Open Access

Historic emissions from deforestation and forest degradation in Mato Grosso, Brazil: 1) source data uncertainties

  • Douglas C Morton1Email author,
  • Marcio H Sales2,
  • Carlos M SouzaJr2 and
  • Bronson Griscom3
Carbon Balance and Management20116:18

https://doi.org/10.1186/1750-0680-6-18

Received: 25 July 2011

Accepted: 30 December 2011

Published: 30 December 2011

Abstract

Background

Historic carbon emissions are an important foundation for proposed efforts to Reduce Emissions from Deforestation and forest Degradation and enhance forest carbon stocks through conservation and sustainable forest management (REDD+). The level of uncertainty in historic carbon emissions estimates is also critical for REDD+, since high uncertainties could limit climate benefits from credited mitigation actions. Here, we analyzed source data uncertainties based on the range of available deforestation, forest degradation, and forest carbon stock estimates for the Brazilian state of Mato Grosso during 1990-2008.

Results

Deforestation estimates showed good agreement for multi-year periods of increasing and decreasing deforestation during the study period. However, annual deforestation rates differed by > 20% in more than half of the years between 1997-2008, even for products based on similar input data. Tier 2 estimates of average forest carbon stocks varied between 99-192 Mg C ha-1, with greatest differences in northwest Mato Grosso. Carbon stocks in deforested areas increased over the study period, yet this increasing trend in deforested biomass was smaller than the difference among carbon stock datasets for these areas.

Conclusions

Estimates of source data uncertainties are essential for REDD+. Patterns of spatial and temporal disagreement among available data products provide a roadmap for future efforts to reduce source data uncertainties for estimates of historic forest carbon emissions. Specifically, regions with large discrepancies in available estimates of both deforestation and forest carbon stocks are priority areas for evaluating and improving existing estimates. Full carbon accounting for REDD+ will also require filling data gaps, including forest degradation and secondary forest, with annual data on all forest transitions.

Keywords

Amazon REDD+ IPCC Tier Approach Landsat

1. Background

Tropical deforestation accounted for approximately 12% of anthropogenic CO2 emissions in 2008 [1]. Forest degradation from fire, logging, and fuel wood collection represents an additional source of carbon emissions from land use activities in tropical forest regions [16]. Recognition of the important contributions from deforestation and forest degradation to anthropogenic greenhouse gas emissions led to proposals for Reduced Emissions from Deforestation and forest Degradation (REDD) to be included in a post-2012 climate agreement under the United Nations Framework Convention on Climate Change [7]. In 2010, the Cancun Agreements expanded the scope for climate mitigation activities in forests to include the conservation and enhancement of forest carbon stocks and sustainable forest management, or REDD+ [8].

Proposed REDD+ mechanisms require a baseline or reference emissions level against which future emissions can be compared [9, 10]. Previous scientific studies have estimated historic deforestation carbon emissions at pan-tropical [1, 3, 1113] or regional spatial scales, such as the Brazilian Amazon [1419]. However, the spatial and temporal resolutions of previous deforestation emissions estimates are likely too coarse for national REDD+ baselines, given the potential inclusion of sub-national activities [8]. In addition, the input data and methods in these studies were not necessarily consistent with guidance on national-scale reporting of emissions from forest lands from the Intergovernmental Panel on Climate Change [20, 21]. A range of forest carbon stock and deforestation data products exist at national and sub-national scales that could be used to establish historic emission levels [21], but the suitability of existing data for estimating historic carbon emissions and associated uncertainties has not been thoroughly evaluated.

The level of uncertainty in historic emissions baselines is critical for REDD+. Uncertainty in forest carbon emissions arises from estimated rates of deforestation and forest degradation, forest carbon stocks [22, 23], and emissions factors [16, 24, 25]. Large uncertainties could undermine the effectiveness of REDD+ by limiting the ability to generate credits from mitigation actions, especially if a conservative approach is used to estimate REDD+ credits [26, 27]. In the absence of a conservative approach, large uncertainties in historic emissions could lead to a situation in which mitigation actions fail to generate climate benefits (i.e., "hot air").

Research to reduce uncertainties in REDD+ baselines at national or sub-national scales may generate both scientific and policy payoffs. Tropical deforestation remains the most uncertain term in the global carbon budget [28]. Attention to the source and magnitude of uncertainties in emissions estimates at the national level can therefore help to constrain the global carbon balance. Reducing uncertainties at the national level may remove potential discounts from REDD+ carbon credits. Recent studies suggest that uncertainties in rates of deforestation and forest degradation [26, 29] and forest carbon stocks [30] can dramatically alter the cost-benefit calculation for REDD+ from the country perspective.

