Comparative performance of classification algorithms for the development of models of spatial distribution of landscape structures

Geoderma

Volumn 219-220, Issue , 2014, Pages 136-144

  Bourennane, Hocine ^a   Couturier, Alain ^a   Pasquier, Catherine ^a   Chartin, Caroline ^b   Hinschberger, Florent ^b   Macaire, Jean Jacques ^b   Salvador Blanes, Sébastien ^b  

^a Centre de recherche d'Orléans   (France)

^b Parc de Grandmont   (France)

Author keywords

Classification and regression trees; Factorial discriminant analysis; Landscape structures; Morphometric attributes; Multinomial logistic regression; Soil thickness

Indexed keywords

CLASSIFICATION AND REGRESSION TREE; FACTORIAL DISCRIMINANT ANALYSIS; LANDSCAPE STRUCTURES; MORPHOMETRIC ATTRIBUTES; MULTINOMIAL LOGISTIC REGRESSION; SOIL THICKNESS;

DISCRIMINANT ANALYSIS; FORESTRY; REGRESSION ANALYSIS; SOILS;

SPATIAL DISTRIBUTION;

ALGORITHM; COMPARATIVE STUDY; LANDSCAPE STRUCTURE; LOGISTICS; MORPHOMETRY; REGRESSION ANALYSIS; SPATIAL DISTRIBUTION;

FORESTRY; REGRESSION ANALYSIS; SOIL;

FRANCE; PARIS BASIN;

EID: 84892981560     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2014.01.001     Document Type: Article

References (46)

