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LTER-Italy terrestrial sites map

Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the terrestrial ecosystem (brown) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org/">https://deims.org</a>).

LTER-Italy marine sites map

Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the marine ecosystem (dark blue) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org/">https://deims.org</a>).

LTER-Italy freshwater sites map

Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) freshwater sites are evidenced. The colours of the dots correspond to the freshwater ecosystem (light blue) typologies. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).

opencc-by-4.0Jul 2021View details →

LTER-Italy sites map

Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) sites are evidenced. The colours of the dots correspond to the main ecosystem typologies: Dark blue = marine, light blue = freshwater, green = transitional water, brown = terrestrial. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).

LTER-Italy marine and transitional sites map

Map of Italy where the Italian Long-Term Ecological Research (LTER-Italy) sites are evidenced. The colours of the dots correspond to the main ecosystem typologies: Dark blue = marine, green = transitional water. The main features of the sites can be found on DEIMS-SDR, the LTER-Europe repository for research sites and datasets (<a href="https://deims.org">https://deims.org</a>).

Comparison of high-resolution global canopy height maps and their applicability to biodiversity modelling - dataset

This repository was created to provide datasets related with an article comparing high-resolution global canopy height maps and exploring their applicability to biodiversity modeling in temperate biomes. EBR stands for Entlebuch Biosphere Reserve, MRF stands for Mount Richmond Forest and TAW stands for Trinity Alps Wilderness. The original airborne laser scanning point clouds used for the generation of the canopy height models were sourced from the LINZ Data Service and OpenTopography, and licensed for reuse under the CC BY 4.0 licence (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fdoi.org.mcas.ms%2F10.5069%2FG97D2SB0%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://doi.org/10.5069/G97D2SB0</a>); Federal Office of Topography swisstopo (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fwww.swisstopo.admin.ch.mcas.ms%2Fen%2Fgeodata%2Fheight%2Fsurface3d.html%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://www.swisstopo.admin.ch/en/geodata/height/surface3d.html</a>); and U.S. Geological Survey (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fapps.nationalmap.gov.mcas.ms%2Fdownloader%2F%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://apps.nationalmap.gov/downloader/</a>). The Global Forest Canopy Height Map - GFCH (Potapov et al. 2021; https://glad.umd.edu/dataset/gedi) and the high-resolution canopy height model of the Earth - HRCH (Lang et al. 2022, https://langnico.github.io/globalcanopyheight/) are provided free of charge, without restriction of use under Creative Commons Attribution 4.0 International License. Publications, models, and data products that make use of these datasets must include proper acknowledgement. P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165. <a href="https://doi.org/10.1016/j.rse.2020.112165">https://doi.org/10.1016/j.rse.2020.112165</a> Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322. R scripts related with this datasets are available at Github (https://github.com/lukasgabor/Comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability; <a href="https://doi.org/10.5281/zenodo.7332716">DOI: 10.5281/zenodo.7332716</a>) In the previous version (1.0) the average was calculated for the canopy height. In this version (1.1), the maximum height is calculated for the canopy height.

opencc-by-4.0Jan 2023View details →

Global maps of area suitability for solar photovoltaics on rooftops and cropland

This dataset consists of the following global maps: 1. The suitable area for installing agri-photovoltaics or agrivoltaics on agricultural land cover in about 10 km x 10 km resolution for three scenarios of policy and social acceptance (optimistic, neutral and conservative) 2. The percentage of PV-suitable building area of total area in 10km x 10km resolution 3. The area suitability of rooftop-photovoltaics in a 10km x 10km resolution  The maps are based on the land cover data from the Copernicus Land Monitoring Service from 2019 and the Open Street Map accessed in 2021. Updates from Version 1.0 : - New maps of agrivoltaic suitability due to updated methodology : inclusion of regional crop distribution

