Mapping global urban extent dynamics
Using Landsat time series data, we developed a mapping framework to extract annual, high resolution (30m), and temporally consistent global urban extent dynamics dataset GAIA, as well as the multi-temporal global settlement boundary dataset GUB.
We developed multiple urban cellular automata models, to support studies of urban environmental change under future shared socioeconomic pathways (SSPs) and climate change scenarios.
We developed a cloud-based mapping framework to generate long-term, high spatial and temporal resolution vegetation phenology datasets in the complicated urban environment. We explored the dynamics of phenological indicators and modeled their responses to the changing urban environment.
We developed a series of products using nighttime light time-series data, including the stepwise calibration of DMSP/OLS data, the harmonization of DMSP/OLI and VIIRS/DNB data, and applications of urban extent mapping and ecological protection.
