Parcel boundary from Kent County GIS Data Library, available at https://www.accesskent.com/GISLibrary/.This data is used in the North Kent Disposal Area PFAS web map. If you have questions regarding the North Kent Disposal Area site contact Karen Vorce at 616-439-8008 or vorcek@michigan.gov.

This feature service is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. It is also available as a map service and a tiled map service. This dataset is a statewide service of municipal parcels (properties) including their geometry (polygon shape) and attributes (tabular information about each parcel). In order to preserve the attributes, each municipality is added individually to the service.

This data is used in the North Kent Study Area Data Summary Web App, which displays environmental sampling data that has been collected as part of Wolverine World Wide’s and EGLE’s per-and poly fluoroalkyl (PFAS) assessment activities in the North Kent Study Area. This data is used in the North Kent Study Area Data Summary Web App (Item Details). You can find more information about the North Kent Study Area by visiting the House Street Disposal Area webpage or the Rockford Tannery webpage on the Michigan PFAS Action Response Team (MPART) website. For questions about this content, reach out to Leah Gies, GiesL1@Michigan.gov. This data was provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the consulting firm AECOM.

This layer is a geographic representation of the municipal boundary for Kent, Washington. It is sourced from property management records, King County Assessor Maps, survey records, and annexation/deannexation actions. It was last updated for the Bridges Neighborhood Deannexation via City of Auburn Ordinance 6928 and City of Kent Resolution 2068, which took effect on 01/01/24.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the Delaware (FIPS 0700) State Plane projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000. Coastal study data as defined in FEMA Gudelines and Specifications, Appendix D: Guidance for Coastal Flooding Analyses and Mapping, submitted as a result of a coastal study. Appendix D notes that a variety of analytical methodologies may be used to establish Base (1-percent-annual-chance) Flood Elevations (BFEs) and floodplains throughout coastal areas of the United States. Appendix D itemizes references for the methodologies currently in use by FEMA for specific coastal flood hazards, provides general guidance for documentation of a coastal flood hazard analysis, specifies flood hazard analysis procedures for the Great Lakes coasts, and outlines intermediate data submissions for coastal flood hazard analyses with new storm surge modeling and revised stillwater flood level (SWFL). (Source: FEMA Guidelines and Specs, Appendix D Guidance for Coastal Flooding Analyses and Mapping, Section D.1)

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

MD/PA Sandy Supplemental Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G14PD00397 Woolpert Order No. 74333 CONTRACTOR: Woolpert, Inc. This task is for a high resolution data set of lidar covering approximately 1,845 square miles. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, one (1) meter pixel raster DEMs of the bare-earth surface in ERDAS IMG Format, and 8-bit intensity images. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format, and LAS swath data. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. Coastal tiles 18SVH065720 and 8SVH095690 contain no lidar points as they exist completely in water. A DEM IMG was generated for these two tiles as the digitized hydro breakline assumed the data extent in the area. As such only 2568 LAS and Intensity files will be delivered along with 2570 DEM IMG's.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://lidar.geodata.md.gov/imap/rest/services/Kent/MD_kent_dem_ft/ImageServer

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

These data were automated to provide an accurate high-resolution historical shoreline of Kent Island, MD suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

Connecticut Parcels for Protected Open Space Mapping is a polygon feature-based layer that includes basic parcel-level information for some towns in Connecticut. This parcel layer includes information provided by individual municipalities. These parcel data are incomplete and out of date. The accuracy, currency and completeness of the data reflect the content of the data at the time DEEP acquired the data from the individual municipalities. Attribute information is comprised of values such as town name and map lot block number. These data are not updated by CT DEEP and should only be used as a general reference. Critical decisions involving parcel-level information should be based on more recently acquired information from the respective municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Largely due to differences in horizontal accuracy among various data layers, do not expect these parcel boundaries to line up exactly with or be properly postioned relative to features shown on other layers available from CT DEEP such as scanned USGS topography quadrangle maps, roads, hydrography, town boundaries, and even orthophotograpy.

The data in the parcel layer was obtained from individual Connecticut municipalities. An effort was made to collect data once from each municipality. The data acquisition date for each set of municipally-supplied parcel data was not recorded and CT DEEP does not keep this information up-to-date. Consequently, these data are out-of-date, incomplete and do not reflect the current state of property ownership in these municipalities. These parcels are not to be considered legal boundaries such as boundaries determined from certain classified survey maps or deed descriptions. Parcel boundaries shown in this layer are based on information from municipalities used for property tax purposes. Parcel boundaries and attribute information have not been updated in this layer since the time the information was originally acquired by CT DEEP. For example, property boundaries are incorrect where subdivisions have occurred. Also, field attribute values are populated only if the information was supplied to CT DEEP. For example, parcels in some towns lack location (street name) information or possibly map lot block values. Therefore, field attributes are inconsistent, may include gaps, and do not represent complete sets of values among all towns. They should not be compared and analyzed across towns. It is emphasized that critical decisions involving parcel-level information be based on more recently obtained information from the respective municipalities. These data are only suitable for general reference purposes. Be cautious when using these data. Many Connecticut municipalities provide access to more up-to-date and more detailed property ownership information on the Internet. This dataset includes parcel information for the following towns: Andover, Ansonia, Ashford, Avon, Beacon Falls, Berlin, Bethany, Bethel, Bethlehem, Bloomfield, Bolton, Branford, Bridgewater, Brookfield, Brooklyn, Canaan, Canterbury, Canton, Chaplin, Cheshire, Chester, Clinton, Colchester, Colebrook, Columbia, Cornwall, Coventry, Cromwell, Danbury, Darien, Deep River, Derby, East Granby, East Haddam, East Hampton, East Hartford, East Lyme, East Windsor, Eastford, Ellington, Enfield, Essex, Farmington, Franklin, Glastonbury, Granby, Greenwich, Griswold, Groton, Guilford, Haddam, Hamden, Hartford, Hebron, Kent, Killingly, Killingworth, Lebanon, Ledyard, Lisbon, Litchfield, Lyme, Madison, Manchester, Mansfield, Marlborough, Meriden, Middlebury, Middlefield, Middletown, Milford, Monroe, Montville, Morri

