dc.creatorBrendel, Andrea
dc.creatorFerrelli, Federico
dc.creatorPiccolo, Maria Cintia
dc.creatorPerillo, Gerardo Miguel E.
dc.date.accessioned2020-04-17T16:03:55Z
dc.date.accessioned2022-10-15T01:47:27Z
dc.date.available2020-04-17T16:03:55Z
dc.date.available2022-10-15T01:47:27Z
dc.date.created2020-04-17T16:03:55Z
dc.date.issued2019-01-14
dc.identifierBrendel, Andrea; Ferrelli, Federico; Piccolo, Maria Cintia; Perillo, Gerardo Miguel E.; Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data; Society of Photo-Optical Instrumentation Engineers; Journal Of Applied Remote Sensing; 13; 1; 14-1-2019; 1-15; 014503
dc.identifier1931-3195
dc.identifierhttp://hdl.handle.net/11336/102892
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4331681
dc.description.abstractThis study is aimed at evaluating the effectiveness of different supervised and unsupervised methods with information derived from Landsat satellite images and fieldwork in order to maximize the land cover classification accuracy in an area with geomorphologic differences and heterogeneous edaphic characteristics located in the southwest of the Pampas (Argentina). We test two datasets: bands-based and indices-based and also we analyze the spectral behavior of each land cover identified by field trips and surveys with farmers to improve the spatial samples employed in the digital processing. Complementarily, we study the spatial and temporal information about the land cover changes during 2000 to 2016. The classification based on indices widely outperforms the analyses based on bands. The best methods to classify the land cover are the Mahalanobis distance and the maximum likelihood. The values of kappa coefficient and overall accuracy obtain from these two methods allow us to realize a multitemporal study. This study provides essential information for semiarid regions with rain-fed agriculture and livestock activities worldwide. The knowledge obtained quickly and accurately about the land covers and their changes provides essential information about the past and current situations and can be used to predict likely future trends.
dc.languageeng
dc.publisherSociety of Photo-Optical Instrumentation Engineers
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-13/issue-01/014503/Assessment-of-the-effectiveness-of-supervised-and-unsupervised-methods/10.1117/1.JRS.13.014503.full
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1117/1.JRS.13.014503
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectremote sensing
dc.subjectland cover map and changes
dc.subjectassessment accuracy
dc.subjectclassification methods
dc.titleAssessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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