WMS Dies ist der WMS Dienst des Thünen Atlas (https://atlas.thuenen.de/) WFS WMS GEOSERVER Beate Oerder Thünen Institut Leiterin Zentrum für Informationsmanagement

Bundesallee 50

Braunschweig 38116 Deutschland geodaten@thuenen.de NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html text/javascript application/json XML INIMAGE BLANK JSON JSONP Dies ist der WMS Dienst des Thünen Atlas (https://atlas.thuenen.de/) EPSG:4326 EPSG:3785 EPSG:3857 EPSG:900913 EPSG:32647 EPSG:32736 EPSG:25832 CRS:84 -180.0 180.0 -89.99892578 90.0 geonode:ABAG_Landnutzung Bodenbedeckung für die ABAG Landnutzung Gut Tellow Bodenbedeckung ABAG features ABAG_Landnutzung Mustergut landwirtschaftliche Anbauflächen Vegetation EPSG:25833 CRS:84 12.410900452072065 12.56739585314893 53.75384730848854 53.88862677495175 other other text/xml other other other other geonode:Abflussbahn_eingebrannt Oberirdischer Verlauf des Wassers und potenzielle Drainagenverläufe Abflussweg Abflussbahn_eingebrannt Gut Tellow Abflussbahn Erosionsgefährdung features Mustergut EPSG:25833 CRS:84 12.41910457364533 12.558020073985425 53.75863880992084 53.88183143241663 other other other other text/xml other other geonode:Analyseraum Für die Untersuchungen im Rahmen dieses Forschungsprojektes wurde zunächst ein hydrologisch, auf Wasserscheiden bzw. Einzugsgebieten beruhender Ansatz gewählt. Dieser ist als „Analyseraum“ gekennzeichnet und bildet die maximale Erstreckung des Untersuchungsraumes bzw. -gebietes ab. landwirtschaftliche Anbauflächen Gut Tellow Analyseraum features Gewässer Hydrologie Mustergut Einzugsgebiet EPSG:25833 CRS:84 12.41876334369838 12.559567071593609 53.75849847863073 53.88399087999198 other other other other text/xml other other geonode:Anbauflaechen_2011_20210 Darstellung der Anbaufrüchte auf den Schlägen des Mustergutes Tellow und Anzahl der Fruchtfolgewechsel in den Jahren 2011 - 2021. Anbauflaechen_2011_20210 Anbauflächen Kulturen Boden Schlag Gut Tellow Fruchtfolge Landwirtschaft features Fruchtwechsel Nährstoffe Mustergut EPSG:25833 CRS:84 12.449771033793146 12.533413270453737 53.77781432769887 53.84172946195206 text/xml other other other other other other geonode:Anbauflaechen_Mit_Faktor_C0 Bodenbedeckungs- und Bodenbearbeitungsfaktor (als Verhältnis des Bodenabtrages bei Kultivierung bestimmter Fruchtarten/Fruchtfolgen zum Bodenabtrag unter Standardbedingungen). Landnutzung Bodenbedeckung Gut Tellow ABAG features Anbauflaechen_Mit_Faktor_C0 Mustergut landwirtschaftliche Anbauflächen Vegetation EPSG:25833 CRS:84 12.449771033793146 12.533413270453737 53.77781432769887 53.84172946195206 other other other other text/xml other other geonode:BVK_Leitbildlebensraumtypen Darstellung der Leitbildlebensraumtypen auf den Probeflächen und Vergleich mit der anzunehmende Artzusammensetzung für Leitbildlebensraumtypen (vor Ort nachgewiesene Arten) nach Flade (1994). Lebensraumbewertung Leitbild Gut Tellow Leitbildlebensraumtyp BVK_Leitbildlebensraumtypen features Mustergut EPSG:25833 CRS:84 12.451915202266447 12.537368615974705 53.77742949296628 53.83777574761725 other other other other text/xml other other geonode:BVK_Reviere Erfassung von Butvögeln von März bis Juni (4 Begehungen) auf den Probeflächen Brutvögel Gut Tellow Revierbildung Bestandsentwicklung features Brutvogelmonitoring BVK_Reviere Dachverband Deutscher Avifaunisten landwirtschaftliche Anbauflächen Mustergut Norddeutsche Tiefebene EPSG:25833 CRS:84 12.452335955828158 12.541676754291695 53.77721873016893 53.837544252874316 other other other other text/xml other other geonode:BVK_gesamt_xyUTF8 Erfassung von Butvögeln von März bis Juni (4 Begehungen) auf den Probeflächen - Grunddaten. Insgesamt liegen 25 Arten mit einem Schutz- oder Gefährdungsstatus vor. Dies sind ca. 30 % des gesamten Artenspektrums, was auf gute Habitatbedingungen hinweist. Kartierung Brutvögel Arterfassung Gut Tellow features Brutvogelmonitoring BVK_gesamt_xyUTF8 Dachverband Deutscher Avifaunisten landwirtschaftliche Anbauflächen Mustergut EPSG:25833 CRS:84 12.443474943544407 12.541846450650628 53.77632963094342 53.839009586031835 other other other other text/xml other other geonode:Biotopkartierung Biotopkartierung nach Biotopkartieranleitung des Landes Mecklenburg-Vorpommern (LUNG M-V 2013). Darstellung der flächenhaften Verteilung der kartierten Biotoptypen der Teilflächen Tellow, inkl. Haupt-, Neben- und Überlagerungscodes sowie Schutzstatus. Die Biotopobergruppen sind beschrieben und werden durch ihre Symbologie auf der Karte farblich unterschieden. Nebencode Schutzstatus Gut Tellow Überlagerungscode Hauptcode Obergruppe Biotopkartierung features Mustergut landwirtschaftliche Anbauflächen EPSG:25833 CRS:84 12.446066744980746 12.539824636633758 53.77730997601118 53.84141575050333 other other other other text/xml other other geonode:C1000_2017_2021_classified No abstract provided Fernerkundung C1000_2017_2021_classified GeoTIFF Bodenbedeckung WCS Bodenerosion Erosionsgefährdung EPSG:25832 CRS:84 5.558647641385569 15.586479192345632 47.139376915226116 55.06469862747793 text/xml other other other other other other rs:CTM_GER_2017_V2_opt CTM_GER_2017_V2_opt WCS GeoTIFF EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 geonode:CTM_GER_2017_rst_v201 No abstract provided WCS Landnutzung landwirtschaftliche Anbauflächen GeoTIFF Fernerkundung CTM_GER_2017_rst_v201 EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other other other text/xml other other other rs:CTM_GER_2018_V2_opt CTM_GER_2018_V2_opt WCS GeoTIFF EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 geonode:CTM_GER_2018_rst_v201 No abstract provided WCS Landnutzung CTM_GER_2018_rst_v201 landwirtschaftliche Anbauflächen GeoTIFF Fernerkundung EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other other other text/xml other other other rs:CTM_GER_2019_V2_opt CTM_GER_2019_V2_opt WCS GeoTIFF EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 geonode:CTM_GER_2019_rst_v201 No abstract provided WCS Landnutzung landwirtschaftliche Anbauflächen GeoTIFF Fernerkundung CTM_GER_2019_rst_v201 EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other other other text/xml other other other rs:CTM_GER_2020_V2_opt CTM_GER_2020_V2_opt WCS GeoTIFF EPSG:3035 CRS:84 5.23116544029572 15.548774658103367 46.907375930555425 55.09896344025221 geonode:CTM_GER_2020_rst_v201 No abstract provided WCS Landnutzung landwirtschaftliche Anbauflächen GeoTIFF Fernerkundung CTM_GER_2020_rst_v201 EPSG:3035 CRS:84 5.23116544029572 15.548774658103367 46.907375930555425 55.09896344025221 other other other other text/xml other other other geonode:CTM_GER_2021_rst_v201 No abstract provided WCS Landnutzung landwirtschaftliche Anbauflächen GeoTIFF Fernerkundung CTM_GER_2021_rst_v201 EPSG:3035 CRS:84 5.23116544029572 15.548774658103367 46.907375930555425 55.09896344025221 other other other other text/xml other other other geonode:Dominant_Species_Class <p>Spatially explicit and detailed information on tree species composition is critical for forest management, nature conservation and the assessment of forest ecosystems services. In many countries, forest attributes are monitored regularly through sample-based forest inventories. In combination with satellite imagery, data from such forest inventories have a great potential for developing large area tree species maps. Here, the high temporal and spatial resolution of Sentinel-1 and Sentinel-2 has been useful for extracting vegetation phenology, information that may also be useful for improving forest tree species mapping.</p> <p>Thus, we generate cloud free time series with 5-day intervals from Sentinel-2 imagery and combine those with monthly Sentinel-1 backscatter composites to map the dominant tree species throughout Germany. Further, we incorporate information on topography, meteorology, and climate to account for environmental gradients in Germany. We use the German NFI as source for training data for a random forest classifier and for validation data to assess map accuracy.</p> <p>The optical Sentinel-2 data were downloaded, processes and structured in an analysis-ready data cube using with the open-source software FORCE (Framework for operational radiometric correction for environmental monitoring; Frantz, D., 2019, doi.org/10.3390/rs11091124; https://force-eo.readthedocs.io/en/latest/ last accessed: 02. Nov. 2022). The preprocessed Sentinel-1 SAR data was obtained through the CODE-DE platform (Sentinel-1 L3 BS - Monthly Composite, https://code-de.org/en/portfolio/, last accessed: 02. Nov. 2022) and included in the FORCE data cube along with the environmental data.