This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW.

Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product.

Soil data confidence is described using a 4 class system between high and very low as outlined below.:

• Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps.

• Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only.

Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area.

Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

GISCO (Geographic Information System of the COmmission) is responsible for meeting the European Commission's geographical information needs at three levels: the European Union, its member countries, and its regions.

In addition to creating statistical and other thematic maps, GISCO manages a database of geographical information, and provides related services to the Commission. Its database contains core geographical data covering the whole of Europe, such as administrative boundaries, and thematic geospatial information, such as population grid data. Some data are available for download by the general public and may be used for non-commercial purposes. For further details and information about any forthcoming new or updated datasets, see http://ec.europa.eu/eurostat/web/gisco/geodata.

This metadata refers to the whole content of GISCO reference database extracted in June 2020, which contains both public datasets (also available for the general public through http://ec.europa.eu/eurostat/web/gisco/geodata) and datasets to be used only internally by the EEA (typically, but not only, GISCO datasets at 1:100k). The document GISCO-ConditionsOfUse.pdf provided with the dataset gives information on the copyrighted data sources, the mandatory acknowledgement clauses and re-dissemination rights. The license conditions for EuroGeographic datasets in GISCO are provided in a standalone document "LicenseConditions_EuroGeographics.pdf".

The database is provided in GPKG files, with datasets at scales from 1:60M to 1:100K, with reference years spanning until 2021 (e.g. NUTS 2021). Attribute files are provided in CSV. The database manual, a file with the content of the database, a glossary, and a document with the naming conventions are also provided with the database.

The main updates with respect to the previous version of the full database in the SDI (from Jul. 2018) are the addition of the following datasets: - Administrative boundaries at country level, 2020 (CNTR_2020) - Administrative boundaries at commune level, 2016 (COMM_2016) - Coastline boundaries, 2016 (COAS_2016) - Exclusive Economic Zones, 2016 (EEZ_2016)

- Farm Accountancy Data Network based on NUTS 2016, 2018 (FADN_2018)

             Local Administrative Units, 2018 (LAU_2018)

• Nomenclature of Territorial Units for Statistics, 2021 (NUTS_2021)
• Political regions (NB.: defined by the Committee of the Regions), 2018 (POLREG_2018)
• Pan-European Settlements, 2016 (STLL_2016) and 2018 (STLL_2018)
• Transport Networks (NB.: railway lines, railway stations, roads, road junctions, levelcrossings, ferry routs and custom points), 2019 (TRAN_2019)
• Urban Audit Areas, 2018 (URAU_2018) and 2020 (URAU_2020)

NOTE: This metadata file is only for internal EEA purposes and in no case replaces the official metadata provided by Eurostat. For specific GISCO datasets included in this version there are individual EEA metadata files in the SDI: NUTS_2021 and CNTR_2020. For other GISCO datasets in the SDI, it is recommended to use the version included in this dataset. The original metadata files from Eurostat for the different GISCO datasets are available via ECAS login through the Eurostat metadata portal on https://webgate.ec.europa.eu/inspire-sdi/srv/eng/catalog.search#/home. For the public products metadata can also be downloaded from https://ec.europa.eu/eurostat/web/gisco/geodata. For more information about the full database or any of its datasets, please contact the SDI Team (sdi@eea.europa.eu).

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

The map title is Rock Types. Map scale. North arrow pointing to the north. Map projection is Hammer-Aitoff. Border of Canada. Great Lakes Border for each theme category within Canada. Neat line around the map. Each theme category is identified by a number that corresponds to the legend. Legend is divided into three categories: Metamorphic rocks, Deformed Sedimentary and Igneous rocks, Flat Lying Sedimentary rocks. Tactile maps are designed with Braille, large text, and raised features for visually impaired and low vision users. The Tactile Maps of Canada collection includes: (a) Maps for Education: tactile maps showing the general geography of Canada, including the Tactile Atlas of Canada (maps of the provinces and territories showing political boundaries, lakes, rivers and major cities), and the Thematic Tactile Atlas of Canada (maps showing climatic regions, relief, forest types, physiographic regions, rock types, soil types, and vegetation). (b) Maps for Mobility: to help visually impaired persons navigate spaces and routes in major cities by providing information about streets, buildings and other features of a travel route in the downtown area of a city. (c) Maps for Transportation and Tourism: to assist visually impaired persons in planning travel to new destinations in Canada, showing how to get to a city, and streets in the downtown area.