1. Agresti A. Categorical Data Analysis 2002, John Wiley and Sons, New York. 2nd edition.
2. Akgün A., Türk N. Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalik region, NW Turkey. Comput. Geosci. 2011, 37:1515-1524.
3. Anderson R.H., Farrar D.B., Thoms S.R. Application of discriminant analysis with clustered data to determine anthropogenic metals contamination. Sci. Total Environ. 2009, 408:50-56.
4. Bailey N., Clements T., Lee J.T., Thompson S. Modelling soil series data to facilitate targeted habitat restoration: a polytomous logistic regression approach. J. Environ. Manag. 2003, 67:395-407.
5. Bater C.W., Coops N.C. Evaluating error associated with lidar-derived DEM interpolation. Comput. Geosci. 2009, 35:289-300.
6. Behrens T., Scholten T. A comparison of data-mining techniques in predictive soil mapping. Developments in Soil Science 2006, vol. 31:353-364. Elsevier, Amsterdam. P. Lagacherie, A.B. McBratney, M. Voltz (Eds.).
7. Bellemlih S. Stocks particulaires holocènes et bilans de matières dans un bassin fluviatile en domaine sédimentaire - Le bassin du Négron, Sud-ouest du Bassin Parisien, France 1999, Ph.D. thesis, Université de Tours, France.
8. Boutin D., Froger D., Rassineux J. Feuille Loudun (1724-1624), Carte des sols du Département de la Vienne et de la région Centre au 1:50000 1990, Chambre d'Agriculture de la Vienne - IGN - INRA.
9. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth.
10. Campling P., Gobin A., Feyen J. Logistic modeling to spatially predict the probability of soil drainage classes. Soil Sci. Soc. Am. J. 2002, 66:1390-1401.
11. Chartin C., Bourennane H., Salvador-Blanes S., Hinschberger F., Macaire J.-J. Classification and mapping of anthropogenic landforms on cultivated hillslopes using DEMs and soil thickness data - example from the SW Parisian Basin, France. Geomorphology 2011, 135:8-20.
12. Cox G.M., Martin W.M. Use of a discriminant function for differentiating soils with different azotobacter populations. Iowa State Coll. J. Sci. 1937, 11:323-332.
13. 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.
14. FAO World reference base for soil resources. World Soil Resources Report No 84 1998, FAO, Rome.
15. Fernández-Getino A.P., Hernández Z., Piedra Buena A., Almendros G. Assessment of the effects of environmental factors on humification processes by derivative infrared spectroscopy and discriminant analysis. Geoderma 2010, 158:225-232.
16. Friedl M.A., Brodley C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61:399-409.
17. Friedman J.H. Multivariate adaptive regression splines (with discussion). Ann. Stat. 1991, 19(1):1-82.
18. Geissen V., Kampichler C., López-de Llergo-Juárez J.J., Galindo-Acántara A. Superficial and subterranean soil erosion in Tabasco, tropical Mexico: development of a decision tree modeling approach. Geoderma 2007, 139:277-287.
19. Hansen M., Dubayah R., DeFries R. Classification trees: an alternative to traditional land cover classifiers. Int. J. Remote Sens. 1996, 17:1075-1081.
20. Hengl T., Toomanian N., Reuter H.I., Malakouti M.J. Methods to interpolate soil categorical variables from profile observations: lessons from Iran. Geoderma 2007, 140:417-427.
21. Hirmas D.R., Graham R.C., Kendrick K.J. Soil-geomorphic significance of land surface characteristics in an arid mountain range, Mojave Desert, USA. Catena 2011, 87:408-420.
22. Hosmer D.W., Lemeshow S. Applied Logistic Regression 2000, John Wiley and Sons, New York. Second edition.
23. Houben P. Scale linkage and contingency effects of field-scale and hillslope-scale controls of long-term soil erosion: anthropogeomorphic sediment flux in agricultural loess watersheds of Southern Germany. Geomorphology 2008, 101:172-191.
24. Jungmann M., Kopal M., Clauser C., Berlage T. Multi-class supervised classification of electrical borehole wall images using texture features. Comput. Geosci. 2011, 37:541-553.
25. Kempen B., Brus D.J., Heuvelink G.B.M., Stoorvogel J.J. Updating the 1:50,000 Dutch soil map using legacy soil data: a multinomial logistic regression approach. Geoderma 2009, 151:311-326.
26. King D., Bourennane H., Isambert M., Macaire J.-J. Relationship of the presence of a non-calcareous clay-loam horizon to DEM attributes in a gently sloping area. Geoderma 1999, 89:95-111.
27. Lagacherie P., Robbez-Masson J.M., Nguyen-The N., Barthes J.P. Mapping of reference area representativity using a mathematical soilscape distance. Geoderma 2001, 101:105-118.
28. Ließ M., Glaser B., Huwe B. Uncertainty in the spatial prediction of soil texture Comparison of regression tree and Random Forest models. Geoderma 2012, 170:70-79.
29. Loh W.Y., Vanichsetakul N. Tree-structured classification via generalized discriminant analysis. J. Am. Stat. Assoc. 1988, 83:715-728.
30. Marchetti A., Piccini C., Santucci S., Chiuchiarelli I., Francaviglia R. Simulation of soil types in Teramo province (Central Italy) with terrain parameters and remote sensing data. Catena 2011, 85:267-273.
31. May R., Van Dijk J., Wabakken P., Swenson J.E., Linnell J.D.C., Zimmermann B., Odden J., Pedersen H.C., Andersen R., Landa A. Habitat differentiation within the large-carnivore community of Norway's multiple-use landscapes. J. Appl. Ecol. 2008, 45:1382-1391.
32. Mitchie D., Spiegelhalter D.J., Taylor C.C. Machine Learning, Neural and Statistical Classification 1994, Ellis Horwood, New York, (298 pp.).
33. Mulder V.L., de Bruin S., Schaepman M.E., Mayr T.R. The use of remote sensing in soil and terrain mapping - a review. Geoderma 2011, 162:1-19.
34. Müller D., Zeller M. Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation. Agric. Econ. 2002, 27:333-354.
35. Munoz J., Felicisimo A.M. Comparison of statistical methods commonly used in predictive modelling. J. Veg. Sci. 2004, 15:285-292.
36. Rhemtulla J.M., Mladenoff D.J., Clayton M.K. Regional land-cover conversion in the U.S. upper Midwest: magnitude of change and limited recovery (1850-1935-1993). Landsc. Ecol. 2007, 22:57-75.
37. Schmidt K., Behrens T., Scholten T. Instance selection and classification tree analysis for large spatial datasets in digital soil mapping. Geoderma 2008, 146:138-146.
38. Scull P., Franklin J., Chadwick O.A. The application of classification tree analysis to soil type prediction in a dessert landscape. Ecol. Model. 2005, 181:1-15.
39. Suring L.H., Goldstein M.I., Howell S.M., Nations C.S. Response of the cover of berry-producing species to ecological factors on the Kenai Peninsula, Alaska, USA. Can. J. For. Res. 2008, 38:1244-1259.
40. Tabachnick B.G., Fidell L.S. Using Multivariate Statistics 1996, Harper Collins, New York.
41. Taylor J.A., Coulouma G., Lagacherie P., Tisseyre B. Mapping soil units within a vineyard using statistics associated with high-resolution apparent soil electrical conductivity data and factorial discriminant analysis. Geoderma 2009, 153:278-284.
42. Tomassone R., Danzart M., Daudin J.J., Masson J.P. Discrimination et Classement 1988, Masson, Paris.
43. Varol M., Gökot B., Bekleyen A., Şen B. Spatial and temporal variations in surface water quality of the dam reservoirs in the Tigris River basin, Turkey. Catena 2012, 92:11-21.
44. Venkataraman K., Uddameri V. Modeling simultaneous exceedance of drinking-water standards of arsenic and nitrate in the Southern Ogallala aquifer using multinomial logistic regression. J. Hydrol. 2012, 458-459:16-27.
45. Viscarra Rossel R.A., Behrens T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010, 158:46-54.
46. Webster R., Burrough P.A. Multiple discriminant analysis in soil survey. J. Soil Sci. 1974, 25:120-134.