Jun.-Prof. Dr. Christoph Raab

Christoph is a geographer by training, with a focus on remote sensing and nature conservation. He is especially interested in the application of machine learning algorithms to understand changes in agricultural land use and grassland ecosystems.

https://orcid.org/0000-0002-3044-6826

• Satellite remote sensing
• Machine learning
• Change analysis
• Grassland ecosystems
• Biodiversity and nature conservation

2019-2023

Postdoctoral Researcher University for Sustainable Development in Eberswalde, focussing on agricultural land cover change and sustainable land use in Central and South Asia as part of the junior research group TRANSECT (www.transect.de).

2016-2019

PhD Researcher, Georg-August-University Göttingen

Thesis: Combining remote sensing data at different spatial, temporal and spectral resolutions to characterise semi-natural grassland habitats for large herbivores in a heterogeneous landscape

2014-2015

Research Assistant, University College Cork

Project: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery – TaLAM

2011-2014

M.Sc. Geographical Science – Environmental Hydrology, Freie Universität Berlin

Thesis: Land cover pattern in a sub-catchment of the Voi River, Kenya, from remote sensing and field data – A comparison of pixel- and object-based classification method

2007-2011

B.Sc. Georesources Management, RWTH Aachen

Thesis: Wildbäche und Muren in den Hochgebirgsregionen der Alpen: Größenordnung, Verbaumaßnahmen und ökologische Konzepte

Peer-reviewed Journal Articles

Zwick, M., Cardoso, J. A., Gutierrez-Zapata, D. M., Cerón-Muñoz, M., Gutierrez, J. F., Raab, C., ... & Barrett, B. (2024). Pixels to Pasture: Using Machine Learning and Multispectral Remote Sensing to Predict Biomass and Nutrient Quality in Tropical Grasslands. Remote Sensing Applications: Society and Environment, 101282.

Raab, C., & Spies, M. (2023). Characterising cropland fragmentation in post-Soviet Central Asia, using Landsat remote-sensing time series data. Applied Geography, 156, 102968.

Zuberi, M., Raab, C., Spies, M. (2022) Landnutzungswandel im Baumwollgürtel Pakistans. Gen-Baumwolle, Agrardiversifizierung und ökologische Herausforderungen im Süden des Punjab, Geographische Rundschau 75/4, 20-25.

Spies, M.; Zuberi, M.; Mählis, M.; Zakirova, A.; Alff, H.; Raab, C. (2022) Towards a participatory systems approach to managing complex bioeconomy interventions in the agrarian sector. Sustainable Production and Consumption. DOI: 10.1016/j.spc.2022.03.020

Raab, C., Riesch, F., Tonn, B., Barrett, B., Meißner, M., Balkenhol, N., & Isselstein, J (2020). Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data. Remote Sensing in Ecology and Conservation.

Richter, L., Balkenhol, N., Raab, C., Reinecke, H., Meißner, M., Herzog, S., Isselstein, J. & Signer, J. (2020). So close and yet so different: The importance of considering temporal dynamics to understand habitat selection. Basic and Applied Ecology.

Raab, C., Tonn, B., Meißner, M., Balkenhol, N., & Isselstein, J. (2019). Multi-temporal RapidEye Tasselled Cap data for land cover classification. European Journal of Remote Sensing, 52(1), 653-666.

Raab C., Stroh H.-G., Tonn B., Meißner M., Rohwer N., Balkenhol N., Isselstein J. (2018): Mapping semi-natural grassland communities using multi-temporal RapidEye remote sensing data. International Journal of Remote Sensing. DOI: 10.1080/01431161.2018.1504344

Tonn B., Raab C., Isselstein J. (2018): Sward patterns created by patch grazing are stable over more than a decade. Grass and Forage Science. DOI: 10.1111/gfs.12389

Barrett B., Raab C., Cawkwell F., & Green S. (2016): Upland vegetation mapping using Random Forests with optical and radar satellite data. Remote Sensing in Ecology and Conservation. DOI: 10.1002/rse2.32

Raab C., Barrett B., Green S., Cawkwell F. (2015): Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land cover accounting and monitoring in Ireland. Remote Sensing Letters. DOI: 10.1080/2150704X.2015.1076950