Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus

Catena

Volumn 156, Issue , 2017, Pages 161-175

  Sindayihebura, Anicet ^a,b   Ottoy, Sam ^b   Dondeyne, Stefaan ^b   Van Meirvenne, Marc ^c   Van Orshoven, Jos ^b  

^a Burundi University   (Burundi)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c GHENT UNIVERSITY   (Belgium)

Author keywords

Auto correlation; Choropleth map; Digital elevation model; Prediction; Residual kriging; SCORPAN

Indexed keywords

AUTOCORRELATION; CLAY SOIL; DIGITAL ELEVATION MODEL; DIGITAL MAPPING; KRIGING; LAND MANAGEMENT; LAND USE PLANNING; LANDSAT; NDVI; ORGANIC CARBON; PREDICTION; SATELLITE IMAGERY; SOIL CARBON; TOPSOIL;

BURUNDI;

EID: 85017474987     PISSN: 03418162     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.catena.2017.04.003     Document Type: Article

References (63)

1. Aertsen, W., Kint, V., Von Wilpert, K., Zirlewagen, D., Muys, B., Van Orshoven, J., Comparison of location-based, attribute-based and hybrid regionalization techniques for mapping forest site productivity. Forestry 85:4 (2012), 1–12.
2. Anselin, L., Syabri, I., Kho, Y., GeoDa: an introduction to spatial data analysis. Geogr. Anal. 38 (2006), 5–22.
3. Baize, D., Guide des analyses en pédologie. 2000, Quae.
4. Bellocchi, G., Rivington, M., Donatelli, M., Matthews, K., Validation of biophysical models: issues and methodologies. A review. Agron. Sustain. Dev. 30 (2010), 109–130.
5. Boucneau, G., Van Meirvenne, M., Thas, O., Hofman, G., Integrating properties of soil map delineations into ordinary kriging. Eur. J. Soil Sci. 49 (1998), 213–229.
6. de Brogniez, D., Quantification of Topsoil Organic Carbon in Europe: A Statistical Approach Combining Harmonized Soil and Land Cover Point Data with Spatial Layers. (PhD thesis), 2015, Université Catholique de Louvain, Belgium http://hdl.handle.net/2078.1/158816.
7. Brunsdon, C., Fotheringham, A.S., Charlton, M.E., Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28:4 (1996), 1–18.
8. Carré, F., McBratney, A.B., Minasny, B., Estimation and potential improvement of the quality of legacy soil samples for digital soil mapping. Geoderma 141 (2007), 1–14.
9. Dai, F., Zhou, Q., Lv, Z., Wang, X., Liu, G., Spatial prediction of organic matter content integrating artificial neural networks and ordinary kriging in Tibetan Plateau. Ecol. Indic. 45 (2014), 184–194.
10. Dent, D., Young, A., Soil Survey and Land Evaluation. 1993, 1981, E & FN SPON (reprint).
11. Elith, J., Leathwick, J.R., Hastie, T., A working guide to boosted regression trees. J. Anim. Ecol. 77:4 (2008), 802–813.
12. Engeman, R.M., Sugihara, R.T., Pank, L.F., Dusenberry, W.E., A comparison of plotless density estimators using Monte Carlo simulation. Ecology 75:6 (1994), 1769–1779.
13. FAO and ISRIC, Towards Global Soil Information: Activities Within the Geo Task Global Soil Data. Workshop Report, 20–23 March 2012. 2012, FAO Headquarters, Rome, Italy http://eusoils.jrc.ec.europa.eu/projects/esoter/Documents/GSP_eSOTER.pdf.
14. Fernandez-Alonso, M., Cutten, H., De Waele, B., Tack, L., Tahon, A., Baudet, D., Barritt, S.D., The Mesoproterozoic Karagwe-Ankole Belt (formerly the NE Kibara Belt): the result of prolonged extensional intracratonic basin development punctuated by two short-lived far-field compressional events. Precambrian Res. 216–2019 (2012), 63–86.
15. Florinsky, I.V., Eilers, R.G., Manning, G.R., Fuller, L.G., Prediction of soil properties by digital terrain modeling. Environ. Model. Softw. 17 (2002), 295–311.
16. Fotheringham, A.S., Brunsdon, C., Charlton, M., Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. 2002, Wiley.
17. Gee, G.W., Bauder, J.W., Methods of Soil Analyses, Part 1. Physical and Mineralogical Methods–Agronomy Monograph No. 9. second ed., 1986.
