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
GGIS 477 - Intro to Remote Sensing
GGIS 478 - Techniques of Remote Sensing
GGIS 489 - Programming for GIS
External Links
Recent Publications
Diao, C., & Li, G. (2022). Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology. Remote Sensing, 14(9), [1957]. https://doi.org/10.3390/rs14091957
Kang, B., & Diao, C. (2022). Walking School Bus Program Feasibility in a Suburban Setting. Journal of Planning Education and Research, 42(3), 365-374. https://doi.org/10.1177/0739456X18817353
Lyu, F., Yang, Z., Xiao, Z., Diao, C., Park, J., & Wang, S. (2022). CyberGIS for Scalable Remote Sensing Data Fusion. In PEARC 2022 Conference Series - Practice and Experience in Advanced Research Computing 2022 - Revolutionary: Computing, Connections, You [35] (PEARC 2022 Conference Series - Practice and Experience in Advanced Research Computing 2022 - Revolutionary: Computing, Connections, You). Association for Computing Machinery, Inc. https://doi.org/10.1145/3491418.3535145
Diao, C., Yang, Z., Gao, F., Zhang, X., & Yang, Z. (2021). Hybrid phenology matching model for robust crop phenological retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 308-326. https://doi.org/10.1016/j.isprsjprs.2021.09.011
Gao, F., Anderson, M. C., Johnson, D. M., Seffrin, R., Wardlow, B., Suyker, A., Diao, C., & Browning, D. M. (2021). Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset. Remote Sensing, 13(24), [5074]. https://doi.org/10.3390/rs13245074
