Widespread subsidence and carbon emissions across Southeast Asian peatlands

Abstract

Over the last three decades, most of the 25 million hectares of tropical peatlands in Southeast Asia have been deforested and drained. As a consequence, declining water tables are exposing peat to oxidation, converting plant material accumulated over millennia to carbon dioxide, and causing land subsidence. Here, we quantify the widespread peat carbon loss by using InSAR remote sensing to map subsidence at 90-m resolution across 2.7 Mha of peatland area from 2007 to 2011. Over 90% of the surveyed area is subsiding, with a mean rate of 2.2 cm yr−1. Consequently, the region now faces loss of productive land and flooding because many peatlands are near sea level. Our measurements reveal that smallholder agricultural areas and degraded peatlands are subsiding at rates comparable to those of plantations, and that subsidence rates increase away from rivers and decrease over time following drainage. Because of its detailed spatial resolution, InSAR provides a valuable tool to identify emissions by land use and geography and to target hotspots for better management. Finally, we use remotely sensed maps to update IPCC emissions factors and calculate regional CO2 emissions from peat oxidation of 155 ± 30 MtC yr−1 in 2015, similar in magnitude to both regional fossil-fuel emissions and peat fires.

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Fig. 1: Subsidence rates and time series in peat and non-peatland areas in ALOS frame 1 of 8.
Fig. 2: Subsidence and land-use maps.
Fig. 3: Subsidence rates over time since oil palm plantation establishment.

Data availability

The calculated mean subsidence rates that support the findings of this study are archived on Zenodo at https://doi.org/10.5281/zenodo.3694667. The raw SAR data that support the findings of this study are publicly available through the Alaska Satellite Facility data portal at https://vertex.daac.asf.alaska.edu/.

Change history

  • 25 June 2020

    The ‘Editor recognition’ statement has been amended to additionally include the contribution of Xujia Jiang; the sentence now reads ‘Primary Handling Editors: Clare Davis; Xujia Jiang.’

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Acknowledgements

We thank D. Sheehan, A. Graham, M. Wrable and J. Murack of the MIT Libraries’ GIS and Data Lab for sharing their time, expertise and the computing resources of the Lab. The ALOS‐PALSAR data are copyrighted by the Japanese Aerospace Exploration Agency and Ministry of Economy, Trade and Industry and were made available by the US Government Research Consortium through the Alaska Satellite Facility. ISCE is an InSAR processing software package developed at NASA JPL, Caltech and Stanford, which was made available through the Western North America InSAR Consortium. Peatland land-use maps1 were made available by the Centre for Remote Imaging, Sensing and Processing at the National University of Singapore. This work benefited from access to the University of Oregon high performance computer, Talapas. This research was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, the Singapore–MIT Alliance for Research and Technology, the US National Science Foundation (grants 1114155, 1114161 and 1923491 to C.F.H.) and the Environmental Solutions Initiative at MIT.

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Affiliations

Authors

Contributions

A.M.H., E.C. and C.F.H. conceived of the study. E.C. and A.M.H. completed InSAR data processing and time-series analysis. A.M.H. and S.S.S. performed land-use and spatial analysis, as well as regional upscaling. A.M.H. wrote the manuscript with contributions from E.C., S.S.S. and C.F.H.

Corresponding author

Correspondence to Alison M. Hoyt.

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The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editors: Clare Davis; Xujia Jiang.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Industrial Plantations and Non-Peatland Areas at all Sites.

Density plot of subsidence rates for industrial plantations (2007) and non-peatland areas for each ALOS frame. Non-peatland areas are shown for pixels >15 km from the edge of the peat to exclude transition zones between peatland and mineral soils. Positive values indicate subsidence and negative values indicate uplift.

Extended Data Fig. 2 Subsidence Rate by Land Use.

Mean subsidence rates (positive values) for each 2007 land use category on peat within each ALOS frame, where available. Numbers indicate number of InSAR measurements averaged for each bar. Dashed lines indicate the mean subsidence rate of 2.24 cm/yr across all peatland measurements. Non-peatland areas (light blue) are shown for pixels >15 km from the edge of the peat to exclude transition zones between peatland and mineral soils. Error bars represent the standard deviation of the data (not the standard error of the mean).

Extended Data Fig. 3 Example of Upscaled Subsidence Map and Regression Tree.

