Urban expansion (urbanization) often causes significant disturbance to ecosystems surrounding cities, sometimes resulting in the removal of large amounts of biomass and in turn putting the human-nature systems at risk. Landsat imagery has long been utilized to monitor urbanization and ecosystem change at regional and local scales. However, few studies use Landsat time series to monitor urbanization at higher temporal frequencies, especially for applications focusing on large geographic areas, mainly due to the lack of efficient algorithms and computation facilities to handle large data volume.
Here we extract annual vegetated land loss to urbanization information with Landsat time series and implement it on the Google Earth Engine (GEE), a cloud-computing platform, for large area applications. We first generate annual Landsat cloud/shadow free NDVI mosaics and then NDVI time series spanning the period from 2000 to 2010. We then develop change and stable models to identify change time points in the time series.
We evaluate the performance of the proposed method in Shanghai, China, which has experienced rapid urbanization during the past few decades. Results show annual ecosystem disturbance caused by urban expansion is well captured, with a change detection accuracy higher than 80%. Our method is fast, simple, and can be easily extended to large areas on the Google Earth Engine cloud-computing platform.