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Improvement of Soil Property Mapping in Northern Ontario's Great Clay Belt Using Multi-Source Remotely Sensed Data

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dc.contributor.advisorHu, Baoxin
dc.contributor.authorPittman, Rory Clifford
dc.date.accessioned2021-03-08T17:23:31Z
dc.date.available2021-03-08T17:23:31Z
dc.date.copyright2020-11
dc.date.issued2021-03-08
dc.date.updated2021-03-08T17:23:31Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThe prediction for the soil properties of texture, calcareous substrate reaction to acid, and ELC (Ecological Land Classification) moisture regime from environmental covariates derived from multi-source remotely sensed data was conducted for study areas located in the District of Cochrane in Ontario (49 50 N, 81 84 W). Random forest (RF) and support vector machine (SVM) approaches were applied to model soil property classifications for 3 adjacent regions: Hearst, Gordon Cosens Forest (GCF) and Abitibi River Forest (ARF). LiDAR (light detection and ranging) data was exploited to derive the detailed vegetation properties of canopy height model (CHM) and gap fraction, as well as a digital elevation model (DEM) for generating topographic covariates. The results indicate that vegetation covariates, particularly LiDAR-derived vegetation properties, were important in the prediction of the soil properties of interest. The most accurate models had accuracy scores greater than 0.7 and Cohens kappa greater than 0.5.
dc.identifier.urihttp://hdl.handle.net/10315/38190
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectsoil sciences
dc.subject.keywordsdigital soil mapping
dc.subject.keywordssoil classification
dc.subject.keywordsLiDAR
dc.subject.keywordscanopy height model
dc.subject.keywordsgap fraction
dc.titleImprovement of Soil Property Mapping in Northern Ontario's Great Clay Belt Using Multi-Source Remotely Sensed Data
dc.typeElectronic Thesis or Dissertation

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