</p> <p>We used Sentinel-2, Sentinel-1 and environmental data from the two years 2017 and 2018 to generate the map with 11 tree species groups, including two broader deciduous tree classes (ODH: other deciduous trees with high life expectancy, ODL: other deciduous trees with low life expectancy), within the extent of a previously produced tree map: Langner et al. (2022,&nbsp; doi.org/10.3220/DATA20221205151218).</p> <p>The reference data for model training and map validation is based on the third German National Forest Inventory which was recorded in 2011/2012 (https://www.bundeswaldinventur.de/en/third-national-forest-inventory, last accessed: 02. Nov. 2022). To exclude inventory plots where tree species composition might have changed in between 2012 and 2018, we used the forest disturbance maps by Senf et al. (2020, doi.org/10.1038/s41893-020-00609-y). The map accuracies were estimated based on a stratified estimator according to Stehman (2014, doi.org/10.1080/01431161.2014.930207).</p> <p>Map accuracies were estimated for pure species stands in a first step and for the entire map, including mixed species stands, in a second step. The overall accuracy of pure-species stands is 87.07 &plusmn; 0.3 % and 75.53 &plusmn; 0.07 % for the entire map, including mixed-species stands. For further details please contact <em>lukas.blickensdoerfer@thuenen.de</em> and see upcoming publication: &nbsp;Blickensd&ouml;rfer, L., Oehmichen, K., Pflugmacher, D., Kleinschmidt, B., Hostert, P. (in prep.). Large-area tree species mapping across environmental gradients: Combining Sentinel-2 and Sentinel-1 time series with German National Forest Inventory data.</p> Wald WCS 2018 Baumarten GeoTIFF Deutschland Waldinventur Dominant_Species_Class 2017 EPSG:32632 CRS:84 5.568678471893083 15.56595534155325 47.15070926692559 55.03221276288581 other other other other text/xml other other other geonode:Einzelgehoelze Einzelbäume ab 25 cm Stammdurchmesser, Altbäume und Jungbäume inkl. Schutzstatus Mehrstämmig Baumart Gut Tellow Einzelgehoelze Brusthöhendurchmesser Allee features Einzelbaum Baumreihe landwirtschaftliche Anbauflächen Mustergut EPSG:25833 CRS:84 12.494790445566748 12.535791458570445 53.82321990245538 53.839751270326104 other other other other text/xml other other geonode:Ertragskartierung_Tellow_2021 No abstract provided Ernteertrag Ertragskartierung_Tellow_2021 features EPSG:25833 CRS:84 12.451332657628662 12.533330418064622 53.778192969031615 53.83741091101413 other other other other text/xml other other geonode:Ertragskartierung_Tellow_2022 No abstract provided Ertragskartierung_Tellow_2022 Ernteertrag features EPSG:25833 CRS:84 12.449850274344856 12.529413007428772 53.77777688128923 53.841626750202714 other other other other text/xml other other geonode:Fledermaeuse_Ergebnis_HB_20220 Nachgewiesenes Artenspektrum von Fledermäusen während der Horchboxerfassung an den Untersuchungsstandorten. Abundanz Horchboxerfassung Strukturreichtum Fledermäuse Habitateignung features Quartiere Fledermaeuse_Ergebnis_HB_20220 Strukturbindung Jagd-/Leitstrukturen EPSG:25833 CRS:84 12.449935699558404 12.533148095372212 53.78331401317493 53.83869499720123 other other other other text/xml other other geonode:Flugrouten_Drohnenbefliegung0 Geplante Flugrouten der Drohnenbefliegung für Luftbildaufnahmen der Anbauflächen. Gut Tellow Schrägluftbilder features Drohnenbefliegung Flugroute Waypoints Mustergut Flugrouten_Drohnenbefliegung0 EPSG:25833 CRS:84 12.448625397898489 12.534681148341226 53.777080660071725 53.8423295238238 other other other other text/xml other other geonode:Fotos_Drohnenbefliegung0 Aufgezeichnete Fotoposition während der Drohnenbefliegung mit horizontaler und vertikaler Richtungsangabe. Fotoposition Landschaftsbild Gut Tellow Fotos_Drohnenbefliegung0 Schrägluftbilder features Drohnenbefliegung landwirtschaftliche Anbauflächen Mustergut EPSG:25833 CRS:84 12.448622560922637 12.534562314908326 53.77707946732879 53.84232239738922 text/xml other other other other other other geonode:GLU_GER_2017_SUM_DOY1_PGL_CGL_FAL_v201 <p>The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Th&uuml;nen Institute since 2017 on the basis of satellite data. The maps cover the entire grassland area, i.e. permanent grassland, potentially permanent grassland (e.g. fodder crops) and other extensive areas. The map was derived from dense time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112795">Schwieder et al. (2022)</a>.</p> <p>The dataset includes two layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first detected mowing event. DOYs of all other detected mowing events together with ancillary data layers are available on request.</p> <p>The map extent covers all areas in Germany that are defined as permanent grassland, cultivated grassland or fallow land according to the maps of agricultural land use that are available here. Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM. The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in <a href="https://doi.org/10.1109/ACCESS.2021.3105903">Tetteh et al. (2021)</a>.</p> <p>Data are available on request either in gridded (raster) format or in aggregated (vector) format. For metadata details please see the specifications below.</p> Fernerkundung GLU_GER_2017_SUM_DOY1_PGL_CGL_FAL_v201 GeoTIFF Grünland Landnutzung WCS EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other other other text/xml other other geonode:GLU_GER_2017_seg_sum_doy1_v201_PGL_CGL_FAL Fernerkundung Grünland Landnutzung features GLU_GER_2017_seg_sum_doy1_v201_PGL_CGL_FAL EPSG:3035 CRS:84 5.4759094951522345 15.486924106185029 47.17143854810996 55.04909497131256 other other other other text/xml other other geonode:GLU_GER_2018_SUM_DOY1_PGL_CGL_FAL_v201 No abstract provided Fernerkundung GeoTIFF Grünland Landnutzung WCS GLU_GER_2018_SUM_DOY1_PGL_CGL_FAL_v201 EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other text/xml other other other other geonode:GLU_GER_2018_seg_sum_doy1_v201_PGL_CGL_FAL Fernerkundung Grünland GLU_GER_2018_seg_sum_doy1_v201_PGL_CGL_FAL Landnutzung features EPSG:3035 CRS:84 5.4746405381121575 15.488487142659878 47.17141659746356 55.05619383875036 other other other other text/xml other other geonode:GLU_GER_2019_SUM_DOY1_PGL_CGL_FAL_v201 No abstract provided Fernerkundung GeoTIFF Grünland Landnutzung WCS GLU_GER_2019_SUM_DOY1_PGL_CGL_FAL_v201 EPSG:3035 CRS:84 5.231165440295703 15.548774658103348 46.90737593055544 55.098963440252206 other other other other text/xml other other geonode:GLU_GER_2019_seg_sum_doy1_v201_PGL_CGL_FAL Fernerkundung Grünland Landnutzung GLU_GER_2019_seg_sum_doy1_v201_PGL_CGL_FAL features EPSG:3035 CRS:84 5.474390519522835 15.4881831714596 47.17143854810996 55.05844157018468 other other other other text/xml other other geonode:GLU_GER_2020_SUM_DOY1_PGL_CGL_FAL_v201 No abstract provided Fernerkundung GeoTIFF Grünland Landnutzung WCS GLU_GER_2020_SUM_DOY1_PGL_CGL_FAL_v201 EPSG:3035 CRS:84 5.23116544029572 15.548774658103367 46.907375930555425 55.09896344025221 other other other other text/xml other other geonode:GLU_GER_2020_seg_sum_doy1_v201_PGL_CGL_FAL Fernerkundung Grünland Landnutzung GLU_GER_2020_seg_sum_doy1_v201_PGL_CGL_FAL features EPSG:3035 CRS:84 5.477036879025717 15.48398979891311 47.17190125235065 55.038853716274836 other other other other text/xml other other geonode:GLU_GER_2021_SUM_DOY1_PGL_CGL_FAL_v201 No abstract provided Fernerkundung GeoTIFF Grünland Landnutzung WCS GLU_GER_2021_SUM_DOY1_PGL_CGL_FAL_v201 EPSG:3035 CRS:84 5.23116544029572 15.548774658103367 46.907375930555425 55.09896344025221 other other other other other text/xml other geonode:GLU_GER_2021_seg_sum_doy1_v201_PGL_CGL_FAL Landnutzung GLU_GER_2021_seg_sum_doy1_v201_PGL_CGL_FAL features Fernerkundung Grünland EPSG:3035 CRS:84 5.4762198884154305 15.48659020022602 47.171903302581654 55.04199558096079 other other other other other other text/xml other geonode:HB_Standorte_mTab0 Standorte der Horchboxen und nachgewiesene Fledermausarten an den Standorten mit Angaben zu Gefährdungsgrad und Schutzstatus Habitateignung Strukturreichtum HB_Standorte_mTab0 Fledermäuse Quartiere Abundanz features Strukturbindung Horchboxerfassung Jagd-/Leitstrukturen EPSG:25833 CRS:84 12.449942324691403 12.533149148211674 53.78331403934615 53.838692948507905 other other other other other text/xml other geonode:Lichtfang_2022 Lichtfang zur