Forschungsschwerpunkte

• Remote Sensing based Quantification of Soil Properties
• Analysis of Hyperspectral Data
• Remote Sensing in Precision Agriculture
• UAV Remote Sensing
• Monitoring in Remote Sensing
• Laboratory and Field Spectroscopy

Lebenslauf

Publikationen

Peer-reviewed journal paper

Reuter, T., Nahrstedt, K., Jarmer, T., Broll, G., Trautz, D., 2025. Delineation of management zones in clover-grass for site-specific management of subsequent crops. Precision Agriculture, 26, 61. https://doi.org/10.1007/s11119-025-10260-2

Reuter, T., Nahrstedt, K., Wittstruck, L., Jarmer, T., Broll, G., Trautz, D., 2025. Site-specific mechanical weed management in maize (Zea mays) in North-West Germany. Crop Protection. https://doi.org/10.1016/j.cropro.2025.107123

Storch, M., Kisliuk, B., Jarmer, T., Waske, de Lange, N., 2025. Comparative analysis of UAV-based LiDAR and photogrammetric systems for the detection of terrain anomalies in a historical conflict landscape. Science of Remote Sensing, 11, 100191. https://doi.org/10.1016/j.srs.2024.100191

Wittstruck, L., Jarmer, T., Waske, B., 2024. Multi-Stage Feature Fusion of Multispectral and SAR Satellite Images for Seasonal Crop-Type Mapping at Regional Scale Using an Adapted 3D U-Net Model. Remote Sensing, 16(17), 3115. https://doi.org/10.3390/rs16173115

Nahrstedt, K., Reuter, T., Trautz, D., Waske, B., Jarmer, T., 2024. Classifying stand compositions in clover grass based on high-resolution multispectral UAV images. Remote Sensing, 16(14), 2684. https://doi.org/10.3390/rs16142684

Ubben, N., Pukrop, M. Jarmer, T., 2024. Spatial resolution as a factor for efficient UAV-based weed mapping – A soybean field case study. Remote Sensing, 16(10), 1778. https://doi.org/10.3390/rs16101778

Seiche, A.T., Wittstruck, L., Jarmer, T., 2024. Weed detection from UAV imagery using deep learning – A multispectral sensor comparison between high-end and low-cost. Sensors, 24, 1544.

Niemeyer, M., Renz, M., Pukrop, M., Hagemann, D., Zurheide, T., di Marco, D., Höferlin, M., Stark, P., Rahe, F., Igelbrink, M., Jenz, M., Jarmer, T., Trautz, D., Stiene, S., Hertzberg, J., 2024. Cognitive Weeding: an approach to single‑plant specific weed regulation. Künstliche Intelligenz. https://doi.org/10.1007/s13218-023-00825-6

Pöttker, M., Kiehl, K., Jarmer, T. & Trautz, D., 2023. Convolutional Neural Network maps plant communities in semi-natural grasslands using multispectral Unmanned Aerial Vehicle imagery. Remote Sensing, 15(7), 1945.

Storch, M., de Lange, N., Jarmer, T., Waske, B., 2023. Detecting historical terrain anomalies with UAV-LiDAR data using spline-approximation and support vector machines. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 16, 3158-3173.

Wittstruck, L., Jarmer, T., Trautz, D. & Waske, B., 2022. Estimating LAI from winter wheat using UAV data and CNNs. IEEE Geoscience and Remote Sensing Letters, 19, 1-5 (Art no. 2503405).

Stojanovic, O., Siegmann, B., Jarmer, T., Pipa, G., Leugering, J., 2022. Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets. Frontiers in Environmental Science, 9, 780814.

Storch, M., Jarmer, T., Adam, M., de Lange, N., 2022. Systematic approach for remote sensing of historical conflict landscapes with UAV-based Laserscanning. Sensors, 22(1), 217.

Hänel, T., Jarmer, T., & Aschenbruck, N., 2021. Learning a transform base for the multi- to hyperspectral sensor network with K-SVD. Sensors, 21, 7296.

Wittstruck, L., Kühling, I., Trautz, D., Kohlbrecher, M., Jarmer, T., 2021. UAV-based RGB imagery for Hokkaido pumpkin (Cucurbita max.) detection and yield estimation. Sensors, 21, 118.

Hänel, T., Jarmer, T. & Aschenbruck, N., 2019. Using distributed compressed sensing to derive continuous hyperspectral imaging from a wireless sensor network. Computers and Electronics in Agriculture, 166, 9p.

Bauer, J., Jarmer, T., Schittenhelm, S., Siegmann, B. & Aschenbruck, N., 2019. Processing and filtering of leaf area index time series assessed by in-situ wireless sensor networks. Computers and Electronics in Agriculture, 165, 14p.

Kanning, M., Kühling, I., Trautz, D. & Jarmer, T., 2018. High resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, 10(12), 2000.

