This is a video demonstrating how to create an offline map in ArcGIS Pro. Steps:Start with creating a vector tile package (.vtpk) from vector data.Add the vector tile package on top of other relevant data in a basemap view. The other data can be a raster image or any of the Esri's default basemaps.Add the basemap into another map view. In this map, you can add other operational layers on top of the basemap.Create a mobile map package (.mmpk) from the multi-layered map.The mobile map package can then be shared through ArcGIS Enterprise portal or manually copied to mobile devices.Author: Irvan Salim - Solution Engineer from Esri IndonesiaCopyright © 2020 Esri Indonesia. All rights reserved.

This dataset is available for download from: Wetlands (File Geodatabase).Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader land cover raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Change LogVersion 1.1 (January 26, 2023)Full resolution of wetlands replaced a coarser resolution version that was previously shared. Also, file type changed from polygon to raster (feature service to tile layer service).

This web map contains the new Hybrid Reference Layer vector tile layer, which is designed to be used to overlay imagery. The vector tile layer is similar in content and style to the popular Imagery with Labels map, which is delivered as a map service with raster tiles, with additional labels for transportation features.The 'Imagery with Labels' basemap contains the World Imagery map service and the World Boundaries and Places map service, so when you use that basemap you get boundaries and places, but you don't get highways and streets at small scales or street labels at large scale.If you prefer a map that uses raster tiles for both boundary and transportation features, you can use the Imagery with Labels and Transportation map.

Abstract

The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.
The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).
The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

Data acquisition

The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

Data collection

Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

Value of the data

The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

Data description

A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

Experimental design, materials and methods

The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

Acknowledgments

This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

References

[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

Global patterns of current and future road infrastructure - Supplementary spatial data

Authors: Johan Meijer, Mark Huijbregts, Kees Schotten, Aafke Schipper

Research paper summary: Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.

Contents: The GRIP dataset consists of global and regional vector datasets in ESRI filegeodatabase and shapefile format, and global raster datasets of road density at a 5 arcminutes resolution (~8x8km). The GRIP dataset is mainly aimed at providing a roads dataset that is easily usable for scientific global environmental and biodiversity modelling projects. The dataset is not suitable for navigation. GRIP4 is based on many different sources (including OpenStreetMap) and to the best of our ability we have verified their public availability, as a criteria in our research. The UNSDI-Transportation datamodel was applied for harmonization of the individual source datasets. GRIP4 is provided under a Creative Commons License (CC-0) and is free to use. The GRIP database and future global road infrastructure scenario projections following the Shared Socioeconomic Pathways (SSPs) are described in the paper by Meijer et al (2018). Due to shapefile file size limitations the global file is only available in ESRI filegeodatabase format.

Regional coding of the other vector datasets in shapefile and ESRI fgdb format:

• Region 1: North America
• Region 2: Central and South America
• Region 3: Africa
• Region 4: Europe
• Region 5: Middle East and Central Asia
• Region 6: South and East Asia
• Region 7: Oceania

Road density raster data:

• Total density, all types combined
• Type 1 density (highways)
• Type 2 density (primary roads)
• Type 3 density (secondary roads)
• Type 4 density (tertiary roads)
• Type 5 density (local roads)

Keyword: global, data, roads, infrastructure, network, global roads inventory project (GRIP), SSP scenarios

Standardized data sets produced from the Massachusetts Coast Flood Risk Model (MC-FRM)include annual exceedance probability (AEP) ARC-GIS based rasters across all modeled climatetime horizons, and depth of flooding at three AEP levels across all modeled climate timehorizons (present day, 2030, 2050, and 2070). However, additional non-standardized datasetswere requested by the Massachusetts Executive Office of Energy and Environmental Affairs(EEA) to be utilized in the development of climate resiliency tools for the State ofMassachusetts. These datasets were produced using the same probabilistic modelingframework created and utilized for the original MC-FRM and Boston Harbor Flood Risk Model(BH-FRM) datasets. These new non-standardized data consist of (1) Water Surface Elevations,(2) Maximum Wave Heights, and (3) calculations of Design Flood Elevations (DFEs). These weredetermined for six (6) selected annual exceedance probabilities (AEPs) and three (3) timehorizons. Additionally, EEA also requested (4) development of Tidal Benchmarks across thestate for 2030, 2050, and 2070 projected future sea level rise conditions. For these requestedoutputs, Woods Hole Group created a set of geographic data features and raster datasets.These datasets were calculated directly from the MC-FRM model simulation results butrequired additional calculation and processing efforts to create the required GIS based outputs.