Here, we use a structured approach to evaluate the source and magnitude of uncertainties in historic forest carbon emissions for the Brazilian State of Mato Grosso. Mato Grosso is a hotspot of recent deforestation, accounting for more than 15% of humid tropical forest losses worldwide during 2001-2005 [3133]. Compared to other tropical forest regions, Mato Grosso also has a wealth of data with which to evaluate historic emissions. In this, the first of two research articles, we review the available data for Mato Grosso on deforestation, forest degradation, and forest carbon stocks to identify important data gaps and research needs to reduce source data uncertainties in historic forest carbon emissions estimates. We concentrate on five annual deforestation datasets and six estimates of forest carbon stocks in Mato Grosso (see Sections 5.3 and 5.4). We conclude this study with a roadmap for research in support of REDD+ based on the spatial and temporal patterns of disagreement among available data products. In the second manuscript, we describe a new model, the Carbon Emissions Simulator, to quantify the contribution from source data evaluated in this study and model parameters to total uncertainties in forest carbon emissions. The Carbon Emissions Simulator uses both Monte Carlo and error propagation techniques to quantify uncertainties in deforestation carbon emissions. By separating data and model-based uncertainties, the Carbon Emissions Simulator can be used to evaluate tradeoffs for improving historic emissions estimates by year, region, and source term. Together, these papers provide a comprehensive look at the data and research methods needed to quantify and reduce uncertainties in historic forest carbon emissions estimates for REDD+.

2. Results

2.1 Deforestation

All five deforestation data products identified periods of increasing (2001-2004) and decreasing (2005-2007) deforestation rates in Mato Grosso (Figure 1, Table 1). Annual deforestation rates were highly variable during the study period, ranging between 2,203 and 11,082 km2 yr-1 during 1990-2008. Three years with deforestation rates greater than 10,000 km2 yr-1 accounted for more than 25% of the total forest loss during this period (1995, 2003, and 2004).
Figure 1

Annual deforestation in Mato Grosso State during 1990-2008. Blue numbers indicate the ratio between low and high annual deforestation estimates. Individual data products are described in Table 1.

Table 1

Data sources for historic deforestation in Mato Grosso, Brazil.

Dataset

Approach

Temporal Coverage

MMU 1

Reference

Sensor

Method

INPE-PRODES

2

1987-2008

6.25 ha

[58]

Landsat

Single image, visual interpretation

PRODES-Digital 2

3

1997-2008

1 ha

[33]

Landsat

Single image, digital processing, visual interpretation

SEMA 2

3

1992-2005

1 ha

[32]

Landsat

Single image, digital processing, visual interpretation

IMAZON-SAD 3

3

2005-2008

12.5 ha

[59]

MODIS2

Two images, digital processing, automated analysis

INPE-DETER 3

3

2004-2008

25 ha

[60]

MODIS2

Single image, digital processing, visual interpretation

1 Minimum mapping unit (MMU): the smallest area of new deforestation identified in any year.

2 Annual deforestation estimates were not available from PRODES-Digital during 1997-2000 or SEMA for 1996, 1998, and 2000. Average values of forest loss between image dates were used in these years (e.g., forest area change between 1995-1997 images was divided equally between 1996 and 1997).

3 Alert data products provide near-real time monitoring of deforestation using imagery from the MODIS sensors at 250 m resolution. These data are primarily intended to identify the location of new deforestation, especially for deforestation events > 25 ha, rather than provide robust estimates of forest area change.

On an annual basis, deforestation rates from different data products exhibited considerable variability (Figure 1). In three consecutive years (2000-2002), deforestation rates from SEMA were approximately half those from PRODES-Digital, despite reliance on similar Landsat base data for both products. The range of annual deforestation rates exceeded the expected performance of satellite-based approaches (80-95% accuracy, [34]) in more than half of the years with multiple satellite-based deforestation products (1998-2008). Using a confidence interval of ± 20%, low and high estimates of annual deforestation did not overlap in these six years. The inclusion of MODIS-based deforestation data increased the range of annual deforestation estimates in 2005-2008, yet the INPE-DETER and Imazon-SAD estimates only represented the high and low values in 2008.

The legacy of differences in satellite-based deforestation datasets can also be seen in the spatial distribution of cumulative forest loss (Figure 2). In 1997, SEMA deforestation estimates indicated greater cumulative forest losses than PRODES-Digital data in northern Mato Grosso (Figure 2a). By 2005, cumulative forest losses derived from PRODES-Digital data were higher across the state, with the greatest differences around the Xingu River basin in eastern Mato Grosso (Figure 2b). These areas of greatest uncertainty highlight the need for additional field and remote sensing research to identify the causes of consistent spatial discrepancies among satellite-based estimates of forest area change.
Figure 2

Spatial differences between PRODES-Digital and SEMA estimates of cumulative deforestation through 1997 (a) and 2005 (b), summarized as the difference in remaining forest area (km 2 ) within each 0.25° cell. Areas outside of the PRODES forest mask appear gray.