Polinova, M., Jarmer, T. & Brook, A., 2018. Spectral data source effect on crop state estimation by vegetation indices. Environmental Earth Sciences, 77, 752.

Bauer, J., Siegmann, B., Jarmer, T. & Aschenbruck, N., 2016. On the potential of wireless sensor networks for the in-situ assessment of crop leaf area index. Computers and Electronics in Agriculture, 128, 149-159.

Kanning, M., Siegmann, B. & Jarmer, T., 2016. Regionalization of uncovered agricultural soils based on organic carbon and soil texture estimations. Remote Sensing, 8, 927-943.

Jarmer, T. & Shoshany, M., 2016. Relationships between soil spectral and chemical properties along a climatic gradient in the Judean Desert. Arid Land Research and Management, 30(2), 123-137.

Siegmann, B. & Jarmer, T., 2015. Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data. International Journal of Remote Sensing, 36(18), 4519-4534.

Siegmann, B., Jarmer, T., Beyer, F. & Ehlers, M., 2015. The potential of pan-sharpened EnMAP data for the assessment of wheat LAI. Remote Sensing, 7, 12737-12762.

Beyer, F., Jarmer, T. & Siegmann, B., 2015. Identification of agricultural crop types in Northern Israel using multitemporal RapidEye data. Photogrammetrie-Fernerkundung-Geoinformation, 1/2015, 21-32.

Jarmer, T., 2013. Spectroscopy and hyperspectral imagery for monitoring summer barley. International Journal of Remote Sensing, 34(17), 6067-6078.

Schwanghart, W. & Jarmer, T., 2011. Linking spatial patterns of soil organic carbon to topography - a case study from south-eastern Spain. Geomorphology, 126, 252-263.

Shoshany, M., Kizel, F., Netanyahu, N.S., Goldshlager, N., Jarmer, T. & Even-Tzur, G., 2011. An iterative search in end-member fraction space for spectral unmixing. Geoscience and Remote Sensing Letters, 99, 706-709.

Jarmer, T., Hill, J., Lavée, H. & Sarah, P., 2010. Mapping soil organic carbon in semi-arid and arid ecosystems of Israel. Photogrammetric Engineering and Remote Sensing, 75(1), 85-94.

Richter, N., Jarmer, T., Chabrillat, S., Oyonarte, C., Hostert, P. & Kaufmann, H., 2009. Free iron oxide determination in Mediterranean soils using diffuse reflectance spectroscopy. Soil Science Society of America Journal, 73, 72-81.

Jarmer, T., Vohland, M., Lilienthal, H. & Schnug, E., 2008. Estimation of some chemical properties of an agricultural soil by spectroradiometric measurements. Pedosphere, 18(2), 163-170.

Vohland, M. & Jarmer, T., 2008. Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL). International Journal of Remote Sensing, 29(1), 191-209.

Udelhoven, T., Emmerling, C. & Jarmer, T., 2003. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial-least-square regression: A feasibility study. Plant and Soil, 251(2), 319-329.

Udelhoven, T., Hostert, P., Jarmer, T. & Hill, J., 2003. Klassifikation von Getreideflächen mit hyperspektralen Bilddaten des HyMap-Sensors. Photogrammetrie-Fernerkundung-Geoinformation, 1/2003, 35-42.

Hostert, P., Röder, A, Jarmer, T., Udelhoven, T. & Hill, J., 2001. The potential of remote sensing and GIS for desertification monitoring and assessment.- Annals of Arid Zone, 40, 2,     
103-140.

Books

Jarmer, T., 2005. Der Einsatz von Reflexionsspektrometrie und Satellitenbilddaten zur Erfassung pedochemischer Eigenschaften in semi-ariden und ariden Gebieten Israels (= Trierer Geographische Studien, Heft 29). Promotionsschrift, Trier.

Book chapters

Jarmer, T., Lavée, H., Sarah, P. & Hill, J., 2009. Using reflectance spectroscopy and Landsat data to assess soil inorganic carbon in the Judean Desert (Israel). In: Röder, A. & Hill, J. (eds.): Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment. ISPRS Book Series. London, Taylor & Francis, 227-241.

Hill, J., Udelhoven, T., Jarmer, T. & Yair, A., 2008. Land cover in the Nizzana sandy arid ecosystem. Mapping surface properties with multi-spectral remote sensing data. In: Arid Dune Ecosystems. The Nizzana Sands in the Negev Desert. (= Ecological Studies, Vol. 200), Berlin, Springer, 157-172.

Jarmer, T., 1997. Ein optimiertes Verfahren zur multispektralen Klassifikation von Waldbeständen nach Baumart und Altersstruktur. In: Aktuelle Forschungen aus dem Fachbereich VI Geographie / Geowissenschaften, Hrsg.: R. Baumhauer. (= Trierer Geographische Studien, 16), Trier, 229-240.

Lehre