The Gap Analysis Program (GAP) is an element of the U.S. Geological Survey (USGS). GAP helps to implement the Department of Interior?s goals of inventory, monitoring, research, and information transfer. GAP has three primary goals: 1 Identify conservation gaps that help keep common species common; 2 Provide conservation information to the public so that informed resource management decisions can be made; and 3 Facilitate the application of GAP data and analysis to specific resource management activities. To implement these goals, GAP carries out the following objectives: --Map the land cover of the United States --Map predicted distributions of vertebrate species for the U.S. --Map the location, ownership and stewardship of protected areas --Document the representation of vertebrate species and land cover types in areas managed for the long-term maintenance of biodiversity --Provide this information to the public and those entities charged with land use research, policy, planning, and management --Build institutional cooperation in the application of this information to state and regional management activities. GAP provides the following data and web services: The Protected Areas Database of the United States (PAD-US) is a geodatabase that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The PADUS GAP Status Layer web service can be found at http://gis1.usgs.gov/arcgis/rest/services/gap/PADUS_Status/MapServer . The Land Cover Data creates a seamless data set for the contiguous United States from the four regional Gap Analysis Projects and the LANDFIRE project. The 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 . In addition 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 The GAP species range data show a coarse representation of the total areal extent of a species or the geographic limits within which a species can be found (Morrison and Hall 2002). The GAP species distribution models represent the areas where species are predicted to occur based on habitat associations. A full report documenting the parameters used in each species model can be found via: http://gis1.usgs.gov/csas/gap/viewer/species/Map.aspx Web map services for species distribution models can be accessed from: http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Birds http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Mammals http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Amphibians http://gis1.usgs.gov/arcgis/rest/services/NAT_Species_Reptiles A table listing all of GAP's available web map services can be found here: http://gapanalysis.usgs.gov/species/data/web-map-services/

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.The specific raster datasets in this publication include:Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

Abstract: Annual average wind resource potential for the state of South Carolina at a 50 meter height.

Purpose: Provide information on the wind resource development potential within the state of South Carolina.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a WGS 84 projection system.

Other Citation Details: The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This dataset is a geographic shapefile generated from the original raster data. The original raster data resolution is a 200-meter cell size. The data provide an estimate of annual average wind speed at 90 meter height above surface for specific offshore regions of the United States. To learn more, please see the Assessment of Offshore Wind Energy Resources for the United States.

These data were produced in cooperation with U.S. Department of Energy, and have been validated by NREL. To download state wind resource maps, visit Wind Powering America.

In order to ensure the downloadable shapefile is current, please compare the date updated on this page to the last updated date on the NREL GIS Wind Data webpage.

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations. DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This raster dataset includes tree canopy cover derived from Lidar data.Due to the data type restrictions, tree canopy data couldn't be downloaded through our open data hub. Anybody interested are encouraged to send an enquiry at Toledo GIS.Created by:University of Toledo students in Digital Image Analysis (Nafula Barasa, Abdullah Bin Tamam, Nick Bonfigliio, Logan Dickson, Luke Folup, Nicholas Koenig, Donovan Morgan, Pramila Paudyal)

