基于图神经网络的林分空间结构优化

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中图分类号：S750 文献标识码：A DOI：10.7525/j.issn.1006-8023.2025.03.002

Abstract：The optimizationof stand spatial structure is akey issue inachieving sustainable forest management.Traditionaloptimizationmethodsoftenexhibitloweficiencyinhandlingcomplexspatialrelationshipsandlarge-scaledata.This study proposed a stand spatial structure optimization method basedon Graph Atention Networks （GAT）.An integrated spatial structure evaluation system was established using the entropy-weighted mater-element analysis method，and a graph neural network modelwas constructedbasedonstanddata fromthe Tanglin ForestFarmof the Xinqing Forestry bureauin northern Yichun，Heilongjiang Province.Themodel wasapplied to perform multi-objectiveoptimization analysis of stand spatial structure. Experimental results showed that at a 2 5 % harvesting intensity，the integrated spatial structure index improvedfrom4.336 to 7.256. The GAT model demonstrated superior performance incapturing complex spatial relationships andoptimizing multi-objectivetasks.This study provides aninnovativeandintellgentapproach foroptimizing standspatial structure and managing forests，contributing to the enhancementofforest ecosystem health and stability.

Keywords：Stand spatial structure；graph neural networks；mattr-element analysis；graph atention network；entropy weighting method

0 引言

森林生态系统是地球上最重要的生态系统之一，不仅为人类提供丰富的资源，还在调节气候、保持水土和防风固沙等方面发挥着重要作用1。（剩余15752字）