dc.creatorde Mello A.Y.I.
dc.creatorAlves D.S.
dc.creatorLinhares C.A.
dc.creatorde Lima F.B.
dc.date2012
dc.date2015-06-26T20:28:45Z
dc.date2015-11-26T14:24:46Z
dc.date2015-06-26T20:28:45Z
dc.date2015-11-26T14:24:46Z
dc.date.accessioned2018-03-28T21:27:06Z
dc.date.available2018-03-28T21:27:06Z
dc.identifier
dc.identifierRevista Arvore. , v. 36, n. 3, p. 537 - 547, 2012.
dc.identifier1006762
dc.identifier10.1590/S0100-67622012000300016
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84864831012&partnerID=40&md5=c9d4c9ca44d486ae66a787cbca64af59
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/96740
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/96740
dc.identifier2-s2.0-84864831012
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1245522
dc.descriptionRemotely sensed imagery has been widely used for land use/cover classification thanks to the periodic data acquisition and the widespread use of digital image processing systems offering a wide range of classification algorithms. The aim of this work was to evaluate some of the most commonly used supervised and unsupervised classification algorithms under different landscape patterns found in Rondônia, including (1) areas of mid-size farms, (2) "fish-bone" settlements and (3) a gradient of forest and Cerrado (Brazilian savannah). Comparison with a reference map based on the kappa statistics resulted in good to superior indicators (best results - K-means: k=0.68; k=0.77; k=0.64 and MaxVer: k=0.71; k=0.89; k=0.70 respectively for three areas mentioned). Results show that choosing a specific algorithm requires to take into account both its capacity to discriminate among various spectral signatures under different landscape patterns as well as a cost/benefit analysis considering the different steps performed by the operator performing a land cover/use map. it is suggested that a more systematic assessment of several options of implementation of a specific project is needed prior to beginning a land use/cover mapping job.
dc.description36
dc.description3
dc.description537
dc.description547
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dc.languageen
dc.languagept
dc.publisher
dc.relationRevista Arvore
dc.rightsaberto
dc.sourceScopus
dc.titleClassification Techniques For Landsat Tm Imagery Under Different Landscape Patterns In Rondônia [avaliação De Técnicas De Classificação Digital De Imagens Landsat Em Diferentes Padrões De Cobertura Da Terra Em Rondônia]
dc.typeArtículos de revistas


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