{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:23:17Z","timestamp":1780384997263,"version":"3.54.1"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2014,4,3]],"date-time":"2014-04-03T00:00:00Z","timestamp":1396483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Per-pixel and sub-pixel are two common classification methods in land cover studies. The characteristics of a landscape, particularly the land cover itself, can affect the accuracies of both methods. The objectives of this study were to: (1) compare the performance of sub-pixel vs. per-pixel classification methods for a broad heterogeneous region; and (2) analyze the impact of land cover heterogeneity (i.e., the number of land cover classes per pixel) on both classification methods. The results demonstrated that the accuracy of both per-pixel and sub-pixel classification methods were generally reduced by increasing land cover heterogeneity. Urban areas, for example, were found to have the lowest accuracy for the per-pixel method, because they had the highest heterogeneity. Conversely, rural areas dominated by cropland and grassland had low heterogeneity and high accuracy. When a sub-pixel method was used, the producer\u2019s accuracy for artificial surfaces was increased by more than 20%. For all other land cover classes, sub-pixel and per-pixel classification methods performed similarly. Thus, the sub-pixel classification was only advantageous for heterogeneous urban landscapes. Both creators and users of land cover datasets should be aware of the inherent landscape heterogeneity and its potential effect on map accuracy.<\/jats:p>","DOI":"10.3390\/ijgi3020540","type":"journal-article","created":{"date-parts":[[2014,4,3]],"date-time":"2014-04-03T11:26:03Z","timestamp":1396524363000},"page":"540-553","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Land Cover Heterogeneity Effects on Sub-Pixel and  Per-Pixel Classifications"],"prefix":"10.3390","volume":"3","author":[{"given":"Trung","family":"Tran","sequence":"first","affiliation":[{"name":"Department of Geography and Environmental Sustainability, University of Oklahoma, Norman,  OK 73019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Julian","sequence":"additional","affiliation":[{"name":"Department of Geography, Texas State University, San Marcos, TX 78666, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kirsten","family":"De Beurs","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Sustainability, University of Oklahoma, Norman,  OK 73019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2014,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"PNAS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. 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