SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report

Imagery provided by the NOAA Advanced Very High Resolution Radiometer (A VHRR) has proved to be very important for studying the dynamics of the Earth surface at global and regional scales. The success of the use of A VHRR imagery for global and continental studies spurred its application to finer sc...

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Detalles Bibliográficos
Autores: Lobo, Agustín, Pineda, N., Dédieu, G., Fernandez-Turiel, J. L.
Tipo de recurso: otro
Fecha de publicación:1997
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/176311
Acceso en línea:http://hdl.handle.net/10261/176311
Access Level:acceso abierto
Palabra clave:VEGETATION, satellite, segmentation, forest, shrubland
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spelling SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch ReportLobo, AgustínPineda, N.Dédieu, G.Fernandez-Turiel, J. L.VEGETATION, satellite, segmentation, forest, shrublandImagery provided by the NOAA Advanced Very High Resolution Radiometer (A VHRR) has proved to be very important for studying the dynamics of the Earth surface at global and regional scales. The success of the use of A VHRR imagery for global and continental studies spurred its application to finer scales. This implies the integration of the low-spatial, high-temporal resolution imagery, such as AVHRR and VEGETATION, and the high-spatial, low-temporal resolution imagery, such as SpotHRVIR or Landsat-TM. In SPATEM we use a segmentation-based classification for a down-scaling of the spatially-coarse multi-temporal imagery, with particular interest on Mediterranean forests and shrublands. In the pre-launch phase we have used AVHRRlkm multi-temporal imagery and a LISS-ID as simulations of, respectively, VEGETATION and HRVIR imagery. We have produced a classification of a Mediterranean forested area by means of segmentation, hierarchical classification and canonical analysis. We have used the classification to select pure AVHRR-lkm temporal signatures of NDVI for some of the classes and found that the ordering from higher to lower values of annual NDVI is coincident with the ordering of the same classes along a greeness axes produced by the canonical analysis of the LISS-ID image. We conclude that consistent differences in temporal NDVI are detectable with A VHRRlkm data for different types of Mediterranean forests. Such differences are likely to be more evident using SPOT-4 VEGETATION and HRVIR data, which improved radiometric and geometric characteristics will allow for an spectral mixture analysis.NoCSIC - Instituto de Ciencias de la Tierra Jaume Almera (ICTJA)Centre National D'Etudes Spatiales (France)0000-0002-4383-799X201920191997info:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_18ghinfo:eu-repo/semantics/reporthttp://hdl.handle.net/10261/176311reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#794/97/CNES/68858/00794/97/CNES/68858/00Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1763112026-05-22T06:33:51Z
dc.title.none.fl_str_mv SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
title SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
spellingShingle SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
Lobo, Agustín
VEGETATION, satellite, segmentation, forest, shrubland
title_short SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
title_full SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
title_fullStr SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
title_full_unstemmed SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
title_sort SPATEM - Spatial-temporal integration of satellite data: An image segmentation approach for the improvement of environmental monitoring and modeling with the VEGETATION instrument - Pre-Launch Report
dc.creator.none.fl_str_mv Lobo, Agustín
Pineda, N.
Dédieu, G.
Fernandez-Turiel, J. L.
author Lobo, Agustín
author_facet Lobo, Agustín
Pineda, N.
Dédieu, G.
Fernandez-Turiel, J. L.
author_role author
author2 Pineda, N.
Dédieu, G.
Fernandez-Turiel, J. L.
author2_role author
author
author
dc.contributor.none.fl_str_mv Centre National D'Etudes Spatiales (France)
0000-0002-4383-799X
dc.subject.none.fl_str_mv VEGETATION, satellite, segmentation, forest, shrubland
topic VEGETATION, satellite, segmentation, forest, shrubland
description Imagery provided by the NOAA Advanced Very High Resolution Radiometer (A VHRR) has proved to be very important for studying the dynamics of the Earth surface at global and regional scales. The success of the use of A VHRR imagery for global and continental studies spurred its application to finer scales. This implies the integration of the low-spatial, high-temporal resolution imagery, such as AVHRR and VEGETATION, and the high-spatial, low-temporal resolution imagery, such as SpotHRVIR or Landsat-TM. In SPATEM we use a segmentation-based classification for a down-scaling of the spatially-coarse multi-temporal imagery, with particular interest on Mediterranean forests and shrublands. In the pre-launch phase we have used AVHRRlkm multi-temporal imagery and a LISS-ID as simulations of, respectively, VEGETATION and HRVIR imagery. We have produced a classification of a Mediterranean forested area by means of segmentation, hierarchical classification and canonical analysis. We have used the classification to select pure AVHRR-lkm temporal signatures of NDVI for some of the classes and found that the ordering from higher to lower values of annual NDVI is coincident with the ordering of the same classes along a greeness axes produced by the canonical analysis of the LISS-ID image. We conclude that consistent differences in temporal NDVI are detectable with A VHRRlkm data for different types of Mediterranean forests. Such differences are likely to be more evident using SPOT-4 VEGETATION and HRVIR data, which improved radiometric and geometric characteristics will allow for an spectral mixture analysis.
publishDate 1997
dc.date.none.fl_str_mv 1997
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/other
http://purl.org/coar/resource_type/c_18gh
dc.type.openaire.fl_str_mv info:eu-repo/semantics/report
format other
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/176311
url http://hdl.handle.net/10261/176311
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
794/97/CNES/68858/00
794/97/CNES/68858/00
No
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv CSIC - Instituto de Ciencias de la Tierra Jaume Almera (ICTJA)
publisher.none.fl_str_mv CSIC - Instituto de Ciencias de la Tierra Jaume Almera (ICTJA)
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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