Search Results (11)

Integrating multi-source remote sensing can improve the accuracy of forest aboveground biomass (AGB) estimation. However, the accuracy and stability of the forest AGB estimation results are affected by multiple remote sensing feature variables as well as parameter tuning of machine learning algorithms. To this end, this study employed six types of remote sensing data—Landsat 8 OLI, Sentinel-2A, GEDI, ICESat-2, ALOS-2, and SAOCOM. A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of Pinus kesiya var. langbianensis forest in Wuyi Village, Zhenyuan County. The dual-variable selection strategy integrates SHAP with the Pearson correlation coefficient (PC), RF, EN, and Lasso to enhance feature screening robustness and interpretability. The results of the study showed that Lasso-SHAP dual-variate screening was more stable than SHAP univariate screening. In particular, the Lasso-SHAP strategy improved the average R² from 0.59 (using SHAP alone) to above 0.70, achieving an enhancement of 11%. Among GA-optimized parametric machine learning models, the linear GA-Lasso achieved the best performance, with an R² of 0.91 and an RMSE of 12.94 Mg/ha, followed by the GA-EN model (R² = 0.89, RMSE = 14.46 Mg/ha). For nonlinear models, GA-SVR performed the best (R² = 0.74, RMSE = 22.07 Mg/ha), surpassing the GA-CatBoost model (R² = 0.64, RMSE = 25.88 Mg/ha). In summary, the Lasso-SHAP dual-variable selection strategy effectively improves the estimation accuracy of AGB for Pinus kesiya var. langbianensis forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment. Full article

Pinus kesiya var. langbianensis, a species endemic to Yunnan, China, accounts for over 90% of Yunnan’s Pinus resin production. However, there is significant variation in resin yield among individuals, and molecular markers for identifying high-yielding individuals have yet to be developed. In this study, a comparative analysis of complete chloroplast genomes of P. kesiya var. langbianensis was conducted to perform a phylogenetic analysis and differentiate high-yielding individuals. Both high-yielding (HY) and low-yielding (LY) trees possess a typical quadripartite structure, with respective genome sizes of 119,812 bp and 119,780 bp. Each chloroplast genome contains 112 genes, including 72 protein-coding genes, 36 tRNAs, and 4 rRNAs. Furthermore, HY and LY trees contain 30 and 34 SSRs, respectively, with mononucleotide repeats being predominant; neither genome exhibited trinucleotide or pentanucleotide repeats. Six highly variable regions were identified: trnI-CAU-psbA, trnH-GUG-trnI-CAU, rpl16, rrn4.5-rrn5, petG-petL, and psaJ. Phylogenetic analysis based on 72 Pinus species revealed that HY and LY trees clustered separately, with the HY tree grouping with P. kesiya and the LY tree with P. yunnanensis. This study provides a theoretical foundation for the molecular identification of high-yield P. kesiya var. langbianensis individuals and enriches the understanding of its phylogenetic relationships. Full article

This study presents a comprehensive examination of Pinus kesiya var. langbianensis (Pinus kesiya var. langbianensis), the primary resin-extraction tree species in Yunnan Province, China. In this study, we formulated different concentration gradients of 0.25%, 0.5%, 1%, and 2% of diquat solution as tapping stimulant to test the effect of different concentrations on the resin gain rate of Pinus kesiya, and analyzed the relationship between anatomical structure, major chemical composition of turpentine and resin yield by methods such as wood anatomy and chemical composition analysis of turpentine. The primary focus of the investigation was on exploring the interrelationships among resin-tapping stimulants, anatomical structures, turpentine components, and resin yield. Research findings demonstrate a significant enhancement in resin production due to the application of stimulants, with the highest increase rate reaching 55% in a specific group, while others achieved approximately 30% increments. Moreover, measurement data about resin duct dimensions indicate a noteworthy increase in resin duct area for the stimulant-treated group compared to the control group. However, it should be noted that the impact on resin duct area by varying stimulant concentrations was relatively minor. Furthermore, continuous observation of resin extraction from different resin-yield classes of P. kesiya revealed insignificant variation in resin yield over time for the low and moderate resin-yield groups. In contrast, the high resin-yield group exhibited a gradual increase in resin production. Interestingly, the high resin-yield group exhibited the smallest resin duct area, but the highest resin duct density, indicating an interconnectedness of resin duct-related data that influences resin yield. Additionally, correlative investigations between anatomical structures and resin yield demonstrate a positive correlation between resin duct area and resin yield, total resin production, and average resin yield. This underscores the importance of resin duct area as a significant factor in resin production. On the other hand, the influence of stimulant concentrations on the turpentine components was found to be negligible. Overall, the correlation results suggest that turpentine components cannot reliably predict or differentiate between high and low resin-yield trees. This study provides a comprehensive analysis of the interrelationships among stimulants, anatomical structures, and turpentine components, offering a theoretical foundation for the resin extraction and resin processing industries in Yunnan Province. Full article

