A Holistic GIS-based Approach for Thematic Extraction of the Soil
Erosion Estimates Using Predictors as a Function of Climate, Land Cover,
Relief, Soil and Topography
Affiliations: Department of Soil Science & Agricultural
Chemistry, Faculty of Agriculture Annamalai University, Chidambaram, India.
senthil.selvaradjou@gmail.com
Note: [] Corresponding Author
Abstract: Present claim for update of existing soil information has taken a
heavy toll to fit the needs of the current environmental modelling data
demands. The information derived from the age old data of 1960's
and 1970's that are being used in most cases at present situation
are losing its relevance to represent the reality of now existing soil status.
Due to various transformations that have undergone in the land use, crop
management practices, intensive cultivation integrated with unscrupulous
fertilization (imbalanced fertilization), certain fertile soils of the past
have reached a status of degraded lands or unproductive lands. Henceforth,
present focus is visualized on developing modelling approaches through
exploitation of the new GIS and remote sensing techniques as a feasible option
and to cut down the cost factor that would be a certain unaffordable demand
through conventional approaches. In this study, "SEIMS
network" (Soil and Environment based Mapping System) approach was
adopted to update information on the soil loss due to water erosion.
Conceptually, this approach is based on the principles of Data Mining and
Knowledge Discovery (KDD) method. The spatial relationships among the
independent variable related to the soil erosion process (predictors) are
accounted to estimate soil erosion through spatial modelling. In this study,
about four climatic variables (temperature, rainfall, potential
evapotranspiration and rainfall seasonality), one for land cover (derived from
MODIS spectral bands), three variables for soil attributes (soil crusting, soil
erodibility, top soil organic carbon content) and two terrain parameters
(altitude and slope) were chosen as predictors for modelling soil erosion
process. The reclassified soil erosion map derived through SEIMS network scheme
exhibited a better correlation (r^2 = 0.891) with the published class-based soil
erosion map of Tamil Nadu (NBSS & LUP, 1997). Thereby, holistic GIS-based
approach was found to be efficient in transforming the useful subjective,
qualitative and categorical information into objective and quantitative
information serving the present demands of soil information update.
Keywords: Soil erosion, Geographical Information System (GIS), spatial modelling, data mining, digital soil mapping
