FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU

Descripción del Articulo

The loss of forests is one of the main environmental problems in Peru and in various parts of the world. The present work aims to calculate the forest cover by 2021 within the Leoncio Prado province, located in the Huánuco region, Peru. Sentinel-2 images were used, which were classified on the Googl...

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Detalles Bibliográficos
Autores: Puerta Tuesta, Ronald, Fajardo-Gamarra, Raí
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad Nacional Federico Villarreal
Repositorio:Revistas - Universidad Nacional Federico Villarreal
Lenguaje:español
OAI Identifier:oai:ojs2.revistas.unfv.edu.pe:article/1319
Enlace del recurso:https://revistas.unfv.edu.pe/rtb/article/view/1319
Nivel de acceso:acceso abierto
Materia:Artificial intelligence
deforestation
Google Earth Engine
Random Forest
Sentinel-2
Deforestación
inteligencia artificial
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spelling FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERUCOBERTURA BOSCOSA AL 2021 EN LA PROVINCIA LEONCIO PRADO, PERÚPuerta Tuesta, Ronald Fajardo-Gamarra, Raí Artificial intelligencedeforestationGoogle Earth EngineRandom ForestSentinel-2DeforestaciónGoogle Earth Engineinteligencia artificialRandom ForestSentinel-2The loss of forests is one of the main environmental problems in Peru and in various parts of the world. The present work aims to calculate the forest cover by 2021 within the Leoncio Prado province, located in the Huánuco region, Peru. Sentinel-2 images were used, which were classified on the Google Earth Engine platform using the Random Forest artificial intelligence algorithm. Likewise, the thematic accuracy of the resulting classification was evaluated using high spatial resolution Planet images. As results, it was found that the study area includes 349,811.47 ha, which represents more than 70% of the total area, while the degraded and intervened areas add up to a total of 131,392.12 ha, which come mainly from the change in use of forest to agricultural areas. Regarding the metrics that evaluate the thematic accuracy of the classification, a value of 0.77 was found in the Kappa Index and 89.14% global accuracy. Therefore, it is concluded that the forest cover is the most predominant in the Leoncio Prado province, which was classified with high thematic accuracy.La pérdida de bosques es uno de los principales problemas ambientales en el Perú y en diversas partes del mundo, en ese sentido el presente trabajo tiene por objetivo calcular la cobertura boscosa al 2021 dentro de la provincia Leoncio Prado, ubicada en la región Huánuco, Perú. Para ello, se utilizó las imágenes Sentinel-2 que fueron clasificadas en la plataforma Google Earth Engine utilizando el algoritmo de inteligencia artificial Random Forest. Asimismo, se evaluó la exactitud temática de la clasificación resultante utilizando imágenes de alta resolución espacial Planet. Como resultados se encontró que la zona de estudio presenta 349 811,47 ha lo que representa más del 70% del área total, mientras que las áreas degradas e intervenidas suman un total de 131 392,12 ha que proceden principalmente del cambio de uso de bosque a zonas agrícolas. Respecto a las métricas que evalúan la exactitud temática de la clasificación, se encontró un valor de 0,77 en el Índice de Kappa y 89,14% de exactitud global. Por lo que se concluye que la cobertura boscosa es la de mayor predominancia en la provincia Leoncio Prado, la cual fue clasificada con alta exactitud temática.Universidad Nacional Federico Villarreal. Facultad de Ciencias Naturales y Matemática. Escuela Profesional de Biología2022-02-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/epub+zipapplication/pdfhttps://revistas.unfv.edu.pe/rtb/article/view/1319The Biologist; Vol. 20 No. 1 (2022): The Biologist (Lima); 93-101The Biologist; Vol. 20 Núm. 1 (2022): The Biologist (Lima); 93-1011994-90731816-0719reponame:Revistas - Universidad Nacional Federico Villarrealinstname:Universidad Nacional Federico Villarrealinstacron:UNFVspahttps://revistas.unfv.edu.pe/rtb/article/view/1319/1573https://revistas.unfv.edu.pe/rtb/article/view/1319/1574https://revistas.unfv.edu.pe/rtb/article/view/1319/1421https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessoai:ojs2.revistas.unfv.edu.pe:article/13192023-01-18T17:40:04Z
