Digital cartography of soil classes with fuzzy logic in mountain areas

Ángel R. Valera, María C. Pineda, Jesús A. Viloria

Article ID: 1674
Vol 5, Issue 2, 2022, Article identifier:68-76

VIEWS - 85 (Abstract) 18 (PDF)

Abstract


In order to strengthen the study of soil-landscape relationships in mountain areas, a digital soil mapping approach based on fuzzy set theory was applied. Initially, soil properties were estimated with the regression kriging (RK) method, combining soil data and auxiliary information derived from a digital elevation model (DEM) and satellite images. Subsequently, the grouping of soil properties in raster format was performed with the fuzzy c-means (FCM) algorithm, whose final product resulted in a fuzzy soil class variation model at a semi-detailed scale. The validation of the model showed an overall reliability of 88% and a Kappa index of 84%, which shows the usefulness of fuzzy clustering in the evaluation of soil-landscape relationships and in the correlation with soil taxonomic categories.


Keywords


Fuzzy Logic; FCM Algorithm; Regression Kriging; Digital Soil Mapping; Soil Classes

Full Text:

PDF


References


Zhu AX, Moore A, Burt JE. Prediction of soil properties using fuzzy membership values. Geoderma 2010; 158: 199–206.

Sun W, Minasny B, Mcbratney AB. Analysis and prediction of soil properties using local regression-kriging. Geoderma 2021; (171–172): 23–30.

Zadeh LA. Fuzzy sets. Inform. Control 1965; 8: 338–353.

Minasny B, Mcbratney AB. Spatial prediction of soil properties using EBLUP with the Matérn covariance function. Geoderma 2007; 140(4): 324–336.

Penizek V, Boruvka L. Soil depth prediction supported by primary terrain attributes: A comparison of methods. Plant Soil Environ 2006; 52(9): 424–430.

Hengl T, Heuvelink GBM, Rossiter DG. About regression-kriging: From theory to interpretation of results. Computers & Geosciences 2007; 33(10): 1301–1315.

Mcbratney AB, De Gruijter JJ, Brus DJ. Spatial prediction and mapping of continuous soil classes. Geoderma 1992; 54: 39–64.

Odeh, IOA, Mcbratney AB, Chittleborough DJ. Soil pattern recognition with fuzzy c-means: Application to classification and soil landform interrelationships. Soil Science Society of America Journal 1992; 56: 505–516.

Mazaheri AS, Koppi AJ, Mcbratney AB. A fuzzy allocation scheme for the Australian great soil groups classification system. European Journal of Soil Science1995; 46: 601–612.

Bragato G. Fuzzy continuous classification and spatial interpolation in conventional soil survey for soil mapping of the lower Piave plain. Geoderma 2004; 118: 1–16.

Chen J, Chen C, Chen S. Application of fuzzy k-mean cluster and fuzzy similarity in soil classification. Proceedings of 15th International off Shore and Polar Engineering Conference; 2005. p. 459–465.

Lagacherie P, Mcbratney AM. Spatial soil information systems. Perspective for digital soil mapping. Developments in Soil Science 2006; 31: 4–24.

Boruvka L, Pavlu L, Vasat R, et al. Delineating acidified soils in the Jizera Mountains Region using Fuzzy Classification. Digital Soil Mapping with Limited Data; 2008. p. 233–245.

Bhargavi P, Tech M. Fuzzy C-Means classifier for soil data. International Journal of Computer Applicationsm 2010; 6(4): 1–5.

Burrough PA. Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science 1989; 40: 477–492.

Kollias VJ, Kalivas DP, Yassoglou NJ. Mapping the soil resources of a recent alluvial plain in Greece using fuzzy sets in a GIS environment. European Journal of Soil Science 1999; 50: 261–273.

Zhu AX, Band L, Vertessy R, et al. Derivation of soil properties using a soil land inference model (SoLIM). Soil Science Society of America Journal 1997; 61(2): 523–533.

De Bruin S, Stein A. Soil-landscape modelling using fuzzy c-means clustering of attribute data derived from a Digital Elevation Model (DEM). Geoderma 1998; 83: 17–33.

