Abstract
Forest carbon density is an important indicator for evaluating forest carbon sink capacities. Accurate carbon density estimation is the basis for studying the response mechanisms of forest ecosystems to global climate change. Airborne light detection and ranging (LiDAR) technology can acquire the vertical structure parameters of forests with a higher precision and penetration ability than traditional optical remote sensing. Combining top of canopy height model (TCH) and allometry models, this paper constructed two prediction models of aboveground carbon density (ACD) with 94 square plots in northwestern China: one model is plot-averaged height-based power model and the other is plot-averaged daisy-chain model. The correlation coefficients (R2) were 0.6725 and 0.6761, which are significantly higher than the correlation coefficients of the traditional percentile model (R2 = 0.5910). In addition, the correlation between TCH and ACD was significantly better than that between plot-averaged height (AvgH) and ACD, and Lorey’s height (LorH) had no significant correlation with ACD. We also found that plot-level basal area (BA) was a dominant factor in ACD prediction, with a correlation coefficient reaching 0.9182, but this subject requires field investigation. The two models proposed in this study provide a simple and easy approach for estimating ACD in coniferous forests, which can replace the traditional LiDAR percentile method completely.
Highlights
Forest carbon storage accounts for 82.5% of terrestrial vegetation carbon storage, which is the main component of the vegetation carbon sink (Cusack et al, 2014; Kauranne et al, 2017)
Using the comparison between the simple power-law model (Eq 8) of aboveground carbon density (ACD) and the three plot-averaged metrics (AvgH, Lorey’s height (LorH), and basal area (BA))calculated from the field inventory, we found that the BA explains 91.8% of the variation in ACD, which is much higher than the 39.5% explained by averaged height (AvgH) and 10.1% explained by LorH (Table 2), and the convergence of BA is much better than that of AvgH and LorH (Figure 3), which indicates that BA is the optimal plot-averaged indicator for the inversion of ACD
It is not surprising that BA is a stronger predictor of aboveground biomass (AGB) than height is, because BA can be measured with a lot better accuracy than height, and because diameter at breast height (DBH) is weighted higher than tree height in Eq 7
Summary
Forest carbon storage accounts for 82.5% of terrestrial vegetation carbon storage, which is the main component of the vegetation carbon sink (Cusack et al, 2014; Kauranne et al, 2017). Large-scale estimations of forest carbon sinks are mainly realized by means of traditional optical remote sensing. Traditional optical remote sensing can extract the spectral information and horizontal structure information of vegetation. With increasing biomass, saturation occurs which affects the estimation accuracy of forest carbon storage (Zhao et al, 2016). LiDAR can acquire high-precision three-dimensional information of the object. LiDAR has a certain penetrating ability and can obtain vertical structure information of forests, improving the estimation accuracy of forest height and structure and forest carbon storage (Dubayah and Drake, 2000; Naesset and Bjerknes, 2001; Hudak et al, 2002; Gwenzi and Lefsky, 2014)
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