An Open and Explainable Google Earth Engine Workflow for Wildfire Danger and Burn Severity Mapping in Mediterranean Ecosystems

Wildfires in Mediterranean ecosystems are increasing in frequency, extent, and severity under the combined influence of climate change and human pressure. This trend is intensifying the need for operational hazard products that are not only accurate, but also transparent, auditable, and easy to justify to decision-makers. Here we present an open-access, fully reproducible remote-sensing workflow for (i) pre-fire danger mapping and (ii) post-fire burn severity assessment, explicitly designed around explainability rather than black-box prediction. The workflow is implemented in Google Earth Engine using only freely available data sources: Sentinel-2 and Landsat 8 optical imagery, ERA5-Land meteorological reanalysis, and OpenStreetMap ancillary layers.

Post-fire impacts are standardized through NBR (Normalized Burn Ratio) and dNBR (difference Normalized Burn Ratio), converted into burn-severity classes using established USGS-style thresholds. Pre-fire danger is mapped using a physically interpretable, rule-based score derived from six binary, pixel-level indicators representing necessary conditions for elevated danger: (1) fuel availability (vegetation presence), (2) fuel dryness (SWIR-based moisture proxies), (3) heat (2 m temperature and/or LST), (4) atmospheric dryness (relative humidity), (5) wind speed, and (6) antecedent moisture deficit (recent precipitation/soil moisture). This structure provides built-in explainability, because each pixel’s class is directly traceable to the specific conditions that triggered it.

We demonstrate the workflow through a comparative analysis across four major Greek wildfire contexts, Attica, Euboea, Rhodes, and Evros, spanning different seasons and synoptic regimes. Using consistent pre-fire (multi-week) and post-fire compositing windows, we quantify how danger conditions co-occur prior to ignition, assess concordance between high-danger classes and observed fire perimeters, and relate pre-fire signatures to subsequent dNBR patterns, including differences associated with fuel structure, topography, and human exposure (proxied by proximity to roads and settlements from OpenStreetMap).

To move beyond qualitative map interpretation, we complement the rule-based danger score with two lightweight, fully explainable modeling layers that quantify driver effects and test cross-region generalization. First, we fit generalized additive models (GAMs) using continuous satellite- and reanalysis-derived predictors to recover nonlinear response curves and threshold-like behavior. Second, we use a hierarchical ordinal logistic regression in which baseline levels and selected driver effects can differ by region, enabling us to identify which driver–severity relationships are consistent across Mediterranean landscapes and which are site-specific.  We keep the models fully interpretable by reporting GAM response curves and logistic-regression odds ratios (with uncertainty), so predicted danger can be directly linked to physical drivers rather than opaque feature-importance scores. We generate all satellite/reanalysis-derived layers and danger/severity maps in Google Earth Engine, then export pixel-level predictor and outcome samples to fit the GAM and hierarchical logistic models in open-source Python, enabling transparent estimation of driver effects with uncertainty. Finally, we evaluate transferability using leave-one-region-out validation to identify where learned driver–danger relationships remain robust under differing regimes and where localized recalibration may be required for operational deployment.

Keywords

wildfire danger; burn severity; Google Earth Engine; Sentinel-2; Landsat 8; ERA5-Land; dNBR; generalized additive models; hierarchical logistic regression; explainable AI; transparent hazard mapping; Mediterranean ecosystems