Carbon stock classification for tropical forests in Brazil: Understanding the effect of stand and climate variables

- A method to classify forests into carbon stock classes (Mg·ha−1) is proposed.
- Classification rules reached nearly 100% of forests correctly classified.
- Classifications in the Atlantic Forest biome was more sensitive to climate than the Savanna biome.
- Climate variables had more positive contributions in absence of stand variables.

Forest ecosystems play an important role in the global carbon cycle and with this there is an increasing need for quantifying carbon at large scales. The aim of this research was to develop a system for classifying tropical forests in Brazil into carbon stock classes, applicable to large areas, emphasizing different sets of stand and climate variables. We used data from forests inventoried in two Brazilian biomes: Atlantic Forest and Savanna. We applied discriminant analysis to generate a classification rule by biome. Three types of variables were used: climatic (mean annual temperature and precipitation, or MAT and MAP), geographical (latitude and longitude), and stand variables (density of trees, mean height or h¯, mean square diameter or dg, and basal area or G). We combined these into three scenarios for analysis: (1) all variables; (2) all variables, except h¯; (3) all variables, except h¯, dg, and G, to determine their contribution to classifying carbon stocks. We also assessed each set of variables in the presence/absence of MAP and MAT, used simultaneously or not. The best classification rules resulted in 83.9% and 98.5% of correct classifications for Atlantic Forest and Savanna biomes, respectively. Stand variables contributed significantly to successful classification; for the Atlantic Forest biome, dg and G contributed from 36% to 42% and h¯ from 2% to 5%, yet for the Savanna biome the gains ranged from 31% to 42% and 6%-9%, respectively. For the climate variables, the simultaneous use of MAT and MAP played an important role in the classification in all cases in the Atlantic Forest biome, contributing up to 9.2% for the classification. In the Savanna biome, we found significant positive gains by the simultaneous use in the absence of h¯, dg, and G, on the other hand, the simultaneous use exerted negative effects when h¯ was used. We concluded that climate variables are most helpful when stand variables are not included in the analysis. In terms of carbon stock variation, the Atlantic Forest biome tended to be more sensitive to both MAT and MAP, whereas the Savanna biome had no significant climatic dependence in the classification. The variable h¯ exerted a greater effect in the Savanna biome than in the Atlantic Forest, however, basal area and mean square diameter were the most important in both biomes.