Applying the Optimum Index Factor to Multiple Data Types in Soil Survey

Abstract

Digital soil mapping requires simple, straight-forward methods that can be easily implemented into daily activities of soil survey. The Optimum Index Factor (OIF) was developed by Chavez et al. (1982, 1984) as a method for determining the three-band combination that maximizes the variability in a particular multispectral scene. The OIF is based on the amount of total variance and correlation within and between all possible band combinations in the dataset. Although the OIF method was developed for Landsat TM data, the concept and methodology are applicable to any multilayer dataset. We used the OIF method in a subset area of the initial soil survey of the Duchesne Area, Utah, USA, to help determine which combination of data layers would be most useful for modeling soil distribution. Unique multiband images created from layers of multiple data types (elevation and remote sensing derivatives) were evaluated using the OIF method to determine which data layers would maximize the biophysical variability in the study area. A multiband image was created from the optimum combinations of data layers and used for classification and modeling in ERDAS Imagine. The output from the classification and modeling are being evaluated as pre-maps for soil mapping activities in the study area.