Forest canopies embody a dynamic set of ecological factors, acting as a pivotal interface between the Earth and its atmosphere. They are not only the result of an ecosystem's ability to maintain its inherent ecological processes, structures, and functions but also a reflection of human disturbance. This study introduces a methodology for extracting a comprehensive and human-interpretable set of features from the Canopy Height Model (CHM), which are then analyzed to identify reliable indicators for the degree of naturalness of forests in Southern Sweden. Utilizing these features, machine learning models - specifically, the perceptron, logistic regression, and decision trees - are applied to predict forest naturalness with an accuracy spanning from 89% to 95%, depending on the area of the region of interest. The predictions of the proposed method are easy to interpret, something that various stakeholders may find valuable.

翻译：森林冠层体现了一系列动态的生态因子，是地球与大气之间关键的界面。它们不仅是生态系统维持其固有生态过程、结构和功能能力的结果，也反映了人为干扰的程度。本研究提出了一种从冠层高度模型中提取一套全面且易于人类理解的特征的方法，并通过对这些特征进行分析，以确定瑞典南部森林自然度的可靠指标。利用这些特征，本研究应用机器学习模型——具体为感知器、逻辑回归和决策树——来预测森林自然度，其准确率在89%至95%之间，具体取决于感兴趣区域的面积。所提出方法的预测结果易于解释，这可能对各类利益相关者具有重要价值。