Evaluation of the impact of wildfires and predicting vegetation cover in the sub-humid area using Google earth engine and Stacking Ensemble Models
Year: 2025
Type: article
Abstract: Using Google Earth Engine (GEE) to assess wildfire impacts, this study focused on a sub-humid area affected by 2021 wildfires. Sentinel-2 imagery (10 m) was used to compute NDVI and NBR+ indices, revealing nearly 50% vegetation loss, with weakened correlation post-fire. Accurate NDVI prediction is critical post-fires due to environmental changes. Two standalone machine learning models, Random Forest and XGBoost, along with a Stacking model combining their outputs, were employed to predict NDVI for 2030–2080. Historical data (2000–2023) for NDVI, climate variables (precipitation, LST) from CMIP6 model ACCESS-CM2, and future projections were utilized. Model evaluation showed close results, with MAE (0.0306–0.0310), RMSE (0.0410–0.0419), and R² (0.521-0.541). Predictions suggest slight NDVI variations over future decades, with stacking and Random Forest models providing stability, while XGBoost exhibited higher variability. Compared to current values (post-fires) NDVI projections indicate a long-term decline in vegetation, exacerbating aridity, reducing atmospheric humidity, decreasing rainfall, increasing runoff, heightening soil erosion, and lowering soil permeability. The study demonstrates the stacking model's strength in handling spatial data and improving prediction accuracy for regions prone to fires. It emphasizes the importance of early warnings for vegetation degradation, supporting vegetation management and environmental planning, especially during emergencies.
DOI: 10.4491/eer.2024.732
Authors: Kamel Nouadi, Fahad Alshehri, Nadir Marouf
Language: English
Topic: Fire effects on ecosystems
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: diamond
OpenAlex ID: W4409904932