View Metadata
NLCD Tree Canopy (Cartographic), New York, 2011
- Identification Information
- Data Quality Information
- Spatial Data Organization Information
- Entity and Attribute Information
- Distribution Information
- Distribution Information
- Metadata Reference Information
- Identification Information
- Citation
- Publication Date
- 20140331
- Title
- NLCD Tree Canopy (Cartographic), New York, 2011
- Edition
- 2011 Edition
- Geospatial Data Presentation Form
- raster digital data
- Collection Title
- NLCD 2011
- Series Information
- Series Name
- National Land Cover Database
- Issue Identification
- 2011 Tree Canopy (Cartographic)
- Publication Information
- Publication Place
- Publisher
- U.S. Geological Survey
- Other Citation Details
- References: Brand, Gary J.; Nelson, Mark D.; Wendt, Daniel G.; Nimerfro, Kevin K. 2000. The hexagon/panel system for selecting FIA plots under an annual inventory. In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C., eds. Proceedings of the First Annual Forest Inventory and Analysis Symposium; Gen. Tech. Rep. NC-213. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station: 8-13. Breiman, L. 2001. Random forests. Machine Learning 45:15–32. Chander, G.; Markham, B.L.; Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113(2009): 893-903. Chavez, P.S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24(1988): 459-479. Coulston, John W.; Jacobs, Dennis M.; King, Chris R.; Elmore, Ivey C. 2013. The influence of multi-season imagery on models of canopy cover: a case study. Photogrammetric Engineering Remote Sensing 79(5):469–477. Coulston, John W.; Moisen, Gretchen G.; Wilson, Barry T.; Finco, Mark V.; Cohen, Warren B.; Brewer, C. Kenneth. 2012. Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering Remote Sensing 78(7): 715–727. Cutler, R.D.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. 2007. Random forest for classification in ecology. Ecology 88 (11):2783-2792. Huang, C.; Yang, L.; Wylie, B.; Homer, C. 2001. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. In: Third International Conference on Geospatial Information in Agriculture and Forestry; November 5-7, 2001; Denver, Colorado. CD-ROM, 1 disk. Moisen, Gretchen G.; Coulston, John W.; Wilson, Barry T.; Cohen, Warren B.; Finco, Mark V. 2012. Choosing appropriate subpopulations for modeling tree canopy cover nationwide. In: McWilliams, Will; Roesch, Francis A., eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: US Department of Agriculture, Forest Service, Southern Research Station: 195-200. Tipton, John; Moisen, Gretchen; Patterson, Paul; Jackson, Thomas A.; Coulston, John. 2012. Sampling intensity and normalizations: Exploring cost-driving factors in nationwide mapping of tree canopy cover. In: McWilliams, Will; Roesch, Francis A., eds. 2012. Monitoring Across Borders: 2010 Joint Meeting of the Forest Inventory and Analysis (FIA) Symposium and the Southern Mensurationists. e-Gen. Tech. Rep. SRS-157. Asheville, NC: US Department of Agriculture, Forest Service, Southern Research Station: 201-208. Zhu, Z.; Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83-94.
- Online Linkage
- https://cugir.library.cornell.edu/catalog/cugir-009006
- Abstract
- The National Land Cover Database 2011 (NLCD2011) USFS percent tree canopy product was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). 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 (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate current, consistent, and seamless national land cover, percent tree canopy, and percent impervious cover at medium spatial resolution. This product is the cartographic version of the NLCD2011 percent tree canopy cover dataset for CONUS at medium spatial resolution (30 m). It was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). Tree canopy values range from 0 to 100 percent. The analytic tree canopy layer was produced using a Random Forests™ regression algorithm. The cartographic product is a filtered version of the regression algorithm output.
- Purpose
- The goal of this project is to provide the Nation with complete, current and consistent public domain information on its tree canopy cover.
- Temporal Extent
- Currentness Reference
- Ground condition
- Time Instant
- 2011
- Bounding Box
- West
- -80.035689
- East
- -70.516534
- North
- 45.847801
- South
- 40.086438
- Theme Keyword
- Percent Tree Canopy
- Tree Canopy Cover
- Theme Keyword Thesaurus
- None
- Theme Keyword
- ImageryBaseMapEarthCover
- environment
- Theme Keyword Thesaurus
- ISO 19115 Category
- Theme Keyword
- environment
- Theme Keyword Thesaurus
- CUGIR Category
- Place Keyword
- New York
- Place Keyword Thesaurus
- None
- Temporal Keyword
- Access Restrictions
- None
- Use Restrictions
- Any hardcopy or electronic products utilizing these datasets will clearly indicate their source. If the user has modified the data in any way, they are obligated to describe the types of modifications they have performed. User specifically agrees not to misrepresent these data sets, nor to imply that the MRLC approved the changes. Any data downloaded must be properly cited.
