Thünen Earth Observation (ThEO)

 (c) Fotolia/Andrey Armyagov

Satellite data and aerial photographs provide an image of the earth's surface with unprecedented precision and timeliness. At Thünen Institute, we use the information from various earth observation sensors to support and further develop monitoring tasks on the state and change of fields, forests and oceans. The area-wide coverage of land and sea surface indicators provides an objective data basis for policy advice and policy impact assessment.

With the start of the Copernicus program of the European Union, a new era for remote sensing began in 2014. Since then, large areas (regions, states, continents) can be covered with a high spatial resolution and at intervals of a few days, and the data are promptly and freely available.

At the same time, rapid technical developments in aerial photography (e.g., LiDAR, drones) have greatly improved the collection of features down to the individual level. This progress has been accompanied by the establishment of AI techniques for digital processing and analysis of the enormous amounts of data generated by these data.

The Thünen Earth Observation (ThEO) working group is coordinated by Dr. Stefan Erasmi. Its goal is to systematically utilise data from current and planned satellite missions for monitoring tasks and other research activities at the Thünen Institute.

In accordance with the breadth of the subject areas at the Thünen Institute, the activities of ThEO are divided into the following application areas:

  • Land use: How is agricultural land used and how is its use changing? Satellites of the Copernicus program provide the basis for an area-wide and accurate inventory for the whole of Germany.
  • Climate protection and climate impacts: Does climate change affect agricultural use and forests? What are the effects of environmental and climate measures? Satellite data can be used to visualise spatial and tem-poral patterns of climate impacts and management measures.
  • Biodiversity: The type and intensity of use has a massive impact on the ecosystem functions of agricultural landscapes and forests. The analysis of remote sensing data over long periods of time provides indicators of ecosystem condition and stress.
  • Forest structure and dynamics: The Thünen Institute tests and analyses various cross-scale remote sensing data and new remote sensing technologies in order to record forest structure and management.
  • Deforestation and restoration: Remote sensing is used to investigate deforestation patterns, their drivers and causes in order to develop solutions for sustainable forest development in the tropics and subtropics.
  • Oceanography and climate: In oceanography, remote sensing data provide large-scale information on environmental factors such as temperature, salinity, and circulation of specific ocean areas. We use them to study the distribution and population dynamics of North Atlantic fish stocks.

Application areas of Thünen Earth Observation (ThEO)

Landscape in Harz foreland (©  Thünen-Institut/Tania Runge)
Land use in agricultural landscapes
How is agricultural land used and how is its use changing? Satellites of the Copernicus program provide the basis for an area-wide and accurate inventory for the whole of Ger-many.
stubble field (©  hykoe -
Climate protection and climate impacts
Does climate change affect agricultural use and forests? What are the effects of environmental and climate measures? Satellite data can be used to visualise spatial and temporal patterns of climate impacts and management measures
UAV image of a mixed forest in autumn (©  Thünen-Institut/Katja Clemens)
Forest structure and dynamics
In order to record and monitor the forest in its structure and dynamic development, the Thünen Institute tests and analyses various cross-scale remote sensing data and new remote sensing technologies.
dry grassland on Weper mountain range near Fredelsloh (©  Thünen-Institut/Manfred Bathke)
The type and intensity of use influences the ecosystem functions of agricultural landscapes and forests. Remote sensing data can be used to show the condition and stress of ecosystems over a wide area.
Floating ice sheets in the Weddell Sea (Antarctica) (©  Boris Cisewski)
Oceanography and climate
Remote sensing data provide large-scale information on environmental factors such as tempera-ture, salinity and circulation of specific ocean areas. We use them to study the distribution and population dynamics of North Atlantic fish stocks.
Deforestation in Nord-Luzon, Philippines (©  Thünen-Institut/Melvin Lippe)
Deforestation and restoration
In order to develop solutions for sustainable forest development in the tropics and subtropics, remote sensing is used to investigate deforestation patterns, their drivers and causes. On this basis, we analyse how these changes affect ecosystem services and what potential there is for restoring forests.