Land Information Ontario coordinates public and private sector organizations to collect high resolution aerial imagery for Ontario through a partnership funding model.Digital Raster Acquisition Project Eastern Ontario (DRAPE) was acquired in the spring of 2008 under the best conditions possible to achieve cloud free, snow free, ice free, smoke free and leaf off captures. The orthophotography has a pixel resolution of 20 centimetres.Time of Capture: April 18th 2008 - November 7th 2008, and April 15th 2009 - November 17th 2009Coverage: 45,000 square kilometresCanopy Coverage: Leaf-OffResolution: 20 centimetreBit-depth: 8-bitNumber of Image bands: Four band (RGBN)Accuracy: Within 1 meter horizontal accuracy at 95% confidence.Format: GeoTIFF (and compressed GeoTIFF)Participants to share in the cost of acquisition with the Ontario government are being sought and can include public, private, First Nations and non-government organizations. Participants get access to their imagery products of interest as soon as they are available.Use the Geospatial Ontario Imagery Orders App to select and request uncompressed or compressed digital imagery tiles from Geospatial Ontario for transfer directly to you.The acquisition area spans two Universal Transverse Mercator (UTM) Zones of UTM Zone 17 and UTM Zone 18. View the acquisition area for each zone by clicking the links near the bottom of this record. A new page opens and you may need to zoom in to see the individual one kilometre squared tiles. To download the index, click the ‘View Full Details’ button and finally the ‘Download’ button near the top of the page that opens. Alternatively, you can click HERE to bring the map up in an ArcGIS online map viewer. If Stereo imagery is required, please see the related Stereo Index entry on GeoHub.Online Web Map Service - Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2008 - Web Map ServiceAvailable Products:Orthorectified TilesDigital Raster Acquisition Project Eastern Ontario (DRAPE) 2008 - 1km Ortho IndexFull Quality Sample (95MB)Compressed Sample (7MB)Stereo FramesDigital Raster Acquisition Project Eastern Ontario (DRAPE) 2008 - Stereo Frame Index1.9 - 2.4GB per frameAdditional Resources:Imagery User Guide (.docx)Status:CompletedMaintenance and Update Frequency:Not planned: There are no plans to update the data.Contact:Ontario Ministry of Natural Resources - Geospatial Services, imagery@ontario.ca

NDFD temperature, max and min temperature, apparent temperature and relative humidity forecastsLink to graphical web page: https://digital.weather.govLink to data download (grib2): https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have two options for determining the latest time information about the service:Issue a returnUpdates=truerequest for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following relevant fields returned:idp_validtime - valid time of forecastidp_validendtime - end time of forecastidp_fcst_hour - start time of number of hours from current time forecast is validIn ArcGIS.com this option can be turned on by clicking the three dots under "NDFD Temp" heading and choosing "Enable Time Animation".

Abstract: Annual average wind resource potential for the state of Georgia at a 50 meter height.

Purpose: Provide information on the wind resource development potential within the state of Georgia.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a UTM zone 17, datum WGS 84 projection system.

Other_Citation_Details: The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

This layer quantifies the yearly economic value of nitrogen removal in Maryland's forest and wetland areas. Economic values are based on a number of factors, including the average cost to remove nutrients using best management practices, amount of funds provided for the BMP cost share program through the state and the Bay Restoration Fund , and the price on nutrient trading markets. The averaege value is $8.36 per lb nitrogen. Urban and agricultural lands are particularly important nutrient sources, and forests and wetlands in watersheds with high incidence of these land-uses tend to have high values of the nitrogen removal service. This service totals $402.6 million for Maryland yearly.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/17Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0

