Li Jialing, He Bohao, Ahmad Shahid, Mao Wei
School of Ecology and Environment Hainan University Haikou China.
Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province Hainan University Haikou China.
Ecol Evol. 2023 Sep 25;13(9):e10558. doi: 10.1002/ece3.10558. eCollection 2023 Sep.
Global forest area has declined over the past few years, forest quality has declined, and ecological and environmental events have increased with climate change and human activity. In the context of ecological civilization, forest health issues have received unprecedented attention. By improving forest health, forests can better perform their ecosystem service functions and promote green development. This study was carried out in the WuZhi Shan area of Hainan Tropical Rainforest National Park. We employed a decision tree algorithm, a machine learning technique, for our modeling due to its high accuracy and interpretability. The objective weighted method using criteria of importance through intercriteria correlation (CRITIC) was used to determine forest health classes based on survey and experimental data from 132 forest samples. The results showed that species diversity is the most important metric to measure forest health. An interpretable decision tree machine learning model was proposed to incorporate forest health indicators, providing up to 90% accuracy in the classification of forest health conditions. The model demonstrated a high degree of effectiveness, achieving an average precision of 90%, a recall of 67%, and an F1 score of 70.2% in predicting forest health. The interpretable decision tree classification results showed that breast height diameter is the most important variable in classifying the health status of both primary and secondary forests. This study highlights the importance of using interpretable machine learning methods for the decision-making process. Our work contributes to the scientific underpinnings of sustainable forest development and effective conservation planning.
在过去几年中，全球森林面积减少，森林质量下降，并且随着气候变化和人类活动，生态环境事件有所增加。在生态文明背景下，森林健康问题受到了前所未有的关注。通过改善森林健康状况，森林能够更好地发挥其生态系统服务功能并促进绿色发展。本研究在海南热带雨林国家公园五指山地区开展。由于决策树算法具有较高的准确性和可解释性，我们采用了这种机器学习技术进行建模。基于132个森林样本的调查和实验数据，运用基于准则间相关性的客观加权方法（CRITIC）来确定森林健康等级。结果表明，物种多样性是衡量森林健康的最重要指标。提出了一个可解释的决策树机器学习模型，该模型纳入了森林健康指标，在森林健康状况分类中准确率高达90%。该模型显示出高度的有效性，在预测森林健康方面，平均精度达到90%，召回率为67%，F1分数为70.2%。可解释的决策树分类结果表明，胸径是对原始林和次生林健康状况进行分类的最重要变量。本研究强调了在决策过程中使用可解释机器学习方法的重要性。我们的工作为可持续森林发展和有效的保护规划提供了科学依据。
