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.

[Additive aboveground biomass equations based on different predictors for natural Tilia Linn]. / åŸºäºŽä¸åŒé¢„æµ‹å˜é‡çš„å¤©ç„¶æ¤´æ ‘å¯åŠ æ€§åœ°ä¸Šç”Ÿç‰©é‡æ¨¡åž‹æž„å»º.

Biomass is a basic quantitative character of forest ecosystem. Biomass data are foundation of researching many forestry and ecology problems. 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. Constructing biomass models is considered a good approach to estimate forest biomass. Based on biomass data of 97 sampling trees of natural Tilia Linn. in Xiaoxing'an Mountains and Zhangguangcai ranges, three additive systems of individual tree biomass equations were developed: based on tree diameter at breast height (D) only, based on tree diameter at breast height and height (H), and based on the best models. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The heteroscedasticity in model residuals was addressed by applying a unique weight function to each equation. The individual tree biomass model validation was accomplished by Jackknifing technique. The results showed that three additive systems of individual tree biomass equations could fit and predict the biomass of Tilia Linn. well (adjusted coefficient of determination Ra2>0.84, mean predicted error percentage MPE<8.5%, mean absolute error MAE<16.3 kg,mean standard error percentage MPSE<28.5%). The biomass equations of stem and aboveground were better than biomass equations of branch, foliage and crown. Adding total tree height and crown factor in the additive systems of biomass equations could significantly improve model fitting performance and predicting precision (Ra2 improved from 0.01 to 0.04, MAE decreased from 0.01 to 4.55 kg), narrow the confidence interval of the predicted value and the biomass of stem, foliage and aboveground were increased more than the biomass of branch and crown. In general, the equations of the additive system based on the best models produced the best model fitting, followed by that of the additive system based on D and H, and that based on D. It was essential to develop biomass model by adding total tree height and crown factor.

[Stand-level biomass estimation models for the tree layer of main forest types in East Daxing'an Mountains, China.] / å¤§å ´å®‰å²­ä¸œéƒ¨ä¸»è¦æž—åˆ†ç±»åž‹ä¹”æœ¨å±‚ç”Ÿç‰©é‡ä¼°ç®—æ¨¡åž‹.

Forest biomass estimation methods of regional scale attract most attention of the resear-chers, with developing stand-level biomass model being a research trend. Based on the biomass data from fix forest types, two additive systems of biomass equations based one- and two-variable were developed. The model error structure (additive vs. multiplicative) of the allometric equation was evaluated using the likelihood analysis. The nonlinear seemingly unrelated regression (NSUR) was used to estimate the parameters in the additive system of stand-level biomass equations. The results showed that the assumption of multiplicative error structure was strongly supported for the stand-level biomass equations of total and components for those forest types. Thus, the additive system of log-transformed biomass equations was developed. The adjusted coefficient of determination of the additive system of biomass equations was 0.78-0.99, the mean relative error was between -2.3%-6.9%, and the mean absolute relative error was between 6.3%-43.3%. Adding mean tree height in the additive systems of biomass equations could significantly improve the model fitting performance and predicting precision for most of the models. The biomass equations of total, aboveground and stem were better than biomass equations of root, branch, foliage and crown. In order to estimate model parameters more effectively, the additivity property of estimating tree total, sub-totals, and component biomass should be taken into account. Overall, the stand-level biomass models established in this study would be suitable for predicting stand-level biomass of six forest types in Daxing'an mountains.

[Predicting models of leaf area for trees in Larix olgensis plantation.] / 人工长白落叶松立木叶面积预估模型.

