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dc.contributor.author
Brendel, Andrea
dc.contributor.author
Ferrelli, Federico
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Piccolo, Maria Cintia
dc.contributor.author
Perillo, Gerardo Miguel E.
dc.date.available
2020-04-17T16:03:55Z
dc.date.issued
2019-01-14
dc.identifier.citation
Brendel, 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.identifier.issn
1931-3195
dc.identifier.uri
http://hdl.handle.net/11336/102892
dc.description.abstract
This 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.format
application/pdf
dc.language.iso
eng
dc.publisher
Society of Photo-Optical Instrumentation Engineers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
remote sensing
dc.subject
land cover map and changes
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assessment accuracy
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classification methods
dc.subject.classification
Geociencias multidisciplinaria
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-01-13T14:38:11Z
dc.journal.volume
13
dc.journal.number
1
dc.journal.pagination
1-15; 014503
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Brendel, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentina
dc.description.fil
Fil: Ferrelli, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentina
dc.description.fil
Fil: Piccolo, Maria Cintia. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
dc.description.fil
Fil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; Argentina
dc.journal.title
Journal Of Applied Remote Sensing
dc.relation.alternativeid
info: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.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1117/1.JRS.13.014503
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