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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pontone, Nicholas; Millard, Koreen; Thompson, Dan K.; Guindon, Luc; +1 Authors
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Mar, Ariza-Sentís; Kaiwen, Wang; Zhen, Cao; Sergio, Vélez; +1 Authors

    This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gershon, D;

    A Playbook for the Next Decade - Understanding How Venture Investors WillDeploy CapitalThe playbook for deploying capital has seen dynamic shifts over the past two yearsas inflation forces central banks to hike interest rates. Alongside these macrodynamics, new technologies like AI, ML, and Deep Learning offer largeenterprises the possibility of solving significant industry problems. Yet, as the costof money increases, execution risk and pathways for successful integration of thesetechnologies rise, investment and system-wide adoption risk profiles cause concernand disrupt the growth and commercialization of innovative digital healthcareproducts and services. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mendeley Dataarrow_drop_down
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; +1 Authors

    The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: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 and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science ___________________________________________________________________________ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Székely, Árpád;

    This dataset is an additional data to the following article: Temporal Changes of Leaf Spectral Properties and Rapid Chlorophyll—A Fluorescence under Natural Cold Stress in Rice Seedlings, Plants, 2023, DOI: 10.3390/plants12132415 . The dataset contains absorbance, reflectance and transmittance data of albino seedlings, measured by handheld leaf spectrometer (CI-710s SpectraVue Leaf Spectrometer; Cid-Bioscience, Camas, WA, U.S.A.). The albino plants were established in the temperate rice growing area in spring under the influence of natural cold temperatures. These data can be used as a reference to estimate the rate of chlorophyll degradation. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mendeley Dataarrow_drop_down
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2024
      Data sources: B2FIND
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; +1 Authors

    The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: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 and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science ___________________________________________________________________________ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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    Authors: Rocha, K;

    Data collection was carried out through the acquisition of images from the Landsat 8 satellite obtained from the Earth Explorer platform, from the United States Geological Survey (USGS, 2023). The images were obtained for the years 2013, 2018 and 2023, for the purpose of evaluating the temporal evolution of vegetation and land use and occupation in the study area considering a period of 10 years. One image was selected for each year, corresponding to the dry period, totaling three images. As pointed out by Pereira et al., (2020), precipitation can influence responses to vegetation indices. Therefore, the choice to analyze images from the same period aims to eliminate interpretations of changes in indices due to different climatic conditions. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
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      Mendeley Data
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      Mendeley Data
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    Authors: Kerr, Vince;

