Abstract
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.
Highlights
Soil salinization is a major land-degradation problem in arid and semi-arid environments [1,2,3,4].Mapping techniques that can be used to inventory and monitor soil salinization over large areas in more efficient, time-effective and less expensive ways are required for precision agriculture and sustaining soil productivity in many parts of the world
Across the area we find a significant correlation between electrical conductivity (EC) values and surface elevation (Table 4)
We found that the wetness index has a significant correlation with the EC values which could be due to the tendency of salt-affected soils to retain high moisture content
Summary
Mapping techniques that can be used to inventory and monitor soil salinization over large areas in more efficient, time-effective and less expensive ways are required for precision agriculture and sustaining soil productivity in many parts of the world Predictive mapping techniques, such as linear and multiple regression, geostatistics (i.e., Kriging and CoKriging), fuzzy logic, neural network, and classification and regression trees [5,6,7,8,9] have been used to develop soil and natural resource maps. Geostatistics, for example, yield significant results when data are normally distributed and stationary (mean and variance do not vary significantly in space); where significant deviations from normality and stationarity arise, the analysis becomes problematic [10,11] This normality issue is difficult to constrain at smaller scales, especially when values of environmental parameters and soil properties dramatically change from one location to another across the soilscape. Mapping salt-affected soils in the field is difficult as they are interspersed with normal soils and form no contiguous pattern [12]
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