This layer shows the existing water mains prior to installation of new municipal water mains for the North Kent Study Area. This data is used in the North Kent Disposal Area PFAS web map.The fields found in this dataset are:Field NameDescriptionLocationLocations of water main: City of Rockford or Plainfield TownshipYou can find more information about the North Kent Study Area by visiting the House Street Disposal Area webpage or the Rockford Tannery webpage on the Michigan PFAS Action Response Team (MPART) website. For questions about this content, reach out to Leah Gies, GiesL1@Michigan.gov.This data was provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the consulting firm AECOM.

U.S. Census Bureau QuickFacts statistics for Kent city, Washington. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

An overview map of Chatham-Kent and some associated Stats data for various communities

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The special soil features point layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped.

"The map of Section 216, 218, Melbourne Road, Water & Kent Streets. This map is georeferenced by CeRDI using a projective transformation ( linear rotation and translation of coordinates: scale 1:25000). More Information: www.access.prov.vic.gov.au, www.landata.vic.gov.au Author: City of Ballarat Owner: Department of Environment, Land, Water & Planning"

Parcel boundaries are symbolized by parcel classification in new municipal or filter areas from the February 19, 2020 Consent Decree between the State of Michigan (Plaintiff), Plainfield and Algoma Townships (Intervening Plaintiffs) and Wolverine (Defendant). Parcel boundaries originated from Kent County GIS Data Library, available at www.accesskent.com/GISLibrary.The fields used in this dataset are: Field Name Description

PPN Parcel Pin Number in number format

Address Street Address of parcel

City City of parcel

Zip_Code State and Zip Code of parcel

PNUM Parcel Pin Number in text format

Township Algoma or Plainfield

NK Used to query parcels within North Kent Study Area

GWOrdinance Used to query parcels within Groundwater Ordinance Affected Area

Symbology2 Parcel Status from February 2020 Consent Decree: Existing Municipal Area, Filter Area, Filter Area Vacant, New Municipal Area, New Municipal Area Vacant This data is used in the North Kent Disposal Area PFAS web map. You can find more information about the North Kent Study Area by visiting the House Street Disposal Area webpage or the Rockford Tannery webpage on the Michigan PFAS Action Response Team (MPART) website. For questions about this content, reach out to Leah Gies, GiesL1@Michigan.gov. This data was provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the consulting firm AECOM.

Hcropland30:A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model

***Please note this dataset is undergoing peer review***

Version: 1.0

Authors: Qiong Hu ^{a, 1}, Zhiwen Cai ^{b, 1}, Liangzhi You ^{c, d}, Steffen Fritz ^e, Xinyu Zhang ^c, He Yin ^f, Haodong Wei^c, Jingya Yang ^g, Zexuan Li ^a, Qiangyi Yu ^g, Hao Wu ^a, Baodong Xu ^{b *}, Wenbin Wu ^{g, *}

^a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China

^b College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

^c Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China

^d International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA

^e Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria

^f Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA

^g State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Introduction

We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.

Dataset

1. Hcropland30: A hybrid 30-m global cropland map in 2020

****Data format: GeoTiff

****Spatial resolution: 30 m

****Projection: EPSG: 4326 (WGS84)

****Values: 1 denotes cropland and 0 denotes non-cropland

The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.

2. 1°×1° Grids: This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

Id: The grid’s ID number.

area: The area of the grid.

mode: Indicates the representative sample grid.

climate: The climate type the grid belongs to.

dem: Average DEM value of the grid.

ndvi_s1 to ndvi_s4: Average NDVI values for four seasons within the grid.

esa, esri, fcs30, fromglc, glad, globeland30: Proportion of cropland pixels of different publicly available cropland products.

inconsistent: Proportion of inconsistent pixels within the grid according to different public cropland products.

hcropland30: Proportion of cropland pixels of our Hcropland30 dataset.

3. Samples: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.

****Data format: ESRI shapefile

****Projection: EPSG: 4326 (WGS84)

****Attribute Fields:

type: 1 denotes cropland sample and 0 denotes non-cropland sample.

Citation

If you use this dataset, please cite the following paper:

Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.

License

The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Disclaimer

This dataset is provided as-is, without any warranty, express or implied. The dataset author is not

responsible for any errors or omissions in the data, or for any consequences arising from the use

of the data.

Contact

If you have any questions or feedback regarding the dataset, please contact the dataset author

Qiong Hu (huqiong@ccnu.edu.cn)

These data are from collecting events beginning in ~1960s through early ~2000s by Ross and Joyce Bell and associates, as well as reliable records from literature gathered by the authors. Most locations of specimens were georeferenced by the authors on paper maps for each species using township borders as a reference when placing a location. Points from each of these species maps were digitized in a GIS.