Abstract
There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height−diameter (H−D) and projected crown area−diameter (C−D) allometries of individual trees. We show that a power function relationship emerges when the C−D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven−aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness−of−fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks.
Highlights
Aboveground carbon density (ACD) is an important forest property to map in the context of the global carbon cycle [1,2,3]
Fitting the model with ground−measured BP and ρP increased the R2 to 0.41, but BP was poorly predicted from LiDAR estimates of height derived from LiDAR (HL) and GL (R2 = 0.09; Table 3), and ρP was unrelated to the LiDAR metrics (Figs 1, 2)
We explore specific explanations for low goodness−of−fit, including that (1) the basal area and wood density of plots are not closely correlated with top canopy height or gap fraction as measured by LiDAR; (2) tree size distributions are not conserved across the landscape; and (3) the exponents of the allometries are affected by systematic changes in species composition, and the exponent of the crown area allometry deviates from 2
Summary
Aboveground carbon density (ACD) is an important forest property to map in the context of the global carbon cycle [1,2,3]. A common approach has been to estimate ACD in field plots and use regression to relate these measurements to various LiDAR metrics [9]. This approach can deliver accurate estimation models within sampling regions, but the models lack physical underpinnings because they are purely empirical. Asner and Mascaro [8] have developed a General Model ( AM−GM) for predicting ACD, which uses measures of the top canopy height derived from LiDAR (HL ), along with local relationships predicting basal area (BP ) and basal−area−weighted mean wood density (ρP ): ACD = aHL b1 BP b2 ρP b3
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