Evaluation of the Spatio-Temporal of Soil Salinity Variation Using Data Mining Approach
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 intergrates 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 form its corresponding temporal mean value. Landsat and Aster images data was uxed to provide frequent Normalized Difference Vegetition Index (NDVI) in cultivation phase for a period of 22 years.Evolution of salinity condition befor 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 hand 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.
Data Mining Technique Fuzzy Logic Soil Salinity Monitoring Vegetation Index(NDVI) Electrical Conductivity (EC),Remote Sensing Data
Khatereh Polous Abbas Farshad Manouchehr Zarinkafsh Mohammad Hassan Roozitalab
Soil Science Department,Azad University Karaj P.O.Box 21485-313,Karaj,Iran Faculty of Geo-Information Science and Earth Observation, Tewente University P.O.Box 217,7500 AE Ens International Center for Agricultural Research in the Dry Areas (ICARDA/AREEO)
国际会议
福州
英文
81-86
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）