Erfassung der Nachtfalter (Großschmetterlinge) Kartierung Lichtfang_2022 Lepidoptera landwirtschaftliche Anbauflächen Gut Tellow Lichtfang features Schmetterling Nachtfalter Mustergut Großschmetterlinge EPSG:25833 CRS:84 12.449894522675784 12.523351943537381 53.77894907643079 53.83841539272664 other other other other other text/xml other geonode:MZB_Probestellen Darstellung der Lage der Probestellen im Untersuchungsraum Graben aus Tenze Makrozoobenthos Gräben Gut Tellow MZB_Probestellen STI Graben Sölle Kleingewässer Algenmatten features Thürkower Bach landwirtschaftliche Anbauflächen Soll Mustergut Perlodes EPSG:25833 CRS:84 12.456738491243414 12.522981727509038 53.80651808426695 53.83698312771907 other other other other text/xml other other geonode:OW_Chemie Monatliche Analysedaten des eigenen Messprogramms zur Gewässergüte von Dez. 2021 - Sept. 2022 Durchflüsse OW_Chemie Gut Tellow Gewässergüte Schwermetalle Pflanzenschutzmittel WRRL features Stofftransport Nährstoffe OGewV Mustergut EPSG:25833 CRS:84 12.520074091176319 12.539506178510726 53.83546086984874 53.84354751521793 other other other other text/xml other other geonode:PS_Heuschrecken Lage der Untersuchungsbereiche Saumbiotop Hygrophilie PS_Heuschrecken Gut Tellow Heuschrecken features Randstruktur Saltatoria Vegetationsstruktur Mustergut landwirtschaftliche Anbauflächen EPSG:25833 CRS:84 12.453212948690659 12.531525534278549 53.77802728876654 53.840727392120364 other other text/xml other other other other geonode:PS_Kaefer_mTabelle No abstract provided naturschutzfachliche Bewertung ökologische Bewertung PS_Kaefer_mTabelle Carabiden features Laufkäfer EPSG:25833 CRS:84 12.453212948690659 12.531525534278549 53.77802728876654 53.840727392120364 other other other other text/xml other other geonode:Pflanzenarten Gefährdete und geschützte Pflanzenarten werden dargestellt (Einzelindividuen sowie Bestände). Bundesartenschutzverordnung Gut Tellow Gefährdete Pflanzenarten Rote Liste Geschützte Pflanzenarten BArtSchV features Pflanzenarten Mustergut landwirtschaftliche Anbauflächen EPSG:25833 CRS:84 12.450655464541574 12.534442119922712 53.77794927641667 53.8408508452422 other other other text/xml other other other geonode:Pot_Laichgewaesser Lage der untersuchten Kleingewässer und Vorkommen der Arten in den potentiellen Laichgewässern. Kammmolch Gewässerstruktur Mustergut features Rotbauchunke Pot_Laichgewaesser landwirtschaftliche Anbauflächen Laichgewässer Laubfrosch Gut Tellow Gewässerumfeld Amphibien Landhabitate EPSG:25833 CRS:84 12.454490771015045 12.52791211727374 53.77853087245645 53.83626857798043 other other other other text/xml other other geonode:Raster_250x250_BK Beurteilung des Randeffektes (bezogen auf Rastergröße 250 m x 250 m) naturnaher (und auch optisch attraktiver) linienhafter Strukturen (Polygone), ergänzt und geändert nach MARKS et al. (1992). Raster MARKS Raster_250x250_BK Strukturelemente Gut Tellow Bewertung Biotop naturnah Landschaftsstrukturmaße Biotopkartierung features Gewässer Randefffekte Mustergut Vegetation EPSG:25833 CRS:84 12.446028109888774 12.540811755642178 53.77730997601118 53.84207060311127 other other other other text/xml other other geonode:ReptFunde Nachgewiesene Reptilienarten und ihre Fundorte im Analyseraum ReptFunde Gut Tellow Sichtbeobachtung Nachweis features landwirtschaftliche Anbauflächen Reptilienfunde Mustergut EPSG:25833 CRS:84 12.450334001855659 12.516038681624421 53.77845186548226 53.83176204034902 other other other other text/xml other other geonode:SRC_atlas_comp No abstract provided Fernerkundung GeoTIFF SRC_atlas_comp WCS Boden landwirtschaftliche Anbauflächen EPSG:3035 CRS:84 5.231165439609108 15.548774657497983 47.44626425673059 55.09896344057053 other other other other text/xml other other geonode:Tagfang_2022 Kartierung der Tagfalter und tagfliegender Nachtfalter Kartierung tagfliegende Nachtfalter Tagfalter Lepidoptera Tagfang_2022 Gut Tellow features Schmetterling Mustergut landwirtschaftliche Anbauflächen EPSG:25833 CRS:84 12.44976384311627 12.525369653879435 53.777899134776774 53.84110490886236 other other other other text/xml other other geonode:Transekt_BvK Transekte zur Erfassung der Brutvögel in allen Leitbildlebensraumtypen Brutvögel Linienkartierung Transekt_BvK Gut Tellow features Transektbegehung Dachverband Deutscher Avifaunisten Mustergut EPSG:25833 CRS:84 12.454396079379087 12.534734318365292 53.777881580541404 53.83634176858473 other other other other text/xml other other geonode:Transekte1 Transekte zur Sichtbeobachtung von Reptilien Transekte Gut Tellow Sichtbeobachtung Begehung features Transekte1 Mustergut EPSG:25833 CRS:84 12.449735518204783 12.539553766410497 53.7778935896259 53.84104476794061 other other other other text/xml other other geonode:Untersuchungsflaechen_Drohnenbefliegung Die Raumabgrenzung für die einzelnen Untersuchungen ist natürlich von der Lage der Anbauflächen abhängig; allerdings sind hier in den kommenden Jahren noch teilweise erhebliche Verschiebungen zu erwarten. Die Landgesellschaft Mecklenburg-Vorpommern ist aktuell dabei, die Anbauflächen insbesondere durch Tausch von Landesfläche weiter zu arrondieren. Schlagkartei Anbauflächen Schlag Gut Tellow Untersuchungsflaechen_Drohnenbefliegung Landwirtschaft features Mustergut EPSG:25832 CRS:84 12.451516018023836 12.533325367475594 53.77602496933701 53.843383022873745 other other other other text/xml other other geonode:ZugRast_Abgabe Erfassung der rastenden, nahrungssuchenden und überfliegenden Vögel auf den Probeflächen 9/2021-4/2022 Gut Tellow ZugRast_Abgabe Rastvögel Energiebedarf Mais features Gänsetrupps intensive Monokultur landwirtschaftliche Anbauflächen Mustergut EPSG:25833 CRS:84 12.45014707188558 12.537421916448654 53.77943652661373 53.84067363002602 other other other other text/xml other other geonode:bg_europe_naturalEarth No abstract provided Thünen CD bg_europe_naturalEarth features background Hintergrundkarte EPSG:4326 CRS:84 -180.0 180.0 -89.99892578 90.0 other other other other text/xml other other other geonode:bs_Gadus_morhua_biomass_2050_scenario No abstract provided Baltic Sea Habitat Suitability bs_Gadus_morhua_biomass_2050_scenario features Cod Commercial fishes Climate Change Biomass EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Gadus_morhua_decades_difference No abstract provided Baltic Sea Habitat Suitability features bs_Gadus_morhua_decades_difference Commercial fishes Climate Change Cod EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Gadus_morhua_first_decade_hs_1990 No abstract provided Baltic Sea Habitat Suitability features Commercial fishes Climate Change bs_Gadus_morhua_first_decade_hs_1990 Cod EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Platichthys_flesus_biomass_2050_scenario No abstract provided Baltic Sea bs_Platichthys_flesus_biomass_2050_scenario Habitat Suitability features Commercial fishes Climate Change Flounder Biomass EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Platichthys_flesus_decades_difference No abstract provided Baltic Sea bs_Platichthys_flesus_decades_difference Habitat Suitability features Commercial fishes Climate Change Flounder EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Platichthys_flesus_first_decade_hs_1990 No abstract provided Baltic Sea Habitat Suitability features bs_Platichthys_flesus_first_decade_hs_1990 Commercial fishes Climate Change Flounder EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Pleuronectes_platessa_biomass_2050_scenari No abstract provided Baltic Sea Habitat Suitability Plaice features bs_Pleuronectes_platessa_biomass_2050_scenario Commercial fishes Climate Change Biomass EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Pleuronectes_platessa_decades_difference No abstract provided Baltic Sea Plaice Habitat Suitability features bs_Pleuronectes_platessa_decades_difference Commercial fishes Climate Change EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Pleuronectes_platessa_first_decade_hs_1990 No abstract provided Baltic Sea bs_Pleuronectes_platessa_first_decade_hs_1990 Plaice Habitat Suitability features Commercial fishes Climate Change EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Solea_solea__first_decade_hs_1990 No abstract provided Baltic Sea Habitat Suitability features bs_Solea_solea__first_decade_hs_1990 Commercial fishes Climate Change Common sole EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Solea_solea_biomass_2050_scenario No abstract provided Baltic Sea Habitat