Publications
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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
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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
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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
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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
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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
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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
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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
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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
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Lobert F (2025) Remote sensing of cropland dynamics for climate-related agricultural monitoring in Germany. Berlin: Humboldt-Univ, xxi, 175 p, Berlin, Humboldt-Univ, Mathematisch-Naturwiss Fak, Diss, DOI:10.18452/34374
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Brög T (2025) Remote sensing-based monitoring of cropland soil organic carbon. Tübingen: Eberhard Karls Univ, xxvii, 283 p, Tübingen, Univ, Mathematisch-Naturwiss Fak, Diss, 2025, DOI:10.15496/publikation-114189
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Wang Y, Rammig A, Blickensdörfer L, Wang Y, Zhu XX, Buras A (2025) Species-specific responses of canopy greenness to the extreme droughts of 2018 and 2022 for four abundant tree species in Germany. Sci Total Environ 958:177938, DOI:10.1016/j.scitotenv.2024.177938
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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
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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
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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
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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
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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
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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
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Peters F, Kempe A, Kübler D, Günter S (2024) Evaluating forest degradation, deforestation, and reforestation in Boeny and DIANA: Current efforts and future opportunities. Braunschweig: Johann Heinrich von Thünen-Institut, 116 p, Thünen Working Paper 248, DOI:10.3220/WP1728377983000
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Langner N, Puhm M, Deutscher J, Wimmer A, Adler P, Backa J, Eisenecker P, Reinosch E, Wiesehahn J, Hoffmann K, Oehmichen K (2024) FNEWS-Jahresprodukte 2018 bis 2022 [Datenpublikation] [online]. Braunschweig: Thünen-Atlas, zu finden in <https://atlas.thuenen.de/layers/geonode_data:geonode:fnews_jp_18_22> [zitiert am 08.03.2024], DOI:10.3220/DATA20240307175924-0
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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
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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
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Schmidt F, Kruse M, Paulsen HM (2024) Kohlenstoffspeicherung in Böden und Gehölzen in einem landwirtschaftlichen Betrieb. In: Bruder V, Röder-Dreher U, Breuer L, Herzig C, Gattinger A (eds) Landwirtschaft und Ernährung - Transformation macht nur gemeinsam Sinn : 17. Wissenschaftstagung Ökologischer Landbau, 5.-8. März 2024, Justus-Liebig-Universität Gießen ; Tagungsband. 1. Auflage. Frankfurt am Main: FiBL Deutschland eV, pp 372-373, DOI:10.5281/zenodo.11204339
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May PB, Schlund M, Armston J, Kotowska MM, Brambach F, Wenzel A, Erasmi S (2024) Mapping aboveground biomass in Indonesian lowland forests using GEDI and hierarchical models. Remote Sens Environ 313:114384, DOI:10.1016/j.rse.2024.114384
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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
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Langner N, Oehmichen K, Backa J, Eisenecker P, Reinosch E, Wiesehahn J, Hoffmann K, Adler P, Beckschäfer P (2024) Referenzdaten aus dem Projekt FNEWs [Datenpublikation] [online]. 1 GeoPackage, 2 PDF-Dateien. Göttingen: OpenAgrar Repository, zu finden in <https://atlas.thuenen.de/layers/geonode:referenzdaten_fnews_3_0> [zitiert am 12.01.2024], DOI:10.3220/DATA20240111153336-0
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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
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Brög T, Don A, Gocht A, Scholten T, Taghizadeh-Mehrjardi R, Erasmi S (2024) Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma 444:116850, DOI:10.1016/j.geoderma.2024.116850
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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
