​Currently this work is funded by a NASA Carbon Cycle grant to Jiquan Chen​ (MSU), Kyla, and others. More information on that project is here.

SAST Phenology

​Coarse grain (meaning bigger than a single plant canopy, in this context) seasonal changes in vegetation leaf area is called 'land surface phenology'. These seasonal changes are important because their timing and magnitude influence the carbon cycle, energy balance, surface roughness, biogeochemical cycling, and more. We are particularly interested in semi-arid and savanna-type (SAST) land surface phenology, which is additionally challenging because in many parts of the world there is no really cold season to 're-set' plant growth. In addition, in some regions (especially eastern and southern Africa) there are often two rainy seasons per year, meaning two green seasons per year, which goes against many of the assumptions that go in to phenology models. We have been looking at SAST phenology from a number of different angles, including Earth system models, meta-analysis, correlations with climate teleconnection patterns (see figure), and more recently work looking at abiotic factors' influence on fine grain phenoregion patterns (Desanker et al in prep). There are many satellite remote sensing products (e.g. soil moisture, LiDAR & radar, fine spatial & temporal resolution small sats) that could contribute to this research, which we hope to use in future projects.
Correlations between leaf area index (LAI3g) and four different teleconnection patterns (AMM = Atlantic Meridonal Mode; ENSO = El Nino-Southern Oscillation; IODM = Indian Ocean Dipole Mode; NAO = North Atlantic Oscillation) across East Africa. Zoomed in from Dahlin & Ault 2018.

Socioecological Carbon

How important are humans to the carbon cycle? At the global scale, humans are increasing greenhouse gas concentrations in the atmosphere and changing land surface properties via deforestation, agriculture, and urbanization. But what about at the local scale? Can we quantify the influence of land use practices and land cover change on the carbon cycle at the watershed scale? We are doing this in the Kalamazoo River watershed in Michigan via a combination of remote sensing, land surface modeling, life cycle analysis, eddy covariance, and interviews with members of the Michigan Centennial Farm Association. This is a large collaborative project led by Dr. Jiquan Chen in MSU GEO/CGCEO. The ERSAM Lab component is currently looking at how the Community Land Model represents these ecosystems (Dahlin, Akanga, et al in prep), and in the future will use PT CLM to model specific past, current, and future states in this system.
Land cover change over time in the Kalamazoo River watershed, Michigan. Image credit: Rong Zhang.

Recent Presentations

Publications on this topic