[Metadata] Description: Land Use Land Cover of main Hawaiian Islands as of 1976

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Geography Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 1994 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994. Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

This hosted feature layer has been published in RI State Plane Feet NAD 83.A statewide, seamless, vector-formatted geospatial dataset depicting 2020 land use and land cover ground conditions. The product was developed by comparing high resolution 2020 and 2011 leaf-off aerial orthoimagery and employing both automated and manual processes to detect, delineate and photointerpret changes since 2011. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts, or to the limits of the source orthoimagery. The minimum mapping unit for this dataset is 0.5 acre.The classification scheme is based on the same RI-modified Anderson Level III scheme used in previous classifications (1988, 1995, 2003/2004, and 2011) with the addition of two new classes (148) Ground-mounted Solar Energy Systems and (149) Wind Energy Systems. If data are used for change detection using the 2003/2004 edition be aware that marinas were coded from other transportation and developed recreation to commercial in the 2020 data to more accurately fit the classification system. The RI classification is based upon Anderson Level III coding described in the United States Geological Survey Publication: "A Land Use And Land Cover Classification System for Use With Remote Sensor Data, Geological Survey Professional Paper 964" Available Online at: https://landcover.usgs.gov/pdf/anderson.pdfPlease consider the source, spatial accuracy, attribute accuracy, and scale of these data before incorporating them into your project. These data were derived from both automated and manual photointerpretation processes and should be used for planning purposes only. The wetland areas contained in this dataset do not include all wetlands previously identified in other RIGIS land use and land cover datasets or in other separate GIS wetland datasets and interpretation of wetland areas should lean toward the side of caution. Wetland areas previously classified as forested wetlands are shown as forested areas in this dataset. Statistical comparisons with RIGIS land use and land cover data prior to 2003 should be treated with caution since some differences in the methodologies used to delineate features were employed

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

High resolution land cover dataset for Virginia Beach, VA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3x3 square feet.

The primary sources used to derive this land cover layer were (1) NAIP 2008 imagery derived from the compressed county mosaic, (2) Normalized digital surface model (nDSM) for Virginia Beach, VA, generated from 2004 light detection and ranging (LiDAR) data representing a 2ft surface. Ancillary data sources used were GIS vector planimetrics layers containing buildings, roads, utility lines, and surface water, provided by the City of Virginia Beach to aid in classification. This land cover dataset is considered current as of November, 2009.

Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing.

No accuracy assessment was conducted, but the dataset was subject to a thorough manual quality control. 18194 corrections were made to the classification.

This series of three-period land use land cover (LULC) datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources (exception is Tchad at 4 kilometers). To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation using Esri’s ArcGIS Desktop ArcMap software. Citation: Trochain, J.-L., 1957, Accord interafricain sur la définition des types de végétation de l’Afrique tropicale: Institut d’études centrafricaines.

A polygon feature class of existing land use that is produced by the research section of the Regulatory and Economic Resources (RER) Departments Planning Division. Existing Land Use is the source of all current land use data updates that could be traced back to 1994, and it is updated weekly based on the most current aerial photography, property appraisal data, thematic layers, development and environmental information.Updated: Weekly-Sat The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

The land cover classes consist of vegetation types (such as forest, wetlands and agricultural crops or pasture) and categories of non-vegetated surface (such as water bodies, bedrock outcrops or settlements).

These classes reflect the nature of the land surface rather than actual or potential land use. The 2000 Edition of the Ontario Land Cover Data Base is the Second Edition of this provincial land cover classification. The coverage is derived wholly from Landsat-7 Thematic Mapper (TM) satellite data frames recorded between 1999 and 2002, most from 2000 onward.

The Provincial Land Cover (2000) Data Base is divided into 4 individual Universal Transverse Mercator (UTM) grid zone tiles (15, 16, 17, and 18) and is distributed in Tagged Image File Format (TIFF) format. Documentation is provided with this database in the form of a user's guide and general use caveats.

This dataset shows specific areas of land use/cover conversion in the Chesapeake Bay Watershed during the period 2013/14 to 2017/18. Change in land use/cover from 2013/14 to 2017/18 was interpreted by translating changes in land cover to changes in land use consistent with the 54 unique land use/cover classes in the 2017/18 land use dataset. Changes in land cover were primarily based on multi-date LiDAR imagery if available followed by multi-date NAIP imagery (available for all counties). Similar rules and logic used to classify the 2013/14 land cover data were applied to the change objects to produce a comparable land cover dataset for 2017/18. While some changes in land cover translate directly into changes in land use (e.g., impervious structures), others had to be interpreted based on context (e.g., small fragmented patches of tree canopy reconstituted as forest in 2013/14; turf grass in a newly developed parcel interpreted as cropland prior to development in 2013/14). Transitions between turf grass, cropland, pasture, and natural succession are not evident in the land cover data but are evident in the land use data. For this reason, the extent of land use change is greater than the extent of land cover change. For more information on input data please see: https://docs.google.com/spreadsheets/d/1e0Uy7DVUe_bXY4jJ1TUPUFvwNs9QbyHrSRY8JQs5GxE/edit?usp=sharing For detailed methods and documentation, please see: https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/lulc-data-project-2022/