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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
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Langner N, Puhm M, Deutscher J, Wimmer A, Adler P, Backa J, Eisenecker P, Reinosch E, Wiesehahn J, Hoffmann K, Oehmichen K (2023) FNEWS-Jahresprodukte 2018 bis 2022 [Datenpublikation] [online]. Braunschweig: Thünen-Atlas, zu finden in <https://atlas.thuenen.de/layers/geonode_data:geonode:fnews_jp_18_22> [zitiert am 19.12.2023], DOI:10.3220/DATA20230907171359-0
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Holtgrave A-K, Lobert F, Erasmi S, Röder N, Kleinschmit B (2023) Grassland mowing event detection using combined optical, SAR, and weather time series. Remote Sens Environ 295:113680, DOI:10.1016/j.rse.2023.113680
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Brög T, Blaschek M, Seitz S, Taghizadeh-Mehrjardi R, Zepp S, Scholten T (2023) Transferability of covariates to predict soil organic carbon in cropland soils. Remote Sensing 15(4):876, DOI:10.3390/rs15040876
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Schlund M, Wenzel A, Camarretta N, Stiegler C, Erasmi S (2023) Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM-X supported by GEDI data. Methods Ecol Evol 14(7):1639-1656, DOI:10.1111/2041-210X.13933
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Herrmann GA, Padel S (2023) Verbesserung des Ökokontroll- und Zertifizierungssystems durch die Integration von digitalen Zertifizierungs- und Produkttransaktionsdaten und von geografischen Daten und die Entwicklung eines umsetzbaren technologischen Konzepts am Beispiel der Getreidekette [online]. Bonn: BLE, 66 p, zu finden in <https://orgprints.org/id/eprint/51755/> [zitiert am 15.12.2023]
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Hagemann N, Magdon P, Schnell S, Pommerening A (2022) Analysing gap dynamics in forest canopies with landscape metrics based on multi-temporal airborne laser scanning surveys - A pilot study. Ecol Indic 145:109627, DOI:10.1016/j.ecolind.2022.109627
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Gnilke A, Sanders TGM (2022) Distinguishing abrupt and gradual forest disturbances with MODIS-Based phenological anomaly series. Front Plant Sci 13:863116, DOI:10.3389/fpls.2022.863116
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Blickensdörfer L, Oehmichen K, Pflugmacher D, Kleinschmit B, Hostert P (2022) Dominant tree species for Germany (2017/2018) [Datenpublikation] [online]. Version 1.0, 1 Rasterdatei (tif). Eberswalde: Thünen-Institut für Waldökosysteme, zu finden in <https://www.openagrar.de/receive/openagrar_mods_00084346> [zitiert am 05.01.2023], DOI:10.3220/DATA20221214084846
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Schaber M, Gastauer S, Cisewski B, Hielscher NN, Janke M, Pena M, Sakinan S, Thorburn J (2022) Extensive oceanic mesopelagic habitat use of a migratory continental shark species. Sci Rep 12:2047, DOI:10.1038/s41598-022-05989-z
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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
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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
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Weber R, Lippe M, Günter S (2022) Mapping tropical forests: implications and challenges for deforested landscapes and forest restoration. Examples from Zambia, Ecuador and Philippines : [paper for] XV World Forestry Congress, Coex, Soul, Republic of Korea, 2-6 May 2022. 9 p
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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
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Ferrer Velasco R, Lippe M, Tamayo F, Mfuni T, Sales-Come R, Mangabat C, Schneider T, Günter S (2022) Towards accurate mapping of forest in tropical landscapes: A comparison of datasets on how forest transition matters. Remote Sens Environ 274:112997, DOI:10.1016/j.rse.2022.112997
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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
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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
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Klinge M, Dulamsuren C, Schneider F, Erasmi S, Bayarsaikhan U, Sauer D, Hauck M (2021) Geoecological parameters indicate discrepancies between potential and actual forest area in the forest-steppe of Central Mongolia. For Ecosyst 8:55, DOI:10.1186/s40663-021-00333-9
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Schulz C, Holtgrave A-K, Kleinschmit B (2021) Large-scale winter catch crop monitoring with Sentinel-2 time series and machine learning - An alternative to on-site controls? Comput Electron Agric 186:106173, DOI:10.1016/j.compag.2021.106173
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Erasmi S, Klinge M, Dulamsuren C, Schneider F, Hauck M (2021) Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe. Environ Monit Assessm 193:200, DOI:10.1007/s10661-021-08996-1
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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
