Mapping deep peat carbon stock from a LiDAR based DTM and field measurements, with application to eastern Sumatra

• Published in: Carbon balance and management Vol. 15; no. 1; p. 4
• Main Authors: Vernimmen, Ronald, Hooijer, Aljosja, Akmalia, Rizka, Fitranatanegara, Natan, Mulyadi, Dedi, Yuherdha, Angga, Andreas, Heri, Page, Susan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 23.03.2020; Springer Nature B.V; BMC
• Subjects: Airborne sensing; Below-ground carbon stock; Biodiversity; Bogs; Carbon; Carbon emissions; Climate change; Climate change mitigation; Correlation analysis; Earth and Environmental Science; Ecosystems; Elevation; Emissions; Environment; Environmental Management; Forest conservation; Forest management; Forestry; Forests; Global climate; LiDAR; Lowland; Mapping; Peat; Peat bogs; Peat thickness; Peatlands; Sumatra; Thickness measurement; Asia; Sumatra; Indonesia; ICESat-2 DTM; Sumatra; LiDAR; Lowland; Peat; Peat thickness; Below-ground carbon stock
• ISSN: 1750-0680; 1750-0680
• DOI: 10.1186/s13021-020-00139-2

Background Reduction of carbon emissions from peatlands is recognized as an important factor in global climate change mitigation. Within the SE Asia region, areas of deeper peat present the greatest carbon stocks, and therefore the greatest potential for future carbon emissions from degradation and fire. They also support most of the remaining lowland swamp forest and its associated biodiversity. Accurate maps of deep peat are central to providing correct estimates of peat carbon stocks and to facilitating appropriate management interventions. We present a rapid and cost-effective approach to peat thickness mapping in raised peat bogs that applies a model of peat bottom elevation based on field measurements subtracted from a surface elevation model created from airborne LiDAR data. Results In two raised peat bog test areas in Indonesia, we find that field peat thickness measurements correlate well with surface elevation derived from airborne LiDAR based DTMs (R 2 0.83–0.88), confirming that the peat bottom is often relatively flat. On this basis, we created a map of extent and depth of deep peat (> 3 m) from a new DTM that covers two-thirds of Sumatran peatlands, applying a flat peat bottom of 0.61 m +MSL determined from the average of 2446 field measurements. A deep peat area coverage of 2.6 Mha or 60.1% of the total peat area in eastern Sumatra is mapped, suggesting that deep peat in this region is more common than shallow peat and its extent was underestimated in earlier maps. The associated deep peat carbon stock range is 9.0–11.5 Pg C in eastern Sumatra alone. Conclusion We discuss how the deep peat map may be used to identify priority areas for peat and forest conservation and thereby help prevent major potential future carbon emissions and support the safeguarding of the remaining forest and biodiversity. We propose rapid application of this method to other coastal raised bog peatland areas in SE Asia in support of improved peatland zoning and management. We demonstrate that the upcoming global ICESat-2 and GEDI satellite LiDAR coverage will likely result in a global DTM that, within a few years, will be sufficiently accurate for this application.