Classifying riparian forests of the Okanagan Basin, British Columbia, Canada, through random forest modelling with LiDAR and spectral derived parameters
department or school: Science, Irving K. Barber Faculty of (Okanagan)
department or school: Earth, Environmental and Geographic Sciences, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Scinocca, Michael
issued: 2024-12-12T20:52:45Z
issued: 2024
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Riparian forests are integral components of landscapes due to their ecological significance and the services they provide to both terrestrial and aquatic ecosystems. These unique ecosystems act as buffers, regulating water quality, stabilizing streambanks, providing habitat for diverse flora and fauna, and influencing local microclimates. Accurate assessment and classification of riparian forests are essential for effective land management and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change. Recent advancements in remote sensing (RS) technologies, particularly the integration and availability of multispectral and LiDAR data, offer opportunities to predict and delineate riparian forest composition at larger scales and over potentially inaccessible terrain, enabling comprehensive assessments of these ecosystems' spatial distribution, structural attributes, and species composition. This study integrated fine-scale RGB RS imagery with LiDAR data to classify riparian forest tree species in select fish-bearing streams within the Okanagan River system, to develop a reproducible and cost-effective process for predicting ten native tree species classes and one infrastructure class. To inform the random forest (RF) classification model, a total of 23 explanatory covariates created from LiDAR and RGB data were incorporated. The model was trained using 1,611 observations, collected through in-situ field visits and supplemented by open-access tree inventory data, sourced from various reaches of the study area, encompassing diverse locations, elevations, and ecosystem types. The RF model achieved an overall accuracy of 96.03%, evaluated through a confusion matrix. In the 100 m buffers used to describe riparian environments of the study area, (129,802 ha), 85,679 ha (66% of the area) were classified into 11 classes. The spruce class was the most predicted class (54.16…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89863
note: Classifying Riparian Forests of the Okanagan Basin, British Columbia, Canada, through Random Forest Modelling with LiDAR and Spectral Derived Parameters by Michael F. Scinocca B.Sc., University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) December 2024 © Michael F. Scinocca, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Classifying Riparian Forests of the Okanagan Basin, British Columbia, Canada, through Random Forest Modelling with LiDAR and Spectral Derived Parameters submitted by Michael F. Scinocca in partial fulfillment of the requirements of the degree of Master of Science. Dr. Mathieu Bourbonnais, Department of Earth, Environmental and Geographic Sciences Supervisor Dr. Hillary Ward, BC Ministry of Water, Land and Resource Stewardship Supervisory Committee Member Dr. Lael Parrott, Department of Biology, Earth, Environmental and Geographic Sciences Supervisory Committee Member Dr. John Richardson, Department of Forest and Conservation Sciences University Examiner iii Abstract Riparian forests are integral components of landscapes due to their ecological significance and the services they provide to both terrestrial and aquatic ecosystems. These unique ecosystems act as buffers, regulating water quality, stabilizing streambanks, providing habitat for diverse flora and fauna, and influencing local microclimates. Accurate assessment and classification of riparian forests are essential for effective land management and conservation efforts, especially in regions like the Okanagan Basin, where water resources are particularly vulnerable to anthropogenic impacts and climate change. Recent advancements in remote sensing (RS) technologies, particularly the integration and…
has type: Thesis/Dissertation
date issued: 2025-02
is shown at: 10.14288/1.0447495
language: eng
degree discipline: Earth and Environmental Sciences
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Bourbonnais, Mathieu
type: Text
identifier URI: http://hdl.handle.net/2429/89863