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.

Application of traditional Chinese and Western medicine combined with chronic disease management in pulmonary rehabilitation and evaluation of efficacy.

OBJECTIVE: This study evaluated the efficacy of traditional Chinese and western medicine combined with chronic disease management on rehabilitation of chronic obstructive pulmonary disease (COPD) patients. METHODS: A total of 199 COPD patients in Shanghai Construction Group (SCG) Hospital were recruited as research objects. The control group (CG) consisted of 100 patients treated with conventional western chronic disease management, and the research group (RG) consisted of 99 patients treated with chronic disease management with combined traditional Chinese and western medicine. The efficacy, pulmonary rehabilitation performance, compliance score, 6-minute walk test (6MWT), modified Medical Research Council dyspnoea scale (MMRC), COPD assessment test (CAT), pulmonary function (PaO2, PaCO2, FEV1, PEF), self-rating anxiety scale (SAS), self-rating depression scale (SDS) and patient satisfaction between the two groups were compared. RESULTS: Pulmonary rehabilitation performance, 6MWT results, and patient satisfaction in the RG were significantly better than those in the CG. The total effective rate, compliance score, PaO2, FEV1 and PEF of the RG were significantly higher than those of the CG. After treatment, the COPD symptom score, CAT score, PaCO2, SAS score and SDS score in the RG were significantly lower than those in the CG. CONCLUSION: Chronic disease management with combined traditional Chinese and western medicine has great application value and high efficacy in pulmonary rehabilitation.

Biomass Functions and Carbon Content Variabilities of Natural and Planted Pinus koraiensis in Northeast China.

The population of natural Korean pine (Pinus koraiensis) in northeast China has sharply declined due to massive utilization for its high-quality timber, while this is vice versa for Korean pine plantations after various intensive afforestation schemes applied by China's central authority. Hence, more comprehensive models are needed to appropriately understand the allometric relationship variations between the two origins. In this study, we destructively sampled Pinus koraiensis from several natural and plantation sites in northeast China to investigate the origin's effect on biomass equations. Nonlinear seemingly unrelated regression with weighted functions was used to present the additivity property and homogenize the model residuals in our two newly developed origin-free (population average) and origin-based (dummy variable) biomass functions. Variations in biomass allocations, carbon content, and root-to-shoot ratio between the samples obtained from plantations and natural stands were also investigated. The results showed that (1) involving the origin's effect in dummy variable models brought significant improvement in model performances compared to the population average models; (2) incorporating tree total height (H) as an additional predictor to diameter at breast height (D) consistently increase the models' accuracy compared to using D only as of the sole predictors for both model systems; (3) stems accounted for the highest partitioning proportions and foliage had the highest carbon content among all biomass components; (4) the root-to-shoot ratio ranged from 0.18-0.35, with plantations (0.28 ± 0.04) had slightly higher average value (±SD) compared to natural forests (0.25 ± 0.03). Our origin-based models can deliver more accurate individual tree biomass estimations for Pinus koraiensis, particularly for the National Forest Inventory of China.

[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.

Forest age mapping based on multiple-resource remote sensing data.

Forest age is an important stand description factor and plays an important role in the carbon cycle of forest ecosystems. However, forest age data are typically lacking or are difficult to acquire at large spatial scale. Thus, it is important to develop a method of spatial forest age mapping. In this study, a method of forest age estimation based on multiple-resource remote sensing data was developed. Forest age was estimated by using average tree height estimated from the ICESat/GLAS and MODIS BRDF products. The results showed that forest age was significantly related to average tree height with a correlation coefficient of 0.752. Then, the average tree height was inversed using a waveform parameter extracted from ICESat/GLAS and was extended to continuous space with the help of the MODIS BRDF product. Thus, forest age mapping was realized. Lastly, the structure of forest age in the study area was evaluated. The results indicated that this method can be used to estimate forest age at the local scale and at large scale and can facilitate understandings of the real forest age structure features of a research area.

Determination of the most effective design for the measurement of photosynthetic light-response curves for planted Larix olgensis trees.

A photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process and has been widely applied in many eco-physiological models. To date, many PLR measurement designs have been suggested, although their differences have rarely been explored, and the most effective design has not been determined. In this study, we measured three types of PLR curves (High, Middle and Low) from planted Larix olgensis trees by setting 31 photosynthetically active radiation (PAR) gradients. More than 530 million designs with different combinations of PAR gradients from 5 to 30 measured points were conducted to fit each of the three types of PLR curves. The influence of different PLR measurement designs on the goodness of fit of the PLR curves and the accuracy of the estimated photosynthetic indicators were analysed, and the optimal design was determined. The results showed that the measurement designs with fewer PAR gradients generally resulted in worse predicted accuracy for the photosynthetic indicators. However, the accuracy increased and remained stable when more than ten measurement points were used for the PAR gradients. The mean percent error (M%E) of the estimated maximum net photosynthetic rate (Pmax) and dark respiratory rate (Rd) for the designs with less than ten measurement points were, on average, 16.4 times and 20.1 times greater than those for the designs with more than ten measurement points. For a single tree, a unique PLR curve design generally reduced the accuracy of the predicted photosynthetic indicators. Thus, three optimal measurement designs were provided for the three PLR curve types, in which the root mean square error (RMSE) values reduced by an average of 8.3% and the coefficient of determination (R2) values increased by 0.3%. The optimal design for the High PLR curve type should shift more towards high-intensity PAR values, which is in contrast to the optimal design for the Low PLR curve type, which should shift more towards low-intensity PAR values.

