This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine, we examined inundation dynamics in California croplands from 2003 –2020 by intersecting monthly surface water maps (n=216 months) with mapped locations of precipitation amounts, rice, field, truck (which comprises truck, nursery, and berry crops), deciduous (deciduous fruits and nuts), citrus (citrus and subtropical), vineyards, and young perennial crops. Surface water maps were produced using the Dynamic Surface Water Extent (DSWE) model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating the monthly occurrence of “high confidence” water from each satellite collection across eight cropland types and 58 counties. The resulting tabular data have been joined to a county GIS shapefile covering the state of California. The file includes attributes summarizing each crop contained within the county boundaries along with a summary of how much cropland intersects past locations of cropland inundation, the relative percentage of cropland inundated, and the frequency of crop inundation. These summaries were generated using both the Landsat and MODIS water inundation maps, and are presented separately in the data release.

This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

1. This dataset is compiled from data provided by various government agencies and projects commissioned by government agencies to create river maps for public reference. For central government-managed rivers, the river boundary points should be based on those announced by the Ministry of Economic Affairs for central and inter-provincial rivers.2. River (tributary) data is provided for use by government agencies and private organizations, groups, or academic units commissioned by government agencies. The fields display the Chinese names of rivers, and the spatial data is represented in line format, with a total of 2,015 data entries.3. In hydrology, a tributary refers to a river (or other body of water) that flows into another river without directly reaching the sea. The river into which a tributary flows is known as the main stream or trunk stream, and the confluence of two or more rivers is called a confluence. The opposite situation is referred to as a distributary. Along the flow direction of the river water, tributaries can be referred to as left tributaries and right tributaries.4. When opening this file using Google Earth software, there may be some discrepancies in the overlay of layers due to the lack of precise orthorectification in the base map images provided by Google.

The Water Resources Agency and its affiliated agencies provide relevant range information for various river basins for use by government agencies and private organizations, groups, or academic units commissioned by government agencies for specific projects. This dataset is linked to a Keyhole Markup Language (KML) file list, a markup language based on the XML (eXtensible Markup Language) syntax standard, used to express geographic annotations. KML files, written in the KML language, use the XML file format and are applied in Google Earth-related software for displaying geographic data (including points, lines, polygons, models, etc.). Many GIS systems also use this format for exchanging geographic data. The KML data in this dataset uses the UTF-8 encoding.

This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

The outdoor parks for this dataset were identified using Google Maps. Each data point was geocoded using the latitude and longitude points of each outdoor park that Google Maps identified as being a part of Newark and inputted into a spreadsheet. Using the NJ Municipalities layer on ArcGIS Pro helped to finalize the list, ensuring that each park point was inside the Newark boundary.

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries.
This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset.
This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose.
Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata.
To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only.
This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services.
See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

Service Area 2009 - 30 cm Aerials for ArcGIS Online/Bing Maps/Google Maps, etc.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: DigitalGlobePROJECT SPECIFICATIONS -- ACCURACY REPORTProject: WA Seattle-30cm-0709_4488Datum: NAD 83Projection: UTMZone: 10Units: metersGSD (pixel size): 30 cmCamera Type: Leica Geosystems ADS40-SH51Average Acquisition Altitude: 9,600 feet above ground levelDEM Source: National Elevation Dataset (NED)Reference Source: Airborne GPS/IMUPhoto Date Range: July 2 through July 22, 2009Cloud cover: Entire Market is cloud freeResolution: Entire Market acquired at 30 cm GSDSun Angle: Entire Market acquired with sun angle above 30 degreesAccuracy Points Measured: 47Accuracy: 2.01 meters CE90Accuracy report, date prepared: September 30, 2009Accuracy report prepared by: Mark MeyerOriginal ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.655808 East longitude: -121.999192 North latitude: 47.352623 South latitude: 46.791574Extent in the item's coordinate system: West longitude: 1105996.657074 East longitude: 1264997.323324 South latitude: 539001.060567 North latitude: 740002.627067

This layer contains detailed outlines of Maryland counties. The Maryland land county boundaries were built using political county boundaries and the National Hydrology Data (NHD). Land boundaries are a key geographic featue in our mapping process.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Last Updated: UnknownFeature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PhysicalBoundaries/FeatureServer/0