2022Schwieder 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.2021.112795pdf document
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2022Blickensdörfer 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.2021.112831pdf document
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2021Lobert 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
2021Klinge 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/s40663-021-00333-9pdf document
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2021Schulz 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
2021Erasmi 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/s10661-021-08996-1pdf document
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2021Lobert 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.2021.112751pdf document
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2021Schlund 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
2021Burkhardt 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.201142pdf document
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2021Schlund 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.2021.119497pdf document
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2021Camarretta 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/rs13234794pdf document
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2021Taylor 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 pdf document
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2020Siemon 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/rs12020203pdf document
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2020Holtgrave 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/rs12182919pdf document
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2020Schwieder 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/s41064-020-00120-1pdf document
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2020Martinez 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.2019.11.010pdf document
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2020Ackermann 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
2020Tetteh 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.2020.105696pdf document
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2020Schlund 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
2020Smith 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
2020Holtgrave 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
2020Tetteh 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/rs12183096pdf document
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2019Tetteh 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.
2019Lüken 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
2019Ortmann 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.
2019Asmuß 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/rs11141659pdf document
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2019Große-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
2019Erasmi 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/rs11242966pdf document
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2019Castaldi 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/rs11182121pdf document
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2019Krause 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
2019Krause 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/rs11070758pdf document
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2019Nguyen 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/land8090130pdf document
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2018Pisek 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
2018Langkamp-Wedde T, Kraft M, Neeland H, Matschiner K, Kottmann L, Schittenhelm S (2018) Drohnenbasierte Fernerkundung in der Weizenzüchtung. Bornimer Agrartechn Ber 99:29-43
2018Bechtold 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/rs10040536pdf document
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2018Schnell 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
2018Beckschäfer 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
2018Hartmann 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
2017Vohland 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/rs9111103pdf document
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2016Cisewski 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
2016Oehmichen 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
2016Kraft 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
2016Klatt 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
2016Vohland 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
2016Vicca 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/srep28269pdf document
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2016Schneider 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:10.1088/1748-9326/11/1/014004pdf document
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2015Wang 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
2014Gocht 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
2013Neeland 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.KGA94.10pdf document
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2013Marshall M, Tu K, Funk CC, Michaelsen J, Williams P, Williams CA, Ardö J, Boucher M, Cappelaere B, De Grandcourt A, Nickless A, Nouvellon Y, Scholes RJ, Kutsch WL (2013) Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach. Hydrol Earth Syst Sci 17(3):1089-1091, DOI:10.5194/hess-17-1079-2013pdf document
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2013Baldauf T (2013) Monitoring reduced emissions from deforestation and forest degradation (REDD+) : capabilities of high-resolution active remote sensing . Hamburg: Universität, 152 p, Hamburg, Univ, Diss
2013Cui J, Xiao X, Merbold L, Arneth A, Veenendaal EM, Kutsch WL (2013) Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa. Remote Sens Environ 135:189-201, doi:10.1016/j.rse.2013.03.033
2011Kuntz S, Poncet F von, Baldauf T, Plugge D, Kenter B, Köhl M (2011) A multi-stage inventory scheme for REDD inventories in tropical countries. In: Proceedings of 34th International Symposium for Remote Sensing of the Environment. pp 1-4
2011Sjöström M, Ardö J, Arneth A, Boulain N, Cappelaere B, Eklundh L, De Grandcourt A, Kutsch WL, Merbold L, Nouvellon Y, Scholes RJ, Schubert P, Seaquist J, Veenendaal EM (2011) Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote Sens Environ 115(4):1081-1089, doi:10.1016/j.rse.2010.12.013
2010Stümer W (2010) Auswertung von Fernerkundungsdaten mit Self Organizing Maps für die Herleitung von Kohlenstoffkarten. Publ Dt Gesellsch Photogrammetrie Fernerkundung Geoinf 19:175-186
2010Plugge D, Baldauf T, Ratsimba HR, Rajoelison G, Köhl M (2010) Combined biomass inventory in the scope of REDD (Reducing Emissions from Deforestation and Forest Degradation) [online]. Madagascar Conserv Dev 5(1):23-34, zu finden in <> [zitiert am 24.06.2010]
2010Iost A, Oehmichen K, Riedel T (2010) Evaluierung satellitengestützter Stichprobenkonzepte für die Bundeswaldinventur. Berlin: Rhombos-Verl, 236 p
2010Köhl M (2010) Resource assessment techniques for continuous cover forests systems. Manag Forest Ecosyst 4:13-26
2010Oehmichen K (2010) Satellitengestützte Waldflächenkartierung für die deutsche Treibhausgasberichterstattung. Publ Dt Gesellsch Photogrammetrie Fernerkundung Geoinf 19:195-202
2009Granke O, Kenter B, Kriebitzsch W-U, Köhl M, Köhler R, Olschofsky K (2009) Biodiversity assessment in forests - from genetic diversity to landscape diversity. iForest 1:1-3, DOI:10.3832/ifor0474-002
2008Montzka C, Canty M, Kreins P, Kunkel R, Menz G, Vereecken H, Wendland F (2008) Multispectral remotely sensed data in modelling the annual variability of nitrate concentrations in the leachate. Environ Modelling Software 23(8):1070-1081, DOI:10.1016/j.envsoft.2007.11.010
2008Oehmichen K, Köhl M (2008) Verfahrensvorschlag zur satellitengestützten Waldflächenkartierung für die Bundeswaldinventur. Photogrammetrie Fernerkund Geoinf(6):499-507
2007Köhl M, Baldauf T, Plugge D (2007) Einsatz von Fernerkundung zur Erfassung der Entwaldung : Pilotstudie Madagaskar: Vermiedene Entwaldung als Klimaschutzoption. AFZ Wald 62(23):1262-1263pdf document
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2007Kleinschmit B, Förster M, Frick A, Oehmichen K (2007) QuickBird Data - experiences with ordering, quality and pan sharpening. Photogrammetrie Fernerkund Geoinf(2):73-83
2007Oehmichen K (2007) Satellitengestützte Waldflächenkartierung für die Bundeswaldinventur. Hamburg: Univ, 112 p, Hamburg, Univ, Fakultät für Mathematik, Informatik und Naturwissenschaften, Diss, 2007
2006Köhl M, Magnussen S, Marchetti M (2006) Sampling methods, remote sensing and GIS : multiresource forest inventory. Heidelberg; Berlin: Springer, 403 p
2005Stümer W, Köhl M (2005) Kombination von terrestrischen Aufnahmen und Fernerkundungsdaten mit Hilfe der k-Nächste-Nachbar-Methode zur Klassifizierung und Kartierung von Wäldern. Photogrammetrie Fernerkund Geoinf(1):23-36
1998Kraft M, Brandes F (1998) Erste Erfahrungen bei der optischen Messung des Stickstoffversorgungsgrades von Raps- und Grünlandbeständen. KTBL Arbeitspap 250:61-67
1990Kraft M (1990) Fernerkundung in der Landwirtschaft : Möglichkeiten und Probleme bei der Verwendung von Satellitendaten. KTBL Arbeitspap 145:101-108


Dr. Boris  Cisewski

Dr. Boris Cisewski

Institute of Sea Fisheries
Herwigstraße 31
27572 Bremerhaven
Phone: +49 471 94460 454
Fax: +49 471 94460 199

Involved Institutes