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies. Full article

(1) The Hoop Head Tenon-mortise Joint (HHTMJ) in the Tusi Manor in Tibetan areas in Yunnan, China, has a serious decay phenomenon. To understand the effect of decay on the seismic performance of HHTMJ, (2) the five groups of HHTMJ and small-size Pinus kesiya var. langbianensis wood mechanical property testing specimens were placed in an artificially set decay environment and cultivated together with wood decay fungi for 0, 6, 12, 18, and 24 weeks, respectively. Low-cycle repeated loading tests were conducted to compare the failure mode, hysteresis curve, skeleton curve, and cumulative energy consumption of the HHTMJ under different decay cycles. (3) The results indicate that the failure mode of the HHTMJ is fractured at the tenon shoulder, and the deformation and failure of the tenon increase with the increase in decay. Compared with the non-decayed specimens, the ultimate bearing performance of the specimens after 6, 12, 18, and 24 weeks of decay decreased by 8.83%, 16.97%, 19.69%, and 30.22%, respectively. The cumulative energy consumption decreased by 21.6%, 27.4%, 33.2%, and 41.3%, respectively. (4) Decay primarily occurs on the exterior of the tenon, with minimal decay on the interior. The degradation of seismic performance in HHTMJ is relatively close to the degradation observed in small-size wood specimens during mechanical property testing. Full article

To enhance forest carbon sequestration capacity, it is important to optimize forest structure by revealing the spatial effects of the aboveground biomass of individual trees, with particular emphasis on stand origin and tree size. Here, 0.3 ha clear-cut plots of Pinus kesiya var. langbianensis forest were selected in a typical plantation and natural stand. Then, the ordinary least squares model and spatial regression models were used to analyze the different responses between spatial position and individual tree biomass based on the stand origin and diameter at breast height (DBH) of the tree. Our study shows the following: (1) The spatial effect produced a stronger response in the natural stand than in the plantation. The amount of change in the adjusted R-squared (Δ_Radj²) of tree component totaled 0.34 and 0.57 for Pinus kesiya var. langbianensis and other trees in the natural stand, compared to only 0.2 and 0.42 in the plantation; (2) Spatial effects had a stronger impact on the accuracy of the fit for the crown (ΔR_adj² = 0.52) compared to the wood and bark (ΔR_adj² = 0.03) in the plantation, and there were no significant differences in the natural stand (ΔR_adj² = 0.42, ΔR_adj² = 0.43); (3) When DBH reached a certain size, the impact of spatial effect for the crown showed a significant change from positive to negative. The sizes of DBH were 19.5 cm, 14 cm and 34.6 cm, 19 cm for branches of Pinus kesiya var. langbianensis and other tree species in the plantation and natural stand, and were 20.3 cm and 31.4 cm for the foliage of Pinus kesiya var. langbianensis. Differences in stand structure led to varied responses in the biomass of tree components to spatial effects. Full article

It is essential to analyze the spatial autocorrelation and heterogeneity of aboveground biomass (AGB). But it is difficult to accurately describe due to the lack of data in clear-cutting plots. Thus, measuring the AGB directly in a clear-cutting plot can provide a reference for accurately describing the spatial variation. Therefore, a 0.3-hectare clear-cutting sample plot of Pinus kesiya var. langbianensis natural forest was selected, and the AGB was calculated by each component. The intra-group variance was quantitatively described in terms of spatial heterogeneity, and the spatial autocorrelation was explored by global and local Moran’s I. The results indicated that (1) there was different spatial heterogeneity for the different trees and organs. The intra-group variance tended to be stable after 20 m for P. kesiya var. langbianensis (PK) and other upper trees (UPs) and after 10 m for the other lower trees (LTs). (2) The spatial autocorrelation of AGB and wood biomass was similar, while the bark biomass and foliage biomass were consistent. PK and other UPs also exhibited strong spatial autocorrelation, with maximum Moran’s I values of 0.1537 and 0.1644, respectively. (3) There was spatial heterogeneity in the different components except for the bark of PK. The lowest spatial heterogeneity was found for LT. Full article

