Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA

Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding the forest carbon cycle. In the upper Yangtze River region, where ecosystems are incredibly fragile, the driving factors that make AGB changes differ from other regions. This study aims to investigate AGB’s spatial and temporal variation of Pinus densata in Shangri-La and decompose the direct and indirect effects of spatial attribute, climate, stand structure, and agricultural activity on AGB in Shangri-La to evaluate the degree of influence of each factor on AGB change. The continuous sample plots from National Forest Inventory (NFI) and Landsat time series were used to estimate the AGB in 1987, 1992, 1997, 2002, 2007, 2012, and 2017. The structural equation model (SEM) was used to analyze the different effects of the four factors on AGB based on five scales: entire, 1987–2002, 2007–2017, low population density, and high population density. The results are as follows: (1) The AGB of Pinus densata in Shangri-La decreased from 1987 to 2017, with the total amount falling from 9.52 million tons to 7.41 million tons, and the average AGB falling from 55.49 t/ha to 40.10 t/ha. (2) At different scales, stand structure and climate were the drivers that directly affect the AGB change. In contrast, the agricultural activity had a negative direct effect on the AGB change, and spatial attribute had a relatively small indirect effect on the AGB change. (3) Analyzing the SEM results at different scales, the change of the contribution of the agricultural activity indicates that human activity is the main negative driver of AGB change in Shangri-La, especially at the high population density region. In contrast, the change of the contribution of the stand structure and climate indicates that the loss of old trees has an important influence on the AGB change. Forest resources here and other ecologically fragile areas should be gradually restored by adhering to policies, such as strengthening forest protection, improving forest stand quality, and limiting agricultural production activities.

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

According to the forest resources inventory data for different periods and the latest estimation parameters of forest carbon reserves in China, the carbon reserves and carbon density of forest biomass in the Tibet Autonomous Region from 1999 to 2019 were estimated using the IPCC international carbon reserves estimation model. The results showed that, during the past 20 years, the forest area, forest stock, and biomass carbon storage in Tibet have been steadily increasing, with an average annual increase of 1.85×104 hm2, 0.033×107 m3, and 0.22×107 t, respectively. Influenced by geographical conditions and the natural environment, the forest area and biomass carbon storage gradually increased from the northwest to the southeast, particularly in Linzhi and Changdu, where there are many primitive forests, which serve as important carbon sinks in Tibet. In terms of the composition of tree species, coniferous forests are dominant in Tibet, particularly those containing Abies fabri, Picea asperata, and Pinus densata, which comprise approximately 45% of the total forest area in Tibet. The ecological location of Tibet has resulted in the area being dominated by shelter forest, comprising 68.76% of the total area, 64.72% of the total forest stock, and 66.34% of the total biomass carbon reserves. The biomass carbon storage was observed to first increase and then decrease with increasing forest age, which is primarily caused by tree growth characteristics. In over-mature forests, trees’ photosynthesis decreases along with their accumulation of organic matter, and the trees can die. In addition, this study also observed that the proportion of mature and over-mature forest in Tibet is excessively large, which is not conducive to the sustainable development of forestry in the region. This problem should be addressed in future management and utilization activities.

Interspecific variation and phylogenic architecture of Pinus densata and the hybrid of Pinus tabuliformis×Pinus Yunnanensis in the Pinus densata habitat: an Electrical Impedance Spectra perspective

We evaluated a novel and non-destructive method of the electrical impedance spectroscopy (EIS) to elucidatethe genetic and evolutionary relationship of homoploid hybrid conifer of Pinus densata (P.d) and its parental species Pinus tabuliformis (P.t) and Pinus yunnanensis(P.y), as well as the artificial hybrids of the P.t and P.y. Field common garden tests of96 trees sampled from 760 seedlings and 480 EIS records of 1,440 needles assessed the interspecific variation of the P.d, P.t, P.y and the artificial hybrids. We found that (1) EIS at different frequencies diverged significantly among germplasms; P.ywasthe highest, P.t was the lowest, and their artificial hybrids were within the range of P.t and P.y; (2) maternal species effect of EIS magnitudes inthe hybrids and P.d was stronger than the paternal species characteristics; (3)EIS of the artificial hybrid confirmed the mid-parent and partial maternal species characteristics;(4) unified exponential modelof EIS for the interspecific and hybrids canbe constructedas; (5) cluster analysis for species and hybrid combinationsin total corroborated with the previous hybrid model ofPinus densata. Our non-destructive EIS method complemented the previous finding that Pinus densata was originated from P.t and P.y. We conclude that the impedance would be a viable indicator to investigate the interspecific genetic variations of conifers.

