Estimation of chlorophyll content for urban trees from UAV hyperspectral images

Abstract

Urban trees provide important ecosystem services to improve cities’ liveability and sustainability. Leaf chlorophyll content (Cab) estimation by remote sensing can help monitor tree health efficiently. However, the Cab retrieval of urban trees is challenging because of the complex canopy structures, backgrounds, and illuminations conditions. This paper proposed an automatic method for partitioning sunlit/shaded pixels and removal of bright-specular/dark-hole and background pixels. In addition, we proposed a new index, the Urban Tree Chlorophyll Index (UTCI), defined as UTCI=(ρ709-ρ697)/(ρ709-ρ686), based on the simulated hyperspectral images of urban tree using radiative transfer model. This proposed UTCI index outperforms existing narrow-band indices (NBIs) for estimating Cab for complex canopy structures, backgrounds, and illumination conditions evaluated using simulated hyperspectral images. The advantage of the UTCI was also demonstrated when applied to UAV hyperspectral images validated with direct field foliar measurements of Cab. It surpasses existing NBIs, demonstrating a moderate correlation (R2 = 0.34) with Cab under varying irradiance and a strong correlation (R2 = 0.62) with Cab under stable diffuse illumination. This study, for the first time, extensively investigated NBIs for Cab estimation of urban trees from UAV hyperspectral images, providing a theoretical and operational basis for future monitoring of Cab in the management of urban trees. This new method can be potentially applied to other vegetation types with complex canopy structures, backgrounds, and illuminations conditions.