Naturalness indicators of forests in Southern Sweden derived from the canopy height model

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) with a resolution of 1 meter. These features are then analyzed to identify reliable indicators of the degree of naturalness of forests in Southern Sweden. Using these features, machine learning models–specifically, the perceptron, logistic regression, and decision trees–are trained with examples of forests exhibiting known high and low degrees of naturalness. These models achieve prediction accuracies ranging from 89% to 95% on unseen data, depending on the area of the region of interest. The predictions of the proposed method are easy to interpret, making them particularly valuable to various stakeholders involved in forest management and conservation.