{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:29:44Z","timestamp":1776072584897,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC18K0341"],"award-info":[{"award-number":["80NSSC18K0341"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input.<\/jats:p>","DOI":"10.3390\/rs14051219","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T22:53:25Z","timestamp":1646261605000},"page":"1219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Assessing the Accuracy and Potential for Improvement of the National Land Cover Database\u2019s Tree Canopy Cover Dataset in Urban Areas of the Conterminous United States"],"prefix":"10.3390","volume":"14","author":[{"given":"Mehdi","family":"Pourpeikari Heris","sequence":"first","affiliation":[{"name":"Department of Urban Policy and Planning, Hunter College, City University of New York, 695 Park Ave., New York, NY 10065, USA"}]},{"given":"Kenneth J.","family":"Bagstad","sequence":"additional","affiliation":[{"name":"US Geological Survey, Geosciences & Environmental Change Science Center, Lakewood, CO 80225, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3003-2532","authenticated-orcid":false,"given":"Austin R.","family":"Troy","sequence":"additional","affiliation":[{"name":"College of Architecture and Planning, University of Colorado Denver, Denver, CO 80202, USA"}]},{"given":"Jarlath P. M.","family":"O\u2019Neil-Dunne","sequence":"additional","affiliation":[{"name":"Spatial Analysis Laboratory, University of Vermont, Burlington, VT 05405, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,2]]},"reference":[{"key":"ref_1","unstructured":"(2018, January 15). Multi-Resolution Land Characteristics (MRLC) Consortium|Multi-Resolution Land Characteristics (MRLC) Consortium, Available online: https:\/\/www.mrlc.gov\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.rse.2012.12.001","article-title":"Accuracy Assessment of NLCD 2006 Land Cover and Impervious Surface","volume":"130","author":"Wickham","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s10980-013-9950-5","article-title":"Relationships between Land Cover and the Surface Urban Heat Island: Seasonal Variability and Effects of Spatial and Thematic Resolution of Land Cover Data on Predicting Land Surface Temperatures","volume":"29","author":"Zhou","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6343","DOI":"10.1080\/01431160902849503","article-title":"Development of an Object-Based Framework for Classifying and Inventorying Human-Dominated Forest Ecosystems","volume":"30","author":"Zhou","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1111\/j.1752-1688.2007.00139.x","article-title":"Mechanistic Simulation of Tree Effects in an Urban Water Balance Model 1","volume":"44","author":"Wang","year":"2008","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.apgeog.2010.12.005","article-title":"High-Resolution Land Cover Datasets, Composite Curve Numbers, and Storm Water Retention in the Tampa Bay, FL Region","volume":"31","author":"Reistetter","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_7","unstructured":"Nowak, D., and Heisler, G.M. (2010). Air Quality Effects of Urban Trees and Parks. Natl. Recreat. Park Assoc. Res. Ser., 1\u201344. Available online: https:\/\/www.fs.usda.gov\/treesearch\/pubs\/52881."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.ecolecon.2009.09.004","article-title":"Cost of Potential Emerald Ash Borer Damage in U.S. Communities, 2009\u20132019","volume":"69","author":"Kovacs","year":"2010","journal-title":"Ecol. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ufug.2012.10.003","article-title":"Assessing Net Carbon Sequestration on Urban and Community Forests of Northern New England, USA","volume":"12","author":"Zheng","year":"2013","journal-title":"Urban For. Urban Green."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ufug.2016.12.004","article-title":"Residential Building Energy Conservation and Avoided Power Plant Emissions by Urban and Community Trees in the United States","volume":"21","author":"Nowak","year":"2017","journal-title":"Urban For. Urban Green."