18. Gollini, I., Lu, B., Charlton, M., Brunsdon, C., Harris, P., GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. J. Stat. Softw. 63:17 (2015), 1–5.
19. Goovaerts, P., Geostatistics for Natural Resources Evaluation. 1997, Oxford University Press.
20. Günther, F., Fritsch, S., Neuralnet: training of neural networks. R Journal 2:1 (2010), 30–38.
21. Hengl, T., Heuvelink, B.M., Stein, A., A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120 (2004), 75–93.
22. Heremans, S., Dong, Q., Zhang, B., Bydekerke, L., Van Orshoven, J., The potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed NDVI and additional data sources. J. Appl. Remote. Sens., 9(1), 2015, 10.1117/1.JRS.9.097095.
23. Heuvelink, G.B.M., Webster, R., Modeling soil variation: past, present, and future. Geoderma 100 (2001), 269–301.
24. Hiemstra, P.H., Pebesma, E.J., Twenhöfel, C.J.W., Heuvelink, G.B.M., Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput. Geosci. 35 (2009), 1711–1721.
25. Hijmans, R.J., Philips, S., Leathwick, J., Elith, J., Dismo: Species Distribution Modeling. (R package version 1.0–12) http://CRAN.R-project.org/package=dismo, 2015.
26. Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw. 2 (1989), 359–366.
27. James, G., Witten, D., Hastie, T., Tibshirani, R., An Introduction to Statistical Learning with Applications in R. 2013, Springer.
28. Jenny, H., Factors of Soil Formation. A System of Quantitative Pedology. 1941, Dover Publications, Inc., New York.
29. Kempen, B., Brus, D.J., Stoorvogel, J.J., Heuvelink, G.B.M., de Vries, F., Efficiency comparison of conventional and digital soil mapping for updating soil maps. Soil Sci. Soc. Am. J. 76 (2012), 2097–2115.
30. Köthe, R., A new system of terrain classification. FAO and ISRIC, 2012: Towards Global Soil Information: Activities Within the Geo Task Global Soil Data. Workshop Report, 20–23 March 2012, 2012, FAO Headquarters, Rome, Italy http://esdac.jrc.ec.europa.eu/projects/eSoter/FinalMeeting/GSP_SoilInformation_WorkshopReport.pdf.
31. Kuhn, M., Building predictive models in R using the caret package. J. Stat. Softw. 28 (2008), 1–26.
32. Kumar, S., Lal, R., Liu, D., A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189–190 (2012), 627–634.
33. Laslett, G.M., McBratney, A.B., Pahl, P.J., Hutchinson, M.F., Comparison of several spatial prediction methods for soil pH. J. Soil Sci. 38 (1987), 325–341.
34. Lettens, S., Van Orshoven, J., Van Wesemael, B., Muys, B., Soil organic and inorganic carbon contents of landscape units in Belgium derived using data from 1950 to 1970. Soil Use Manag. 20 (2004), 40–47.
35. Li, L., Wu, J., Wilhelm, M., Ritz, B., Use of generalized additive models and cokriging of spatial residuals to improve land-use regression estimates of nitrogen oxides in South California. Atmos. Environ. 55 (2012), 220–228.
36. MacMillan, R.A., LandMapR© Software Toolkit- C++ Version: Users Manual. 2003, LandMapper Environmental Solutions Inc., Edmonton, AB.
37. MacMillan, R.A., Pettapiece, W.W., Nolan, S.C., Goddard, T.W., A generic procedure for automatically segmenting landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets Syst. 113 (2000), 81–109.
38. Martin, M.P., Orton, T.G., Lacarce, E., Meersmans, J., Sably, N.P.A., Paroissien, J.B., Jolivet, C., Boulonne, L., Arrouays, D., Evaluation of modeling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma 223–225 (2014), 97–107.
39. Matheron, G., Principles of geostatistics. Econ. Geol. 58 (1963), 1246–1266.
40. McBratney, A.B., Santos, M.L.M., Minasny, B., On digital soil mapping. Geoderma 117 (2003), 3–52.
41. Meersmans, J., De Ridder, F., Canters, F., De Baets, S., Van Molle, M., A multiple regression approach to assess the spatial distribution of soil organic carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma 143 (2008), 1–13.
