Mengjiagang Forest Farm Research Articles

The influence of BRDF effects and representativeness of training data on tree species classification using multi-flightline airborne hyperspectral imagery

Airborne hyperspectral imagery provides detailed spatial and spectral information that can be used for tree species classification, but its application in practical forest inventories over large areas is limited. This study investigates the impact of (i) bidirectional reflectance distribution function (BRDF) effects on multi-flightline airborne imagery and (ii) the selection strategy for training data on tree species classification accuracy over large forested areas. Hyperspectral imagery was acquired using the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) over the Mengjiagang Forest Farm (MFF, ∼155 km2) in northeastern China, which includes common keystone coniferous species in northern China’s temperate-boreal ecozones. A kernel-driven BRDF model based on mNDVI704.50 stratification was applied to airborne hyperspectral data acquired from multi-flightlines. We developed three training data selection strategies, namely #1 sampling over the entire area, #2 localized sampling over the area for each flight date, and #3 gradually decreasing sampling in size. Additionally, two validation assessment strategies were respectively employed: independent data over the entire area and independent data in areas with inconsistent classification labeling between using BRDF corrected and uncorrected hyperspectral imagery. These strategies were implemented to perform a comprehensive cross-comparison of tree species classification accuracy using BRDF corrected and uncorrected airborne hyperspectral imagery. The study found that the representativeness of the training data (in terms of solar-viewing geometry distribution and size) has a stronger impact on the classification accuracy of tree species than the BRDF effects in airborne hyperspectral imagery. By utilizing BRDF corrected hyperspectral imagery with a majority of representative training data, the mean overall accuracy of tree species classification improves by ≤ 2.9 % compared to uncorrected imagery. Notably, BRDF correction has improved classification accuracy by 11.5 % in inconsistently labeled areas, mainly concentrated in the spatial overlap regions of adjacent flightlines. These findings provide valuable insights for improving tree species classification accuracy in large forested areas using multi-flightline airborne hyperspectral imagery.

Construction of universal equations for knot attributes of three coniferous species.

We constructed base model, dummy variable model, and mixture model with three variables including knot diameter, loose knot length, and sound knot length with three typical coniferous species, Pinus koraiensis, Larix olgensis, and Pinus sylvestris var. mongolica, from the Linkou Forestry Bureau and Mengjiagang forest farm in Heilongjiang Province in 2020. We analyzed the differences in knot properties among different tree species and simplified the modeling work. Firstly, we collected relevant knot property data through the sectioning method based on relevant literature, transformation of the model form and substitution of related variables to conduct a base model. We transformed the species into dummy variables as qualitative factors, and introduced the dummy variable model of the relevant attributes into the base model. We introduced the random effects of sample trees and sample plots when constructing the mixture model. By comparing evaluation indicators, such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the mixture model with the best fitting effect was selected. We selected the optimal universal equation by comparing the fitting accuracy of the base model, dummy variable model and mixture model. The fitting accuracy of the dummy variable model and mixture model was higher than that of the basic model. The evaluation indicators (AIC and BIC) showed that the mixture model had a better fitting effect on knot properties than the dummy variable model. In the model comparison results, R2 of mixture models for sound knot length, the loose knot length, and knot diameter increased by 13.2%, 84.8% and 40.3%, respectively. The predictive accuracy of the three base models for different tree species' knot attributes was above 90%, and both the prediction accuracy of the dummy variable model and mixture model were above 94%, indicating that the constructed models could well predict knot-related properties. From the perspective of tree species, the sound knot length, knot diameter, and loose knot length was in order of P. sylvestris var. mongolica > P. koraiensis > L. olgensis. Fitted results of the dummy variable model and the mixture model were superior to the basic model, with higher accuracy.

Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis

Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

Construction of the additive model system for heartwood, sapwoodand bark taper of Pinus koraiensis plantation in different regions of Heilongjiang Province, China

With the commonly used variable-exponent taper models developed by Kozak (1988), Muhairwe (1999), Lee (2003) and Kozak (2004), we aimed to develop taper functions of diameter outside bark (DOB), heartwood diameter (HD), sapwood width (SW), and bark thickness (BT) based on the data of 2977 discs from 103 sample trees of Pinus koraiensis plantations under different conditions in Mengjiagang Forest Farm, Dongjingcheng and Linkou Forestry Bureau in Heilongjiang Province. We selected the optimal basic model after comparison. By using seemingly unrelated regression (SUR) method in the PROC MODEL of SAS software, we established an additive model system for the taper equations of DOB, HD, SW and BT. By introducing the region as a dummy variable, the models were evaluated by coefficient determination (Radj2), root mean square error (RMSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results showed that the taper model developed by Kozak (2004) was the optimal one for the DOB, HD, SW and BT. When satisfying the additivity of each component and total amount, the additivity model system got better prediction effect, with a prediction precision of more than 98%. The additive model system with dummy variables improved the prediction ability to varying degrees, especially the prediction precision of HD and SW models. Minor differences existed in DOB and BT among different regions, while enormous differences in HD and SW. The additive model system with dummy variables not only had high prediction accuracy but also satisfied the additive logic of DOB, HD, SW and BT, which provided a basis for accurate estimation of heartwood, sapwood and bark volume of P. koraiensis.

Deriving Individual-Tree Biomass from Effective Crown Data Generated by Terrestrial Laser Scanning

Biomass reflects the state of forest management and is critical for assessing forest benefits and carbon storage. The effective crown is the region above the lower limit of the forest crown that includes the maximum vertical distribution density of branches and leaves; this component plays an important role in tree growth. Adding the effective crown to biomass equations can enhance the accuracy of the derived biomass. Six sample plots in a larch plantation (ranging in area from 0.06 ha to 0.12 ha and in number of trees from 63 to 96) at the Mengjiagang forest farm in Huanan County, Jiamusi City, Heilongjiang Province, China, were analyzed in this study. Terrestrial laser scanning (TLS) was used to obtain three-dimensional point cloud data on the trees, from which crown parameters at different heights were extracted. These parameters were used to determine the position of the effective crown. Moreover, effective crown parameters were added to biomass equations with tree height as the sole variable to improve the accuracy of the derived individual-tree biomass estimates. The results showed that the minimum crown contact height was very similar to the effective crown height, and an increase in model accuracy was apparent (with R a 2 increasing from 0.846 to 0.910 and root-mean-square error (RMSE) decreasing from 0.372 kg to 0.286 kg). The optimal model for deriving biomass included tree height, crown length from minimum contact height, crown height from minimum contact height, and crown surface area from minimum contact height. The novelty of the article is that it improves the fit of individual-tree biomass models by adding crown-related variables and investigates how the accuracy of biomass estimation can be enhanced by using remote sensing methods without obtaining diameter at breast height.

Nonlinear mixed-effects height to crown base and crown length dynamic models using the branch mortality technique for a Korean larch (Larix olgensis) plantations in northeast China

Korean larch (Larix olgensis) is one of the main tree species for afforestation and timber production in northeast China. However, its timber quality and growth ability are largely influenced by crown size, structure and shape. The majority of crown models are static models based on tree size and stand characteristics from temporary sample plots, but crown dynamic models has seldom been constructed. Therefore, this study aimed to develop height to crown base (HCB) and crown length (CL) dynamic models using the branch mortality technique for a Korean larch plantation. The nonlinear mixed-effects model with random effects, variance functions and correlation structures, was used to build HCB and CL dynamic models. The data were obtained from 95 sample trees of 19 plots in Meng JiaGang forest farm in Northeast China. The results showed that HCB progressively increases as tree age, tree height growth (HT growth) and diameter at breast height growth (DBH growth). The CL was increased with tree age in 20 years ago, and subsequently stabilized. HT growth, DBH growth stand basal area (BAS) and crown competition factor (CCF) significantly influenced HCB and CL. The HCB was positively correlated with BAS, HT growth and DBH growth, but negatively correlated with CCF. The CL was positively correlated with BAS and CCF, but negatively correlated with DBH growth. Model fitting and validation confirmed that the mixed-effects model considering the stand and tree level random effects was accurate and reliable for predicting the HCB and CL dynamics. However, the models involving adding variance functions and time series correlation structure could not completely remove heterogeneity and autocorrelation, and the fitting precision of the models was reduced. Therefore, from the point of view of application, we should take care to avoid setting up over-complex models. The HCB and CL dynamic models in our study may also be incorporated into stand growth and yield model systems in China.

Effects of thinning on soil carbon and nitrogen fractions in a Larix olgensis plantation.