Existing land use categories for current land uses in Fairfax County as of the VALID_TO date in the attribute table. For methodology and a data dictionary please visit: https://www.fairfaxcounty.gov/demogrph/opendata/ipls-methodology-data-dictionary.pdf

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

The GIS shapefile and summary tables provide irrigated agricultural land-use for Hendry and Palm Beach Counties, Florida through a cooperative project between the U.S Geological Survey (USGS) and the Florida Department of Agriculture and Consumer Services (FDACS), Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated land field verified for 2019, crop type, irrigation system type, and primary water source used in Hendry and Palm Beach Counties, Florida. A map image of the shapefile is provided in the attachment.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

Every five years, since 1990, the Delaware Valley Regional Planning Commission has produced a GIS Land Use layer for its 9-county region. As it was in 2000, digital orthophotography was flown by DVRPC in 2010. Digitizing was done using these 2010 true-color aerials on the ESRI ArcGIS software platform at a 1:2400 (1 inch = 200 feet) scale.

CSV Table. This table includes coded descriptions for Land Use Codes in the St. Louis County, Missouri parcel dataset. This is the land use description for a property. Please see field LUCODE in the Parcel dataset. Link to Metadata.

DVRPC’s 2023 land use file is based on digital orthophotography created from aerial surveillance completed in the spring of 2023. This dataset supports many of DVRPC's planning analysis goals. Every five years, since 1990, the Delaware Valley Regional Planning Commission (DVRPC) has produced a GIS Land Use layer for its 9-county region.

lu20cat

Land use main category two-digit code. lu20catn

Land use main category name. lu20cat lu20catn

1- Residential, 3 - Industrial, 4 - Transportation, 5- Utility, 6 - Commercial, 7 - Institutional, 8 - Military, 9 - Recreation, 10 - Agriculture, 11 - Mining, 12 - Wooded, 13 - Water, 14 - Undeveloped lu20sub

Land use subcategory five-digit code. (refer to this data dictionary for code description) lu20subn

Land use subcategory name. lu20dev

Development status. mixeduse

Mixed-Use status (Y/N). Features belonging to one of the Mixed-Use subcategories (Industrial: Mixed-Use, Multifamily Residential: Mixed-Use, or Commercial: Mixed-Use). acres

Area of feature, in US acres geoid

10-digit geographic identifier. In all DVRPC counties other than Philadelphia, a GEOID is assigned by municipality. In Philadelphia, it is assigned by County Planning Area (CPA). state_name , co_name , mun_name

State name, county name, municipal/CPA name. In Philadelphia, County Planning Area (CPA) names are used in place of municipal names.

The Metropolitan Council routinely compiles individual land use plans and plan amendments from communities within the seven-county Twin Cities metropolitan area into a single regional data layer. A principal goal of the Regional Planned Land Use dataset is to allow users to view, analyze and display planned land use data for anywhere in the seven county metropolitan area with a consistent land use classification scheme. The Metropolitan Council uses the Regional Planned Land Use (PLU) data to help monitor growth and plan for regional services such as regional parks, transit service, and wastewater collection and treatment.

Although the planned land use data is based on the locally adopted land use plans and designations for each community, it represent only data that has been submitted to the Metropolitan Council for review per the Metropolitan Land Planning Act of 1995 (Minn. Stat 473.864, Subd 2 and 473.175, Subd 1). See Data Quality Information (Section 2 of this metadata) for specifics about the Metropolitan Land Planning Act of 1995 under Completeness information.

Since there is no official State or Regional land use coding scheme that communities must conform with, the variability of content and codes between communities' land use plans is nearly as vast as the number of communities themselves (187). Differences among communities can range from the implementation of different land use categories to conflicting definitions of similar categories. The PLU dataset attempts to effectively level out the variability among communities by translating communities land use categories and descriptions into a common classification scheme developed and endorsed by MetroGIS (a regional GIS data sharing consortium) participants while retaining each communities' original categories. Although the comparability of land use plans between communities has greatly improved as a result of this translation or "regionalization" of communities' land use codes, it is possible that not all community land use definitions have been precisely translated into the most appropriate regional land use category.

In conjunction with other regional information (i.e., land use trend data, households and jobs forecasts), the PLU data can help communities more easily understand regional and sub-regional planning goals and Council staff, working with individual local units of government, can better plan for the future needs and financing of regional services.

- Contact individual communities for more information on their locally adopted planned land use categories.

- See Data Quality Information (Section 2 of this metadata) for specifics about the development of the regional dataset and its accuracy.

- See Entities and Attributes Information (Section 5 of this metadata) for specifics about the regional land use codes and categories.

About this layerThe Land Use Database held by the Northern Ireland Environment Agency (NIEA) provides a record of approximately 14,000 sites that have had previous industrial land use(s).What can you do with the layer?Visualisation: This layer can be used for visualisation online in web maps and in ArcGIS Pro.Analysis: This layer can be used in dashboards.Download: The data is downloadable.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps and apps.

This is collection of DWR County Land Use Surveys. You may scroll the list below to download any individual survey of interest. Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer. For Statewide Crop Mapping follow the link below : https://data.cnra.ca.gov/dataset/statewide-crop-mapping For Region Land Use Surveys follow link below: https://data.cnra.ca.gov/dataset/region-land-use-surveys Questions about the survey data may be directed to Landuse@water.ca.gov.