DOI: 10.5281/zenodo.6506502
title: Forest Aboveground Biomass 1984-2016 for Maryland, USA

description: This dataset provides 30-m resolution maps of estimated forest aboveground biomass (AGB) in the state of Maryland between 1984-2016. This dataset was produced by a novel forest carbon monitoring system which utilizes high resolution remote sensing of contemporary tree cover and canopy height as powerful constraints within a process-based ecosystem model to reconstruct the spatial and temporal dynamics in AGB while considering impacts of spatially and temporally transient meteorology, elevated CO2 and disturbance. This dataset reports AGB in unit of kg C/m2. This forest carbon monitoring system was built on a process-based ecosystem model called Ecosystem Demography (ED) (Hurtt et al 1998; Moorcroft et al 2001; Ma et al 2022), which can simulate plant dynamics including growth, mortality, and reproduction; carbon dynamics within the simulated plants; and dynamics of carbon pools in forest ecosystems. This forest carbon monitoring system ingests transient meteorology from Daymet (Thornton et al 2016) and MERRA2 (Gelaro et al. 2017) and CO2 concentrations from NOAA, remote sensing of forest change from the North American Forest Dynamics (NAFD), contemporary tree cover and canopy height from airborne lidar (e.g. Tang et al. 2021) and aerial imagery from National Agriculture Imagery Program (NAIP). More details about the system development can be found in Hurtt et al. 2022. This data is currently utilized in the State of Maryland’s Greenhouse Gas Inventory and is scheduled to be updated at least triennially as part of updates to the State’s inventory. This data is also serves as the basis for calculations within the University of Maryland Peer-Reviewed Offset Protocol for Maryland Reforestation/Afforestation Projects. For questions and support please contact lma6@umd.edu, rachlamb@umd.edu and gchurtt@umd.edu. <strong>Reference</strong> Hurtt, G.C., P.R. Moorcroft, S.W. Pacala, and S.A. Levin. 1998 Terrestrial models and global change: challenges for the future. Global Change Biology 4:581-590. https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x Hurtt et al 2022. Beyond Forest Carbon Monitoring: Integrating High-Resolution Remote Sensing and Ecosystem Modeling for Geospatial Assessment and Attribution of Changes in Forest Carbon Stocks Over Maryland, USA (in prep). Ma, L., G. Hurtt, L. Ott, R. Sahajpal, J. Fisk, R. Lamb, H. Tang, S. Flanagan, L. Chini, A. Chatterjee, and J. Sullivan. 2022a. Global evaluation of the Ecosystem Demography model (ED v3.0). Geoscientific Model Development 15:1971–1994. https://doi.org/10.5194/gmd-15-1971-2022 Moorcroft, P. R., G.C. Hurtt. and S.W. Pacala, 2001 A method for scaling vegetation dynamics: the ecosystem demography model (ED) Ecol. Monogr. 71 557–86. https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2 Tang, H., L. Ma, A.J. Lister, J. O'Neil-Dunne, J. Lu, R. Lamb, R.O. Dubayah, and G.C. Hurtt. 2021. LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1854. Dr. Pieter Tans, NOAA/GML (gml.noaa.gov/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).