Batch Extraction of Tree Stem From Natural Forest Based on Terrestrial Laser Scanning

Background: Aiming at the problems of low accuracy of tree stem extraction from point cloud data of natural forest and poor universality, a method for batch extraction of tree stem from natural forest point cloud data based on terrestrial laser scanning is proposed.Methods: First, the principal component analysis method is used to calculate the point cloud eigenvalues and eigenvectors, and the information entropy is minimized as a constraint to achieve the best neighborhood scale selection; Then combined with the spatial distribution features of the three-dimensional forest, using the Z-axis component of normal vector as the feature variable, the threshold method is used to filter out a large number of non-stem point clouds, and the 3D features are used for rough extraction of tree stem point cloud; Finally, density clustering is used to realize the precise extraction of tree stem point cloud. Results: Select the two typical representative natural forest sample plots of Pinus densata Mast. and Picea asperata Mast. in Shangri-La as the experimental data to extract stem. All the stem of the two natural forest sample plots were detected and extracted. Using the extracted individual tree stem point cloud and the true tree stem point cloud for correlation analysis, the R2 of the Pinus densata Mast. sample plot was 0.990, and the R2 of the Picea asperata Mast. sample plot with a more complex growth environment was 0.982. Conclusions: The results show that this method can well achieve batch extraction of tree stem point cloud from natural forest, and has the characteristics of high extraction accuracy and strong adaptability.

Capacitance characteristics of Pinus densata, Pinus tabuliformis, Pinus yunnanensis and the hybrids Pinus tabuliformis × Pinus Yunnanensis

We employed capacitance to evaluate the kinship and interspecific variation of homoploid hybrid conifer Pinus densata, P. tabuliformis, P. yunnanensis and artificial hybrids of P. tabuliformis (maternal parent) and P. yunnanensis (paternal parent) which were cultivated and selected in the common garden experiment. By measuring capacitance spectra under different voltage frequencies, we could differentiate different germplasms based on the electrical response. We aims to demonstrate that P. densata as the hybrid of P. tabuliformis and P. yunnanensis based on the capacitance values of the species, and to provide new evidence to the previously known biological evidence, as well as and the parental effect on the hybrids. Our results revealed that capacitance values between the species are significantly different in the spectra where P. yunnanensis positioned at the lowest and P. densata was much higher than all other species, indicating that P. densata had possessed a great capacity to store electrical energy. The capacitance spectra of P. densata and the artificial hybrid are not similar, which rejected our hypothesis. Both of the capacitance values of P. densata and the hybrids were closer to P. tabuliformis than to P. yunnanensis, which shows that the maternal influence was stronger than the paternal influence. Correlation analysis on the relationship between capacitance and fitness-related characteristics showed that capacitance is negatively correlated to mortality rate, and positively correlated with second-year survival rate. High capacitance values of P. densata and some of the hybrids reveal their superior adaptability to harsh environment in the Tibet Plateau. We concluded that capacitance as a new indicator for plant fitness and evolution evidence of homoploid hybrid conifers.

Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.

Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison

Optical remote sensing data have been widely used for estimating forest aboveground biomass (AGB). However, the use of optical images is often restricted by the saturation of spectral reflectance for forests that have multilayered and complex canopy structures and high AGB values and by the effect of spectral reflectance from underlayer shrub, grass, and bare soil for young stands. This usually leads to overestimations and underestimations for smaller and larger values, respectively, and makes it very challenging to improve the estimation accuracy of forest AGB. In this study, a novel methodology was proposed by incorporating stand age as a dummy variable into four models to improve the estimation accuracy of the Pinus densata forest AGB in Yunnan of Southwestern China. A total of eight models, including two parametric models (LM: linear regression model and LMC: LM with combined variables), two nonparametric models (RF: random forest and ANN: artificial neural network) without the age dummy variable, and four corresponding models with the age dummy variable (DLM, DLMC, DRF, and DANN), were compared to estimate AGB. Landsat 8 Operational Land Imager (OLI) images and 147 sample plots were acquired and utilized. The results showed that (1) compared with the two parametric models, the two nonparametric algorithms resulted in significantly greater estimation accuracies of Pinus densata forest AGB, and the increases of accuracy varied from 8% to 32% for 100 modeling plots and from 12% to 35% for 47 test plots based on root mean square error (RMSE); (2) compared with the models without the age dummy variable, the models with the age dummy variable greatly reduced the overestimations for the plots with AGB values smaller than 70 Mg/ha and the underestimations for the plots with AGB values larger than 180 Mg/ha and, thus, significantly improved the overall estimation accuracy by 14% to 42% for the modeling plots and by 32% to 44% for the test plots based on RMSE; and (3) the texture measures derived from the Landsat 8 OLI images contributed more to improving the estimation accuracy than the original spectral bands and other transformations. This implied that two nonparametric models, coupled with the use of the age dummy variable and texture measures, offered a great potential for improving the estimation accuracy of Pinus densata forest AGB.