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Ökologischer Betrieb
Ökologischer Betrieb
Institute of

BW Farm Economics

All publications of Marcel Schwieder

  1. 0

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.15055561

  2. 1

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 202. Genève: Zenodo, DOI:10.5281/zenodo.15309479

  3. 2

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.16949898

  4. 3

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.17122420

  5. 4

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 202. Genève: Zenodo, DOI:10.5281/zenodo.17122646

  6. 5

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17197830

  7. 6

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17197761

  8. 7

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17190438

  9. 8

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17181387

  10. 9

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2024) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17182245

  11. 10

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17181503

  12. 11

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 302. Genève: Zenodo, DOI:10.5281/zenodo.17182293

  13. 12

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 302/2. Genève: Zenodo, DOI:10.5281/zenodo.17197871

  14. 13

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (raster): National-scale crop type maps for Germany from combined time series of Sentinel-2 and Landsat data (2025) [Datenpublikation]. 1 ZIP archive; Version 301. Genève: Zenodo, DOI:10.5281/zenodo.17197766

  15. 14

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2025) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2023) [Datenpublikation]. 1 ZIP archive; Version 201. Genève: Zenodo, DOI:10.5281/zenodo.17135735

  16. 15

    Lobert F, Schwieder M, Hostert P, Gocht A, Erasmi S (2025) Characterizing spatio-temporal patterns of winter cropland cover in Germany based on Landsat and Sentinel-2 time series. Int J Appl Earth Observ Geoinf 142:104728, DOI:10.1016/j.jag.2025.104728

    https://literatur.thuenen.de/digbib_extern/dn069891.pdf

  17. 16

    Schwieder M, Lobert F, Tetteh GO, Erasmi S (2025) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2023 [Datenpublikation]. 1 ZIP archive; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.16941138

  18. 17

    Schwieder M, Lobert F, Tetteh GO, Erasmi S (2025) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2024 [Datenpublikation]. 1 ZIP archive; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.16942505

  19. 18

    Muro J, Blickensdörfer L, Don A, Köber A, Asam S, Schwieder M, Erasmi S (2025) Hedgerow mapping with high resolution satellite imagery to support policy initiatives at national level. Remote Sens Environ 328:114870, DOI:10.1016/j.rse.2025.114870

    https://literatur.thuenen.de/digbib_extern/dn069821.pdf

  20. 19

    Kasiske T, Klimek S, Dauber J, Harpke A, Kühn E, Levers C, Schwieder M, Settele J, Sietz D, Tetteh GO, Musche M (2025) Identifying typical patterns of land-use and landscape structure in citizen science butterfly monitoring. Ecol Indic 180:114317, DOI:10.1016/j.ecolind.2025.114317

    https://literatur.thuenen.de/digbib_extern/dn070250.pdf

  21. 20

    Levers C, Schwieder M, Dieker P, Erasmi S, Rodríguez RA, Bayr U, Calvo Obando AJ, Fjellstad W, Furubayashi S, Heliölä J, Herzog F, Hyvönen T, Ievina L, Lakovskis P, Meier E, Ojanen H, Pitkänen T, Tomas WM (2025) Implementing an OECD Farmland Habitat Biodiversity Indicator : lessons and guidelines from eight pilot studies. 49 p

  22. 21

    Muro J, Blickensdörfer L, Köber A, Schwieder M, Don A, Erasmi S (2025) Spatially explicit distribution of hedgerows across German agricultural landscapes [Datenpublikation]. 1 TIFF file; Version 1. Genève: Zenodo, DOI:10.5281/zenodo.15654506

  23. 22

    Muro J, Blickensdörfer L, Köber A, Schwieder M, Don A, Erasmi S (2025) Spatially explicit distribution of hedgerows across German agricultural landscapes [Datenpublikation]. 1 TIFF file; Version 2. Genève: Zenodo, DOI:10.5281/zenodo.15782863