42. Moore, I.D., Gessler, P.E., Nielsen, G.A., Peterson, G.A., Soil attributes prediction using terrain analysis. Soil Sci. Soc. Am. J. 57 (1993), 443–452.
43. Mora-Vallejo, A., Claessens, L., Stoorvogel, J., Heuvelink, G.B.M., Small scale digital soil mapping in Southeastern Kenya. Catena 76 (2008), 44–53.
44. Odeh, I.O.A., McBratney, A.B., Chittleborough, D.J., Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma 63 (1994), 197–214.
45. Ottoy, S., Beckers, V., Jacxsens, P., Hermy, M., Van Orshoven, J., Multi-level statistical soil profiles for assessing regional soil organic carbon stocks. Geoderma 253–254 (2015), 12–20.
46. Pebesma, E., Multivariate geostatistics in S: the gstat package. Comput. Geosci. 30 (2004), 683–691.
47. Pebesma, E., Cornford, D., Dubois, G., Heuvelink, G.B.H., Hristopulos, D., Pilz, J., Stöhlker, U., Morin, G., Søien, J.O., INTAMAP: the design and implementation of an interoperable automated interpolation web service. Comput. Geosci. 37 (2011), 343–352.
48. Rossi, J., Govaerts, A., De Vos, B., Verbist, B., Vervoort, A., Poesen, J., Spatial structures of soil organic carbon in tropical forests – a case study of Southeastern Tanzania. Catena 77 (2009), 19–27.
49. Shi, W., Liu, J., Du, Z., Song, Y., Chen, C., Yue, T., Surface modeling of soil pH. Geoderma 150 (2009), 113–119.
50. Sindayihebura, A., Van Meirvenne, M., Verstraete, S., Nsabimana, S., Accounting for shape reliability in modeling contour-derived topographic properties for use in soil-terrain correlation. Catena 74 (2008), 165–174.
51. Sys, K., Van Wambeke, A., Frankart, R., Gilson, P., Jongen, P., Pecrot, A., Berge, J.M., Jamagne, M., La cartographie des sols au Congo, ses principes et méthodes. Série Technique no. 66, 1961, Publ. INEAC, Bruxelles.
52. Tack, L., Wingate, M.T.D., De Waele, B., Meert, J., Belousova, E., Griffin, B., Tahon, A., The 1375 MA “Kibaran event” in Central Africa: prominent emplacement of bimodal magmatism under extensional regime. Precambrian Res. 180 (2010), 63–84.
53. Tessens, E., Mvuyekure, E., Muganza, J.-P., Bigura, C., Guide Synoptique pour la Carte Pédologique Semi-détaillée du Burundi. 1991 (Publication ISABU no. 164).
54. Tiessen, H., Cuevas, E., Chacon, P., The role of soil organic matter in sustaining soil fertility. Nature 371 (1994), 783–785.
55. Triantafilis, J., Odeh, I.O.A., McBratney, A.B., Five geostatistical models to predict soil salinity from electromagnetic induction data across irrigated cotton. Soil Sci. Soc. Am. J. 65 (2001), 869–878.
56. Van Meerbeek, K., Van Beek, J., Bellings, L., Aertsen, W., Muys, B., Hermy, M., Quantification and prediction of biomass yield of temperate low-input high-diversity ecosystems. BioEnergy Res. 7:4 (2014), 1120–1130.
57. Van Orshoven, J., Deckers, J.A., Vandenbroucke, D., Feyen, J., The completed database of Belgian soil profile data and its applicability in planning and management of rural land. Bull. Rech. Agron. Gembloux 28 (1993), 197–222.
58. Vaysse, K., Lagacherie, P., Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc –Roussillon (France). Geoderma Reg. 4 (2015), 20–30.
59. Walkley, A., Black, I.A., An examination of the Degjareff method for determining organic carbon in soils: effect of variations in digestion conditions and of organic soil constituents. Soil Sci. 63 (1934), 251–263.
60. Willmott, C.J., Matsuura, K., Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in assessing average model performance. Clim. Res. 30 (2005), 79–82.
61. Wood, J.D., The Geomorphological Characterization of Digital Elevation Models. (PhD Thesis), 1996, University of Leicester, UK http://www.soi.city.ac.uk/~jwo/phd.
62. Wood, S., Generalized Additive Models: An Introduction With R. 2006, Chapman and Hall/CRC.
63. Wood, J.D., The Landserf Manual. www.landserf.org, 2009.