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  1. 0

    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

    https://literatur.thuenen.de/digbib_extern/dn064834.pdf

  2. 1

    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

  3. 2

    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

  4. 3

    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

  5. 4

    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. Publ Dt Gesellsch Photogrammetrie Fernerkundung Geoinf 30:117-126

  6. 5

    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

    https://literatur.thuenen.de/digbib_extern/dn064744.pdf

  7. 6

    Schlund M, Wenzel A, Camarretta N, Stiegler C, Erasmi S (2022) Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM-X supported by GEDI data. Methods Ecol Evol:in Press, DOI:10.1111/2041-210X.13933

    https://literatur.thuenen.de/digbib_extern/dn065101.pdf

  8. 7

    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

  9. 8

    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

  10. 9

    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

    https://literatur.thuenen.de/digbib_extern/dn063892.pdf

  11. 10

    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

  12. 11

    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

    https://literatur.thuenen.de/digbib_extern/dn063505.pdf

  13. 12

    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

  14. 13

    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

  15. 14

    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

    https://literatur.thuenen.de/digbib_extern/dn063644.pdf

  16. 15

    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

    https://literatur.thuenen.de/digbib_extern/dn063766.pdf

  17. 16

    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

    https://literatur.thuenen.de/digbib_extern/dn064242.pdf

  18. 17

    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. J Geophys Res Oceans 126(12):e2021JC017686, DOI:10.1029/2021JC017686

    https://literatur.thuenen.de/digbib_extern/dn064301.pdf

  19. 18

    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

    https://literatur.thuenen.de/digbib_extern/dn061949.pdf

  20. 19

    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

    https://literatur.thuenen.de/digbib_extern/dn062663.pdf

  21. 20

    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

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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 G, 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

    https://literatur.thuenen.de/digbib_extern/dn062674.pdf

  25. 24

    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 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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    Tetteh G (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

    https://literatur.thuenen.de/digbib_extern/dn061159.pdf

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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

    https://literatur.thuenen.de/digbib_extern/dn061759.pdf

  35. 34

    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

    https://literatur.thuenen.de/digbib_extern/dn061373.pdf

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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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    Cisewski B, Strass VH (2016) Acoustic insights into the zooplankton dynamics of the eastern Weddell Sea. Progr Oceanogr 144:42-92, DOI:10.1016/j.pocean.2016.03.005

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    Oehmichen K, Bauerhansl C, Ginzler C, Kroiher F, Straub C, Waser LT (2016) Comparison of different definitions for wooded land using high resolution remote sensing techniques - a cross-country case study. In: Wezyk P (ed) 3rd EARSel Workshop SIG on Forestry and Young Scientist Days on Forestry Conference - Braking dimensions and resolutions of forest remote sensing data, Krakow, September 15-16 ; book of abstracts. Krakow: University of Agriculture in Krakow, Faculty of Forestry, p 88

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    Kraft M, Schittenhelm S, Kottmann L, Schroetter S, Langkamp T, Neeland H, Matschiner K (2016) Fernerkundliche Beurteilung der Trocken- und Hitzetoleranz von Weizengenotypen auf Selektionsstandorten mit begleitenden Untersuchungen zu Durchwurzelungstiefe, Wurzelmorphologie und Wasserhaushalt (Phaenokopter). In: Innovationstage 2016 : Die Zukunft ins Jetzt holen ; 15. bis 26. Oktober in Bonn. Bonn: Bundesanstalt für Landwirtschaft und Ernährung, pp 301-305

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    Klatt S, Breidenbach J, Astrup R (2016) Measuring tree diameters with close-range photogrammetry. In: Wezyk P (ed) 3rd EARSel Workshop SIG on Forestry and Young Scientist Days on Forestry Conference - Braking dimensions and resolutions of forest remote sensing data, Krakow, September 15-16 ; book of abstracts. Krakow: University of Agriculture in Krakow, Faculty of Forestry, p 110

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    Vohland M, Harbich M, Ludwig M, Emmerling C, Thiele-Bruhn S (2016) Quantification of soil variables in a heterogeneous soil region with VIS-NIR-SWIR data using different statistical sampling and modeling strategies. IEEE J Selected Topics Appl Earth Observ Remote Sens 9(9):4011-4021, DOI:10.1109/JSTARS.2016.2572879

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    Vicca S, Balzarolo M, Filella I, Granier A, Herbst M, Knohl A, Longdoz B, Mund M, Nagy Z, Pintér K, Rambal S, Verbesselt J, Verger A, Zeileis A, Zhang C, Penuelas J (2016) Remotely-sensed detection of effects of extreme droughts on gross primary production. Sci Rep 6:28269, DOI:10.1038/srep28269

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    Schneider J, Jungkunst HF, Wolf U, Schreiber P, Gazovic M, Miglovets M, Mikhaylov O, Grunwald D, Erasmi S, Wilmking M, Kutzbach L (2016) Russian boreal peatlands dominate the natural European methane budget. Environ Res Lett 11(1):14004, DOI:10.1088/1748-9326/11/1/014004

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    Wang S, Pan M, Mu Q, Shi X, Mao J, Brümmer C, Jassal RS, Krishnan P, Li J, Black TA (2015) Comparing evapotranspiration from eddy covariance measurements, water budgets, remote sensing, and land surface models over Canada. J Hydrometeorol 16(4):1540-1560, DOI:10.1175/JHM-D-14-0189.1

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    Gocht A, Röder N (2014) Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany. Int J Geogr Inf Sci 28(9):1744-1764, doi:10.1080/13658816.2014.897348

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    Neeland H, Kraft M (2013) Construction and measurement technology of the ThünoCopter for contactless inspection of crop canopies: first measurements with a low-cost image analysing system. Kölner Geogr Arb 94:67-73, DOI:10.5880/TR32DB.KGA94.10

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