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Past and future assessment of vegetation activity for the state of Amazonas-Brazil

R. S. Vilanova,R. Delgado,E. L. da Silva Abel,P. Teodoro,C. A. S. Silva Junior,H. S. Wanderley,G. F. Capristo-Silva

Published 2020 in Remote Sensing Applications: Society and Environment

ABSTRACT

Abstract: Tropical forest activities have been sensitive to climate change, making monitoring of these forests increasingly necessary. In the context of climate change, the Legal Amazon is a region of great global importance. In this study, the variables air temperature, rainfall, soil moisture, fire foci, land surface temperature, Normalized Difference Vegetation Index (NDVI) and the Vegetation Health Index (VHI) from 2001 to 2018 on a monthly scale were used to describe the past condition and future health of vegetation in the state of Amazonas-Brazil. The Autoregressive Integrated Moving Average (ARIMA) model was applied to the VHI series and its capacity was analyzed in the forecast of the observed (2001–2018) and future (2019–2030) time series. The results showed a decrease in VHI values for the period considered dry in the Amazon. Of each year, 2015 registered the lowest average of 30.73%, placing it in the mild drought class. However, in some months, such as September, October and November, which presented VHI values of 8.42%, 6.08% and 9.47% respectively, indicated extreme droughts. For the Mann-Kendall analysis, the variables soil moisture and air temperature showed a negative and positive trend with significant values for most months. The Heatmap clustering based on the Euclidean distance revealed that the most influential variables for the region were VHI and air temperature. The generated and validated ARIMA modeling well simulated VHI, presenting an average of the willmott coefficient (d) of 1 for the study period. The future 12-year projection (2019–2030) of VHI for the state of Amazonas showed that the model able to represent the seasonality of the series.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Remote Sensing Applications: Society and Environment

  • Publication date

    Unknown publication date

  • Fields of study

    Environmental Science

  • Identifiers
    DOI 10.1016/j.rsase.2019.100278
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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