Bridging Tree-Level Quantification and Continuous Forest Structural Characterization: A Framework Integrating Tree Counts and Crown Structure from UAV Imagery Using Forest Structural Zones

Abstract

Object detection and instance-segmentation methods have been widely used to locate trees and delineate individual crowns from aerial imagery, providing valuable tree-level information for estimating tree abundance, density, and spatial distribution. These variables form important inputs for quantitative assessments of forests and urban greenery, including analyses of thermal environments, pollutant dispersion and removal, and carbon storage and sequestration. Yet, existing approaches remain centered on individual trees or crowns and lack a spatially continuous representation of forest structural conditions, leaving a gap between discrete tree-level objects and continuous forest structural patterns. To address this gap, this study develops a geospatial multilayer supervised-classification framework that jointly estimates visible tree-top abundance and maps continuous forest structural zones (FSZs), thereby integrating tree-count information with spatially explicit structural characterization. The FSZ workflow was evaluated against YOLO11n and FastViT-T8-FPN Faster R-CNN in Domain A and independently evaluated in Domain B (an urban-fringe planted landscape). It achieved an OA of 0.825 and absolute TCRE of 14.29% in Domain A and an OA of 0.856 and absolute TCRE of 19.15% in Domain B, demonstrating good applicability across both scenarios. By linking tree-top abundance with landscape-scale structural patterns, the FSZ framework provides a complementary spatial data product for forest and urban-greenery quantification and offers a foundation for subsequent assessments of microclimate regulation, air-pollution management, carbon accounting, and green-infrastructure performance.