin S, Schaepman ME, Mayr TR. The use of remote sensing in soil and terrain mapping—A review. Geoderma. 2011;162: 1–19. 10.1016/j.geoderma.2010.12.018 DOI
Vaze J, Teng J, Spencer G. Impact of DEM accuracy and resolution on topographic indices. Environ Model Softw. 2010;25: 1086–1098. 10.1016/j.envsoft.2010.03.014 DOI
Wilson J. P, Repetto P. L and S RD. Effect of Data Source, Grid Resolution, and Flow-Routing Method on Computed Topographic Attributes In: Wilson J. P. and G JC, editor. Terrain Analysis: Principles and Applications. Wiley; 2000. pp. 133–161.
Mashimbye ZE, de Clercq WP, Van Niekerk A. An evaluation of digital elevation models (DEMs) for delineating land components. Geoderma. 2014;213: 312–319. 10.1016/j.geoderma.2013.08.023 DOI
Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg RD, Mukhopadhyay A. Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). Int J Appl Earth Obs Geoinf. 2013;21: 205–217. 10.1016/j.jag.2012.09.004 DOI
Gallant JC, Hutchinson MF. Scale dependence in terrain analysis. Math Comput Simul. 1997;43: 313–321. 10.1016/S0378-4754(97)00015-3 DOI
Smith MP, Zhu A-X, Burt JE, Stiles C. The effects of DEM resolution and neighborhood size on digital soil survey. Geoderma. 2006;137: 58–69. 10.1016/j.geoderma.2006.07.002 DOI
Thompson JA, Bell JC, Butler CA. Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma. 2001;100: 67–89. 10.1016/S0016-7061(00)00081-1 DOI
Wu S, Li J, Huang GH. Modeling the effects of elevation data resolution on the performance of topography-based watershed runoff simulation. Environ Model Softw. 2007;22: 1250–1260. 10.1016/j.envsoft.2006.08.001 DOI
Sørensen R, Seibert J. Effects of DEM resolution on the calculation of topographical indices: TWI and its components. J Hydrol. 2007;347: 79–89. 10.1016/j.jhydrol.2007.09.001 DOI
Ariza-Villaverde AB, Jiménez-Hornero FJ, Gutiérrez de Ravé E. Influence of DEM resolution on drainage network extraction: A multifractal analysis. Geomorphology. Elsevier B.V.; 2015;241: 243–254. 10.1016/j.geomorph.2015.03.040 DOI
Wu S, Li J, Huang GH. A study on DEM-derived primary topographic attributes for hydrologic applications: Sensitivity to elevation data resolution. Appl Geogr. 2008;28: 210–223. 10.1016/j.apgeog.2008.02.006 DOI
Shi X, Girod L, Long R, DeKett R, Philippe J, Burke T. A comparison of LiDAR-based DEMs and USGS-sourced DEMs in terrain analysis for knowledge-based digital soil mapping. Geoderma. 2012;170: 217–226. 10.1016/j.geoderma.2011.11.020 DOI
Maynard JJ, Johnson MG. Scale-dependency of LiDAR derived terrain attributes in quantitative soil-landscape modeling: Effects of grid resolution vs. neighborhood extent. Geoderma. Elsevier B.V.; 2014;230–231: 29–40. 10.1016/j.geoderma.2014.03.021 DOI
Taylor JA, Jacob F, Galleguillos M, Prévot L, Guix N, Lagacherie P. The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping). Geoderma. 2013;193–194: 83–93. 10.1016/j.geoderma.2012.09.009 DOI
Chaplot V, Walter C, Curmi P. Improving soil hydromorphy prediction according to DEM resolution and available pedological data. Geoderma. 2000;97: 405–422. 10.1016/S0016-7061(00)00048-3 DOI
Chaplot V. Impact of DEM mesh size and soil map scale on SWAT runoff, sediment, and NO3–N loads predictions. J Hydrol. 2005;312: 207–222. 10.1016/j.jhydrol.2005.02.017 DOI
Napieralski J, Nalepa N. The application of control charts to determine the effect of grid cell size on landform morphometry. Comput Geosci. 2010;36: 222–230. 10.1016/j.cageo.2009.06.003 DOI
Guth J. Komplexní průzkum zemědělských půd-Průvodní zpráva k výsledků průzkumu v hospodářském obvodu ČSSS Vysoká, hospodářství Vidim. 1964.
Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, et al. System for Automated Geoscientific Analyses (SAGA) v. 2.1.4. Geosci Model Dev. Copernicus GmbH; 2015;8: 1991–2007. 10.5194/gmd-8-1991-2015 DOI
Hengl T. Finding the right pixel size. Comput Geosci. 2006;32: 1283–1298. 10.1016/j.cageo.2005.11.008 DOI
StatSoftInc. Statistica 12. 2015.
Lemercier B, Lacoste M, Loum M, Walter C. Extrapolation at regional scale of local soil knowledge using boosted classification trees: A two-step approach. Geoderma. 2012;171–172: 75–84. 10.1016/j.geoderma.2011.03.010 DOI
Heung B, Ho HC, Zhang J, Knudby A, Bulmer CE, Schmidt MG. An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma. Elsevier B.V.; 2016;265: 62–77. 10.1016/j.geoderma.2015.11.014 DOI
Wu W, Fan Y, Wang Z, Liu H. Assessing effects of digital elevation model resolutions on soil–landscape correlations in a hilly area. Agric Ecosyst Environ. 2008;126: 209–216. 10.1016/j.agee.2008.01.026 DOI
Lassueur T, Joost S, Randin CF. Very high resolution digital elevation models: Do they improve models of plant species distribution? Ecol Modell. 2006;198: 139–153. 10.1016/j.ecolmodel.2006.04.004 DOI
Tarolli P, Dalla Fontana G. Hillslope-to-valley transition morphology: New opportunities from high resolution DTMs. Geomorphology. 2009;113: 47–56. 10.1016/j.geomorph.2009.02.006 DOI
Vianello A, Cavalli M, Tarolli P. LiDAR-derived slopes for headwater channel network analysis. CATENA. 2009;76: 97–106. 10.1016/j.catena.2008.09.012 DOI
Gillin CP, Bailey SW, McGuire KJ, Prisley SP. Evaluation of Lidar-derived DEMs through Terrain Analysis and Field Comparison. Photogramm Eng Remote Sens. American Society for Photogrammetry and Remote Sensing; 2015;81: 387–396. 10.14358/PERS.81.5.387 DOI
Laamrani A, Valeria O, Bergeron Y, Fenton N, Cheng LZ, Anyomi K. Effects of topography and thickness of organic layer on productivity of black spruce boreal forests of the Canadian Clay Belt region. For Ecol Manage. 2014;330: 144–157. 10.1016/j.foreco.2014.07.013 DOI
Greve MH, Kheir RB, Greve MB, Bøcher PK. Using Digital Elevation Models as an Environmental Predictor for Soil Clay Contents. Soil Sci Soc Am J. The Soil Science Society of America, Inc.; 2012;76: 2116 10.2136/sssaj2010.0354 DOI