Leaf area influences dry matter production of trees, as well as the growth of trees and forest stands. The accurate estimation of leaf area plays an important role in analyzing the growth of trees and forest stands. Based on data of 76 Larix olgensis trees in a plantation of Heilongjiang Province, predicting models of branch leaf area (BLA) and crown leaf area (CLA) were constructed, respectively. The results showed that a form of lnBLA=ß1+(ß2+b2)lnBD+(ß3+b3)lnRDINC+ß4lnDBH+ß5lnHT/DBH+(ß6+b6)lnCR was selected as the optimal BLA mixed-effect model with the considera-tion of tree-level random effects, composed of three random-effect on lnBD, lnRDINC and lnCR (ßi represented model fixed parameters, bi represented model random-effect parameters, BD was branch diameter, RDINC was the relative depth into crown from tree apex, DBH was tree diameter at breast height, HT/DBH represented the ratio of tree height to DBH, and CR represented the ratio of crown length to tree height). The adjusted coefficient of determination (Ra2), residual mean squares error (RMSE), mean error (ME), mean absolute error (MAE) and precision estimation (P) of the optimal BLA mixed model were 0.90, 0.5477, -0.03, 0.24 and 91%, respectively, indicating the model had a good performance in predicting. The CLA was calculated by predicted values of all branches based on developed BLA model and the final form of CLA model was as follows: lnCLA=γ0+γ1lnDBH+γ2CR (γi, model parameters). Results of likelihood ratio test (P>0.05) showed that plot-level random effect had no influence on the model performance, which can be ignored. The CLA model got a good-fitting effect with R2 and RMSE being 0.87 and 0.3847, respectively. The CLA predicting model developed in this study could provide a good prediction of CLA for L. olgensis trees and provided a theoretical basis for the research on distribution of leaf area and photosynthesis.

[Change pattern of heartwood of Larix olgensis plantation.] / 长白落叶松人工林心材变化规律.

Based on the data of 1179 discs and whorls of 49 trees from larch (Larix olgensis) plantations located in Mengjiagang forest farm in Heilongjiang Province, China, we analyzed the longitudinal variation pattern of heartwood radius. The results showed that the heartwood radius decreased with the increases of tree height, which was basically the same as the trunk shape. The relationship between the xylem radius (XR), diameter at breast height (DBH) and cambial age (CA) with the heartwood radius was significant. The stepwise regression analysis was used to develop heartwood radius (HR) and heartwood area (HA) models: HR=b1+b2XR2+b3CA+b4XR, HA=b1+b2DBH·XR+b3CA+b4DBH·XR2. We used the evaluation statistics such as AIC, BIC, Log Likelihood and Likelihood ratio test to compare the heartwood radius and heartwood area models which fitted with the plot effect and tree effect. The heartwood radius and heartwood area models with parameters b1, b2, b3 as mixed effects performed best when the tree effect was considered. The prediction accuracy of the mixed model was better than that of the basic model. In the application, the total heartwood radius and area could be predicted by the mixed model. Beta regression model was used to simulate the heartwood proportion. In this model, all parameters were significant, and the coefficients of determination were relatively high, with a good simulation effect.

[Knot attributes of Korean pine plantation based on mixed effect model.] / 基于混合效应模型的人工红松节子属性.

Based on 1534 knot data from 60 sample trees in a Korean pine plantation in Mengjiagang Forest Farm, Heilongjiang Province, China, mixed effect model of knot attributefactors (knot diameter, sound knot length, year of death of knot and knot angle) of Korean pine plantation was established using NLMIXED and GLIMMIX procedures of SAS software. The prediction accuracy of models was compared using evaluation statistics, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), -2Log likelihood(-2LL), and likelihood ratio test (LRT). Results showed that all of the mixed effect models that considered tree effect performed better than conventional fixed-effect models. For knot diameter models, the model with random parameter combination of b1, b2 had the best performance. For sound knot length models, the model with random parameter combination of b1, b3 had the best performance. For the models of year of death of knot, the model with random variables of knot diameter was proved to be the optimal generalized linear mixed model. For the models of knot angle, the model with randomvariables of intercept, knot diameter, sound knot length was proved to be the optimal generalized linear mixed model. Mixed effect model was more effective than conventional fixed-effect model for describing knot attributes. The combination of knot attributes models and reasonable prunning schemes could improve timber quality of Korean pine which is one of the main commercial tree species in Northeast China.