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent …Show full descriptionThis map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW. Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product. Soil data confidence is described using a 4 class system between high and very low as outlined below.: Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps. Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only. Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

L_ME_RIV_SDAGE_2022-2027_ETATLIEUX_2019_L_019

Stream-type water bodies whose elementary watersheds cut off the department of Corrèze. They are derived from the benchmarks used for the preparation of the 2019 state of the watersheds Adour-Garonne and Loire-Bretagne serving as the basis for the development of the water management master plans (Sdage) of the Adour-Garonne and Loire-Bretagne watersheds of the next 2022-2027 management cycle. For each body of water the values of its state (ecological and chemical) are given. These were determined in the context of the State of the Sites (EDL) of the Adour-Garonne and Loire-Bretagne basins approved in December 2019 by the coordinator prefects of these basins.

The data assembled in this layer were published in Q1 2020 by the water agencies Adour-Garonne and Loire-Bretagne.

River-type water bodies and data from the Adour-Garonne and Loire-Bretagne site reports were provided by the water agencies in Q1 2020. For Adour-Garonne, stream-type water bodies and data from the 2019 EDL can be accessed from the website: http://adour-garonne.eaufrance.fr/catalogue/10ff23eb-2079-4afe-bbca-f0a470a2c3bf For Loire-Bretagne, stream-type water bodies and data from the 2019 EDL can be accessed from the website: https://sdage-sage.eau-loire-bretagne.fr/home/projet-de-sdage-preparer-la-re-1/les-documents-du-sdage-2022-2027/etat-des-lieux-2019.html Water agencies to establish their water bodies are part of the BD Carthage repository (database on thematic mapping of water agencies and the Ministry responsible for the environment).

Meaning of the fields awarded European Water Body Code name of body of water ‘Class of the ecological status or ecological potential of the body of water: 1=very good, 2=good, 3=average, 4=poor, 5=bad, U=uncategorised’ ‘Chemical status class without ubiquistic molecules: 2=good, 5=bad, U=uncategorised’ nature of the body of water: Natural, Artificial, Strongly Modified ‘Class of the ecological status or ecological potential of the body of water: 1=very good, 2=good, 3=average, 4=poor, 5=bad, U=unclassified; and class of chemical state without ubiquitous molecules of the body of water:
2=good, 5=bad, U=uncategorised’

This dataset supports a bibliometric and modeling analysis on the topic of Fintech in Entrepreneurship covering the period from 1973 to 2024. The dataset was derived from the Scopus database and includes a total of 461 peer-reviewed documents that meet the following clear inclusion criteria: (a) relevance to fintech in entrepreneurship, (b) coverage of a complete publication year, (c) peer-reviewed status, and (d) accessibility for analysis. The dataset comprises metadata from 1108 authors affiliated with institutions in 85 different countries. It contains extensive bibliographic fields such as author names, document titles, publication years, EIDs, source titles, volumes, issues, pages, citation counts, document and source types, publication stages, DOIs, open access status, bibliographic information, affiliations, serial identifiers (e.g., ISSN), PubMed IDs, publishers, editors, languages, correspondence addresses, abbreviated source titles, abstracts, keywords, indexed keywords, funding details, numbers, acronyms, sponsors, funding texts, tradenames and manufacturers, accession numbers and chemicals, and conference information, including references. Bibliometric visualizations were generated using Microsoft Excel and R Biblioshiny. Microsoft Excel was used to visualize main information and annual scientific production, while R Biblioshiny enabled the visualization of main information summaries, thematic maps, and trending topics. In addition, the dataset supports a polynomial regression model (level 2) implemented in Python for forecasting the number of scientific publications from 2025 to 2034. The modeling output includes a projection chart in PNG format, and bibliometric visualizations include images of thematic maps, annual scientific production trends, trend topic maps, and research stage diagrams, also in PNG format. The primary dataset is provided in CSV format and is titled “Fintech in Entrepreneurship Dataset.” This dataset serves as a comprehensive resource for researchers interested in exploring publication trends, thematic developments, and predictive modeling in the interdisciplinary field of fintech and entrepreneurship.

Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independently of ArcMap, but output is then limited to a tabular report. The soil survey attribute database associated with the spatial soil map is a complicated database with more than 50 tables. Soil Data Viewer provides users access to soil interpretations and soil properties while shielding them from the complexity of the soil database. Each soil map unit, typically a set of polygons, may contain multiple soil components that have different use and management. Soil Data Viewer makes it easy to compute a single value for a map unit and display results, relieving the user from the burden of querying the database, processing the data and linking to the spatial map. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Resources in this dataset:Resource Title: Soil Data Viewer. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053620 Soil Data Viewer is a tool built as an extension to ArcMap that allows a user to create soil-based thematic maps. The application can also be run independent of ArcMap, but output is then limited to a tabular report. Soil Data Viewer contains processing rules to enforce appropriate use of the data. This provides the user with a tool for quick geospatial analysis of soil data for use in resource assessment and management. Links to download and install Download Soil Data Viewer 6.2 for use with ArcGIS 10.x and Windows XP, Windows 7, Windows 8.x, or Windows 10. Earlier versions are also available.

222: Cultivated parcels planted with fruit trees and shrubs, intended for fruit production, including nuts. The planting pattern can be by single or mixed fruit species, both in association with permanently grassy surfaces.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Vegetation map development for KNRI has somewhat different protocols than for other Parks. Normally photointerpretation is preceded by extensive field work which includes plot selection and vegetation sampling using detailed descriptions which are subsequently analyzed using ordination and other statistical techniques. The data are then summarized and association descriptions are assigned to each plot or, if the association is previously unrecognized, then a new association name is assigned. Subsequently, the plots locations are compared to its photographic signature and a photointerpretive key is developed. Given the very small size of KNRI and the extensive historical impact and alteration of the vegetation a simplified technique was used. NatureServe developed a list of potential vegetation types prior to any field work. This list was referenced during the field visit and modified after comparison of site characteristics and vegetation descriptions. Aerial photographs were viewed prior to the field visit and areas of like signature were differentiated. All vegetation and land-use information was then transferred to a GIS database using the latest grayscale USGS digital orthophoto quarter-quads as the base map and using a combination of on-screen digitizing and scanning techniques. Overall thematic map accuracy for the Park is considered 100% as all interpreted polygons received a filed visit for verification.

