Opening for postdoctoral researcher with Dr. Maggi Kelly, Department of Environmental Science, Policy and Management, University of California, Berkeley
Using Remote Sensing to Quantify Carbon Capture Capacity in Wetlands (http://kellylab.berkeley.edu/carbon-sequestration-wetlands/) Join a team of U.C. Berkeley and USGS researchers to develop methods to quantify and map belowground biomass and belowground net primary productivity (BNPP) of emergent marsh vegetation from remotely sensed estimates of aboveground plant characteristics and aboveground net primary productivity (ANPP) under controlled experimental treatments. Research will be conducted at a USGS long-term experimental wetland restoration site on drained peatland in the Sacramento-San Joaquin River Delta and surrounding areas. This project is in conjunction with Kristin Byrd, USGS (http://geography.wr.usgs.gov/staff.php), under a NASA New Investigator in Earth Sciences award, and the USGS Carbon Capture Wetland Farm project (http://ca.water.usgs.gov/Carbon_Farm/). Background: Temperate peatlands typically dominated by grasses and sedges generate among the greatest annual rates of net primary productivity (NPP, up to 4 kg C m-2) and soil carbon storage (up to 1 kg C m-2) for natural ecosystems. Belowground tissues represent 20-80% of total NPP, thus understanding the controls on BNPP in these wetland ecosystems is particularly important to quantifying peatland carbon balances. In addition, there is a growing need to quantify large-scale belowground carbon sequestration rates in wetlands to better understand marsh resilience to sea level rise and to help define eligibility for carbon offset credits. Since plant productivity influences wetland carbon budgets, combining field and remote sensing techniques for estimating above and belowground productivity of wetland vegetation over a large spatial extent will help to address these needs. Data from repeat field spectroradiometer readings throughout the growing season is the primary tool for exploring relationships between spectral signatures and ANPP, plant biophysical characteristics, and belowground biomass and BNPP. We are analyzing reflectance data to develop hyperspectral indices that predict the biophysical characteristics of wetland vegetation biomass, leaf area index (LAI), and the fraction of absorbed photosynthetically active radiation (APAR) which may be used to infer belowground biomass and productivity. Soil cores and root in-growth bags are being used to calculate root biomass and productivity rates. Remote sensing-based estimates of these parameters may be used to model NPP, which is determined at the site through CO2 flux measurements at the leaf scale, eddy correlation flux at the field scale, and stem and leaf turnover measurements. Multiple band combinations of simulated hyperspectral, hyperspatial, and multispectral data will be explored through statistical modeling to identify new indices that predict plant biophysical features and productivity rates that are related to belowground biomass and carbon sequestration. Our goal is to develop the capability to extrapolate measurements of plant productivity to other wetland sites using indices that are field-tested and calibrated for wetland plant species that contribute significantly to belowground carbon storage. Multiple opportunities exist for the postdoctoral researcher to build upon the project. Some broad research questions include: 1. Can a significant relationship between ANPP and BNPP in cattail/bulrush dominated marsh be established, including how they change under varying environmental conditions, to be able to model BNPP based on remote sensing estimates of ANPP? 2. Can vegetation indices related to plant pigments and other biochemical characteristics that indicate plant stress and reduced photosynthetic activity serve as a predictor of BNPP? 3. How does error associated with these estimates change with frequency of imaging and remote sensing data characteristics? How does error of these estimates change under varying environmental conditions? Required Qualifications: Ph.D. in remote sensing, geography, or equivalent Preferred Qualifications: Experience with field work; Knowledge of plant ecology/botany, especially wetland; Remote sensing background, experience with hyperspectral and field spectrometry a plus; Extensive experience with: ESRI GIS software and applications (ArcGIS Desktop); Statistical analysis software (e.g. SAS, R); and Remote sensing software (e.g. Erdas Imagine, eCognition, ENVI). Salary & Benefits: Annual salary of $41,496 plus benefits, commensurate upon experience and qualification Start Date and Duration: January 1, 2012; Duration 15 to 18 months, renewed after 12 months For Questions Contact Dr. Kristin Byrd USGS Western Geographic Science Center Menlo Park, CA [email protected]; 650-329-4279 Dr. Maggi Kelly Department of Environmental Science, Policy and Management U.C. Berkeley, [email protected]; 510-642-7272 To Apply Contact Maggi Kelly [email protected], please put Wetland Postdoc in subject line, and cc. Kristin Byrd at [email protected].