  24. 23

    Lobert F, Schwieder M, Alsleben J, Brög T, Kowalski K, Okujeni A, Hostert P, Erasmi S (2025) Unveiling year-round cropland cover by soil-specific spectral unmixing of Landsat and Sentinel-2 time series. Remote Sens Environ 318:114594, DOI:10.1016/j.rse.2024.114594

    https://literatur.thuenen.de/digbib_extern/dn069411.pdf

  25. 24

    Tetteh GO, Schwieder M, Pham V-D, Blickensdörfer L, Gocht A, Neuenfeldt S, van der Linden S, Erasmi S (2024) Agrarflächennutzung aus dem All kartiert: Daten zur Quantifizierung von Klimaschutzmaßnahmen. In: Köchy M (ed) Agrarforschung zum Klimawandel: Konferenz der Deutschen Agrarforschungsallianz, 11.-14.03.2024, Potsdam, unter der Schirmherrschaft des Bundesministeriums für Ernährung und Landwirtschaft; Programm und Beiträge, Stand: 7. Mai 2024. Braunschweig: DAFA, p 61, DOI:10.3220/DAFA1713767287000

    https://literatur.thuenen.de/digbib_extern/dn068079.pdf

  26. 25

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) ; Version 201 [Datenpublikation] [online]. 5 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10617623> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10617623

  27. 26

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) ; Version 202 [Datenpublikation] [online]. 5 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10640528> [zitiert am 03.12.2024], DOI:10.5281/zenodo.10640528

  28. 27

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) ; Version v201 [Datenpublikation] [online]. 6 TIFF files, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10628809> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10628809

  29. 28

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) ; Version v202 [Datenpublikation] [online]. 1 TIFF file, 1 PDF file, 2 CLR files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10645427> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10645427

  30. 29

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 2 PDF files, 5 FGB files, 1 SLD file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10619783> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10619783

  31. 30

    Tetteh GO, Schwieder M, Blickensdörfer L, Gocht A, Erasmi S (2024) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2022) [Datenpublikation] [online]. 1 FGB file, 1 PDF file, 1 SLD file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10621629> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10621629

  32. 31

    Kasiske T, Dauber J, Dieker P, Harpke A, Klimek S, Kühn E, Levers C, Schwieder M, Settele J, Musche M (2024) Assessing landscape-level effects of permanent grassland management and landscape configuration on open-land butterflies based on national monitoring data. Biodiv Conserv 33(8-9):2381-3404, DOI:10.1007/s10531-024-02861-6

    https://literatur.thuenen.de/digbib_extern/dn068253.pdf

  33. 32

    Schwieder M, Lobert F, Tetteh GO, Erasmi S (2024) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the year 2022 [Datenpublikation] [online]. 1 TIFF file. Genève: Zenodo, zu finden in <https://zenodo.org/records/10610283> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10610283

  34. 33

    Schwieder M, Lobert F, Tetteh GO, Erasmi S (2024) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat time series for the years 2017 - 2021 [Datenpublikation] [online]. 5 TIFF files. Genève: Zenodo, zu finden in <https://zenodo.org/records/10609590> [zitiert am 07.03.2024], DOI:10.5281/zenodo.10609590

  35. 34

    Weber D, Schwieder M, Ritter L, Koch T, Psomas A, Huber N, Ginzler C, Boch S (2024) Grassland-use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications. Remote Sensing Ecol Conserv 10(3):312-327, DOI:10.1002/rse2.372

    https://literatur.thuenen.de/digbib_extern/dn067152.pdf

  36. 35

    Weber D, Schwieder M, Huber N, Ginzler C, Boch S (2024) Kartierung der Grünlandnutzung aus dem All - methodisches Vorgehen und ökologische Anwendung für die Schweiz. N+L Inside(1):35-39

  37. 36

    Erasmi S, Muro J, Brög T, Blickensdörfer L, Fuß R, Gocht A, Don A, Schwieder M (2024) KlimaFern - Fernerkundung für eine Verbesserung der Klimaberichterstattung. In: Köchy M (ed) Agrarforschung zum Klimawandel: Konferenz der Deutschen Agrarforschungsallianz, 11.-14.03.2024, Potsdam, unter der Schirmherrschaft des Bundesministeriums für Ernährung und Landwirtschaft; Programm und Beiträge, Stand: 7. Mai 2024. Braunschweig: DAFA, p 60, DOI:10.3220/DAFA1713767287000

