Land evaluation is sensitive to the effects of variability of ecologically complex phenomena. A probability map incorporating some of these phenomena is proposed to account for local uncertainty of areas affected by soil degradation in the Apennines of south Italy. To be useful, a method for assessing soil degradation should integrate several kinds of data. We present here an overview of the geostatistical approach to solving this problem: non-linear estimation. The following factors have been considered: the soil erosion by water (geomorphologic indicator), the station aridity (bioclimate indicator), and top-soil depth (pedologic indicator). We convert the continuous data values of each variable at each location using a binary variable indicator transform based on critical thresholds. The indicator transform values for individual variables are then integrated to form multiple variable indicator transform (MVIT) to evaluate the degree of soil degradation. Areas suited to soil degradation maps delineated by geographical information system (GIS), showed that the joint probabilities of meeting specific criteria indicator Kriging were influenced by the critical threshold values used to transform each individual variable and the method of integration. So, the understanding of soil vulnerability to degradation is increased to providing a way to classify degraded regions. On the basis of this information different land uses strategies could be identified to develop sustainable assessment models of soils. For example, many countries of these disadvantaged areas, should have agro-forestation programmes that increase the heterogeneity in vegetation cover contrasting hydrological properties, thus promoting a self-regulating system for runoff and erosional soil degradation control.

Multivariate indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands

Ceccarelli M.
2004

Abstract

Land evaluation is sensitive to the effects of variability of ecologically complex phenomena. A probability map incorporating some of these phenomena is proposed to account for local uncertainty of areas affected by soil degradation in the Apennines of south Italy. To be useful, a method for assessing soil degradation should integrate several kinds of data. We present here an overview of the geostatistical approach to solving this problem: non-linear estimation. The following factors have been considered: the soil erosion by water (geomorphologic indicator), the station aridity (bioclimate indicator), and top-soil depth (pedologic indicator). We convert the continuous data values of each variable at each location using a binary variable indicator transform based on critical thresholds. The indicator transform values for individual variables are then integrated to form multiple variable indicator transform (MVIT) to evaluate the degree of soil degradation. Areas suited to soil degradation maps delineated by geographical information system (GIS), showed that the joint probabilities of meeting specific criteria indicator Kriging were influenced by the critical threshold values used to transform each individual variable and the method of integration. So, the understanding of soil vulnerability to degradation is increased to providing a way to classify degraded regions. On the basis of this information different land uses strategies could be identified to develop sustainable assessment models of soils. For example, many countries of these disadvantaged areas, should have agro-forestation programmes that increase the heterogeneity in vegetation cover contrasting hydrological properties, thus promoting a self-regulating system for runoff and erosional soil degradation control.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12070/2951
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