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A map of global peatland extent created using machine learning (Peat-ML)

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Title A map of global peatland extent created using machine learning (Peat-ML)
 
Creator Melton, Joe R.
Chan, Ed
Millard, Koreen
Fortier, Matthew
Winton, R. Scott
Martín-López, Javier M.
Cadillo-Quiroz, Hinsby
Kidd, Darren
Verchot, Louis V.
 
Subject peatlands
machine learning
climate change
turberas
aprendizaje electrónico
cambio climático
 
Description Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed by Earth system models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here, we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning (ML) techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of peatland formation and include spatially distributed climate, geomorphological and soil data, and remotely sensed vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualitative comparisons to other maps in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r2 of 0.73 with a root-mean-square error and mean bias error of 9.11 % and −0.36 %, respectively. Our second error estimate was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping products generated through more traditional approaches, at least for boreal peatlands.
 
Date 2022-06-20
2022-06-28T08:06:59Z
2022-06-28T08:06:59Z
 
Type Journal Article
 
Identifier Melton, J.R.; Chan, E.; Millard, K.; Fortier, M.; Winton, R.S.; Martín-López, J.M.; Cadillo-Quiroz, H.; Kidd, D.; Verchot, L.V. (2022) A map of global peatland extent created using machine learning (Peat-ML). Geoscientific Model Development 15 (12) p. 4709–4738. ISSN: 1991-959X
1991-959X
https://hdl.handle.net/10568/119957
https://doi.org/10.5194/gmd-15-4709-2022
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format p. 4709-4738
application/pdf
 
Publisher Copernicus GmbH
 
Source Geoscientific Model Development