These datasets represent a systematic collection of harmonized data concerning geological events. GIS layers display data on the Portal at a resolution of 1:100,000 and 1:250,000 scale concerning earthquakes, submarine landslides, volcanoes, tsunamis, fluid emissions and Quaternary tectonics, subdivided according to their geometry (polygons, points and lines). They provide information on the type of events which have taken place in the past and might potentially occur again. Where available, details include dimensions, state of activity, morphological type and lithology. The elaboration of guidelines to compile GIS layers was aimed at identifying parameters to be used to thoroughly characterize each event. Particular attention has been devoted to the definition of the Attribute tables in order to achieve the best degree of harmonization and standardization complying with the European INSPIRE Directive. Shapefiles can be downloaded from the Portal and used locally in order to browse through the details of the different features, consulting their Attribute tables. Information contained therein provide an inventory of available data which can be fruitfully applied in the management of coastal areas and support planning of further surveys. By combining the diverse information contained in the different layers, it might be possible to elaborate additional thematic maps which could support further research. Moreover, they potentially represent a useful tool to increase awareness of the hazards which might affect coastal areas. Data sources include detailed information held by the Project Partners plus any further publicly available third-party data (last update Sep. 2021). All products delivered by Partners have been collated, verified and validated in order to achieve the best degree of harmonization and INSPIRE compliance. Each layer is complemented by an Attribute table which provides, in addition to the location, type of geological event and its references (mandatory), further information for each occurrence (where available). Since features considered within WP6 have a scattered distribution, the additional layer “Geological events distribution” provides basic information on areas of occurrences, no occurrences and no data for the marine areas surrounding European countries.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the digital map, a combination of 1:12,000-scale true color aerial photography, 1:12,000-scale true color ortho-rectified imagery, and 3 years of ground-truthing were used to interpret the complex patterns of vegetation and land-use. In the end, 52 map units were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. 1,122 accuracy assessment data points were collected and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 82%.

Map of summer pyrological risk levels of forest typologies carried out as part of the implementation of the Regional Plan for the planning of forecasting, prevention and active fight against forest fires, art 3 Law n. 353/2000 - Years 2011-2012. Regional thematic map derived from the rating classification of elements of the regional thematic map of Forest Types 2006 (summer classification by Tammaro F.)

Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes describing the basic properties of soil derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Attributes in this layer include:Soil Phase 1 and Soil Phase 2 - Phases identify characteristics of soils important for land use or management. Soils may have up to 2 phases with phase 1 being more important than phase 2.Other Properties - provides additional information important for agriculture.Additionally, 3 class description fields were added by Esri based on the document Harmonized World Soil Database Version 1.2 for use in web map pop-ups:Soil Phase 1 DescriptionSoil Phase 2 DescriptionOther Properties DescriptionThe layer is symbolized with the Soil Unit Name field.The document Harmonized World Soil Database Version 1.2 provides more detail on the soil properties attributes contained in this layer.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentMore information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database - Bulk DensityWorld Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – HydricWorld Soils Harmonized World Soil Database – TextureThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Living Atlas Discussion GroupSoil Data Discussion GroupThe Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This dataset was superseded by the State Vegetation Type Map (https://datasets.seed.nsw.gov.au/dataset/nsw-state-vegetation-type-map) on 24.06.2022.

Please note, Upper Hunter v1.0. VIS_ID 4894 web service and zipped dataset will be archived and will no longer be available on line after 31st March 2025.

The NSW Office of Environment and Heritage (OEH) is producing a new map of the State’s native vegetation. This seamless map of NSW’s native vegetation types will enable government, industry and the community to better understand the composition and the relative significance of the native vegetation in their local area. The State Vegetation Type Map (SVTM) (http://www.environment.nsw.gov.au/vegetation/state-vegetation-type-map.htm) is constructed from the best available imagery, site survey records, and environmental information.

The primary thematic layer in this dataset is a regional scale map of Plant Community Type (PCT) - "quickview" map.

Where spatially coincident, this map of Upper Hunter (v1.0) supersedes the Greater Hunter Native Vegetation Mapping v4.0. VIS ID 3855 and was generated sourcing the following improvements:

• A comprehensive revision of vegetation plot allocation to Plant Community Types (PCT), superseding GHM v4 Map Units.
• Addition of 463 vegetation plots.
• Comprehensive revision of aerial photo interpretation of Vegetation Photo Patterns (VPP) at 1:10,000. A relevant selection of PCT’s were nested and modelled within each VPP.
• Utilisation of Boosted Regression Tree modelling in place of Generalised Dissimilarity Modelling
• All manual aerial photo interpretation of VPP’s modelled PCT’s performed using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.
• Semi-automated line work generated using high resolution 50cm ADS-40 aerial imagery in place of SPOT-5 2.5m imagery.
• Climatic and topographic rule based envelopes were generated to constrain the maximum spatial envelope for each PCT. Each envelope was further manually edited.
• Dry Sclerophyll communities further constrained by exposure and landform envelopes.
• Selective integration of the following pre-existing maps to PCT: VIS1849, VIS3863, VIS3913, VIS4184, VIS4778
• 312 vegetation communities mapped as PCT’s compared to 185 GHMv4 map units over this region.

