Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning

[EN] Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essent...

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Autores: Pacheco-Prado, Diego, Bravo-López, Esteban, Ruiz Fernández, Luis Ángel|||0000-0003-0073-7259
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/220067
Acceso en línea:https://riunet.upv.es/handle/10251/220067
Access Level:acceso abierto
Palabra clave:Andes Mountains
Google Earth Engine
NICFI
Random forest
RFE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica
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repository_id_str
spelling Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine LearningPacheco-Prado, DiegoBravo-López, EstebanRuiz Fernández, Luis Ángel|||0000-0003-0073-7259Andes MountainsGoogle Earth EngineNICFIRandom forestRFE06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica[EN] Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1¿SC6, combining 5 m resolution data, and SC7¿SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.This research was funded by UNIVERSIDAD DEL AZUAY in the context of investigation of projects 2020-0125 and 2022-0159 denominated Caracterización de unidades forestales a partir de datos espectrales, espaciales y de relieve a distintas escalas. Aplicación a los bosques andinos del cantón Cuenca (Ecuador) . Fase 2 y 4.MDPI AGDepartamento de Ingeniería Cartográfica Geodesia y FotogrametríaEscuela Técnica Superior de Ingeniería Geodésica, Cartográfica y TopográficaGrupo de Cartografía Geoambiental y TeledetecciónUniversidad del Azuay, EcuadorRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/220067reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengUniversidad del Azuay, Ecuador Universidad del Azuay, Ecuador 2020-0125 Caracterizacion de unidades forestales a partir de datos espectrales, espaciales y de relieve a distintas escalas. Aplicacion a los bosques andinos del canton Cuenca (Ecuador), Fase 2Universidad del Azuay, Ecuador Universidad del Azuay, Ecuador 2022-0159open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2200672026-06-13T07:49:27Z
dc.title.none.fl_str_mv Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
title Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
spellingShingle Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
Pacheco-Prado, Diego
Andes Mountains
Google Earth Engine
NICFI
Random forest
RFE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica
title_short Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
title_full Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
title_fullStr Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
title_full_unstemmed Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
title_sort Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning
dc.creator.none.fl_str_mv Pacheco-Prado, Diego
Bravo-López, Esteban
Ruiz Fernández, Luis Ángel|||0000-0003-0073-7259
author Pacheco-Prado, Diego
author_facet Pacheco-Prado, Diego
Bravo-López, Esteban
Ruiz Fernández, Luis Ángel|||0000-0003-0073-7259
author_role author
author2 Bravo-López, Esteban
Ruiz Fernández, Luis Ángel|||0000-0003-0073-7259
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría
Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica
Grupo de Cartografía Geoambiental y Teledetección
Universidad del Azuay, Ecuador
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Andes Mountains
Google Earth Engine
NICFI
Random forest
RFE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica
topic Andes Mountains
Google Earth Engine
NICFI
Random forest
RFE
06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos
15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica
description [EN] Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1¿SC6, combining 5 m resolution data, and SC7¿SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/220067
url https://riunet.upv.es/handle/10251/220067
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Universidad del Azuay, Ecuador Universidad del Azuay, Ecuador 2020-0125 Caracterizacion de unidades forestales a partir de datos espectrales, espaciales y de relieve a distintas escalas. Aplicacion a los bosques andinos del canton Cuenca (Ecuador), Fase 2
Universidad del Azuay, Ecuador Universidad del Azuay, Ecuador 2022-0159
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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