Monitoring the change of tree coverage with remote sensors and Deep Learning

Arboreal coverage plays an important role both for the environment and for humans, fulfilling a fundamental role in the climate regulation due to the ability of trees to absorb carbon dioxide and other greenhouse gases, helping to reduce global heating and climate change. In addition, from the point of view of biodiversity they provide the ideal habitat for plant and animal species. Although several international organizations such as the United Nations Framework Convention on Climate Change (CMNUCC) promote the sustainable management of forests as a strategy to mitigate climate change, and that the scientific community has evidenced the importance of tree cover through thousands of studies in recent decades; The FAO report between 2015 and 2020 indicates that there has been a 0.06% decrease in tree coverage worldwide, being an important and latent problem especially in South America; Where deforestation continues, the non -sustainable exploitation of timber resources, forest fires, unplanned urban expansion, mining, etc. In Latin America, a deforestation rate of 2.8 million hectares between 2010 and 2020 (FAO, 2022) is estimated. In this context, the 2030 Agenda for the Sustainable Development of the United Nations Organization (UN)

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