Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach

[EN] Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at unde...

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Autores: Álvarez Taboada, María Flor, Paredes, Claudio, Julián Peláez, Julia
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/18818
Acceso en línea:https://www.mdpi.com/2072-4292/9/9/913
https://hdl.handle.net/10612/18818
Access Level:acceso abierto
Palabra clave:Ingeniería forestal
Topografía
High spatial resolution
Pan-sharpening
Texture
OBIA
Invasive plant
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spelling Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented ApproachÁlvarez Taboada, María FlorParedes, ClaudioJulián Peláez, JuliaIngeniería forestalTopografíaHigh spatial resolutionPan-sharpeningTextureOBIAInvasive plant[EN] Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at understanding and quantification of their location, extent and evolution, namely the monitoring of the phenomenon. In this sense, the techniques and methods of remote sensing can be very useful. The aim of this paper was to identify and quantify the areas covered by the invasive plant Hakea sericea using high spatial resolution images obtained from aerial platforms (Unmanned Aerial Vehicle: UAV/drone) and orbital platforms (WorldView-2: WV2), following an object-oriented image analysis approach. The results showed that both data were suitable. WV2reached user and producer accuracies greater than 93% (Estimate of Kappa (KHAT): 0.95), while the classifications with the UAV orthophotographs obtained accuracies higher than 75% (KHAT: 0.51). The most suitable data to use as input consisted of using all of the multispectral bands that were available for each image. The addition of textural features did not increase the accuracies for the Hakea sericea class, but it did for the general classification using WV2.SIMDPIIngeniería Cartografica, Geodesica y FotogrametriaEscuela de Ingeniería Agraria y Forestal2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www.mdpi.com/2072-4292/9/9/913https://hdl.handle.net/10612/18818reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/188182026-06-24T12:43:27Z
dc.title.none.fl_str_mv Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
title Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
spellingShingle Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
Álvarez Taboada, María Flor
Ingeniería forestal
Topografía
High spatial resolution
Pan-sharpening
Texture
OBIA
Invasive plant
title_short Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
title_full Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
title_fullStr Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
title_full_unstemmed Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
title_sort Mapping of the Invasive Species Hakea sericea Using Unmanned Aerial Vehicle (UAV) and WorldView-2 Imagery and an Object-Oriented Approach
dc.creator.none.fl_str_mv Álvarez Taboada, María Flor
Paredes, Claudio
Julián Peláez, Julia
author Álvarez Taboada, María Flor
author_facet Álvarez Taboada, María Flor
Paredes, Claudio
Julián Peláez, Julia
author_role author
author2 Paredes, Claudio
Julián Peláez, Julia
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Cartografica, Geodesica y Fotogrametria
Escuela de Ingeniería Agraria y Forestal
dc.subject.none.fl_str_mv Ingeniería forestal
Topografía
High spatial resolution
Pan-sharpening
Texture
OBIA
Invasive plant
topic Ingeniería forestal
Topografía
High spatial resolution
Pan-sharpening
Texture
OBIA
Invasive plant
description [EN] Invasive plants are non-native species that establish and spread in their new location, generating a negative impact on the local ecosystem and representing one of the most important causes of the extinction of local species. The first step for the control of invasion should be directed at understanding and quantification of their location, extent and evolution, namely the monitoring of the phenomenon. In this sense, the techniques and methods of remote sensing can be very useful. The aim of this paper was to identify and quantify the areas covered by the invasive plant Hakea sericea using high spatial resolution images obtained from aerial platforms (Unmanned Aerial Vehicle: UAV/drone) and orbital platforms (WorldView-2: WV2), following an object-oriented image analysis approach. The results showed that both data were suitable. WV2reached user and producer accuracies greater than 93% (Estimate of Kappa (KHAT): 0.95), while the classifications with the UAV orthophotographs obtained accuracies higher than 75% (KHAT: 0.51). The most suitable data to use as input consisted of using all of the multispectral bands that were available for each image. The addition of textural features did not increase the accuracies for the Hakea sericea class, but it did for the general classification using WV2.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.mdpi.com/2072-4292/9/9/913
https://hdl.handle.net/10612/18818
url https://www.mdpi.com/2072-4292/9/9/913
https://hdl.handle.net/10612/18818
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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