Trees play a crucial role in mitigating climate change by absorbing CO2 and providing biophysical cooling. In Cyprus, an EU member state located in the Eastern Mediterranean, and a climate change hotspot, increasingly impacted by forest fires and more arid conditions, the absence of a comprehensive tree monitoring system hinders effective carbon stock assessment and land-based mitigation strategies for the achievement of the European Commission goal of substantial greenhouse gas reductions by 2030.

This study presents a deep learning neural network model applied to high resolution (10 cm) airborne images collected during the year 2019, to generate segmented tree crowns and the number of individual trees over selected areas of Cyprus, including a large national forest park, a forest park in the capital city, and a small urban area, encompassing a total studied area of 107 square kilometers.