Automatic identification of forest disturbance drivers based on their geometric pattern in Atlantic forests

Abstract

Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.