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Schlund M, Lobert F, Erasmi S (2021) Potential of Sentinel-1 time series data for the estimation of season length in winter wheat phenology. In: Institute of Electrical and Electronics Engineers (ed) IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium : proceedings ; 12-16 July 2021, Virtual Symposium, Brussels, Belgium. IEEE, pp 5917-5920, DOI:10.1109/IGARSS47720.2021.9554454
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Burkhardt E, Opzeeland IC van, Cisewski B, Mattmüller R, Meister M, Schall E, Spiesecke S, Thomisch K, Zwicker S, Boebel O (2021) Seasonal and diel cycles of fin whale acoustic occurrence near Elephant Island, Antarctica. Royal Soc Open Sci 8:201142, DOI:10.1098/rsos.201142
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Schlund M, Kotowska MM, Brambach F, Hein J, Wessel B, Camarretta N, Silalahi M, Surati Jaya IN, Erasmi S, Leuschner C, Kreft H (2021) Spaceborne height models reveal above ground biomass changes in tropical landscapes. Forest Ecol Manag 497:119497, DOI:10.1016/j.foreco.2021.119497
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Camarretta N, Ehbrecht M, Seidel D, Wenzel A, Zuhdi M, Merk MS, Schlund M, Erasmi S, Knohl A (2021) Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics. Remote Sensing 13:4794, DOI:10.3390/rs13234794
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Taylor MH, Akimova A, Bracher A, Kempf A, Kühn B, Helaouet P (2021) Using dynamic ocean color provinces to elucidate drivers of North Sea hydrography and ecology. JGR Oceans 126(12):e2021JC017686, DOI:10.1029/2021JC017686
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Siemon B, Ibs-von Seht M, Frank S (2020) Airborne electromagnetic and radiometric peat thickness mapping of a bog in Northwest Germany (Ahlen-Falkenberger Moor). Remote Sensing 12(2):203, DOI:10.3390/rs12020203
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Holtgrave A-K, Röder N, Ackermann A, Erasmi S, Kleinschmit B (2020) Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing 12:2919, DOI:10.3390/rs12182919
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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
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Martinez B, Gilabert MA, Sanchez-Ruiz S, Campos-Taberner M, Garcia-Haro FJ, Brümmer C, Carrara A, Feig G, Grünwald T, Mammarella I, Tagesson T (2020) Evaluation of the LSA-SAF gross primary production product derived from SEVIRI/MSG data (MGPP). ISPRS J Photogramm Remote Sens 159:220-236, DOI:10.1016/j.isprsjprs.2019.11.010
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Ackermann J, Adler P, Aufreiter C, Bauerhansl C, Bucher T, Franz S, Engels F, Ginzler C, Hoffmann K, Jütte K, Kenneweg H, Koukal T, Martin K, Oehmichen K, Rüffer O, Sagischewski H, Seitz R, Straub C, Tintrup G, Wasser L, Zielewska-Büttner K (2020) Oberflächenmodelle aus Luftbildern für forstliche Anwendungen : Leitfaden AFL 2020. 60 p WSL Ber 87
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Tetteh GO, Gocht A, Conrad C (2020) Optimal parameters for delineating agricultural parcels from satellite images based on supervised Bayesian optimization. Comput Electron Agric 178:105696, DOI:10.1016/j.compag.2020.105696
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Schlund M, Erasmi S (2020) Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sens Environ 246:111814, DOI:10.1016/j.rse.2020.111814
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Smith NE, Kooijmans LMJ, Koren G, Schaik E van, Woude A van der, Wanders N, Ramonet M, Xueref-Remy I, Siebicke L, Manca G, Brümmer C, Baker IT, Haynes KD, Luijkx IT, Peters W (2020) Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern Europe. Philos Trans Royal Soc B 375(1810):20190509, DOI:10.1098/rstb.2019.0509
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Holtgrave A-K, Ackermann A, Röder N, Kleinschmit B (2020) Towards a dual-polarisation radar vegetation index for Sentinel-1 for grassland monitoring. Grassl Sci Europe 25:596-598
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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
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Tetteh GO (2019) Establishment of a time-sensitive crop database of Germany based on multi-temporal Sentinel-1 and Sentinel-2 Data. In: Living Planet Symposium, Milan (Italy), May 13-17 2019.
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Lüken T (2019) Improving the reliability of FREL/FRL by different remote sensing systems. Hamburg: Univ Hamburg, Fakultät für Mathematik, Informatik und Naturwissenschaften, 41 p, Hamburg, Univ, Fak f Mathematik, Informatik und Naturwissenschaften, Fachber Biologie, Masterarb, 2019
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Ortmann A, Feilhauer H, Klimek S, Thiele J (2019) Mapping extensively used grassland types at a regional scale using multispectral remote sensing. In: 62nd Symposium of the International Association for Vegetation Science (IAVS). 14-19 July, Bremen, Germany.
