Biography
My research interests lie at the confluence of remote sensing, GIScience, and biogeography. The broad goal of my research is to develop advanced remote sensing frameworks to understand the interactions among land cover dynamics, hydrologic regimes, climate changes, and human activities. In today’s big data era, large volumes of remotely sensed information open up new paradigms for exploring these interactions. To date, my research has mostly focused on advancing time series of remote sensing at multiple spatial, temporal and spectral scales, to better understand vegetation phenological dynamics in response to climate and environmental changes. I am also interested in building advanced computational solutions to model large-scale agricultural and forest system dynamics under various natural and anthropogenic disturbances by integrating remote sensing, process-based models, deep learning, and high performance computing.
Education
- Ph.D., Geography, State University of New York at Buffalo
- M.A., Biostatistics, State University of New York at Buffalo
- B.S., Beijing Normal University
Courses Taught
Recent Publications
Diao, C. (2020). Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages. Remote Sensing of Environment, 248, [111960]. https://doi.org/10.1016/j.rse.2020.111960
Tian, J., Wang, L., Yin, D., Li, X., Diao, C., Gong, H., Shi, C., Menenti, M., Ge, Y., Nie, S., Ou, Y., Song, X., & Liu, X. (2020). Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion. Remote Sensing of Environment, 242, [111745]. https://doi.org/10.1016/j.rse.2020.111745
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T. A. (2020). A summary of the special issue on remote sensing of land change science with Google earth engine. Remote Sensing of Environment, 248, [112002]. https://doi.org/10.1016/j.rse.2020.112002
Diao, C. (2019). Complex network-based time series remote sensing model in monitoring the fall foliage transition date for peak coloration. Remote Sensing of Environment, 229, 179-192. https://doi.org/10.1016/j.rse.2019.05.003
Diao, C. (2019). Innovative pheno-network model in estimating crop phenological stages with satellite time series. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 96-109. https://doi.org/10.1016/j.isprsjprs.2019.04.012
