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Dr. Di Yang

Assistant Professor
Turlington Hall 3205
yangdi1031@ufl.edu

ORCID: 0000-0002-4010-6163
Twitter: yangdi1031

Curriculum Vitae

Google Scholar

LinkedIn

Focus Areas

Areas of Specialization

  • Remote sensing applications on human-environment interactions
  • Geospatial analytics, digital image processing, cloud computing
  • Machine learning and GeoAI for environmental monitoring
  • Citizen Science and Volunteered Geographic Information (VGI)
  • Land change science

Educational Background

  • Ph.D. in Geography, University of Florida, 2019
  • M.S. in Environmental Engineering, Texas A&M University-Kingsville, 2013
  • B.S. in Environmental Science, Liaoning University of Petroleum and Chemical Technology, 2011

Research Statement

My work focuses on integrating cutting-edge technologies like machine learning, cloud computing, and citizen science to address critical environmental challenges. I specialize in developing innovative approaches to monitor and analyze land use changes, ecosystem dynamics, and human-environment interactions from local, to regional, continental, and global scales. In our lab, we are interested in the dynamics of human-modified landscape systems, their response to environmental change (e.g., land use and climate), as well as the scientific basis for their management, conservation, and restoration. The main topics we are currently investigating include 1) NEON (National Ecological Observatory Network) macrosystems for forest management and biodiversity; 2) incorporating VGI data into geospatial modeling of extreme events under changing climates; and 3) processing and analyzing large ecological and remote sensing databases using geospatial cloud computing (e.g., Google Earth Engine, Microsoft Azure, and Web-GIS) to benefit conservation, resource management, landowners, and policymakers.

Recent Funded Projects

  • NASA Early-Career: “Evaluating Long-term Impacts of Land-use Transformation on Fire Regimes”
  • BLM: “Monitoring the Yermo xanthocephalus (desert yellowhead) using high-resolution UAS data and novel AI/ML workflows”
  • NASA: “A Meta-Learning Framework for Characterizing and Accessing Machine Learning Training Data for GLOBE Observer Protocols”
  • NASA: “Quantifying Effects of Land Cover Change-Climate Interaction on Ecosystem Productivity Over Western North America”
  • NCAR Large Allocation Grant: “Harnessing HPC for Predictive Geospatial Analytics”
  • NSF Engines Development Award: “Advancing Precision Forestry and Rangeland Technologies” (as Remote Sensing Team Lead)

Current Graduate Students

  • Shuai Li
  • Wei Liu
  • Kaitlyn Anderson

Recent Publications (select)

Yang, A., Cheng, W., Yang, J., Cai, C., Li, H., & Yang, D. (2024). Unveiling disparities: a social media analysis of urban park usage and sentiments in Oklahoma City. GeoJournal89(4), 173.

Thomas, A., Kolb, T., Biederman, J. A., Venturas, M. D., Ma, Q., Yang, D., … & Tai, X. (2024). Mitigating drought mortality by incorporating topography into variable forest thinning strategies. Environmental Research Letters19(3), 034035.

Huang, X., Wang, S., Yang, D., Hu, T., Chen, M., Zhang, M., … & Hohl, A. (2024). Crowdsourcing Geospatial Data for Earth and Human Observations: A Review. Journal of Remote Sensing4, 0105.