High Resolution Ecosystem Modeling as Part of a Robust Carbon Monitoring System

Amanda Armstrong, UMD, aha@umd.edu (Presenter)
George Hurtt, University of Maryland, gchurtt@umd.edu
Ralph Dubayah, University of Maryland, dubayah@umd.edu
Justin Fisk, University of Maryland, fisk@umd.edu
Naiara Pinto, University of Maryland, npinto@umd.edu
Juan Suarez, University of Maryland, jcsuarez@umd.edu
Shannon Franks, NASA GSFC/UMD, shannon.franks@nasa.gov
Rourke Oliver, University of Maryland, oliverrourke@gmail.com
Steve Flanagan, University of Maryland Geography Dept, saf204@gmail.com

The development of high-resolution ecosystem models is of key importance in the advancement of carbon assessment and monitoring systems. Ecosystem models that have the ability to incorporate fine-scale in situ vegetation measurements as well as high-resolution satellite imagery, such as LIDAR, will improve larger-scale estimates of carbon and reduce uncertainties in our overall understanding of global carbon cycle dynamics. The aim of our study was to develop and test a high-resolution version of the Ecosystem Demography (ED) Model, as well as develop a framework with the capability to model vegetation dynamics at 90m resolution over large geographic areas. Our experimental approach was designed to quantify the individual effect of each input dataset by running multi-scale model tests using combinations of biotic and abiotic input data. Through this methodology we sought to answer the following: 1) What are the high resolution carbon stocks and fluxes (past, present, future) over the domain and 2) How do different input datasets improve and/or constrain model estimates? From model results, we developed aboveground biomass and flux maps for Anne Arundel and Howard Counties in Maryland that in conjunction with other comparative metrics highlight the importance of the high spatial resolution climate, soil and structural inputs (LIDAR and forest masking) toward advancing our ability to predict carbon dynamics across heterogeneous landscapes with unprecedented accuracy and precision. The application of this system will monitor changes in carbon stocks over large continental areas through time and will introduce predictive capability for future planning and management purposes at a more human-relevant scale than has been previously achieved.

Presentation Type:  Poster

Session:  Global Change Impact & Vulnerability   (Tue 11:30 AM)

Associated Project(s): 

• Related Activity

Poster Location ID: 109