EnMAP Core Science Team - Monitoring Ecosystem Transitions
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 analyze the spatial-temporal dynamics of (semi-)natural ecosystems and their services. Data synergies with other sensors and data types are also a focus of the project's research.
This first phase of the EnMAP project focusses on the analysis of gradual transitions and dynamics of (semi-) natural ecosystems and der services. Regional and thematic foci are set both on shrubland ecosystems and shrub encroachment processes on former agricultural regions in southern Portugal and on the urban to rural gradient of Berlin. Analyses integrated simulated EnMAP data, Landsat time-series and spatially and temporally transferable classification and regression models from the field of machine learning. This project was followed by ECST phases II on “Natural Ecosystems and Ecosystem Transitions” in the Brazilian Cerrado.
Financer
BMBF - HU als Unterauftragnehmerin
Duration of project
Start date: 01/2010
End date: 12/2012
Research Areas
Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
Research Areas
Biodiversität, Hyperspektrale Fernerkundung, Klassifikations- und Regressionsansätze, Veränderung der Landnutzung/-bedeckung, Zeitreihenanalyse
Publications
Leitão, P.J., Schwieder, M., Suess, S., Okujeni, A., Galvão, L., Linden, S., & Hostert, P. (2015). Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP. Remote Sensing, 7, 13098-13119.
Leitão, P.J., Schwieder, M., Suess, S., Catry, I., Milton, E.J., Moreira, F., Osborne, P.E., Pinto, M.J., van der Linden, S., & Hostert, P. (2015). Mapping beta diversity from space: Sparse Generalised Dissimilarity Modelling (SGDM) for analysing high-dimensional data. Methods in Ecology and Evolution, 6, 764-771.
Okujeni, A., van der Linden, S., & Hostert, P. (2015). Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning. Remote Sensing of Environment, 158, 69-80
Okujeni, A., van der Linden, S., Jakimow, B., Rabe, A., Verrelst, J., & Hostert, P. (2014). A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover. Remote Sensing, 6, 6324-6346
Okujeni, A., van der Linden, S., Tits, L., Somers, B., & Hostert, P. (2013). Support vector regression and synthetically mixed training data for quantifying urban land cover. Remote Sensing of Environment, 137, 184-197
Schwieder, M., Leitão, P.J., Suess, S., Senf, C., & Hostert, P. (2014). Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques. Remote Sensing, 6, 3427-3445.
Suess, S., Linden, S.v.d., Leitão, P.J., Okujeni, A., Waske, B., & Hostert, P. (2014). Import Vector Machines for Quantitative Analysis of Hyperspectral Data. IEEE Geoscience and Remote Sensing Letters, 11, 449-453
Suess, S., van der Linden, S., Okujeni, A., Leitão, P., Schwieder, M., & Hostert, P. (2015). Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data. Remote Sensing, 7, 10668
Suess, S., van der Linden, S., Okujeni, A., Griffiths, P., Leitão, P.J., Schwieder, M., & Hostert, P. (2018). Characterizing 32 years of shrub cover dynamics in southern Portugal using annual Landsat composites and machine learning regression modeling. Remote Sensing of Environment, 219, 353-364