Thinning, an important forest management strategy, can alter forest structure and stability, and consequently affect ecosystem biogeochemical cycles. The effects of thinning on soil carbon and nitrogen is far from conclusive especially due to the lack of long-term experiments. Here, we investigated soil carbon and nitrogen in Larix olgensis plantations in Mengjiagang Forest Farm, Heilongjiang Province, with four thinning treatments (i.e., 4 times low-intensity thinning, LT4; 3 times medium-intensity thinning, MT3; 2 times high-intensity thinning, HT2; and un-thinned control). The effects of thinning on soil total organic carbon and total nitrogen were examined from the perspective of the composition of labile and recalcitrant pools (labile carbon or nitrogen pool I; labile carbon or nitrogen pool II; and recalcitrant carbon or nitrogen pool) by an acid hydrolysis approach. The results showed that thinning significantly increased soil total organic carbon and nitrogen by 48.7%-50.3% and 28.9%-42.7%, respectively. The carbon and nitrogen contents in all the labile I, labile II, and recalcitrant pools were increased by thinning, with the magnitudes varying across different pools and thinning types. LT4, MT3, and HT2 improved the recalcitrant carbon by 71%, 69% and 75%, respectively, which was significantly higher than the increment of two labile carbon pools. In addition, the percentage of recalcitrant carbon in total organic carbon was increased by thinning. LT4 significantly increased microbial biomass and microbial quotient, but no significant change was found in MT3 and HT2 treatments. Overall, our results indicated that thinning might increase the input of soil recalcitrant carbon components such as suberin and lignin by producing more coarse woody residues, thus leading to decline of organic matter decomposition and ultimately enhancement of soil organic carbon.

Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models

Korean pine (Pinus koraiensis Sieb. et Zucc.) is one of the highly commercial woody species in Northeast China. In this study, six nonlinear equations and artificial neural network (ANN) models were employed to model and validate height-diameter (H-DBH) relationship in three different stand densities of one Korean pine plantation. Data were collected in 12 plots in a 43-year-old even-aged stand of P. koraiensis in Mengjiagang Forest Farm, China. The data were randomly split into two datasets for model development (9 plots) and for model validation (3 plots). All candidate models showed a good perfomance in explaining H-DBH relationship with error estimation of tree height ranging from 0.61 to 1.52 m. Especially, ANN models could reduce the root mean square error (RMSE) by the highest 40%, compared with Power function for the density level of 600 trees. In general, our results showed that ANN models were superior to other six nonlinear models. The H-DBH relationship appeared to differ between stand density levels, thus it is necessary to establish H-DBH models for specific stand densities to provide more accurate estimation of tree height.

Change pattern of heartwood of Larix olgensis plantation.

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.

Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China

Forest aboveground biomass (AGB) and leaf area index (LAI) are two important parameters for evaluating forest growth and health. It is of great significance to estimate AGB and LAI accurately using remote sensing technology. Considering the temporal resolution and data acquisition costs, digital aerial photographs (DAPs) from a digital camera mounted on an unmanned aerial vehicle or light, small aircraft have been widely used in forest inventory. In this study, the aerial photograph data was acquired on 5 and 9 June, 2017 by a Hasselblad60 digital camera of the CAF-LiCHy system in a Y-5 aircraft in the Mengjiagang forest farm of Northeast China, and the digital orthophoto mosaic (DOM) and photogrammetric point cloud (PPC) were generated from an aerial overlap photograph. Forest red-green-blue (RGB) vegetation indices and textural factors were extracted from the DOM. Forest vertical structure features and canopy cover were extracted from normalized PPC. Regression analysis was carried out considering only DOM data, only PPC data, and a combination of both. A recursive feature elimination (RFE) method using a random forest was used for variable selection. Four different machine-learning (ML) algorithms (random forest, k-nearest neighbor, Cubist and supporting vector machine) were used to build regression models. Experimental results showed that PPC data alone could estimate AGB, and DOM data alone could estimate LAI with relatively high accuracy. The combination of features from DOM and PPC data was the most effective, in all the experiments considered, for the estimation of AGB and LAI. The results showed that the height and coverage variables of PPC, texture mean value, and the visible differential vegetation index (VDVI) of the DOM are significantly related to the estimated AGB (R2 = 0.73, RMSE = 20 t/ha). The results also showed that the canopy cover of PPC and green red ratio index (GRRI) of DOM are the most strongly related to the estimated LAI, and the height and coverage variables of PPC, the texture mean value and visible atmospherically resistant index (VARI), and the VDVI of DOM followed (R2 = 0.79, RMSE = 0.48).

