The statewide dataset contains a combination of land cover mapping from 2016 aerial imagery and land use derived from standardized assessor parcel information for Massachusetts. The data layer is the result of a cooperative project between MassGIS and the National Oceanic and Atmospheric Administration’s (NOAA) Office of Coastal Management (OCM). Funding was provided by the Mass. Executive Office of Energy and Environmental Affairs.

This land cover/land use dataset does not conform to the classification schemes or polygon delineation of previous land use data from MassGIS (1951-1999; 2005).In this map service layer hosted at MassGIS' ArcGIS Server, all impervious polygons are symbolized by their generalized use code; all non-impervious land cover polygons are symbolized by their land cover category. The idea behind this method is to use both cover and use codes to provide a truer picture of how land is being used: parcel use codes may indicate allowed or assessed, not actual use; land cover alone (especially impervious) does not indicate actual use.

See the full datalayer description for more details.This map service is best displayed at large (zoomed in) scales. Also available are a Feature Service and a Tile Service (cache). The tile cache will display very quickly in in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.

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.

To download this dataset, click below:Zipped TIFF File: LC_FCD_RECLASS_2016.zip (2GB)The reclassified landcover dataset was derived from the 2016 landcover, one of the products available as part of the the LARIAC program.NOTE: The extent of the derived dataset only covers the area located within the County's flood control district. This raster dataset was combined with the County's parcel layer to produce a file geodatabase of impermeable and permeable areas by parcel for use by the County's Safe Clean Water program.Attributes0 = Permeable1 = ImpermeableThe 2016 landcover dataset was reclassified as follows:Tree Canopy - PermeableGrass/Shrubs - PermeableBare Soil - PermeableWater - PermeableBuildings - ImpermeableRoads/Railroads - ImpermeableOther Paved - ImpermeableTall Shrubs - PermeableFor more information, please contact Bowen Liang (bliang@dpw.lacounty.gov)

This update to the Alaska National Land Cover Database (NLCD) 2016 replaces the files dated 20200213. In this update the landcover footprint was extended along the northern coast to include the islands that were missed in previous versions, and several duplicate roads (offset by 1 or 2 pixels) were removed on the Aleutian Islands. The Alaska National Land Cover Database 2016 was created using change detection between the nominal dates of 2011 and 2016 utilizing Google Earth engine composites of Landsat imagery. Traditionally, previous classifications of Alaska used path row data and spectral comparisons between path rows along with ancillary data to derive areas of change. Alaska has many challenges for land cover classification, with the largest of these being a very short “leaf on” imagery season for acceptable phenology. This is compounded by persistent cloud cover during this growing season, and increasing terrain shadow and sun angle problems outside of this season. For this reason, the timeframe of 10 years was needed for the first update to the Alaska land cover in order to have enough acceptable imagery between these 2001 and 2011 nominal dates that met these criteria in order to provide reasonable change detection. For 2016, there was not enough complete path row imagery to do this similar change detection. Google Earth engine was employed to create a composite imagery mosaic that uses much smaller pieces of Landsat imagery to create a complete Landsat imagery snapshot used to create change detection between 2011 and 2016. Although this composite technique is not as scientifically rigorous as utilizing direct Landsat imagery, it was the only method available to us during production that would allow completion of change detection in the desired five year timeframe. The original published 2001 and 2011 Alaska NLCD classifications are generally unchanged, except for slight updates in the northern part of the state to remove perennial ice and snow that were a result of lack of suitable imagery in the 2001 timeframe representing minimum snow and ice extent. See the 2001 and 2011 Land Cover products for the specific metadata process steps associated with the creation of those datasets.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. 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 2019. The NLCD 2019 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 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on 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 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011 and 2016. U.S. EPA leads the accuracy assessment of NLCD land cover products in coordination with USGS and SUNY-ESF. The posted dataset includes map labels and reference labels for 4629 sample locations (pixels) from the NLCD 2016 database. The sample pixels were selected using a stratified random design based on the dual 2011 – 2016 map labels. The data can be used to re-create the accuracy results reported in a forthcoming paper and in support of other applications. Accuracy results are reported for the 2011 and 2016 land cover products in the NLCD2016 database and for selected 2011-2006 changes.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. 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 2019. The NLCD 2019 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 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on 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 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Legend for land cover categories: https://www.mrlc.gov/data/legends/national-land-cover-database-2016-nlcd2016-legend

