Boosted carbon emissions from Amazon deforestation

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L14810, doi: /2009gl037526, 2009 Boosted carbon emissions from Amazon deforestation Scott R. Loarie, 1 Gregory P. Asner, 1 and Christopher B. Field 1 Received 25 February 2009; accepted 22 June 2009; published 21 July [1] Standing biomass is a major, often poorly quantified determinate of carbon losses from land clearing. We analyzed maps from the PRODES deforestation time series with recent regional predeforestation aboveground biomass estimates to calculate carbon emission trends for the Brazilian Amazon basin. Although the annual rate of deforestation has not changed significantly since the 1990s (ANOVA, p = 0.3), the aboveground biomass lost per unit of forest cleared increased from 2001 to 2007 (183 to 201 Mg C ha 1 ; slope of regression significant: p < 0.01). Remaining unprotected forests harbor significantly higher aboveground biomass still, averaging 231 Mg C ha 1. This difference is large enough that, even if the annual area deforested remains unchanged, future clearing will increase regional emissions by 0.04 Pg C yr 1 a25% increase over annual carbon emissions. These results suggest increased climate risk from future deforestation, but highlight opportunities through reductions in deforestation and forest degradation (REDD). Citation: Loarie, S. R., G. P. Asner, and C. B. Field (2009), Boosted carbon emissions from Amazon deforestation, Geophys. Res. Lett., 36, L14810, doi: /2009gl Introduction [2] Tropical forests are increasingly recognized as major resources for mitigating climate change [Canadell and Raupach, 2008; Phillips et al., 1998; Prentice et al., 2001] as they sequester and store large quantities of carbon [Houghton, 2005b; Malhi and Grace, 2000]. During the 80s and 90s, the Brazilian Amazon was responsible for more than a quarter of global annual carbon emissions from tropical deforestation [DeFries et al., 2002]. However, since carbon losses cannot be directly measured at fine scales across large areas, there is a great deal of disagreement among emission estimates [Achard et al., 2002; Eva et al., 2003; Fearnside and Laurance, 2003; 2004; Houghton et al., 2001]. Carbon loss from deforestation is a function of the area cleared, the fate of the cleared carbon, and the biomass of the vegetation [Ramankutty et al., 2007]. Uncertainty in aboveground forest biomass is the largest source of disagreement in the carbon balance from the tropical region [Houghton, 2003]. [3] While spatially explicit monitoring of tropical deforestation and logging has become increasingly sophisticated [Achard et al., 2007; Asner et al., 2005; Hansen and DeFries, 2004; Hansen et al., 2008; Morton et al., 2005; 1 Department of Global Ecology, Carnegie Institution of Washington, Stanford, California, USA. Copyright 2009 by the American Geophysical Union /09/2009GL Skole and Tucker, 1993], the treatment of biomass in emission calculations remains simplistic [Gibbs et al., 2007]. Several previous studies [Achard et al., 2002, 2004; Houghton et al., 2000] use local-scale or interpolated estimates from small samples of forest plot data [Baker et al., 2004; Brown, 1997; Programa de Integração et al., 1978]. Others [DeFries et al., 2002] use maps produced by assigning vegetation-specific estimates of standing biomass stocks [Houghton and Hackler, 1995]. [4] Satellites are increasingly used to produce spatiallyexplicit models of aboveground biomass, enabling a potentially more data-rich approach [Lu, 2006; Wulder et al., 2008]. Satellites cannot measure biomass directly, but they gather large quantities of spectral data that often correlate well with vegetation parameters. These features have enabled satellites to become the primary source for large area aboveground live biomass estimation [Lu, 2006]. Because belowground and aboveground dead biomass are highly correlated with aboveground live biomass, remote sensing approaches can be extrapolated to produce estimates of total biomass [Cairns et al., 1997; Houghton et al., 2001]. Remote sensing-based biomass maps have been made in boreal [Houghton et al., 2007] tropical Asian [Foody et al., 2001], Afro-tropical [Baccini et al., 2008] and Neo-tropical [Saatchi et al., 2007] biomes. [5] The Saatchi et al. [2007] biomass estimate is the only remote sensing-based biomass estimate for the Brazilian Amazon. It incorporated 544 biomass plots across the Amazon basin. Seven previous basin wide estimates compared by Houghton et al. [2001] use the 44 RADAMBRAZIL plots. The Saatchi et al. [2007] estimates had an accuracy of better than 70% and, extrapolated to total biomass, estimated the total carbon content of the Amazon forest between 77 and 95 Pg C with and average of 86 Pg C. These are within the range of previously published estimates from other approaches [Houghton et al., 2001]. [6] In this study we combine Landsat-based estimates of deforestation [Instituto Nacional de Pesquisas Espaciais (INPE), 2007], radar-based satellite estimates of aboveground biomass [Saatchi et al., 2007] and a bookkeeping carbon model [DeFries et al., 2002; Houghton et al., 2000] to produce new regional estimates of carbon emissions from the Brazilian Amazon for the years We ask how recent carbon losses compare to past estimates, and how spatial biomass patterns interact with patterns of deforestation to drive past and future carbon losses. [7] We explored uncertainty and bias in the biomass estimates in three ways. First, to accommodate uncertainty in biomass estimates, we propagated biomass uncertainty reported by Saatchi et al. [2007] throughout our analysis. Second, the biomass estimates were less resolved spatially and temporally than the deforestation estimates making it difficult to exactly match the dimensions of the datasets. L of5

