Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. The layer is organized into a time series with years 2001, 2006, 2011, and 2016, for the lower 48 conterminous US states. This information may be used in conjunction with the USA NLCD Land Cover layer. Time SeriesBy default, this service will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every five years, in 2001, 2006, 2011, and 2016. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are not included in the time series. No new data were created for these areas since the last time MRLC updated the NLCD imperviousness layer. The older service USA NLCD Impervious Surface 2011 includes a portion of Alaska around Anchorage, but there is as yet no time series available for this part of Alaska.Dataset SummaryPhenomenon Mapped: The proportion of the landscape that is impervious to waterUnits: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area ConicExtent: Contiguous United StatesSource: Multi-Resolution Land Characteristics ConsortiumPublication Date: 2019ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Impervious layers are a compilation of GIS layers which include buildings, structures, paved surfaces (road, sidewalk, parking lots, driveways), patio, concrete pads, plaza, transmission tower pad, electric boxes, and irrigation devices, This is a snapshot from April 14, 2010City of Cambridge, MA GIS basemap development project encompasses the land area of City of Cambridge with a 200 foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft. interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Feature class which was used to create the impervious surfaces layer

The Santa Cruz County Impervious Surfaces map is a 5-class fine-scale polygon vector representation of all artificial impervious surfaces in Santa Cruz County. There are 242,471 features in the dataset. Non-impervious areas are not mapped and are not covered by polygons. The impervious map represents the state of the landscape in summer, 2020. This data product was produced by the impervious mapping team at the University of Vermont Spatial Analysis Lab. Table 1 lists download locations for the dataset.

Santa Cruz County impervious surfaces data product availability






  Description


  Link




  File GDB


  https://vegmap.press/Santa_Cruz_Impervious_FileGDB




  ArcGIS Pro Layer Package


  https://vegmap.press/Santa_Cruz_Impervious_Layer_Package




  Vector Tile Layer


  https://vegmap.press/Santa_Cruz_Impervious_Vector_Tile_Layer

Detailed Dataset Description: The impervious map was created using “expert systems” rulesets developed in Trimble Ecognition. These rulesets combine automated image segmentation with-object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for impervious mapping included: high resolution (6 inch or greater) 4-band orthophotography (2020), the lidar point cloud (2020), and lidar derived rasters such as the canopy height model. After it was produced using Trimble Ecognition, the preliminary impervious map product was manually edited by a team of UVM’s photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. The impervious map has 5 classes, which are described below:

Building – Structures including homes, commercial buildings, outbuildings, and other human-made structures such as water tanks and silage silos. Structures fully occluded by vegetation will not be mapped.




Paved Road – Roads that are paved and wide enough for a vehicle.




Dirt/Gravel Road – Dirt or gravel roads wide enough for a vehicle. Non-ephemeral fire roads, ranch roads and long driveways. Polygons representing narrow unpaved (single track) trails are not included in this data product.




Other Dirt/Gravel Surface – Dirt or gravel surfaces that are highly compacted and used by humans and equipment, such as parking lots, road pull-offs, some dirt or gravel paths, and highly compacted areas around commercial activities. This class DOES NOT include natural turf playing fields, very lightly used dirt roads, livestock areas, naturally occurring bare soil or rock, or bare areas around ponds.




Other Paved Surface – Includes parking lots, sidewalks, paved walking paths, swimming pools, tennis courts.

Miscellaneous quality control and processing notes:

Zoom level used during manual quality control was no finer than 1 to 500.


Vector data was created with no overlapping polygons.

Data Limitations: This is not a planimetric data product and was created using semi-automated techniques. It provides a reasonable and useful depiction of impervious surfaces for planner and managers but does not have the accuracy or precision to support engineering. Please note that this dataset does not contain information about ownership potential access restrictions. Appropriate uses of the data product include:

As an input to storm water models




For planners to assess % imperviousness in a parcel/watershed




To help identify areas of human infrastructure for fuels and fire management




As an input to fuel models that are used in fire behavior and fire spread models




For cartography and mapping




Generally for use at scales 1:1,000 and smaller




Inappropriate uses of this product include:




Measuring exact square footage of structures or impervious features for building projects




Using the impervious as geographically precise information in transportation and public works




Determining ownership or maintenance responsibility of a particular feature, such as a paved or dirt road




Identifying publicly accessible areas for recreation or other uses

Confirming the suitability of a surface for any use including driving, hiking, bicycling, etc.

