Remote Sensing Projects

These projects apply remote sensing, satellite imagery, and raster-based spatial analysis to investigate environmental and landscape change across space and time. Using Landsat multispectral imagery, spectral indices, time-series analysis, and Google Earth Engine, they examine vegetation conditions, wildfire impacts and recovery, land cover dynamics, and longer-term patterns of environmental change. The projects demonstrate how Earth observation data can be processed, analyzed, and integrated with GIS and data visualization to identify patterns, monitor changing conditions, and communicate findings relevant to environmental planning, conservation, climate resilience, and natural resource management.

Vegetation Recovery Following the 2016 Fort McMurray Wildfire

Remote Sensing Analysis of Burn Severity & Vegetation Recovery

Analyzed post-fire vegetation recovery following the 2016 Fort McMurray wildfire using Landsat 8 satellite imagery and Google Earth Engine. Applied the differenced Normalized Burn Ratio (dNBR) to classify burn severity and the Normalized Difference Vegetation Index (NDVI) to evaluate vegetation recovery from pre-fire conditions through 2025. Integrated raster analysis, multitemporal analysis, and zonal statistics to examine recovery trajectories across burn severity classes and produced maps, charts, and time-series animations communicating patterns of wildfire disturbance and landscape recovery.

Software & Tools: Google Earth Engine, Landsat 8 Surface Reflectance

Remote Sensing & Earth Observation: NDVI, dNBR, Spectral Indices, Multispectral Imagery, Multitemporal Analysis, Burn Severity Classification, Vegetation Change Detection

Raster & Spatial Analysis: Raster Analysis, Zonal Statistics, Spatial Analysis, Data Classification

Environmental Applications: Wildfire Ecology, Vegetation Recovery Analysis, Forest Monitoring, Landscape Change Analysis, Natural Resource Management

Cartography & Data Visualization: Cartography, Data Visualization, Scientific Communication, Time-Series Animation

Research Question:
How did burn severity during the 2016 Fort McMurray wildfire relate to patterns of vegetation recovery between 2016 and 2025?

Key Findings

Higher burn severity was associated with greater initial vegetation loss and slower vegetation recovery.
Unburned and low-severity areas maintained relatively high NDVI values following the fire and returned more rapidly toward pre-fire vegetation conditions.
Moderate- and high-severity areas exhibited slower recovery trajectories, with NDVI values remaining below those of unburned and lower-severity areas through 2025.
Approximately 53.7% of the study area experienced forest loss following the 2016 wildfire, illustrating the substantial spatial extent of landscape disturbance.
Overall, the analysis identified a clear relationship between initial burn severity and subsequent vegetation recovery, with higher-severity areas exhibiting slower and less complete recovery over the study period.