Abstract This is a source dataset created by the Bioregional Assessment Programme without the use of source data. This dataset contains all of the surface water footprint polygons that were created from mining reports that were used in the surface water modelling. There is also a document with the source references for all of the footprints included in the dataset. ## Dataset History Environmental impact statements and similar documents were downloaded from New South Wales Department of Planning and Environment Major Projects website, and from mining companies' websites. To obtain mine footprints for surface water modelling, the mining reports were searched for past and future projected mine layouts and surface water contributing areas. Each figure was digitised and georeferenced using one of four methods: 1. The preferred method was to use maps or plans with coordinates already on them. 2. If there were no coordinates, then three point locations were matched with points on Google Earth and the latitude and longitude from Google Earth were used to georeference the image. 3. If there were not three clearly identifiable point locations in the image, then supplementary points were found by matching contour information to the Shuttle Radar Topography Mission Smoothed Digital Elevation Model (SRTM DEM-S) grid Dataset GUID - 12e0731d-96dd-49cc-aa21-ebfd65a3f67a 4. Other site-specific approaches: a. Mangoola Coal Mine did not have adequate georeferencing points in the Year 10 and Final Landform images so these images were georeferenced to the matching project boundary in the other Mangoola Coal Mine images. b. The West Wallsend Colliery existing pit top surface facilities image, containing a satellite photo background, was georeferenced using Google Earth. The West Wallsend Colliery pit top facility outline was used to georeference the water management system image as they both contained the same outline. These areas were exported as polygon files (*.poly) using Geosoft Oasis Montaj software. A list of documents used for creating these polygon files are also included in the dataset ## Dataset Citation Bioregional Assessment Programme (2016) HUN SW footprint shapefiles v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/2a9520c8-1569-4e0e-8bd8-26e2c7b9e9e0.

The V1 ('flying bomb' or 'doodlebug') and V2 (a ballistic missile) were two new weapons developed by Nazi Germany. In 1944 and 1945 thousands were fired at London. They killed thousands of people and injured many more.

This dataset includes all impact sites for V1s and V2s within the London County Council boundary. These were manually compiled from bomb maps published in 'The London County Council Bomb Damage Maps 1939-1945' by Laurence Ward (Thames and Hudson, 2015). The original LCC Bomb Damage maps are held at the London Archives.

**Please note that this is not a comprehensive dataset of all V1s and V2s. Only those within the London County Council boundary are included.**

We previously analysed this dataset in 'The flying bomb and the actuary', Significance (2019). doi: 10.1111/j.1740-9713.2019.01315.x

The impact sites can also be viewed as a layer on Google Maps. Data is separated into two layers: V1 sites and V2 sites. Each point represents an impact site, with the closest street name (to help with possible cross-reference) and page number in the LCC Bomb Damage Maps: https://www.google.com/maps/d/viewer?mid=1VwyxV_e_LAwzbyJPCAF-C7aCRVNA5W7N&ll=51.509018493447314%2C-0.05324588962980492&z=14

This dataset contains information on Sri Lanka's provinces, districts, and cities.

Contents

• Provinces: 9 provinces in Sri Lanka
• Districts: 25 districts, each associated with a province
• Cities: 2213 cities, each associated with a district, including sub-city names, postal codes, latitude, and longitude

Usage

This dataset is useful for a variety of applications including:

• Geospatial Analysis: Utilize the latitude and longitude data to analyze geographic patterns, proximity, and regional distribution across Sri Lanka.
• Mapping and Visualization: Integrate with mapping services like Google Maps to visualize city locations, provincial boundaries, and district affiliations.
• Location-Based Services: Develop applications that identify nearby cities, districts, or points of interest within a specified radius of any given coordinates.

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

While the Waimakariri District Council has taken all reasonable care in providing correct information, all information should be considered as being illustrative and indicative only. Your use of this information is entirely at your own risk. You should independently verify the accuracy of any information before taking any action in reliance upon it.Read full disclaimer here.Abstract:Physical addresses in Waimakariri. This dataset contains the street number, street name and suburb of an address.There can be multiple addresses on a property and an example of these are granny flats, farm cottages etc. When this occurs and where known, the address point has been located on top of the appropriate building otherwise it has been located in the centre of the property boundary.Note, address boundaries match the entire property boundary and where there are multiple addresses on a property, these boundaries are stacked on top of each other.Other information:Click here to view Address Point LayerProperties:Properties are derived from current primary parcels, as per the NZ Parcels layer on the LINZ Data Service, joined to data on matching current/future properties in WDC’s rating database. (Note: properties only contain the primary address as identified in WDC’s rating database, not all addresses on a property)Click here to view Property Boundary LayerUpdate Frequency:DailyPoint of Contact:Waimakariri District CouncilLineage:Data has been compiled from a number of sources and its accuracy may vary (e.g. Field Verification, Deposited Plans, AsBuilt plans and forms, sketches, aerial photo, Google Street View). There may be delays before data is updated to reflect changes in an area.