[Estimating in situ maximum net photosynthetic rate of Larix olgensis based on abscised mea-surement]. / åŸºäºŽç¦»ä½“æµ‹é‡ä¼°è®¡é•¿ç™½è½å¶æ¾åŽŸä½æœ€å¤§å‡€å ‰åˆé€ŸçŽ‡çš„æ–¹æ³•.

The data of needle in situ maximum net photosynthetic rate (SPn max) and abscised maximum net photosynthetic rate (APn max) were measured for the 15 year-old planted Larix olgensis stand in the Maoershan Forest Farm, Heilongjiang Province, China. The change pattern between APn max and abscised time (ta) was analyzed and the functional relationship between SPn max and APn max with ta was also established. Finally, the prediction model of SPn max for planted L. olgensis trees was developed by analyzing the effect of tree size and environmental factors on the decline of APnmax. The results showed that needle APn max decreased with the increase of ta without restoring water supply. The higher vapor pressure deficit (VPD) and the leaf temperature (Tleaf) would lead to faster and lager reduction of APn max. Taking VPD and ta as the independent variables of the linear regression model had the best goodness of fit for SPn max (Ra2 were 0.774 and RMSE was 20.73). The model prediction precision decreased with the increase of ta, but after 20 min it would be stabilized at 97%. Overall, estimating SPn max of L. olgensis trees by developing regression model based on abscised measurement not only had a well predictive ability but also had a stable predictive precision, and greatly improved the efficiency of field measurement. The results of this study could be suitably applied to measure SPn max in practice.

[Individual tree diameter increment model for natural Betula platyphylla forests based on meteorological factors]. / åŸºäºŽæ°”è±¡å› å­çš„ç™½æ¡¦å¤©ç„¶æž—å•æœ¨ç›´å¾„ç”Ÿé•¿æ¨¡åž‹.

Based on the 212 re-measured permanent plots for natural Betula platyphylla fore-sts in Daxing'an Mountains and Xiaoxing'an Mountains and 30 meteorological stations data, an individual tree growth model based on meteorological factors was constructed. The differences of stand and meteorological factors between Daxing'an Mountains and Xiaoxing'an Mountains were analyzed and the diameter increment model including the regional effects was developed by dummy variable approach. The results showed that the minimum temperature (Tg min) and mean precipitation (Pg m) in growing season were the main meteorological factors which affected the diameter increment in the two study areas. Tg min and Pg m were positively correlated with the diameter increment, but the influence strength of Tg min was obviously different between the two research areas. The adjusted coefficient of determination (Ra2) of the diameter increment model with meteorological factors was 0.56 and had an 11% increase compared to the one without meteorological factors. It was concluded that meteorological factors could well explain the diameter increment of B. platyphylla. Ra2 of the model with regional effects was 0.59, and increased by 18% compared to the one without regional effects, and effectively solved the incompatible problem of parameters between the two research areas. The validation results showed that the individual tree diameter growth model with regional effect had the best prediction accuracy in estimating the diameter increment of B. platyphylla. The mean error, mean absolute error, mean error percent and mean prediction error percent were 0.0086, 0.4476, 5.8% and 20.0%, respectively. Overall, dummy variable model of individual tree diameter increment based on meteorological factors could well describe the diameter increment process of natural B. platyphylla in Daxing'an Mountains and Xiaoxing'an Mountains.

[A two-step approach for modeling tree mortality in Larix olgensis plantation based on effects of thinning]. / 基于抚育间伐效应的长白落叶松人工林两阶段枯死模型.

