Registering small-scale wildfires in Belgium using satellite data

For a long time, wildfires in Belgium were not considered a major risk. However, climate change is causing more frequent and longer periods of droughts, and when combined with high population density, a considerable wildland urban interface, and limited expertise and awareness, Belgium is facing an emerging wildfire risk. Belgium has already experienced wildfires that were difficult to control, such as those in Baelen (2011) and Achouffe (2025). Simultaneous wildfire events have also occurred, such as in April 2020, when three wildfires in the provinces Antwerp and Limburg stretched the capacity of the emergency services.Belgium currently lacks a standardized method for wildfire data collection. The only available database, compiled by our research group at Ghent University (Prof. J. Baetens), is based on digitized newspapers dating back to 1830 and intervention reports. However, this database is incomplete: for many events only the date and municipality are known, with no additional information on burnt area, fire perimeter or flame height, nor any environmental data such as landcover type or meteorological conditions.To improve wildfire data collection in Belgium, we are developing a semi-automatic method to register wildfires using satellite imagery. A major challenge is the small size of most wildfires in Belgium, often limited to a few hectares, which makes existing satellite-based systems such as the EFFIS Current Situation Viewer unsuitable. Our approach starts from emergency phone calls, where wildfire related calls are identified using a specific incident code, providing a date and approximate location. A spatial buffer is applied to account for the fact that callers are not located directly at the fire site. This results in a list of potential wildfire events.For each potential event, time series of Sentinel-1 and Sentinel-2 images are collected. Pre- and post-fire images are processed using a customized wildfire detection algorithm designed specifically for the Belgian landscape. Based on spectral indices (e.g., NDVI or NBR), backscatter differences and thermal anomalies, the algorithm distinguishes true wildfire events from false positives by analysing conditions before and after the reported incident.The detection results are validated using field-based wildfire perimeter measurements, which we collected for the wildfire season of 2025, covering approximately 100 events identified from newspaper reports. Combined with the historical database from 1830, these data enable us to understand the wildfire dynamics in Belgium. Finally, based on the historic dataset, we developed the Belgian Wildfire Viewer, an interactive dashboard that allows users to explore wildfires events and increases public awareness of wildfire risk. This viewer not only shows information about the number of wildfires we had in Belgium but also provides derived information such as the landcover type and meteorological conditions.