Effects of thinning on structural complexity of Larix olgensis plantation.

In this paper, we collected the individual tree point cloud data in the plots of Larix olgensis plantations with different thinning intensities in Mengjiagang Forest Farm, applied the fractal analysis theory to extract box dimensions (Db) on MATLAB platform, and characterized the structural complexity of L. olgensis. We assessed the effect of different thinning intensities and tree attributes on the structural complexity of L. olgensis. The results showed significant differences in L. olgensis Db between control (CK: 1.68±0.07), low and medium intensity thinning (T1, T2, T3: 1.74±0.07), and high intensity thinning (T4: 1.81±0.06), which indicated that the thinning intensity increased tree structural complexity. For trunk attribute, the diameter at breast height and tree height was significantly positively correlated with Db, while the height-to-diameter ratio was significantly negatively correlated with Db. For canopy attribute, crown volume, surface area, projected area, and crown diameter was significantly positively correlated with Db. Hegyi competition index was significantly negatively correlated with Db in the control and low-moderate-intensity thinning treatments, but not significantly correlated with Db in the high-intensity thinning treatment. It indicated that thinning influenced L. olgensis structural complexity, with trunk attribute and canopy attribute as the main drivers of L. olgensis structural complexity.

Growth difference of planted Pinus koraiensis from different provenances in Maoer Mountain, China.

In order to analyze the growth pattern of tree height of planted Pinus koraiensis and screen the provenances with fastest growth, we grouped the provenances using the differences in tree height, diameter at breast height (DBH) and volume of timber of 234 individuals of planted P. koraiensis from 26 provenances in Maoershan Experimental Forest Farm. We constructed the growth equation for tree height by combining the base models of Gompertz, Korf, Richards, Logistic, and Schumacher, and then selected the optimal one. We introduced the prove-nance grouping as a dummy variable into the base model, and evaluated the optimal tree height growth equation by a comprehensive evaluation of the model according to the coefficient of determination (R2), the root-mean-square error (RMSE), the Akaikei Information Criterion (AIC), and the model's predictive precision (FP). The results showed that the growth traits of the 26 provenances had significant difference among the groups, and that tree height and DBH showed significant differences among the provenances. According to the comprehensive consideration of different growth traits, the four groups of provenance growth were divided into group A (Wuying, Hebei, Linjiang, Dongfanghong, Huanan, Lushuihe, Fangzheng) >group B (Aihuisanzhan, Liangshui, Tieli, Qinghe) > group C (Wuyiling, Zhanhe, Liangzihe, Baihe, Chaihe, Caohekou, Bajiazi) >group D (Tongzigou, Dashitou, Wangqing, Helong, Yanshou, Dahailin, Xiaobeihu, Muling). The optimal base tree height growth model of the four groups was the Gompertz model, and the fitting accuracy of the model after the introduction of dummy variables (R2=0.9353) was higher than that of the base model (R2=0.9303), and the model prediction accuracy was also improved. The tree height growth curves of each provenance group conformed to the "S"-shaped rule of change. There were obvious differences among the groups, with the best performance of the provenances in group A. The growth of P. koraiensis from different provenances was different, and the tree height growth model with dummy variables of provenance groups could effectively improve the prediction accuracy of the model, reflect the differences in height growth of P. koraiensis of different provenances, which could provide the scientific basis for the selection and cultivation of P. koraiensis plantations.

Model construction for height to crown base of Larix olgensis based on mixed-effects model and quantile regression.

Height to crown base is an important index reflecting the characteristics of tree crown. It is of great significance to accurately quantify height to crown base for forest management and increasing stand production. We used nonlinear regression to construct the height to crown base generalized basic model, and further extended that to the mixed-effects model and quantile regression model. The prediction ability of the models was evaluated and compared by the 'leave-one-out' cross-validate. Four sampling designs and different sampling sizes were used to calibrate the height to crown base model, and the best model calibration scheme was selected. The results showed that based on the height to crown base generalized model including tree height, diameter at breast height, basal area of the stand and average dominant height, the prediction accuracy of the expanded mixed-effects model and the combined three-quartile regression model were obviously improved. The mixed-effects model was slightly better than the combined three-quartile regression model, and the optimal sampling calibration scheme was to select five average trees. The mixed-effects model with five average trees was recommended to predict the height to crown base in practice.

