A Hmm-based Approach For Historic And Up-to-date Land Cover Mapping Through Landsat Time-series In The State Of São Paulo, Brazil

dc.creatorIannini
dc.creatorL.; Molijn
dc.creatorR.; Mousivand
dc.creatorA.; Hanssen
dc.creatorR.; Lamparelli
dc.creatorR.
dc.date2016
dc.date2017-11-13T13:44:36Z
dc.date2017-11-13T13:44:36Z
dc.date.accessioned2018-03-29T05:59:15Z
dc.date.available2018-03-29T05:59:15Z
dc.identifier978-1-5090-3332-4
dc.identifier2016 Ieee International Geoscience And Remote Sensing Symposium (igarss). Ieee, p. 5457 - 5460, 2016.
dc.identifier2153-6996
dc.identifierWOS:000388114605097
dc.identifierhttp://ieeexplore.ieee.org/document/7730421/
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328810
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365835
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThe paper debates a novel approach for land cover (LC) mapping based on the Hidden Markov Model. The proposed methodology is aimed to address both the urgent demand of off-line (or historic) LC information retrieval and of near-real time LC monitoring. The discrete-time model employs short steps of 16 days, that conveniently fits the Landsat revisit time while providing a continuous and temporally dense representation of the land cover dynamics. Two temporal pattern typologies were identified and modeled within the proposed Markov chain architecture: a seasonal and synchrounous behavior which can be associated to the observables of LC classes such as forest and grasses, and a highly asynchronous behaviour, which characterizes the crop observables. The first typology is addressed by introducing time-dependency in state output probabilities, whereas the latter is rendered through a sequence of (sub-class) states interlinked by means of a 'left-right' based model. Such model inherently incorporates crop growth tracking functionalities as an added value. In this paper the methodology has been tailored to Sao Paulo state (Brazil) scenario, showing overall accuracy above 80% on the test sample. A particular emphasis is attributed to the identification of sugarcane plantations, that are indeed responsible for major land use changes.
dc.description5457
dc.description5460
dc.descriptionBE-Basic FAPESP [2013/50943-9]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.descriptionJUL 10-15, 2016
dc.descriptionBeijing, PEOPLES R CHINA
dc.description
dc.languageEnglish
dc.publisherIEEE
dc.publisherNew York
dc.relation2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.rightsfechado
dc.sourceWOS
dc.subjectLand Cover
dc.subjectClassification
dc.subjectChange Detection
dc.subjectLandsat Time-series
dc.subjectHidden Markov Models
dc.subjectCrop Growth Tracking
dc.titleA Hmm-based Approach For Historic And Up-to-date Land Cover Mapping Through Landsat Time-series In The State Of São Paulo, Brazil
dc.typeActas de congresos


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