GEOGRAPHICALLY WEIGHTED REGRESSION AND MULTIPLE LINEAR REGRESSION FOR TOPSOIL TEXTURE PREDICTION
Keywords:Regression, Geographically Weighted Regression, Soil Texture Modelling, Terrain Analysis, Digital Elevation Model
Land resource management requires extensive land mapping. Conventional soil mapping takes a long time and is expensive; therefore, geographic information system data as a predictor in soil texture modeling can be used as an alternative solution to shorten time and reduce costs. Through digital elevation model data, topographic variability can be obtained as an independent variable in predicting soil texture. Geographically weighted regression is used to observe the effects of spatial heterogeneity. This study uses a data set of 50 observation points, each of which had soil particle-size fraction attributes and eight local morphological variables. The covariates used in this study are eastness aspects, northness aspects, slope, unsphericity curvature, vertical curvature, horizontal curvature, accumulation curvature, and elevation. Prediction using geographically weighted regression shows more results compared to multiple linear regression models. The spatial location can affect product Y, with the R2 value of 0.81 in the sand fraction, 0.57 in the silt fraction, and 0.33 in the clay fraction.
P. E. Gessler, I. D. Moore, N. J. McKenzie, and P. J. Ryan, “Soil-landscape modelling and spatial prediction of soil attributes,” Int. J. Geogr. Inf. Syst., vol. 9, no. 4, pp. 421–432, 1995. DOI: https://doi.org/10.1080/02693799508902047
H. Saraiva Koenow Pinheiro, W. de Carvalho Junior, C. da Silva Chagas, L. Helena Cunha dos Anjos, and P. Ray Owens, “Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions,” Artic. Rev Bras Cienc Solo, vol. 42, p. 170167, 2018.
V. L. Mulder, S. de Bruin, M. E. Schaepman, and T. R. Mayr, “The use of remote sensing in soil and terrain mapping — A review,” Geoderma, vol. 162, no. 1–2, pp. 1–19, Apr. 2011. DOI: https://doi.org/10.1016/j.geoderma.2010.12.018
M. Ließ, B. Glaser, and B. Huwe, “Uncertainty in the spatial prediction of soil texture,” Geoderma, vol. 170, pp. 70–79, Jan. 2012. DOI: https://doi.org/10.1016/j.geoderma.2011.10.010
C. da S. Chagas, W. de Carvalho Junior, S. B. Bhering, and B. Calderano Filho, “Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions,” CATENA, vol. 139, pp. 232–240, Apr. 2016. DOI: https://doi.org/10.1016/j.catena.2016.01.001
J. B. Lindsay, J. M. H. Cockburn, and H. A. J. Russell, “An integral image approach to performing multi-scale topographic position analysis,” Geomorphology, vol. 245, pp. 51–61, 2015. DOI: https://doi.org/10.1016/j.geomorph.2015.05.025
J. P. Holcomb, N. R. Draper, H. Smith, J. O. Rawlings, S. G. Pantula, and D. A. Dickey, “Applied Regression Analysis Applied Regression Analysis: A Research Tool,” Am. Stat., 1999. DOI: https://doi.org/10.2307/2685739
A. S. Fotheringham and T. M. Oshan, “Geographically weighted regression and multicollinearity: dispelling the myth,” J. Geogr. Syst., 2016. DOI: https://doi.org/10.1007/s10109-016-0239-5
J. Tamas, P. Reisinger, P. Burai, and I. David, “Geostatistical analysis of spatial heterogenity of Ambrosia artemisiifolia on Hungarian acid sandy soil,” in Journal of Plant Diseases and Proctectio, Supplement, 2006.
 C. Brunsdon, S. Fotheringham, and M. Charlton, “Geographically Weighted Regression,” J. R. Stat. Soc. Ser. D (The Stat., vol. 47, no. 3, pp. 431–443, 1998. DOI: https://doi.org/10.1111/1467-9884.00145
P. A. Shary, “Land surface in gravity points classification by a complete system of curvatures,” Math. Geol., vol. 27, no. 3, pp. 373–390, 1995. DOI: https://doi.org/10.1007/BF02084608
I. V. Florinsky, Digital Terrain Analysis in Soil Science and Geology. 2012. DOI: https://doi.org/10.1016/B978-0-12-385036-2.00001-8
J. I. Daoud, “Multicollinearity and Regression Analysis,” J. Phys. Conf. Ser., vol. 949, no. 1, 2018. DOI: https://doi.org/10.1088/1742-6596/949/1/012009
M. Fischer and A. Getis, Handbook of Applied Spatial Analysis. New York: Springer, 2010. DOI: https://doi.org/10.1007/978-3-642-03647-7
L. Anselin, Spatial econometrics: methods and models, vol. 4. Springer Science & Business Media, 2013.
A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons, 2003.
C. Chasco, “Modeling spatial variations in household disposable income with Geographically Weighted Regression,” no. January, 2007.
How to Cite
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.