    # GIS data of urchin barren mapping in Northeastern New Zealand GIS mapping resources supporting the research article: Kerr, V.C. Grace R.V. (deceased), and Shears N.T., 2004. Estimating the extent of urchin barrens and kelp forest loss in northeastern Aotearoa, New Zealand. Kerr and Associates, Whangarei, New Zealand. ## Description of the data and file structure Four folders in this archive contain ArcGIS shapefiles with the extension (.shp). The shapefiles can be uploaded to ArcGIS or any ArcGIS-compatible software to view and access the files' spatial data and habitat attributes. It is essential to retain the associated files in each folder as these are system files required by ArcGIS to open and use the shapefiles. Each shapefile has six associated files with extensions: .avi, .CPG, .dbf, .prf, .sbn, and .sbx. In this archive are maps based on polygons drawn to depict habitat boundaries of biological and physical habitats in the shallow coastal areas of Northeastern New Zealand. Seven study areas, which feature urchin barrens and kelp forest habitats, are mapped at fine scales. Also in the archive are two Regional scale habitat maps of the coastal area created at a larger scale, providing full spatial coverage of the Northeast Region. The Regional scale maps provide spatial area data of shallow rocky reefs and other prominent habitats. Details of additional GIS resources to assist the use of the habitat maps are listed for each folder below: 1) Folder: /*7 Habitat maps full data 2023*. Includes the seven fine-scale habitat maps drawn at a scale allowing for mapping urchin barrens. The locations are Maitai Bay, Doubtless Bay, Waewaetoria Island, Maunganui Bay, Mimiwhangata, Leigh Marine Reserve and Tawharanui Marine Reserve. Each mapping file has the location name in the file name. For each of these map shapefiles, there is a layer file (extn: .lyr). The layer file is a GIS format file that should be kept in the same folder as the mapping shapefiles. The layer file contains a symbology that can be applied to the open map shapefile to display the different habitats (polygons) in a colour scheme, which aids the interpretation of the habitat mapping data. 2) *Folder: /Associated Mapping resources* has a large-scale GIS shapefile (extn: shp) of the New Zealand coast and a GIS shapefile (extn: shp) of the Marine Protection Area boundaries for Mimiwhangata Marine Park, Leigh Marine Reserve and Tawharanui Marine Reserve. These two files add a useful context to the display and interpretation of the mapping files. 3) *Folder: /Regional scale base maps* have the shapefiles (extn: .shp) for the two base maps used in the study for the Northland East and North coast and a Northern section of the Hauraki Gulf where the Leigh and Tawharanui areas are located. The file names include these location names. The base maps entirely cover shallow reefs in the Regional study area but were not mapped at a scale that allowed for the mapping of urchin barrens. Both of these shapefiles for the Regional maps have associated layer files (extn: .lyr), which can be used to display a suggested symbology for the habitat polygons, making it easier to interpret the maps. 4) *Folder: /Time series analysis* has the shape files (extn: .shp) of habitat boundaries (polygons) for two locations at Maitai Bay and Mimiwhangata where urchin barrens and kelp forest were mapped using aerial phtography for years ranging from 1944 to 2023 creating an opportunity to analyse loss of kelp forests over time due to the expansion of urchin barrens. ## Sharing/Access information The collection of GIS files of the mapping studies archived here, except for the Tawharanui Marine Reserve and the Regional Base map for Hauraki Gulf, have either published articles detailing the methods used or have unpublished technical reports with details of the fieldwork supporting the mapping process. Researchers are welcome to contact the Author, Vince Kerr, at for queries about these resources. Access links to these mapping studies are listed below. Kerr, V.C., Rutene, W., Bone, O., 2020. Marine habitats of Maitai Bay and the exposed coast of the Karikari Peninsula. A report prepared for Te Whānau Moana/Te Rorohuri, Maitai Bay, Karikari Peninsula, Northland and the Mountains to Sea Conservation Trust. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-56131-4, Download link Grace, R.V., Kerr, V.C., 2005. Intertidal and subtidal habitats of Doubtless Bay, Northland, N.Z. Contract report for the Department of Conservation, Northland Conservancy, Whangarei. Available online from: (Download archive) The New Zealand National Library digital library, ISBN, 978-0-473-69908-6 Download link Kerr, V.C., Grace, R.V., 2015. Marine habitats of the proposed Waewaetorea Marine Reserve. A report prepared for Fish Forever, Bay of Islands Maritime Park Inc. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-34573- Download link Kerr, V.C., 2016. Marine habitats of the proposed Maunganui Bay Marine Reserve. A report prepared for Fish Forever, Bay of Islands Maritime Park Inc. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-39499-8, Download link Kerr, V.C., Grace, R.V., 2005. Intertidal and subtidal habitats of Mimiwhangata Marine Park and adjacent shelf. Department of Conservation Research and Development Series 201, 55 p. Available online from: (Download archive) Department of Conservation, New Zealand ISSN 1176 8886 ISBN 0478226780 Download link Leleu, K., Remy-Zephir, B., Grace, R., & Costello, M. J. (2012). Mapping habitats in a marine reserve showed how a 30-year trophic cascade altered ecosystem structure. Biological Conservation, 155, 193-201. Regional Base Map Kerr, V.C., 2010. Marine Habitat Map of Northland: Mangawhai to Ahipara Vers. 1. Technical Report, Department of Conservation, Northland Conservancy, Whangarei, New Zealand. Department of Conservation ISBN: 978-0-478-14789-6 (Printed copy) ISBN: 978-0-478-14790-2 (Web PDF)\ ## Code/Software The ArcGIS format shapefiles and layer files in this archive were created in ArcGIS 10.3. These files are compatible with all ArcGIS and ArcPro software and other third-party GIS applications that can use ArcGIS format files. On shallow rocky reefs in northeastern Aotearoa, New Zealand, urchin barrens are recognised as indicators of the ecosystem effects of overfishing reef predators. Yet, information on their extent and variability is lacking. We use aerial imagery to map the urchin barrens and kelp forests on reefs (<30 m depth) across seven locations, including within two long-established marine reserves and a marine protected area that allows recreational fishing. Urchin barrens were present in all locations and were restricted to reefs <10-16 m deep. This archive contains ArcGIS shapefiles and layer files for all of the maps used in this study. The study area extends from Cape Reinga in the far north of the North Island to Tawharanui in the Hauraki Gulf near Auckland. Regional scale base maps of the prominent marine habitats were included along with the seven fine-scale maps where the kelp forests and urchin barrens were mapped. The GIS shapefiles produced in this study were hand-drawn over layers of low-level aerial photography taken in specific conditions, which maximised the visible depth observable to create polygons to depict the habitat boundaries of the shallow reef. Of particular interest was the mapping of urchin barrens. Ground truthing surveys creating point data and underwater imagery were also brought into the GIS project to assist in drawing the reef habitat polygons. Arc layer files contain a common symbology across the seven study maps to aid the interpretation of the mapping. Further information on the methodology used in the mapping can be found in two published papers and four technical reports corresponding to the maps. The Readme file details where technical reports and published reports can be downloaded from the internet.