Suitability bs_Solea_solea_biomass_2050_scenario features Commercial fishes Climate Change Common sole Biomass EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bs_Solea_solea_decades_difference No abstract provided Baltic Sea Habitat Suitability features bs_Solea_solea_decades_difference Commercial fishes Climate Change Common sole EPSG:4326 CRS:84 10.2 21.6 54.5 58.5 other other other other text/xml other other geonode:bwi3-tnr-voll3-shp_GVs_ReinbestandGattung_mitSp_PolyVar GVs_ReinbestandGattung_mitSp_PolyVar Forstwirtschaft Wald features EPSG:31467 CRS:84 5.091021658626396 16.69621024420553 47.00955396647776 55.10462523705822 other other other other text/xml other other geonode:bze_lw_standorte_verschleiert carbon Lagerungsdichte Feinbodenvorrat Bodenprofil bze_lw_standorte_verschleiert special permanent crop nitrogen soil organic matter Sonderkultur soil fertility Humus Stickstoff Organische Bodensubstanz pedogenic oxides Bodenfruchtbarkeit Monitoring Probenahme Boden organic grid sampling Soil carbonate Acker pH Landnutzung organisch cropland bulk density Pedogene Oxide Korngrößenverteilung soil profile fine soil stock Grünland land use Kohlenstoff features Raster Karbonat grassland soil texture LULUCF EPSG:25832 CRS:84 5.716176557815534 15.5121653660219 47.30780625717842 54.907618966926144 other other other other text/xml other other geonode:ctm_ger_2017_seg_v201 Landnutzung landwirtschaftliche Anbauflächen features Fernerkundung ctm_ger_2017_seg_v201 EPSG:3035 CRS:84 5.453099197962894 15.524758361727972 47.150383389678396 55.06175021229205 other other other other text/xml other other other geonode:ctm_ger_2017to2021_lsm_dis_v201 Fernerkundung Landnutzung ctm_ger_2017to2021_lsm_dis_v201 landwirtschaftliche Anbauflächen Vielfalt features EPSG:3035 CRS:84 5.473023880259563 15.490107590788723 47.171246216790934 55.0678113073229 other other other other text/xml other other geonode:ctm_ger_2017to2021_lsm_fed_v201 Fernerkundung Landnutzung ctm_ger_2017to2021_lsm_fed_v201 landwirtschaftliche Anbauflächen Vielfalt features EPSG:3035 CRS:84 5.473023880259563 15.490107590788723 47.171246216790934 55.0678113073229 other other other other text/xml other other geonode:ctm_ger_2017to2021_lsm_grd_v201 Fernerkundung Landnutzung ctm_ger_2017to2021_lsm_grd_v201 landwirtschaftliche Anbauflächen Vielfalt features EPSG:3035 CRS:84 5.199260096991065 15.585849297030917 46.90737488329842 55.36855840384982 other other other other text/xml other other geonode:ctm_ger_2017to2021_lsm_hex_v201 Fernerkundung Landnutzung landwirtschaftliche Anbauflächen Vielfalt ctm_ger_2017to2021_lsm_hex_v201 features EPSG:3035 CRS:84 5.462448759833015 15.500865156648416 47.165595909975934 55.07383871264814 other other other other text/xml other other geonode:ctm_ger_2017to2021_lsm_mun_v201 Fernerkundung Landnutzung ctm_ger_2017to2021_lsm_mun_v201 landwirtschaftliche Anbauflächen Vielfalt features EPSG:3035 CRS:84 5.473023880259563 15.490107590788723 47.171246216790934 55.0678113073229 other other other other text/xml other other geonode:ctm_ger_2018_seg_v201 Landnutzung landwirtschaftliche Anbauflächen ctm_ger_2018_seg_v201 features Fernerkundung EPSG:3035 CRS:84 5.463179779373968 15.48202089594726 47.17766938786413 54.943916064010565 other other other other text/xml other other other geonode:ctm_ger_2019_seg_v201 Landnutzung landwirtschaftliche Anbauflächen features Fernerkundung ctm_ger_2019_seg_v201 EPSG:3035 CRS:84 5.47474772298736 15.531068219646762 47.14563897067323 55.04555224878124 other other other other text/xml other other other geonode:ctm_ger_2020_seg_v201 Landnutzung landwirtschaftliche Anbauflächen features Fernerkundung Anbauflächen ctm_ger_2020_seg_v201 EPSG:3035 CRS:84 5.537225354298493 15.514823366090695 47.15206755348747 54.95267210247077 other other other other text/xml other other other geonode:ctm_ger_2021_seg_v201 Landnutzung features Fernerkundung Anbauflächen 2021 ctm_ger_2021_seg_v201 EPSG:3035 CRS:84 5.468360986016852 15.515805772429523 47.16010973989298 54.97038214041805 other other other text/xml other other other other geonode:emissionen_lawi_1990_2021_luftschadstoffe Landwirtschaft Lachgas Methan emissionen_lawi_1990_2021_luftschadstoffe Ammoniak Treibhausgase features Emissionen Luftschadstoffe EPSG:31467 CRS:84 5.565364323069865 15.57141007189034 47.14120596469726 55.05887116696665 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z other other other other text/xml other other other geonode:emissionen_lawi_1990_2021_thg Landwirtschaft Lachgas Methan Ammoniak Treibhausgase features emissionen_lawi_1990_2021_thg Emissionen Luftschadstoffe EPSG:31467 CRS:84 5.565364323069865 15.57141007189034 47.14120596469726 55.05887116696665 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z other other other other text/xml other other other geonode:emissionen_lawi_1990_2022_luftschadstoffe Als Vertragsstaat internationaler Abkommen zur Luftreinhaltung ist es das erkl&auml;rte Ziel Deutschlands, den Aussto&szlig; an Luftschadstoffen zu reduzieren. Wissenschaftliche Bestandsaufnahmen liefern die notwendigen Daten. (https://www.thuenen.de/de/themenfelder/klima-und-luft). Die Bundesregierung hat sich im Rahmen von internationalen Vereinbarungen zur Luftreinhaltung dazu verpflichtet, nationale anthropogene Emissionen von Luftschadstoffen zu berichten. Die Berichterstattung zu Luftschadstoffemissionen liegt beim Bundesministerium f&uuml;r Umwelt und Verbraucherschutz (BMUV). Die Ermittlung der Emissionen aus der Landwirtschaft unterliegt der Federf&uuml;hrung des Bundesministeriums f&uuml;r Ern&auml;hrung und Landwirtschaft (BMEL). Im Auftrag des BMEL erstellt das Institut f&uuml;r Agrarklimaschutz (AK) des Johann Heinrich von Th&uuml;nen-Instituts (TI) das j&auml;hrliche landwirtschaftliche Emissionsinventar. Gezeigt werden Emissionen von NH3, NMVOC, NO und St&auml;uben aus dem Bereich, der durch die Berichterstattung im Berichtssektor &bdquo;Landwirtschaft&ldquo; abgedeckt wird, d. h. die Nutztierhaltung und der Pflanzenbau. Die Verg&auml;rung von Energiepflanzen (Emissionen aus Fermenter, G&auml;rrestlager und G&auml;rrestausbringung) wird mit ber&uuml;cksichtigt. Emissionen aus Vorleistungsbereich (z. B. D&uuml;ngerherstellung) und Energieverbrauch in der Landwirtschaft, die nach den IPCC-und EMEP-Regeln in gesonderten Sektoren berichtet werden, finden hier keine Ber&uuml;cksichtigung. Dies gilt insbesondere auch f&uuml;r die Emissionen aus der mobilen und station&auml;ren Feuerung in der Land- und Forstwirtschaft sowie Fischerei, die in der Einteilung nach Bundesklimaschutzgesetz der Landwirtschaft zugerechnet werden. Die Emissionen der einzelnen Treibhausgase und Luftschadstoffe wird auf Landkreisebene aggregiert dargestellt. F&uuml;r diese Darstellung wurde die Aufteilung von Aktivit&auml;tsdaten auf Kreise teilweise gesch&auml;tzt, da sie nicht statistisch erhoben wird. Die Anzahl der Stellen der zum Download bereitgestellten Daten l&auml;sst keinen R&uuml;ckschluss auf deren Unsicherheit zu. &nbsp;Methodische Einzelheiten zur Ermittlung von Aktivit&auml;tsdaten und Emissionen sind im ausf&uuml;hrlichen Methodenbericht (verlinkt auf https://www.thuenen.de/de/fachinstitute/agrarklimaschutz/arbeitsbereiche/emissionsinventare) nachzulesen. Landwirtschaft Lachgas Methan Ammoniak Treibhausgase emissionen_lawi_1990_2022_luftschadstoffe features Emissionen Luftschadstoffe EPSG:31467 CRS:84 5.565364323069865 15.57141007189034 47.14120596469726 55.05887116696665 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z,2022-01-01T00:00:00.000Z other other other other text/xml other other other geonode:emissionen_lawi_1990_2022_thg Landwirtschaft Lachgas emissionen_lawi_1990_2022_THG Methan Ammoniak Treibhausgase features Emissionen Luftschadstoffe EPSG:31467 CRS:84 5.565364323069865 15.57141007189034 47.14120596469726 55.05887116696665 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z,2022-01-01T00:00:00.000Z other other other other text/xml other other other geonode:emissionen_lawi_1990_2023_luftschadstoffe Landwirtschaft Lachgas Methan Ammoniak Treibhausgase emissionen_lawi_1990_2023_luftschadstoffe features Emissionen Luftschadstoffe EPSG:31467 CRS:84 5.565364856049344 15.571406802144768 47.14121012950651 55.05886881652001 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z,2022-01-01T00:00:00.000Z,2023-01-01T00:00:00.000Z other other other other other text/xml other other geonode:emissionen_lawi_1990_2023_thg Treibhausgase Landwirtschaft Methan