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
• Smallholder farming Pakistan

EuroPa - A coupled remote sensing-field assessment approach taking two Biosphere Reserves - European National Parks/Nature Conservation Areas as a comparative case study 

October 2025 – September 2028

Role: Funding acquisition and project lead 

Project volume: 195.000 €

Funding agency: EuroPA is funded by Marianne und Dr. Fritz Walter foundation (Grant Number - T0192/46979/202)

The loss of biodiversity from the species to the landscape level as well as the loss of ecosystem services continue to be a major problem of global environmental change and thus, calls for improving existing nature conservation strategies. Large Protected Areas such as National Parks, Nature Conservation Areas and Biosphere Reserves play an important role in protecting biodiversity, either without human intervention or with the integration of sustainable land use. Nevertheless, how different concepts of Protected Areas contribute to biodiversity maintenance has so far been insufficiently investigated. Our project follows the overarching hypothesis that in the densely populated cultural landscapes of Europe, characterized by land use for thousands of years, Biosphere Reserves contribute differently than National Parks and Nature Conservation Areas to biodiversity maintenance and ecosystem services, particularly for those land-use types of the traditional cultural landscape. This fundamentally calls into question whether the National Park and Nature Conservation Area protection strategies, at least for Europe, is always appropriate. As a case study, the paired protected areas Hortobágy Biosphere Reserve and National Park (Hungary), as well as Spreewald Biosphere Reserve and Nature Conservation Areas (Germany), are examined, where different conservation strategies have been implemented in the same area. These regions comprise natural, near-natural ecosystems as well as numerous land-use systems. An assessment of the biodiversity and ecosystem services of the various ecosystems and landuse types respectively will be carried out for the different Protected Areas concepts. The focus will be on vegetation and soil, with the investigation of spatial and temporal changes being supported by the use of satellite-based remote sensing methods. 

SMALLPAK - Innovations for Resilient Smallholder Production Systems in Punjab, Pakistan

November 2025 – December 2028

Role: Funding acquisition (Teilvorhaben) and principle investigator 

Verbundprojekt: 

• University of Hildesheim, Germany (Teilprojekt)
• Eberswalde University for Sustainable Development, Germany (project lead)
• MNS-University of Agriculture Multan, Pakistan
• Lahore University of Management Sciences, Pakistan

Project volume: 1.346.506 EURO 

Funding agency: 

SMALLPAK is funded by the German Federal Ministry of Agriculture, Food, and Regional Identity (BMLEH). The project is managed by the Federal Office for Agriculture and Food (BLE) within the research scheme “NIPS: Innovative Sustainable Production Systems” (Grant Number - 2822NIPS07)

Website: www.smallpak.info

Summary:

The vast majority of agricultural producers in Pakistan are smallholder farmers. Strengthening the resilience and long‑term productivity of their farming systems is crucial for the country’s food security.

Smallholder farmers in Pakistan face a wide range of challenges: low yields, increasing climatic and other environmental stresses, high input prices, and volatile markets. Nevertheless, their needs and vulnerabilities are often neglected in agricultural policy, which prioritizes modernization approaches that primarily benefit larger agricultural holdings and enterprises. National agricultural research primarily focuses on technical innovations and pays limited attention to the specific challenges, perspectives, and local knowledge of smallholder farmers. SMALLPAK addresses this gap in current agricultural research and policy in Pakistan. The aim is to place smallholder agriculture more firmly at the centre and to promote integrative and transdisciplinary perspectives on agricultural and food systems in both research and practice.

More than half of Pakistan’s rural population lives in Punjab, the province that SMALLPAK focuses on. The predominantly small‑scale farming practiced there is crucial for the country’s food security and contributes around 40% to national agricultural production.

The aim is to develop and examine locally tested, socially adapted, and ecologically sustainable innovations for smallholder production systems that can be disseminated within the region and beyond by the involved decision-makers and other actors. Additional goals include capacity building for researchers, smallholder communities, and decision-makers to strengthen agricultural policy strategies that explicitly address the needs of smallholder farmers.

The project focuses on the province of Punjab and examines how smallholder production systems can be strengthened in socially adapted and ecologically sustainable ways. Building on local knowledge and new technologies, innovative approaches are identified, assessed, and developed in close connection with practical application. The project is structured into three core modules: 

(1) local innovations that emphasize multifunctionality and integrated, resilient production practices; 

(2) digital innovations that support smallholder farmers in enhancing their production and managing climatic and environmental risks; and

(3) organizational innovations that improve smallholders’ access to markets, inputs, and knowledge through new forms of social and economic cooperation. 

SMALLPAK follows an integrative and participatory approach that involves policy makers and, most importantly, smallholder farmers themselves, from the beginning in both research activities and project design. The project contributes to improving agricultural practice and policymaking. In addition, it contributes to capacity building among researchers, smallholder communities, and decision-makers, helping to ensure that the often politically neglected smallholder farmers receive greater attention on the agricultural policy agenda.

Since 2023

Junior professorship in applied geoecology with a focus on remote sensing at the University of Hildesheim

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

Raab, C., Nguyen, V. D., Barrett, B., Zakirova, A., Zuberi, M., Alff, H., & Spies, M. (2026). Crop type classification in smallholder agriculture of central and South Asia using Sentinel-1/2 data fusion. Remote Sensing Applications: Society and Environment, 42, 102007. https://doi.org/10.1016/j.rsase.2026.102007

Riesch, F., Isselstein, J., Balkenhol, N., Beckmann, J., Bojarska, K., Gerber, N., Herzog, S., Jarmer, E., Meißner, M., Raab, C., Tonn, B., Zetsche, M., & Signer, J. (2025). From here to there: Free-ranging large herbivores redistribute nutrients from grassland to forest soil. Landscape Ecology, 40(5), 96. doi.org/10.1007/s10980-025-02116-x

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