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.foreco.2014.07.025","article-title":"Using a Remote Sensing-Based, Percent Tree Cover Map to Enhance Forest Inventory Estimation","volume":"331","author":"McRoberts","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.landurbplan.2012.04.005","article-title":"Tree and Impervious Cover in the United States","volume":"107","author":"Nowak","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1093\/biosci\/biy135","article-title":"The Natural Capital Accounting Opportunity: Let\u2019s Really Do the Numbers","volume":"68","author":"Boyd","year":"2018","journal-title":"BioScience"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101226","DOI":"10.1016\/j.ecoser.2020.101226","article-title":"Piloting Urban Ecosystem Accounting for the United States","volume":"48","author":"Heris","year":"2021","journal-title":"Ecosyst. Serv."},{"key":"#cr-split#-ref_15.1","unstructured":"(2021, November 29). City of New York, Land Cover Raster Data (2017)-6in Resolution|NYC Open Data 2018"},{"key":"#cr-split#-ref_15.2","unstructured":"Accessed in May 2019. Available online: https:\/\/data.cityofnewyork.us\/Environment\/Land-Cover-Raster-Data-2017-6in-Resolution\/he6d-2qns."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.14358\/PERS.75.11.1279","article-title":"Assessment of 2001 NLCD Percent Tree and Impervious Cover Estimates","volume":"75","author":"Greenfield","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1007\/s00267-010-9536-9","article-title":"Evaluating The National Land Cover Database Tree Canopy and Impervious Cover Estimates Across the Conterminous United States: A Comparison with Photo-Interpreted Estimates","volume":"46","author":"Nowak","year":"2010","journal-title":"Environ. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.ecolecon.2010.03.011","article-title":"The Value of Urban Tree Cover: A Hedonic Property Price Model in Ramsey and Dakota Counties, Minnesota, USA","volume":"69","author":"Sander","year":"2010","journal-title":"Ecol. Econ."},{"key":"ref_19","unstructured":"Landry, S.M., Koeser, A.K., Northrop, R.J., McLean, D., Donovan, G., Andreu, M.G., and Hilbert, D. (2021, November 29). City of Tampa Tree Canopy and Urban Forest Analysis 2016. Available online: https:\/\/waterinstitute.usf.edu\/upload\/documents\/TampaUEA2016_FinalReport-lowres.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101099","DOI":"10.1016\/j.ecoser.2020.101099","article-title":"Testing Ecosystem Accounting in the United States: A Case Study for the Southeast","volume":"43","author":"Warnell","year":"2020","journal-title":"Ecosyst. Serv."},{"key":"ref_21","unstructured":"Huang, C., Yang, L., Wylie, B., and Homer, C. (2001, January 5\u20137). A Strategy for Estimating Tree Canopy Density Using Landsat 7 ETM and High Resolution Images Over Large Areas. Proceedings of the Third International Conference on Geospatial Information in Agriculture and Forestry, Denver, Colorado."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"715","DOI":"10.14358\/PERS.78.7.715","article-title":"Modeling Percent Tree Canopy Cover: A Pilot Study","volume":"78","author":"Coulston","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","first-page":"345","article-title":"Completion of the 2011 National Land Cover Database for the Conterminous United States\u2013Representing a Decade of Land Cover Change Information","volume":"81","author":"Homer","year":"2015","journal-title":"Photogramm. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","article-title":"A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies","volume":"146","author":"Yang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"829","DOI":"10.14358\/PERS.70.7.829","article-title":"Development of a 2001 National Land-Cover Database for the United States","volume":"70","author":"Homer","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_26","unstructured":"(2019, February 15). U.S. EPA EnviroAtlas, Available online: https:\/\/www.epa.gov\/enviroatlas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1080\/10106049.2012.689015","article-title":"An Object-Based System for LiDAR Data Fusion and Feature Extraction","volume":"28","author":"MacFaden","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_28","unstructured":"(2019, May 06). US EPA Ecoregions, Available online: https:\/\/www.epa.gov\/eco-research\/ecoregions."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10980-009-9402-4","article-title":"Urban Heat Islands and Landscape Heterogeneity: Linking Spatiotemporal Variations in Surface Temperatures to Land-Cover and Socioeconomic Patterns","volume":"25","author":"Buyantuyev","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0378-7788(95)00927-P","article-title":"Mitigation of Urban Heat Islands: Materials, Utility Programs, Updates","volume":"22","author":"Rosenfeld","year":"1995","journal-title":"Energy Build."