PDF(Pubmed)
Abstract：
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.
摘要：
全球森林面积在过去几年有所下降,森林质量下降了，生态和环境事件随着气候变化和人类活动而增加。在生态文明的背景下,森林健康问题受到了前所未有的关注。通过改善森林健康，森林可以更好地发挥其生态系统服务功能，促进绿色发展。本研究在海南五指山地区热带雨林国家公园进行。我们采用了决策树算法，机器学习技术，由于其高精度和可解释性，我们的建模。根据132个森林样本的调查和实验数据，使用通过标准间相关性（CRITIC）使用重要性标准的客观加权方法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。提出了一种可解释的决策树机器学习模型，以纳入森林健康指标，在森林健康状况分类中提供高达90%的准确率。该模型表现出高度的有效性，达到90%的平均精度，67%的召回，预测森林健康的F1评分为70.2%。可解释的决策树分类结果表明，胸高直径是对原始森林和次生林健康状况进行分类的最重要变量。这项研究强调了在决策过程中使用可解释的机器学习方法的重要性。我们的工作有助于可持续森林发展和有效保护规划的科学基础。