[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.

Soil nutrients, forest structure and species traits drive aboveground carbon dynamics in an old-growth temperate forest.

Forests store a substantial amount of terrestrial carbon (C), but the drivers of forest C dynamics remain poorly understood, especially in old-growth forests. Here, we evaluate how aboveground C dynamics (i.e., net C change and its demographic processes: C gain from the growth of surviving trees (∆C-surv), C gain from the growth of recruited trees (∆C-recr) and C loss by tree mortality (∆C-mort)) are driven by vegetation attributes (diversity, trait composition and forest structure) and habitat conditions (soil properties and light environment), as well as how ∆C-surv, ∆C-recr and ∆C-mort contribute to net C change. Using 10-year interval demographic data from a 9-ha permanent plot in an old-growth temperate forest in northeastern China, we performed structural equation model to relate the C dynamics to the vegetation attributes and habitat conditions. The net C change is most strongly determined by ∆C-mort. High soil phosphorus concentrations increased ∆C-surv, soil moisture increased ∆C-recr, and leaf area index increased both ∆C-surv and ∆C-recr. Diversity (i.e., structural diversity) had a positive relationship with ∆C-surv but was not related to ∆C-recr or ∆C-mort. Trait composition was significantly related to all three demographic processes. Forest structure was the best predictor of ∆C-surv and ∆C-recr. The net C change increased with higher soil phosphorus concentrations and basal area and in communities dominated by conservative traits (i.e., high wood density). This study highlights that soil nutrients, forest structure and trait composition are important drivers of net C change in old-growth temperate forests. Better insights into C storage and productivity can be gained by simultaneously evaluating the vegetation attributes and habitat conditions of C dynamics in natural ecosystems.

[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.

Spatial heterogeneity of leaf area index in a temperate old-growth forest: Spatial autocorrelation dominates over biotic and abiotic factors.

Leaf area index (LAI) controls many eco-physiological processes and can be widely used to scale-up leaf processes to ecosystem, landscape and regional levels. However, the macro-scale spatial heterogeneity of LAI and its controlling factors are not fully understood. We estimated annual maximum LAI using an LAI-2200 plant canopy analyzer in a 9-ha, old-growth, mixed broadleaved-Korean pine (Pinus koraiensis) forest in China. We analyzed the spatial heterogeneity of LAI and mapped its distribution using geostatistical methods; then, through variance partitioning, we examined the influences of several biotic factors, abiotic factors and spatial autocorrelation on the LAI distribution. Variance partitioning showed that these factors altogether explained 59% of the variation in the distribution of LAI. Compared to biotic and abiotic factors, spatial autocorrelation controlled more spatial heterogeneity of LAI by explaining 51.4% of the total variation in LAI. For biotic and abiotic factors, the mean diameter at breast height (DBH) of large trees (DBH > 10 cm), elevation, soil temperature and soil mass moisture content significantly affected the LAI distribution (P < 0.01). Notably, spatial autocorrelation unexpectedly explained the most variation in the LAI values, and it also varies with cardinal direction and is a key descriptor of LAI spatial variability. These results suggest that the influence of spatial autocorrelation on LAI distribution should attract more attention and that both the relative importance of and interactions among different determining factors is helpful for better understanding the mechanistic determinants of LAI distributions in temperate mixed forests.

[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.

Evaluation of Four Methods for Predicting Carbon Stocks of Korean Pine Plantations in Heilongjiang Province, China.

A total of 89 trees of Korean pine (Pinus koraiensis) were destructively sampled from the plantations in Heilongjiang Province, P.R. China. The sample trees were measured and calculated for the biomass and carbon stocks of tree components (i.e., stem, branch, foliage and root). Both compatible biomass and carbon stock models were developed with the total biomass and total carbon stocks as the constraints, respectively. Four methods were used to evaluate the carbon stocks of tree components. The first method predicted carbon stocks directly by the compatible carbon stocks models (Method 1). The other three methods indirectly predicted the carbon stocks in two steps: (1) estimating the biomass by the compatible biomass models, and (2) multiplying the estimated biomass by three different carbon conversion factors (i.e., carbon conversion factor 0.5 (Method 2), average carbon concentration of the sample trees (Method 3), and average carbon concentration of each tree component (Method 4)). The prediction errors of estimating the carbon stocks were compared and tested for the differences between the four methods. The results showed that the compatible biomass and carbon models with tree diameter (D) as the sole independent variable performed well so that Method 1 was the best method for predicting the carbon stocks of tree components and total. There were significant differences among the four methods for the carbon stock of stem. Method 2 produced the largest error, especially for stem and total. Methods 3 and Method 4 were slightly worse than Method 1, but the differences were not statistically significant. In practice, the indirect method using the mean carbon concentration of individual trees was sufficient to obtain accurate carbon stocks estimation if carbon stocks models are not available.

