Multi-Temporal analysis of remotely sensed information using wavelets

Autores
Campos, Alfredo Nicolas; Di Bella, Carlos Marcelo
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification.
Instituto de Clima y Agua
Fil: Campos, Alfredo Nicolas. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Departamento de Electrónica; Arentina
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina
Fuente
Journal of geographic information system 4 (4) :ID: 22158. (2012)
Materia
Land Use
Vegetation
Remote Sensing
Moderate Resolution Imaging Spectroradiometer
Land Cover Change
Utilización de la Tierra
Vegetación
Teledetección
Espectrorradiómetro de Imágenes de Resolución Moderada
Alteración de la Cubierta Vegetal
Wavelet Transform
MODIS NDVI Series
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/4478
Acceder
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oai_identifier_str oai:localhost:20.500.12123/4478
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spelling Multi-Temporal analysis of remotely sensed information using waveletsCampos, Alfredo NicolasDi Bella, Carlos MarceloLand UseVegetationRemote SensingModerate Resolution Imaging SpectroradiometerLand Cover ChangeUtilización de la TierraVegetaciónTeledetecciónEspectrorradiómetro de Imágenes de Resolución ModeradaAlteración de la Cubierta VegetalWavelet TransformMODIS NDVI SeriesLand cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification.Instituto de Clima y AguaFil: Campos, Alfredo Nicolas. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Departamento de Electrónica; ArentinaFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaScientific Research Publishing2019-02-20T18:43:36Z2019-02-20T18:43:36Z2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/4478https://file.scirp.org/Html/11-8401156_22158.htm2151-19502151-1969 (Online)10.4236/jgis.2012.44044Journal of geographic information system 4 (4) :ID: 22158. (2012)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-09-29T13:44:34Zoai:localhost:20.500.12123/4478instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-09-29 13:44:35.086INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Multi-Temporal analysis of remotely sensed information using wavelets
title Multi-Temporal analysis of remotely sensed information using wavelets
spellingShingle Multi-Temporal analysis of remotely sensed information using wavelets
Campos, Alfredo Nicolas
Land Use
Vegetation
Remote Sensing
Moderate Resolution Imaging Spectroradiometer
Land Cover Change
Utilización de la Tierra
Vegetación
Teledetección
Espectrorradiómetro de Imágenes de Resolución Moderada
Alteración de la Cubierta Vegetal
Wavelet Transform
MODIS NDVI Series
title_short Multi-Temporal analysis of remotely sensed information using wavelets
title_full Multi-Temporal analysis of remotely sensed information using wavelets
title_fullStr Multi-Temporal analysis of remotely sensed information using wavelets
title_full_unstemmed Multi-Temporal analysis of remotely sensed information using wavelets
title_sort Multi-Temporal analysis of remotely sensed information using wavelets
dc.creator.none.fl_str_mv Campos, Alfredo Nicolas
Di Bella, Carlos Marcelo
author Campos, Alfredo Nicolas
author_facet Campos, Alfredo Nicolas
Di Bella, Carlos Marcelo
author_role author
author2 Di Bella, Carlos Marcelo
author2_role author
dc.subject.none.fl_str_mv Land Use
Vegetation
Remote Sensing
Moderate Resolution Imaging Spectroradiometer
Land Cover Change
Utilización de la Tierra
Vegetación
Teledetección
Espectrorradiómetro de Imágenes de Resolución Moderada
Alteración de la Cubierta Vegetal
Wavelet Transform
MODIS NDVI Series
topic Land Use
Vegetation
Remote Sensing
Moderate Resolution Imaging Spectroradiometer
Land Cover Change
Utilización de la Tierra
Vegetación
Teledetección
Espectrorradiómetro de Imágenes de Resolución Moderada
Alteración de la Cubierta Vegetal
Wavelet Transform
MODIS NDVI Series
dc.description.none.fl_txt_mv Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification.
Instituto de Clima y Agua
Fil: Campos, Alfredo Nicolas. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Buenos Aires. Departamento de Electrónica; Arentina
Fil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentina
description Land cover changes (LCC) are an important component of Global Change. LCC can be described not only by its occur-rence, but also by the land cover replacement, causal agent and change duration or recuperation. Nowadays, remote sensing offers the opportunity to assemble reliable time series, however this fails to make a characterization of LCC since the series represents dynamics due to the combination of several processes occurring simultaneously. In this arti-cle we proposed an approach to the study of LCC using wavelet transform (WT) and MODIS vegetation time series. Through this work we have demonstrated the capacity of this tool in order to recognize and characterize four different LLC documented in scientific publications, presenting the results divided in frequency scales as interannual, seasonal and rapid changes. The information decomposed in frequency allows the interpretation of each involved process with-out the interference of others. The uses of WT in an image time series give us the possibility of joining temporal and spatial dimension in a single raster. Layers generated with WT might be used to pattern recognition in LCC and to im-prove an image classification.
publishDate 2012
dc.date.none.fl_str_mv 2012
2019-02-20T18:43:36Z
2019-02-20T18:43:36Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/4478
https://file.scirp.org/Html/11-8401156_22158.htm
2151-1950
2151-1969 (Online)
10.4236/jgis.2012.44044
url http://hdl.handle.net/20.500.12123/4478
https://file.scirp.org/Html/11-8401156_22158.htm
identifier_str_mv 2151-1950
2151-1969 (Online)
10.4236/jgis.2012.44044
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Scientific Research Publishing
publisher.none.fl_str_mv Scientific Research Publishing
dc.source.none.fl_str_mv Journal of geographic information system 4 (4) :ID: 22158. (2012)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 12.559606

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