The clearing of tropical forests and their conversion to other land uses has resulted in gross emissions of 0.45 – 1.7 Pg C year-1 (90% prediction interval) from 2000–2007, equivalent to 5-19% of global anthropogenic CO2 emissions [1–3]. Intact tropical forests are, however, thought to be serving as a carbon sink of similar magnitude, capturing an estimated 0.55-1.49 Pg C year-1, equivalent to 6-17% of anthropogenic CO2 emissions, over the same period . While there are many other reasons to protect tropical forests, the preservation of their carbon stocks and their potential as a future carbon sink has motivated a policy priority among the international community for their protection in order to reduce greenhouse gas (GHG) emissions, with associated benefits to society provided by their ecosystem services .
Many different schemes have been pursued to conserve tropical forests, but all rely on the quantification of stored carbon stocks to allow a calculation of avoided GHG emissions. The UN Framework Convention on Climate Change (UNFCCC) initiative “Reducing Emissions from Deforestation and forest Degradation” (REDD+, ) may create both social and economic incentives for conservation of forests in tropical countries. At an international level, REDD+ remains in negotiation within the UNFCCC, with the goal to include REDD+ in the next global climate change agreement. However, pilot and preparatory activities are already occurring at a national level, largely funded by UN-REDD (a consortium of the FAO, UN and UNEP), the Forest Carbon Partnership Facility (World Bank), and individual governments, especially Norway . Parallel to the main REDD+ process, Norway has set up bilateral deals with Brazil, Guyana, Indonesia and Tanzania that allow for the transfer of up to US$1 billion for conservation and development, in return for the countries meeting targets for reducing deforestation rates . Furthermore there are already many voluntary REDD+ projects, generating credits primarily under the Verified Carbon Standard (VCS), with total REDD+ credit sales equal to $85 million in 2010 . These projects are increasing in number, meaning that there is already some implementation of REDD+ in many tropical forest regions.
Under the UNFCCC, countries planning to participate in the REDD+ mechanisms are required to use the Intergovernmental Panel on Climate Change (IPCC) GHG accounting framework for estimating their anthropogenic emissions caused by deforestation and forest degradation . One of the key inputs into the IPCC framework is the carbon stocks of the forests undergoing change. The difference between the pre- and post- deforestation or degradation carbon stocks is the 'emission factor’, which is the carbon emissions per unit area due to forest cover change. The product of the emission factor and the area of forest change provides the estimate of the total carbon emissions.
Countries participating in a future UNFCCC agreement will likely need to assess and monitor their carbon stocks regardless of their inclusion in REDD+. One approach often followed to obtain carbon stock estimates is to map vegetation types within a landscape and assign a carbon density value to each vegetation type, using either international or locally-derived values from field-based inventory . However this method can have high uncertainty, especially over large areas or when using generic carbon density values, so to maximise potential financial benefits countries may opt to produce spatial maps of their biomass stocks, using field-calibrated remote sensing observations. No current satellite can directly estimate aboveground biomass (AGB), so proxies related to forest canopy colour, seasonality parameters, elevation, or the canopy structure are used to estimate and spatially model AGB [10–14].
Two recent maps have been published using this approach to estimate biomass across the tropics at a 1 km resolution [15
], subsequently described as 'RS1’, and a 463 m resolution [16
], 'RS2’. These resolutions are considered high enough to be used by carbon forestry projects [9
]. Both maps use spaceborne LiDAR data from the Geoscience Laser Altimeter System (GLAS) as samples of forest structure distributed across the tropics, but the two approaches use a different method to extend the isolated GLAS footprints to full-coverage AGB maps. The differences can be summarized as follows:
GLAS datasets: Both studies independently downloaded, processed and filtered the GLAS dataset for cloud and slope effects and other potential artefacts. In RS1, filters were introduced to remove all GLAS shots over slopes > 20% and ground elevations with > 100 m difference from a global digital elevation model, the Shuttle Radar Topography Mission (SRTM) data at 90 m resolution; in RS2, the filter removed all GLAS shots that differed from SRTM elevation by > 25 m. In both cases this was done because forest height estimates over sloped terrains may have large biases, causing overestimation of the estimated tree height. Both methods included a series of filters based on the shape of the waveform and the signal-to-noise ratio.
