Search Results (20)

Forests are affected by a wide range of disturbances globally, resulting in the decline or death of large areas of them. There is a lack of comparative studies on how soil properties change in forests that die under the influence of disturbances, especially considering different levels of disturbance. For this study, we took Larix olgensis—a major plantation forest species in northeast China—as the research object, one in which a large outbreak of bark beetle led to large-scale forest death, and set up fixed sample plots characterized by different disturbance intensities. We investigated the responses of soil physicochemical properties and microbial community compositions to different disturbance intensities through the determination of soil nutrient indices and high-throughput sequencing. The results show that there were significant differences (p < 0.05) in the effects of different disturbance intensities on soil physicochemical properties, where the soil moisture content, total nitrogen, total carbon, and total phosphorus in the control group were significantly higher than those in the disturbed groups. The soil pH was highest under low-intensity disturbance and the soil total potassium content was highest under high-intensity disturbance. At different disturbance intensities, the highest soil moisture content was found in the high-intensity group. Proteobacteria, Actinobacteria, Verrucomicrobia, Acidobacteria, Candidatus_Rokubacteria, Chloroflexi, Gemmatimonadetes, and Thaumarchaeota were the dominant populations with higher abundances; meanwhile, the relative abundance of Bacteroidetes, Tenericutes, and a tentatively unclassified fungus differed significantly (p < 0.05) across disturbance intensities. Among the dominant microbial populations, Acidobacteria showed a significant negative correlation with soil pH and a significant positive correlation with total potassium content, Thaumarchaeota showed significant positive correlations with soil moisture content and total nitrogen content, and Firmicutes and Gemmatimonadetes showed significant negative correlations with total carbon content in the soil. Furthermore, soil total nitrogen content was the key factor driving changes in microbial communities. The results of this study provide a scientific basis for the study of the long-term effects of tree mortality caused by insect pests on soil microbial communities and their response mechanisms, which is of great theoretical value for the establishment of scientific and effective methods for woodland restoration. Full article

Forest biomass is an important indicator of forest ecosystem productivity, and it plays vital roles in the global carbon cycling, global climate change mitigating, and ecosystem researches. Multiscale, rapid, and accurate extraction of forest biomass information is always a research topic. In this study, comprehensive investigation of a larch (Larix olgensis) plantation was performed using remote sensing and field-based monitoring methods, in combination with LiDAR-based multisource data and machine learning methods. On this basis, a universal, multiscale (single tree, stand, management unit, and region), and unit-high-precision continuous monitoring method was proposed for forest biomass components. The results revealed the following. (1) Airborne LiDAR point cloud variables exhibited significant correlation with the aboveground components (except leaves) and the whole-plant biomass ($R_{adj}^2$ > 0.91), suitable for extraction or estimation of forest parameters such as biomass and stock volume. (2) In terms of biomass monitoring at forest stand and management unit scale, a random forest model performed well in fitting accuracy and generalization ability, whereas a multiple linear regression model produced clearer explanation regarding the biomass of each forest component. (3) Using seasonal phenological characteristics in the study area, larch distribution information was extracted effectively. The overall accuracy reached 90.0%, and the kappa coefficient reached 0.88. (4) A regional-scale forest biomass component estimation model was constructed using a long short-term memory model, which effectively reduced the probability of biomass underestimation while ensuring good estimation accuracy, with R² exceeding 0.6 for the biomass of the aboveground and whole-plant components. This research provides theoretical support for rapid and accurate acquisition of large-scale forest biomass information. Full article