Indiana's Statewide Lidar data is produced at 1.5-meter average post spacing for all 92 Indiana Counties covering more than 36,420 square miles. New Lidar data was captured except where previously captured Lidar data exists, or the participating County bought-up to a higher resolution of 1.0-meter average post spacing Lidar data. Existing Lidar data exists for: Porter, Steuben, Noble, De Kalb, Allen, Madison, Delaware, Hendricks, Marion, Hancock, Morgan, Johnson, Shelby, Monroe, and portions of Vermillion, Parke, Vigo, Clay, Sullivan, Knox, Gibson, and Posey. These existing Lidar datasets were seamlessly integrated into this new statewide dataset. From this seamless Lidar product a statewide 5-foot post spacing hydro-flattened DEM product was created and is also available. See the FGDC Metadata provided for more details. This statewide project is divided into three geographic areas captured over a 3-year period (2011-2013):Area 1 (2011) Indiana central counties: St. Joseph, Elkhart, Starke, Marshall, Kosciusko, Pulaski, Fulton, Cass, Miami, Wabash, Carroll, Howard, Clinton, Tipton, Boone, Hendricks, Marion, Morgan, Johnson, Monroe, Brown, Bartholomew, Lawrence, Jackson, Orange, Washington, Crawford, and Harrison. Area 2 (2012) Indiana eastern counties: LaGrange, Steuben, Noble, DeKalb, Whitley, Allen, Huntington, Wells, Adams, Grant, Blackford, Jay, Hamilton, Madison, Delaware, Randolph, Hancock, Henry, Wayne, Shelby, Rush, Fayette, Union, Decatur, Franklin, Jennings, Ripley, Dearborn, Ohio, Scott, Jefferson, Switzerland, Clark, and Floyd. Area 3 (2013) Indiana western counties: Lake, Porter, LaPorte, Newton, Jasper, Benton, White, Warren, Tippecanoe, Fountain, Montgomery, Vermillion, Parke, Putnam, Vigo, Clay, Owen, Sullivan, Greene, Knox, Daviess, Martin, Gibson, Pike, Dubois, Posey, Vanderburgh, Warrick, Spencer, and Perry.Funders of OpenTopography Hosting of the Indiana Statewide Lidar and DEM data: USDA NRCS, Indiana, ISPLS Foundation, Indiana Geographic Information Office, Indiana Office of Technology, Indiana Geological Survey, Surdex Corporation, Vectren Energy Delivery, Indiana, Woolpert, Inc., and Individual IGIC Member Donations from Jim Stout, Jeff McCann, Cele Morris, Becky McKinley, Phil Worrall, and Andy Nicholson.

This layer shows the raster data of land utilization of Hong Kong. It is a set of data made available by the Planning Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is in GeoTiff format (Dataset URL: https://portal.csdi.gov.hk/geoportal/?lang=en&datasetId=pland_rcd_1696577406166_85973 ) and has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk .

The imagery products described in this metadata record are open data. Use of any imagery indicates your acceptance of the Open Government Licence – Ontario.The Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 imagery acquisition was part of a Geospatial Ontario coordinated project that included funding contributions from Ontario, and other public and private sector organizations to collect high-resolution aerial imagery. This aerial project is part of a five-year plan (2018-2022) to acquire high-resolution, leaf-off/low leaf imagery of settled areas across the province.

The DRAPE 2019-2020 was acquired in the spring of 2019/2020 under the best conditions possible to achieve cloud free, snow free, ice free, smoke free and leaf off/low leaf captures. The orthophotography has a pixel resolution of 16 centimetres and a stated accuracy of 45 centimetres on the ground.

Time of Capture: April 25th, 2019 to May 22nd, 2020 Coverage: 36,103 square kilometres Canopy Coverage: Leaf-Off/Low-Leaf (various stages of Leaf-On in some areas)Resolution: 16 centimetreBit-depth: 8-bitNumber of Image bands: Four band (RGBN)Accuracy: Within 45 centimetres on the ground at 95% confidence.Format: GeoTIFF (and compressed GeoTIFF)

Regularly check the Geospatial Ontario Imagery Page for the availability of online geospatial services that contain DRAPE 2019-2020. Use the Geospatial Ontario Imagery Orders App to select and request uncompressed or compressed digital imagery tiles from Geospatial Ontario for transfer directly to you.Stereo imagery and associated stereo models are available. Please see the related Stereo Index record on GeoHub for more information. Available Products:Orthorectified TilesDigital Raster Acquisition Project Eastern Ontario (DRAPE) 2019 - 1km Index (Shapefile)Full Quality Sample (95MB)Compressed Sample (7MB)Stereo FramesDigital Raster Acquisition Project Eastern Ontario (DRAPE) 2019 - Stereo Frame Index (Shapefile)1.9 - 2.4GB per frameAdditional Resources:Imagery User Guide (.docx)Sample DRAPE 2019-2020 Imagery in uncompressed .TIFF formatStatusCompleted: Production of the data has been completedMaintenance and Update Frequency:Imagery data will be updated as needed.Contact:Ontario Ministry of Natural Resources - Geospatial Services, imagery@ontario.ca