Soil salinity is a major environmental constraint in Northeast Thailand. Sustainable land use and management in this region require careful mapping of the salinity status. This study was conducted to investigate performances of some digital classification techniques for soil salinity mapping in the Northeast. The techniques investigated were based on the use of LANDSAT-7 ETM+ with different combinations of three kinds of ancillary data (i.e., topography, geology, and underground water quality). In this study, the Maximum Likelihood classification method was employed. Statistics including KAPPA analysis and Z-statistic, overall accuracy, producer's accuracy, and user's accuracy, were used as the bases for assessments of mapping accuracies and, in turn, performances of the classification techniques. Results have shown that the use of ETM+ data bands 4,5 and 7, with the combination of all three kinds of the ancillary data yielded the most accurate soil salinity map with 83.6 % overall accuracy. The same subset of ETM+ data when used with any combination of two kinds of the ancillary data could serve as well. Other classification techniques yielded significantly less accurate results. It was, therefore, concluded that techniques based on the use of the selected ETM+ data subset with combinations of two or three kinds of the ancillary data were promising.
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Katawatin et al. (2005) studied this question.