2.2 Forest Carbon Stocks

Average forest carbon stock estimates from Tier 1 and Tier 2 data products for Mato Grosso varied by a factor of two (Table 2). The Tier 1 estimate of forest carbon stocks in Mato Grosso was the highest estimate of total carbon (206 Mg C ha), based on the value for humid tropical forests in South America. The Tier 1 root-shoot ratio (0.37) was much higher than for other products (0.21-0.26), suggesting that below ground biomass (BGB) accounts for part of the difference between Tier 1 and Tier 2 carbon stock estimates. Among Tier 2 data products, the source of plot data, number of parameters in the biomass expansion factor, and methods to interpolate between plot locations all contributed to the difference in carbon stock estimates. The wide range of average forest carbon stocks for Mato Grosso suggests that per-product uncertainty could be greater than ± 50%, similar to an earlier assessment of biomass data products by [22].
Table 2

Tier 1 and Tier 2 data sources for tropical rainforest carbon stocks in Mato Grosso.

Source

Total C: AGLB+AGDB+BGB

(Mg C/ha) 1

AGDB, BGB (% AGLB)

Plot Data

Carbon Fraction (CF)

Tier 2

IPCC 3

SA: 206

9%, 37%

N/A

0.47

1

Houghton et al. 2001

BA: 192

9%, 21%

Literature Review

0.5

2.a

Brown & Lugo 1992

BA: 156

9%, 21%

RADAM 4

0.5

2.a

Nogueira et al. 2009

MT: 159.7

13.91%, 25.8% 5

RADAM 4

0.485

2.a

Imazon; Sales et al. 2007

MT: 130.4 ± 44.8

13.91%, 25.8% 5

RADAM 4

0.485

2.m

Saatchi et al. 2007

MT: 99.0 ± 58.0

9%, 21%

Houghton et al. 2001

0.5

2.m

Tier 1 data are the IPCC default values for forest carbon stocks, whereas Tier 2 indicates country-specific data (see Table 4). Total forest carbon (C) was estimated from aboveground live biomass (AGLB) using conversion factors from each source for aboveground dead biomass (AGDB) and below-ground biomass (BGB) as a percentage of AGLB.

1 Average total carbon in forest biomass for tropical rainforest South America (SA), Brazilian Amazon (BA), or Mato Grosso (MT).

2 Tier 2 biomass data products were divided between regional or state-wide average values (Tier 2.a) and spatially-explicit maps of forest biomass (Tier 2.m).

3 As reported by [80]

4 The RADAMBRASIL floristic inventory (DPNM, 1973-1983).

5 Nogueira et al (2009) applied additional correction factors for AGLB in dense (10.5%) and non-dense (15.7%) forest types. These factors were also included in the Imazon product.

The spatial distribution of forest carbon stocks in Mato Grosso differed markedly between Tier 2.m data products considered in this study. The Saatchi et al. and Imazon estimates of aboveground live biomass (AGLB) disagreed by ~50 Mg ha-1 in central and eastern portions of the state, and differences between the two products exceeded 100 Mg ha-1 in northwest Mato Grosso (Figure 3). The conversion from AGLB to total biomass amplified the spatial discrepancies in Figure 3 because expansion factors for BGB and aboveground dead biomass (AGDB) for the Imazon product were larger than in the Saatchi et al. data product (see Table 2).
Figure 3

Map of differences between Saatchi et al. [75] and Imazon [76] estimates of AGLB in northern Mato Grosso state. Imazon estimates exceed those of Saatchi et al. in red, orange, and yellow areas, while green areas indicate higher AGLB estimates from Saatchi et al. Deforestation through 2005 is shown in gray, and non-forest areas within Mato Grosso appear white. Individual data products are described in Table 2.

2.3 Deforested Biomass

Deforestation in Mato Grosso during 1993-2008 was concentrated in low biomass forest types. Average biomass in deforested regions increased during the study period (Table 3) but remained below state-wide averages (Table 2). For the combination of SEMA deforestation data with Imazon biomass estimates, average AGLB in deforested regions increased by 2.3 Mg ha-1 yr-1 during 1993-2005 (R2 = 0.85, Table 3). PRODES-Digital data also suggest an increasing trend in average AGLB in deforested areas using Imazon biomass estimates during 2001-2008 (1.8 Mg ha-1yr-1, R2 = 0.43). Differences in the location of recent deforestation between PRODES-Digital and SEMA had little impact on the average biomass in deforested areas during 2001-2005 from the IMAZON product (< 2 Mg ha-1), as discrepancies between these products were widely distributed across low and high biomass forests in Mato Grosso by 2005 (Figure 2b).
Table 3

Mean aboveground live biomass ± 1 SD in areas of recent deforestation (Mg ha-1).

