{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:05:42Z","timestamp":1760241942275,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T00:00:00Z","timestamp":1540944000000},"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>In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data.<\/jats:p>","DOI":"10.3390\/rs10111717","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T11:55:41Z","timestamp":1540986941000},"page":"1717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Land Cover Classifications with Multiangular Data: MISR Data in Mainland Spain"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3501-7051","authenticated-orcid":false,"given":"Carlos J.","family":"Novillo","sequence":"first","affiliation":[{"name":"Departamento de Tecnolog\u00eda Qu\u00edmica y Ambiental, ESCET, Universidad Rey Juan Carlos, C\/Tulip\u00e1n s\/n, 28933 M\u00f3stoles, Spain"}]},{"given":"Patricia","family":"Arrogante-Funes","sequence":"additional","affiliation":[{"name":"Departamento de Tecnolog\u00eda Qu\u00edmica y Ambiental, ESCET, Universidad Rey Juan Carlos, C\/Tulip\u00e1n s\/n, 28933 M\u00f3stoles, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5700-2184","authenticated-orcid":false,"given":"Ra\u00fal","family":"Romero-Calcerrada","sequence":"additional","affiliation":[{"name":"Geography Group, Departamento de Ciencias de la Educaci\u00f3n, Lenguaje, Cultura y Artes, Ciencias Hist\u00f3rica-Jur\u00eddicas y Human\u00edsticas y Lenguas Modernas, Facultad de Ciencias Jur\u00eddicas y Sociales, Universidad Rey Juan Carlos, Paseo de los Artilleros s\/n, 28032 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.4155\/cmt.13.49","article-title":"Approaches to monitoring changes in carbon stocks for REDD+","volume":"4","author":"Birdsey","year":"2013","journal-title":"Carbon Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14784","DOI":"10.1073\/pnas.261555198","article-title":"A large carbon sink in the woody biomass of Northern forests","volume":"98","author":"Myneni","year":"2001","journal-title":"Proc. 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