Ammoniak Emissionen Lachgas Luftschadstoffe emissionen_lawi_1990_2023_thg features EPSG:31467 CRS:84 5.565364856049344 15.571406802144768 47.14121012950651 55.05886881652001 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z,2022-01-01T00:00:00.000Z,2023-01-01T00:00:00.000Z other other other other other text/xml other other geonode:emissions_co2_eq_lulucf_20250 emissions_co2_eq_lulucf_20250 Treibhausgase Kohlenstoff features LULUCF Emissionen EPSG:5677 CRS:84 5.565031809580257 15.571669014242538 47.141212321844336 55.060105179431694 1990-01-01T00:00:00.000Z,1991-01-01T00:00:00.000Z,1992-01-01T00:00:00.000Z,1993-01-01T00:00:00.000Z,1994-01-01T00:00:00.000Z,1995-01-01T00:00:00.000Z,1996-01-01T00:00:00.000Z,1997-01-01T00:00:00.000Z,1998-01-01T00:00:00.000Z,1999-01-01T00:00:00.000Z,2000-01-01T00:00:00.000Z,2001-01-01T00:00:00.000Z,2002-01-01T00:00:00.000Z,2003-01-01T00:00:00.000Z,2004-01-01T00:00:00.000Z,2005-01-01T00:00:00.000Z,2006-01-01T00:00:00.000Z,2007-01-01T00:00:00.000Z,2008-01-01T00:00:00.000Z,2009-01-01T00:00:00.000Z,2010-01-01T00:00:00.000Z,2011-01-01T00:00:00.000Z,2012-01-01T00:00:00.000Z,2013-01-01T00:00:00.000Z,2014-01-01T00:00:00.000Z,2015-01-01T00:00:00.000Z,2016-01-01T00:00:00.000Z,2017-01-01T00:00:00.000Z,2018-01-01T00:00:00.000Z,2019-01-01T00:00:00.000Z,2020-01-01T00:00:00.000Z,2021-01-01T00:00:00.000Z,2022-01-01T00:00:00.000Z,2023-01-01T00:00:00.000Z other other other other other text/xml other other geonode:fnews_holzbodenmaske_2018_32632 Als Grundlage für das fernerkundungsbasierte Monitoring von Waldökosystemen muss die zu betrachtende Waldfläche eingegrenzt werden. Zu diesem Zweck wird in der Regel eine Wald- oder Holzbodenkarte hergestellt, welche auf einer Walddefinition basiert. Die hier vorgestellte bestockte Holzbodenkarte wurde auf Basis der Walddefinition der Bundeswaldinventur produziert (https://www.bundeswaldinventur.de/service/fachbegriffe-und-abkuerzungen). Ziel der Karte ist die Erfassung der mit Forstpflanzen bestocken Waldfläche. Dabei werden Landnutzungs- und Landbedeckungseigenschaften berücksichtigt, zum Beispiel gehören Weihnachtsbaumkulturen, Stadtparks oder Gehölzstreifen nach dem Bundeswaldgesetzt nicht zur Waldfläche und werden nicht erfasst. Die Karte basiert auf den Klassen AX_Wald, AX_Sumpf, AX_Moor des Basis-DLM 2018 (https://gdz.bkg.bund.de/index.php/default/digitales-basis-landschaftsmodell-ebenen-basis-dlm-ebenen.html), sowie den Produkten High Resolution Layer Tree Cover Density 2015/2018 (https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/tree-cover-density-2018?tab=metadata) und Feature Aditional Support Layer 2018 (https://land.copernicus.eu/user-corner/technical-library/forest-2018-user-manual.pdf). Flächen mit einem Überschirmungsgrad ≤ 50% und Gebiete mit einer Minimalen Flächeneinheit (MMU) ≤ 0,25 Hektar werden von der Holzbodenkarte nicht berücksichtigt. Die Genauigkeit der Karte wurde nach Stehman (2014, doi.org/10.1080/01431161.2014.930207) basierend auf den Waldentscheiden der Kohlenstoffinventur 2017/2018 geschätzt. Dabei erreicht die Karte eine Gesamtgenauigkeit von 96,49 ± 0,05%, ein Produzentengenauigkeit für Wald von 94,06 ± 0,12%  und eine Nutzergenauigkeit für Wald von 94,56 ± 0,11%. Die deutschlandweite bestockte Holzbodenfläche umfasst 10.962.638,95 Hektar. Damit liegt die kartierte Fläche 0,45% (97.80,91 Hektar) unter der bestockten Holbodenfläche nach BWI3 (https://bwi.info/). Für weitere Einzelheiten wenden Sie sich bitte an niklas.langner@thuenen.de WCS fnews_holzbodenmaske_2018_32632 GeoTIFF Wald Holzboden 2018 EPSG:32632 CRS:84 5.568678471893082 15.56595534155325 47.15070926692558 55.03221276288581 other other other other text/xml other other geonode:fnews_jp_18_22 No abstract provided Forstwirtschaft Monitoring Borkenkäfer features damage fnews_jp_18_22 Wald forest Fernerkundung Sturm Schaden EPSG:32632 CRS:84 7.531828329458276 15.121718042704348 47.408920260623375 52.350123703673766 other other other other text/xml other other rs:germany_bundesland features germany_bundesland EPSG:3035 CRS:84 5.473027754825263 15.490103808740873 47.17124653895502 55.06780919361057 rs:germany_bundesland_landscape_metrics_2017 features germany_bundesland_landscape_metrics_2017 EPSG:3035 CRS:84 5.473027754825263 15.490103808740873 47.17124653895502 55.06780919361057 rs:germany_bundesland_landscape_metrics_2018 features germany_bundesland_landscape_metrics_2018 EPSG:3035 CRS:84 5.473027754825263 15.490103808740873 47.17124653895502 55.06780919361057 rs:germany_bundesland_landscape_metrics_2019 features germany_bundesland_landscape_metrics_2019 EPSG:3035 CRS:84 5.473027754825263 15.490103808740873 47.17124653895502 55.06780919361057 rs:germany_bundesland_landscape_metrics_2020 features germany_bundesland_landscape_metrics_2020 EPSG:3035 CRS:84 5.473027754825263 15.490103808740873 47.17124653895502 55.06780919361057 rs:germany_gemeinde_landscape_metrics_2017 features germany_gemeinde_landscape_metrics_2017 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_gemeinde_landscape_metrics_2018 features germany_gemeinde_landscape_metrics_2018 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_gemeinde_landscape_metrics_2019 features germany_gemeinde_landscape_metrics_2019 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_gemeinde_landscape_metrics_2020 features germany_gemeinde_landscape_metrics_2020 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_grids_landscape_metrics_2017 features germany_grids_landscape_metrics_2017 EPSG:3035 CRS:84 5.142417654894223 15.643422026471653 46.86373123952001 55.41034610219899 rs:germany_grids_landscape_metrics_2018 features germany_grids_landscape_metrics_2018 EPSG:3035 CRS:84 5.142417654894223 15.643422026471653 46.86373123952001 55.41034610219899 rs:germany_grids_landscape_metrics_2019 features germany_grids_landscape_metrics_2019 EPSG:3035 CRS:84 5.142417654894223 15.643422026471653 46.86373123952001 55.41034610219899 rs:germany_grids_landscape_metrics_2020 features germany_grids_landscape_metrics_2020 EPSG:3035 CRS:84 5.142417654894223 15.643422026471653 46.86373123952001 55.41034610219899 rs:germany_hexagons_landscape_metrics_2017 features germany_hexagons_landscape_metrics_2017 EPSG:3035 CRS:84 5.450749564607207 15.540632185347773 47.14924271960287 55.11269582939279 rs:germany_hexagons_landscape_metrics_2018 features germany_hexagons_landscape_metrics_2018 EPSG:3035 CRS:84 5.424665219332527 15.52553835432937 47.140309859816064 55.09839063651324 rs:germany_hexagons_landscape_metrics_2019 features germany_hexagons_landscape_metrics_2019 EPSG:3035 CRS:84 5.481435253590354 15.53941372490114 47.158551629350775 55.10312171152659 rs:germany_hexagons_landscape_metrics_2020 features germany_hexagons_landscape_metrics_2020 EPSG:3035 CRS:84 5.424880415189075 15.552513608343936 47.13975508677214 55.09313917864755 rs:germany_landkreis_landscape_metrics_2017 germany_landkreis_landscape_metrics_2017 features EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_landkreis_landscape_metrics_2018 features germany_landkreis_landscape_metrics_2018 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_landkreis_landscape_metrics_2019 features germany_landkreis_landscape_metrics_2019 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 rs:germany_landkreis_landscape_metrics_2020 features germany_landkreis_landscape_metrics_2020 EPSG:3035 CRS:84 5.418657441981665 15.545301435807062 47.1304800290207 55.10678467232186 geonode:governance0 No abstract provided protected area features participation Deforestation property rights institutions governance0 Tropics forest owner community forests stakeholder EPSG:4326 CRS:84 -79.79211699199999 122.33991585735245 -14.878320461288913 18.470007233870085 other other other other text/xml other other geonode:ground_control_points No abstract provided land use features forest type Tropics ground_control_points mapping land cover EPSG:4326 CRS:84 -79.77902683 125.170887 -14.85045 18.426164999999997 other other other other text/xml other other geonode:hydro_survey_bts water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery Oceanographic geographical features temperature SF inspireidentifiziert features hydro_survey_bts Hydrography EPSG:4326 CRS:84 0.337300002574921 9.315999984741211 52.16529846191406 57.5004997253418 other other other other other text/xml other other other other other text/xml other geonode:hydro_survey_dogs water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery temperature SF