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

Contextual information:

• elevation (European Union and ESA, 2021)
• land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)
• administrative boundaries from geoBoundaries (Runfola et al., 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024)
• Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved)
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866.
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

This resource is a repository of the map products for the Annual Irrigation Maps - Republican River Basin (AIM-RRB) dataset produced in Deines et al. 2017. It also provides the training and test point datasets used in the development and evaluation of the classifier algorithm. The maps cover a 141,603 km2 area in the northern High Plains Aquifer in the United States centered on the Republican River Basin, which overlies portions of Colorado, Kansas, and Nebraska. AIM-RRB provides annual irrigation maps for 18 years (1999-2016). Please see Deines et al. 2017 for full details.

Preferred citation: Deines, J.M., A.D. Kendall, and D.W. Hyndman. 2017. Annual irrigation dynamics in the US Northern High Plains derived from Landsat satellite data. Geophysical Research Letters. DOI: 10.1002/2017GL074071

Map Metadata Map products are projected in EPSG:5070 - CONUS Albers NAD83 Raster value key: 0 = Not irrigated 1 = Irrigated 254 = NoData, masked by urban, water, forest, or wetland land used based on the National Land Cover Dataset (NLCD) 255 = NoData, outside of study boundary

Training and test point data sets supply coordinates in latitude/longitude (WGS84). Column descriptions for each file can be found below in the "File Metadata" tab when the respective file is selected in the content window.

Corresponding author: Jillian Deines, jillian.deines@gmail.com

This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

In 2016 NYC Parks contracted with the UVM Spatial Analysis Lab to use modern remote sensing and object-based image analysis to create a new wetlands map for New York City. Data inputs include Light Detection and Ranging Data, State and Federal Wetland Inventories, soils, and field data. Because the map was conservative in its wetlands predictions, NYC Parks staff improved the map through a series of desktop and field verification efforts. From June to November 2020, NYC Parks staff field verified the majority of wetlands on NYC Parks' property.

The map will be opportunistically updated depending on available field information and delineations. Another dedicated field verification effort has not been planned. As of June 2021, no subsequent updates to the data are scheduled.

Original field names were updated to field names that are easier to understand.

This dataset was developed to increase awareness regarding the location and extent of wetlands to promote restoration and conservation in New York City. This map does not supersede U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) and New York State Department of Environmental Conservation (NYSDEC) wetlands maps and has no jurisdictional authority. It should be used alongside NWI and NYSDEC datasets as a resource for identifying likely locations of wetlands in New York City. Mapped features vary in the confidence of their verification status, ranging from "Unverified" (meaning the feature exists in its original remotely mapped form and has not been ground truthed) to "Verified - Wetland Delineation" (meaning the boundaries and type of wetland have been verified during an official wetland delineation). Because of the rapid nature of the protocol and the scale of data collection, this product is not a subsitute for on-site investigations and field delineations. The dataset also includes broad classifications for each wetland type, e.g. estuarine, emergent wetland, forested wetland, shrub/scrub wetland, or water. Cowardin classifcations were not used given rapid verfication methods.

The accuracy of the wetlands map has improved over time as a result of the verification process. Fields were added over time as necessitated by the workflow and values were updated with information, either from the field verifications, delineation reports, or desktop analysis.

OBJECTID, Shape, Class_Name_Final, Verification_Status, Create_Date, Last_Edited_Date, Verification_Status_Year, SHAPE_Length, SHAPE_Area

https://www.nycgovparks.org/greening/natural-resources-group

Data Dictionary: https://docs.google.com/spreadsheets/d/1a45qCho45MV-AuOlGxyaRp0cg3cRFKw4lAYBIaU3zi4/edit#gid=260500519

Map: https://data.cityofnewyork.us/dataset/NYC-Wetlands/7piy-bhr9