2 Figure 1. The history of deforestation in the Brazilian Amazon from 2001 (blue) through 2007 (red). White areas were not forested in Green areas were forested in Because the biomass estimates are influenced by previously cleared land, spatial and temporal mismatches could bias estimates of carbon emissions. To minimize this bias we modified the Saatchi et al. [2007] biomass estimates to represent pre-deforestation biomass. Third, we explored the possibility of forest degradation and selective logging biasing the biomass estimates. 2. Data and Methods [8] Using the Brazilian Space Research Institute s PRODES annual deforestation summaries [INPE, 2007], we first tested whether the deforestation rate for the 1990s was significantly different from the 2000s. PRODES provides 60-m gridded maps of deforested areas each year from 2000 through We reclassified these maps into land not forested in 2000, forested in 2007, and deforested in each intervening year. Areas forested in 2000 then subsequently obscured by clouds make up less than 6% of the study area. We assume these areas remained forested resulting in conservative deforestation magnitudes and rates. The mapped values in Figure 1 are weighted averages of the PRODES year deforested, summarized across a km grid. [9] The Saatchi et al. [2007] aboveground live biomass map has 1 km 2 grid cells and was developed from field and remotely sensed satellite data collected from the 1990s and early 2000s. This data set has 19 remote sensing image layers, including RADAR data from JERS, QSCAT, and SRTM. It has 11 biomass classes each spanning 25 to >400 Mg C ha 1. We used the middle, upper, and lower values of each class to calculate average estimates and uncertainty bounds. [10] Deforestation signals in these remote sensing datasets carry through to the biomass estimates. Because the biomass data are compiled from several dates and at a courser 1 km resolution, we cannot precisely match the biomass data to the PRODES areas on the scale of a deforestation event. As an alternate approach, we performed the analysis over a km grid. We estimated predeforestation biomass so that patches of low biomass from previous deforestation signals would not bring down the biomass of the entire grid cell. To do this, we excluded all pixels in the lowest three biomass classes from Saatchi et al. [2007] (Figure S1a of the auxiliary material). 1 These classes comprise a distinct mode of low biomass lands in the dataset from Saatchi et al. [2007] and correspond well with the PRODES areas not forested in 2000 (Figure S1b). To quantify errors resulting from these spatio-temporal mismatches, we calculated the correlation between the proportion of PRODES non-forested areas in 2000 (Figure S1c) and the proportion of biomass in the three lowest classes (Figure S1d) across the grid of km kernels. The strength of this correlation indicates how faithfully masking the lowest biomass classes eliminates the signal of previous deforestation. [11] Forest degradation and selective logging may precede forest clearing, which could lead to the observed biomass gradient increasing away from the arc of deforestation. To address this possibility, we plotted the average biomass binned by distance from lands cleared before 2001 and also grouped into lands inside versus outside of protected areas (Figure S2). Assuming that there is relatively little logging within protected areas [Asner et al., 2005], this analysis tests for the contribution of logging to observed regional biomass patterns from Saatchi et al. [2007]. [12] To predict changes in carbon emissions from deforestation in the future, we excluded indigenous and federal reserves from our calculations because they are unlikely to be cleared [Nepstad et al., 2006]. The bookkeeping model follows Houghton et al. [2000]: 20% of cleared land is burned immediately, 70% is converted to slash, 8% is converted to wood products, and 2% is converted to elemental carbon. We used 0.1 as the annual decay rate for slash and wood products and for elemental carbon. We assumed that in the 25 years following clearing, regrowth recovers 70% of the original forest biomass. Following Houghton et al. [2000], we ignored relatively small and poorly known changes in soil carbon. Following DeFries et al. [2002], we added 7% to the emissions to account for cryptic logging. [13] To create distributions of biomass loss for each year between 2000 and 2007, and remaining forested biomass, we broke the km grid cells into smaller 1 1km kernels. We designated each smaller kernel forested or cleared proportional to the deforestation percentage of the larger km grid cell. We assigned the average biomass of the km grid cell to each 1 1km kernel designated as cleared. To test whether the average annual biomass per unit area cleared has been increasing from 2001 through 2007, we used a linear model: y ¼ b 0 þ b 1 x þ N0; ð sþ where y is the average annual biomass per unit area cleared and x is the number of years after We tested the significance of the slope term, b 1. We used two-tailed 1 Auxiliary materials are available in the HTML. doi: / 2009GL of5

