LAUSR

Abstract Climate change due to global warming has significant impacts on the Atlantic forest biome. Many forest fires are caused by extreme drought events, which present an uncertain future for their vegetation, and their associated risks are sensitive mainly at the local scale. In this context, the present study evaluated and correlated the normalized multi-band drought index (NMDI) with biophysical variables in the monthly period from 2001 to 2019 in 12 land cover classes. The Auto Regressive Integrated Moving Average (ARIMA) model was applied to the NMDI series and its ability to simulate data from the observed time series (2001–2019) and the future (2020–2030). The results showed a decrease in the NMDI values for the period considered dry in the State of Rio de Janeiro (SRJ), mainly for the classes of pasture and savannah, which presented greater sources of heat. The non-parametric analysis was performed using the Mann-Kendall test for all biophysical variables. The variables soil moisture and NMDI showed negative trends (Z = −1.68 and Z = −0.76), whereas gross primary productivity (GPP) showed a positive trend (Z = 1.89). The generated and validated ARIMA modeling simulated NMDI well and the Willmott coefficient (d) was approximately 1.0 for the study period. The 10-year projection (2020–2030) from NMDI for SRJ pointed to a change in class from wet to dry in the mixed forest area (D) and cultivated land (L). The ARIMA model can represent the drought index in the seasonality of the series for the different classes of vegetation. These results showed that the applicability of NMDI in predicting fire risk conditions would be adequate in other areas of tropical forests, standing out mainly for being a drought index that can be used in future modeling.