EnFireMap

Remote sensing plays a vital role in providing timely information on pre-fire, active fire and post-fire conditions. New satellite missions such as the Environmental Mapping and Analysis Program (EnMAP) offer hyperspectral time series, enhancing the information provided for fire management and risk reduction. In this light, the EnFireMap project will make use of EnMAP time series to address monitoring needs in fire-prone ecosystems. Our project focuses on five Californian study sites, which are characterized by various vegetation communities, diverse environmental gradients and almost annually recurring fires. Our specific objectives are (1) to advance quantitative machine learning workflows to leverage the benefits of intra-annual and multi-year hyperspectral EnMAP time series, (2) to quantify ecosystem parameters relevant for pre-fire risk and post-fire impact assessment, and (3) to improve our understanding of post-fire vegetation recovery processes through synergistic analyses with multispectral Landsat/Sentinel-2 time series. Post-fire analyses will be supported by in situ surveys and field photography. Through this work, we will show the potential of hyperspectral satellite missions for deriving more detailed and accurate environmental parameters relevant to describe pre and post fire dynamics.

Funding

‘Innovative multi-temporal unmixing approaches for monitoring fire-prone ecosystems with EnMAP’ (EnFireMap) is funded by the German Aerospace Centre (DLR) Project Management Agency and granted by the Federal Ministry for Economic Affairs and Climate Action (BMWK; grant number 50EE2227).

Project members

Akpona Okujeni (Principle investigator)
Neija Elvekjaer (Project scientist)