    https://literatur.thuenen.de/digbib_extern/dn068078.pdf

  38. 37

    Klein K, Ogan S, Tönshoff C, Böhner HGS, Dauber J, Erasmi S, Gocht A, Hellwig N, Klimek S, Krüger L, Lakemann L, Levers C, Lindermann L, Richter A, Röder N, Schwieder M, Sickel W, Sommerlandt FMJ, Stahl J, Tebbe CC, et al (2024) MonViA Indikatorenbericht 2024 : Bundesweites Monitoring der biologischen Vielfalt in Agrarlandschaften. Bonn: BLE, 199 p

    https://literatur.thuenen.de/digbib_extern/dn069418.pdf

  39. 38

    Follath T, Mickisch D, Hemmerling J, Erasmi S, Schwieder M, Demir B (2024) Multi-modal vision transformers for crop mapping from satellite image time series. In: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium : Athens, Greece, 17-12 July 2024. IEEE, DOI:10.1109/IGARSS53475.2024.10641794

  40. 39

    Pham V-D, Tetteh GO, Thiel F, Erasmi S, Schwieder M, Frantz D, van der Linden S (2024) Temporally transferable crop mapping with temporal encoding and deep learning augmentations. Int J Appl Earth Observ Geoinf 129:103867, DOI:10.1016/j.jag.2024.103867

    https://literatur.thuenen.de/digbib_extern/dn068235.pdf

  41. 40

    Frank C, Hertzog LR, Klimek S, Schwieder M, Tetteh GO, Böhner HGS, Röder N, Levers C, Katzenberger J, Kreft H, Kamp J (2024) Woody semi-natural habitats modulate the effects of field size and functional crop diversity on farmland birds. J Appl Ecol 61(5):987-999, DOI:10.1111/1365-2664.14604

    https://literatur.thuenen.de/digbib_extern/dn067799.pdf

  42. 41

    Lobert F, Löw J, Schwieder M, Gocht A, Schlund M, Hostert P, Erasmi S (2023) A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level. Remote Sens Environ 298:113800, DOI:10.1016/j.rse.2023.113800

    https://literatur.thuenen.de/digbib_extern/dn066907.pdf

  43. 42

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2023) Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 5 TIFF-Dateien, 2 Textdateien. Braunschweig: Thünen-Atlas, zu finden in <https://www.openagrar.de/receive/openagrar_mods_00087489> [zitiert am 10.07.2023], DOI:10.3220/DATA20230707103051-0

  44. 43

    Schwieder M, Tetteh GO, Blickensdörfer L, Gocht A, Erasmi S (2023) Agricultural land use (vector) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) [Datenpublikation] [online]. 5 Geopackages, 2 Textdateien. Braunschweig: Thünen-Atlas, zu finden in <https://www.openagrar.de/receive/openagrar_mods_00087490> [zitiert am 10.07.2023], DOI:10.3220/DATA20230707103117-0

  45. 44

    Tetteh GO, Schwieder M, Erasmi S, Conrad C, Gocht A (2023) Comparison of an optimised multiresolution segmentation approach with deep neural networks for delineating agricultural fields from Sentinel-2 images. J Photogramm Remote Sensing Geoinf Sci 91(4):295-312, DOI:10.1007/s41064-023-00247-x

    https://literatur.thuenen.de/digbib_extern/dn066421.pdf

  46. 45

    Holtgrave A-K, Förster M, Rossi M, Morel J, Ali I, Burchard-Levine V, Raya Serano MD, Fastnacht F, Rocchini D, Schwieder M, Hostert P, Kleinschmit B (2023) Remote sensing indices for environmental grassland monitoring in Europe. In: Brückner D, Kietzmann K (eds) Book of abstracts : 52nd Annual Meeting of the Ecological Society of Germany, Austria and Switzerland ; Leipzig - 12-16 September 2023. Berlin: Gesellschaft für Ökologie, p 697

  47. 46

    Kasiske T, Klimek S, Dauber J, Dieker P, Harpke A, Kühn E, Musche M, Schwieder M, Settele J (2023) Testing the effects of grassland mowing regimes and landscape configuration on butterflies at large spatial scales. In: Brückner D, Kietzmann K (eds) Book of abstracts : 52nd Annual Meeting of the Ecological Society of Germany, Austria and Switzerland ; Leipzig - 12-16 September 2023. Berlin: Gesellschaft für Ökologie, p 389