Ten permanent plots of Larix olgensis plantation were established in 1972 and 1974 at Jiangshanjiao and Mengjiagang forest farms in Heilongjiang Province, respectively. The plots including 8 thinning plots and 2 control plots were measured annually. The effects of thinning on the probability of plot mortality and individual tree mortality were analyzed. Based on the binary logistic regression, two-step models of the probability of mortality were developed. The approach consisted of estimating the probability of mortality after thinning on a sample plot (1) and the mortality of individual tree within mortality plots (2). The generalized estimating equations (GEE) method was adopted to estimate the parameters of models. An optimal cutpoint was determined for each model by plotting the sensitivity curve and the specificity curve and choosing the cutpoint at which the specificity and sensitivity curves cross. The results showed that four models (models 1-4) were developed based on the data of plots which was divided into 4 groups by thinning times, respectively. The significant explicatory variables of model 1 were site index, the logarithm of stand age, thinning age and thinning intensity. Principal component analysis was used to develop models 2-4. The primal variables of the principal components were stand age, tree numbers per hectare, mean square diameter at breast height and thinning factors. This showed that thinning significantly affected the probability of plot mortality. The effect of thinning was not significant for the pro-bability of individual tree mortality. The significant variables of the individual tree mortality model were planting density, age, the inverse of diameter at breast height and the basal area of all trees larger than the subject tree. Hosmer and Lemeshow goodness of fit tests were not significant for the mortality models of plots and individual trees (P＞0.05). The areas under the receiver operating characteristic curve (AUC) of the models were all greater than 0.91, the accuracies were all above 80%, suggesting the fitting results of the models performed very well.

[Spatial distribution of soil total nitrogen in Liangshui National Nature Reserve based on local model].

Based on LiDAR data of Liangshui National Nature Reserve, digital elevation model (DEM) was constructed and both primary terrain attributes (slope, aspect, profile curvature, etc.) and secondary terrain attributes (wetness index, sediment transport index, relative stream power index, etc.) were extracted. According to the theory of soil formation, geographically weighted regression (GWR) was applied to predict soil total nitrogen (TN) of the area, and the predicted results were compared with those of three traditional interpolation methods including inverse distance weighting (IDW), ordinary Kriging (OK) and universal Kriging (UK). Results showed that the prediction accuracy of GWR (77.4%) was higher than that of other three interpolation methods and the accuracy of IDW (69.4%) was higher than that of OK (63.5%) and UK (60.6%). The average of TN predicted by GWR reached 4.82 g . kg-1 in the study area and TN tended to be higher in the region with higher elevation, bigger wetness index and stronger relative stream power index than in other areas. Further, TN also varied partly with various aspects and slopes. Thus, local model using terrain attributes as independent variables was effective in predicting soil attribute distribution.

[Maximum density-size line for Larix olgensis plantations based on quantile regression.] / 基于分位数回归的长白落叶松人工林最大密度线.

Based on 378 permanent and 415 temporary plots from Northeast China, the relationship of maximum stand density and quadratic mean diameter at breast height of treesfor Larix olgensis plantation was developed. Linear quantile regression model with different quantiles (τ=0.90, 0.95, 0.99) was used and the optimal model for the maximum density-size line model was selected. The ordinary least square (OLS) and maximum likelihood (ML) regression were also employed to develop the maximum density-size line by using the arbitrary selected data. Generalized Pareto model of extreme value theory was used to calculate the number of limited maximum trees based on the current stands so that the limited density-size line was developed. The linear quantile regression model was compared with the other methods. The results showed that selecting 5 points within the whole diameter class for the maximum density-size line model development would get the satisfying prediction model. The fitting line would deviate from the maximum density-size line with the increasing points selected. The method of ML was superior to OLS in parameter estimation. The linear quantile regression model with the quantile of 0.99 achieved similar fitting results compared with ML regression and the estimation results was much stable. Traditional approach that selecting fittng data was considered arbitrary so that linear quantile regression with quantile of 0.99 was selected as the best model to construct the maximum density-size line with the estimates for the parameters as k=11.790 and ß=-1.586, and k=11.820 and ß=-1.594 for the limited density-size line model. The determined limited density-size line was above the maximum density-size line but the difference was not pronounced. The validation results by using the data of permanent sample plots showed the models were suitable to predict the maximum and limited density line of the current forest stands, which would provide basis for the sustainable management of L. olgensis plantation.

