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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 (R2 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. [ABSTRACT FROM AUTHOR]

Water management in lowland areas usually aims to keep water tables within a narrow range to avoid flooding and drought conditions. A common water management target parameter is the depth of the canal water table below the surrounding soil surface. We demonstrated a method that rapidly determines canal water table depth (CWD) from airborne LiDAR data. The water table elevation was measured as the minimum value determined in a grid of 100 m × 100 m applied to a 1 m × 1 m digital terrain model (DTM), and the soil surface was calculated as the median value of values in each grid cell. Results for areas in eastern Sumatra and West Kalimantan, Indonesia, were validated against 145 field measurements at the time of LiDAR data collection. LiDAR-derived CWD was found to be accurate within 0.25 m and 0.5 m for 86% and 99% of field measurements, respectively, with an R2 value of 0.74. We demonstrated the method for CWD conditions in a drained peatland area in Central Kalimantan, where we found CWD in the dry season of 2011 to be generally below −1.5 and often below −2.5 m indicating severely overdrained conditions. We concluded that airborne LiDAR can provide an efficient and rapid mapping tool of CWD at the time of LiDAR data collection, which can be cost-effective especially where LiDAR data or derived DTMs are already available. The method can be applied to any LiDAR-based DTM that represents a flat landscape that has open water bodies. [ABSTRACT FROM AUTHOR]