Peer-reviewte Publikationen

2025

  • Grinnell, N. A., Hamidi, D., Komainda, M., Riesch, F., Horn, J., Traulsen, I., Palme, R., & Isselstein, J. (2025). Supporting rotational grazing systems with virtual fencing: paddock transitions, beef heifer performance, and stress response. animal, 19(2), 101416. https://doi.org/10.1016/j.animal.2024.101416.
  • Wilms, L., Hamidi, D., Lüntzel, C. H. U., Hamidi, M., Komainda, M., Palme, R., Isselstein, J., Waiblinger, S., & Egerbacher, M. (2025). Assessing learning, behaviour, and stress level in goats while testing a virtual fencing training protocol. animal, 19(2), 101413. https://doi.org/10.1016/j.animal.2024.101413.

2024

  • Hamidi, D., Grinnell, N. A., Komainda, M., Wilms, L., Riesch, F., Horn, J., Hamidi, M., Traulsen, I., & Isselstein, J. (2024). Training cattle for virtual fencing: Different approaches to determine learning success. Applied Animal Behaviour Science, 273, 106220. https://doi.org/10.1016/j.applanim.2024.106220.
  • Hütt, C., Isselstein, J., Komainda, M., Schöttker, O., & Sturm, A. (2024). UAV LiDAR-based grassland biomass estimation for precision livestock management. Journal of Applied Remote Sensing, 18(1), 017502-017502. https://doi.org/10.1117/1.JRS.18.017502.
  • Kiefer, A., Stumpe, C., Hütt, C., & Bahrs, E. (2024). Comparing economic effects of remote herbage mass estimation in small-scale farms in mountain regions. Landtechnik, 79(1). https://doi.org/10.15150/lt.2024.3302.
  • Kowalski, K., Senf, C., Okujeni, A., Hostert, P. (2024). Large‐scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand. Global Change Biology 30, e17315. https://doi.org/10.1111/gcb.17315.
  • Lussem, U., Bolten, A., Kleppert, I., Jasper, J., Gnyp, M.L., Schellberg, J., Bareth, G. (2022). Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning. Remote Sensing 14:3066. https://doi.org/10.3390/rs14133066
  • Möck, M., Feindt, P. H., 2024: Policy Learning in the Face of Ambiguity: Puzzling and Powering in Multiple Streams, in: International Review of Public Policy, 6(2), 267-304. https://doi.org/10.4000/130zk.
  • Möck, M., & Feindt, P. H. (2024). Learning mode misfits in policy learning: typology, case study and lessons learnt. Journal of European Public Policy, 31(7), 2050-2075. https://doi.org/10.1080/13501763.2023.2280678.
  • Okujeni, A., Kowalski, K., Lewińska, K.E., Schneidereit, S., Hostert, P. (2024). Multidecadal grassland fractional cover time series retrieval for Germany from the Landsat and Sentinel-2 archives. Remote Sensing of Environment 302, 113980. https://doi.org/10.1016/j.rse.2023.113980.
  • Wätzold, F., Jauker, F., Komainda, M., Schöttker, O., Horn, J., Sturm, A., & Isselstein, J. (2024). Harnessing virtual fencing for more effective and adaptive agri-environment schemes to conserve grassland biodiversity. Biological Conservation, 297, 110736. https://doi.org/10.1016/j.biocon.2024.110736.
  • Wilms, L., Komainda, M., Hamidi, D., Riesch, F., Horn, J., & Isselstein, J. (2024). How do grazing beef and dairy cattle respond to virtual fences? A review. Journal of Animal Science, 102, skae108. https://doi.org/10.1093/jas/skae108.

