Rapid Post-Wildfire Burned Vegetation Assessment with Google Earth Engine (Case Study: 2023 Canada Wildfires)

Abstract

Wildfires are significant environmental threats, requiring precise and prompt assessment to mitigate damage and guide recovery efforts. Remote sensing, mainly through satellite imagery multispectral data, provides practical tools for monitoring and evaluating wildfire impacts. Canada experiences significant wildfires each year, causing substantial damage to the country’s environment, particularly its vegetation. This study proposed a fast and efficient method using Google Earth Engine (GEE) cloud-based computing to rapidly assess burned vegetation following a wildfire in Canada in 2023, utilizing Sentinel-2 imagery data. This method computed NDVI, GNDVI, and EVI spectral indices for classifying pre-fire vegetation cover and NBR, dNBR, and MIRBI for classifying post-fire burn severity. These spectral indices served as input data for machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and Support Vector Machine (SVM). Ultimately, the results of vegetation cover and burn severity classifications, performed separately by these models, were combined using a decision-level fusion with a weighting approach based on an accuracy approach to produce integrated and final classifications. Subsequently, by overlapping the results of these fused classifications, the burned vegetation was assessed, and its area was estimated. According to the study's results, significant damage was observed in the vegetation after the wildfire. 4489 km2 of the study area, which was a Military Grid Reference System (MGRS) tile with an area of 12,000 km2, was burned due to the wildfire. 34.06% of this area was specifically burned vegetation, equating to approximately 4,088 km2.