EnMAP Science Advisory Group - Monitoring Vegetation under Global Change

EnMAP (Environmental Mapping and Analysis Program) is a German hyperspectral satellite mission which will provide high quality hyperspectral image data on a timely and frequent basis. The mission aims to support the retrieval of a variety of terrestrial and aquatic parameters essential for the quantification and modelling of ecosystem processes. EnMAP will both contribute to a better understanding of the of complex Earth systems and to the sustainable management of natural resources. As part of the scientific preparation of the EnMAP mission, the Earth Observation Lab of the Humboldt-Universität zu Berlin focuses on the development of algorithms and the assessment of EnMAP’s potential to analyse the spatial-temporal dynamics of (semi-)natural ecosystems and their services.
This third phase of the EnMAP project is focusing on the monitoring of different (semi-) natural ecosystems in California, USA. Analyses focus on the characterization of vegetation types, conditions, phenology and ecosystem disturbances like fire or drought. Simulated multi-temporal EnMAP data covering large areas across a wide range of different ecoregions are used for this purpose, and analyses focus on the implementation of spatially and temporally generalized empirical machine learning models as well as potential synergies with Landsat and Sentinel-2. This project follows ECST phases I & II entitled “Natural Ecosystems and Ecosystem Transitions”.

Principal investigators
Hostert, Patrick Prof. Dr. (Details) (Geomatics)

Bundesministerium für Wirtschaft und Technologie

Duration of project
Start date: 01/2017
End date: 11/2020

Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography

Research Areas
Hyperspektrale Fernerkundung, Klassifikations- und Regressionsansätze, Ökosystemstörungen, Vegetationsfernerkundung, Zeitreihenanalyse

Cooper, S., Okujeni, A., Jaenicke, C., Clark, M., van der Linden, S., & Hostert, P. (under review). Disentangling fractional vegetation cover: regression-based unmixing of simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment
Jänicke, C., Okujeni, A., Cooper, S., Clark, M., Hostert, P., & van der Linden, S. (2020). Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California. Remote Sensing Letters, 11, 1-10
Okujeni, A., Canters, F., Cooper, S.D., Degerickx, J., Heiden, U., Hostert, P., Priem, F., Roberts, D.A., Somers, B., & van der Linden, S. (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment, 216, 482-496

Last updated on 2022-08-09 at 23:08