- Maintenance and Update Frequency
- As needed
- Point of Contact
- Contact Organization
- U.S. Geological Survey
- Delivery Point
- USGS/EROS
- Delivery Point
- 47914 252nd Street
- City
- Sioux Falls
- State
- SD
- Postal Code
- 57198-0001
- Country
- US
- Contact Telephone
- 605/594-6151
- Contact Facsimile Telephone
- 605/594-6589
- Contact Electronic Mail Address
- custserv@usgs.gov
- Hours of Service
- 0800 - 1600 CT, M - F (-6h CST/-5h CDT GMT)
- Credit
- USDA Forest Service Remote Sensing Applications Center
- Native Data Set Environment
- Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; ESRI ArcGIS 10.0.3.3600
- Collection
- Publication Date
- 20140101
- Title
- NLCD 2011
- Edition
- 4.0
- Geospatial Data Presentation Form
- raster digital data
- Series Information
- Series Name
- none
- Issue Identification
- none
- Publication Information
- Publication Place
- Publisher
- U.S. Geological Survey
- Other Citation Details
- References: Fry, J.; Xian, G.; Jin, S.; Dewitz, J.; Homer, C.; Yang, L.; Barnes, C.; Herold, N.; Wickham, J. 2011. Completion of the 2006 National Land Cover Database for the conterminous United States, Photogrammetric Engineering Remote Sensing 77(9):858-864. Homer, C.; Gallant, A. 2001. Partitioning the conterminous United States into mapping zones for Landsat TM land cover mapping, USGS Draft White Paper. http://landcover.usgs.gov/pdf/homer.pdf Homer, C.; Huang, C.; Yang, L.; Wylie, W.; Coan, M. 2004. Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering Remote Sensing 70(7): 829-840.
- Online Linkage
- http://www.mrlc.gov/nlcd2006.php
- Data Quality Information
- Attribute Accuracy Report
- No formal independent accuracy assessment of this product has been made. The Random Forests™ regression algorithm (Breiman 2001; Cutler et al. 2007) employed in creating this product calculates the mean of squared residuals along with percent variability explained by the model for assessing prediction reliability. The Random Forests™ models consisted of 500 decision trees, which were used to determine the final response value. The response of each tree depended on a randomly chosen subset of predictor variables chosen independently (with replacement) for evaluation by that tree. The responses of the trees were averaged to obtain an estimate of the dependent variable. The standard error is the square root of the variance of the estimates given by all trees. A summary of the Random Forests™ models is available in the supplemental metadata associated with the analytic version of this product.
- Completeness Report
- This product is the cartographic version of the NLCD2011 USFS percent tree canopy product, version 1, dated 2014.
- Lineage
- Source
- Originator
- Publication Date
- 20110101
- Title
- NLCD 2006 Land Cover
- Geospatial Data Presentation Form
- raster digital data
- Publication Information
- Publication Place
- Publisher
- U.S. Geological Survey
- Type of Source Media
- None
- Contribution
- land cover information
- Source
- Originator
- Publication Date
- 20140331
- Title
- NLCD 2011 USFS Percent Tree Canopy
- Geospatial Data Presentation Form
- raster digital data
- Publication Information
- Publication Place
- Publisher
- U.S. Geological Survey
- Type of Source Media
- None
- Contribution
- percent tree canopy cover
- Spatial Data Organization Information
- Direct Spatial Reference Method
- Raster
- Raster Object Information
- Raster Object Type
- Grid Cell
- Row Count
- 16989
- Column Count
- 22610
- Entity and Attribute Information
- Entity Type
- Entity Type Label
- nlcd2011_usfs_conus_canopy_cartographic.img.vat
- Attributes
- OID
- Internal feature number. (Sequential unique whole numbers that are automatically generated.)
- Definition Source
- ESRI
- Value
- Percent tree canopy cover (0 to 100 Percent)
- Count
- Distribution Information
- Format Name
- GeoTIFF
- Format Name
- metadata
- Format Name
- HTML metadata
- Format Name
- OGC:WMS
- Distributor
- Albert R. Mann Library
- Online Access
- https://cugir-data.s3.amazonaws.com/00/90/06/cugir-009006.zip
- Online Access
- https://cugir-data.s3.amazonaws.com/00/90/06/fgdc.xml
- Online Access
- https://cugir-data.s3.amazonaws.com/00/90/06/fgdc.html
- Online Access
- https://cugir.library.cornell.edu/geoserver/cugir/wms?version=1.1.0request=GetMaplayers=cugir009006bbox=-80.32126365,39.91359711,-70.23095934999999,46.02064189width=256height=154srs=EPSG:4326format=image/png
- Name
- Distribution Information
- Distributor
- U.S. Geological Survey
- Name
- Metadata Reference Information
- Metadata Date
- 20190604
- Metadata Contact
- Contact Information
- Contact Organization Primary
- Contact Organization
- Albert R. Mann Library
- Contact Address
- Address
- Albert R. Mann Library
- City
- Ithaca
- State or Province
- New York
- Postal Code
- 14853
- Country
- USA
- Contact Voice Telephone
- 607-255-5406
- Contact Electronic Mail Address
- mann-ref@cornell.edu
- Metadata Standard Name
- FGDC Content Standard for Digital Geospatial Metadata
- Metadata Standard Version
- FGDC-STD-001-1998
