A new map of global peatland extent as estimated by machine learning
Abstract
Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. However, global peatland extent is relatively poorly known, especially in low latitudes with large peatland complexes even recently being discovered. To address this issue, we used machine learning techniques to develop a global map of peatland fractional extent. This global map of fractional peatland coverage was generated on a 5 arc minute grid using remotely-sensed vegetation characteristics and climate, geomorphological, and soil information as predictors of peatland extent. Our peatland map was then used as a mask for the peatland module of the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) to generate estimates of global peatland C pools and fluxes at 1 degree global resolution . We additionally ran CLASSIC using a polygon-based meta-analysis product (PEATMAP ) and the histosols of the Harmonized World Soils Database as the peatland mask. Our simulations shed some light on the impact of peatland extent on both regional and global C fluxes.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMEP042..03M
- Keywords:
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- 0497 Wetlands;
- BIOGEOSCIENCES;
- 1211 Non-tectonic deformation;
- GEODESY AND GRAVITY;
- 1632 Land cover change;
- GLOBAL CHANGE;
- 4306 Multihazards;
- NATURAL HAZARDS