{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T03:18:53Z","timestamp":1762658333950,"version":"build-2065373602"},"reference-count":18,"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>Much remote sensing (RS) research focuses on fusing, i.e., combining,  multi-resolution\/multi-sensor imagery for land use\/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100\u2013120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR\/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.<\/jats:p>","DOI":"10.3390\/rs71013436","type":"journal-article","created":{"date-parts":[[2015,10,15]],"date-time":"2015-10-15T12:44:06Z","timestamp":1444913046000},"page":"13436-13439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"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. Remote Sens. 2015, 7(7),  8368\u20138390"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1911-3585","authenticated-orcid":false,"given":"Brian","family":"Johnson","sequence":"first","affiliation":[{"name":"Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi, Hayama,  Kanagawa 240-0115, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.3390\/rs70708368","article-title":"The improvement of land cover classification by thermal remote sensing","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","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."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/01431161.2012.709329","article-title":"Tropical forest mapping using a combination of optical and microwave data of ALOS","volume":"34","author":"Hoan","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2014.10.001","article-title":"Fusing Landsat and SAR time series to detect deforestation in the tropics","volume":"156","author":"Reiche","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.04.004","article-title":"Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data","volume":"93","author":"Jia","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1080\/10106049.2012.756940","article-title":"Data fusion and classifier ensemble techniques for vegetation mapping in the coastal Everglades","volume":"29","author":"Zhang","year":"2014","journal-title":"Geocarto Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.isprsjprs.2014.06.005","article-title":"Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem","volume":"104","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.3390\/rs61111518","article-title":"Land cover classification of Landsat data with phenological features extracted from time series MODIS NDVI data","volume":"6","author":"Jia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1109\/JSTARS.2013.2245101","article-title":"Feature level fusion of multi-temporal ALOS PALSAR and Landsat data for mapping and monitoring of tropical deforestation and forest degradation","volume":"6","author":"Reiche","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","first-page":"218","article-title":"An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery","volume":"33","author":"Johnson","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6969","DOI":"10.1080\/01431161.2013.810825","article-title":"A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees","volume":"34","author":"Johnson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"345","DOI":"10.2747\/1548-1603.48.3.345","article-title":"A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon","volume":"48","author":"Lu","year":"2011","journal-title":"GISci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/JSTARS.2011.2176467","article-title":"Classification of pansharpened urban satellite images","volume":"5","author":"Palsson","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1109\/TGRS.2003.814627","article-title":"A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas","volume":"41","author":"Shackelford","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Frequently Asked Questions about the Landsat Missions, Available online: http:\/\/landsat.usgs.gov\/band_designations_landsat_satellites.php."},{"key":"ref_17","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/10\/13436\/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\/13436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,15]]},"references-count":18,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2015,10]]}},"alternative-id":["rs71013436"],"URL":"https:\/\/doi.org\/10.3390\/rs71013436","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2015,10,15]]}}}