Urbanization and global change are the two major processes that have been altering global land surface, causing a series of environmental impacts and challenging human beings’ future development. Remote Sensing has provided powerful, fast and cost-efficient tools to monitor global land surface in real time. Landsat, AVHRR (Advanced Very High Resolution Radiometer), and DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) have accumulated more than 30 years records, and MODIS (Moderate Resolution Imaging Spectroradiometer) has accumulated more than 10 years records of global land. These and other remote sensing data form an invaluable resource to monitor land surface changes over the past decades, and also make it a big challenge to take full advantage of them.
Dr. Zhang’s research focuses on the following fields:
1. Monitoring urban expansion and global change with Landsat, AVHRR, MODIS, and DMSP/OLS long archives.
2. Generating spatially and temporally complete remote sensing data by removing gaps caused by clouds and cloud shadows. Dr. Zhang’s previous work has focused on gap-filling the MODIS BRDF (Bidirectional Reflectance Distribution Function) products. The gap-filled MODIS BRDF products can be used to generate reflectance at any given illumination-observation geometry, providing a practical way to normalize observations obtained at various conditions. His recent efforts include generating global cloud-free Landsat mosaics on Google Cloud platform, cooperating with the Google Earth Engine team.
3. Reconstructing land cover land use temporal dynamics through analyzing high temporal frequency remote sensing time series. Traditional methods developed to process remote sensing images obtained at a single time point are no longer effective enough to deal with high temporal frequency data. With the long time series from Landsat, AVHRR, MODIS, and DMSP/OLS freely available to public, new algorithms need to be developed to reconstruct land cover land use dynamics with a high temporal frequency. One of his current projects is to reconstruct the history of agriculture land loss due to urban expansion in China using cloud-free Landsat mosaics generated on Google Earth Engine.
4. Developing new tools to assess high temporal time series algorithms. The classical confusion matrix used to assess traditional classification algorithms needs to be redesigned in order to assess and compare new algorithms developed to analyze high temporal frequency time series data. Or new error matrices must be developed in this new era.
5. Urbanization and its environmental impacts. Urban sprawl can be detected with satellite data at different scales. With the rapid economic development in countries such as China and India, the world will witness fast urban expansion in the near future. Is urbanization good or bad to environment? Should we think of urban as a sustainable form or unsustainable form for us human being to live in?
|Ph.D., Geography||2008||Boston University|
|M.A., Geographic Information Science||2004||Central Michigan University|
|M.S., Environmental Science||2002||Chinese Academy of Sciences|
|B.A., Geography||1997||Hunan Normal University|