The National Land Cover Database (NLCD) is a land cover monitoring program providing land cover information for the United States. NLCD2016 extended temporal coverage to 15 years (2001–2016). We collected land cover reference data for the 2011 and 2016 nominal dates to report land cover accuracy for the NLCD2016 database 2011 and 2016 land cover components. We measured land cover accuracy at Level II and Level I, and change accuracy at Level I. For both the 2011 and 2016 land cover components, single-date Level II overall accuracies (OA) were 72% (standard error of ±0.9%) when agreement was defined as match between the map label and primary reference label only and 86% (± 0.7%) when agreement also included the alternate reference label. The corresponding level I OA for both dates were 79% (± 0.9%) and 91% (± 1.0%). For land cover change, the 2011–2016 user’s and producer’s accuracies (UA and PA) were ~ 75% for forest loss. PA for water loss, grassland loss, and grass gain were > 70% when agreement included a match between the map label and either the primary or alternate reference label. Depending on agreement definition and level of the classification hierarchy, OA for the 2011 land cover component of the NLCD2016 database was about 4% to 7% higher than OA for the 2011 land cover component of the NLCD2011 database, suggesting that the changes in mapping methodologies initiated for production of the NLCD2016 database have led to improved product quality.

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework and has been clipped to the Oregon boundary and reprojected to Oregon Lambert (2992). The 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. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The Virginia Geographic Information Network (VGIN) and its partners have coordinated the development of a statewide Land Cover Dataset, improving land coverage data to assist localities in planning and implementing stormwater management programs. The Land Cover product leverages the Commonwealth's investment in the VBMP digital orthophotography and is focused on creating a consistent, statewide 1 meter digital land classification. Land Cover data extraction is being publicly provided back to local governments and state agencies in both raster and vector data formats.Resources:Classification Summary by County (PDF)Classification Summary by County (XLSX)Land Cover Use Case Scenarios (PDF)Technical Plan of Operations (PDF) Change Detection County Report (XLSX)TPO Land Cover Change Stream Corridor (PDF)For questions, please contact Virginia DCR GIS.

The 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. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

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. The timeframe for this metadata is summer 2016. These maps are developed utilizing high resolution National Agriculture Imagery Program (NAIP) imagery, and can be used to track changes in the landscape through time. This trend information gives important feedback to managers on the success or failure of management policies and programs and aid in developing a scientific understanding of the Earth system and its response to natural and human-induced changes. This understanding allows for the prediction of impacts due to these changes and the assessment of their cumulative effects, helping coastal resource managers make more informed regional decisions. NOAA C-CAP is a contributing member to the Multi-Resolution Land Characteristics consortium and C-CAP products are included as the coastal expression of land cover within the National Land Cover Database.

This is the Land Cover & Land Use data (2016) collected by Satellite imagery utilizing MODIS-based Global Land Cover Climatology - Geometry calculation through ESRI ArcMap Desktop software.

As part of the next generation NLCD 2016 mapping process, the NLCD research team developed a suite of intermediate products that were used to generate the final NLCD Land Cover products. Some of those products also have value as independent products and are provided here. Please read the product descriptions to understand what the product represents. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. 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 2019. The NLCD 2019 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 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on 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 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Layer can be downloaded at: https://asu.maps.arcgis.com/home/item.html?id=0f3f2464210544868499f4f45af889ed#overviewThe 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. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.*This land cover layer was extracted from the United States land cover layer and reprojected to UTM12.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

This data set serves as documentation of land cover and land use (LCLU) within the South Florida Water Management District as it existed in 2014-16. Land Cover Land Use data was updated from 2008-09 LCLU by photo-interpretation from 2014-16 aerial photography and classified using the SFWMD modified FLUCCS classification system. Features were interpreted from the county-based aerial photography (4 in - 2 ft pixel), see imagery year in the "AERIAL DATE" field. The features were updated on screen from the 2008-09 vector data. Horizontal accuracy of the data corresponds to the positional accuracy of the county aerial photography. The minimum mapping unit for classification was 0.5 acres for wetlands and 5 acres for uplands. This dataset is now complete.2014 Photointerpretation Key: https://geoext.geoapps.sfwmd.gov/TPubs/2014_SFWMD_LULC_Photointerpretation_Key.pdf To download zipped file geodatabse: https://sfwmd.maps.arcgis.com/home/item.html?id=266a64a897514090842e283311c12d30

30cm Object based image analysis land cover dataset based on WorldView 3 and nDSM, stored as a .shp file.

Class

Vector

NumCodec

(16bit)

Raster

NumCodec

(8bit)

Building

100

10

0 Lowest rise building

110

11

1 Low rise building

120

12

2 Mid rise building

130

13

3 High rise building

140

14

4 Highest rise building

150

15

Mineral surface

210

21

Bare soil

220

22

Artificial grass

230

23

Grass

310

31

Shrub round

410

41

Shrub linear

420

42

Evergreen

510

51

Deciduous

520

52

Lake

610

61

River

620

62

Sea

630

63

Undergrowth

710

71

Agriculture, intensive temporary crops

810

81

Agriculture, intensive permanent crops

820

82

Agriculture, extensive

830

83

unclassified

999

99

NonAOI

999

99