LAUSR

The forest dynamics are usually explained by the precipitation and temperature through fixed effects models using ordinary least squares and geographically weighted regression methods. However, forest dynamics were found insufficiently explained by meteorological factors as the fixed effects models were not designed to account for random effects. In this study, we utilized three types of forests located in the Gulf of Mexico Coast region, including softwood, hardwood, and mixed forests to investigate the underlying forest dynamics to meteorological variations by incorporating random effects into fixed effects models. Four types of linear mixed effects models (LMMs) were developed for regressing the normalized difference of vegetation index (NDVI) against two explanatory variables: precipitation and temperature. By assuming that the intercept and slope parameters estimated from LMMs would vary randomly, we intended to explore if the amount of variation in the NDVI variables could be reduced by the use of random effects variables. The results suggested that the random intercept and random slope model fitted the data better than the random intercept model with higher R2, lower Akaike information criterion, and Bayesian information criterion values. The R2 value indicated that the explanatory power of the LMM varies between forest types. Moreover, this study revealed that a linear mixed effects model could significantly reduce the unexplained variance by introducing random effects variables, and forest dynamics is a synthetic result of the mixed effects of temperature and fixed effects of precipitation.