{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:16:23Z","timestamp":1773436583818,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,10,1]],"date-time":"2016-10-01T00:00:00Z","timestamp":1475280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path\/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path\/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps.<\/jats:p>","DOI":"10.3390\/rs8100811","type":"journal-article","created":{"date-parts":[[2016,10,3]],"date-time":"2016-10-03T10:17:01Z","timestamp":1475489821000},"page":"811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Evaluation of the Initial Thematic Output from a Continuous Change-Detection Algorithm for Use in Automated Operational Land-Change Mapping by the U.S. Geological Survey"],"prefix":"10.3390","volume":"8","author":[{"given":"Bruce","family":"Pengra","sequence":"first","affiliation":[{"name":"SGT Inc., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3029-6637","authenticated-orcid":false,"given":"Alisa","family":"Gallant","sequence":"additional","affiliation":[{"name":"U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]},{"given":"Zhe","family":"Zhu","sequence":"additional","affiliation":[{"name":"Inuteq., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9594-1249","authenticated-orcid":false,"given":"Devendra","family":"Dahal","sequence":"additional","affiliation":[{"name":"SGT Inc., Contractor to the U.S. Geological Survey, Earth Resources Observation and Science Center, 47914 252nd St., Sioux Falls, SD 57198, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,1]]},"reference":[{"key":"ref_1","first-page":"337","article-title":"Completion of the 2001 National Land Cover Database for the counterminous United States","volume":"73","author":"Homer","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1023\/A:1013051420309","article-title":"Effects of land cover conversion on surface climate","volume":"52","author":"Bounoua","year":"2002","journal-title":"Clim. Chang."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1126\/science.1159607","article-title":"Ecosystem disturbance, carbon, and climate","volume":"321","author":"Running","year":"2008","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.biocon.2014.11.048","article-title":"Free and open-access satellite data are key to biodiversity conservation","volume":"182","author":"Turner","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"1091","article-title":"A strategy for estimating the rates of recent United States land-cover changes","volume":"68","author":"Loveland","year":"2002","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sleeter, B.M., Wilson, T.S., and Acevedo, W. (2012). Status and Trends of Land Change in the Western United States\u20141973 to 2000.","DOI":"10.3133\/pp1794A"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Taylor, J.L., Acevedo, W., Auch, R.F., and Drummond, M.A. (2015). Status and Trends of land Change in the Great Plains of the United States\u20141973 to 2000.","DOI":"10.3133\/pp1794B"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Auch, R.F., and Karstensen, K.A. (2015). Status and Trends of Land Change in the Midwest-South Central United States\u20141973 to 2000.","DOI":"10.3133\/pp1794C"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Soulard, C.E., Acevedo, W., Auch, R.F., Sohl, T.L., Drummond, M.A., Sleeter, B.M., Sorenson, D.G., Kambly, S., Wilson, T.S., and Taylor, J.L. (2014). Land Cover Trends Dataset, 1973\u20132000.","DOI":"10.3133\/ds844"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Anderson, J.R. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data.","DOI":"10.3133\/pp964"},{"key":"ref_13","unstructured":"U.S. Geological Survey Land Cover Trends Project Classification System, Available online: http:\/\/landcovertrends.usgs.gov\/main\/classification.html."},{"key":"ref_14","unstructured":"U.S. Environmental Protection Agency (1999). Level III Ecoregions of the Continental United States (Revision of Omernik, 1987)."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1111\/j.1467-8306.1987.tb00149.x","article-title":"Map supplement: Ecoregions of the conterminous United States","volume":"77","author":"Omernik","year":"1987","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_16","unstructured":"Giri, C. (2012). Remote Sensing of Land Use and Land Cover, Principles and Applications, CRC Press."},{"key":"ref_17","unstructured":"U.S. Geological Survey National Land Cover Dataset 1992 (NLCD1992), Available online: http:\/\/www.mrlc.gov\/nlcd1992.php."},{"key":"ref_18","unstructured":"Cochran, W.G. (1977). Sampling Techniques, Wiley and Sons."},{"key":"ref_19","unstructured":"U.S. Geological Survey Landsat 4\u20137 Climate Data Record (CDR) Surface Reflectance\u2014User\u2019s Guide, Available online: http:\/\/landsat.usgs.gov\/documents\/cdr_sr_product_guide.pdf."},{"key":"ref_20","unstructured":"U.S. Geological Survey Provisional Landsat 8 Surface Reflectance Product\u2014Product Guide, Available online: http:\/\/landsat.usgs.gov\/documents\/provisional_l8sr_product_guide.pdf."},{"key":"ref_21","unstructured":"U.S. Geological Survey Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) on Demand Interface\u2014User Guide, Available online: http:\/\/landsat.usgs.gov\/documents\/espa_odi_userguide.pdf."},{"key":"ref_22","unstructured":"U.S. Geological Survey Landsat Processing Details, Available online: http:\/\/landsat.usgs.gov\/Landsat_Processing_Details.php."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.rse.2014.06.012","article-title":"Automated cloud, cloud shadow, and snow detection in multitemporal landsat data: An algorithm designed specifically for monitoring land cover change","volume":"152","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.rse.2015.02.009","article-title":"Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time","volume":"162","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Davis, J.C., and Sampson, R.J. (1986). Statistics and Data Analysis in Geology, Wiley."},{"key":"ref_28","unstructured":"Rayner, J.N. (1971). Introduction to Spectral Analysis, Pion."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, Z. (2016). Optimizing selection of training and auxillary data for operational land cover classification for the LCMAP initiative. ISPRS J. Photogramm. Remote Sens., submitted.","DOI":"10.1016\/j.isprsjprs.2016.11.004"},{"key":"ref_31","unstructured":"U.S. Geological Survey National Land Cover Database 2006 (NLCD 2006), Available online: http:\/\/www.mrlc.gov\/nlcd2006.php."},{"key":"ref_32","unstructured":"U.S. Fish and Wildlife Service National Wetlands Inventory, Available online: http:\/\/www.fws.gov\/wetlands\/."},{"key":"ref_33","unstructured":"Natural Resources Conservation Service Soil Survey Geographic Database, Available online: http:\/\/www.nrcs.usda.gov\/wps\/portal\/nrcs\/site\/soils\/home\/."},{"key":"ref_34","unstructured":"Wilen, B.O., and Bates, M. (1995). Classification and Inventory of the World\u2019s Wetlands, Springer Netherlands."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/811\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:32:20Z","timestamp":1760211140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/10\/811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10,1]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2016,10]]}},"alternative-id":["rs8100811"],"URL":"https:\/\/doi.org\/10.3390\/rs8100811","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,10,1]]}}}