森林工程2025，Vol.41Issue(3)：451-461,11.DOI:10.7525/j.issn.1006-8023.2025.03.002
基于图神经网络的林分空间结构优化
Stand Spatial Structure Optimization Using Graph Neural Networks
摘要
Abstract
The optimization of stand spatial structure is a key issue in achieving sustainable forest management.Tradi-tional optimization methods often exhibit low efficiency in handling complex spatial relationships and large-scale data.This study proposed a stand spatial structure optimization method based on Graph Attention Networks(GAT).An integrated spatial structure evaluation system was established using the entropy-weighted matter-element analysis method,and a graph neural network model was constructed based on stand data from the Tanglin Forest Farm of the Xinqing Forestry bu-reau in northern Yichun,Heilongjiang Province.The model was applied to perform multi-objective optimization analysis of stand spatial structure.Experimental results showed that at a 25%harvesting intensity,the integrated spatial structure index improved from 4.336 to 7.256.The GAT model demonstrated superior performance in capturing complex spatial re-lationships and optimizing multi-objective tasks.This study provides an innovative and intelligent approach for optimizing stand spatial structure and managing forests,contributing to the enhancement of forest ecosystem health and stability.关键词
林分空间结构/图神经网络/物元分析法/图注意力网络/熵权法Key words
Stand spatial structure/graph neural networks/matter-element analysis/graph attention network/entropy weighting method分类
林学引用本文复制引用
张雨晨,董希斌,张甜,郭奔,张佳旺,滕弛,宋梓恺..基于图神经网络的林分空间结构优化[J].森林工程,2025,41(3):451-461,11.基金项目
国家重点研发计划项目(2022YFD2201001) （2022YFD2201001）
山西省基础研究计划项目(20210302123375). （20210302123375）
