The past, present and future of vegetation in the Central Atlantic Forest Corridor, Brazil

R. O. Santana,R. Delgado,A. Schiavetti

Published 2020 in Remote Sensing Applications: Society and Environment

ABSTRACT

Abstract The Brazilian Atlantic Forest is one of 34 biodiversity hotspots worldwide and is considered one of the five most important for conservation. In this context, the present study examines meteorological and biophysical variables from 2001 to 2016, on a monthly scale, to describe the past, present and future of vegetation in the Central Atlantic Forest Corridor for different land use and land cover categories. The Autoregressive Integrated Moving Average model was applied to the Enhanced Vegetation Index series and its ability to simulate data from the observed (2001–2016) and future (2020–2030) time series was analyzed. The results showed an increase in fire foci in extreme climatic El Nino and La Nina years in the land use and land cover categories of Agriculture and Forest. For the Mann-Kendall analysis, soil moisture and the Enhanced Vegetation Index showed a negative trend in all land use categories and fire foci showed a positive trend, especially in the Agriculture category. The Heatmap showed that evapotranspiration (−0.73) and soil moisture (0.79) were the variables with the highest correlations with vegetation. The results of the validation of the future modeling were satisfactory with an average R2 of 0.53 for Farming, 0.64 for Forest and 0.76 for Others. The Mann-Kendall test for future data showed a significant negative trend for Forest and Others (−1.99) in all simulated future years. The eminent risks due to agricultural expansion and the dismantling of environmental inspection bodies in Brazil increase the need for studies that can assist in the management of areas such as the Atlantic Forest.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Remote Sensing Applications: Society and Environment

  • Publication date

    2020-11-01

  • Fields of study

    Geography, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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