dc.title.none.fl_str_mv FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
COBERTURA BOSCOSA AL 2021 EN LA PROVINCIA LEONCIO PRADO, PERÚ
title FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
spellingShingle FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
Puerta Tuesta, Ronald
Artificial intelligence
deforestation
Google Earth Engine
Random Forest
Sentinel-2
Deforestación
Google Earth Engine
inteligencia artificial
Random Forest
Sentinel-2
title_short FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
title_full FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
title_fullStr FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
title_full_unstemmed FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
title_sort FOREST COVER BY 2021 IN THE LEONCIO PRADO PROVINCE, PERU
dc.creator.none.fl_str_mv Puerta Tuesta, Ronald
Fajardo-Gamarra, Raí
author Puerta Tuesta, Ronald
author_facet Puerta Tuesta, Ronald
Fajardo-Gamarra, Raí
author_role author
author2 Fajardo-Gamarra, Raí
author2_role author
dc.subject.none.fl_str_mv Artificial intelligence
deforestation
Google Earth Engine
Random Forest
Sentinel-2
Deforestación
Google Earth Engine
inteligencia artificial
Random Forest
Sentinel-2
topic Artificial intelligence
deforestation
Google Earth Engine
Random Forest
Sentinel-2
Deforestación
Google Earth Engine
inteligencia artificial
Random Forest
Sentinel-2
description The loss of forests is one of the main environmental problems in Peru and in various parts of the world. The present work aims to calculate the forest cover by 2021 within the Leoncio Prado province, located in the Huánuco region, Peru. Sentinel-2 images were used, which were classified on the Google Earth Engine platform using the Random Forest artificial intelligence algorithm. Likewise, the thematic accuracy of the resulting classification was evaluated using high spatial resolution Planet images. As results, it was found that the study area includes 349,811.47 ha, which represents more than 70% of the total area, while the degraded and intervened areas add up to a total of 131,392.12 ha, which come mainly from the change in use of forest to agricultural areas. Regarding the metrics that evaluate the thematic accuracy of the classification, a value of 0.77 was found in the Kappa Index and 89.14% global accuracy. Therefore, it is concluded that the forest cover is the most predominant in the Leoncio Prado province, which was classified with high thematic accuracy.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-06
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://revistas.unfv.edu.pe/rtb/article/view/1319
url https://revistas.unfv.edu.pe/rtb/article/view/1319
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.unfv.edu.pe/rtb/article/view/1319/1573
https://revistas.unfv.edu.pe/rtb/article/view/1319/1574
https://revistas.unfv.edu.pe/rtb/article/view/1319/1421
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
application/epub+zip
application/pdf
dc.publisher.none.fl_str_mv Universidad Nacional Federico Villarreal. Facultad de Ciencias Naturales y Matemática. Escuela Profesional de Biología
publisher.none.fl_str_mv Universidad Nacional Federico Villarreal. Facultad de Ciencias Naturales y Matemática. Escuela Profesional de Biología
dc.source.none.fl_str_mv The Biologist; Vol. 20 No. 1 (2022): The Biologist (Lima); 93-101
The Biologist; Vol. 20 Núm. 1 (2022): The Biologist (Lima); 93-101
1994-9073
1816-0719
reponame:Revistas - Universidad Nacional Federico Villarreal
instname:Universidad Nacional Federico Villarreal
instacron:UNFV
instname_str Universidad Nacional Federico Villarreal
instacron_str UNFV
institution UNFV
reponame_str Revistas - Universidad Nacional Federico Villarreal
collection Revistas - Universidad Nacional Federico Villarreal
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 13.7211075
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