Synthesizing Global and Local Datasets to Estimate Jurisdictional Forest Carbon Fluxes in Berau, Indonesia

<div><p>Background</p><p>Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale.</p><p>Principal Findings and Significance</p><p>We find that Berau generated 8.91 ± 1.99 million tonnes of net CO₂ emissions per year during 2000–2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85%) of Berau’s original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total), oil palm (28%), and fiber plantations (9%). Most of the remainder was due to legal commercial selective logging (17%). Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate–which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values.</p></div

Reduced-impact logging in Borneo to minimize carbon emissions and impacts on sensitive habitats while maintaining timber yields

We define two implementation levels for reduced-impact logging for climate mitigation (RIL-C) practices for felling, skidding, and hauling in dipterocarp forest concessions of East and North Kalimantan. Each implementation level reduces logging emissions by a consistent proportion below the business-as-usual emissions baseline, which varies with harvest intensity. Level 1 reflects the best recorded emissions performance for each type of practice. Level 2 is more ambitious but feasible based on workshop feedback from concession managers and forestry experts, and confirmed by a recent demonstration. At Level 1 emissions can be reduced by 33%, avoiding emissions of 64.9 ± 22.2 MgCO per ha harvested, on average. At Level 2 emissions can be reduced by 46%, avoiding 88.6 ± 22.7 MgCO ha . The greatest emissions reductions derive from (i) not felling trees that will be left in the forest due to commercial defects, and (ii) use of long-line cable winching to avoid bulldozer impacts. We also quantify the potential to avoid logging steep slopes and riparian habitats, while holding to our RIL-C accounting assumption that timber yields are maintained to avoid problems of leakage and product substitution. Logging damage to riparian areas 40% could similarly be avoided. The combined areas of these sensitive habitats (steep slopes and riparian buffers) represented 16% of each cutting block on average. Implementation of RIL-C practices would deliver 8% (Level 1) and 11% (Level 2) of Indonesia's pledged reductions to their forest reference emissions level as a nationally determined contribution to the Paris Climate Agreement. In concert with RIL-C practices, 30% of logging concession areas could be permanently protected from logging and conversion to minimize impacts on biodiversity, soils, and water quality, thereby expanding Indonesia's protected areas by one third and achieving 93% of Indonesia's Aichi Target 11 (the effective conservation of at least 17% of lands). Both these Paris Climate Agreement and Aichi outcomes could be delivered with no reductions in timber yields and substantial improvements in worker safety and sustainability of the natural forest timber sector

Forest loss in Berau from 2000–2012 as detected by Hansen et al. (2013) is depicted in red (25% canopy cover threshold).

<p>Remaining forests are shown in green. Forest loss in Berau is associated with multiple land uses. Oil palm and other agriculture mostly occurs in zones designated for “non-forest” (APL). Fiber tree plantations occur in HP zones. Commercial selective logging concessions are located in HP and HPT zones. Small scale swidden-fallow agriculture is dispersed throughout the landscape. Large coal mining permits overlap all zones except protection forests (HL). Source for spatial plan: Global Forest Watch accessed on February 20, 2014. <a href="http://www.globalforestwatch.org/" target="_blank">www.globalforestwatch.org</a>.</p

Spatial distribution of forest carbon flux, in MgC ha^-1, is represented in (a) shades of brown for forest loss emissions, and (b) shades of green for forest regrowth.

<p>Monotone grey zones in (a) and (b) represent HA logging concessions–within which logging emissions and regrowth occurred, but specific locations are not known.</p

Aboveground carbon stocks in 13 forest biomass classes.

<p>Red bars depict mean aboveground biomass (MgC ha-¹) for the disturbance-elevation-soil forest biomass classes we derived using GLAS footprint estimates (N = 7573). For most classes, mean biomass derived from the latest pantropical forest biomass datasets [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.ref005" target="_blank">5</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.ref006" target="_blank">6</a>] did not fall with 95% confidence intervals (error bars) from the GLAS-derived means. GLAS-derived means for the two most extensive biomass classes (primary low fertility highland and lowland) did overlap with 95% confidence intervals independent variable radius field plot means (green bars). See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.s003" target="_blank">S3 File</a> for information on carbon stocks, area change, and percent emissions by class.</p

Location of aboveground forest biomass samples.

<p>Black dots indicate the location of GLAS lidar footprints (N = 7,574). Red circles are locations of co-located 40 x 40 m calibration plots (N = 15) in Berau (60 more calibration plots were located in SE Asia). Blue squares indicate the location of transects of variable radius plots (N = 80).</p

Spatial distribution of forest biomass and biomass classes.

<p>We developed a new forest biomass map for Berau (a) using mean values of GLAS footprint estimates assigned to each disturbance-elevation-soil forest biomass class (b). See Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.s003" target="_blank">S3 File</a> for details on the derivation of biomass classes.</p

Gross and net emissions, in millions of tonnes (Tg) of CO₂, are represented according to forest loss, logging, and sequestration components.

<p>Values represent annual means during the 2000–2010 period acrossBerau. The wider bars just above and below the top of the net emissions column represent the width of error bars (±1%) if we had only accounted for uncertainty in our activity dataset and map-based uncertainty in our biomass dataset.</p

Comparison of alternative emissions estimates for Berau.

<p>Two alternative historic emissions estimates for Berau were higher than the upper end of our modeled uncertainty range (error bars are 95% confidence intervals). Those two estimates are from our use of emissions tools by Sidmanet al.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.ref056" target="_blank">56</a>] and Harjaet al.[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.ref057" target="_blank">57</a>]. A fourth estimate by Forclime[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0146357#pone.0146357.ref022" target="_blank">22</a>] fell below ours and within our uncertainty range.</p