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Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 14 No. 1 (2024)

DOI
10.11591/ijece.v14i1.pp732-740
ISSN
2722-2578, 2088-8708
Published
2024-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value

Collaboration network of the authors

35 authors · 72 co-authorship links · 36 across different organisations

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  • Universidad Católica de Manizales
  • Universidad Nacional de Colombia
  • Universidad de Caldas
  • Universidad del Cauca
  • Universidad de Manizales
  • Universidad de la Amazonia
  • Nigerian Institute For Oil Palm Reasearch
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nicolás Toro GarcíaUniversidad Nacional de Colombia3
Fredy E. HoyosUniversidad Nacional de Colombia3
Paula Andrea López JimenezUniversidad Católica de Manizales2
LUIS FERNANDO DIAZ CADAVIDUniversidad Nacional de Colombia2
María Nancy Marín OlayaUniversidad Católica de Manizales2
Angie Marcela Galvez ValenciaAffiliation unknown1
Erwin Leandro Lemus RodriguezAffiliation unknown1
Miguel Andres Arango ArgotiAffiliation unknown1
Alejandro RincónUniversidad Católica de Manizales1
Gloria Yaneth Flórez YépesUniversidad Católica de Manizales1
Juan Carlos Granobles ToresUniversidad de Manizales1
Rubén Darío Lara-EscobarUniversidad Católica de Manizales1
José Israel Cardenas-JimenezUniversidad Nacional de Colombia1
Carlos-Augusto González-CorreaUniversidad de Caldas1
Clara Helena González-CorreaUniversidad de Caldas1
Jesús Emilio Pinto LoperaUniversidad de la Amazonia1
Diana Carolina Meneses-CabezasUniversidad Católica de Manizales1
Yuliana Zapata-SernaUniversidad Católica de Manizales1
Jose Manuel Monsalve-TellezUniversidad Católica de Manizales1
Jorge Luís Torres-LeónNigerian Institute For Oil Palm Reasearch1
Christian Javier Pinza-JiménezUniversidad Católica de Manizales1
Astrid Lorena Urbano-CanoUniversidad del Cauca1
Diana Jimena López MesaCorporación Universitaria Comfacauca - Unicomfacauca1
Rosa Elvira Álvarez RoseroUniversidad del Cauca1
William Efrén Chirán-AlpalaUniversidad Católica de Manizales1
Lorena Paola Cárdenas EspinosaUniversidad Católica de Manizales1
Diego Alexander Gómez-MoyaAffiliation unknown1
Laura Valeria Avendaño-GarcíaUniversidad Católica de Manizales1
Luisa Fernanda Cuellar-EscobarUniversidad Católica de Manizales1
Cristian Gómez-GuzmánUniversidad Católica de Manizales1
Erick Salvador Reyes-GalvánUniversidad Católica de Manizales1
Fredy Alexander Bolivar-GomezAffiliation unknown1
Gloria Matilde Gil DuqueUniversidad Católica de Manizales1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia</dc:title><dc:creator>Henao-Céspedes, Vladimir</dc:creator><dc:creator>Garcés-Gómez, Yeison Alberto</dc:creator><dc:description>&lt;jats:p&gt;This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value &amp;amp;lt;0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2024-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v14i1.pp732-740</dc:identifier><dc:identifier>10.11591/ijece.v14i1.pp732-740</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 14 No 1 (2024); 732</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/33234/17131</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>