inspireidentifiziert features Oceanographic geographical features hydro_survey_dogs Hydrography EPSG:4326 CRS:84 1.0 4.84600019454956 54.29100036621094 56.05799865722656 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_gaseez hydro_survey_gaseez water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery temperature SF inspireidentifiziert features Oceanographic geographical features Hydrography EPSG:4326 CRS:84 3.389800071716309 9.295499801635742 53.716800689697266 57.41529846191406 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_groenland water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery temperature SF hydro_survey_groenland inspireidentifiziert features Oceanographic geographical features Hydrography EPSG:4326 CRS:84 -60.839298248291016 -13.631699562072754 53.22829818725586 70.56500244140625 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_has water salinity Johann Heinrich von Thünen hydro_survey_has WCG Sea fisheries Downloadable Data oceanography abiotic factor hydrography opendata fishery Oceanographic geographical features temperature SF inspireidentifiziert features Hydrography EPSG:4326 CRS:84 -1.467000007629394 8.201199531555176 52.08319854736328 56.39970016479492 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_ibts1q water salinity Johann Heinrich von Thünen hydro_survey_ibts1q oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery Oceanographic geographical features temperature SF inspireidentifiziert features Hydrography EPSG:4326 CRS:84 -7.093299865722656 8.161199569702148 53.63669967651367 61.41669845581055 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_ibts3q water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery temperature SF inspireidentifiziert features Oceanographic geographical features hydro_survey_ibts3q Hydrography EPSG:4326 CRS:84 -5.500199794769287 7.982800006866455 52.1870002746582 60.450801849365234 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_irmsee hydro_survey_irmsee water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery Oceanographic geographical features temperature SF inspireidentifiziert features Hydrography EPSG:4326 CRS:84 -53.931800842285156 -26.006200790405273 52.49980163574219 65.67430114746094 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_megs water salinity Johann Heinrich von Thünen oceanography hydro_survey_megs Sea fisheries Downloadable Data WCG abiotic factor hydrography opendata fishery temperature SF inspireidentifiziert features Oceanographic geographical features Hydrography EPSG:4326 CRS:84 -15.249199867248535 3.22000002861023 43.62670135498047 58.9463005065918 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_soles water salinity hydro_survey_soles Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery temperature SF inspireidentifiziert features Oceanographic geographical features Hydrography EPSG:4326 CRS:84 4.09119987487793 8.325699806213379 53.711700439453125 55.88779830932617 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_vectors water salinity hydro_survey_vectors Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor hydrography fishery Oceanographic geographical features temperature SF inspireidentifiziert features Hydrography EPSG:4326 CRS:84 -3.248500108718872 5.68120002746582 50.109798431396484 59.964500427246094 other other other other text/xml other other other other other other text/xml other geonode:hydro_survey_wcs water salinity Johann Heinrich von Thünen oceanography WCG Sea fisheries Downloadable Data opendata abiotic factor fishery temperature SF inspireidentifiziert features Oceanographic geographical features hydro_survey_wcs Hydrography EPSG:4326 CRS:84 4.638999938964844 8.4266996383667 53.44329833984375 57.10179901123047 other other text/xml other other other other other other other other text/xml other geonode:inventories0 No abstract provided features timber inventories0 carbon tree species biodiversity Tropics Deforestation soil anayses EPSG:4326 CRS:84 -79.75 125.17 -14.8 18.42 other other other other text/xml other other other rs:krs10_geom_ts features krs10_geom_ts EPSG:31467 CRS:84 5.565364856049342 15.57140680214477 47.141210129506504 55.05886881652001 geonode:land_use Land use has been mapped in more than 70 community workshops in the LaForeT landscapes in 2017-2019. Participatory mapping was used to produce maps at the landscape level from the perspective of individuals. Mapping was carried out in workshops assembling representatives from the research landscapes. Polygons were delineated in focus group discussions on print outs of satellite images derived from Google Earth or Bing of approximately 80*120 cm. All mapped information was digitized by project staff after the workshops. Classification into land use categories was based on a project specific and four-tiered land cover and land use classification scheme.  For project information see www.la-foret.org. land_use features Deforestation Tropics mapping land cover EPSG:4326 CRS:84 -79.79211699199999 125.1734800224315 -14.879508297000259 18.468718655401837 other other other other text/xml other other geonode:land_use_zmb_tile Land use was mapped in 24 community workshops in the LaForeT landscapes in 2017-2019. In Zambia, not the whole landscape according to the definition of the LaForeT project were mapped in these participatory exercises. Therefore, additionally the complete landscapes were mapped and classified by project staff in order to complete missing parts that were not mapped in community exercises. This layer depicts the results of the second mapping. features Deforestation Tropics mapping land cover land_use_zmb_tile EPSG:32735 CRS:84 24.137231244783628 31.30108546921698 -14.91062767130237 -11.696269503103958 other other other other text/xml other other aa_at_geoserver_99_20:levl_1999_2003_bkr features levl_1999_2003_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2003_lau2 features levl_1999_2003_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2003_nuts3 levl_1999_2003_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2007_bkr features levl_1999_2007_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2007_lau2 levl_1999_2007_lau2 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2007_nuts3 features levl_1999_2007_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2010_bkr features levl_1999_2010_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2010_lau2 features levl_1999_2010_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2010_nuts3 features levl_1999_2010_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2016_bkr features levl_1999_2016_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2016_lau2 features levl_1999_2016_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2016_nuts3 features levl_1999_2016_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2020_bkr features levl_1999_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2020_lau2 features levl_1999_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_2020_nuts3 features levl_1999_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_bkr features levl_1999_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_1999_lau2 features levl_1999_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_1999_lau2 Die deutsche Agrarstatistik ist eine wichtige Informationsquelle, wenn Umwelteffekte zu bewerten und Trends in der Landwirtschaft abzuschätzen sind. Leider wird der Zugang auf kleinräumiger Ebene durch den Datenschutz beschränkt. Deshalb wurde das Projekt Agraratlas initialisiert, um einen Gemeindedatensatz zur Landnutzung und zum Tierbesatz zu erstellen, den keine Auflagen beschränken und der gleichzeitig alle Datenschutzerfordernisse erfüllt. Dieser Datensatz enthält die Ergebnisse auf LAU2 Ebene für das Jahr 1999. agraratlas levl_1999_lau2 features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other geonode:levl_1999_lau2_v2022 agraratlas levl_1999_lau2_v2022 features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_1999_nuts3 features levl_1999_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2007_bkr features levl_2003_2007_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2007_lau2 features levl_2003_2007_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2007_nuts3 features levl_2003_2007_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2010_bkr features levl_2003_2010_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2010_lau2 features