[Constructing biomass models for natural Quercus mongolica based on Bayesian seemingly unrelated regression]. / åŸºäºŽè´å¶æ–¯ä¼¼ä¹Žä¸ç›¸å ³å›žå½’æ–¹æ³•çš„å¤©ç„¶è’™å¤æ Žç”Ÿç‰©é‡æ¨¡åž‹æž„å»º.

In this study, the biomass models for natural Quercus mongolica in Heilongjiang Province were constructed based on the predictors of diameter at breast height (D) and tree height (H) by several methods including multivariate likelihood analysis and seemingly unrelated regression. The results showed that the H could significantly improve the stem biomass model, with the coefficient of determination (R2) being increased from 0.953 to 0.988 and the root mean square error (RMSE) being reduced by 14 kg, but it had no significant improvement for the biomass model of branch, foliage, and root. The error structures of both biomass model systems (only D and D-H) were multiplicative, indicating that the linear models after logarithmic transformation were more appropriate. The R2 for the biomass models of stem, branch, foliage and root were 0.953-0.988, 0.982-0.983, 0.916-0.917, and 0.951-0.952, while the RMSE were 13.42-27.03, 6.84-7.00, 1.95-1.97 and 9.71-9.84 kg. Compared with the feasible generalized least squares (FGLS) approach, Bayesian estimation had similar fitting performance and provided parameter estimates with different variations. The standard errors of parameters for FGLS were 0.054-0.211. There were similar variations (standard deviations of 0.055-0.221) for the two Bayesian estimation with no prior information (DMC and Gibbs1). The Gibbs sampler with a multivariate normal distribution with a mean vector of 0, variances of 1000 and covariances of 0 (Gibbs2) or the prior information from the historical researches summary for Quercus trees biomass models (Gibbs3) produced greater variation than those of FGLS, DMC, and Gibbs1 (stan-dard deviations were 0.080-0.278), while Gibbs sampler with the prior information obtained from own data (Gibbs4) provided the lower variations than others (standard deviations were 0.004-0.013). The Gibbs4 approach provided the narrowest 95% prediction interval and produced the smaller prediction biases, with the average absolute error percentage (MAPE) for stem, branch, foliage, root and total of the only-D biomass model being 19.8%, 24.7%, 24.6%, 29.0% and 13.1%, while MAPE for the corresponding components of D-H biomass model kept same except for stem and total decreased to 10.5% and 9.8%, which indicated that Gibbs4 could provide more accurate biomass predictions. Compared with classical statistics, accurate prior information made Bayesian seemingly unrelated regression an advantage in estimation stability and uncertainty reduction.

[Effects of mixing proportions on carbon storage and allocation in mixed plantation of Larix olgensis and Fraxinus mandshurica]. / æ··äº¤æ¯”ä¾‹å¯¹é•¿ç™½è½å¶æ¾å’Œæ°´æ›²æŸ³æ··äº¤æž—ç¢³å‚¨é‡åŠå ¶åˆ†é æ ¼å±€çš„å½±å“.

In this study, four types of mixed Larix olgensis and Fraxinus mandshurica plantations were selected according to the rows-mixing proportions (type Ⅰ: 5:3, type Ⅱ: 6:4, type Ⅲ: 5:5, type Ⅳ: 1:1). The see-mingly unrelated biomass models of L. olgensis and F. mandshurica were developed for obtaining biomass values, and the difference and composition of carbon storage in each forest layer and ecosystem were analyzed. The results showed that carbon storage of arbor layer in different stand types was 39.86-50.12 t·hm-2, the carbon storage of arbor layer inⅠ, Ⅱ and Ⅳ was significantly higher than that in type Ⅲ. The carbon storage of understory was 0.10-0.30 t·hm-2, with that in type Ⅱ being significantly higher than other types. Carbon storage of litter layer was 4.43-6.96 t·hm-2, with type Ⅱ and Ⅲ being significantly higher than those of the other types. In the soil layer, carbon storage was 34.97-54.66 t·hm-2. The carbon storage of soil layer in type Ⅱ was significantly greater than those in the other types. At the whole ecosystem level, carbon storage of type Ⅰ-Ⅳ was 90.43, 108.27, 85.83 and 89.92 t·hm-2, respectively. Type Ⅱ had significantly greater carbon storage than the other types. The arbor layer and soil layer were the major carbon pools in the ecosystem, which accounted for 43.3%-55.7% and 38.7%-50.5% of the total, respectively. Our results suggested that mixing by six rows of L. olgensis and four rows of F. mandshurica was better for future planting.

