EnMAP Core Science Team - Natural Ecosystems and Ecosystem Transition

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 second phase of the EnMAP project builds on the previous research and further extends the focus from the study sites in southern Portugal, to more natural and complex ecosystems, such as the Brazilian savannas, known as the Cerrado. The Cerrado covers approximately 2 mio. km², is rich in biodiversity and hosts a wealth of endemic species. However, a growing demand on agricultural products has led to largescale landcover transitions, with approximately 50% of the natural vegetated areas being already converted for agricultural use. As this trend is not expected to halt soon, spatially explicit information over large extents are mandatory. The remote sensing data mentioned above offer great potential to deliver this data and will thus be examined in more detail in this project phase.

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

Bundesministerium für Wirtschaft und Technologie

Duration of project
Start date: 06/2013
End date: 12/2016

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

Research Areas
Biodiversität, Hyperspektrale Fernerkundung, Klassifikations- und Regressionsansätze, Kohlenstoff in oberirdischer Vegetation, Veränderung der Landnutzung/-bedeckung, Zeitreihenanalyse

Lausch, A., Bastian, O., Herzog, F., Hostert, P., Jung, A., Klotz, S., Leitão, P.J., Rocchini, D., Schaepman, M.E., Skidmore, A.K., Tischendorf, L. & Knapp, S. (2018). Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods in Ecology and Evolution, 9: 1799-1809.
Leitão, P.J., Schwieder, M., Pedroni, F., Sanchez, M., Pinto, J.R.R., Maracahipes, L., Bustamante, M., & Hostert, P. (2019). Mapping woody plant community turnover with space-borne hyperspectral data – a case study in the Cerrado. Remote Sensing in Ecology and Conservation, 5: 107-115.
Leitão, P.J., Schwieder, M., Pötzschner, F., Pinto, J.R.R., Teixeira, A.M.C., Pedroni, F., Sanchez, M., Rogass, C., Linden, S., Bustamante, M.M.C., & Hostert, P. (2018). From sample to pixel: multi-scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes. Ecosphere, 9, e02298.
Leitão, P.J., Schwieder, M., & Senf, C. (2017). sgdm: An R Package for Performing Sparse Generalized Dissimilarity Modelling with Tools for gdm. ISPRS International Journal of Geo-Information, 6, 23.
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

Last updated on 2022-08-09 at 19:09