About
Andrew Richardson is a Regents' Professor at Northern Arizona University, jointly affiliated with the School of Informatics, Computing, and Cyber Systems and the Center for Ecosystem Science and Society. His research focuses on ecosystem dynamics, plant phenology, climate change impacts, and carbon cycling, utilizing advanced monitoring technologies and data science approaches.
His research interests span phenology, vegetation monitoring, carbon cycling, remote sensing, and ecological modeling. He is a leading figure in the use of PhenoCam networks and eddy covariance flux data to understand ecosystem responses to environmental change. His work integrates field observations with machine learning and large-scale data synthesis.
The most recent articles reflect a strong trend in combining machine learning with ecosystem-scale CO2 flux measurements, analyzing forest phenology across temperate and mesic systems, and investigating long-term carbon reserves in tree species. His publications emphasize data-driven approaches to understanding climate-vegetation interactions and are published in high-impact environmental science journals.
Andrew Richardson has contributed significantly to open science through the release of major datasets, including PhenoCam imagery and greenhouse gas flux measurements from Howland Forest. He collaborates extensively across institutions and has developed tools for continental-scale evaluation of carbon cycle models using flux tower data.
He advises students and researchers in ecosystem science and environmental informatics, though specific advisees are not listed. His work is supported by long-term ecological research initiatives and collaborative grants focused on climate change impacts and carbon dynamics.
Richardson leads or contributes to major research infrastructure including the PhenoCam network and SPRUCE experimental plots, enabling ground-based phenological observations under controlled warming conditions. His lab emphasizes open data, reproducibility, and interdisciplinary collaboration between ecologists, computer scientists, and climate modelers.
