Modeling Forest Growth, Mortality And Recruitment For Hinese Pine In Beijing

The forest is the main body of terrestrial ecosystem. It plays a very crucial role inmaintaining ecological balance, improving ecological environment, as well as regulating globalclimate change. It is very important to forecast and evaluate forest resource accurately in timefor managing forest reasonably. The key problem of monitoring forest is how to know thedynamic change of foret growth, mortality and recruitment. As we know, the modelingtechnique is the basic method for knowing the forest dynamic change. Based on the forestdynamic models, we can know fully the forest development mode, which is helpful to monitorforest effectively and mange forest in reason. The dynamic models are composed of forestgrowth model, mortality model and recruitment model. In this study, based on the permanentdata of Chinese pine (Pinus tabulaeformis Carrière), the forest dynamic models includinggrowth, mortality and recruitment were developed using the modern biomathematics modeland statistical analysis method:(1) The annual individual tree diameter model was developed with constant rate methodand variable rate method. Results showed that the variable rate method (RMSE=1.0182,R2=0.9310) outperformed the constant rate method (RMSE=1.1393, R2=0.9136) in predictingfuture individual tree diameter growth because the former accounted for the variable rate ofannual diameter growth, which was caused by changes of stand (basal area, dominant height)and tree attributes. It reflects the fact of tree growth. Also the whole stand models wereestablished with the variable rate method, which provided the annual forest stand changes. Theparameters of stand models were estimated via seemingly unrelated regression (SUR). Basedon the estimation method, the parameters had no obvious biases, and the precision of parameterestimation was more effectively.(2) Forest combination method is a good method for improving model performance. Itefficiently uses information generated from different models to improve predictions byreducing errors from a single model. Results showed that the forecast combination method (R2=0.9298) provided overall better predictions of stand basal area than tree level model(R2=0.9255), stand level model (R2=0.9282) and distribution model (R2=0.9244). It alsoimproved the compatibility of stand basal area growth predicted from models of differentresolutions. In other words, it resolved the inconsistency of stand variable predictions atdifferent levels. It provided a method for integration of stand basal area. But we should alsorecognize that the method of calculating weights in combined models is very important. If themethod for calculating weights is good, then we will get the better results for combined model.In this thesis, the sum of squared errors method, variance-covariance method and optimalweight method were used to calculate the weights. The optimal weight method was superior toother two models, which removes the biased impact of single model on combined model, andthen gets the unbiased estimators.(3) Disaggregation is a good method for improving prediction of tree models. In thismethod, individual-tree model predictions are adjusted so that the resulting sums would matchoutputs from a stand-level model. In this research, three disaggregation methods were used foradjusting tree mortality, which are power function method, proportional adjustment method,and addition method. Results showed that the disaggregation approach improved theperformance of tree survival models and the addition method performed slightly better than theother two disaggregation methods. An advantage of the addition method is that it alloweddirect computation of the adjusting coefficient, whereas the other methods required that theadjusting coefficient be resolved in an iterative manner. Meanwhile, we also showed thatstand-level prediction played a crucial role in refining outputs from a tree survival model,especially when it is a very simple model. Because the forecast combination method producedbetter stand-level prediction, we prefer the use of this method in conjunction with thedisaggregation approach, even though the performance gain in using the forecast combinationmethod shown for this data set was modest. And the results showed that the tree mortalityprediction was improved using the two methods together.(4) Stand mortality and recruitment are very important variables for describing the standcharacters. Considering the fact that in permanent sample plots a relatively high number of the plots have no occurrences of recruitment or mortality even over periods of several years, itmeans that data are bounded and characteristically exhibit varying degrees of dispersion andskewness in relation to the mean. Additionally, the data often contain an excess number of zerocounts. Yet least squares method implicitly presumes that the data are Gaussian distributed withconstant variance, or at least satisfy the Gauss-Markov conditions. If the method is still used todeal with the data with large proportion of zero counts, the estimated results will be biased.Based on the theory of count models, poisson model, negative binomial model, zero-inflatedmodels and Hurdle models were used to analyze stand mortality and recruitment. The bestmodel was chose according to the AIC value, Pearson redidual plot and vuong test. Resultsshowed that: Poisson model was not suitable for stand mortality and recruitment, and negativebinomial was superior to the Poisson model. But both of them were not competent for theover-dispersion data. Zero-inflated model and hurdle model were fitted into the data.Additionally, zero-inflated negative binomial model (ZINB) and Hurdle-negative binomialmodel (HNB) outperformed than other models. The two models performed similarly inmodeling stand mortality and recruitment. The result provided a feasible method for analyzingstand mortality and recruitment.Finally, integration system of forest growth, mortality and incruitment dynamic modelsfor Chinese pine was implemented. System interface was setup based on the C#. NET, linkedwith SAS through the SAS IOM programming.