levl_2003_2010_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2010_nuts3 features levl_2003_2010_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2016_bkr features levl_2003_2016_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2016_lau2 features levl_2003_2016_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2016_nuts3 features levl_2003_2016_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2020_bkr features levl_2003_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2020_lau2 levl_2003_2020_lau2 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_2020_nuts3 features levl_2003_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_bkr features levl_2003_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2003_lau2 features levl_2003_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_2003_lau2 Die deutsche Agrarstatistik ist eine wichtige Informationsquelle, wenn Umwelteffekte zu bewerten und Trends in der Landwirtschaft abzuschätzen sind. Leider wird der Zugang auf kleinräumiger Ebene durch den Datenschutz beschränkt. Deshalb wurde das Projekt Agraratlas initialisiert, um einen Gemeindedatensatz zur Landnutzung und zum Tierbesatz zu erstellen, den keine Auflagen beschränken und der gleichzeitig alle Datenschutzerfordernisse erfüllt. Dieser Datensatz enthält die Ergebnisse auf LAU2 Ebene für das Jahr 2003. features levl_2003_lau2 EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other geonode:levl_2003_lau2_v2022 agraratlas Landnutzung features levl_2003_lau2_v2022 EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_2003_nuts3 features levl_2003_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2010_bkr features levl_2007_2010_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2010_lau2 features levl_2007_2010_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2010_nuts3 levl_2007_2010_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2016_bkr features levl_2007_2016_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2016_lau2 features levl_2007_2016_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2016_nuts3 levl_2007_2016_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2020_bkr features levl_2007_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2020_lau2 features levl_2007_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_2020_nuts3 features levl_2007_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_bkr features levl_2007_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2007_lau2 features levl_2007_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_2007_lau2 Die deutsche Agrarstatistik ist eine wichtige Informationsquelle, wenn Umwelteffekte zu bewerten und Trends in der Landwirtschaft abzuschätzen sind. Leider wird der Zugang auf kleinräumiger Ebene durch den Datenschutz beschränkt. Deshalb wurde das Projekt Agraratlas initialisiert, um einen Gemeindedatensatz zur Landnutzung und zum Tierbesatz zu erstellen, den keine Auflagen beschränken und der gleichzeitig alle Datenschutzerfordernisse erfüllt. Dieser Datensatz enthält die Ergebnisse auf LAU2 Ebene für das Jahr 2007. features levl_2007_lau2 EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other text/xml other other other geonode:levl_2007_lau2_v2022 agraratlas levl_2007_lau2_v2022 features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_2007_nuts3 levl_2007_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2016_bkr features levl_2010_2016_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2016_lau2 features levl_2010_2016_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2016_nuts3 features levl_2010_2016_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2020_bkr features levl_2010_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2020_lau2 features levl_2010_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_2020_nuts3 features levl_2010_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_bkr features levl_2010_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2010_lau2 features levl_2010_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_2010_lau2 Die deutsche Agrarstatistik ist eine wichtige Informationsquelle, wenn Umwelteffekte zu bewerten und Trends in der Landwirtschaft abzuschätzen sind. Leider wird der Zugang auf kleinräumiger Ebene durch den Datenschutz beschränkt. Deshalb wurde das Projekt Agraratlas initialisiert, um einen Gemeindedatensatz zur Landnutzung und zum Tierbesatz zu erstellen, den keine Auflagen beschränken und der gleichzeitig alle Datenschutzerfordernisse erfüllt. Dieser Datensatz enthält die Ergebnisse auf LAU2 Ebene für das Jahr 2010. features levl_2010_lau2 EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other geonode:levl_2010_lau2_v2022 agraratlas levl_2010_lau2_v2022 features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_2010_nuts3 features levl_2010_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2016_2020_bkr features levl_2016_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2016_2020_lau2 features levl_2016_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2016_2020_nuts3 features levl_2016_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2016_bkr features levl_2016_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2016_lau2 features levl_2016_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_2016_lau2 Die deutsche Agrarstatistik ist eine wichtige Informationsquelle, wenn Umwelteffekte zu bewerten und Trends in der Landwirtschaft abzuschätzen sind. Leider wird der Zugang auf kleinräumiger Ebene durch den Datenschutz beschränkt. Deshalb wurde das Projekt Agraratlas initialisiert, um einen Gemeindedatensatz zur Landnutzung und zum Tierbesatz zu erstellen, den keine Auflagen beschränken und der gleichzeitig alle Datenschutzerfordernisse erfüllt. Dieser Datensatz enthält die Ergebnisse auf LAU2 Ebene für das Jahr 2016. features levl_2016_lau2 EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other other geonode:levl_2016_lau2_v2022 agraratlas levl_2016_lau2_v2022 features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_2016_nuts3 features levl_2016_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2020_bkr features levl_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:levl_2020_lau2 features levl_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:levl_2020_lau2_v2022 levl_2020_lau2_v2022 agraratlas features Landnutzung EPSG:31467 CRS:84 5.511902201080288 15.627533568041306 47.100393691901935 55.098601094087336 other other other other text/xml other other aa_at_geoserver_99_20:levl_2020_nuts3 levl_2020_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:livelihoods0 No abstract provided livelihoods0 features forestproducts Deforestation Tropics household income rural livelihoods EPSG:4326 CRS:84 -79.7921169916891 125.195223965258 -14.856731019150693 18.4277873706725 other other other other text/xml other other aa_at_geoserver_99_20:lu_1999_2003_bkr features lu_1999_2003_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2003_lau2 features lu_1999_2003_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2003_nuts3 features lu_1999_2003_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2007_bkr features lu_1999_2007_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2007_lau2 features lu_1999_2007_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2007_nuts3 features lu_1999_2007_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2010_bkr features lu_1999_2010_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2010_lau2 features lu_1999_2010_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:lu_1999_2010_nuts3 lu_1999_2010_nuts3 features EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 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geonode:ns_Gadus_morhua_decades_difference No abstract provided Habitat Suitability features Commercial fishes North Sea Climate Change ns_Gadus_morhua_decades_difference Cod EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Gadus_morhua_first_decade_hs_1970 No abstract provided Habitat Suitability features Commercial fishes North Sea Climate Change ns_Gadus_morhua_first_decade_hs_1970 Cod EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Melanogrammus_aeglefinus_biomass_2050_scen No abstract provided ns_Melanogrammus_aeglefinus_biomass_2050_scenario Habitat Suitability features Commercial fishes North Sea Haddock Climate Change Biomass EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Melanogrammus_aeglefinus_decades_differenc No abstract provided