https://hdl.handle.net/20.500.11811/711 Sun, 12 Apr 2026 17:55:11 GMT 2026-04-12T17:55:11Z https://hdl.handle.net/20.500.11811/13798 Satellite remote sensing-based crop cover classification over Europe Donmez, Elif; Heckelei, Thomas; Storm, Hugo Crop maps play an important role in a variety of applications, from calculating crop areas and forecasting food production quantities to the analysis of agri-environmental interactions, highlighting the necessity of timely and accurate information on agricultural land use. The availability of remote sensing data has permitted numerous crop classification studies, which have investigated a variety of methods to improve classification performance, such as the selection of remote sensing sources, classification algorithms, and preprocessing methods. This paper compares these approaches with respect to classification accuracy in a European context. The study also investigates aspects such as classification level, study area division, and class granularity. The review shows that optical products provide more information for crop identification than radar products, however, combining optical data with radar backscatter increases accuracy. Classification accuracy benefits from specific features such as red-edge and spectral indices for optical products and Haralick textures for radar. Compared to traditional machine learning and distance-based classification methods, deep learning algorithms have been shown to achieve superior performance. Nevertheless, random forest's comparative accuracy at relatively low computational cost makes it a viable alternative for largescale applications. Finally, preprocessing methods and data on topography, climate, and crop growth patterns appear to improve accuracy. Thu, 09 Oct 2025 00:00:00 GMT https://hdl.handle.net/20.500.11811/13798 2025-10-09T00:00:00Z https://hdl.handle.net/20.500.11811/13614 Climate-relevant land cover composition and configuration trajectories in Europe Ferro, Marco; Dutta, Trishna; Hüttel, Silke; Lindner, Marcus; Poll, Stefan; Börner, Jan Land use and land cover change (LULCC) can affect the climate system by altering biophysical surface characteristics. At the local scale, climate regulating functions are co-determined by land cover composition and configuration, i.e. the proportions and the spatial arrangement of land cover types. However, research on the relationship between LULCC and climate often focuses individually either on compositional or configurational aspects. As a result, there is a gap in our knowledge about the spatiotemporal distribution of land cover composition and configuration patterns influencing the local climate regulating functions. Here, we used a range of LULCC metrics between 1992 and 2015 and applied Self-Organizing Maps to characterize dominant land cover composition and configuration trajectories in Europe. We then tested the climate relevance of the five most dominant trajectories with a high-resolution regional climate model. Land cover composition and configuration simultaneously changed in more than 20% of the European landmass, with cropland transition to forest patches and bare soil representing the major trajectory. Climate model simulations predict a general increase in the topsoil temperature due to only changes in land cover composition and configuration. All trajectories showed increasing topsoil temperature variability during the crop growing season, with forest transition trajectories showing a greater increase. Our findings demonstrate the relevance of changes in both land cover composition and configuration for the local climate and warrant further empirical and model-based research with an explicit focus on quantifying the effects of simultaneous changes in both these LULCC dimensions. Fri, 11 Apr 2025 00:00:00 GMT https://hdl.handle.net/20.500.11811/13614 2025-04-11T00:00:00Z https://hdl.handle.net/20.500.11811/13335 Digital platforms in the agricultural sector Sauvagerd, Monja; Mayer, Maximilian; Hartmann, Monika This paper introduces the concept of 'oligopolistic platformisation' to capture the specific dynamics of collaboration and competition between multinational upstream agribusinesses and Big Tech companies in the agricultural (ag) sector. We examine this phenomenon through the lens of Van Dijck et al.'s platform mechanisms: datafication, selection and commodification. Multinational agribusinesses operate sectoral ag platforms that analyse spatial, weather and agronomic data to optimise farming operations, whilst Big Tech companies provide the digital infrastructure, including cloud computing, data analytics and artificial intelligence. We explore how these pre-existing oligopolistic market structures influence the process and outcomes of platformisation in the ag sector. Using expert interviews, field observations, economic relationship mapping and an extensive literature review, we investigate relationships amongst multinational agribusinesses and between agribusinesses and Big Tech companies. Our findings reveal that Big Tech and multinational agribusinesses are collaboratively establishing digital platforms as the core organisational form of digital agriculture, aiming to consolidate most services. This collaboration blurs the lines between traditionally distinct industries, fostering overlapping ecosystems and mutually beneficial economic relationships in an already highly concentrated market. This dynamic has the potential to reinforce the market position of established companies, increase farmers' dependency on agribusinesses and contribute to fragmented and siloed data systems. Thu, 26 Dec 2024 00:00:00 GMT https://hdl.handle.net/20.500.11811/13335 2024-12-26T00:00:00Z https://hdl.handle.net/20.500.11811/13324 The multiple dimensions of resilience in agricultural trade networks Jafari, Yaghoob; Engemann, Helena; Zimmermann, Andrea The global food and agricultural trade network is crucial for food security. Shocks such as those posed by extreme weather events, conflicts, pandemics, and economic crises can test the resilience of the trade network to the sudden interruption of trade flows. Depending on the level of connectivity in the trade network and its structure, such shocks have the potential to propagate through the entire network and can affect countries' food availability and variety. This paper contributes to the literature on food and agricultural trade networks in two main ways: (1) understanding the global trade network as a complex system that can be affected by and responds to shocks, we define and operationalize its resilience as a multidimensional concept, which is shaped by the interdependencies in the network and their structure; and (2) applying techniques from network analysis to examine the evolution of three dimensions of resilience within the global food and agricultural trade network between 1995 and 2019. The main findings show that, between 1995 and 2007, trade connectivity among countries increased. Overall, this bolstered countries' and the network's resilience to trade shocks. However, vulnerabilities persisted in terms of ensuring sufficient product variety and quantity. Adding to these vulnerabilities, trade integration stalled in the second half of the series, pointing to a slight tendency towards trade disintegration and potentially lower resilience of countries to trade shocks already in 2019. Tue, 24 Sep 2024 00:00:00 GMT https://hdl.handle.net/20.500.11811/13324 2024-09-24T00:00:00Z