A review of the use of Sentinel-2 imagery for global forest cover monitoring
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The objective of this work was to carry out a systematic review of the use of Sentinel-2 images for monitoring forest cover at a global level, for which the protocol proposed by Prisma 2009 was used. The search for scientific articles published between 2015 and 2021 was carried out in the databases: Scopus and Science Direct, analyzing a total of 65 articles that detail the different types of classifiers used to process the S-2 images, the thematic accuracy achieved in the cartography, as well as the increase, maintenance or decline of forests and its main causes worldwide. As results, it was found that Random Forest (RF) is the most used classifier for the digital processing of S-2 images, which in most cases achieves a thematic accuracy greater than 85%. In multi-temporal work, it has been found that forest cover in South America and Africa has been decreased by activities such as agriculture and livestock. While, in some Asian countries, forest cover has increased as a result of the implementation of reforestation and community forest management programs. Therefore, the results suggest that Sentinel-2 images have enormous potential to carry out continuous and systematic monitoring of forest loss or gain across the planet.
Ronald Hugo Puerta Tuesta, Universidad Nacional Agraria de la Selva, Tingo María, Perú
https://orcid.org/0000-0001-5777-7855
José Alberto Iannacone Oliver, Universidad Nacional Federico Villarreal, Lima, Perú.
https://orcid.org/0000-0003-3699-4732
Manuel Emilio Reategui Inga, Universidad Nacional Intercultural de la Selva Central Juan Santos Atahualpa, Perú.
https://orcid.org/0000-0002-5417-6509
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Accepted 2023-09-28
Published 2023-06-26
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