Stumps and coarse roots form an important C pool and nutrient pool in a Larix olgensis (Larix olgensis Henry) plantation ecosystem, and their decomposition processes would affect nutrient cycling dynamics of the overall Larix olgensis plantation. We studied the decomposition and release of nutrients from stumps and coarse roots that were cleared 0, 6, 16, 26 and 33 years ago in Northeast China. The stumps and coarse roots were divided into stump discs (SD), stump knots (SK), coarse roots (>10 cm in diameter) (CR1), medium-coarse roots (5–10 cm in diameter) (CR2) and fine-coarse roots (2–5 cm in diameter) (CR3). During the entire 33-year study period, SK, CR1, CR2 and CR3 lost 87.37%, 96.24%, 75.76% and 91.98% of their initial mass, respectively. The average annual decomposition rate (k) was 0.068 for SD, 0.052 for SK, 0.092 for CR1, 0.068 for CR2 and 0.066 for CR3. After 33 years of decomposition, CR3 lost 5% of its initial C, CR2 lost 2%, and SK accumulated 1%, indicating slow C release. The N residues in SK, CR1, CR2 and CR3 were 186%, 109%, 158% and 170%, respectively. Coarse roots released P significantly faster than SD and SK, with 13% of the initial P released in CR1. SD and SK release cellulose, hemicellulose and lignin faster than coarse roots. The results show that Larix olgensis stumps and coarse roots could contribute to soil fertility recovery and serve as a long-term nutrient reservoir for forest vegetation. Full article

Stumps are a significant component of coarse woody debris in plantations, but their effect on microsite soil organic carbon (C) and enzyme activities remains understudied. Soil (Alfisol) samples were collected at varying distances from larch (Larix olgensis Henry) stumps and at different soil depths (0–20 cm and 20–40 cm) to analyze soil total organic C (TOC), particulate organic C (POC), easily oxidizable C (EOC), microbial biomass C (MBC), and enzyme activities. Results indicated that stumps significantly affected TOC and POC contents, with the greatest horizontal range of impact reaching up to 15 cm in both the topsoil and subsoil layers. Stumps also significantly affected MBC content, with the greatest horizontal range of impact reaching up to 55 cm in the subsoil layer. EOC content was the most affected, with the stumps’ impact extending to 55 cm in both soil layers. Additionally, the study showed that stumps had a significant impact on the activities of β-glucosidase and β-cellobiohydrolase, with the greatest horizontal range of impact reaching up to 15 cm for glucosidase and 35 cm for cellobiohydrolase in the topsoil layer. Stumps also significantly affected the activities of phenol oxidase and peroxidase, with the maximum horizontal range of stump impact extending up to 35 cm for phenol oxidase and 55 cm for peroxidase in the topsoil layer. This study enhances our understanding of the role of stumps in plantation ecosystems and offers valuable insights for future management strategies to maintain soil fertility and improve site productivity. Full article

The pipe model theory has been applied to estimate allometry of trees in many regions; however, its reliability and generality need more verification for estimating crown biomass in China. In the present study, the crown biomass of Larix olgensis plantations in four sites in northeast China was estimated using the pipe model, and the correlation efficiency index of larch crown biomass for pipe model estimation was 0.953. The crown biomass of larch plantations could be accurately estimated by the tree height, crown base height, and stem area at breast height. Meanwhile, the effects of site, stand density, and age on the accuracy of crown biomass estimated by the pipe model were detected. The covariance analysis showed that the effect of age on crown biomass was 0.024, indicating that age had a significant effect on the estimation accuracy in this model, while site and stand density had no significant effects (p = 0.180 and p = 0.169). Our study showed that the crown biomass of L. olgensis plantations in northeast China could be accurately estimated using the pipe model, and we recommend considering the age effect in practical applications. Full article

This study quantified the horizontal influence range of larch stumps and coarse roots on the phosphorus (P) fraction and availability of microsite soils and explored whether this influence range changes with different plantation types. The total P, available P and P fractions were measured in microsite soils at 0–75 cm horizontal distances from stumps and coarse roots at soil depths of 0–40 cm in a pure larch (Larix olgensis Henry) plantation and a mixed larch–ash (Fraxinus mandshurica Rupr.) plantation. Soils at horizontal distances of 85–95 cm from the stumps and coarse roots were used as the controls. Larch stumps and coarse roots affected the total P concentration at depths of 0–40 cm in the mixed plantations, and the maximum horizontal influence range reached 75 cm. However, in the pure plantation, only the total P at 0–10 cm depths were affected, and the maximum influence range was 35 cm. The NaOH-P_i and NaOH-P_o changes in the pure plantation were similar to those of total P, while those of HCl-P_i, HCl-P_o and NaHCO₃-P_o in the mixed plantation were similar to those of total P. Larch stumps and coarse roots could affect the total P and P fraction concentrations in microsite soils. The horizontal distance of soil total P and P fractions concentrations affected by larch stumps and coarse roots in the mixed plantation was greater than that in the pure plantation. These results suggest that the position of stumps and coarse roots should be considered when reforestation sites are selected. Full article

