Construction of crown profile prediction model of Pinus yunnanensis based on CNN-LSTM-attention method

TL;DR

This paper introduces a new deep learning model to predict the crown shape of Pinus yunnanensis trees, improving accuracy and capturing vertical and directional crown competition.

Contribution

The study proposes a novel CNN-LSTM-Attention hybrid model combined with a Crown Profile Competition Index (CPCI) for improved crown profile prediction.

Findings

The CNN-LSTM-Attention model achieved the best performance with R²=0.98161 and significant improvements over the Vanilla LSTM model.

Incorporating CPCI improved prediction accuracy across all models, especially for the Vanilla LSTM model.

The hybrid model demonstrated superior stability and performance in handling directional crown profile datasets.

Abstract

Pinus yunnanensis is a significant tree species in southwest China, crucial for the ecological environment and forest resources. Accurate modeling of its crown profile is essential for forest management and ecological analysis. However, existing modeling approaches face limitations in capturing the crown’s spatial heterogeneity and vertical structure. This study aims to propose a novel approach that combines deep learning with a crown competition index to overcome the limitations of traditional models in capturing crown asymmetry and vertical structure, thereby enhancing prediction accuracy. Thus, we developed a hybrid CNN-LSTM-Attention deep learning model combined with a novel Crown Profile Competition Index (CPCI), based on data collected from 629 trees across five age-stratified permanent plots on Cangshan Mountain, Dali, Yunnan Province. Experimental results showed that the hybrid CNN-LSTM and CNN-LSTM-Attention models significantly outperformed the Vanilla LSTM model. In particular, the CNN-LSTM-Attention model achieved the best performance (MSE=0.00755 m2, RMSE=0.08691 m, MAE=0.05198 m, R²=0.98161), with absolute R² improvements of 0.16 and 0.17 over the Vanilla LSTM model by the CNN-LSTM and CNN-LSTM-Attention models, respectively. Additionally, the CNN-LSTM-Attention model demonstrated superior stability and performance in handling directional crown profile datasets. Incorporating CPCI improved prediction accuracy across all models, especially benefiting the Vanilla LSTM model. In conclusion, the proposed hybrid deep learning framework significantly enhances crown profile prediction for Pinus yunnanensis, and the introduction of CPCI provides a more precise representation of vertical and directional crown competition. This improvement facilitates more accurate assessment of tree crown dynamics, which is critical for understanding forest structure and competition.

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