Main content

Refining Ontario’s soil property maps based on legacy soil data

Show full item record

Title: Refining Ontario’s soil property maps based on legacy soil data
Author: Lepp, Sarah
Department: School of Environmental Sciences
Program: Environmental Sciences
Advisor: Biswas, Asim
Abstract: Accessible, explicit, high resolution soil information is crucial for land management, resource allocation, and agriculture. This thesis investigates how to build a comprehensive methodological framework to improve Canada’s soil maps by updating existing soil property maps for Middlesex County, Ontario. First, the most accurate soil depth functions to standardized depths per soil property are determined. Next, the highest accuracy covariates per soil property are defined. Finally, interpolation and machine learning algorithms are explored, to find the highest accuracy soil property maps per soil property. Geostatistical and deterministic algorithms can work well to interpolate soil organic matter data; the equal area quadratic spline function accurately standardizes soil profile depths - all horizons being present; and different covariates and soil property prediction methods are necessary for accurate 3D soil property maps for different soil properties at different depths. This methodological framework can be used to refine soil maps for Ontario, Canada.
URI: http://hdl.handle.net/10214/17382
Date: 2019-08
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Terms of Use: All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


Files in this item

Files Size Format View Description
Lepp_Sarah_201908_Msc.pdfuntranslated 5.650Mb PDF View/Open Interpolation, soil depth functions, and soil property prediction with covariates for legacy data across Middlesex County, Ontario.

This item appears in the following Collection(s)

Show full item record

Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International