[Carbon storage of forest vegetation and allocation for main forest types in the east of Da-xing'an Mountains based on additive biomass model]. / åŸºäºŽå¯åŠ æ€§ç”Ÿç‰©é‡æ¨¡åž‹çš„å¤§å ´å®‰å²­ä¸œéƒ¨ä¸»è¦æž—åž‹æ£®æž—æ¤è¢«ç¢³å‚¨é‡åŠå ¶åˆ†é .

Based on the biomass investigation data of main forest types in the east of Daxing'an Mountains, the additive biomass models of 3 main tree species were developed and the changes of carbon storage and allocation of forest community of tree layer, shrub layer, herb layer and litter layer from different forest types were discussed. The results showed that the carbon storage of tree layer, shrub layer, herb layer and litter layer for Rhododendron dauricum-Larix gmelinii forest was 71.00, 0.34, 0.05 and 11.97 t·hm-2, respectively. Similarly, the carbon storage of the four layers of Ledum palustre-L. gmelinii forest was 47.82, 0.88, 0, 5.04 t·hm-2, 56.56, 0.44, 0.04, 8.72 t·hm-2 for R. dauricum-mixed forest of L. gmelinii-Betula platyphylla, 46.21, 0.66, 0.07, 6.16 t·hm-2 for L. palustre-mixed forest of L. gmelinii-B. platyphylla, 40.90, 1.37, 0.04, 3.67 t·hm-2 for R. dauricum-B. platyphylla forest, 36.28, 1.12, 0.18, 4.35 t·hm-2 for L. palustre-B. platyphylla forest. The carbon storage of forest community for the understory vegetation of R. dauricum was higher than that of the forest with L. palustre. In the condition of similar circumstances for the understory, the order of carbon storage for forest community was L. gmelinii forest > the mixed forest of L. gmelinii-B. platyphylla > B. platyphylla forest. The carbon storage of different forest types was different with the order of R. dauricum-L. gmelinii forest (83.36 t·hm-2)> R. dauricum-mixed forest of L. gmelinii-B. platyphylla (65.76 t·hm-2) > L. palustre-L. gmelinii forest (53.74 t·hm-2)> L. palustre-mixed forest of L. gmelinii-B. platyphylla (53.10 t·hm-2)> R. dauricum-B. platyphylla forest (45.98 t·hm-2) > L. palustre-B. platyphylla forest (41.93 t·hm-2). The order of carbon storage for the vertical distribution in forest communities with diffe-rent forest types was the tree layer (85.2%-89.0%) > litter layer (8.0%-14.4%) > shrub layer (0.4%-2.7%) > herb layer (0-0.4%).

[Comparison of three stand-level biomass estimation methods]. / 三种林分生物量估算方法的比较.

At present, the forest biomass methods of regional scale attract most of attention of the researchers, and developing the stand-level biomass model is popular. Based on the forestry inventory data of larch plantation (Larix olgensis) in Jilin Province, we used non-linear seemly unrelated regression (NSUR) to estimate the parameters in two additive system of stand-level biomass equations, i.e., stand-level biomass equations including the stand variables and stand biomass equations including the biomass expansion factor (i.e., Model system 1 and Model system 2), listed the constant biomass expansion factor for larch plantation and compared the prediction accuracy of three stand-level biomass estimation methods. The results indicated that for two additive system of biomass equations, the adjusted coefficient of determination (Ra2) of the total and stem equations was more than 0.95, the root mean squared error (RMSE), the mean prediction error (MPE) and the mean absolute error (MAE) were smaller. The branch and foliage biomass equations were worse than total and stem biomass equations, and the adjusted coefficient of determination (Ra2) was less than 0.95. The prediction accuracy of a constant biomass expansion factor was relatively lower than the prediction accuracy of Model system 1 and Model system 2. Overall, although stand-level biomass equation including the biomass expansion factor belonged to the volume-derived biomass estimation method, and was different from the stand biomass equations including stand variables in essence, but the obtained prediction accuracy of the two methods was similar. The constant biomass expansion factor had the lower prediction accuracy, and was inappropriate. In addition, in order to make the model parameter estimation more effective, the established stand-level biomass equations should consider the additivity in a system of all tree component biomass and total biomass equations.