QuickView map fields:

• PCTID – Plant Community Type identifier.
• PCTName – Plant Community Type common names
• vegClass – The PCT’s Keith Class
• vegFormation – The PCT’s Keith Formation
• mapSource - The source of the polygon’s PCT attribution.
• MapName – The 100k sheet map name

Note that this is a dissolved surface and does not highlight the fine internal line-work within each map unit. Please refer to the 100k full data sheets for the complete editable internal linework, which are available by request to Data.Broker@environment.nsw.gov.au.

The data are provided in an ArcGIS 10.4 compatible file geodatabase.

Fields in the undissolved 100k sheet fine scale linework:

• polygonID – Unique map polygon identifier
• PCTID – Plant Community Type identifier
• PCTName – Plant Community Type common name
• vegetationClass – The PCT’s Keith Class
• vegetationFormation – The PCT’s Keith Formation

• mapSource - The source of the polygon’s PCT attribution. Possible values are:

  • Manual editing
  • Site Survey
  • Spatial Modelling
  • Pre-existing mapping: VIS1849
  • Pre-existing mapping: VIS3863
  • Pre-existing mapping: VIS3913
  • Pre-existing mapping: VIS4184
  • Pre-existing mapping: VIS4778
  • Expert Rules (see note on grassland attribution below)

• PCTIDMod1 - The most likely Plant Community Type identifier as derived from the spatial model.

• PCTIDMod2 - The second most likely Plant Community Type identifier as derived from the spatial model.

• PCTIDMod3 - The third most likely Plant Community Type identifier as derived from the spatial model.

• vegStruct - Vegetation Photo Pattern (VPP) as derived from manual aerial photo interpretation of 50cm ADS40 imagery.

Possible values for vegStruct include direct attribution of some PCT’s where possible in addition to these Vegetation Photo Patterns listed below:

• vegStruct (VPP) Description

  • 0 Non Native
  • 1 Candidate Grasslands
  • 2 Dry Sclerophyll
  • 3 Wet Sclerophyll
  • 5 Floodplain Forest
  • 7 Non Woody Wetlands
  • 8 Grass Open Woodlands
  • 10 Rainforests
  • 11 Riparian Forests
  • 12 Acacia Woodlands
  • 13 Shrublands
  • 15 Mallee
  • 16 Rocky Outcrops
  • 17 Belah
  • 100 Dry Rainforest

• PCTmapAccuracyConfidence - Modelling Confidence for PCTIDMod1 – Note that this reflects the modelling surface (PCTIDMod1) only and may not reflect the confidence of the mapped attribution (PCTID). PCTallocationConfidence can only be accurately applied to the published map surface (PCTID) where mapSource = ‘Spatial Modelling’.

• PCTSiteValidation - Type of field validation used to assess PCT reliability: Possible Values are:

  • Not validated
  • RPD (Rapid)
  • Full floristic validation
  • Unknown

Full details will be provided in the pending Technical Report.

