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.

For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

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.

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.

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.

Larix gmelinii is the major tree species in Northeast China. The wood properties of different Larix gmelinii are quite different and under strong genetic controls, so it can be better improved through oriented breeding. In order to detect the longitudinal compressive strength (LCS), modulus of rupture (MOR) and modulus of elasticity (MOE) in real-time, fast and non-destructively, a prediction model of wood mechanical properties with high precision and stability is constructed based on visible-near-infrared spectroscopy (Vis-NIRS) technology. The featured wavelengths were selected with the algorithms of competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE), synergy interval partial least squares (SiPLS) and their combinations. The prediction models were then developed based on the partial least square regression (PLSR). The predictive ability of models was evaluated with coefficient of determination (R²) and root mean square error (RMSE). It indicated that CARS performed the best among the four methods examined in terms of wavelength-variable selection. The combined featured wavelength selecting method of SiPLS-CARS showed better performance than the single wavelength selection method. The optimal models of LCS, MOR and MOE are the SiPLS-CARS-PLSR model, with the R² of the calibration set and the validation set are both greater than 0.99, and RMSE the smallest. The NIR optimal models for wood mechanical properties predictions has high predictive accuracy and good robustness.