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    DRYAD; ZENODO
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    Authors: Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan;

    Grasslands provide a wide range of ecosystem services within agricultural landscapes. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the years 2017 to 2021. The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 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. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019). Each annual dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose. We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly. The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. References Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795. _____________________________________________________________________________Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
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    Authors: Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan;

    Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the year 2022. The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 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. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019). The dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose. We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly. The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. References Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795. _____________________________________________________________________________Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
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    Authors: Pontone, Nicholas; Millard, Koreen; Thompson, Dan K.; Guindon, Luc; +1 Authors
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      ZENODO
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    Authors: Mar, Ariza-Sentís; Kaiwen, Wang; Zhen, Cao; Sergio, Vélez; +1 Authors

    This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s.

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    Authors: Gershon, D;

    A Playbook for the Next Decade - Understanding How Venture Investors WillDeploy CapitalThe playbook for deploying capital has seen dynamic shifts over the past two yearsas inflation forces central banks to hike interest rates. Alongside these macrodynamics, new technologies like AI, ML, and Deep Learning offer largeenterprises the possibility of solving significant industry problems. Yet, as the costof money increases, execution risk and pathways for successful integration of thesetechnologies rise, investment and system-wide adoption risk profiles cause concernand disrupt the growth and commercialization of innovative digital healthcareproducts and services. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; +1 Authors

    The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which have been produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: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 and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science ___________________________________________________________________________ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    Authors: Székely, Árpád;

    This dataset is an additional data to the following article: Temporal Changes of Leaf Spectral Properties and Rapid Chlorophyll—A Fluorescence under Natural Cold Stress in Rice Seedlings, Plants, 2023, DOI: 10.3390/plants12132415 . The dataset contains absorbance, reflectance and transmittance data of albino seedlings, measured by handheld leaf spectrometer (CI-710s SpectraVue Leaf Spectrometer; Cid-Bioscience, Camas, WA, U.S.A.). The albino plants were established in the temperate rice growing area in spring under the influence of natural cold temperatures. These data can be used as a reference to estimate the rate of chlorophyll degradation. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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      Data sources: B2FIND
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      Mendeley Data
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    Authors: Tetteh, Gideon Okpoti; Schwieder, Marcel; Blickensdörfer, Lukas; Gocht, Alexander; +1 Authors

    The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: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 and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References:Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science ___________________________________________________________________________ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
    Dataset . 2024
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    ZENODO
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      ZENODO
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      ZENODO
      Dataset . 2024
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    Authors: Rocha, K;

    Data collection was carried out through the acquisition of images from the Landsat 8 satellite obtained from the Earth Explorer platform, from the United States Geological Survey (USGS, 2023). The images were obtained for the years 2013, 2018 and 2023, for the purpose of evaluating the temporal evolution of vegetation and land use and occupation in the study area considering a period of 10 years. One image was selected for each year, corresponding to the dry period, totaling three images. As pointed out by Pereira et al., (2020), precipitation can influence responses to vegetation indices. Therefore, the choice to analyze images from the same period aims to eliminate interpretations of changes in indices due to different climatic conditions. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    DANS-EASY
    Dataset . 2024
    Data sources: B2FIND
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
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      DANS-EASY
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      Mendeley Data
      Dataset . 2024
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      Mendeley Data
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    Authors: Kerr, Vince;