Bacteria are among the most critical components in soil. The application of nutrients as an important management measure to maintain soil fertility can affect the structure of soil bacterial communities. The objective of this study was to explore the influence of the application of nutrients on the soil bacterial community composition and diversity in a Larix olgensis Henry plantation after thinning using Illumina high-throughput sequencing technology. In July 2015, a middle-aged (27 years old) Larix olgensis forest, afforested in the spring of 1988 (thinning was conducted in the winter of 2012), in MengJiagang National Forest Farm, Jiamusi City, China, was assessed. Four fertilizer treatments were applied, each replicated three times: nitrogen (N, 250 kg/ha); nitrogen + phosphorus (NP, nitrogen 250 kg/ha + phosphorus 50 kg/ha); nitrogen + phosphorus + potassium (NPK, nitrogen 250 kg/ha + phosphorus 50 kg/ha + potassium 30 kg/ha); and a control (CK, no fertilizer). In mid-August 2018, soil samples of a 0–10 cm soil layer were collected; the diversity and composition of soil bacteria under different the application of nutrients conditions were determined by Illumina high-throughput sequencing technology on the MiSeq platform. Our results found that: (1) compared with the CK treatment, long-term the application of nutrients significantly reduced the soil pH and soil total potassium content (p < 0.05); and (2) the continued application of nutrients increased the Chao1 richness index of the soil bacteria in the Larix olgensis plantation (p < 0.05); (3) soil organic carbon and soil total nitrogen were key drivers of the soil bacterial community structure. Therefore, the different long-term the application of nutrients regimes did not affect the stability of the soil ecosystem in the Larix olgensis plantation. Full article

The stump and coarse root biomass remaining after tree harvesting are often overlooked by researchers, which may lead to underestimation of their role in carbon cycling, so we constructed two sets of additive models for larch (Larix olgensis Henry) plantations in Northeast China. Due to the absence of tree diameter at breast height data after harvesting, only the sole predictor variable stump disc diameter could be used to predict stump and coarse root biomass, and the results showed that stump disc diameter predicted stump biomass with higher accuracy than coarse root biomass predictions. In addition, to investigate the effect of the site class of complex stands on the predictive capability of the model, the generic model in this study was employed with all site class data and a specific model was developed and employed with all the site class data. We found that the generic model had different degrees of error compared to the predicted results for each site class, overestimating the total biomass by 15% and underestimating it by 10%, especially for site class IV. In conclusion, to obtain a biomass prediction model with reliable results, the impact of more complex site class effects should be considered. Full article

Soil enzymes play an important role in nutrient biogeochemical cycling in terrestrial ecosystems. Previous studies have emphasized the variability of soil enzyme activities and stoichiometric ratios in forest ecosystems in northern China. However, much less is known about soil enzyme activity, enzymatic stoichiometry ratios and microbial nutrient limitations in Larix olgensis plantations under different microsites. In this study, four specific extracellular enzyme activities (β-glucosidase, β-1,4-N-acetylglucosaminidase, L-leucine aminopeptidase, Acid phosphatase), and soil physicochemical properties were measured in the 0–20 cm soil layer. The results showed that slope aspect and slope position had a significant effect on soil moisture, soil bulk density, soil porosity, soil organic matter, ammonium nitrogen and nitrate-nitrogen. Meanwhile, slope aspect and slope position had a significant effect on β-glucosidase, β-1,4-N-acetylglucosaminidase, L-leucine aminopeptidase and Acid phosphatase activities while the highest activity of β-glucosidase (or β-1,4-N-acetylglucosaminidase), L-leucine aminopeptidase, and Acid phosphatase was observed in the upper slope of the east, the upper slope of the south, and the upper slope of the north; soil porosity, pH and soil organic matter were the main factors affecting soil extracellular enzyme activities. The log-transformed ratios of soil C-, N-, and P-acquiring enzyme activities were 1.00:1.06:1.17, indicating that soil microbial growth in this region was limited by N and P. Therefore, these findings highlight that N and P inputs should be considered in the management of L. olgensis plantations to improve soil microbial enzyme activity, alleviating N and P limitations. Full article

