Degree Type

Thesis

Date of Award

2006

Degree Name

Master of Science

Department

Agronomy

First Advisor

Andrew Manu

Abstract

Terrain analysis is a powerful tool which is useful to build prediction models in the Northwest Iowa Plains. To effectively explain variability of soil properties across the landscape using terrain analysis, models used for prediction of the former must adequately reflect the processes at relevant scales. Topographic roughness may be used as a tool to guide appropriate resolutions for selected landscapes. Although topography is one of many factors of soil formation, it can be a major factor in some landscapes to explain spatial variation in soil properties. This study was conducted to determine optimal grid resolution for predicting multiple soil properties in a low relief landscape. Multiple primary and secondary terrain attributes were created from a high accuracy Digital Elevation Model (DEM). Regression was then used to determine the relationships between soil properties and terrain attributes at resolutions of 2m to 40m. The variation of soil properties explained by the models ranged from 27% to 70% by changing the grid resolution alone. Spatial prediction models were able to account for 70% of the variability in the depth to till contact. Spatial prediction models were only able to account for 27% of the variability in the prediction of surface sand content. Half of the soil properties had prediction models performed best at resolutions coarser than 25m. Prediction of some properties was not affected by grid resolution. The statistical significance of terrain attributes varied by grid resolution. Relief is an important contributor to processes that modify the landscape and must be considered when attempting to model those changes. This study suggests that soil properties can be effectively predicted using low resolution DEMs in low relief landscapes.

DOI

https://doi.org/10.31274/rtd-180813-12043

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu

Copyright Owner

Daniel Alan Nath

Language

en

Proquest ID

AAI1439918

File Format

application/pdf

File Size

133 pages

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