  48. 47

    Oliveira HFM, Fandos G, Zangrandi PL, Bendini HN, Silva DC, Muylaert RL, Marinho-Filho JS, Fonseca LMG, Rufin P, Schwieder M, Domingos FMCB (2022) Crops, caves, and bats: deforestation and mining threaten an endemic and endangered bat species (Lonchophylla: Phyllostomidae) in the Neotropical savannas. Hystrix 33(2):157-165, DOI:10.4404/hystrix-00541-2022

    https://literatur.thuenen.de/digbib_extern/dn065790.pdf

  49. 48

    Schwieder M, Wesemeyer M, Frantz D, Pfoch K, Erasmi S, Pickert J, Nendel C, Hostert P (2022) Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sens Environ 269:112795, DOI:10.1016/j.rse.2021.112795

    https://literatur.thuenen.de/digbib_extern/dn064247.pdf

  50. 49

    Blickensdörfer L, Schwieder M, Pflugmacher D, Nendel C, Erasmi S, Hostert P (2022) Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sens Environ 269:112831, DOI:10.1016/j.rse.2021.112831

    https://literatur.thuenen.de/digbib_extern/dn064297.pdf

  51. 50

    Lobert F, Röder N, Gocht A, Schwieder M, Erasmi S (2022) Mowing detection from combined Sentinel-1, Sentinel-2, and Landsat 8 time series on fallow cropland with transfer learning. Publikationen der DGPF eV 30:117-126

  52. 51

    Lobert F, Holtgrave A-K, Schwieder M, Pause M, Gocht A, Vogt J, Erasmi S (2021) Detection of mowing events from combined Sentinel-1, Sentinel-2, and Landsat 8 time series with machine learning. Grassl Sci Europe 26:123-125

  53. 52

    Buddeberg M, Schwieder M, Orthofer A, Kowalski K, Pfoch K, Hostert P, Bach H (2021) Estimating grassland biomass from Sentinel 2 - a study on model transferability. Grassl Sci Europe 26:211-213

  54. 53

    Tetteh GO, Gocht A, Erasmi S, Schwieder M, Conrad C (2021) Evaluation of sentinel-1 and sentinel-2 feature sets for delineating agricultural fields in heterogeneous landscapes. IEEE Access 9:116702-116719, DOI:10.1109/ACCESS.2021.3105903

    https://literatur.thuenen.de/digbib_extern/dn063902.pdf

  55. 54

    Schwieder M, Wesemeyer M, Frantz D, Pfoch K, Erasmi S, Pickert J, Nendel C, Hostert P (2021) Grassland mowing events across Germany detected from combined Sentinel-2 and Landsat 8 time series for the years 2017 - 2020 [Datenpublikation] [online]. Genève: Zenodo, zu finden in <https://zenodo.org/records/5571613> [zitiert am 03.12.2024], DOI:10.5281/zenodo.5571613

  56. 55

    Wesemeyer M, Schwieder M, Pickert J, Hostert P (2021) Identifying areas of homogeneous grassland management based on iterative segmentation of Sentinel-1 and Sentinel-2 data. Grassl Sci Europe 26:208-210

  57. 56

    Lobert F, Holtgrave A-K, Schwieder M, Pause M, Vogt J, Gocht A, Erasmi S (2021) Mowing event detection in permanent grasslands: Systematic evaluation of input features from Sentinel-1, Sentinel-2, and Landsat 8 time series. Remote Sens Environ 267:112751, DOI:10.1016/j.rse.2021.112751

    https://literatur.thuenen.de/digbib_extern/dn064075.pdf

  58. 57

    Schwieder M, Buddeberg M, Kowalski K, Pfoch K, Bartsch J, Bach H, Pickert J, Hostert P (2020) Estimating grassland parameters from Sentinel-2: A model comparison study. J Photogramm Remote Sensing Geoinf Sci 88:379-390, DOI:10.1007/s41064-020-00120-1

    https://literatur.thuenen.de/digbib_extern/dn062563.pdf

  59. 58

    Tetteh GO, Gocht A, Schwieder M, Erasmi S, Conrad C (2020) Unsupervised parameterization for optimal segmentation of agricultural parcels from satellite images in different agricultural landscapes. Remote Sensing 12(18):3096, DOI:10.3390/rs12183096

    https://literatur.thuenen.de/digbib_extern/dn062673.pdf

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