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Asmuß T, Bechtold M, Tiemeyer B (2019) On the potential of Sentinel-1 for high resolution monitoring of water table dynamics in grasslands on organic soils. Remote Sensing 11(14):1659, DOI:10.3390/rs11141659
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Große-Stoltenberg A, Hellmann C, Thiele J, Werner C, Oldeland J (2019) Remote sensing of an N-fixing invasive shrub species: Early indicators of high impact. In: GfÖ 2019 : Science meets practice ; 49th Annual Meeting of the Ecological Society of Germany, Austria and Switzerland ; University of Münster, 9 - 13 September 2019 ; book of abstracts. Berlin: Gesellschaft für Ökologie, p 435
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Erasmi S, Semmler M, Schall P, Schlund M (2019) Sensitivity of bistatic TanDEM-X data to stand structural parameters in temperate forests. Remote Sensing 11(24):2966, DOI:10.3390/rs11242966
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Castaldi F, Chabrillat S, Don A, Wesemael B van (2019) Soil organic carbon mapping using LUCAS topsoil database and sentinel-2 data: an approach to reduce soil moisture and crop residue effects. Remote Sensing 11(18):2121, DOI:10.3390/rs11182121
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Krause S, Strer M, Mund J-P, Sanders TGM (2019) UAV remote sensing data handling: A transition from testing to long-term data acquisition for intensive forest monitoring. J Photogramm Remote Sensing Geoinf Sci 28(39):167-174
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Krause S, Sanders TGM, Mund J-P, Greve K (2019) UAV-based photogrammetric tree height measurement for intensive forest monitoring. Remote Sensing 11(7):758, DOI:10.3390/rs11070758
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Nguyen TT, Lippe M, Marohn C, Vien TD, Cadisch G (2019) Using farmer decision rules for mapping historical land use change patterns from 1954 to 2007 in rural northwestern Vietnam. Land 8(9):130, DOI:10.3390/land8090130
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Pisek J, Buddenbaum H, Camacho F, Hill J, Jensen JLR, Lange H, Liu Z, Piayda A, Qu Y, Roupsard O, Serbin SP, Solberg S, Sonnentag O, Thimonier A, Vuolo F (2018) Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory. Remote Sens Environ 215:1-6, DOI:10.1016/j.rse.2018.05.026
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Langkamp-Wedde T, Kraft M, Neeland H, Matschiner K, Kottmann L, Schittenhelm S (2018) Drohnenbasierte Fernerkundung in der Weizenzüchtung. Bornimer Agrartechn Ber 99:29-43
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Bechtold M, Schlaffer S, Tiemeyer B, de Lannoy G (2018) Inferring water table depth dynamics from ENVISAT-ASAR C-band backscatter over a range of peatlands from deeply-drained to natural conditions. Remote Sensing 10(4):536, DOI:10.3390/rs10040536
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Schnell S, Riedel T, Oehmichen K (2018) Integration von Fernerkundungsdaten in die Auswertung der Bundeswaldinventur. In: Ammer C, Bredemeier M, Arnim G von (eds) FowiTa : Forstwissenschaftliche Tagung 2018 Göttingen ; Programm & Abstracts ; 24. bis 26. September 2018. Göttingen: Univ Göttingen, Fakultät für Forstwissenschaften und Waldökologie, p 438
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Beckschäfer P, Schnell S, Kleinn C (2018) Monitoring and assessment of trees outside forests (TOF). In: Dagar JC, Tewari VP (eds) Agroforestry : anecdotal to modern science. Puchong, Selangor DE: Springer Singapore, pp 137-161, DOI:10.1007/978-981-10-7650-3_5
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Hartmann H, Schuldt B, Sanders TGM, Macinnis-Ng C, Boehmer HJ, Allen CD, Bolte A, Crowther TW, Matthew MC, Medlyn BE, Rühr NK, Anderegg WR (2018) Monitoring global tree mortality patterns and trends. Report from the VW symposium 'Crossing scales and disciplines to identify global trends of tree mortality as indicators of forest health'. New Phytol 217(3):984-987, DOI:10.1111/nph.14988
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Vohland M, Ludwig M, Thiele-Bruhn S, Ludwig B (2017) Quantification of soil properties with hyperspectral data: selecting spectral variables with different methods to improve accuracies and analyze prediction mechanisms. Remote Sensing 9(11):1103, DOI:10.3390/rs9111103
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