Development of the First Airborne Tree Inventory for Cyprus and Novel Allometries for Carbon Stock Estimation Using AI Models and High-Resolution Remote Sensing Data

Accurate tree inventories are critical for monitoring forest resources and assessing ecosystem services, particularly carbon storage. This study presents the first airborne tree inventory for Cyprus, a climate change hotspot lacking a comprehensive national forest inventory. Using high-resolution orthophotos, we developed a novel method for tree segmentation and individual-level carbon stock estimation.

Tree identification and segmentation were performed using a published state-of-the-art convolutional neural network (CNN) model, previously applied in Denmark and Finland, which was completely re-tuned using local annotations to account for Cyprus’s specific conditions. This approach achieved 90% accuracy in tree crown delineation. Given the absence of suitable allometric equations for Cyprus' tree species, we developed novel, locally tailored allometric equations for above-ground biomass estimation, achieving 92% accuracy. These equations, derived from crown dimensions and height extracted through CNN models applied to canopy height maps (CHMs), enable accurate carbon stock estimation for individual trees.

The integration of orthophotos and CHMs proved highly effective in capturing detailed structural data across diverse forest landscapes. Our methodology is scalable, cost-effective, and robust, offering a valuable tool for forest management, climate change mitigation, and policy development in Cyprus. This project establishes a comprehensive baseline for Cyprus' forest resources and demonstrates the potential of combining remote sensing and AI technologies for national-scale environmental monitoring, including urban trees.