2023

  • Bareth, G. and Hütt, C. (2023): Evaluation of Direct RTK‑georeferenced UAV Images for Crop and Pasture Monitoring Using Polygon Grids. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. https://doi.org/10.1007/s41064-023-00259-7.
  • Goncalves Bazzo, C.O., Kamali, B., Hütt, C., Bareth, G., and Gaiser, T. (2023): A review of estimation methods for aboveground biomass in grasslands using UAV. Remote Sensing 15 (3), 639, https://doi.org/10.3390/rs15030639.
  • Grinnell, N. A., Komainda, M., Tonn, B., Hamidi, D., & Isselstein, J. (2023). Long-term effects of extensive grazing on pasture productivity. Animal Production Science, 63(12), 1236-1247. https://doi.org/10.1071/AN22316.
  • Hamidi, D., Hütt, C., Komainda, M., Grinell, N.A., Horn, J., Riesch, F., Hamidi, M., Traulsen, I., and Isselstein, J. (2023): Grid grazing: A case study on the potential of combining virtual fencing and remote sensing for innovative grazing management on a grid base. Livestock Science 278, 105373, https://doi.org/10.1016/j.livsci.2023.105373.
  • Hütt, C., Bolten, A., Hüging, H., and Bareth, G. (2023): UAV LiDAR metrics for monitoring crop height, biomass and nitrogen uptake: A case study on a winter wheat field trial. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 91(2), pp.65-76. https://doi.org/10.1007/s41064-022-00228-6.
  • Kowalski, K., Okujeni, A., Hostert, P. (2023). A generalized framework for drought monitoring across Central European grassland gradients with Sentinel-2 time series. Remote Sensing of Environment 286, 113449. https://doi.org/10.1016/j.rse.2022.113449.
  • Schöttker, O., Hütt, C., Jauker, F., Witt, J., Bareth, G., and Wätzold, F. (2023): Monitoring costs of result-based payments for biodiversity conservation: Will UAV-assisted remote sensing be the game-changer?. Journal for Nature Conservation 76, 126494. https://doi.org/10.1016/j.jnc.2023.126494.
  • Sturm, A., Schöttker, O., Kadir, K., Wätzold, F. (2023). „Wann, wo und wie? Ein softwarebasiertes Mehrebenen-
    Informationssystem zur Optimierung von Beweidungssystemen“ in Lecture Notes in Informatics (LNI) –
    Proceedings Series of the Gesellschaft für Informatik (GI) Volume P-330 ISBN 978-3-88579-724-1 ISSN
    1617-5468. https://dl.gi.de/items/e77b74e0-1aed-4530-bc8b-094c716d99c5 

2022

  • Kowalski, K., Okujeni, A., Brell, M. & Hostert, P. (2022). Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series. Remote Sensing of Environment, 268, 112781. https://doi.org/10.1016/j.rse.2021.112781.
  • Stampa, E., & Zander, K. (2022). Backing biodiversity? German consumers’ views on a multi-level biodiversity-labeling scheme for beef from grazing-based production systems. Journal of Cleaner Production, 370, 133471. https://doi.org/10.1016/j.jclepro.2022.133471.
  • Horn, J., & Isselstein, J. (2022). How do we feed grazing livestock in the future? A case for knowledge‐driven grazing systems. Grass and Forage Science, 77(3), 153–166. https://doi.org/10.1111/gfs.12577.
  • Hamidi, D., Grinnell, N. A., Komainda, M., Riesch, F., Horn, J., Ammer, S., … & Isselstein, J. (2022). Heifers don’t care: no evidence of negative impact on animal welfare of growing heifers when using virtual fences compared to physical fences for grazing. animal, 16(9), 100614. https://doi.org/10.1016/j.animal.2022.100614.

2021

  • Bareth, G. & Hütt, C. (2021). Upscaling and validation of RTK direct georeferenced UAV based RGB image data with planet imagery using polygon grids for pastures, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 533–538. https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-533-2021.
  • Hamidi, D., Komainda, M., Tonn, B., Harbers, J., Grinnell, N. A., & Isselstein, J. (2021). The effect of grazing intensity and sward heterogeneity on the movement behavior of suckler cows on semi-natural grassland. Frontiers in Veterinary Science, 8. https://doi.org/10.3389/fvets.2021.639096.
  • Jenal, A., Hüging, H., Ahrends, H.E., Bolten, A., Bongartz, J. & Bareth, G. (2021). Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat. Remote Sensing, 13(9):1697. https://doi.org/10.3390/rs13091697.
  • Schmiedgen, A., Komainda, M., Kowalski, K., Hostert, P., Tonn, B., Kayser, M. & Isselstein, J. (2021). Impacts of cutting frequency and position to tree line on herbage accumulation in silvopastoral grassland reveal potential for grassland conservation based on land use and cover information. Ann Appl Biol aab.12681. https://doi.org/10.1111/aab.12681.

2020

  • Jenal, A., Lussem, U., Bolten, A., Gnyp, M.L., Jasper, J., Bongartz, J., & Bareth, G. (2020). Investigating the potential of a newly developed UAV-based VNIR/SWIR imaging system for forage mass monitoring. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 88 (6), 493-507. https://doi.org/10.1007/s41064-020-00128-7.
  • Lussem, U., Schellberg, J. & Bareth, G. (2020). Monitoring forage mass with low-cost UAV data: case study at the Rengen grassland experiment. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 88 (5), 407–422. https://doi.org/10.1007/s41064-020-00117-w.
  • Stampa, E., Zander, K., Hamm, U. (2020). Insights into German Consumers’ Perceptions of Virtual Fencing in Grassland-Based Beef and Dairy Systems: Recommendations for Communication. Animals, 10(12): 2267. https://doi.org/10.3390/ani10122267.
  • Stampa, E., Schipmann-Schwarze, C., Hamm, U. (2020). Consumer perceptions, preferences, and behavior regarding pasture-raised livestock products: A review. Food Quality and Preference, 82: 103872. https://doi.org/10.1016/j.foodqual.2020.103872.