Effect of thinning intensity on tree growth and temporal variation of seed and cone production in a Pinus koraiensis plantation

Thinning of Korean pine (Pinus koraiensis Sieb. et Zucc.) is used to facilitate timber and cone production. The present study in Northeast China investigated the effects of thinning intensity on individual tree growth, temporal variation in cone yield, and seed quality in Korean pine plantation. In 2005, five thinning intensity levels (none, extreme, heavy, moderate and light) were set in 15 permanent plots in a 32-year-old Korean pine plantation at Mengjiagang Forest Farm, Jiamusi City, Heilongjiang Province. We recorded tree growth and seed cone production from 2013 to 2016, i.e., from 8 to 11 years after thinning. Except for height growth, thinning increased tree growth (diameter at breast height and crown size) and improved cone yield. The extreme thinning treatment (to 300 trees per hectare) resulted in the largest tree diameter, tree volume, crown size and 4-year cone production per tree. The highest cone yield per tree in the mast year (2014) was observed when stands were thinned to 500 trees per hectare (heavy thinning). Although the best cone and seed quality and the largest cone and seed mass per tree were recorded in the heavily thinned stand, no significant differences were found between heavy and moderate thinning stands (750 trees per hectare). At the stand level, the moderately thinned stand had the highest basal area, stock volume and seed cone production per stand. Our results suggest that thinning to 750 trees per hectare will improve timber and cone productivity in 40-year-old P. koraiensis stands.

Knot attributes of Korean pine plantation based on mixed effect model.

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.

Modeling the diameter distribution of Larix olgensis plantation based on thinning effects.

In 1974, five permanent sample plots of Larix olgensis plantation were established in Mengjiagang forest farm in Heilongjiang Province, which included one control plot and four thinning plots with different thinning treatments (remove 3.7%-49.7% of the basal area). Diameter at breast height (DBH) for each tree (19-59 a) of the five plots was remeasured 31 times from 1974 to 2014. The three parameters Weibull function was used to describe the DBH distribution of different ages for the five plots. The dynamic changes and the effects of thinning on the diameter distribution curves and the three parameters of Weibull function were analyzed. Based on the thinning effects, the parameter prediction model (PPM) of simultaneous equations system was developed and the parameters were estimated by three-stage least squares method (3SLS) and error-in-variable method of simultaneous equations. The results showed that the diameter distribution curves with stand age changed from higher peak with narrow DBH range to lower peak with large DBH range for unthinned stand (control plot). For thinning plots, the skewness was more right-hand with higher peak, narrow DBH range, and increased asymmetry of the curves after thinning. Increasing with thinning intensity, more trees of small or middle sizes were cut and the curves of diameter distribution were obvious left-truncted higher peak with narrow DBH range than before thinning. The parameter a of the Weibull function increased with thinning intensity and the parameter b decreased. The parameter c was not influenced by lower intensity thinning, but decreased by higher thinning intensity. The fitting results for the PPM of simultaneous equations system showed that predicting models for the parameter b were best (R2>0.98) and the R2 for model of the parameter c was larger than 0.91. The method of 3SLS was as good as the error-in-variable method, and there was no significant diffe-rence. The results of validation for the two methods showed that, the precision of the three prediction models was >97%, and the fitting efficiency (EF) was >0.92. The parameter prediction mo-dels developed based on the effects of thinning performed well and could be used to predict the dynamic change of diameter distribution and to provide suitable management of L. olgensis plantation.

Branch quantity distribution simulation for Pinus koraiensis plantation in Heilongjiang Pro-vince, China.

