In order to explore the ever-changing law of soil organic matter (SOM) content in the forest of the Greater Khingan Mountains, a prediction model of the SOM content with a high accuracy and stability has been developed based on visible near-infrared (VIS-NIR) technology and multiple regression analysis. A total of 105 soil samples were collected from Cuifeng forest farm in Jagdaqi City, Greater Khingan Mountains region, Heilongjiang Province, China. Five classical preprocessing algorithms, including Savitzky−Golay convolution smoothing (S-G smoothing), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative, second derivative, and the combinations of the above five methods were applied to the raw spectra. Wavelengths were optimized with five methods of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least square (SiPLS), and their combinations, and PLS models were developed accordingly. The results showed that when S-G smoothing is combined with SNV or MSC, both preprocessing strategies can improve the performance of the model. The prediction accuracy of SiPLS-PLS model and SiPLS-UVE-PLS model for the SOM content is higher than for other models, withan Rc2 of 0.9663 and 0.9221, RMSEC of 0.0645 and 0.0981, Rv2 of 0.9408 and 0.9270, and RMSEV of 0.0615 and 0.0683, respectively. The pretreatment strategies and characteristic variable selection methods used in this study could significantly improve the model performance and predicting efficiency.
Natural secondary forests not only contribute to the total balance of terrestrial carbon, but they also play a major role in the future mitigation of climate change. In China, secondary forests have low productivity and carbon sequestration, which seriously restricts the sustainable development of the forest. Thinning is a core measure of scientific management of forest ecosystems and is a primary natural forest management technique. The carbon density of the tree layer is most affected by thinning. Taking larch–birch mixed natural secondary forests in the Greater Khingan Range, Northeast China, as the research object, we analyzed the changes in tree layer carbon density of secondary forests under different thinning intensities. The results showed that in five thinned groups, when intensity was 49.6%, the diameter at breast height (DBH) and individual tree biomass significantly increased. Thinning had no significant effect on the carbon content of the tree stem, branches and bark, but had significant effects on the carbon content of leaves. Our result showed that the carbon content of birch leaves increased and that of larch decreased. As the thinning intensity increases, the proportion of broad-leaved tree species (birch) increased, yet larch decreased. In the short term, thinning will reduce the total biomass and carbon density of tree layers. However, when the thinning intensity was 49.6%, the carbon accumulation was higher than that of the blank control group (CK group) after thinning for 12 years. This shows that after a long period of time, the carbon density of tree layers will exceed that of the CK group. Reasonable thinning intensity management (49.6% thinning intensity) of natural secondary forests can make trees grow better, and the proportion of broad-leaved trees increases significantly. It can also increase the carbon sequestration rate and lead to more accumulation of biomass and carbon density. This can not only promote the growth of secondary forests, but also shows great potential for creating carbon sinks and coping with climate change.
This study aimed to measure the carbon content of tree species rapidly and accurately using visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods. Currently, the carbon content of trees used for calculating the carbon storage of forest trees in the study of carbon sequestration is obtained by two methods. One involves measuring carbon content in the laboratory (K2CrO7-H2SO4 oxidation method or elemental analyzer), and another involves directly using the IPCC (Intergovernmental Panel on Climate Change) default carbon content of 0.45 or 0.5. The former method is destructive, time-consuming, and expensive, while the latter is subjective. However, Vis-NIR detection technology can avoid these shortcomings and rapidly determine carbon content. In this study, 96 increment core samples were collected from six tree species in the Heilongjiang province of China for analysis. The spectral data were preprocessed using seven methods, including extended multiplicative scatter correction (EMSC), first derivative (1D), second derivative (2D), baseline correction, de-trend, orthogonal signal correction (OSC), and normalization to eliminate baseline drifting and noise, as well as to enhance the model quality. Linear models were established from the spectra using partial least squares regression (PLS). At the same time, we also compared the effects of full-spectrum and reduced spectrum on the model’s performance. The results showed that the spectral data processed by 1D with the full spectrum could obtain a better prediction model. The 1D method yielded the highest R2c of 0.92, an RMSEC (root-mean-square error of calibration) of 0.0056, an R2p of 0.99, an RMSEP (root-mean-square error of prediction) of 0.0020, and the highest RPD (residual prediction deviation) value of 8.9. The results demonstrate the feasibility of Vis-NIR spectroscopy coupled with chemometric methods in determining the carbon content of tree species as a simple, rapid, and non-destructive method.
In, this study, we analysed the effects of thinning on stand structure and carbon stocks for a mixed conifer and broadleaf natural secondary forests in the Small Khingan Mountains, China. Stand structure and carbon stocks were assessed in trees from unthinned control (CK), lightly thinned (LT), moderately thinned (MT) and heavily thinned (HT) treatments. Results showed that the heavier the thinning, the larger the crown area became. Under the MT treatment, trees tended to be evenly distributed when compared to trees under the other treatments. All the trees of the LT and HT treatments were mixed strongly compared to those of the CK treatment. As the thinning intensitiy increased, the distributions of size differentiation and crowding degree gradually decreased. As a result, the competitive pressure diminished, and more dominant trees emerged. In addition, there was a significant positive correlation between individual tree carbon stock and canopy under all treatments. Carbon tends to accumulate in individuals with a random distribution, sparse spacing, strong mingling index and large competitive advantage. However, the results varied slightly under the HT treatment. Individuals in a dominant position occupied abundant resources and great niche space.
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