Fig 1.
Methodological approach applied to quantify the relationship between forest change and local climate change.
This approach was applied worldwide (I) to compare close cells sharing a similar regional climate (II and III). Each window had 15 cells of 0.05° resolution (5 longitude x 3 latitude cells). To facilitate visualization, a single window is depicted (red grid in III). In each window, data on local forest cover and local land surface temperature (LST) were compared by subtracting values in different years (2010–2000 for forest cover, 2011–2001 for climate; A and B), generating a georeferenced matrix of forest change and another of LST change (C). From the resulting matrices, two cells were chosen: a “focal” cell, with absolute forest change > 15%, and a “reference” cell, with forest change < 5%. Finally, we calculated the standardized forest change (ΔF) and the standardized LST change (ΔLST) (D), by subtracting the focal cell values by the reference cell values.
Fig 2.
Scheme depicting presumed relationships between forest cover and climatic variables (albedo and evapotranspiration).
These climatic variables are regarded as the main drivers of land surface temperature change. Heat is more readily absorbed by vegetation, soil and water bodies (lower albedo values), than surfaces with snow and ice (higher albedo values). By estimating condensed water from substrate evaporation and vegetation transpiration, we quantify the available water in the air (evapotranspiration). At the same time, heat absorbance induces greater water condensation by both soil and vegetation.
Fig 3.
Effects of forest change on local climate change.
(A) Change in annual land surface temperature (LST). (B) Change in evapotranspiration. (C) Change in albedo. Each cell in the plots represents decadal changes in annual means of climatic variables (2011–2001) following decadal forest changes (2010–2000), calculated originally for 0.05 x 0.05° cells, and grouped into bins of 5° latitude and 10% forest change to facilitate visualization. Note that the number of bins with good-quality data is higher for ET than for albedo.
Fig 4.
Comparative effects of deforestation (red) and forestation (blue) on annual land surface temperature (LST) change.
This analysis considered only cells with ~50% (40–60%) of decadal forest change (2010–2000). Positive (negative) values indicate a warming (cooling) effect of forest change. Bars indicate averages and 95% confidence intervals. The number of cell pairs analyzed (left to right) was 40, 15, 694, 268, 861 and 25.
Fig 5.
Path diagrams showing the direct and indirect effects of forest change on annual land surface temperature (LST).
Indirect effects are assumed to be caused by changes in albedo and evapotranspiration (ET). Arrows are scaled in proportion to the absolute standardized path coefficients (numbers). Red and blue arrows indicate negative and positive coefficients, respectively. Coefficients for non-significant paths (p > 0.05) are not shown (NS). R2 values indicate the coefficient of determination of the component models for each response variable.
Fig 6.
Predicted future changes in annual land surface temperature (LST) for Brazil from 2010 to 2050.
Analyses were based on changing forest cover predicted by two land use change scenarios: (a) Business-as-usual (BAU) scenario, in which land use follows the same pattern observed in 2000; (b) Forest Code (FC) scenario, in which the 2012 Brazilian environmental legislation (“Forest Code”) is fully implemented. Brazilian biomes: AM = Amazon; CE = Cerrado; CA = Caatinga; AF = Atlantic Forest; PM = Pampa; PN = Pantanal.