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.

由于全球变暖导致的气候变化对大西洋森林生物群系产生了重大影响。许多森林大火是由极端干旱事件引起的，这给他们的植被带来了不确定的未来，其相关风险主要在当地范围内敏感。在这种情况下，本研究评估了12个土地覆盖类别中2001年至2019年每月的标准化多波段干旱指数（NMDI）与生物物理变量之间的关系。将自动回归综合移动平均值（ARIMA）模型应用于NMDI序列，该模型具有对观察到的时间序列（2001-2019）和未来的时间（2020-2030）的数据进行仿真的能力。结果表明，在里约热内卢州（SRJ）认为干旱的时期，NMDI值有所下降，主要是牧场和热带稀树草原，呈现出更多的热源。使用Mann-Kendall检验对所有生物物理变量进行非参数分析。土壤水分和NMDI变量显示为负趋势（Z = -1.68和Z = -0.76），而总初级生产力（GPP）显示为正趋势（Z = 1.89）。生成并验证的ARIMA建模很好地模拟了NMDI，在研究期间，Willmott系数（d）约为1.0。NMDI对SRJ的10年预测（2020-2030年）指出，混合林区（D）和耕地（L）的类别从湿性变为干性。ARIMA模型可以代表不同类别植被的季节季节性干旱指数。