The statewide composite of parcels (cadastral) data for New Jersey was developed during the Parcels Normalization Project in 2008-2014 by the NJ Office of Information Technology, Office of GIS (NJOGIS.) The normalized parcels data are compatible with the NJ Department of the Treasury system currently used by Tax Assessors, and those records have been joined in this dataset. This composite of parcels data serves as one of the framework GIS datasets for New Jersey. Stewardship and maintenance of the data will continue to be the purview of county and municipal governments, but the statewide composite will be maintained by NJOGIS.Parcel attributes were normalized to a standard structure, specified in the NJ GIS Parcel Mapping Standard, to store parcel information and provide a PIN (parcel identification number) field that can be used to match records with suitably-processed property tax data. The standard is available for viewing and download at https://njgin.state.nj.us/oit/gis/NJ_NJGINExplorer/docs/NJGIS_ParcelMappingStandardv3.2.pdf. The PIN also can be constructed from attributes available in the MOD-IV Tax List Search table (see below).This feature class includes a large number of additional attributes from matched MOD-IV records; however, not all MOD-IV records match to a parcel, for reasons explained elsewhere in this metadata record. The statewide property tax table, including all MOD-IV records, is available as a separate download "MOD-IV Tax List Search Plus Database of New Jersey." Users who need only the parcel boundaries with limited attributes may obtain those from a separate download "Parcels Composite of New Jersey". Also available separately are countywide parcels and tables of property ownership and tax information extracted from the NJ Division of Taxation database.The polygons delineated in this dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such. Please note that these parcel datasets are not intended for use as tax maps. They are intended to provide reasonable representations of parcel boundaries for planning and other purposes. Please see Data Quality / Process Steps for details about updates to this composite since its first publication.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.
This data layer references data from a high-resolution tree canopy change-detection layer for Seattle, Washington. Tree canopy change was mapped by using remotely sensed data from two time periods (2016 and 2021). Tree canopy was assigned to three classes: 1) no change, 2) gain, and 3) loss. No change represents tree canopy that remained the same from one time period to the next. Gain represents tree canopy that increased or was newly added, from one time period to the next. Loss represents the tree canopy that was removed from one time period to the next. Mapping was carried out using an approach that integrated automated feature extraction with manual edits. Care was taken to ensure that changes to the tree canopy were due to actual change in the land cover as opposed to differences in the remotely sensed data stemming from lighting conditions or image parallax. Direct comparison was possible because land-cover maps from both time periods were created using object-based image analysis (OBIA) and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs). 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 ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to manual review and correction.University of Vermont Spatial Analysis LaboratoryThe dataset covers the following tree canopy categories:Environmental Justice Priority AreasCensus tracts composite / quintileExisting tree canopy percentage & environmental justice priority levelExisting tree canopyPossible tree canopyRelative percentage changeFor more information, please see the 2021 Tree Canopy Assessment.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually).This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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This polygon dataset is part of the three-dimensional (3-D) hydrostratigraphic model of the Sylvan Lake sub-basin in the Edmonton-Calgary Corridor, central Alberta. This dataset provides the areal distribution of greater than or equal to 0.60 net-to-gross sandstone values for the Paskapoo Formation - Middle Composite Slice, defined by an analysis of normalized oil and gas well digital gamma-ray logs to produce discrete slices of net-to-gross sandstone distribution within the Paskapoo Formation. The zonated Paskapoo Formation - Middle Composite Slice is in ESRI shapefile format for input into a groundwater flow model. This dataset supplements Alberta Geological Survey (AGS) Open File Report 2014-10, which includes a full description of the steps taken to produce the dataset.
The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, the locations of other features have been obtained from a variety of sources. The data are included in the data available for download from the provided URLs. The data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. Data described by this metadata record has Dataset_id = 17. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.
