Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas

Expert assessments for crop and range productivity of very-large arid and semiarid areas worldwide are ever more in demand and these studies require greater sensitivity in delineating the different grades or levels of soil salinity. In conjunction with field study in arid southeastern Oregon, we assess the merit of adding decision-tree analysis (DTA) to a commonly used remote-sensing method. Randomly sampled surface soil horizons were analyzed for saturation percentage, field capacity, pH and electrical conductivity (EC). IFSAR data were acquired for terrain analysis and surficial geological mapping, followed by derivation of layers for analysis. Significant correlation was found between EC values and surface elevation, bands 1, 2, 3 and 4 of the Landsat TM image, and brightness and wetness indices. Maximum-likelihood supervised classification of the Landsat images yields two salinity classes: non-saline soils (EC < 4 dSm–1), prediction accuracy of 97%, and saline soils (EC < 4 dSm–1), prediction accuracy 60%. Addition of DTA results in successful prediction of five classes of soil salinity and an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data only compared to that predicted by additionally using DTA. DTA is a promising approach for mapping soil salinity in more productive and accurate ways compared to only using remote-sensing analysis.