[Neural network grade program of natural forest protection]

Area of Science:

• Forestry Science
• Ecological Management
• Artificial Intelligence in Ecology

Background:

• Natural forest protection programs require effective grading systems to manage diverse ecological factors.
• Existing methods may not fully integrate spatial data and complex environmental variables.
• The Moershan National Forest Park presents a case study for advanced forest protection assessment.

Purpose of the Study:

• To summarize the implementation steps of a neural network (NN)-based natural forest protection program grading (NFPPG).
• To analyze the relationships between NFPPG and key ecological factors using Geographic Information System (GIS) data.
• To develop and validate a generalized NFPPG model for Moershan National Forest Park.

Main Methods:

• Summarized implementation steps for NFPPG using NN.
• Utilized Arc/Info GIS to describe tree species diversity, rarity, disturbance, channel protection, and classification management.
• Analyzed relationships between NFPPG and these factors as input for the NN.
• Artificially determined training samples to build the NFPPG model.
• Tested the model's generalization with all park patches.

Main Results:

• A functional NFPPG model for Moershan National Forest Park was successfully built and validated.
• The model demonstrated satisfactory generalization across all park patches.
• The NFPPG model effectively integrated classification management, forest community types, and ecological environments.
• Analysis indicated the inclining factor significantly influenced network optimization without severe excitation function saturation.

Conclusions:

• The developed NN-based NFPPG provides a comprehensive approach to forest protection grading.
• The model's ability to incorporate diverse ecological and management factors enhances its practical applicability.
• The findings highlight the effectiveness of NN and GIS integration for ecological assessment and management.