Amazon forests potentially have strong influence on global carbon dynamics. Temporal change of woody biomass in these forests determines whether they are carbon sinks, sources or neutral. Woody biomass change can be attributed to two sources: 1) disturbance such as tree mortality and regrowth and 2) exogenous factors such as elevated atmospheric carbon input. Linking woody biomass change with the change of forest gap phases, an indicator of disturbance, can help us separate the two sources and improve carbon dynamic models for Amazon forests. Using ground tree census data and ground LiDAR data from a 20 ha inventory plot in Tapajós National Forest near Santarém, Pará, we partitioned observed increases in aboveground biomass between those attributed to changes in gap phase (response to disturbance) and to exogenous factors (including climate change factors).
We found that observed increases are due both to gap phase processes (0.87-0.89% per year) and to exogenous factors (1.14-1.16% per year). Moreover, gap phase changes estimated from ground LiDAR data gave comparable results as those estimated from ground tree census data. Thus, we should be able to extrapolate this analysis to large spatial scale using airborne LiDAR data. We argue that given carefully developed methods that provide reliable estimate of gap phases, it is possible to include large-scale gap phase analysis into studies of forest biomass dynamics. Such analysis can ultimately improve models of global carbon dynamics.