Individual tree detection is an increasing trend in LiDAR-based forest inventories. The locations, heights, and crown areas of the detected trees can be estimated rather directly from the LiDAR data by using the LiDAR-based canopy height model and segmentation methods to delineate the tree crowns. However, the most important tree variable is the diameter of the tree stem at the breast height (DBH) which can seldom be interpreted directly from the LiDAR data. Therefore, the use of individually detected trees in forest planning calculations requires predictions for the DBH. This study tested six methods for predicting the DBH from laser scanning data collected by an unmanned aerial vehicle from Larix olgensis plantations located in northeast China. The tested methods were the linear regression model (LM), a linear model with ridge regularization (LMR), support vector regression (SVR), random forest (RF), artificial neural network (ANN), and the k-nearest neighbors (KNN) method. Both tree-level and stand-level metrics derived from the LiDAR point cloud data (for instance percentiles of the height distribution of the echoes) were used as potential predictors of DBH. Compared to the LM, all other methods improved the accuracy of the predictions. On the other hand, all methods tended to underestimate the DBH of the largest trees, which could be due to the inability of the methods to sufficiently describe nonlinear relationships unless different transformations of the LiDAR metrics are used as predictors. The support vector regression was evaluated to be the best method for predicting individual tree diameters from LiDAR data. The benefits of the methods tested in this study can be expected to be the highest in the case of little prior knowledge on the relationships between the predicted variable and predictors, a high number of potential predictors, and strong mutual correlations among the potential predictors. Full article

We performed a comparative analysis of the prediction accuracy of machine learning methods and ordinary Kriging (OK) hybrid methods for forest volume models based on multi-source remote sensing data combined with ground survey data. Taking Larix olgensis, Pinus koraiensis, and Pinus sylvestris plantations in Mengjiagang forest farms as the research object, based on the Chinese Academy of Forestry LiDAR, charge-coupled device, and hyperspectral (CAF-LiTCHy) integrated system, we extracted the visible vegetation index, texture features, terrain factors, and point cloud feature variables, respectively. Random forest (RF), support vector regression (SVR), and an artificial neural network (ANN) were used to estimate forest volume. In the small-scale space, the estimation of sample plot volume is influenced by the surrounding environment as well as the neighboring observed data. Based on the residuals of these three machine learning models, OK interpolation was applied to construct new hybrid forest volume estimation models called random forest Kriging (RFK), support vector machines for regression Kriging (SVRK), and artificial neural network Kriging (ANNK). The six estimation models of forest volume were tested using the leave-one-out (Loo) cross-validation method. The prediction accuracies of these six models are better, with $R_{Loo}^2$ values above 0.6, and the prediction accuracy values of the hybrid models are all improved to different extents. Among the six models, the RFK hybrid model had the best prediction effect, with an $R_{Loo}^2$ reaching 0.915. Therefore, the machine learning method based on multi-source remote sensing factors is useful for forest volume estimation; in particular, the hybrid model constructed by combining machine learning and the OK method greatly improved the accuracy of forest volume estimation, which, thus, provides a fast and effective method for the remote sensing inversion estimation of forest volume and facilitates the management of forest resources. Full article