3 Figure 2. (a) Pre-deforestation biomass (Mg C ha 1 ) in the Brazilian Amazon. Federal and indigenous reserves are in gray. The color code for Figure 2a is the orange-to-green scheme shown in Figure 2b. (b) Histograms of biomass previously cleared (top) in , (middle) in , and (bottom) biomass in remaining forests in Y-axis represents the frequency of 100 km 2 areas. Kolmogorov Smirnov test to test whether the distribution of biomass remaining in forests in 2007 is significantly different from those cleared between 2000 and Results [14] In the Brazilian Amazon basin, the average area deforested annually from 2001 to 2007 was ha yr 1. This was not significantly different from the annual rate during the 1990s of ha yr 1 (ANOVA, p = 0.3) [INPE, 2007]. Spatially, the arc of deforestation is concentrated along the southeastern rim of the Amazon and has been encroaching toward the interior (Figure 1). There has been relatively little deforestation within federal and indigenous reserves. At the basin scale, we estimate annual carbon emissions from to be 0.16 Pg C yr 1 ( Pg C yr 1 ), which is similar to previous estimates [DeFries et al., 2002; Houghton et al., 2000]. [15] There is a strong one-to-one relationship between the proportion of PRODES non-forested areas in 2000 and the proportion of biomass in the three lowest classes across a km grid (Spearman s rank correlation: r = 0.88, n = 33865, p < 0.001). We therefore conclude that errors from temporal mismatches between the biomass and deforestation data are minimal and approximating pre-deforestation biomass by excluding these lowest biomass classes is a justifiable. [16] From 2001 to 2007 there was very little forest clearing inside of the federal and indigenous reserves that we excluded from our analysis (Figure S2a). Since degradation often precedes clear cutting, we assume that there was also little forest degradation and selective logging in these reserves before This is supported by studies specifically addressing degradation and selective logging [Asner et al., 2005]. If the observed increasing biomass gradient away from the arc of deforestation is an artifact of increasing degradation near to the arc, lands inside protected areas should have more biomass at a given distance from lands cleared before 2001 (Figure S2b). There are no significant differences between these two groups (ANOVA, p = 0.5), suggesting that the observed biomass gradient is not an artifact of forest degradation and selective logging. [17] The distribution of biomass in the Brazilian Amazon is heterogeneous (Figure 2a) and geographically correlated with the spatial pattern of deforestation. Specifically, there has been a geographic trend in deforestation from transitional forests with lower biomass in the southeast to highbiomass systems in the Amazon interior. As a consequence, carbon loss per hectare has been increasing significantly (Figure S3a) from an average of 183 Mg C ha 1 ( Mg C ha 1 ) in 2001 to 201 Mg C ha 1 ( Mg C ha 1 ) in 2007 (slope = 3 in a linear model significant at p < 0.01). Figure 2b shows the proportion of biomass cleared in , cleared in , and remaining in Carbon stocks in remaining forests outside of federal and indigenous reserves are higher still, with a mean of 231 Mg C ha 1 ( Mg C ha 1 ). This is significantly more carbon per unit area than in previously cleared forests (two-tailed Kolmogorov Smirnov test, p < 0.001). 4. Discussion [18] Accurately quantifying the spatial distribution of biomass is critical for assessments of carbon emissions from deforestation [Houghton, 2005a]. In the Amazon, as in many other regions, forests are rich mosaics of stands that differ in age and species composition, and on sites that differ in climate, soils, and disturbance history [Saleska et al., 2003]. As a consequence, they also differ in biomass. The Amazon biomass gradient extending towards the southeast has been previously noted but not explicitly compared with deforestation patterns or used to calculate carbon emissions [Nogueira et al., 2007]. Fearnside [2000] found that per hectare the biomass felled in the 1990s was less than the unlogged portions of the Amazon but did not examine temporal trends. [19] This gradient is not merely an artifact of disturbance from clearing and logging (Figures S1 and S2). Since 3of5

4 logging is concentrated near deforestation [Asner et al., 2005], it is noteworthy that the satellite derived biomass estimates [Saatchi et al., 2007] appear unable to detect the effects of logging on biomass across this gradient. Nogueira et al. [2007] attributed the gradient to increased soil fertility and natural disturbance frequency nearer to the arc of deforestation. Soil fertility is inversely related to wood density [Baker et al., 2004; Ter Steege et al., 2006], but the relationship between soil fertility, productivity and biomass is complex [Keeling and Phillips, 2007; Keeling et al., 2008]. [20] Recent deforestation has occurred primarily in lower biomass, more fertile, more frequently disturbed forests towards the southeastern edge of the Brazilian Amazon. This deforestation is increasingly encroaching into higher biomass forests towards the northwest. These forests differ not only in biomass but also in their ecology [Peacock et al., 2007] and perhaps resilience to disturbances. Biodiversity patterns across the Amazon are poorly understood [Bush and Lovejoy, 2007]. As deforestation proceeds into the higher biomass systems, there may be yet unknown feedbacks on not only carbon but also on water cycles and biodiversity. [21] In the Amazon, the substantially higher initial biomass in remaining forest than on deforested lands adds a challenging new dimension to the implications of future deforestation [Nepstad et al., 2002]. Even if deforestation rates remain constant, Brazil s annual carbon emissions will increase through time. 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