Harvard Forest Core Values Award
I am deeply honored to receive the Harvard Forest Core Values Award. Thank you to my colleagues for nominating me.
👋 I’m currently a Postdoc Researcher at Harvard Forest, Harvard University working in the Thompson Lab. I finished my Ph.D. at the Center for Geospatial Analytics, North Carolina State University, working in the Spatial Ecosystems Analytics Lab (SEAL). I was also a NASA Future Investigator awardee working on using satellite 🛰 images to investigate plant phenology 🌱.
🚀 Passion: develop cool technologies to answer important science questions, and share my knowledge to the whole world!
I play guitar 🎸 and I love singing 🎤
Interests
- Remote Sensing
- Terrestrial Vegetation Dynamics
- Ecosystem Modeling
- Climate Change
- Bayesian
- Open Science
Education
Ph.D. in Geospatial Analytics
North Carolina State University
M.E. in Natural Resources and Environmental Remote Sensing
Chengdu University of Technology & Chinese Academy of Sciences
B.S. in Geography Information System
Chengdu University of Technology
What is my research about?
Geospatial Ecology –– An interdisciplinary field that applies geospatial analytics, particularly remote sensing and spatial modeling, to study ecological patterns and processes across landscapes. My current work focuses on how integrating AI and remote sensing can improve our understanding of forest ecosystem processes.
Recent News
Spatial BLSP extension paper is online
A recent publication led by Dr. Matthew Shisler is online. This paper extends our Bayesian Land Surface Phenology model to use spatial information.
pnetr paper is online
Our paper on pnetr, an R package for the PnET family of forest ecosystem models, is online in Methods in Ecology and Evolution.
Cross-scalar analysis of multisensor land surface phenology is online
Our paper on cross-scalar analysis of multisensor land surface phenology is online in Remote Sensing of Environment.
AGU 2024 talk on phenology-growth research
I gave a talk at AGU 2024 about my current phenology-growth research.
Recent Projects
Mapping Forest Communities at 10-m Resolution Using Deep Learning
Landscape simulation and nature-based climate solutions.
Multiscale Analysis of Land Surface Phenology
Understanding the correlation across land surface phenology datasets derived from satellite sensors with different spatial resolutions.
Featured Publications
Cross-scalar analysis of multisensor land surface phenology
We intercompared LSP with 3, 30, and 500 m resolutions and found that Land cover heterogeneity measured via entropy explains cross-scalar agreement.
Thermal Forcing Versus Chilling? Misspecification of Temperature Controls in Spring Phenology Models
Modeling field phenology observations may not reveal the importance of chilling.