Forest Aboveground Biomass 2001-2100 for New Mexico, USA
Description
This dataset provides 1km resolution maps of estimated forest aboveground biomass (AGB) in the state of New Mexico between 2001-2100. This dataset was produced by a novel forest carbon monitoring and modeling 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. Specifically, the remote sensing data used includes GEDI and ICESat-2 observations of canopy height at footprint scale, canopy cover from USGS RCMAP and ESA CGLS, and meteorology reanalysis from NASA MERRA2 and climate projection from CMIP6. The ecosystem model used is the Ecosystem Demography model (EDv3.0) which has been developed, calibrated and evaluated at global scale (Ma et al 2022).
For questions and support please contact lma6@umd.edu and gchurtt@umd.edu.
This work was supported by contract from the Maryland Department of the Environment. We also gratefully acknowledge the support of NASA Carbon Monitoring System project (80NSSC21K1059) and United States Climate Alliance Grant Program for NWL Research.
Files
AGB_2001_2023_v1.tif
Files
(71.4 MB)
| Name | Size | |
|---|---|---|
|
md5:dae2518c530a5556cd342246a3e0c763
|
63.1 MB | Preview Download |
|
md5:0c4cb844e62b8b5131f291385ed380e0
|
1.4 MB | Preview Download |
|
md5:d99c24b69a4840b380bf92a778058fa0
|
1.4 MB | Preview Download |
|
md5:8649c5ae76cba37d20edb2c308ef3036
|
1.4 MB | Preview Download |
|
md5:44285e05651090d12ffb4bac8cb0952f
|
1.4 MB | Preview Download |
|
md5:a750f178cd54480f33ce6f631d04087b
|
2.7 MB | Preview Download |
|
md5:63e411609d2c40363db631d6f7d5d306
|
7.8 kB | Download |
Additional details
References
- 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
- M. C. Hansen et al. ,High-Resolution Global Maps of 21st-Century Forest Cover Change.Science342,850-853(2013).DOI:10.1126/science.1244693
- Ma, L., Hurtt, G., Tang, H., Lamb, R., Lister, A., Chini, L., Dubayah, R., Armston, J., Campbell, E., Duncanson, L., Healey, S., O'Neil-Dunne, J., Ott, L., Poulter, B., & Shen, Q. (2023). Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling. Global Change Biology, 29, 3378–3394. https://doi.org/10.1111/gcb.16682