Zhu AX, Hudson B, Burt J, et al. Soil mapping using GIS, expert knowledge, and fuzzy logic Soil Science Society of America Journal 2001; 65(5): 1463–1472.

Balkovic J, Čemanova G, Kollar J, et al. Mapping soils using the Fuzzy Approach and Regression kriging—Case study from the Považský Inovec Mountains, Slovakia. Soil & Water Research 2007; 2(4): 123–134.

Zhu AX, Yang L, Li B, et al. Purposive sampling for digital soil mapping for areas with limited data. Digital Soil Mapping with Limited Data; 2008. p. 233–245.

Yang L, You J, Sherif, et al. Updating conventional soil maps through digital soil mapping. Soil Science Society of America Journal 2011; 75: 1044–1053.

Sun X, Zhao Y, Wang H, et al. Sensitivity of digital soil maps based on FCM to the fuzzy exponent and the number of clusters. Geoderma 2021; (171–172): 24–34.

Zhu AX, Moore A, Burt JE. Prediction of soil properties using fuzzy membership. Proceedings of the 2nd Global Workshop on Digital Soil Mapping; 2006 Jul 4–7; Rio de Janeiro. 2006.

Yang L, Zhu AX, Qin C, et al. Soil property mapping using Fuzzy Membership derived by Fuzzy c-Means (FCM) Clustering. The 7th International Workshop of Geographical Information System (IWGIS07); 2007.

Li A, Liang S, Wang A, et al. Estimating Crop Yield from multi-temporal satellite data using multivariate regression and neural network techniques. Photogrammetric Engineering & Remote Sensing 2007; 73(10): 1149–1157.

Burrough PA, Van Gaans PFM, Macmillan RA. High-resolution landform classification using fuzzy k-means. Fuzzy Sets and Systems 2000; 113: 37–52.

Oberthur T, Dobermann A, Aylward M. Using auxiliary information to adjust fuzzy membership functions for improved mapping of soil qualities. International Journal of Geographical Information Science 2000; 14: 451–454.

Torbert HA, Krueger E, Kurtener D. Soil quality assessment using fuzzy modeling. International Agrophysics 2008; 22: 365–370.

Zachwatowicz M. The potential of fuzzy logic for quantitative land cover change analysis basing on historical topographic maps. Miscellanea Geographica 2011; 15: 231–240.

Urbani F, Rodriguez JA. Geological atlas of the Cordillera de la Costa, Venezuela. Maps at 1:25.000 scale. Digital version. Edic. Geos Foundation, UCV. Caracas, Venezuela; 2004.

Pineda MC, Elizalde G, Viloria J. Determinación deáreas susceptible to landslides in a sector of the Cordillera de la Costa Central de Venezuela. Interciencia 2011a; 36(5): 370–377.

Valera A. Inventory of soils and landscapes with the support of digital mapping techniques in mountainous areas—Case of the Caramacate river basin, Aragua State [PhD thesis]. Maracay: Central University of Venezuela; 2015.

Pineda MC, Elizalde G, Viloria J. Soil-landscape relationship in a sector of the Caramacate river basin, Aragua, Venezuela. Revista de la Facultad de Agronomía 2011; 37(1): 27–37.

Bezdek JC. Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press; 1981.

Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 1984; 10: 191–203.

De Gruijter JJ, Mcbratney AB. A modified fuzzy k-means method for predictive classification. Amsterdam: Elsevier Science Publishers BV; 1988.

Minasny B, Mcbratney AB. FuzME version 3.0. Australian Centre for Precision Agriculture. The University of Sydney; 2006.

Chuvieco E. Environmental remote sensing. The observation of the Earth from Space. 3rd ed. Spain; 2008.

Cohen J. Weighted kappa: Nominal scales agreement with provision for scaled disagreement or partial credit. Psichological Bulletin 1968; 70: 213–220.




DOI: http://dx.doi.org/10.24294/jgc.v5i2.1674

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License

This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.