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25 JAN 2024 (THU) 10:05 - 10:25

Multi-source remote sensing data fusion for species-habitat synchronized urban forest monitoring

Mr GUO Yasong

(Supervisor: Prof Wendy Chen)


Urban forests have attracted increasing attention as a green prescription for addressing the sustainable development challenges facing the increasingly urbanized human societies. However, natural and human-induced stresses as presented in urbanized habitats can seriously affect the healthy growth of trees and further jeopardize their ability to provide ecological, environmental and social benefits. The considerable requirements of manpower and financial resource associated with conventional urban tree monitoring have forced relevant authorities to commonly adopt a spatial sampling strategy, with only limited numbers of urban trees being checked once in a while. Hence, only the snapshot of those sampled trees’ condition can be gathered. The lack of a long-term monitoring of the entire tree collection within urban fabrics comprising heterogeneous microhabitats might compromise a clear understanding of the species-habitat interaction, which would determine tree growth performance and the application of appropriate management strategies. Thanks to the development of remote sensing technology and the availability of remote sensing imageries from multiple sources, a nuanced understanding about the species-habitat interaction across the entire urban fabrics for a long time period can be derived. High spatial resolution remote sensing images provide fine-grained information for individual tree canopy segmentation, while multispectral remote sensing data (with high/medium spatial resolution) can facilitate tree species specification and identification via a series of vegetation indicators. This study aims to explore how the properties of multi-source remote sensing data can be integrated to undertake larger-scale, cost-effective and efficient monitoring of urban tree growth, and shed additional light on species-habitat interaction across neighborhood-city-regional scales. A case study at the neighborhood scale would be focused on Kowloon Peninsula, HKSAR, to test and validate the research framework. Then three major cities in China (including Beijing, Guangzhou, and Shenzhen) will be selected to construct an urban tree diversity estimation model, which combines in-situ field data, high spatial resolution remote sensing data, and high temporal resolution remote sensing data, in order to monitor the dynamics of urban tree diversity at the regional scale. Finally, the status and dynamics of urban forests in less developed countries would be evaluated. This study could demonstrate how multi-source remote sensing data can achieve dynamic, objective and long-term urban tree monitoring, for advancing scientific understanding about species-habitat interaction in urbanized areas across varying spatiotemporal scales, and providing practical implications for planning and establishing urban forests that can thrive across urban fabrics and contribute to biodiversity enhancement and urban sustainability.

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