Data from: Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR
Nguyen, Ha T.; Hutyra, Lucy R.; Hardiman, Brady S.; Raciti, Steve M. (2015), Data from: Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR, Dryad, Dataset, https://doi.org/10.5061/dryad.817h6
Tropical peat swamp forests (PSF) are one of the most carbon dense ecosystems on the globe and are experiencing substantial natural and anthropogenic disturbances. In this study we combined direct field sampling and airborne LiDAR to empirically quantify forest structure and aboveground live biomass (AGB) across a large, intact tropical peat dome in Northwestern Borneo. Moving up a 4m elevational gradient, we observed increasing stem density but decreasing canopy height, crown area and crown roughness. These findings were consistent with hypotheses that nutrient and hydrological dynamics co-influence forest structure and stature of the canopy individuals, leading to reduced productivity towards the dome interior. Gap frequency as a function of gap size followed a power law distribution with a shape factor (?) of 1.76 ± 0.06. Ground-based and dome-wide estimates of AGB were 217.7 ± 28.3 Mg C ha-1, and 222.4 ± 24.4 Mg C ha-1, respectively, which were higher than previously reported AGB for PSF and tropical forests in general. However, dome-wide AGB estimates were based on height statistics and we found the coefficient of variation on canopy height was only 0.08, three times less than stem diameter measurements, suggesting LiDAR height metrics may not be a robust predictor of AGB in tall tropical forests with dense canopies. Our structural characterization of this ecosystem advances the understanding of the ecology of intact tropical peat domes and factors that influence biomass density and landscape-scale spatial variation. This ecological understanding is essential to improve estimates of forest carbon density and its spatial distribution in PSF and to effectively model the effects of disturbance and deforestation in these carbon dense ecosystems.