Evaluation of the spatio-temporal of soil salinity variation using data mining approach

Abstract

The issue of temporal and spatial variation in soil salinity is considered as a fundamental element in salinity monitoring. The aim of this study is to develop a framework which integrates image mining techniques with Fuzzy logic methodology to improve the evaluation of spatio-temporal variation of soil salinity in areas with lack of available ground observation. Intensity and duration of salinity was characterized in space by the deviation of the current NDVI at each location from its corresponding temporal mean value. Landsat and ASTER images data was used to provide frequent Normalized Difference Vegetation Index (NDVI) in cultivation phase for a period of 22 years. Evolution of salinity condition before planting season was assessed by applying stepwise regression method on image data for two available dataset. The regression equation was obtained between reflectance value of band three and the measured soil Electrical Conductivity (EC) from field. Validation of the developed algorithm was done by comparing the obtained outputs with 50 ground observations, available salinity reports, and previous soil salinity maps. The result revealed that the proposed framework can be considered as a cost and time effective tool for proper assessment of the spatio-temporal variation of soil salinity.

An Intelligible Illustration of an Epidemic Spatio – Temporal Statistics on GIS

“…The disaster that is dengue is recognized using spatial mining techniques. Then the analyzed data are represented on a map based on the time series for all the quarters of a year for the predicted spatial data [13]. The entire work is a threefold procedure.…”