Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling

Abstract

This paper presents a procedure to optimize parametrization and scale for terrain-based environmental modeling. The workflow was exemplified on crop yield data, which is assumed to represent a proxy for soil productivity. Focal mean statistics were used to generate different scale levels of terrain derivatives by increasing the neighborhood size in calculation. The degree of association between each terrain derivative and crop yield values was established iteratively for all scale levels through correlation analysis. The first peak of correlation indicated the scale level to be further retained. To select the best combination of terrain parameters that explains the variation of crop yield, we ran stepwise multiple regressions with appropriately scaled terrain parameters as independent variables. These techniques proved that the mean curvature, filtered over a neighborhood of 55 m, together with slope, made up the optimal combination to account for patterns of soil productivity. To illustrate the importance of scale, we compared the regression results of unfiltered and filtered mean curvature vs. crop yield. The comparison shows an improvement of R 2 from a value of 0.01 when the curvature was not filtered, to 0.16 when the curvature was filtered within 55×55 m neighborhood size. The results were further used in an object-based image analysis environment to create terrain objects containing aggregated values of both terrain derivatives and crop yield. Hence, we introduce terrain segmentation as an alternative method for generating scale levels in terrain-based environmental modeling, besides existing per-cell methods. At the level of segments, R 2 improved up to a value of 0.47.