opencc-by-sa-4.0Oct 2018View details →

Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon

This is the 2nd update of maps produced by <a href="https://doi.org/10.1088/1748-9326/aabe1c">Sanderman et al (2018)</a>. The improvements to the <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-prediction-of-soil-organic-carbon.html">3D spatial predictions</a> include: <ul> <li> new updated global mangrove coverage map (contact Thomas Worthington), </li> <li> spatiotemporal predictions to account for differences in spectral reflectance at the time of field work, </li> <li> additional SOC points <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41558-018-0162-5/MediaObjects/41558_2018_162_MOESM2_ESM.xlsx">published in Rovai et al. (2018)</a> used in model training (see gpkg file). </li> </ul> To open map in QGIS or similar, drag and drop the *.tif files. You can than add also the gpkg file contain the training points. Production steps (ensemble predictions using SuperLearner) are explained in detail at:  <ul> <li>R code: <a href="https://github.com/whrc/Mangrove-Soil-Carbon/">https://github.com/whrc/Mangrove-Soil-Carbon/</a> (see "R_code/GMW_mangroves_SOC_30m.R")</li> <li>Tutorial: <a href="https://envirometrix.github.io/PredictiveSoilMapping/soilmapping-using-mla.html#ensemble-predictions-using-superlearner-package">"Predictive Soil Mapping with R"</a></li> </ul> Produced for the purpose of Mangrove Restoration Potential Map funded by The Nature Conservancy and IUCN. Contact TNC: Emily Landis <<a href="mailto:elandis@TNC.ORG">elandis@TNC.ORG</a>>. Contact IUCN / University of Cambridge: Thomas Worthington <<a href="mailto:taw52@cam.ac.uk">taw52@cam.ac.uk</a>>. <ul> <li>The mangrove restoration potential map is available at: <a href="https://www.researchgate.net/deref/http%3A%2F%2Fmaps.oceanwealth.org%2Fmangrove-restoration%2F">http://maps.oceanwealth.org/mangrove-restoration/</a></li> </ul>

Sample of 100 place names from 'special maps' by Charles Perthées (1:225,000, 1783-1804)

The dataset contains a sample of 100 place names from ‘special maps’ by Charles Perthées (1:225,000, 1783-1804). Place names are supplemented with possible matches from the “National Register of Geographical Names” (NRGM) or the "Historical Atlas of Poland (HAP). There are 19 266 matches for 100 place names. 85 of them have 'certain' or 'uncertain' matches and 15 remained unmatched and, therefore, not geocoded.

EMOFORTE- Ideological Map of Emotions in Turkey

EMOFORTE Project Deliverable: An ideological map of emotions with a comparative-synchronic approach to the discourses of Turkish political parties on Foreign Policy at the nexus of national identity and Europe as its significant Other.    This diagram has been prepared as a part of EMOFORTE Project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.896311

Emotional Mapping of Turkish Foreign Policy towards Europe

EMOFORTE deliverable: A historical-diachronic emotional mapping which interrogate change in the political discourse of Turkish foreign policy towards Europe with a longitudinal study from 2002-2019 This digital product  has been prepared as a part of EMOFORTE Project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.896311

CHEK To-be Digital Building Permit process map

To-be process map for digital building permit process as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit' (CHEK) https://chekdbp.eu  It is described in the CHEK project deliverable D1.1. This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No.101058559 The file is provided in - SVG format, open vector editable format; - Visio format, proprietary but editable format; - PDF format.

SEN12 Multi-Temporal Urban Mapping Dataset

Monthly mean Sentinel-1 SAR and cloud-free Sentinel-2 MSI images for the SpaceNet 7 training and test sites. Our dataset also includes monthly rasterized built-up area labels for the 60 training sites.

Land Productivity Dynamics (LPD) maps based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region (BSR).

Maps of the land productivity dynamic (LPD) based on approaches recommended by the United Nations Convention to Combat Desertification (UNCCD) for the Brazilian Semiarid Region for the period 2001-2015. LPD from Trends.Earth (TE), LPD from the Joint Research Center (JRC-LPD), and LPD from the Food and Agriculture Organization – World Overview of Conservation Approaches and Technologies (FAO-WOCAT LPD). In addition, TE-based LPD with climate correction based on Rain Use Efficiency (RUE), Residual Trend Analysis (RESTREND), and Water Use Efficiency (WUE) calculated using the procedures described in the second version of the Good Practice Guidance for SDG Indicator 15.3.1. The annual LCLU maps from the MapBiomas project at 30 m spatial resolution for 2001 and 2015, and the 16-day MOD13Q1 NDVI dataset from 2001 to 2015 were used as inputs. ********************** A total of seven GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format are provided at 250 m spatial resolution. Coding for the LPDs. Value  Meaning -32768 No data 1           Declining 2          Moderate decline 3          Stressed 4          Stable 5          Increasing Funding: this study was undertaken as part of the Satellite Desertification Monitoring Program in the Brazilian Semiarid Region [Grant Number 403223/2021-0] supported by the CNPq. It also had the support of Capes, through Notice no. 28/2022 – PDPG Social Vulnerability & Human Rights [Grant Number 88881.705050/2022-01].

