Donnini, Jennifer (2025) Remote Sensing of Forest Composition and Diversity: Assessing Spectral Predictors and Long-Term Changes in Quebec’s Forests. Masters thesis, Concordia University.
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Abstract
Forest biodiversity and composition are shifting in response to anthropogenic pressures, natural disturbances, and climate change, especially in regions like Quebec where many broadleaf and conifer species coexist. This thesis investigates how remote sensing can support large scale forest monitoring through two research components. The first evaluates the relationship between satellite derived spectral diversity and ground measured tree diversity metrics across Quebec’s deciduous, mixed, and boreal forests. Using Sentinel-2 imagery and forest inventory plots, we tested the Spectral Variation Hypothesis (SVH) through spectral analysis, clustering, and machine learning. While models poorly predicted species richness, Shannon diversity, and functional dispersion (r2 < 0.46 for all), they performed well for percent conifer (r2 = 0.77), suggesting that structural traits are more detectable via optical sensors than taxonomic diversity. The second component tracks changes in conifer composition (through conifer basal area percentage index) across three decades from 1985 to 2021 using forest inventory plots and Cubist regression models trained on Landsat imagery. Ground data showed widespread increases in conifer basal area, particularly in mixed forests largely driven by balsam fir (Abies balsamea). Cubist models generated spatially continuous maps of conifer basal area percentage and captured general trends, though their change detection accuracy at the plot scale was moderate. Together, these studies demonstrate both the strengths and limitations of remote sensing for biodiversity assessment and forest composition monitoring and emphasize the value of focusing on structural characteristics when interpreting spectral signals.
| Divisions: | Concordia University > Faculty of Arts and Science > Geography, Planning and Environment |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Donnini, Jennifer |
| Institution: | Concordia University |
| Degree Name: | M. Sc. |
| Program: | Geography, Urban & Environmental Studies |
| Date: | 1 July 2025 |
| Thesis Supervisor(s): | Kross, Angela |
| ID Code: | 995790 |
| Deposited By: | Jennifer Donnini |
| Deposited On: | 04 Nov 2025 16:24 |
| Last Modified: | 04 Nov 2025 16:24 |
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