Growing performance of common urban trees across heterogeneous habitats: An application of multi-source remote sensing

Source abstract

Adequately understanding the growing performance of various tree species is essential for informing species selection and evaluating their sustainability. To date, in-situ inspection is commonly adopted to collect relevant information, which unfortunately suffers from sparse networks of field sample plots and limited spatiotemporal coverage. Taking advantage of multi-source remote sensing imageries, this study proposes a framework to specify tree species and monitor their growing performance across heterogeneous habitats. Using the Kowloon Peninsula in Hong Kong as a case study, this framework is tested by specifying six common tree species (three native and three non-native ones) and tracing their growing performance across residential grounds, road verges, recreational parks, and natural terrains during 2016-2020. The monitoring results of three vegetation indices focusing on tree canopy status (i.e., photosynthetic components, non-photosynthetic components, and water content) reveal that all six tree species exhibit poorer performance in three urbanized habitats as compared to natural terrains. And exotic species consistently outperform native ones in urbanized habitats. Using a permutation-based method, the possibility of exotic trees occupying the ecological niches of native trees to achieve growth advantages is ruled out. This study illustrates how multi-source remote sensing can be applied to monitor urban tree growing performance for sustainable management and contributes to the ongoing debate about native versus exotic species choice in urban greening.