This polygon dataset is part of the three-dimensional (3-D) hydrostratigraphic model of the Sylvan Lake sub-basin in the Edmonton-Calgary Corridor, central Alberta. This dataset provides the areal distribution of greater than or equal to 0.60 net-to-gross sandstone values for the Paskapoo Formation - Lower Composite Slice, defined by an analysis of normalized oil and gas well digital gamma-ray logs to produce discrete slices of net-to-gross sandstone distribution within the Paskapoo Formation. The zonated Paskapoo Formation - Lower Composite Slice is in ESRI shapefile format for input into a groundwater flow model. This dataset supplements Alberta Geological Survey (AGS) Open File Report 2014-10, which includes a full description of the steps taken to produce the dataset.
The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.
The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.
Rauer Group 1:50000 Topographic GIS dataset. Data conforms to SCAR Feature Catalogue which can be searched. 10 metre contour interval on rock, 20 metre contour interval on ice up to 100 metres, 100 metre contour interval on ice above 100 metres.
The datasets that are included in the composite layer making up the protected area layer are given below: Dataset Example Designations Citation or hyperlink PAD-US (CBI Edition) National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas “PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28. Conservation Easements California Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022. Inventoried Roadless Areas “Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437 BLM National Landscape Conservation System Wilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivers https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areas https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areas https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/ Greater Sage Grouse Habitat Conservation Areas (BLM) For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For wind technology: BLMP_Managm = ‘PHMA’ “Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf Other BLM Protected Areas Areas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Area https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-areas-of-critical-environmental-concern https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-az-area-of-critical-environmental-concern-polygon [Big Marias ACEC and Beale Slough Riparian and Cultural ACEC] BLM, personal communication, November 2, 2022. Mono Basin NFSA https://pcta.maps.arcgis.com/home/item.html?id=cf1495f8e09940989995c06f9e290f6b#overview Terrestrial 30x30 Conserved Areas Gap Status 1 and 2 CA Nature. 30x30 Conserved Areas, Terrestrial. 2021. https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial/ Accessed September 2022. CPAD Open Spaces and Parks under city or county level California Protected Areas Database (CPAD), 2022b. 2022. https://www.calands.org/cpad/. Accessed February 22, 2023. USFS Special Interest Management Areas https://data-usfs.hub.arcgis.com/datasets/usfs::special-interest-management-areas-feature-layer/about Proposed Protected Area Molok Luyuk Extension (Berryessa Mtn NM Expansion) CalWild, personal communication, January 19, 2023. 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 Log: Version 1.1 (January 22, 2024 10:40 AM) Layer revised to allow for gaps to remain when combining all components of the protected area layer.
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The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.
The Most Probable Overland Flow Pathway dataset is a polyline GIS vector dataset that describes the likely flow routes of water along with potential accumulations of diffuse pollution and soil erosion features over the land. It is a complete network for the entire country (England) produced from a hydro-enforced LIDAR 1-metre resolution digital terrain model (bare earth DTM) produced from the 2022 LIDAR Composite 1m Digital Terrain Model. Extensive processing on the data using auxiliary datasets (Selected OS Water Network, OS MasterMap features as well as some manual intervention) has resulted in a hydro-enforced DTM that significantly reduces the amount of non-real-world obstructions in the DTM. Although it does not consider infiltration potential of different land surfaces and soil types, it is instructive in broadly identifying potential problem areas in the landscape. The flow network is based upon theoretical one-hectare flow accumulations, meaning that any point along a network feature is likely to have a minimum of one-hectare of land potentially contributing to it. Each segment is attributed with an estimate of the mean slope along it. The product is comprised of 3 vector datasets; Probable Overland Flow Pathways, Detailed Watershed and Ponding and Errors. Where Flow Direction Grids have been derived, the D8 option was applied. All processing was carried out using ARCGIS Pro’s Spatial Analyst Hydrology tools. Outlined below is a description of each of the feature class. Probable Overland Flow Pathways The Probable Overland Flow Pathways layer is a polyline vector dataset that describes the probable locations accumulation of water over the Earth’s surface where it is assumed that there is no absorption of water through the soil. Every point along each of the features predicts an uphill contribution of a minimum of 1 hectare of land. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Every effort has been used to digitally unblock real-world drainage features; however, some blockages remain (e.g. culverts and bridges. In these places the flow pathways should be disregarded. The Ponding field can be used to identify these erroneous pathways. They are flagged in the Ponding field with a “1”. Flow pathways are also attributed with a mean slope value which is calculated from the Length and the difference of the start and end point elevations. The maximum uphill flow accumulation area is also indicated for each flow pathway feature. Detailed Watersheds The Detailed Watersheds layer is a polygon vector dataset that describes theoretical catchment boundaries that have been derived from pour points extracted from every junction or node of a 1km2 Flow Accumulation dataset. The hydro-enforced LIDAR Digital Terrain Model 1-Metre Composite (2022) has been used to derive this data layer. Ponding Errors The Ponding and Errors layer is a polygon vector dataset that describes the presence of depressions in the landscape after the hydro-enforcing routine has been applied to the Digital Terrain Model. The Type field indicates whether the feature is Off-Line or On-Line. Off-Line is indicative of a feature that intersects with a watercourse and is likely to be an error in the Overland Flow pathways. On-line features do not intersect with watercourses and are more likely to be depressions in the landscape where standing water may accumulate. Only features of greater than 100m2 with a depth of greater than 20cm have been included. The layer was derived by filling the hydro-enforced DTM then subtracting the hydro-enforced DTM from the filled hydro-enforced DTM. Please use with caution in very flat areas and areas with highly modified drainage systems (e.g. fenlands of East Anglia and Somerset Levels). There will occasionally be errors associated with bridges, viaducts and culverts that were unable to be resolved with the hydro-enforcement process. Attribution statement: © Environment Agency copyright and/or database right 2023. All rights reserved.
This is a GIS dataset of the vegetation of the Windmill Islands. Interpretation was done by Rod Seppelt (Australian Antarctic Division) based on his field work, Zeiss aerial photography flown in January 1994 and a paper: Melick, D.R., Hovenden, M.J., Seppelt, R.D. (1994) Phytogeography of bryophyte and lichen vegetation in the Windmill Islands, Wilkes Land, Continental Antarctica. Vegetatio 111. 71-87 The data have been formatted according to the SCAR Feature Catalogue (see link below).
Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
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This data, also known as the Linear Highway Referencing System (LHRS), is used to locate events along the highway network. Three separate files make up the LHRS dataset: LHRS route is a spatial (GIS) representation of the highway network; LHRS Base Points divide the highway network into base sections with known driven distances; LHRS features points (composite listing) are additional described locations along the highway network. The location of events can be identified by a driven distance, along the LHRS route from a given LHRS Base point or feature point. *[GIS]: Geographic Information System *[LHRS]: Linear Highway Referencing System
This project developed a set of high quality GIS datasets of the emergent and shallow marine features (reef boundaries, reef tops, islands, and cays) of the Coral Sea Marine Park (CSMP). The goal of this mapping was to improve the precision and spatial detail of existing reef maps. Features mapped as openly available shapefiles: - Coral atoll platform boundary - outer visible extent combined with available multi-beam bathymetry (100 m depth) - Coral reef boundary - coral substrate, plus connected sand, raised off atoll platform, mapped to 50 - 60 m depth. - Depth contours (5 m and 20 m depth) - Coral cays regions (above mean high water over time) The inspiration for this project was to map the Coral Sea in a manner similar to the existing reef mapping of the Great Barrier Reef Marine Park (GBRMP) and Torres Strait (Lawrey, et al. 2016) to assist with the management of the Coral Sea Marine Park. Mapped reef features were to be allocated permanent identifiers to allow robust communication about reefs where no existing name exists. Reef features were mapped primarily from composite satellite imagery (Sentinel 2, Landsat 8-9 and Sentinel 3) with existing bathymetry surveys used to assist in the interpretation, calibration and validation of the mapping approaches. All features were manually mapped, and reviewed extensively. The boundary of coral reefs previously been only loosely defined making it difficult to reliably map their boundaries in a repeatable manner. In this project we develop more robust definition based on the presence of hard substrate built by coral suitable for hard coral growth. Reef patches are clustered together, using rules, to form the reef boundary at the same scale as what would normally be considered a coral reef. This approach help to ensure consistent and repeatable quality across the manual mapping process. References: Lawrey, E. P., and Stewart, M. (2016) Mapping the Torres Strait Reef and Island Features: Extending the GBR Features (GBRMPA) dataset. Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (103pp.).
