Fig 1.
Forest loss in Berau from 2000–2012 as detected by Hansen et al. (2013) is depicted in red (25% canopy cover threshold).
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. www.globalforestwatch.org.
Fig 2.
Our forest carbon emissions equation includes (i) activity data variables: forest area lost (AFdh), area of forest legally logged (Asl), area of forest regrowth (Ar), and (ii) emissions/sequestration factor variables: emissions per unit area forest loss (Cdh * LFdh), emissions per unit area legally logged (EFsl), sequestration per unit area of forest regrowth (SFr), and sequestration per unit area logged (SFsl).
Soil emissions were only included for loss of forested wetlands (AWd * SEFd). We assumed that transitions from secondary forest to mature forest (dashed arrow) played a minimal role in carbon flux accounting for this early frontier landscape.
Fig 3.
Location of aboveground forest biomass samples.
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).
Table 1.
Uncertainty categories for emissions equation parameters.
Fig 4.
Gross and net emissions, in millions of tonnes (Tg) of CO2, are represented according to forest loss, logging, and sequestration components.
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.
Fig 5.
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.
Monotone grey zones in (a) and (b) represent HA logging concessions–within which logging emissions and regrowth occurred, but specific locations are not known.
Table 2.
Emissions and area change during 2001–2010 by three spatial plan zones (in italics), four permit types, and areas without known permits.
Fig 6.
Extent of forest loss and legal selective logging through time (2001–2010) in Berau.
Black and grey lines represent mean annual forest loss and forest logged, respectively. There was not a significant linear trend for either forest loss or for logging activity during this ten year period (P>0.05).
Fig 7.
Aboveground carbon stocks in 13 forest biomass classes.
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 [5,6] 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 S3 File for information on carbon stocks, area change, and percent emissions by class.
Fig 8.
Spatial distribution of forest biomass and biomass classes.
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 S3 File for details on the derivation of biomass classes.
Table 3.
Contributions to overall uncertainty in forest carbon flux estimate for Berau.
Fig 9.
Comparison of alternative emissions estimates for Berau.
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.[56] and Harjaet al.[57]. A fourth estimate by Forclime[22] fell below ours and within our uncertainty range.