Analysis of environmental variables and deforestation in the amazon using logistical regression models.

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions.

Imputation of precipitation data in northeast Brazil.

This article evaluates four statistical methods of multiple imputation to fill in the missing data of daily precipitation in Northeast Brazil (NEB). We used a daily database collected by 94 rain gauges distributed in NEB from January 1, 1986 to December 31, 2015. The methods were: random sampling from the observed values; predictive mean matching, Bayesian linear regression; and bootstrap expectation maximization algorithm (BootEm). To compare these methods, missing data from the original series were initially excluded. The next step was to create three scenarios for each method, in which 10\%, 20\% and 30\% of the data were removed at random. The BootEM method presented the best statistical results. With the average bias between the complete series and the imputed series values ranging between -0.91 and 1.30 mm/day. The values of the Pearson correlation ranging between 0.96, 0.91 and 0.86 respectively for 10\%, 20\% and 30\% missing data. We conclude that this is an adequate method for the reconstruction of historical precipitation data in NEB.