},{"key":"ref_31","unstructured":"U.S. Geological Survey (2019). Landsat 8 (L8) Data Users Handbook: Version 4 2019."},{"key":"ref_32","unstructured":"Microsoft (2018). US Building Footprints, Microsoft."},{"key":"ref_33","unstructured":"Heris, M.P., Foks, N., Bagstad, K.J., and Troy, A. (2020). A National Dataset of Rasterized Building Footprints for the U.S."},{"key":"ref_34","first-page":"7","article-title":"Evaluating Metropolitan Spatial Development: A Method for Identifying Settlement Types and Depicting Growth Patterns","volume":"4","author":"Heris","year":"2017","journal-title":"Reg. Stud. Reg. Sci."},{"key":"ref_35","unstructured":"(2019, February 11). Your Weather Service U.S. Climate Data (1990\u20132018). Available online: https:\/\/www.usclimatedata.com."},{"key":"ref_36","unstructured":"Manson, S., Schroeder, J., Van Riper, D., and Ruggles, S. (2019). National Historical Geographic Information System: Version 14.0, IPUMS."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1007\/s00267-006-0112-2","article-title":"Predicting Opportunities for Greening and Patterns of Vegetation on Private Urban Lands","volume":"40","author":"Troy","year":"2007","journal-title":"Environ. Manag."},{"key":"ref_38","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_39","unstructured":"United Nations Department of Economic and Social Affairs Statistics Division (2021). System of Environmental-Economic Accounting-Ecosystem Accounting: Final Draft, United Nations."},{"key":"ref_40","unstructured":"Heris, M.P. (2021, November 29). Accuracy Assessment of National Land Cover Dataset Tree Cover Code. Available online: https:\/\/github.com\/mehdiheris\/NLCD_Assessment."},{"key":"ref_41","first-page":"101955","article-title":"Accuracy Assessment of NLCD 2011 Percent Impervious Cover for Selected USA Metropolitan Areas","volume":"84","author":"Wickham","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41893-018-0202-1","article-title":"Social-Ecological and Technological Factors Moderate the Value of Urban Nature","volume":"2","author":"Keeler","year":"2019","journal-title":"Nat. Sustain."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1007\/s10980-015-0337-7","article-title":"The Impact of Land Use\/Land Cover Scale on Modelling Urban Ecosystem Services","volume":"31","author":"Grafius","year":"2016","journal-title":"Landsc. Ecol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3389\/fenvs.2019.00093","article-title":"How Land Cover Spatial Resolution Affects Mapping of Urban Ecosystem Service Flows","volume":"7","author":"Rioux","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kerins, P., Guzder-Williams, B., Mackres, E., Rashid, T., and Pietraszkiewicz, E. (2021). Mapping Urban Land Use in India and Mexico Using Remote Sensing and Machine Learning, World Resources Institute.","DOI":"10.46830\/writn.20.00048"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1021\/acs.est.0c05642","article-title":"High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany","volume":"55","author":"Haberl","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jochem, W.C., and Tatem, A.J. (2021). Tools for Mapping Multi-Scale Settlement Patterns of Building Footprints: An Introduction to the R Package Foot. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247535"},{"key":"ref_48","unstructured":"(2021, March 07). Microsoft Building Footprints. Available online: https:\/\/github.com\/microsoft?q=building+footprints&type=&language=."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_50","unstructured":"European Environment Agency (2017). Copernicus Land Monitoring Service-High Resolution Layer Forest Product Specifications Document."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ufug.2018.03.006","article-title":"Declining Urban and Community Tree Cover in the United States","volume":"32","author":"Nowak","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_52","unstructured":"Treglia, M.L., Acosta-Morel, M., Crabtree, D., Galbo, K., Lin-Moges, T., Van Slooten, A., and Maxwell, E.N. (2021). The State of the Urban Forest in New York City, Zenodo."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:42Z","timestamp":1760135442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,2]]},"references-count":53,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051219"],"URL":"https:\/\/doi.org\/10.3390\/rs14051219","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,2]]}}}