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management. Full article

Thinning is an important management practice for reducing plant competition and improving wood production in forests. The residues from thinning can contain large amounts of carbon (C) and nitrogen (N), and the management methods applied directly after thinning can affect the input of nutrients to soil, change the availability of substrates to soil bacterial communities, and thus affect soil bacterial community structure. Our objective was to determine the effects of different thinning residue treatments on soil bacterial community structure and diversity. Illumina high-throughput sequencing technology was used to sequence the bacterial 16SrRNA V3–V4 variable region of the soil (0–10 cm) of a Larix olgensis plantation to compare the composition and diversity of soil bacterial communities following removal of thinning residues (tree stems plus tree crowns) (RM) and retention of thinning residues (crowns retained with stem removal) (RT) treatments. Total soil carbon (TC) and nitrogen (TN) content in the residue retention treatment were significantly greater than in residue removal treatments (p < 0.05). The relative abundance of the dominant soil bacteria phyla were, in descending order: Proteobacteria, Verrucomicrobia, Acidobacteria, Chloroflexi, Actinobacteria, Nitrospirae, Planctomycetes, Gemmatimonadetes, and Bacteroidetes, with a total relative abundance of more than 80%. Acidobacteria were enriched in the RM treatment, while Proteobateria, Actinobacteria and Bacteroidetes were greater in the RT treatment. Rhizobiales and Rhodospirillales (belonging to the α-Proteobacteria) were enriched in the RM treatment. Soil bacteria α diversity was not significantly different among different treatments. Spearman correlation analysis showed that the α diversity index was significantly negatively correlated with TC and TN. Lefse analysis revealed that 42 significant soil bacteria from phylum to genus were found in the two different thinning residue treatments. Redundancy analysis showed that soil TC and TN were the major drivers of variation in soil bacterial community structure. Overall, thinning residue retention increased the availability of resources to the soil bacterial community, thus changing bacterial community structure. This research provides a theoretical basis for the regulation of plantation forest soil fertility and quality. Full article

The mixture of tree species has gradually become the focus of forest research, especially native species mixing. Mixed-species plantations of Manchurian ash (Fraxinus mandshurica Rupr.) and Changbai larch (Larix olgensis Henry) have successfully been cultivated in Northeast China. Height–diameter (H–D) models were found to be effective in designing the silvicultural planning for mixed-species plantations. Thus, this study aimed to develop a new system of H–D models for juvenile ash and larch mixed-species plantations, based on competition information and tree and stand attributes. The leave-one-out cross-validation was utilized for model validation. The result showed that the H–D relationship was affected not only by the tree attributes (i.e., tree size and competition information) but also by stand characteristics, such as site quality and species proportion of basal area. The best model explained more than 80% and 85% variation of the tree height of ash and larch, respectively. Moreover, model validation also confirmed the high accuracy of the newly developed model’s predictions. We also found that, in terms of total tree height, ash in middle rows were higher than those in side rows, while larch in the middle rows were higher in the early growth period but then became lower than those in the side rows, as the diameter increased. The newly established H–D models would be useful for forestry inventory practice and have the potential to aid decisions in mixed-species plantations of ash and larch. Full article

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. Although various biomass models have been developed thus far, most of them lack a detailed investigation of the additivity properties of biomass components and inherent correlations among the components and aboveground biomass. This study compared the nonadditive and additive biomass models for larch (Larix olgensis Henry) trees in Northeast China. For the nonadditive models, the base model (BM) and mixed effects model (MEM) separately fit the aboveground and component biomass, and they ignore the inherent correlation between the aboveground and component biomass of the same tree sample. For the additive models, two aggregated model systems with one (AMS1) and no constraints (AMS2) and two disaggregated model systems without (DMS1) and with an aboveground biomass model (DMS2) were fitted simultaneously by weighted nonlinear seemingly unrelated regression (NSUR) and applied to ensure additivity properties. Following this, the six biomass modeling approaches were compared to improve the prediction accuracy of these models. The results showed that the MEM with random effects had better model fitting and performance than the BM, AMS1, AMS2, DMS1, and DMS2; however, when no subsample was available to calculate random effects, AMS1, AMS2, DMS1, and DMS2 could be recommended. There was no single biomass modeling approach to predict biomass that was best for all aboveground and component biomass except for MEM. The overall ranking of models based on the fit and validation statistics obeyed the following order: MEM > DMS1 > AMS2 > AMS1> DMS2 > BM. This article emphasized more on the methodologies and it was expected that the methods could be applied by other researchers to develop similar systems of the biomass models for other species, and to verify the differences between the aggregated and disaggregated model systems. Overall, all biomass models in this study have the benefit of being able to predict aboveground and component biomass for larch trees and to be used to predict biomass of larch plantations in Northeast China. Full article