"Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"

"Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2...

Descripción completa

Detalles Bibliográficos
Autores Principales: "Clerici, Nicola, Calderón, Cesar Augusto Valbuena, Posada, Juan Manuel"
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: Taylor and Francis Ltd. 2017
Materias:
Acceso en línea:https://repository.urosario.edu.co/handle/10336/22391
https://doi.org/10.1080/17445647.2017.1372316
id ir-10336-22391
recordtype dspace
spelling ir-10336-223912020-06-03T22:15:03Z "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia" "Clerici, Nicola Calderón, Cesar Augusto Valbuena Posada, Juan Manuel" Colombia Data fusion Land cover mapping Segmentation Sentinel-1 Sentinel-2 "Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the Lower Magdalena region, Colombia. Data pre-processing was carried out using the European Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLU classification was performed following an object-based and spectral classification approach, exploiting also vegetation indices. A comparison of classification performance using three commonly used classification algorithms was performed. The radar and visible-near infrared integrated dataset classified with a Support Vector Machine algorithm produce the most accurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappa coefficient of 0.86. The proposed mapping approach has the main advantages of combining the all-weather capability of the radar sensor, spectrally rich information in the visible-near infrared spectrum, with the short revisit period of both satellites. The mapping results represent an important step toward future tasks of aboveground biomass and carbon estimation in the region. © 2017 The Author(s)." 2017 2020-05-25T23:56:18Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 17445647 https://repository.urosario.edu.co/handle/10336/22391 https://doi.org/10.1080/17445647.2017.1372316 eng info:eu-repo/semantics/openAccess application/pdf Taylor and Francis Ltd. instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Colombia
Data fusion
Land cover mapping
Segmentation
Sentinel-1
Sentinel-2
spellingShingle Colombia
Data fusion
Land cover mapping
Segmentation
Sentinel-1
Sentinel-2
"Clerici, Nicola
Calderón, Cesar Augusto Valbuena
Posada, Juan Manuel"
"Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
description "Land cover–land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the Lower Magdalena region, Colombia. Data pre-processing was carried out using the European Space Agency’s Sentinel Application Platform and the SEN2COR toolboxes. LCLU classification was performed following an object-based and spectral classification approach, exploiting also vegetation indices. A comparison of classification performance using three commonly used classification algorithms was performed. The radar and visible-near infrared integrated dataset classified with a Support Vector Machine algorithm produce the most accurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappa coefficient of 0.86. The proposed mapping approach has the main advantages of combining the all-weather capability of the radar sensor, spectrally rich information in the visible-near infrared spectrum, with the short revisit period of both satellites. The mapping results represent an important step toward future tasks of aboveground biomass and carbon estimation in the region. © 2017 The Author(s)."
format Artículo (Article)
author "Clerici, Nicola
Calderón, Cesar Augusto Valbuena
Posada, Juan Manuel"
author_facet "Clerici, Nicola
Calderón, Cesar Augusto Valbuena
Posada, Juan Manuel"
author_sort "Clerici, Nicola
title "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
title_short "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
title_full "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
title_fullStr "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
title_full_unstemmed "Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia"
title_sort "fusion of sentinel-1a and sentinel-2a data for land cover mapping: a case study in the lower magdalena region, colombia"
publisher Taylor and Francis Ltd.
publishDate 2017
url https://repository.urosario.edu.co/handle/10336/22391
https://doi.org/10.1080/17445647.2017.1372316
_version_ 1669098327696736256
score 11,828437