Year

SEMA/Imazon

PRODES-Digital/Imazon

PRODES-Digital/Saatchi et al.

1993

158.9 ± 46.9

  

1994

152.6 ± 43.2

  

1995

167.0 ± 54.9

  

1996

165.5 ± 49.5

  

1997

165.5 ± 49.5

  

1998

174.4 ± 61.0

180.5 ± 55.9

 

1999

174.4 ± 61.0

180.5 ± 55.9

 

2000

179.4 ± 56.2

180.5 ± 55.9

 

2001

179.4 ± 56.2

166.6 ± 54.1

 

2002

181.1 ± 53.4

172.7 ± 57.0

 

2003

179.6 ± 56.9

183.4 ± 58.8

 

2004

185.7 ± 59.0

181.0 ± 56.5

 

2005

180.8 ± 57.6

184.6 ± 61.4

135.5 ± 87.3

2006

 

179.8 ± 63.7

126.7 ± 90.5

2007

 

187.0 ± 67.8

143.8 ± 90.2

2008

 

179.1 ± 56.0

136.5 ± 82.3

Tables 1 and 2 provide additional details regarding Tier 2.m biomass data products (Imazon, Saatchi et al.) and Approach 3 deforestation products (SEMA, PRODES-Digital), respectively.

Overall, the choice of Tier 2.m biomass data had a larger impact on estimates of deforested biomass than the trend of increasing biomass in recently deforested areas from either product. The difference in average AGLB between Imazon and Saatchi et al. data products for deforestation during 2005-2008 (47.8 Mg ha-1) was larger than the total increase in average deforested biomass from either product during the study period (< 30 Mg ha-1, Table 3).

3. Discussion

The range of available deforestation and biomass data products provides a first estimate of source-data uncertainties in historic deforestation carbon emissions. Findings in this study highlight how specific years, regions, and data products contribute to potential variability in deforestation emissions estimates. Large (25-50%) discrepancies remain between estimates of forest carbon stocks and annual deforestation from different data products, even for estimates at the same Tier or Approach. Reconciling these differences is essential to reduce uncertainties in historic deforestation emissions estimates and prevent the propagation of errors from subsequent land-use transitions in disputed areas.

Reducing source data uncertainties requires careful methods to substitute space for time. The archive of Landsat satellite imagery is a rich resource for countries interested in revising estimates of forest area changes from 1972-present [35]. Landsat resolution (30 m) is suitable for detailed estimates of forest area change [34], provided that an accuracy assessment can be conducted using very high resolution (< 5 m) imagery from airborne or satellite data sources [36]. In the case of Mato Grosso, where most deforestation occurs in large clearings (> 25 ha, [37]), forest area change estimates from moderate resolution (250 m) deforestation monitoring systems do not differ much from estimates obtained from Landsat-based deforestation maps (see Figure 1, Table 1). However, deforestation alert systems are inappropriate for monitoring small forest clearings [38] or forest degradation from selective logging [39] for estimates of historic carbon emissions.

In contrast to the rich archive of historic satellite data, there is limited historic forest inventory data for Mato Grosso. Improving estimates of tropical forest carbon stocks will therefore require new data collection. A new National Forest Inventory is already underway in Brazil (http://ifn.florestal.gov.br), with field plots distributed on a regular grid (20 km × 20 km). New technologies offer the possibility to generate spatially explicit biomass maps using a more limited network of forest inventory plots and large-area sampling of forest heights with airborne or spaceborne LiDAR [4042]. However, contemporary estimates of forest carbon stocks at the deforestation frontier must then be paired with data on historic deforestation and forest degradation to account for the impacts of historic land use on contemporary measurements (e.g., [43]). Routine sampling may be needed to maintain updated field or LiDAR-based information on forest carbon stocks for REDD+ [44] because static reference data are unable to account for increases in forest carbon stocks over time (e.g., [45]) or reductions in biomass from forest disturbance (e.g., [46, 47]).

What research is needed to reduce source data uncertainties in Mato Grosso and other Amazon regions? New measurements of forest carbon stocks and new estimates of forest area changes from remotely-sensed data are most critical in regions where existing products disagree (Figure 4). Areas with high uncertainties in both forest biomass and deforestation rates provide an opportunity to collect complementary information on land use and carbon stocks to improve estimates of historic carbon emissions. Improved estimates of forest carbon stocks in areas with concentrated historic deforestation are a specific priority for efforts to quantify historic emissions and establish REDD+ baselines. Additional data collection and analysis in these areas are needed to develop a consistent, validated approach for full carbon accounting from deforestation and forest degradation (Figure 5).
Figure 4

Data needs to reduce uncertainties in historic deforestation carbon emissions from Mato Grosso, summarized at 0.25° spatial resolution. White cells indicate areas where Landsat-based estimates of cumulative deforestation through 2005 differ by > 40 km2. Gray cells indicate regions where average Tier 2.m estimates of AGLB in remaining forest in 2005 differ by > 50 Mg ha-1. Cells with data needs for both deforestation and biomass appear black.