This data, also known as the Linear Highway Referencing System (LHRS), is used to locate events along the highway network. Three separate files make up the LHRS dataset: LHRS route is a spatial (GIS) representation of the highway network; LHRS Base Points divide the highway network into base sections with known driven distances; LHRS features points (composite listing) are additional described locations along the highway network. The location of events can be identified by a driven distance, along the LHRS route from a given LHRS Base point or feature point. [GIS]: Geographic Information System [LHRS]: Linear Highway Referencing System
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
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The LIDAR Composite DTM (Digital Terrain Model) is a raster elevation model covering ~99% of England at 1m spatial resolution. The DTM (Digital Terrain Model) is produced from the last or only laser pulse returned to the sensor. We remove surface objects from the Digital Surface Model (DSM), using bespoke algorithms and manual editing of the data, to produce a terrain model of just the surface.
Produced by the Environment Agency in 2022, the DTM is derived from a combination of our Time Stamped archive and National LIDAR Programme surveys, which have been merged and re-sampled to give the best possible coverage. Where repeat surveys have been undertaken the newest, best resolution data is used. Where data was resampled a bilinear interpolation was used before being merged.
The 2022 LIDAR Composite contains surveys undertaken between 6th June 2000 and 2nd April 2022. Please refer to the metadata index catalgoues which show for any location which survey was used in the production of the LIDAR composite.
The data is available to download as GeoTiff rasters in 5km tiles aligned to the OS National grid. The data is presented in metres, referenced to Ordinance Survey Newlyn and using the OSTN’15 transformation method. All individual LIDAR surveys going into the production of the composite had a vertical accuracy of +/-15cm RMSE.
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The zip file contains a large tiff mosaic stitched together from a series of aerial photographs of the Calhoun CZO area taken in 1933, when the area was being acquired by the US Forest Service. USFS archaeologist Mike Harmon delivered the black-and-white photographs, known to him as the 'Sumter National Forest Purchase Aerials', to us in a box. The photographs include most of the Enoree District of the Sumter National Forest, including the entirety of the Calhoun CZO, not just the long-term plots and small watersheds. The photographs were scanned and georectified, then color-balanced and stitched together following 'seams' - high-contrast features such as rivers and roads ('seamlined'). In addition to the main tiff are four files that can be used to properly geolocate the composite image in ArcGIS.
The multilayer pdf file includes a smaller version of the seamlined 1933 aerial photography mosaic raster layer, as well as this aerial mosaic transparent over slope map (for a 3D-like 1933 image raster). Other layers include contours, roads, boundaries, sampling locations, 1.5 m DEM, 1.5m slope, 1m 2013 NAIP aerial imagery, and 2014 canopy height. The pdf file includes both 'interfluve order' and 'landshed order.' These two layers mean the same thing, but the landshed is the area unit around the interfluve that is used for statistics; this dataset has been QC'ed. The Interfluve Order network was used to delineate the landsheds and agrees with it >95% of the time, but has a few inaccuracies (it was automated by the computer) that were fixed manually. Use the network for viewing and considering the landscape at large, but for the specific interfluve order, check the color of the 'Landshed Order' dataset to verify its accuracy.
