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Ökologischer Betrieb
© BLE, Bonn/Thomas Stephan
Ökologischer Betrieb
Institute of

BW Farm Economics

All publications of Marcel Schwieder

  1. 0

    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

  2. 1

    Schwieder M, Tetteh G, 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

  3. 2

    Schwieder M, Tetteh G, 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

  4. 3

    Tetteh G, 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

  5. 4

    Weber D, Schwieder M, Ritter L, Koch T, Psomas A, Huber N, Ginzler C, Boch S (2023) Grassland-use intensity maps for Switzerland based on satellite time series: Challenges and opportunities for ecological applications. Remote Sensing Ecol Conserv: in Press, DOI:10.1002/rse2.372

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

  6. 5

    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, Deutschland: Gesellschaft für Ökologie, p 697

  7. 6

    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, Deutschland: Gesellschaft für Ökologie, p 389

  8. 7

    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

  9. 8

    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

  10. 9

    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

  11. 10

    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

  12. 11

    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

  13. 12

    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

  14. 13

    Tetteh G, 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

  15. 14

    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

  16. 15

    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

  17. 16

    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

  18. 17

    Tetteh G, 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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