VIS_ID 4893

Overview: 423: Area between the average lowest and highest sea water level at low tide and high tide. Generallynon-vegetated expanses of mud, sand or rock lying between high and low water marks. Traceability (lineage): This dataset was produced with a machine learning framework with several input datasets, specified in detail in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ) Scientific methodology: The single-class probability layers were generated with a spatiotemporal ensemble machine learning framework detailed in Witjes et al., 2022 (in review, preprint available at https://doi.org/10.21203/rs.3.rs-561383/v3 ). The single-class uncertainty layers were calculated by taking the standard deviation of the three single-class probabilities predicted by the three components of the ensemble. The HCL (hard class) layers represents the class with the highest probability as predicted by the ensemble. Usability: The HCL layers have a decreasing average accuracy (weighted F1-score) at each subsequent level in the CLC hierarchy. These metrics are 0.83 at level 1 (5 classes):, 0.63 at level 2 (14 classes), and 0.49 at level 3 (43 classes). This means that the hard-class maps are more reliable when aggregating classes to a higher level in the hierarchy (e.g. 'Discontinuous Urban Fabric' and 'Continuous Urban Fabric' to 'Urban Fabric'). Some single-class probabilities may more closely represent actual patterns for some classes that were overshadowed by unequal sample point distributions. Users are encouraged to set their own thresholds when postprocessing these datasets to optimize the accuracy for their specific use case. Uncertainty quantification: Uncertainty is quantified by taking the standard deviation of the probabilities predicted by the three components of the spatiotemporal ensemble model. Data validation approaches: The LULC classification was validated through spatial 5-fold cross-validation as detailed in the accompanying publication. Completeness: The dataset has chunks of empty predictions in regions with complex coast lines (e.g. the Zeeland province in the Netherlands and the Mar da Palha bay area in Portugal). These are artifacts that will be avoided in subsequent versions of the LULC product. Consistency: The accuracy of the predictions was compared per year and per 30km*30km tile across europe to derive temporal and spatial consistency by calculating the standard deviation. The standard deviation of annual weighted F1-score was 0.135, while the standard deviation of weighted F1-score per tile was 0.150. This means the dataset is more consistent through time than through space: Predictions are notably less accurate along the Mediterrranean coast. The accompanying publication contains additional information and visualisations. Positional accuracy: The raster layers have a resolution of 30m, identical to that of the Landsat data cube used as input features for the machine learning framework that predicted it. Temporal accuracy: The dataset contains predictions and uncertainty layers for each year between 2000 and 2019. Thematic accuracy: The maps reproduce the Corine Land Cover classification system, a hierarchical legend that consists of 5 classes at the highest level, 14 classes at the second level, and 44 classes at the third level. Class 523: Oceans was omitted due to computational constraints.

The data set consists of a LBA study area subset of the IGBP DISCover Data Set. The DISCover data set is one data set contained within the Global Land Cover Characteristics Data Base. The U.S. Geological Survey (USGS), the University of Nebraska-Lincoln (UNL), and the European Commission's Joint Research Centre (JRC) have generated a 1-km resolution global land cover characteristics data base for use in a wide range of environmental research and modeling applications. The global land cover characteristics data base was developed on a continent-by-continent basis. All continental data bases share the same map projections (Interrupted Goode Homolosine and Lambert Azimuthal Equal Area), have 1-km nominal spatial resolution, and are based on 1-km Advanced Very High Resolution Radiometer (AVHRR) data spanning April 1992 through March 1993. Each data base contains unique elements based on the geographic aspects of the specific continent. In addition, a core set of derived thematic maps produced through the aggregation of seasonal land cover regions are included in each continental data base. The continental data bases are combined to make six global data sets, each representing a different landscape based on a particular classification legend. The following derived data sets are included in the global land cover data base: * Global Ecosystems (Olson, 1994a, 1994b) * IGBP Land Cover Classification (Belward, 1996) * U.S. Geological Survey Land Use/Land Cover System(Anderson & others, 1976) * Simple Biosphere Model (Sellers and others, 1986) * Simple Biosphere 2 Model (Sellers and others, 1996) * Biosphere-Atmosphere Transfer Scheme (Dickinson and others, 1986) The legends for each of these derived data sets can be found in the documentation accompanying the data. For a description of the methodology for the global data base, see the global readme file found under the EROS Data Center DAAC home page (http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html).