Figure 5

Land use transitions and related data needs to estimate carbon emissions from deforestation and forest degradation. Full carbon accounting requires data for the rate (R) of area change and associated changes in carbon stocks (ΔC) for deforestation (D), forest degradation (L), and regrowth (R). All forest lands must meet minimum height (h), crown cover (CC), and area (A) requirements, according to each country's national forest definition. Solid arrows represent primary transitions from forest to non-forest or degraded forest lands; dashed arrows represent secondary land-use transitions.

At least two factors likely contributed to the observed spatial and temporal discrepancies in annual deforestation rates for Mato Grosso. First, none of the satellite-based deforestation estimates were developed specifically for REDD+. As a result, forest degradation from logging and fire may have been included in historic deforestation estimates, especially in years with extensive damages from understory forest fires [48]. Incomplete information on forest degradation and secondary forest dynamics also contributes to source data uncertainties for estimating net forest carbon emissions in Mato Grosso. Full carbon accounting from deforestation and forest degradation will require careful consideration of sequential land-use transitions (Figure 5). A time-series approach to track deforestation, degradation, and secondary forest dynamics using annual satellite imagery could improve emissions estimates for Mato Grosso and other tropical forest regions by reducing misclassification and "double counting" errors [48] that occur when degraded forests are deforested for agricultural use [49]. Second, time series methods may also improve the consistency of deforestation estimates over time. Deforestation estimates in this study were based on interpretation of a single satellite image or a comparison between two successive images. Time series methods that consider longer periods of disturbance and recovery may improve the accuracy of change detection [50], especially for retrospective analyses to establish historic baselines. Annual satellite data can be used to confirm continued agricultural use of previously deforested areas, forest recovery following degradation, and the age of secondary forests from land abandonment to improve carbon stock estimates in areas of active land use change.

In addition to reducing source data uncertainties, reanalysis of historic changes in forest area can also facilitate sub-national allocation of deforestation baselines. Brazil recently selected the 1996-2005 period for deforestation baseline calculations [51]. However, annual PRODES-Digital deforestation data are only available beginning in 2000. Allocation of baseline deforestation information to Amazon states can be accomplished using PRODES statistics (Approach 2), but below the state scale, regional or project-scale activities may require a new analysis of historic deforestation and forest degradation to provide Approach 3 data for all years during the baseline period.

The range of available data products provides an indication of the spatial and temporal variability associated with estimates of deforestation and forest carbon stocks. However, total uncertainties in historic emissions cannot be estimated without validation efforts to characterize per-product uncertainties. Validation needs are greatest in areas where existing products disagree (Figure 4), but all deforestation and carbon stock data products should include a robust validation plan with routine field measurements and airborne or spaceborne very high resolution imagery (< 5 m).

Given the need for routine data collection on forest transitions and associated carbon losses, development and maintenance of reporting information for REDD+ will likely require dedicated capacity for satellite and field data analysis. Consistent methods for data analysis are also critical for REDD+ [27]. Even in a well-characterized region such as Mato Grosso, multiple deforestation data products were required to consider forest area changes during 1990-2008 because no single product provided annual estimates during the entire study period. The development of standards for REDD+ monitoring, reporting, and verification (MRV) provides an opportunity to design a system that can lower uncertainties in emissions estimates over time using the comparative approach described in this paper. Ideally, the analysis in this study would be the first iteration of a routine process to target new data collection in regions and years with largest uncertainties in carbon stock and deforestation estimates.

4. Conclusions

This study reviewed available data products for deforestation, forest degradation, and forest carbon stocks in Mato Grosso, Brazil to assess the level of uncertainty in source data for estimating historic forest carbon emissions for REDD+. Deforestation data showed considerable spatial and temporal variability, with Landsat-based estimates of annual deforestation differing by > 20% in most years. Forest carbon stock estimates exhibited even greater variability, with more than a two-fold difference in carbon stock estimates in northwest Mato Grosso. Limited information was available on forest degradation and secondary forest regeneration, suggesting that full carbon accounting for REDD+ cannot be achieved without additional satellite data analysis to quantify annual transitions involving degraded or regenerating forests.

