Biography
My research interests lie at the confluence of remote sensing, GIScience, and biogeography. My current research focuses on computational remote sensing of terrestrial ecosystem dynamics at local to global spatial scales, and daily to decadal temporal scales. I am particularly interested in advancing computational remote sensing paradigms in characterizing land surface patterns and processes, underlying mechanisms, and subsequent feedbacks to the atmosphere. My work combines remote sensing, process-based models, field observations, artificial intelligence, high-performance and cloud computing, to study ecosystem structures, functions, and responses to climate change and human activities. My ongoing research traverses varying types of ecosystems, including natural (e.g., forest), human-dominated (e.g., agriculture), and disturbed (e.g., species invasion) ecosystems. Current research foci include computational remote sensing, multi-scale land surface phenology, intelligent agriculture, and invasive species and biodiversity.
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
External Links
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
