Functional-structrual Model Of Pinus Tabulaeformis Based On GreenLab Methodology

Functional-structural plant models are models explicitly describing the development over time of the 3D architecture of plants as governed by physiological processes which, in turn, are driven by environmental factors. Functional-structural tree models (FSTMs) which combine trees'morphological and functional parts depict the true 3D presentation of trees for analyzing tree growth and interactions between trees and the environment. FSTMs can be as an important means of forest management and decision-making with more outputs expected than traditional empirical models, process-based models and morphological models. As a general plant functional-structural model, GreenLab model has been applied to crop simulation such as maize, tomato, cucumber and sunflower. There are some applications in forestry including Fagus Sylvatica, Pinus sylvestris and Salicaceae. But it is seldom applied to trees with different age class especially to adult trees. There it is never applied to trees at stand or tree population level with feedback mechanism. The goal of this paper was: 1) to construct functional-structural model of Chinese pine based on GreenLab model to analyze its growth process and development with different age class; 2) to test biological hypothesis behind GreenLab models; and 3) to extend the application of GreenLab model to stands or tree population level with light environmental effects. It is expected to provide the method for applying GreenLab model to forest growth and management and implementing biology-driven tree visualization.Experimental sites were located in nursery of Shisanling forest farm, Yuanyiqi nursery and Xishan forest farm in Beijing. Detailed data including tree geometry, tree topology and biomass measurements were collected for 1-5 years old, 10 years old, 13 years old, 18 years old and 41 years old trees. With experiment data, direct parameters of the model were calculated including organ sink parameters and allometric parameters and target files were created. Hidden parameters were acquired by the nonlinear least square method inversely. At the same time, GL3 model which is called feedback model were calibrated by fitting demand satisfaction rate. By the relationship between tree Voronoi area which is called area potentially available index(APA) and hidden parameters, we extended GreenLab model to stand or tree population level and simulated the variation of trees biomass and topological structure with different competition index APA after cutting. Main contents and results are listed as follows:1) Organ allometry is one of important components in functional-structural models. It is essential to analyze the biomass allocation and organ geometry attributes. In this thesis allometric rules between internode biomass and internode length, between internode biomass and needle biomass, between internode sectional area and internode biomass, and between internode sectional area and needle biomass of current year twigs on 1 level branches, 2 level branches and 3 level branches of 4 trees with 10 years old and 13 years old were analyzed by power exponent equation, Independent data were used to test the allometric models. The results showed that the correlation coefficient between predicted and observed values is more than 0.85 except the case between sectional area and needle biomass, and the correlation is statistically significant. The size scale between internode biomass and needle biomass is less than 1 which means that biomass allocation rate is higher for internode biomass than for needle biomass. The size scale between sectional area and needle biomass is close to 1 which means that it is symmetry between these two attributes and accord with pipe model.2)Based on botany rules and the concept of physiological age of architecture, we developed topological code by numerical way. It overcomed the difficulties in dealing with hundreds and thousands components of trees. It is convenient to be used in measurement, data process and programming.3) Functional-structural model of young and adult Chinese pine trees are constructed based on GreenLab methodology. Stratified random sampling was used to measure adult Chinese pine trees. Stratifies were whorl and branching orders. Substructure algorithm was used to fill target file to overcome the difficulties in dealing with thousands and millions of units and saved computation time. The thesis introducedλto combine the Pressler pattern and common pool patter for analyzing the ring growth. It is effective and flexible to apply to trees with different ages and different environment factors. By referring to published empirical models, we tested the accumulated biomass of whole trees, internodes and needles. The results showed that the correlation coefficient is 0.98 for total biomass, 0.95 for total internode biomass and 0.75 for total needle biomass. More samples applied to GreenLab model will be helpful to improve the estimation accuracy.4) The thesis introduced Voronoi area as density of tree group. By comparing the attributes two patterns with different Voronoi areas, the effects on organ biomass and organ dimension of light competition was analyzed. Hidden parameters were also compared after fitting models. By allometric models, the relationship between Voronoi area and hidden parameters which indicate the light competition were established. According to this relationship, we simulated the tree topology and biomass after cutting.The study proved that functional-structural GreenLab model could reasonably describe the feedback between tree structure and functions of Chinese pine, and produced 3D architecture information with biological mechanism. The model has potentials in explaining light competition and simulating cutting effects at stand level, and expected to be broadly applied in forest growth simulation and management decision-making in future. The next research will include linking FSTMs to mechanism of botany and ecology, extending the feedback between tree structure and environmental factors, strengthening model validation, integrating the model with management practice, and optimization of parameter fitting. We hope the model could provide more supports for forest management and decision making.