TerraHeal: An Automated Framework for Identifying Post-Wildfire Vegetation Recovery Anomalies Using Sentinel-2 Imagery and Machine Learning

This paper presents TerraHeal, an automated framework for detecting persistent post-wildfire vegetation recovery anomalies ("recovery cold spots") using Sentinel-2 imagery and machine learning. The framework combines multi-temporal NDVI analysis with heuristic thresholding and Isolation Forest-based anomaly detection. A persistence verification step distinguishes transient recovery delays from sustained failures. The system is implemented using Google Earth Engine and applied to major wildfire events, demonstrating the ability to identify localized areas of delayed vegetation recovery. This is a preliminary preprint version. Results are based on remote sensing analysis and require further validation with field data.