Longleaf Pine Modeling and Simulation (Forest Engineering Department)

Longleaf Pine Modeling and Simulation (Forest Engineering Department)
This project is developed for doctorate thesis about Longleaf pine modeling and simulation. Especially focusing on harvesting methods which are single tree harvesting and group selection. The integrated longleaf pine (Pinus palustris) model was used in this study. The new coupled multiple model framework developed for longleaf pine has the following tripartite structure: (even-aged plantation model) EAM, (artificial neural network) ANN, and (individual tree savanna model) LLM. The purpose of such a combined model is to simulate how the transition from even-aged management to longleaf sand hill savanna ecosystems takes place. The first component of the combined model, EAM, is to simulate factors –variable planting density, thinning, prescribed fire, and biomass dynamics – by combining a growth and yield model with specifically designed allometric equations for longleaf pine. The second component, ANN, is used to generate a list of trees –a cluster of 60 years old individual trees with differential heights and diameters on EAM stand. It is then possible to allow transition from EAM to LLM through this list. The final component, LLM, is to simulate particular factors for longleaf and hardwood trees –competition, growth, recruitment, and mortality. The spatially explicit characteristic of LLM requires random placement of the trees in the simulations, which is obtained through the list generated via ANN.
LLM is a lattice-based approach where trees within cells (5 m×5 m) interacted with nearby cells (trees) based on adjacency and distance as in cellular automata (Silvertown et al., 1992). To evaluate the sensitivity of the LLM to such tree placement, multiple models for the same tree list in different locations are run. A complimentary step for sensitivity analysis is to measure mean fire return within 2 to 10 years interval. In this model, every single tree is simulated over time that is why the model can be characterized as an Individual Based Model (DeAngelis and Mooij, 2005). Python programming language (v. 3.5.0, Python Software Foundation) was employed to develop the model. Periodic boundaries and eliminating edge effects were used to model a reasonable compact area (125 m×125 m; 1.56 ha). Spatial interactions contained seed dispersal or fecundity (clonal rhizomatous spreading), inter-species and intra-species plant competition effects on growth and mortality, as well as impacts of tree density on fine fuel distribution and accumulation. This model was used to explain how to impact different prescribe fire frequency on basal area, height of both species, diameter at breast height (DBH), volume, age of both species, coarse woody debris (CWD), carbon stocks and wiregrass biomass in a reasonable compact area which is 1.56 ha and made out of longleaf pine and hardwoods.
I want you to write an introduction about this project around 10 pages with double space. After that I will send you my methods and discussion parts to improve my English and enhance my sentences.

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