The diversity of deforestation and carbon stock estimates for Mato Grosso provides an initial indication of research needs to address source data uncertainties for REDD+. Spatial and temporal patterns of disagreement show priority areas for new data collection, and a coordinated strategy to estimate forest carbon stocks and validate deforestation estimates in these areas could target the main source data uncertainties in Mato Grosso. Data needs for REDD+ differ from previous uses of deforestation information for enforcement of environment laws and private property rights. The additional focus on source data uncertainties for REDD+ could reduce large uncertainties in current emissions estimates, thereby increasing the likelihood of generating benefits from REDD+ actions.

5. Materials and methods

Below, we synthesize relevant IPCC guidance for source data on changes in the area and carbon stocks in forest lands (Section 5.1), describe the Mato Grosso study area (Section 5.2), and review available data on forest area changes (Section 5.3) and carbon stocks (Section 5.4).

5.1 IPCC Tiers and Approaches

The definition of 'forest' forms the foundation of REDD+ and related initiatives, establishing the spatial extent of forest cover and the criteria for deforestation. The Brazilian government defines their forest land as areas of at least one hectare in size with more than 30% crown cover of trees ≥ 5 m in height. This definition selects the upper end of ranges for area (0.04-1.0 ha), crown cover (10-30%), and tree height (2-5 m) in guidelines established by the UNFCCC for the Clean Development Mechanism of the Kyoto Protocol [52]. Deforestation occurs when any of these thresholds are crossed, typically during the conversion of forest for agricultural use (Figure 5). Within the scope of REDD+, forest degradation is generally considered a reduction in carbon stocks within forest land remaining as forest [21], although a precise definition of forest degradation has not been adopted [53].

Data on the rates of forest transitions and associated changes in carbon stocks are classified according to the methods used for data collection (Table 4). We followed this guidance when reviewing and analyzing available forest area change data (Activity Data) and data on changes in carbon stocks (Emissions Factors) from transitions between forest land and other land uses [21]. For estimates of deforestation, moving from Approach 1 to Approach 3 area change data involves a shift from global or national survey methods (e.g., the Food and Agricultural Organization's periodic Forest Resource Assessment surveys) to spatially-explicit estimates from satellite remote sensing data (Table 4). Approach 3 data are recommended as the basis for establishing REDD+ baselines [21], since fine-scale spatial information (20-60 m) is necessary to track sequential land-use transitions at a given location through time (Figure 5). The specificity of source data on forest carbon stocks also increases from continental-scale averages for each forest type (Tier 1) to country-specific information (Tier 2). Few countries have established Tier 3 efforts to repeatedly measure or model forest carbon stocks that could be used to estimate historic emissions.
Table 4

Summary of IPCC data categories for Activity Data on forest area changes and Emission Factors for changes in carbon stocks from deforestation and forest degradation.

Approaches for Activity Data: Forest Area Changes

Tiers for Emission Factors: Changes in Carbon Stocks

1. Non-spatial country statistics

1. IPCC default values by continent and forest type

2. Maps, surveys, and other national statistical data

2. Country specific data for key factors

3.Spatially explicit data from interpretation of remote sensing imagery

3.National inventory of carbon stocks, via repeated measurements of key stocks through time or modeling

Approach 1 and Tier 3 data products were unavailable for Mato Grosso during 1990-2008. Please see [21] for a more complete discussion of IPCC Good Practice Guidance.

5.2 Study area

The state of Mato Grosso includes the southernmost extent of Amazon forests in Brazil (Figure 6). Data from the RADAMBRASIL floristic surveys (1973-1983) indicate that Amazon forest and transition forest types initially covered two-thirds of the state [54]. The Brazilian Instituto Nacional de Pesquisas Espaciais (INPE) further refined the extent of Amazon forests using Landsat satellite data under the PRODES (Monitoramento da Floresta Amazônica Brasileira por Satélite) program of annual deforestation assessments in the Brazilian Amazon [33]. The PRODES forest mask is a common reference for many Amazon deforestation products. We adopted the PRODES forest mask for our analysis to maintain consistency with results from other studies (Figure 2); however, the PRODES mask does not include all areas that could be classified as forest in Mato Grosso based on the 30% crown cover threshold [55].
Figure 6

Forest cover extent (black) from the INPE PRODES program in the Brazilian State of Mato Grosso (inset, white). Outside of the PRODES forest mask, areas with > 30% tree cover in 2001 appear dark gray [55]. The Pantanal biome in southern Mato Grosso is outlined in white.

5.3 Forest area change data for Mato Grosso

5.3.1 Deforestation

Deforestation in the Brazilian Amazon has been monitored for more than two decades using a variety of satellite sensors (e.g., [33, 37, 56]. Fine-scale mapping efforts have relied on Landsat or other high-resolution data (≤ 30 m pixel size), and the minimum mapping unit from these products is consistent with the one-hectare threshold for individual forest patches in Brazil's national forest definition [34]. Most studies report gross rather than net deforestation, as transitions involving secondary forest (e.g., agricultural abandonment and re-clearing) were not routinely identified in historic assessments (see Figure 5).

