Spectral Trajectory-Based Change Detection and Support Vector Machine Algorithm for Assessing Forest Landscape Disturbance Dynamics in Canada’s Athabasca Oil Sands Region

Monitoring the long-term cumulative impacts of industrial mining and environmental changes in sensitive ecosystems remains a critical challenge for sustainable resource management. This study investigates 41 years (1984–2025) of land use and land cover (LULC) dynamics in the Athabasca oil sands region (Alberta, Canada) using the historical Landsat archive (TM, ETM+, and OLI). The methodology integrated temporal and spectral features through two complementary phases: automated spectral trajectory-based change detection and a Support Vector Machine (SVM) classification combining original bands with multiple spectral indices, including normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), transformed difference vegetation index (TDVI), bare soil index (BSI), and Modified Normalized Difference Water Index (MNDWI). This framework quantified baseline LULC, mining expansion, tailings pond footprints, and reclamation operations across a 12,146-km2 study area. The spectral trajectory analysis effectively tracked multiple temporal disturbances, revealing accelerating mining activities alongside forest harvesting, wildfire stress, and insect damage. Additionally, it captured progressive and significant spectral variations in the Athabasca River’s surface water, reflecting cumulative industrial runoff and long-term environmental pressure. Concurrently, the SVM classifications (Overall accuracy ≥ 96.8% and Kappa coefficient ≥ 94%) measured major LULC shifts. Dense coniferous forests declined by 1200 km2, while mixed forests decreased from 4500 km2 in 1984 to a minimum of 1200 km2 in 2010, before recovering to 3040 km2 by 2025 due to reclamation efforts. Conversely, active mining infrastructure and tailings ponds expanded from 95 km2 (1984) to 900 km2 (2025), occupying 7.41% of the landscape. Ultimately, this integrated remote sensing approach captured the complex interactions between accelerating industrial development, climate stressors, and the localized progress of land reclamation, providing a scalable framework for ecosystem monitoring in heavily disturbed landscapes.