Accurate estimation of small-scale forest biomass is a prerequisite and basis for trading forest carbon sinks and optimizing the allocation of forestry resources. This study aims to develop a plot-scale methodology for estimating aboveground biomass (AGB) that combines a biomass horizontal distribution model (HDM) and sampling techniques to improve efficiency, reduce costs and provide the reliability of estimation for biomass. Simao pine (Pinus kesiya var. langbianensis) from Pu’er City, Yunnan Province, was used as the research subject in this study. A canopy profile model (CPM) was constructed based on data from branch analysis and transformed into a canopy biomass HDM. The horizontal distribution of AGB within the sample plots was simulated using the HDM based on the data from the per-wood survey and compared with the results from the location distribution model (LDM) simulation. AGB sampling estimations were carried out separately by combining different sampling methods with the AGB distribution of sample plot simulated by different biomass distribution models. The sampling effectiveness of all sampling schemes was compared and analyzed, and the best plan for the sampling estimation of AGB in plot-scale forests was optimized. The results are as follows: the power function model is the best model for constructing the CPM of the Simao pine in this study; with visual comparison and the analysis of the coefficient of variation, the AGB simulated by HDM has a larger and more continuous distribution than that simulated by LDM, which is closer to the actual distribution; HDM-based sampling plans have smaller sample sizes and sampling ratios than LDM-based ones; and lastly, the stratified sampling method (STS)-HDM-6 plan has the best sampling efficiency with a minimum sample size of 10 and a minimum sampling ratio of 15%. The result illustrates the potential of the method for estimating plot-scale forest AGB by combining HDM with sampling techniques to reduce costs and increase estimation efficiency effectively. Full article

There is still limited information on how genetic introgression impacts morphological variation and population fitness in long-lived conifer species. Two closely related pine species, Pinus kesiya Royle ex Gordon and Pinus yunnanensis Franch. are widely distributed over Southeast Asia and Yunnan province of China, with a large spatial scale of asymmetric genetic introgression and hybridization, and form a hybrid lineage, P. kesiya var. langbianensis, where their ranges overlap in southeast Yunnan. We compared seed trait variation and germination performance between hybrids and parental species and characterized environmental gradients to investigate the genetic and ecological evolutionary consequences of genetic introgression. We found that seed width (SW) differed significantly among the three pines, and all the seed traits were significantly negatively correlated with latitude and associated with the mean temperatures of the driest and wettest quarters. A higher germination fitness of hybrids was detected at a low temperature, indicating that they had better adaptability to temperature stress than their parental species during the germination process. Our results suggest that environmental factors shape seed phenotypic variation in the pine species and that genetic introgression significantly affects seed germination fitness. Therefore, assisting gene flow in natural forest populations might facilitate their adaptation to climate change. Full article

Even though studies on forest carbon storage are relatively mature, dynamic changes in carbon sequestration have been insufficiently researched. Therefore, we used panel data from 81 Pinus kesiya var. langbianensis forest sample plots measured on three occasions to build an ordinary regression model and a quantile-regression model to estimate carbon sequestration over time. In the models, the average carbon reserve of the natural forests was taken as the dependent variable and the average diameter at breast height (DBH), crown density, and altitude as independent variables. The effects of the DBH and crown density on the average carbon storage differed considerably among different age groups and with time, while the effect of altitude had a relatively insignificant influence. Compared with the ordinary model, the quantile-regression model was more accurate in residual and predictive analyses and removed large errors generated by the ordinary model in fitting for young-aged and over-mature forests. We are the first to introduce panel-data-based modeling to forestry research, and it appears to provide a new solution to better grasp change laws for forest carbon sequestration. Full article

Intra-annual monitoring of tree growth dynamics is increasingly applied to disentangle growth-change relationships with local climate conditions. However, such studies are still very limited in subtropical regions which show a wide variety of climate regimes. We monitored stem radius variations (SRV) of Pinus kesiya var. langbianensis (Szemao pine) over five years (2012–2015 and 2017) in the subtropical monsoon mountain climate of the Ailao Mountains, Yunnan Province, southwest China. On average, the stem radial growth of Szemao pine started in early March and ended in early October, and the highest growth rates occurred during May to June. Stem radius increments were synchronous with precipitation events, while tree water deficit corresponded to the drought periods. Correlation analysis and linear mixed-effects models revealed that precipitation and relative humidity are the most important limiting factors of stem radial increments, whereas air temperature and vapor pressure deficit significantly affected tree water balance and may play an important role in determining the growing season length and seasonality (i.e., duration, start, and cessation). This study reveals that moisture availability plays a major role for tree growth of P. kesiya var langbianensis in the Ailao Mountains, southwest China. Full article