    # GIS data of urchin barren mapping in Northeastern New Zealand GIS mapping resources supporting the research article: Kerr, V.C. Grace R.V. (deceased), and Shears N.T., 2004. Estimating the extent of urchin barrens and kelp forest loss in northeastern Aotearoa, New Zealand. Kerr and Associates, Whangarei, New Zealand. ## Description of the data and file structure Four folders in this archive contain ArcGIS shapefiles with the extension (.shp). The shapefiles can be uploaded to ArcGIS or any ArcGIS-compatible software to view and access the files' spatial data and habitat attributes. It is essential to retain the associated files in each folder as these are system files required by ArcGIS to open and use the shapefiles. Each shapefile has six associated files with extensions: .avi, .CPG, .dbf, .prf, .sbn, and .sbx. In this archive are maps based on polygons drawn to depict habitat boundaries of biological and physical habitats in the shallow coastal areas of Northeastern New Zealand. Seven study areas, which feature urchin barrens and kelp forest habitats, are mapped at fine scales. Also in the archive are two Regional scale habitat maps of the coastal area created at a larger scale, providing full spatial coverage of the Northeast Region. The Regional scale maps provide spatial area data of shallow rocky reefs and other prominent habitats. Details of additional GIS resources to assist the use of the habitat maps are listed for each folder below: 1) Folder: /*7 Habitat maps full data 2023*. Includes the seven fine-scale habitat maps drawn at a scale allowing for mapping urchin barrens. The locations are Maitai Bay, Doubtless Bay, Waewaetoria Island, Maunganui Bay, Mimiwhangata, Leigh Marine Reserve and Tawharanui Marine Reserve. Each mapping file has the location name in the file name. For each of these map shapefiles, there is a layer file (extn: .lyr). The layer file is a GIS format file that should be kept in the same folder as the mapping shapefiles. The layer file contains a symbology that can be applied to the open map shapefile to display the different habitats (polygons) in a colour scheme, which aids the interpretation of the habitat mapping data. 2) *Folder: /Associated Mapping resources* has a large-scale GIS shapefile (extn: shp) of the New Zealand coast and a GIS shapefile (extn: shp) of the Marine Protection Area boundaries for Mimiwhangata Marine Park, Leigh Marine Reserve and Tawharanui Marine Reserve. These two files add a useful context to the display and interpretation of the mapping files. 3) *Folder: /Regional scale base maps* have the shapefiles (extn: .shp) for the two base maps used in the study for the Northland East and North coast and a Northern section of the Hauraki Gulf where the Leigh and Tawharanui areas are located. The file names include these location names. The base maps entirely cover shallow reefs in the Regional study area but were not mapped at a scale that allowed for the mapping of urchin barrens. Both of these shapefiles for the Regional maps have associated layer files (extn: .lyr), which can be used to display a suggested symbology for the habitat polygons, making it easier to interpret the maps. 4) *Folder: /Time series analysis* has the shape files (extn: .shp) of habitat boundaries (polygons) for two locations at Maitai Bay and Mimiwhangata where urchin barrens and kelp forest were mapped using aerial phtography for years ranging from 1944 to 2023 creating an opportunity to analyse loss of kelp forests over time due to the expansion of urchin barrens. ## Sharing/Access information The collection of GIS files of the mapping studies archived here, except for the Tawharanui Marine Reserve and the Regional Base map for Hauraki Gulf, have either published articles detailing the methods used or have unpublished technical reports with details of the fieldwork supporting the mapping process. Researchers are welcome to contact the Author, Vince Kerr, at for queries about these resources. Access links to these mapping studies are listed below. Kerr, V.C., Rutene, W., Bone, O., 2020. Marine habitats of Maitai Bay and the exposed coast of the Karikari Peninsula. A report prepared for Te Whānau Moana/Te Rorohuri, Maitai Bay, Karikari Peninsula, Northland and the Mountains to Sea Conservation Trust. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-56131-4, Download link Grace, R.V., Kerr, V.C., 2005. Intertidal and subtidal habitats of Doubtless Bay, Northland, N.Z. Contract report for the Department of Conservation, Northland Conservancy, Whangarei. Available online from: (Download archive) The New Zealand National Library digital library, ISBN, 978-0-473-69908-6 Download link Kerr, V.C., Grace, R.V., 2015. Marine habitats of the proposed Waewaetorea Marine Reserve. A report prepared for Fish Forever, Bay of Islands Maritime Park Inc. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-34573- Download link Kerr, V.C., 2016. Marine habitats of the proposed Maunganui Bay Marine Reserve. A report prepared for Fish Forever, Bay of Islands Maritime Park Inc. Available online from: (Download archive) The New Zealand National Library digital library, ISBN 978-0-473-39499-8, Download link Kerr, V.C., Grace, R.V., 2005. Intertidal and subtidal habitats of Mimiwhangata Marine Park and adjacent shelf. Department of Conservation Research and Development Series 201, 55 p. Available online from: (Download archive) Department of Conservation, New Zealand ISSN 1176 8886 ISBN 0478226780 Download link Leleu, K., Remy-Zephir, B., Grace, R., & Costello, M. J. (2012). Mapping habitats in a marine reserve showed how a 30-year trophic cascade altered ecosystem structure. Biological Conservation, 155, 193-201. Regional Base Map Kerr, V.C., 2010. Marine Habitat Map of Northland: Mangawhai to Ahipara Vers. 1. Technical Report, Department of Conservation, Northland Conservancy, Whangarei, New Zealand. Department of Conservation ISBN: 978-0-478-14789-6 (Printed copy) ISBN: 978-0-478-14790-2 (Web PDF)\ ## Code/Software The ArcGIS format shapefiles and layer files in this archive were created in ArcGIS 10.3. These files are compatible with all ArcGIS and ArcPro software and other third-party GIS applications that can use ArcGIS format files. On shallow rocky reefs in northeastern Aotearoa, New Zealand, urchin barrens are recognised as indicators of the ecosystem effects of overfishing reef predators. Yet, information on their extent and variability is lacking. We use aerial imagery to map the urchin barrens and kelp forests on reefs (<30 m depth) across seven locations, including within two long-established marine reserves and a marine protected area that allows recreational fishing. Urchin barrens were present in all locations and were restricted to reefs <10-16 m deep. This archive contains ArcGIS shapefiles and layer files for all of the maps used in this study. The study area extends from Cape Reinga in the far north of the North Island to Tawharanui in the Hauraki Gulf near Auckland. Regional scale base maps of the prominent marine habitats were included along with the seven fine-scale maps where the kelp forests and urchin barrens were mapped. The GIS shapefiles produced in this study were hand-drawn over layers of low-level aerial photography taken in specific conditions, which maximised the visible depth observable to create polygons to depict the habitat boundaries of the shallow reef. Of particular interest was the mapping of urchin barrens. Ground truthing surveys creating point data and underwater imagery were also brought into the GIS project to assist in drawing the reef habitat polygons. Arc layer files contain a common symbology across the seven study maps to aid the interpretation of the mapping. Further information on the methodology used in the mapping can be found in two published papers and four technical reports corresponding to the maps. The Readme file details where technical reports and published reports can be downloaded from the internet.

