Policy perils of ignoring uncertainty in oil palm research

Peer reviewe

Denial of long-term issues with agriculture on tropical peatlands will have devastating consequences

Non peer reviewe

New LiDAR‐Based Elevation Model Shows Greatest Increase in Global Coastal Exposure to Flooding to Be Caused by Early‐Stage Sea‐Level Rise

Abstract The latest projections indicate that sea‐level rise (SLR) is certain to exceed 2 m in coming centuries, and a rise by 4 m is considered possible. Radar‐based land elevation models applied to date suggest that the increase of area below mean sea level, that is potentially exposed to permanent flooding, will accelerate as SLR proceeds, being relatively limited initially. However, applying new and more accurate satellite LiDAR elevation data we find the opposite pattern, with the fastest increase in the area of exposed land occurring in the early stages of SLR. In one‐third of countries most of this increase will be caused by the first meter of SLR and in nearly all within the first 2 m. We conclude that in many regions the time available to prepare for increased exposure to flooding may be considerably less than assumed to date, and that better elevation data will support timely preparations. The global LiDAR lowland DTM (GLL_DTM_v2) elevation data set developed for this assessment is available in the public domain

Hydrological and economic effects of oil palm cultivation in Indonesian peatlands

Oil palm has increasingly been established on peatlands throughout Indonesia. One of the concerns is that the drainage required for cultivating oil palm in peatlands leads to soil subsidence, potentially increasing future flood risks. This study analyzes the hydrological and economic effects of oil palm production in a peat landscape in Central Kalimantan. We examine two land use scenarios, one involving conversion of the complete landscape including a large peat area to oil palm plantations, and another involving mixed land use including oil palm plantations, jelutung (jungle rubber; (Dyera spp.) plantations, and natural forest. The hydrological effect was analyzed through flood risk modeling using a high-resolution digital elevation model. For the economic analysis, we analyzed four ecosystem services: oil palm production, jelutung production, carbon sequestration, and orangutan habitat. This study shows that after 100 years, in the oil palm scenario, about 67% of peat in the study area will be subject to regular flooding. The flood-prone area will be unsuitable for oil palm and other crops requiring drained soils. The oil palm scenario is the most profitable only in the short term and when the externalities of oil palm production, i.e., the costs of CO2emissions, are not considered. In the examined scenarios, the social costs of carbon emissions exceed the private benefits from oil palm plantations in peat. Depending upon the local hydrology, income from jelutung, which can sustainably be grown in undrained conditions and does not lead to soil subsidence, outweighs that from oil palm after several decades. These findings illustrate the trade-offs faced at present in Indonesian peatland management and point to economic advantages of an approach that involves expansion of oil palm on mineral lands while conserving natural peat forests and using degraded peat for crops that do not require drainage.</p

From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990

Tropical peatlands of the western part of insular Southeast Asia have experienced extensive land cover changes since 1990. Typically involving drainage, these land cover changes have resulted in increased peat oxidation in the upper peat profile. In this paper we provide current (2015) and cumulative carbon emissions estimates since 1990 from peat oxidation in Peninsular Malaysia, Sumatra and Borneo, utilizing newly published peatland land cover information and the recently agreed Intergovernmental Panel on Climate Change (IPCC) peat oxidation emission values for tropical peatland areas. Our results highlight the change of one of the Earth's most efficient long-term carbon sinks to a short-term emission source, with cumulative carbon emissions since 1990 estimated to have been in the order of 2.5 Gt C. Current (2015) levels of emissions are estimated at around 146 Mt C yr-1, with a range of 132-159 Mt C yr-1 depending on the selection of emissions factors for different land cover types. 44% (or 64 Mt C yr-1) of the emissions come from industrial plantations (mainly oil palm and Acacia pulpwood), followed by 34% (49 Mt C yr-1) of emissions from small-holder areas. Thus, altogether 78% of current peat oxidation emissions come from managed land cover types. Although based on the latest information, these estimates may still include considerable, yet currently unquantifiable, uncertainties (e.g. due to uncertainties in the extent of peatlands and drainage networks) which need to be focused on in future research. In comparison, fire induced carbon dioxide emissions over the past ten years for the entire equatorial Southeast Asia region have been estimated to average 122 Mt C yr-1 (www.globalfiredata.org/-index.html). The results emphasise that whilst reducing emissions from peat fires is important, urgent efforts are also needed to mitigate the constantly high level of emissions arising from peat drainage, regardless of fire occurrence

DeltaDTM: A global coastal digital terrain model

Abstract Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications

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

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

Creating a Lowland and Peatland Landscape Digital Terrain Model (DTM) from Interpolated Partial Coverage LiDAR Data for Central Kalimantan and East Sumatra, Indonesia

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines (&#8216;strips&#8217;) covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7&#8211;96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications

Distance to forest, mammal and bird dispersal drive natural regeneration on degraded tropical peatland.

Restoration of peat swamp forest (PSF) on degraded Southeast Asian peatlands could reduce global carbon emissions and biodiversity loss. However, multiple ecological barriers are believed to hinder natural regeneration of native trees on degraded peatland and make restoration expensive. We evaluated if natural PSF regeneration occurs and what factors may influence it on eight different land use and land cover (LULC) classes with different types of disturbance, including drainage and fire, in a retired Acacia crassicarpa Benth. (Acacia) plantation landscape. The study involved 42 plots inside five PSF LULCs – intact, logged, burnt (1997, 2015), remnant and 212 plots at distances up to 2 km from the PSF edge in three Acacia plantation LULCs – unharvested, harvested, and burnt. The number of species per plot were similar between intact PSF (25 ± 6 (SD) per 20 m × 10 m plot), logged forest (30 ± 6) and 1997 burnt forest (30 ± 13) but lower in 2015 burnt forest (11 ± 10) and remnant forest (18 ± 11). Regeneration away from the PSF across all degraded LULCs varied from fern dominated areas with no regeneration to clusters with high stem densities. The plantation LULCs, unharvested (94 species) and harvested Acacia (71 species), had similar overall species diversity after 3–4 years of regeneration to the intact and logged PSF (90 species). In unharvested Acacia, total species diversity, species per plot and stem density decreased with distance from forest edge (1–300 m – 87 species; 9 ± 6 (SD) species per 20 m × 10 m plot; 1,056 stems/ha; 301–500 m – 33; 5 ± 2; 511 and >500 m – 38; 6 ± 3; 683). In harvested Acacia, there was low plot species diversity irrespective of distance from the forest (1–300 m – 51; 4 ± 2; 578; 301–500 m – 17; 4 ± 2; 1,100; >500 m – 48; 4 ± 2; 780). Factors which may influence regeneration differed between different LULCs, but there was a clear influence of distance from forest edge and dispersal mechanism – i.e. whether a tree was bird or mammal dispersed and the interaction between these two factors. While our study suggests that if not further disturbed by logging, drainage and/or fire, degraded PSF could regenerate naturally to a similar species diversity as intact PSF, the lower levels of natural regeneration further away from the forest may warrant selective planting of species which do not disperse over long distances. More study is needed on the factors facilitating natural regeneration, whether it leads to restoration of PSF ecosystem functioning and the role of Acacia as a potential regeneration catalyst