[Prediction of crown leaf mass per area for young Larix olgensis plantation]. / äººå·¥é•¿ç™½è½å¶æ¾å¹¼é¾„æž—æ ‘å† æ¯”å¶é‡.

Leaf mass per area (LMA) is an important parameter in the construction of the ecosystem process models. Accurate prediction of the dynamic validation of canopy LMA is of significance to improve the accuracy of ecosystem process models. We conducted vertical whorl-by-whorl sampling and analyzed LMA in different seasons for Larix olgensis plantation in Maoershan in Shangzhi, Heilongjiang Province, China. We analyzed the vertical and developmental variations of LMA and their main effective factors, established the dynamic prediction model of LMA for young L. olgensis plantation. The results showed that the LMA decreased with the increases of relative depth into crown (RDINC) in the vertical direction of the crown. The range of LMA in the vertical direction after leaf expanded was significantly larger than that during leaf expanding. During the different development periods of leaves, LMAs increased first and then remained stable, and this trend gra-dually weakened with the increases of crown depth. The Ra2 values were lower than 0.6 when RDINC or DOY were used as the single variable to model LMA, but were increased by 0.19 when both of them being used, and the model performed well in validation (ME=0.54 g·m-2, MAE=5.74 g·m-2). LMA varied across different crown whorls and different leaf development periods. The LMA model constructed with RDINC and DOY could well describe the vertical and temporal variations of LMA. The simulation of crown LMA provided a basis for clarifying crown development and a foundation for the establishment of ecological process model.

[Prediction models of knot volume inside the stem for Korean pine plantation]. / äººå·¥çº¢æ¾æ ‘å¹²å† éƒ¨èŠ‚å­ä½“ç§¯é¢„æµ‹æ¨¡åž‹.

Based on 1207 knots from 49 sample trees of 29 standard plots of Korean pine plantations in Linkou and Dongjingcheng Forest Bureau of Heilongjiang Province, China, we extracted longitudinal sections of knots using the image processing software Digimizer and represented the shape of knots using two-dimensional scatter plots. According to the two-dimensional scatter plots, knots of Korean pine plantation were divided into three types: 1) alive knots (whole knot contained only sound knot portion); 2) non-occluded dead knots (whole knot contained both sound and loose knot portions); 3) occluded dead knots (the sound and loose portion of the knot were partially occluded by the bark). For all the three types of knots, the volume of sound knot was calculated by mathematical integral of the sound knot shape equation. The volume of loose knot was calculated using the volume equation of a cylinder. The total volume of knots was calculated as the sum of sound and loose knot volume. Finally, based on knot variables (diameter, relative height and total length of knots) and tree variable (diameter at breast height), we established the prediction models for sound knot volume, loose knot volume, and total volume of knot using the linear mixed model at plot level and tree level. Compared with fixed-effects model, the mixed effects models of the volume of sound knot, loose knot, and total knots provided more accurate parameter estimation, more uniform residual distribution, and higher model fitting precision. The validation results showed that prediction precision of each fixed-effect model was higher than 90%, while that of the mixed models with plot and tree effect was above 93%, indicating that the established model could well predict the volume of knot for Korean pine plantation.

[Prediction models of sapwood density, heartwood density, and bark density in Larix olgensis plantation]. / äººå·¥é•¿ç™½è½å¶æ¾æ ‘å¹²è¾¹æã€å¿ƒæå’Œæ ‘çš®å¯†åº¦é¢„æµ‹æ¨¡åž‹.

In this study, the Beta regression models of sapwood, heartwood, and bark density of Larix olgensis were constructed. A total of 35 trees were destructively sampled from plantations in three different sites, Linkou Forestry Bureau of Heilongjiang Province, Dongjingcheng Forestry Bureau, and Maoershan Experimental Forest Farm of Northeast Forestry University. AIC, R2, BIAS, RMSE and LRT were used as the goodness-of-fit statistics to compare and select the most optimal models for sapwood, heartwood, and bark density. The jackknife resampling technique was used to verify and evaluate the developed models. The results showed that the independent variables of the optimal sapwood, heartwood, and bark density model were not identical. Sapwood density had a good relationship with tree age, tree height, relative height, and the square of relative height. The independent variables of the optimal heartwood density model were annual growth, relative height, and the square of relative height. The independent variables of the optimal bark density model were tree age, annual growth, relative height, and the square of relative height. The analysis of the optimal model showed that from the base to the tip of the trunk, sapwood density decreased gradually, heartwood density initially decreased and then increased regularly, bark density initially increased and then decreased gradually. The established Beta regression models could predict sapwood, heartwood, and bark density of L. olgensis at any position in the research area and be an essential basis for the study of trunk average density and biomass.