UKRI Digital Research Infrastructure Mapping Survey Dataset (for Net Zero Scoping Project)

This dataset was generated as an output for the DRI Mapping exercise carried out during the UKRI Net Zero Digital Research Infrastructure (DRI) Scoping Project undertaken from 2021-2023. The "README.md" provides more information about the dataset and how to use it. The report associated with this dataset is available at: https://doi.org/10.5281/zenodo.7805987

Monthly maps of Warm Core Ring Occupancy and occurrences of Salinity Maximum Intrusions in the Slope Sea (1990-2019)

This dataset presents two important variables across the shelfbreak in the Northwest Atlantic: (i) Warm Core Ring Occupancy in the Slope sea proximate to the shelfbreak; and (ii) locations of Salinity maximum intrusions in the shelf. Monthly fields of both of these fields together are presented for the period 1990-2019 with file name format smax_ring_mm_yyyy.jpg. The gray and red dots on the shelf represent locations of profiles taken from the Ecosystem Monitoring Program’s (EcoMon) hydrographic data (available from the National Centers for Environmental Information World Ocean Database accessible at <a href="http://www.ncei.noaa.gov/products/world-ocean-database">www.ncei.noaa.gov/products/world-ocean-database</a>). Red dots show locations of profiles which contained mid-depth salinity maximum intrusions, gray dots are profiles without any mid-depth salinity maximum intrusion. Profiles with intrusions were identified using the methodology of Gawarkiewicz et al., 2022. The ring occupancy was calculated from a Warm Core Ring Tracking dataset with ring tracks from 2000-2010 and 2011- 2020 available from Zenodo (<a href="https://doi.org/10.5281/zenodo.6436380">https://doi.org/10.5281/zenodo.6436380</a>, <a href="https://doi.org/10.5281/zenodo.7406675">https://doi.org/10.5281/zenodo.7406675</a>) and ring trajectories from 1978 through 1999 available from the Bedford Institute of Oceanography, Canada. To calculate the ring occupancy the region was sub-divided into 0.1 by 0.1 degree bins. Ring trajectories and approximate geographical range (calculated from the ring area, assuming the ring is a perfect circle) were overlain on this region and the days rings are present in each bin are counted in units of ring days. A ring day is the presence of one single ring in a bin during a given day. These ring day counts were converted to percentages, dividing by days in the given month and multiplying by 100. For more details see Silver et al., 2022 and Salois et al., 2023. From these figures one can see the spatial relationship between Warm Core Rings and Salinity Maximum Intrusions, with clusters of intrusions occurring in areas adjacent to high ring occupancy.  An animation of two particular years is also presented in movie_smax_ring_1993_2012.gif to highlight this relationship: Low ring year (1993) leading to fewer Smax intrusion and high ring year (2012) leading to more intrusions.   Gawarkiewicz, G., Fratantoni, P., Bahr, F., & Ellertson, A. (2022). Increasing Frequency of Mid‐Depth Salinity Maximum Intrusions in the Middle Atlantic Bight. Journal of Geophysical Research: Oceans, 127(7), e2021JC018233. <a href="https://doi.org/10.1029/2021JC018233">https://doi.org/10.1029/2021JC018233</a> Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans, 127(8), e2022JC018737. <a href="https://doi.org/10.1029/2022JC018737">https://doi.org/10.1029/2022JC018737</a>  Salois, S. L., Hyde, K. J., Silver, A., Lowman, B. A., Gangopadhyay, A., Gawarkiewicz, G., ... & Lapp, M. (2023). Shelf break exchange processes influence the availability of the northern shortfin squid, Illex illecebrosus, in the Northwest Atlantic. Fisheries Oceanography. <a href="https://doi.org/10.1111/fog.12640">https://doi.org/10.1111/fog.12640</a>

Digitalised dry heathlands on historical Ferraris Map of Flanders

Dataset description Dataset contains GIS vector layers with the polygons of regions, in the Esri shapefile format. Polygons have been drawn manually using QGIS software according to the borders of paths and roads, capturing schematic patterns of dry heathland from De Coene et al. (2012). CRS: EPSG:3857 - WGS 84 / Pseudo-Mercator Charset Encoding: UTF-8

opencc-by-4.0Jun 2022View details →

Pan-European exposure maps and uncertainty estimates from HANZE v2.0 model, 1870-2020

This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files: - CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP) - Pop: population - GDP: gross domestic product (2020 euros) - FA: fixed asset value (2020 euros) - imp: imperviousness density (%) Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods. Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables. For detailed description of the files, see the documentation provided with the code. This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

Segmentation maps of giant Antarctic icebergs

Segmentation maps of the giant Antarctic icebergs B30, B31, B34, B35, B41, B42 and C34 derived with a U-net approach, Otsu thresholding and k-means. Individual images are roughly one month apart. A description of the method and discussion of results can be found here: Braakmann-Folgmann, A., Shepherd, A., Hogg, D., and Redmond, E.: Mapping the extent of giant Antarctic icebergs with Deep Learning, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-858, 2023. Please cite this paper when using or refering to the data.