{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T02:54:57Z","timestamp":1719024897497},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens\u00ae Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the \u201csalt-and-pepper effect\u201d and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.<\/jats:p>","DOI":"10.3390\/s110201943","type":"journal-article","created":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T16:24:19Z","timestamp":1296577459000},"page":"1943-1958","source":"Crossref","is-referenced-by-count":45,"title":["Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"So-Ra","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136-701, Korea"}]},{"given":"Woo-Kyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136-701, Korea"}]},{"given":"Doo-Ahn","family":"Kwak","sequence":"additional","affiliation":[{"name":"Division of Environmental Science and Ecological Engineering, Korea University, Seoul 136-701, Korea"}]},{"given":"Greg S.","family":"Biging","sequence":"additional","affiliation":[{"name":"Department of Environmental Science, Policy and Management, University of California at Berkeley, Mulford Hall, Berkeley, CA 94720, USA"}]},{"given":"Peng","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Environmental Science, Policy and Management, University of California at Berkeley, Mulford Hall, Berkeley, CA 94720, USA"}]},{"given":"Jun-Hak","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Environmental Science, Policy and Management, University of California at Berkeley, Mulford Hall, Berkeley, CA 94720, USA"}]},{"given":"Hyun-Kook","family":"Cho","sequence":"additional","affiliation":[{"name":"Division of Forest Resources Information, Korean Forest Research Institute, Seoul 136-012, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2011,2,1]]},"reference":[{"key":"ref_1","unstructured":"KFRI (accessed on 29 July 2009)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1080\/01431160120548","article-title":"Tropical forest mapping from coarse spatial resolution satellite data: production and accuracy assessment issues","volume":"22","author":"Achard","year":"2001","journal-title":"Int. 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