[Ground diameter-height models of naturally regenerated seedlings and saplings under broad-leaved mixed forest in Maoershan Mountains.] / å¸½å„¿å±±é˜”å¶æ··äº¤æž—å¤©ç„¶æ›´æ–°å¹¼è‹—å¹¼æ ‘åœ°å¾„-æ ‘é«˜æ¨¡åž‹.

Based on the investigation data of seedlings and saplings from 48 plots in natural broad-leaved forest of Maoershan Experimental Forest Farm of Northeast Forestry University in Heilongjiang Province, the optimum model of ground diameter (D0) - height (H) was selected from eight alternative models as the basic model for the main regeneration tree species, and then the stand factors were parameterized, and the mixed effect model of sampling plot level was developed. The basic model and the mixed effect model were tested by independent samples. The results showed that there was a significant positive correlation between ground diameter and tree height of seedlings and saplings and that power function or model containing power function could better fit the relationship. The introduction of stand factors [dominant height of forest (HT), average diameter at breast height (Dg), basal area of forest (BA)] could improve the fitting effect of the model, with the residual root mean square error (RMSE) of each tree species decreasing by 1.3%-7.4% (average 3.8%), adjusted coefficient of determination (Ra2) only increasing by 0.1%-1.1% (average 0.6%) and Akaike info criterion (AIC) decreasing by 3.2%-35.2% (average 11.4%). Mixed effect models were developed for 10 tree species, such as Ulmus propinqua, Tilia and Fraxinus mandshurica. The Ra2 of mixed effect models was larger than that of the basic model, with an enhancement of 0.5%-3.5% (average 2.2%). RMSE and AIC decreased by 3.9%-20.3% (ave-rage 13.9%) and 4.0%-44.4% (average 22.3%) than that of the basic model. Model test results showed that, compared with the basic model, the average absolute error (MAE) of mixed effect model was reduced by 0.0001-0.46 m, with an average reduction of 0.08 m, and the average prediction error percentage (MPSE) decreased by 0.1%-6.2%, with an average reduction of 2.0%. The mixed effect model could improve the fitting effect and prediction ability of the model. The ground diameter-height model of seedlings and samplings of main regeneration species in broad-leaved mixed forest was developed in this study, which provides a reference for structure analysis and stand growth prediction of natural broad-leaved forest.

Prediction model system with dummy variables for carbon storage of larch plantation in Heilongjiang Province, China.

Based on monitoring data from fixed plots of larch plantation in Heilongjiang Province obtained in 2005 and 2010, we analyzed the relationship among stand variables of larch plantation in Heilongjiang Province and established site class index curve model and stand density index model. A two-stage least square method was used to establish a simultaneous equations system for predicting average basal area and carbon storage of stands. Together, they were called prediction model system for carbon storage of larch plantation in Heilongjiang Province. The system included two dummy variables of age group and region. Results showed that the determination coefficients of those models were all greater than 0.98, and the root mean square errors were less than 4, except for the site class index curve model. The model with dummy variables increased the determination coefficient, and the root mean square error was less than 3, indicating that the model had good stability with accutrate estimated parameters. The average relative error of all models was less than 2%, the absolute value of the average relative error of most models was less than 15%, and the accuracy of all models was above 95%, indicating that the models could be used to accurately predict the carbon storage of larch plantations in different regions and age groups in Heilongjiang Pro-vince. According to the analysis of the site class index curve model and the estimated parameters of the simultaneous equations, the greater stand age, the larger stand average height, average basal area and carbon storage when the survey plots were located in the same area, which fitted natural growth rules. Under the same stand but different regions, the stand average height decreased in the order of plain area, southern slope area of Xiao-xing'anling, eastern slope area of Zhangguangcai-ling, Wandashan area, western slope area of Zhangguangcailing, and northern slope area of Xiao-xing'anling, while the order of stand basal area and carbon storage was eastern slope area of Zhangguangcailing, northern slope area of Xiaoxing'anling, western slope area of Zhangguangcailing, southern slope area of Xiaoxing'anling, Wandashan area, and plain area.