Based on the measurement of 955 branch samples of 65 Korean pine (Pinus koraiensis) trees in 12 plots from Mengjiagang forest farm, Heilongjiang Province, and by using Poisson model and negative binomial model, the second-order branch count models for Korean pine were developed in this paper. AIC, Pseudo-R2, RMSE and Vuong test were selected to compare the goodness-of-fit statistics of the models. The results indicated that the first-order branch count in a whorl was 3 to 5, with mean value of 4, and the first-order branch count in a whorl for Korean pine plantation associated with its own characteristics. The second-order branch count of the first-order standard branch had a large discrete degree. All subset regression techniques were used to develop the second-order branch count model. The negative binomial regression model E(Y)=exp(β0+β1lnRDINC+β2RDINC2+β3HT/DBH+β4CL+β5DBH) was selected as the optimal second-order branch count model (β represented the parameter, RDINC represented the relative depth into crown from tree apex, HT represented the total tree height, DBH represented the tree diameter at breast height, CL represented the crown length). Pseudo-R2 of the optimal model was 0.79, the mean error was close to 0 and the mean absolute error was less than 7. For the developed model, the parameter values of lnRDINC, CL and DBH were negative, and the parameter values of RDINC2 and HT/DBH were positive. With the increase of RDINC, the number of second-order branch had a peak value in the tree crown. On the whole, the precision of the second-order branch count model for Korean pine plantation was 96.4%, which would be suitable for predicting the second-order branch count for the study area and provide a theoretic basis for branch photosynthesis and biomass research.

Individual Tree Growth Model of Larch Plantation based on Crown Competition Factors

Based on the re-measured data between 2007- 2009 from 14 permanent sample plots in larch plantations located in Mengjiagang Forest Farm,Jiamusi city of Heilongjiang province,the relationship between crown variables and increment of tree was analyzed and the distance-dependent individual tree growth model for larch plantation was developed by introducing two crown competition factors( CIaand CIb) as new competition index in this paper. The results showed that two crown competition factors( CIaand CIb) are better than the Hegyi competition index( CI). The correlation between various competition index with basal area increment of individual tree is CIb CIa CI for larch plantation. With enlargement of the competition influence zone of object tree,the competition index tends to be stable and the model fitting effect increases. Tee size is the main affecting factor for the individual tree growth for larch plantation. The basal area increment of individual tree increases as tree diameter at breast height increasing. The goodness of fit for individual tree growth model was improved 5. 6% by introducing new distance-dependent crown competition index. The distance-dependent individual tree growth model developed in this paper can be suitable for predicting basal area increment of individual tree for larch plantation.

Prediction Model of Nut Yields for Korean Pine Plantation

Logistic and non-linear models were developed for predicting nut yields for Korean pine( Pinus koraiensis) plantation based on 579 Korean pines measured in 12 sample plots in Mengjiagang forest farm, Jiamusi,Heilongjiang Province. First,the statistical analysis system( SAS 9. 22) was employed using data of pine nut yields to establish Logistic model for predicting whether individual pine could seed or not. Second,a non-linear regression was set up for predicting nut yields of individual seeded pines using tree variables. The results showed that the predicting accuracy of whether individual pine could seed or not,was above 65% for Logistic model. The optimal model of predicting nut yields was y = a( D2CW)bdue to the best fitting performance,which included the prediction accuracy of 77% and evenly distributed model residuals. The accuracy assessment was 92. 78% for the two models using the observed pine nut yield data of Plot 2,which indicated a good prediction performance. This paper has been provided suitable method for predicting pine nut yields for Korean pine plantation.

The Analysis of the Dynamics of the Landscape Structure of Mengjiagang Forest Farm

This paper,basing on forest type map and forest management survey database of 1989 and 2006 and on a GIS platform and the software of FragStats,calculates the index of landscape pattern,and systematically analyies the dynamic character of the landscape spatial structure of Mengjiagang Forest Farm Heilongjiang Province with the method of landscape index.The result indicates that with the landscape elemental types of the study area increases,and landscape diversity index and landscape dominance increases,the fragment index and isolation index of forest landscape increases.Larix gmelinii(Rupr.)Rup is the dominating landscape element type in this region,but its landscape dominance is decreasing gradually,so the result implies that L.gmelinii(Rupr.)Rup landscape element translate into other landscape elements.This paper firstly applied with landscape internal structural index analysis the dynamics of forest landscape of Mengjiagang Forest Farm,and the result implies that Broad-leaved forest and Mixed forest landscape were mainly made of mature and overmature age.Three main coniferous forest landscape elements are mainly made of middle age and near-mature age.Human activities are the main influence factor on forest,which accelerate the simplification of age structure of coniferous forest.