We evaluated annual satellite-based estimates of deforestation in Mato Grosso beginning in 1990, and we extended our evaluation through 2008 to include new deforestation products that were developed based on moderate resolution (250 m) satellite imagery. We identified five satellite-based estimates of Amazon deforestation in Mato Grosso covering part or all of the 1990-2008 timeframe (Table 1). We limited our review to annual deforestation estimates, thereby excluding available data from regional and global deforestation products with periodic (5-10 year) evaluation periods [3, 11, 31, 56, 57].

Deforestation data products were grouped according to IPCC Approach. We categorized annual deforestation statistics from the PRODES-Analog product (INPE) as an Approach 2 dataset since these data were not spatially explicit [58]. The PRODES-Digital and SEMA data products provided the longest time series of Approach 3 deforestation data. The length of the deforestation data record is critical for estimating annual emissions; a minimum of 10 years of historic deforestation data are recommended to estimate the contribution from previous clearing activity to emissions in any given year [16]. We also included two "alert" data products (INPE-DETER and Imazon-SAD) from efforts to monitor deforestation in near-real time based on moderate resolution (250 m) satellite data [59, 60]. Inclusion of alert data products allowed us to characterize the additional uncertainty in annual deforestation estimates that could arise if only alert-type data on area change were available.

5.3.2 Forest Degradation and Secondary Forests

Few satellite-based estimates of forest degradation exist for Mato Grosso. New algorithms to detect selective logging [4, 61, 62] and understory forest fires [48, 61, 63, 64] using Landsat data were developed in Mato Grosso. However, only one estimate of selective logging was available with statewide coverage over multiple years [4]. [4] estimated that selective logging in Mato Grosso averaged 9,367 km2 yr-1 during 1999-2002. Excluding logged areas that were deforested by 2004, the average annual logged area during 1999-2002 was 6,923 km2 yr-1[49]. No satellite-based estimates of understory forest fires or fuel wood collection were available for Mato Grosso, even for a single year.

Knowledge of the extent and frequency of land abandonment to secondary forest is critical for estimating net carbon emissions from deforestation, since carbon accumulation in secondary forests may partially offset deforestation carbon losses [16, 65]. As in the case of forest degradation, few satellite-based estimates of secondary forest extent were available for Mato Grosso. Most studies estimated secondary forest area for only one period in time rather than following the dynamics of land abandonment and re-clearing of secondary forest. Previous estimates of the amount of historic deforestation in some stage of forest regrowth varied from 12-17% in Mato Grosso in three studies conducted with satellite data from 2000-2008 [6668]. Across the entire Brazilian Amazon, the amount of historic deforestation in some stage of secondary forest regrowth ranged from 20-36% over different epochs [56, 68, 69]. However, a rigorous comparison of secondary forest data products was not possible due to differences in the timing of recent studies.

5.4 Forest carbon stocks

The amount and spatial distribution of forest carbon stocks in Amazonia are major sources of uncertainty in estimates of emissions from deforestation and forest degradation [22]. [70] estimated the total forest carbon storage in the Brazilian Amazon as 39-93 Pg C, but the seven data products reviewed in that study disagreed about the spatial distribution of low and high-biomass forest types within the region. Recent efforts to refine maps of forest carbon stocks in Amazonia have focused on new plot measurements of forest biomass [71], improved allometric relationships relating wood volume to biomass [72, 73], and extrapolation of plot-based data using climate metrics [74], satellite-based estimates of forest canopy reflectance [75], and geostatistical methods [76]. Revised estimates of the total forest carbon stocks in Amazonia fall within the original range described by [70], albeit with lower forest biomass in areas of active deforestation in southern and eastern Amazonia than previously estimated [19, 73, 75]. Remaining uncertainties in the spatial distribution of forest biomass arise from the small number of forest plots [77] and the influence of historic land use on forest carbon stocks, especially along the deforestation frontier [16, 49, 70, 78]. Direct estimates of aboveground biomass from LiDAR or Radar remote sensing instruments have the potential to address these concerns [4042, 79], but no direct satellite-based measurements of forest biomass were available for this study.

We compared one Tier 1 and five Tier 2 datasets of forest carbon stocks in Mato Grosso (Table 2). Tier 1 data for carbon stocks in tropical forests represent continental-scale averages for each forest type [53, 80]. Tier 2 biomass datasets were derived from Amazon forest inventory plots, either from the Brazilian government's RADAMBRASIL survey [54] or a compilation of forest biomass plots from the scientific literature [70, 75]. The RADAMBRASIL inventory is the most intensive survey of timber volumes in Brazilian forests conducted to date, with 440 one-hectare plots in Mato Grosso [54]. Converting timber volume into AGLB, including all plants regardless of timber utility, requires the use of a biomass conversion and expansion factor [18, 73, 81]. Similarly, aboveground dead biomass (AGDB) and belowground biomass (BGB) are typically estimated using relationships among field-measured AGLB, woody debris, and root-shoot ratios [70, 72, 73, 82] (Table 2). Data products from Houghton and Saatchi et al. were based on forest biomass plots from the scientific literature, adjusting for AGDB and BGB in a similar manner when these quantities were not directly measured [70, 75].

