Carbon Conversion

Summary

We have primarily worked towards mapping and modeling of aboveground biomass (AGB), but our ultimate goal is to map and model the five forest carbon pools including:

  • Aboveground carbon
  • Belowground carbon
  • Deadwood carbon
  • Litter carbon
  • Soil carbon

There are quite few published examples of producing map-based predictions for each of these carbon pools, but one common rule prevails: carbon mass is 50% of woody biomass (see US Forest Carbon Accounting Framework, Smith and Heath 2003). Additionally, the FIA relates aboveground to belowground biomass using some empirically established ratios as part of the component-ratio-method.

However, our initial AGB predictions are not 100% accurate, nor do we have the necessary information to apply the component ration methods outside of FIA plots. Instead we have opted to model each of the 5 pools directly, using our AGB predictions as a predictor along with other geodata (climate, topographic) and Landsat spectral information.

Aboveground and belowground biomass/carbon estimates are available for all measured FIA plots, and are made using individual tree measurements. down dead, litter, and soil carbon estimates are also available for all plots, but are made using country-level models (Smith and Heath 2008) rather than in-situ measurements. So, through our partnerships with the FIA, and Grant Domke specifically, we were able to acquire down dead, litter, and soil carbon estimates for phase 3 plots (1/16 of all FIA plots) that are based on in-situ measurements. These estimates are not currently available in the FIA database (as of 08/16/2022), though will be made available ‘soon’.

The limited number of plots within NYS with high-quality down dead, litter, soil estimates have made modeling these pools challenging, and we have mostly avoided machine-learning approaches with these pools, in favor of lasso/ridge and principle components regression. Furthermore, the relationships between AGB and soil, litter, and down deadwood are not direct, making them more difficult to capture with the data we have at hand. Next steps, specifically for soil, litter, and down deadwood pools, might involve a) requesting more data from the FIA for other states in the northeast, b) incorporating more data (e.g. soil specific data), or c) using some simpler estimation approaches (like grouped means with b).

People

Key Papers

  • Cao, Baijing, et al. “Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States.” Science of the total environment 654 (2019): 94-106. doi: 10.1016/j.scitotenv.2018.10.359
  • Domke, Grant M., et al. “Estimating litter carbon stocks on forest land in the United States.” Science of the Total Environment 557 (2016): 469-478. doi: 10.1016/j.scitotenv.2016.03.090
  • Domke, G. M., et al. “Toward inventory‐based estimates of soil organic carbon in forests of the United States.” Ecological Applications 27.4 (2017): 1223-1235. doi: 10.1002/eap.1516
  • Wilson, Barry Tyler, Christopher W. Woodall, and Douglas M. Griffith. “Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage.” Carbon balance and management 8.1 (2013): 1-15. doi: 10.1186/1750-0680-8-1
  • Hoover, Coeli M., Ben Bagdon, and Aaron Gagnon. “Standard estimates of forest ecosystem carbon for forest types of the United States.” Gen. Tech. Rep. NRS-202. Madison, WI: US Department of Agriculture, Forest Service, Northern Research Station. 158 p. 202 (2021): 1-158. doi: 10.2737/NRS-GTR-202

Blog Posts

Date Title Author
Mar 22, 2023 Carbon-conversion Take 5 Lucas Johnson
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