Unified Fusion of Remote Sensing Imagery for Sustainable High-density Urban Environment (1 Oct 12- 30 Sep 14)
• Researchers: HUANG Bo, NG Edward, REN Chao
• Funding Amount: HK$400,000
• Funding source: CUHK Research Committee Funding (Direct Grants)

This project proposes to build a unified theoretical framework for generating high STS resolution data through novel image fusion methods to facilitate monitoring, modeling and planning of complex urban environments in Hong Kong and the PRD region. Deliverables of this project will greatly contribute to the exploration and value-adding of existing image resources for urban environmental applications.
Various environmental variables (e.g. land surface temperature relating to urban heat island, aerosol optical depth (AOD) relating to air pollution etc.) that can only be obtained currently in a few stations and in low spatial details will be made available through retrievals from the high STS resolution satellite images to cover an entire study area on daily, monthly, quarterly and yearly basis depending on requirements. At the same time, the retrieved products will be more accurate owing to the simultaneous high STS resolution and the relationship between environmental variables, anthropogenic activities and carbon emission can also be established. While the fusion of various properties of satellite images is both theoretically and technically challenging, the delivery of the high STS resolution data is of high scientific and commercial value as they are in high demand by numerous organizations including research and education institutions where they may be further explored to support different applications.

Task 4: Sustainable land use planning in face of climate change
(sub-project 4, 1Apr 2013-31Jul 2014)

An analysis of land cover/land use change and change intensity in Hong Kong and the PRD and its contribution to UHI effects and aerosol distribution will be conducted. We will also investigate the relationships between urban heat island and urban structure, aerosol distribution and urban land use, and urban heat island and consumption of urban energy as well as carbon emissions as a result of anthropogenic activities in the form of different land use options. Specifically, the Geographically and Temporally Weighted Regression (GTWR) model will be used to study the relationship variations over both space and time. Further, a multi-objective optimization model will be developed which accounts for objectives relating to the afore-mentioned environmental variables, urban climate and other criteria, such as minimization of change, maximization of housing capacity, and maximization of green space, etc. A urban renewal area of old downtown will be selected to conduct a case study of the multi-objective optimization model for developing sustainable design adaptation measures and planning strategies for policy makers and planners.