{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:20:42Z","timestamp":1766269242532,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,16]],"date-time":"2016-09-16T00:00:00Z","timestamp":1473984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Belgian Science Agency Office (BELSPO)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (&gt;77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna.<\/jats:p>","DOI":"10.3390\/rs8090763","type":"journal-article","created":{"date-parts":[[2016,9,19]],"date-time":"2016-09-19T10:07:43Z","timestamp":1474279663000},"page":"763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9361-0224","authenticated-orcid":false,"given":"\u017baneta","family":"Kaszta","sequence":"first","affiliation":[{"name":"Institut de Gestion de l\u2019Environnement et d\u2019Am\u00e9nagement de Territoire (IGEAT), Universit\u00e9 Libre de Bruxelles, Brussels 1050, Belgium"},{"name":"School of Applied Environmental Sciences, Pietermaritzburg 3209, South Africa"}]},{"given":"Ruben","family":"Van De Kerchove","sequence":"additional","affiliation":[{"name":"Institut de Gestion de l\u2019Environnement et d\u2019Am\u00e9nagement de Territoire (IGEAT), Universit\u00e9 Libre de Bruxelles, Brussels 1050, Belgium"},{"name":"Unit Remote Sensing and Earth Observation Processes, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9917-9754","authenticated-orcid":false,"given":"Abel","family":"Ramoelo","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research, Pretoria 0001, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-5375","authenticated-orcid":false,"given":"Moses","family":"Cho","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research, Pretoria 0001, South Africa"}]},{"given":"Sabelo","family":"Madonsela","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research, Pretoria 0001, South Africa"}]},{"given":"Renaud","family":"Mathieu","sequence":"additional","affiliation":[{"name":"Council for Scientific and Industrial Research, Pretoria 0001, South Africa"},{"name":"Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa"}]},{"given":"El\u00e9onore","family":"Wolff","sequence":"additional","affiliation":[{"name":"Institut de Gestion de l\u2019Environnement et d\u2019Am\u00e9nagement de Territoire (IGEAT), Universit\u00e9 Libre de Bruxelles, Brussels 1050, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chapin, F.S., Matson, P.A., and Vitousek, P. 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