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Exact(5)
However, this effort is hampered by the lack of fine-scale, accurate maps of peat distribution and its thickness.
We evaluated peat volume from two commonly referenced maps of peat distribution and depth published by Wetlands International (WI) and the Indonesian Ministry of Agriculture (MoA), and used regionally specific values of carbon density to calculate carbon stocks.
However, only Wahyunto et al. [5 7] provide a national carbon storage estimate based on maps of peat distribution, thickness, and carbon density for Sumatra, Kalimantan and Papua islands.
Here we revisit Indonesian peat carbon stores and their uncertainty using two current maps of peat area and thickness to calculate peat volume, and geographically specific peat carbon densities to calculate carbon mass.
The agreement between these values is expected, as Dommain et al. [9], used regionally specific values for carbon accumulation and the WI maps of peat distribution and depth in their analysis.
Similar(55)
[26] and Wetlands International map of peat lands to define areas with surface peat >50 cm depth [27 29].
[ 26] and Wetlands International map of peat lands to define areas with surface peat >50 cm depth [ 27- 29].
We expect that the combination of an improved sampling strategy, machine learning models, and kriging will increase the accuracy of peat depth mapping.
The WI maps are widely used for research, and are reference data layers of peatland distribution in recent analyses of land-use change [22 25, 37 39], fire emissions from peatlands [26, 40], land cover mapping [23, 39], and geological history of peat formation [35].
MoA maps indicate that about 58% (14.68 GtC) of peat carbon is contained in peatlands >300 cm deep, and the remaining 42% (10.64 GtC) is in depth classes <300 cm.
Results showed that digital mapping method can accurately predict the thickness of peat, explaining up to 98% of the variation of the data with a median relative error of 5% or an average error of 0.3 m.
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