This feature layer was created by calculating the percentage of impervious surface within a census tract polygon using the tabulate intersection geoprocessing tool. Impervious surface data used in the analysis included is from 2015 through 2023. Updated annually as new data becomes available. If there are any questions about this data, please email dwsdGIS@detroitmi.gov

The NOAA Coastal Change Analysis Program (C-CAP) produces national standardized land cover and change products for the coastal regions of the U.S. C-CAP products inventory coastal intertidal areas, wetlands, and adjacent uplands with the goal of monitoring changes in these habitats through time. The timeframe for this metadata is 2020 through 2021, depending on the latest date of available imagery. These maps were developed utilizing high resolution 30cm or better aerial imagery. In addition, a digital surface model (DSM) derived from the stereo imagery was used to determine vegetation heights. This information provides managers with a detailed accounting of current landscape conditions related to impervious surface, woody canopy, and water feature extents.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2023 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2023.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of impervious surface (patio, pad, other)

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.Units: (Thematic dataset)Cell Size: 30m Source Type: Thematic Pixel Type: Unsigned 8 bitData Projection: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: 50 US States, District of Columbia, Puerto RicoSource: National Land Cover DatabasePublication date: June 30, 2023Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America Albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into Web Mercator projection, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief. MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. 51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or

Three classes of impervious surfaces--buildings, roads, and other impervious--were mapped for New Jersey through a semi-automated process developed using eCognition software. The automated feature extraction workflow used a Geographic Object-Oriented Image Analysis (GEOBIA) framework to extract the three impervious classes from the source datasets which include digital imagery, LiDAR point clouds and several vector data sets including Land use/land cover, road centerlines and hydrographic features, using a rule-based expert system.

Four-band Semantic Segmentation Image Analysis from https://rastervision.io is used to perform land classification of impervious surfaces with ~80% F1-score accuracy using 2020 1-foot resolution NJ-DEP aerial photography of the New Jersey Highlands Region as an indicator of development.

Three classes of impervious surfaces--buildings, roads, and other impervious--were mapped for New Jersey through a semi-automated process developed using eCognition software. The automated feature extraction workflow used a Geographic Object-Oriented Image Analysis (GEOBIA) framework to extract the three impervious classes from the source datasets which include digital imagery, LiDAR point clouds and several vector data sets including Land use/land cover, road centerlines and hydrographic features, using a rule-based expert system.

Impervious surface products give a percent of developed impervious surface for each categorized Landsat pixel. This percentage provides the base for the four types of developed land cover pixels. This percentage gives extra precision for development around the nation and allows more detailed analysis of how developed features interact with natural classes around the nation. These percentages are developed through high-resolution training and applied to Landsat pixels. These pixels are generated through machine learning algorithms and incorporate features like roads, wind turbines, buildings database, and other ancillary data sets.

Three classes of impervious surfaces--buildings, roads, and other impervious--were mapped for New Jersey through a semi-automated process developed using eCognition software. The automated feature extraction workflow used a Geographic Object-Oriented Image Analysis (GEOBIA) framework to extract the three impervious classes from the source datasets which include digital imagery, LiDAR point clouds and several vector data sets including Land use/land cover, road centerlines and hydrographic features, using a rule-based expert system.

The Impervious Surface layer is taken from the Land Use 2012 data set. What is meant by impervious surface is material such as concrete and asphalt that comprise roadways, parking areas, sidewalks and buildings. As the land use/land cover of each polygon was mapped from 2012 aerial photography, a visual estimate was also made of the amount of impervious surface in each. This estimate was recorded as a percentage of the total polygon area, in 5% increments, which are depicted here. These percentages can be used to determine the total acreage of impervious surface in any area of interest. The 2012 LU/LC data set is the fifth in a series of land use mapping efforts that was begun in 1986. Revisions and additions to the initial baseline layer were done in subsequent years from imagery captured in 1995/97, 2002, 2007 and 2012. This present 2012 update was created by comparing the 2007 LU/LC layer from NJDEP's Geographic Information Systems (GIS) database to 2012 color infrared (CIR) imagery and delineating and coding areas of change. Work for this data set was done by Aerial Information Systems, Inc., Redlands, CA, under direction of the New Jersey Department of Environmental Protection (NJDEP), Bureau of Geographic Information System (BGIS). LU/LC changes were captured by adding new line work and attribute data for the 2012 land use directly to the base data layer. All 2007 LU/LC polygons and attribute fields remain in this data set, so change analysis for the period 2007-2012 can be undertaken from this one layer. The classification system used was a modified Anderson et al., classification system. An impervious surface (IS) code was also assigned to each LU/LC polygon based on the percentage of impervious surface within each polygon as of 2007. Minimum mapping unit (MMU) is 1 acre. ADVISORY: This metadata file contains information for the 2012 Land Use/Land Cover (LU/LC) data sets, which were mapped by USGS Subbasin (HU8). There are additional reference documents listed in this file under Supplemental Information which should also be examined by users of these data sets. As stated in this metadata record's Use Constraints section, NJDEP makes no representations of any kind, including, but not limited to, the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the digital data layers furnished hereunder. NJDEP assumes no responsibility to maintain them in any manner or form. By downloading this data, user agrees to the data use constraints listed within this metadata record.

