C and Python code examples for tree growth simulation: geographic and meteorological data, as well as lighting conditions, to predict tree growth rate and growth potential

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Tree growth simulation based on geographic and meteorological data and light conditions in Chengdu, Sichuan, in summer is a complex task, and it can be used to predict tree growth rate and growth potential. The following are detailed explanations, flowcharts, usage scenarios, sample code, and links to literature materials:

1. Detailed explanation of the principle:

The growth of trees is affected by many factors, including soil quality, temperature, humidity, light, carbon dioxide concentration, etc. In tree growth simulations, the main principles include:

  • Growth model: Use mathematical models to represent the growth process of trees. Commonly used models include growth rate models, photosynthesis models, water absorption models, etc.

  • Environmental data: Gather geographic and meteorological data, including soil type, temperature, humidity, precipitation, solar radiation, and more.

  • Light conditions: Analyze sun position and shadow effects to determine the amount of light a tree receives, which is critical for photosynthesis and growth.

  • Tree Attributes: Determine tree attributes such as species, age, health, etc. that affect growth rate and potential.

  • Simulation process: Combine these data and models to simulate the trees under given environmental conditions

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Origin blog.csdn.net/feng1790291543/article/details/132452102