Abstract: [Purposes] To address the limitations of traditional remote sensing methods in tree species classification accuracy under strong background interference,this study proposes an optimized random forest model integrating the northern goshawk optimization(NGO)algorithm and an attention mechanism (NGO-Attention-RF). [Methods] Taking Fuxin city,Liaoning province as the study area,multi-source remote sensing data were fused to construct a multi-dimensional feature set.The NGO algorithm was employed to optimize the hyperparameters of the random forest,thereby avoiding local optima.An attention mechanism was introduced during node splitting to dynamically adjust feature weights,enabling the model to focus on key features that distinguish tree species. [Findings] Experimental results show that the proposed model achieved an overall accuracy of 85.4%.It outperformed methods such as MLP,CNN,RNN,Transformer,and SVM in terms of accuracy for each tree species category and overall classification metrics,validating the effectiveness of the NGO algorithm and the attention mechanism. [Conclusions] The study reveals the spatial distribution patterns of forest tree species in Fuxin city,elucidates the role of different features in tree species discrimination,and provides a methodological reference for forest monitoring in similar ecological regions．

Key words: forest tree species classification, multi-source remote sensing, feature fusion, random forest, northern goshawk optimization algorithm, attention mechanism