Date Range Comments: The exact date these photos were taken is unknown, but the year is thought to be 1933.The flight date is prior to the USFS land purchases for the Enoree District of the Sumter National Forest; the photos are thus known as the "pre-purchase photos").
The datasets that are included in the composite layer making up the protected area layer are given below:
DatasetExample DesignationsCitation or hyperlinkPAD-US (CBI Edition)National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas“PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28.Conservation EasementsCalifornia Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022. Inventoried Roadless Areas“Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437BLM National Landscape Conservation SystemWilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivershttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/Greater Sage Grouse Habitat Conservation Areas (BLM)For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’)For wind technology: BLMP_Managm = ‘PHMA’“Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf Other BLM Protected AreasAreas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Areahttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations
Change Log: Version 1.1 (January 22, 2024 11:05 AM) Layer edited to reflect the Bureau of Land Management (BLM) Land Use Plan Amendment (LUPA) Development Focus Area (DFA), Variance Process Land (VPL) and General Public Land (GPL) areas within the DRECP that allow for geothermal energy development applications.Layer revised to allow for gaps to remain when combining all components of the protected area layer.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data.
The NOAA Coastal Services Center's Marine Jurisdiction dataset was created to assist in marine spatial planning and offshore alternative energy sitting. This is a composite dataset derived from a collection of authoritative marine boundary data provided by the DIO Minerals Management Service and the NOAA Office of Coast Survey. NOT LEGALLY BINDING. This dataset is not an authoritative data source for marine boundaries, please see the Minerals Management Service and NOAA Office of Coast Survey for authoritative data and more comprehensive use constraints.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/UtilityTelecom/MD_OffshoreWindEnergyPlanning/FeatureServer/2
The statewide composite of parcels (cadastral) data for New Jersey was developed during the Parcels Normalization Project in 2008-2014 by the NJ Office of Information Technology, Office of GIS (NJOGIS.) The normalized parcels data are compatible with the NJ Department of the Treasury system currently used by Tax Assessors, and those records have been joined in this dataset. This composite of parcels data serves as one of the framework GIS datasets for New Jersey. Stewardship and maintenance of the data will continue to be the purview of county and municipal governments, but the statewide composite will be maintained by NJOGIS.Parcel attributes were normalized to a standard structure, specified in the NJ GIS Parcel Mapping Standard, to store parcel information and provide a PIN (parcel identification number) field that can be used to match records with suitably-processed property tax data. The standard is available for viewing and download at https://njgin.state.nj.us/oit/gis/NJ_NJGINExplorer/docs/NJGIS_ParcelMappingStandardv3.2.pdf. The PIN also can be constructed from attributes available in the MOD-IV Tax List Search table (see below).This feature class includes a large number of additional attributes from matched MOD-IV records; however, not all MOD-IV records match to a parcel, for reasons explained elsewhere in this metadata record. The statewide property tax table, including all MOD-IV records, is available as a separate download "MOD-IV Tax List Search Plus Database of New Jersey." Users who need only the parcel boundaries with limited attributes may obtain those from a separate download "Parcels Composite of New Jersey". Also available separately are countywide parcels and tables of property ownership and tax information extracted from the NJ Division of Taxation database.The polygons delineated in this dataset do not represent legal boundaries and should not be used to provide a legal determination of land ownership. Parcels are not survey data and should not be used as such. Please note that these parcel datasets are not intended for use as tax maps. They are intended to provide reasonable representations of parcel boundaries for planning and other purposes. Please see Data Quality / Process Steps for details about updates to this composite since its first publication.***NOTE*** For users who incorporate NJOGIS services into web maps and/or web applications, please sign up for the NJ Geospatial Forum discussion listserv for early notification of service changes. Visit https://nj.gov/njgf/about/listserv/ for more information.