Jones, Trevor

ABSTRACT

Development of Forest Degradation Indicators from Long-term Trajectories of Multispectral Satellite Images, and their Projections into the Future under Climate Change, in Ontario, Canada

Md. Mozammel Hoque

Ontario forests are affected by natural and anthropogenic disturbances leading to forest degradation, which significantly impact local ecosystems, health, safety, and economy. This thesis develops a methodology for the continuous assessment, mapping, and monitoring of present and historic (1972–2020) forest disturbances, and future forest degradation trends and projections, using remote sensing data, ground measurements, and predictive models in an Ontario forested area. After testing four supervised classification algorithms, support vector machine was found to be the most robust, consistent, and effective for land cover classification. Seven vegetation indices derived from Landsat and MODIS platforms were used to derive forest degradation indicators (FDIs), which were combined into one composite forest degradation indicator (CFDI) for each year, using the principal component analysis image fusion approach. The CFDI was the most informative indicator. The computed FDIs from available large multispectral image stacks were statistically related to historical climate variables. These relationships were used to project future FDIs related to climate variables derived from General Circulation Models through multiple linear regression models. Spatially-explicit maps of relevant climatic variables and of long-term historical forest degradation were developed from the LandTrendr trajectory analysis. Climate variables P, MA1, MA2, and CFDI were strongly correlated, allowing for the development of a model with a high coefficient of determination, R2 (0.93), and low RMSE (0.28) to predict future values. Forest disturbances (as CFDI) were also monitored from 1972–2020. Overall, these relationships allowed for to the creation of spatially-explicit, long-term historical forest degradation maps derived from the Landtrendr trajectory analysis. Historical and future forest degradation maps identified the areas with projected high vulnerability to climate change, as well as the actual and potential changes in forest cover under climate change. The results indicated 2050 will experience an average temperature increase of 3.0°C, projected yearly decrease in precipitation of 109.5 mm, evapotranspiration increase of 73.0 mm, and moisture deficits of 28.47 mm (MA1) and 37.60 mm (MA2), leading to increased forest degradation.

Author Keywords: Climate change impacts, Forest degradation indicators, Forest disturbance and degradation, Land cover classification, Projections of 2050 forest degradation under climate change, Remote sensing technology