AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus

Trees play a crucial role in mitigating climate change by absorbing (Formula presented.) and providing biophysical cooling. The European Commission’s climate policies underscore the importance of forest monitoring systems to achieve substantial greenhouse gas reductions by 2030. In Cyprus, an EU member state located in the Eastern Mediterranean, and a climate change hot-spot, 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. The exact tree population inside and outside forests is currently unknown. Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. 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 (Formula presented.). The model, previously applied in Denmark and Finland was completely re-tuned using local annotations to account for Cyprus’s specific conditions and achieved an overall accuracy of (Formula presented.) and (Formula presented.) to estimate the area covered by tree crowns and the number of trees, respectively. The results are regressed against coarser resolution tree cover maps to predict the area covered by tree crowns at a national level. The accuracy of the tree cover maps created by this study is compared to those of existing global tree cover maps, such as the Copernicus products. This work lays the foundation for establishing a tree-level inventory for Cyprus using airborne remote-sensing.