BrandSat - Mapping Forest Fire Hazard using Earth Observation and Meteorological data
is a joint project with the Earth Observation Lab at HU Berlin.
Subproject 1 (Trier University): Mapping forest structure and dryness.
Subproject 2 (HU Berlin): Satellite-based mapping and characterization of historic and current forest fires for forest fire hazard modelling
|Funding Organisations||Waldklimafonds at Fachagentur für Nachwachsende Rohstoffe e.V. (FNR), with funds from the Federal Ministry of Agriculture and Food (Bundesministerium für Ernährung und Landwirtschaft und Ernährung, BMEL) and the Federal Ministry for the Environment (Bundesministerium für Umwelt, Naturschutz und nukleare Sicherheit, BMU)|
|Funding amount||117.000 EUR|
|Project duration||01.07.2020 - 30.06.2022, extended to 31.12.2022|
|Project staff||Dr. Henning Buddenbaum (Project management: Prof. Dr. Thomas Udelhoven)|
In recenct years, Germany was hit by several heat and drought periods that increased the forest fire hazard signifcantly, a trend that is likely to continue.
The German Weather Service (Deutscher Wetterdienst, DWD) provides a forest fire hazard index called WBI that is calculated from meteorological and site information at several hundred sites across Germany. It is based on the Canadian Fire Weather Index. The WBI is published daily from March to October and comprises five classes between 1 (very low fire hazard) to 5 (very high fire hazard). The WBI gives a solid estimate of fire hazard on a regional basis, but there are several weaknesses in its concept. The spatial resolution corresponds to several kilometers and differs regionally because it is interpolated from site data. And since a typical forest type that may be more or less prone to fire is assumed for each site, it can represent a worst-case scenario in some places, but a best-case scenario in other places.
In our project, we propose strategies to improve the WBI using satellite remote sensing. Our objective is to create an index that gives spatially explicit fire hazard in a high spatial resolution. To achieve this objective, several remote sensing-based input data sets are to be used.