Open AccessMONITORING OF BRAZILIAN DECIDUOUS SEASONAL FOREST BY REMOTE SENSING
Author(s) -
André Medeiros Rocha,
Marcos Esdras Leite,
Mário Marcos do EspíritoSanto
Publication year - 2020
Publication title -
mercator
Language(s) - English
Resource type - Journals
eISSN - 1984-2201
pISSN - 1676-8329
DOI - 10.4215/rm2020.e19022
Subject(s) - deciduous , geography , vegetation (pathology) , biome , shrubland , deforestation (computer science) , land cover , agroforestry , forestry , land use , resource (disambiguation) , vegetation type , remote sensing , environmental resource management , environmental science , ecology , grassland , ecosystem , medicine , computer network , pathology , computer science , biology , programming language
Among the many characteristics that the Brazilian territory possesses, one precisely excel: the mentioned country hosts the second biggest forest resource of the planet, corresponding for approximately 10% of the total amount of global forest resources. In that scenario, the Seasonally Dry Tropical Forests (SDTF) perform the second less expressive forest type in Brazil, being situated mostly in non-forested biomes, such as Savannas and Scrublands. Thus, its conservation must rely on its correct identification, which becomes difficult because the SDTF areas are generally classified as other vegetation types. Therefore, the present study aimed to perform the land cover-land use monitoring for the years of 2007 and 2016 of the continuous area North of Minas Gerais - South Piauí, with the purpose of evaluating the current situation of Brazilian SDTFs and assessing the main drivers that affect its deforestation and natural regeneration. As a result, the study verified that the significant increase in crop areas and spatial mobility of parturelands contributed decisively for the changes presented by vegetation formations. HOWEVER, such drivers played differentiated roles in losses/gains. Especially, it was concluded that the changes in which deciduous forests have undergone were explained particularly by pasture. The other types of vegetation were also impacted by this class, but with a more incisive participation of the crops. Key-words: Mapping, Deciduous Forests, Remote Sensing, GIS.