{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:25:36Z","timestamp":1760243136265,"version":"build-2065373602"},"reference-count":9,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2015,10,15]],"date-time":"2015-10-15T00:00:00Z","timestamp":1444867200000},"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>Following the suggestion made by Johnson (Johnson B.A., 2015), a polygon-based cross validation (CV) method is compared to the pixel-based CV method to classify different levels of land cover categories using a single-date Landsat 8 image and time series of Landsat TM images. Also, different variants of band combinations, with and without the thermal bands, were considered. The results demonstrate that the inclusion of thermal information into the classification process will improve the classification performance, as was already shown in our original study (Sun and Schulz, 2015). However, it is also demonstrated that the polygon-based CV method produced lower overall accuracy values when compared to the pixel-based CV method. This confirms the argument made by Johnson that a correlation of calibration and validation data due to random sampling of multi-scale data will overestimate the performance of the classifier, and independent polygon-based CV methods have to be applied instead.<\/jats:p>","DOI":"10.3390\/rs71013440","type":"journal-article","created":{"date-parts":[[2015,10,15]],"date-time":"2015-10-15T12:44:06Z","timestamp":1444913046000},"page":"13440-13447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Response to Johnson B.A. Scale Issues Related to the Accuracy Assessment of Land Use\/Land Cover Maps Produced Using Multi-Resolution Data: Comments on \u201cThe Improvement of Land Cover Classification by Thermal Remote Sensing\u201d. Remote Sens. 2015, 7, 8368\u20138390"],"prefix":"10.3390","volume":"7","author":[{"given":"Liya","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Geography, Ludwig Maximilian University of Munich, Munich 80333, Germany"},{"name":"Institute for Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences, Vienna 1180, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6616-2876","authenticated-orcid":false,"given":"Karsten","family":"Schulz","sequence":"additional","affiliation":[{"name":"Institute for Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences, Vienna 1180, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13436","DOI":"10.3390\/rs71013436","article-title":"Scale issues related to the accuracy assessment of land use\/land cover maps produced using multi-resolution data: Comments on \u201cthe improvement of land cover classification by thermal remote sensing\u201d. 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Environ."},{"key":"ref_5","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. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1080\/01431160600649254","article-title":"Influence of image fusion approaches on classification accuracy: A case study","volume":"27","author":"Colditz","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Anderson, J.R. (1976). A Land Use and Land Cover Classification System for Use with Remote Sensor Data, U.S. Government Printing Office (GPO).","DOI":"10.3133\/pp964"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-015-4489-3","article-title":"Land cover mapping based on random forest classification of multitemporal spectral and thermal images","volume":"187","author":"Eisavi","year":"2015","journal-title":"Environ. Monit. Assess."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/10\/13440\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:50:08Z","timestamp":1760215808000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/10\/13440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,15]]},"references-count":9,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2015,10]]}},"alternative-id":["rs71013440"],"URL":"https:\/\/doi.org\/10.3390\/rs71013440","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2015,10,15]]}}}