ii) Estimating AGB from GLAS using field plots: Field plots are used to convert millions of individual LiDAR waveforms collected by the GLAS sensor with an approximately 65 m footprint into AGB estimates. RS1 uses a two-stage process, first building a model to predict Lorey’s height (basal-area weighted height) from the LiDAR waveforms using 295 field plots located under GLAS footprints in South America , and then deriving three separate continental equations relating Lorey’s height to AGB using a set of 493 field plots . The AGB values for the field plots are derived from the 3-parameter tropical forest allometric equations including tree diameter, wood density, and height from . The field plots were distributed over three continents, had sizes ranging from 0.2 to 1.0 ha, with the majority of plots being at least 0.25 ha, and included all trees > 10 cm in diameter measured above buttresses.
RS2 instead builds a model directly relating GLAS waveform characteristics to AGB from 283 calibration field plots located under GLAS footprints . The plots are 40 m × 40 m (0.16 ha) in size and include all trees > 5 cm in DBH. Unlike RS1, in RS2, the field data are converted to AGB using allometric equations without tree height from the same study : RS1 uses the 3-parameter equation, whereas RS2 uses the 2-parameter equation, including diameter and wood density but excluding height.
The conversion of the GLAS data to AGB in both approaches ignores the potential variations of forest wood density over the landscape and at regional scales: while biomass estimation of the plot data for both maps was based on equations that included wood density as one of the independent variables, the functions that related the GLAS data to the plot-based biomass estimates did not include any parameter to reflect the spatial variability of wood density.
iii) Creation of training and test datasets from GLAS: For RS1, GLAS AGB estimates are only used in creating the map if at least 5 LiDAR footprints fall within the same 1 km pixel; this gave 160,918 pixels (with the AGB estimate for each the average of at least 5 LiDAR footprints) for use in training and testing the AGB prediction model. For RS2 GLAS AGB estimates were used if more than 5 footprints were located in a 463 m pixel for America and Africa, and 3 or more for Asia, giving 58,476 pixels available for training and testing.
iv) Additional training dataset from field plots:
Additionally for RS1
4,079 field plots were included in the model although, as these were clustered, they were averaged if multiple plots occurred within the same 1 km pixel, reducing the total to 1,877 pixels. No field dataset was used directly for training or testing of RS2.
Creating continuous AGB maps: The point AGB estimates were averaged to give single AGB estimates at the pixel level, then extrapolated across the full pantropics using visible- and infra-red spectrum optical data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, elevation data from SRTM, and in the case of RS1, QUIKSCAT scatterometer data. The precise MODIS data layers used and cloud filtering applied differ considerably between the studies, with RS1 using Leaf Area Index (LAI) and the Normalised Difference Vegetation Index (NDVI), and RS2 using all the land bands excluding the blue band from the Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance (BRDF), the Enhanced Vegetation Index (EVI2), the Normalized Difference Infrared Index (NDII2), and the MODIS Land Surface Temperature products. The extrapolation of biomass is performed using non-linear, non-parametric models, Maxent in RS1 and Random Forest in RS2, with in both cases a percentage of input data held back for testing (40% for RS1, 10% for RS2).
vi) Uncertainty estimates: RS1 additionally produced a spatial uncertainty map, giving an error estimate for every pixel, through bootstrapping the input ground and LiDAR datasets and propagating errors through the model. RS2 estimated uncertainty at the dataset and country level using a Monte Carlo approach.
Here we present a detailed comparison of the outputs of both maps, both directly at the pixel level, and in aggregate over different landcover type classes and countries. However, while comparisons between the maps are interesting, they are of limited use in either confirming the validity of the mapping approach, or stating whether one map should be used preferentially to the other. We cannot use comparisons to field plots to provide these assessments for two reasons: first, the vast majority of well-geolocated recent scientific field plots known to the authors were used in one or other of the maps; and second, all field plots are very much smaller than the pixel size of the maps, and thus only useful in showing if there is large divergence between the maps and ground data, not in providing a quantitative accuracy assessment . We therefore compare the maps to two entirely independent, large-scale ancillary AGB datasets: the country biomass stocks from the FAO Forest Resource Assessment (FRA) estimates , and a high resolution (100 m) LiDAR-derived map for a 16.5 million hectare region of the Colombian Amazon (RS3) .