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    Authors: Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan;

    Grasslands provide a wide range of ecosystem services within agricultural landscapes. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the years 2017 to 2021. The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 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. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019). Each annual dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose. We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly. The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. References Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795. _____________________________________________________________________________Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
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    Authors: Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan;

    Grasslands provide a wide range of important ecosystem services. Mapping and assessing the status and use intensity of grasslands is thus important for environmental monitoring. We here provide maps with detected mowing events, as a proxy for grassland use intensity, for grassland areas across Germany for the year 2022. The dataset contains maps of grassland mowing activity in Germany, which have been produced annually at the Thünen Institute beginning with the year 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. They are derived from dense time series of Sentinel-2, Landsat 8 (and 9) data. Map production is based on the methods described in Schwieder et al. (2022). The algorithm used to derive the maps is available as a user-defined function for the FORCE environment (Frantz, D., 2019). The dataset includes seven layers: (1) the number of detected mowing events, (2) the day of year (DOY) of the first to sixth detected mowing event. Ancillary data layers are available on request. The maps include all areas that have at least once been classified as permanent grassland, cultivated grassland or fallow in the maps of agricultural land use between 2017 and 2021 that are provided by Thünen Institute. Please consider to use the respective annual agricultural land use map or any other data source to generate a mask for your purpose. We provide this dataset "as is" without any warranty regarding the quality or completeness and exclude all liability. Please refer to Schwieder et al. (2022) for the related accuracy assessment and potential limitations and / or contact the authors directly. The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. References Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Schwieder, M., Wesemeyer, M., Frantz, D., Pfoch, K., Erasmi, S., Pickert, J., Nendel, C., & Hostert, P. (2022). Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series. Remote Sensing of Environment, 269, 112795. _____________________________________________________________________________Grassland mowing events across Germany © 2022 by Schwieder, Marcel; Lobert, Felix; Tetteh, Gideon Okpoti; Erasmi, Stefan; licensed under CC BY 4.0. Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (MonViA, Monitoring of biodiversity in agricultural landscapes).

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    ZENODO
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    ZENODO
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    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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