Habitat Suitability features ns_Melanogrammus_aeglefinus_decades_difference Commercial fishes North Sea Haddock Climate Change EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Melanogrammus_aeglefinus_first_decade_hs_1 No abstract provided Habitat Suitability features Commercial fishes North Sea Haddock Climate Change ns_Melanogrammus_aeglefinus_first_decade_hs_1970 EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Merlangius_merlangus_biomass_2050_scenario No abstract provided Habitat Suitability ns_Merlangius_merlangus_biomass_2050_scenario features Commercial fishes Whiting North Sea Climate Change Biomass EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Merlangius_merlangus_decades_difference No abstract provided Whiting Habitat Suitability features North Sea Climate Change Commercial fishes ns_Merlangius_merlangus_decades_difference EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Merlangius_merlangus_first_decade_hs_1970 No abstract provided Habitat Suitability features Commercial fishes Whiting North Sea Climate Change ns_Merlangius_merlangus_first_decade_hs_1970 EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Merluccius_merluccius_biomass_2050_scenari No abstract provided ns_Merluccius_merluccius_biomass_2050_scenario Habitat Suitability features Commercial fishes Hake North Sea Climate Change European hake Biomass EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other other text/xml other geonode:ns_Merluccius_merluccius_decades_difference No abstract provided Habitat Suitability features Hake Commercial fishes ns_Merluccius_merluccius_decades_difference North Sea Climate Change European hake EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Merluccius_merluccius_first_decade_hs_1970 No abstract provided ns_Merluccius_merluccius_first_decade_hs_1970 Habitat Suitability features Commercial fishes North Sea Climate Change European hake oceans EPSG:4326 EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Pleuronectes_platessa_biomass_2050_scenari No abstract provided Habitat Suitability Plaice features Commercial fishes North Sea Climate Change ns_Pleuronectes_platessa_biomass_2050_scenario Biomass EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Pleuronectes_platessa_decades_difference No abstract provided Plaice Habitat Suitability ns_Pleuronectes_platessa_decades_difference features Commercial fishes North Sea Climate Change EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Pleuronectes_platessa_first_decade_hs_1970 No abstract provided Habitat Suitability Plaice features Commercial fishes North Sea Climate Change ns_Pleuronectes_platessa_first_decade_hs_1970 EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Pollachius_virens_biomass_2050_scenario No abstract provided Biomass ns_Pollachius_virens_biomass_2050_scenario Saithe Commercial fishes Habitat Suitability Climate Change North Sea features EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other other geonode:ns_Pollachius_virens_decades_difference No abstract provided Habitat Suitability features ns_Pollachius_virens_decades_difference Commercial fishes North Sea Saithe Climate Change EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Pollachius_virens_first_decade_hs_1970 No abstract provided Habitat Suitability features ns_Pollachius_virens_first_decade_hs_1970 Commercial fishes North Sea Saithe Climate Change EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Solea_solea_biomass_2050_scenario No abstract provided Habitat Suitability features Commercial fishes North Sea Climate Change ns_Solea_solea_biomass_2050_scenario Common sole Biomass EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Solea_solea_decades_difference0 No abstract provided ns_Solea_solea_decades_difference Habitat Suitability features Commercial fishes North Sea Climate Change Common sole EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 other other other other text/xml other other geonode:ns_Solea_solea_first_decade_hs_1970 No abstract provided Habitat Suitability ns_Solea_solea_first_decade_hs_1970 features Commercial fishes North Sea Climate Change Common sole EPSG:4326 CRS:84 -3.8 9.8 51.300000000000004 61.10000000000001 text/xml other other other other other other rs:nuts1_geom_ts features nuts1_geom_ts EPSG:31467 CRS:84 5.565292053988015 15.571545253344784 47.1412101296326 55.05972140783298 geonode:pH_map_0_30 No abstract provided Waldboden pH_map_0_30 Monitoring Raster WCS GeoTIFF Bodenprofil Machine Learning landwirtschaftlicher Boden pH Bodenzustandserhebung Boden EPSG:31468 CRS:84 4.725561167633385 15.624390158183392 47.10066713723891 55.219629217452265 other other other other text/xml other other geonode:pH_map_30_100 No abstract provided pH_map_30_100 Waldboden Monitoring Raster WCS GeoTIFF Bodenprofil Machine Learning landwirtschaftlicher Boden pH Bodenzustandserhebung Boden EPSG:31468 CRS:84 4.725561167633385 15.624390158183392 47.10066713723891 55.219629217452265 other other other other text/xml other other geonode:pot_Reptilienmatten0 Standorte der ausgebrachten Reptilienmatten im Umfeld potentieller Habitate Künstliche Verstecke Reptilienmatten Dachpappen Gut Tellow pot_Reptilienmatten0 features landwirtschaftliche Anbauflächen Mustergut EPSG:25833 CRS:84 12.449799332911505 12.537251261319927 53.77793697812832 53.84099693694891 other other other other text/xml other other geonode:referenzdaten_fnews_3_0 No abstract provided features Fernerkundung Borkenkäfer Sturmwurf Waldschaden Referenzdaten referenzdaten_fnews_3_0 EPSG:32632 CRS:84 7.767573775822795 14.798479035533441 47.442037367685685 51.983123381436855 other other other other other text/xml other aa_at_geoserver_99_20:rlevl_arab_1999_2003_bkr features rlevl_arab_1999_2003_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:rlevl_arab_1999_2003_lau2 features rlevl_arab_1999_2003_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 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features rlu_uaa_2016_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:rlu_uaa_2020_bkr features rlu_uaa_2020_bkr EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:rlu_uaa_2020_lau2 features rlu_uaa_2020_lau2 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 aa_at_geoserver_99_20:rlu_uaa_2020_nuts3 features rlu_uaa_2020_nuts3 EPSG:31467 CRS:84 5.280008339907525 15.567521369638 47.11153289418112 55.03612288404488 geonode:sample_areas_1km2 No abstract provided features sample_areas_1km2 EPSG:3035 CRS:84 5.6190354908237605 15.342823144053243 47.45796287893388 54.89717029731439 other other other other text/xml other other geonode:sample_areas_3km3 No abstract provided features sample_areas_3km3 EPSG:3035 CRS:84 5.6025423943769574 15.3594994230356 47.448427414816834 54.90615641205116 other other text/xml other other other other geonode:ti_kulisse_kat_final_v10 No abstract provided Torfböden Niedermoor ti_kulisse_kat_final_v1 features Hochmoor Moorkarte Moorböden Moorverbreitung EPSG:25832 CRS:84 5.59087323032788 15.523292224796222 47.15027906083776 54.90692515205589 other other other other text/xml other other geonode:tiles Limits of the landscapes given as polygons. In the LaforeT project they are called "tiles". Three countries (Ecuador, the Philippines, and Zambia) were selected for the study. The countries as a whole represent different forest transition (FT) stages and a diverse array of tropical forest cover change dynamics captured by different FT phases at the local scale (landscape). Three regions were selected to capture the different FT phases within each country, totalling up to nine regions. Within each of the nine regions, four landscapes of approximately 100-150km2 each were selected, thus resulting in 36 landscapes. Each of the 36 landscapes is located within adistinct country-specific administration unit (i.e., chiefdoms, parroquias, and municipalities in Zambia, Ecuador, and the Philippines, respectively), selected to ensure homogenous formal administration across landscapes. The landscapes represent their respective regions' typical land-use, socioeconomic, demographic, and biophysical attributes. For project information see www.la-foret.org. see 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 Sensing of Environment 274. Gordillo, F., Eguiguren, P., Köthke, M., Ferrer Velasco, R., Elsasser, P., 2021. Additionality and Leakage Resulting from PES Implementation? Evidence from the Ecuadorian Amazonia. 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