(a) Map of upscaled subsidence. (b) Regression tree schematic. Mean regional subsidence and associated uncertainties were calculated based on a bootstrapping analysis of 1,000 regression trees and corresponding upscaled subsidence maps (Methods). To generate a single regression tree, 2007 land use was used as the Current Land Use, concurrent with ALOS measurements from 2007–2011. For regional upscaling, 2015 land use was used as the Current Land Use, as the most recently available regional mapping. All land use maps were generated by Miettinen et al.1. Total regional CO2 emissions and emissions factors were based on the mean of eight regression trees (each removing data from one ALOS frame). The displayed regression tree, for example purposes only, is based on all data and was used to generate the example regional subsidence map above. The upscaling map is provided as an illustration of the method. It is not our intent to make site-specific predictions due to the wide variability in drainage practices.

Extended Data Fig. 4 InSAR Validation with Ground-Based Subsidence Data.

(a) ALOS frame 6 measurements of industrial plantations which overlap in space and time with measurements on oil palm plantations in Sarawak, Malaysia by Othman et al.8 This is the only area of direct overlap in the region. (b) InSAR data from all other industrial plantations compared to ground-based pole measurements also made on plantations, but at different times and/or places. (c) InSAR data from all non-plantation land uses on peat (excluding pristine peat swamp forest) compared to ground-based pole measurements made on non-plantation land uses in the region, but at different times and/or places. In all panels, where multiple points are shown from a single study, they represent plantation areas established at different times or distinct land uses with distinct subsidence rates. Error bars indicate the standard deviation of the ground-based measurements within these study groups.

Extended Data Fig. 5 Schematic explanation of two methods for calculating carbon loss from bulk density profiles in a subsiding peatland with water table decline.

Method #1: The approach first applied to tropical peat by Couwenberg and Hooijier9. Bulk density profiles are plotted relative to the land surface, which has subsided. It is evident from these bulk density profiles, before and after subsidence, that the carbon loss can be calculated from the change in thickness of the anoxic peat, the small yellow rectangle on the far right. Method #2: Bulk density profiles are plotted relative to elevation. The net carbon lost must be the same regardless of how the plot is constructed, but the calculation is more complex for Method #2. Here the net carbon lost is shown as loss in carbon due to the drop in the land surface, minus the apparent gain in carbon from the increased bulk density below the elevation that is now the land surface. The carbon lost is the difference between the gain and loss shown by the yellow regions on the far right. Our results rely on Method #1. However, calculations of CO2 emissions from Method #2 yield similar results with higher uncertainties. Assuming a dry bulk density of 0.11 ± .03 g/cm3 and carbon content of 55 ± 2 % in the compacted oxic peat, a mean subsidence rate of 2.24 ± 0.23 cm/yr, a peat area of 15.7 Mha, and the fraction of subsidence due to compaction of 75 ± 15 % as found by Hooijer et al.2, we calculate CO2 emissions equivalent to 160 ± 61 MtC/yr (compared to 155 ± 30 MtC/yr using Method #1).

Extended Data Fig. 6 Frame 1 Subsidence and Land Use Map.

ALOS Frame 1 mean subsidence rates (top), 1990 land use (left) and 2007 land use (right). Positive values indicate subsidence and negative values indicate uplift. Peatland extent and land-use maps adapted with permission from ref. 1, Elsevier.

Extended Data Fig. 7 Frame 3 Subsidence and Land Use Map.

ALOS Frame 3 mean subsidence rates (top), 1990 land use (left) and 2007 land use (right). Positive values indicate subsidence and negative values indicate uplift. Peatland extent and land-use maps adapted with permission from ref. 1, Elsevier.

Extended Data Fig. 8 Frame 4 Subsidence and Land Use Map.

ALOS Frame 4 mean subsidence rates (top), 1990 land use (left) and 2007 land use (right). Positive values indicate subsidence and negative values indicate uplift. Peatland extent and land-use maps adapted with permission from ref. 1, Elsevier.

Extended Data Fig. 9 Frame 6 Subsidence and Land Use Map.

ALOS Frame 6 mean subsidence rates (top), 1990 land use (left) and 2007 land use (right). Positive values indicate subsidence and negative values indicate uplift. Peatland extent and land-use maps adapted with permission from ref. 1, Elsevier.

Extended Data Table 1 Study sites (ALOS frames) listed from north to south, first in Sumatra, then in Borneo

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Supplementary Figs. 1–11 and Tables 1–3.

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Hoyt, A.M., Chaussard, E., Seppalainen, S.S. et al. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020). https://doi.org/10.1038/s41561-020-0575-4

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