[Error structure and additivity of individual tree biomass model for four natural conifer species in Northeast China].

Based on the biomass data of 276 sampling trees of Pinus koraiensis, Abies nephrolepis, Picea koraiensis and Larix gmelinii, the mono-element and dual-element additive system of biomass equations for the four conifer species was developed. The model error structure (additive vs. multiplicative) of the allometric equation was evaluated using the likelihood analysis, while nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The results indicated that the assumption of multiplicative error structure was strongly supported for the biomass equations of total and tree components for the four conifer species. Thus, the additive system of log-transformed biomass equations was developed. The adjusted coefficient of determination (Ra 2) of the additive system of biomass equations for the four conifer species was 0.85-0.99, the mean relative error was between -7.7% and 5.5%, and the mean absolute relative error was less than 30.5%. Adding total tree height in the additive systems of biomass equations could significantly improve model fitting performance and predicting precision, and the biomass equations of total, aboveground and stem were better than biomass equations of root, branch, foliage and crown. The precision of each biomass equation in the additive system varied from 77.0% to 99.7% with a mean value of 92.3% that would be suitable for predicting the biomass of the four natural conifer species.

[Multiple-scale analysis on spatial distribution changes of forest carbon storage in Heilongjiang Province, Northeast China based on local statistics].

Taking 4163 permanent sample plots from Chinese National Forest Inventory (CNFI) and key ecological benefit forest monitoring plots in Heilongjiang Province as basic data, and by using local Moran I and local statistics (local mean and local standard deviation), the spatial pattern, spatial variation and spatial autocorrelation of forest carbon storage in Heilongjiang Province with four bandwidths of 25, 50, 100 and 150 km were investigated, and the change in forest carbon storage across 2005 to 2010 was studied. The results showed that the spatial distribution of forest carbon storage in Heilongjiang Province had significantly positive spatial correlation, which indicated that the changes of carbon storage tended to be similar with their neighbors without a non-random manner. Forest carbon storage was affected by environmental factors, and the spatial heterogeneity strongly existed with a large variation in the study area. The spatial distribution of forest carbon storage was significantly different between 2005 and 2010 with an increasing trend. Local statistics are useful tools for characterizing forest carbon storage change across time and space, which are visualized by ArcGIS.

[Prediction of spatial distribution of forest carbon storage in Heilongjiang Province using spatial error model].

Abstract: Based on the data from Chinese National Forest Inventory (CNFI) and Key Ecological Benefit Forest Monitoring plots (5075 in total) in Heilongjiang Province in 2010 and concurrent meteorological data coming from 59 meteorological stations located in Heilongjiang, Jilin and Inner Mongolia, this paper established a spatial error model (SEM) by GeoDA using carbon storage as dependent variable and several independent variables, including diameter of living trees (DBH), number of trees per hectare (TPH), elevation (Elev), slope (Slope), and product of precipitation and temperature (Rain_Temp). Global Moran's I was computed for describing overall spatial autocorrelations of model results at different spatial scales. Local Moran's I was calculated at the optimal bandwidth (25 km) to present spatial distribution residuals. Intra-block spatial variances were computed to explain spatial heterogeneity of residuals. Finally, a spatial distribution map of carbon storage in Heilongjiang was visualized based on predictions. The results showed that the distribution of forest carbon storage in Heilongjiang had spatial effect and was significantly influenced by stand, topographic and meteorological factors, especially average DBH. SEM could solve the spatial autocorrelation and heterogeneity well. There were significant spatial differences in distribution of forest carbon storage. The carbon storage was mainly distributed in Zhangguangcai Mountain, Xiao Xing'an Mountain and Da Xing'an Mountain where dense, forests existed, rarely distributed in Songnen Plains, while Wanda Mountain had moderate-level carbon storage.

[Branch growth of Korean pine plantation based on nonlinear mixed model].

Based on the branch analysis data from 36 sample trees in a Korean pine plantation in Mengjiagang Forest Farm of Heilongjiang Province, Northeast China, and by using Mitcherlich and Richards equations as the models of branch diameter and branch length growth, respectively, the effects of sampling plot and sample tree were investigated, and the nonlinear mixed models of branch diameter and branch length growth were established by the PROC NLMIXED procedure of SAS software. The evaluation statistics such as Akaike information criterion (AIC), Bayesian information criterion (BIC), -2Log likelihood, and likelihood ratio test (LRT) were used to compare the prediction precisions of the models. When considering plot effect, and taking alpha1 and alpha3 and beta1 and beta3 as the random parameters, respectively, the models of branch diameter and branch length growth had the best performance. When considering tree effect, and taking alpha2 and alpha3 and beta2 and beta3 as the random parameters, respectively, the models of branch diameter and branch length growth had the best performance. The nonlinear mixed model could not only reflect the mean variation of branch growth, but also show the differences among the individual trees. No matter considering plot effect or tree effect, the fitting precision of the nonlinear mixed model was better than that of the ordinary regression analysis model. Moreover, the fitting precision of the nonlinear mixed model was better when considering tree effect than considering plot effect.

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.