This data set maps impervious surfaces as of 2021 within the 52 towns of the Piscataqua Region Estuaries Partnership (PREP). It also includes impervious features as of 2015, thereby facilitating change analysis. The data set identifies human-made surfaces that do not allow water to permeate through them. Naturally occurring impervious cover, such as exposed bedrock, is not included in the impervious class.

The data set was derived by updating previously delineated impervious features (developed using 2015 orthophotography) using 60-cm resolution, leaf-on, 4-band National Agriculture Imagery Program (NAIP) orthophotography acquired in 2021 as the basis for the update. Additional reference data sets, including older orthophoto imagery from 2015, were also used in the delineation of features.

Funding for this project was provided by a grant from the Piscataqua Regional Estuaries Partnership as authorized by the U.S. Environmental Protection Agency's National Estuary Program.

Three classes of impervious surfaces--buildings, roads, and other impervious--were mapped for New Jersey through a semi-automated process developed using eCognition software. The automated feature extraction workflow used a Geographic Object-Oriented Image Analysis (GEOBIA) framework to extract the three impervious classes from the source datasets which include digital imagery, LiDAR point clouds and several vector data sets including Land use/land cover, road centerlines and hydrographic features, using a rule-based expert system.

(Link to Metadata) Impervious polygons derived from circa 2016 high-resolution remotely sensed data. Object Based Image Analysis (OBIA) techniques were employed to automatically extract building, roads, other paved,and railroads polygons from a combination of 2013 - 2017 Lidar and 2016 Orthoimagery. The resultant Impervious polygons were then subjected to a manual review at a scale of 1:3000.

A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSSThe U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.

The National Urban Change Indicator (NUCI) is a change indicator dataset covering the lower 48 United States that uses Maxar’s PCM®, imagery-derived change detection, to map persistent changes to the landscape resulting from urban development. The input data for the PCM process are a multi-temporal stack of precision, co-registered Landsat multispectral scenes. This NUCI 2016 layer provides a history of change areas on an annual basis from 1987 through 2016Co-Registered Geospatial DataIn addition to capturing the PCM-determined date of change, the NUCI 2016 dataset is attributed with data elements extracted from the following co-registered geospatial data sets:2011 National Land Cover Data (NLCD 2011) Land Cover: Each change polygon is attributed with NLCD 2011 land cover name and class number of the area covered by the polygon. If more than one land cover category is present, attributes are also provided for the secondary (by percentage pixel count) and tertiary classes. The percentage of polygon area for each class is also captured and provided.Urban Gravity: Each change polygon is attributed with an “Urban Gravity” value. The Urban Gravity is calculated by treating the Impervious Surface (percent impervious by pixel) data of the NLCD 2011 dataset as units of mass and then calculating a “gravitational pull” as the inverse square distance measure at the center of the change polygon. The higher the Urban Gravity value, the closer the polygon center is to existing concentrations of NLCD 2011 mapped impervious surface areas.Distance to Water: Distance, in meters, to the nearest water body as defined by the NLCD land cover dataset. Values greater than 2,000 meters are shown as “999999”.Shuttle Radar Topography Mission (SRTM)Height Variance: Each change polygon is attributed with a measure of the average elevation variance, in meters, across the polygon. The measure is calculated from the 3 arc-second SRTM digital elevation data using a standard variance filter over a 7x7 kernel.Additional NotesThis tile layer is intended for visualization purposes. The NUCI feature layer can be used as input to spatial analysis tools and applications.A NUCI 2016 change is defined as one which meets the “3-observation change (3oc)” criteria where the detected state of change has persisted for three independent date observations.This version of NUCI 2016 was filtered to focus on changes related to human activity in order to mute spurious changes and false positives.

This geodatabase contains data from the 2017 State of Narragansett Bay and Its Watershed Technical Report (nbep.org), Chapter 6: "Impervious Cover." Impervious cover was analyzed using a raster dataset created by the Rhode Island Department of Environmental Management for the Technical Report. The 25-foot raster combines and reconciles data from Rhode Island (RIGIS 2011) and Massachusetts (MassGIS 2005) to produce a seamless impervious cover for the Narragansett Bay, Little Narragansett Bay, and Southwest Coastal ponds watersheds. The dataset is intended for general planning, graphic display, and GIS analysis at the watershed and subwatershed scale. The Little Narragansett Bay watershed does not include data from Connecticut. Summary tables catalog the area and percent area of impervious cover for various geoscales.