Tier 2 biomass maps for Amazonia rely on statistical methods to extrapolate plot-based measurements across the spatial extent of forest cover. Initial maps of forest biomass used simple interpolation between plot locations [70] or land cover information to assign average plot biomass values to each forest type [18, 81]. Recently, additional variables such as climate, soils, topography, and forest phenology metrics derived from satellite data have been used to characterize forest biomass between plot locations [7376]. We selected the most recent map product from each plot data source (RADAMBRASIL: Imazon, scientific literature: Saatchi et al.) for comparisons with Approach 3 deforestation data. The Imazon and Saatchi et al. data products represent substantial methodological advances over simple interpolation or forest type maps for estimating the spatial distribution of forest biomass in Amazonia [75, 76]. These data products are labeled as Tier 2.m for 'map' in Table 3 to differentiate these spatially-explicit biomass maps at 1 km spatial resolution from spatially-averaged forest biomass data by forest type, state, or country (Tier 2.a for 'average'). Although both Tier 2.m data products include internal estimates of map accuracy based on cross-validation techniques, neither Saatchi et al. nor Imazon data products have been rigorously validated using independent estimates of contemporary forest carbon stocks. Therefore, all Tier 2 data products (2.m and 2.a) were treated equally in our summary of data products according to Tier/Approach.

9. Authors' information

1NASA Goddard Space Flight Center, Greenbelt MD USA, douglas.morton@nasa.gov

2Instituto do Homem e Meio Ambiente da Amazônia (Imazon), Belém, PA, Brazil, marcio@imazon.org.br; souzajr@imazon.org.br

3The Nature Conservancy, Arlington, VA USA, bgriscom@tnc.org

6. List of abbreviations

AGDB: 

Aboveground Dead Biomass

AGLB: 

Aboveground Live Biomass

BGB: 

Below Ground Biomass

Imazon: 

Instituto do Homem e Meio Ambiente da Amazônia

INPE: 

Instituto Nacional de Pesquisas Espaciais

IPCC: 

Intergovernmental Panel on Climate Change

PRODES: 

Program for the Annual Estimation of Deforestation in the Amazon

REDD+: 

Reducing Emissions from Deforestation and forest Degradation and enhancing forest carbon stocks through conservation and sustainable forest management

SEMA: 

Secretaria Estadual do Meio Ambiente

UNFCCC: 

United Nations Framework Convention on Climate Change.

Declarations

10. Acknowledgements

Support for this study was provided by The Nature Conservancy and the Ford Foundation. Additional support for DCM was provided by an appointment to the NASA Postdoctoral Program Fellowship at the Goddard Space Flight Center, administered by Oak Ridge Associated Universities. Additional support to Imazon was provided by the David and Lucile Packard Foundation. We thank Patrick Gonzalez for a series of discussions that led to the initiation of this study, and we thank Philip Fearnside, Robert Ewers, Jim Collatz, Kelly McManus, and three anonymous reviewers for helpful comments on a previous version of this manuscript.

Authors’ Affiliations

(1)
NASA Goddard Space Flight Center
(2)
Instituto do Homem e Meio Ambiente da Amazônia (Imazon)
(3)
The Nature Conservancy

References

  1. van der Werf GR, Morton DC, DeFries RS, Oliver JGJ, Kasibhatla P, Jackson RB, Collatz GJ, Randerson JT: C02 emissions from deforestation. Nature Geoscience 2009, 2: 737–738. 10.1038/ngeo671View ArticleGoogle Scholar
  2. Houghton RA: Carbon Flux to the Atmosphere from Land-Use Changes: 1850–2005. In TRENDS: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A; 2008.Google Scholar
  3. Achard F, Eva HD, Mayaux P, Stibig H-J, Belward A: Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s. Global Biogeochemical Cycles 2004., 18: doi:10.1029/2003GB002142 doi:10.1029/2003GB002142Google Scholar
  4. Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller M, Silva JN: Selective logging in the Brazilian Amazon. Science 2005, 310: 480–482. 10.1126/science.1118051View ArticleGoogle Scholar
  5. Putz FE, Zuidema PA, Pinard MA, Boot RGA, Sayer JA, Sheil D, Sist P, Elias JKV: Improved tropical forest management for carbon retention. PLOS Biology 2008, 6: 1368–1369.View ArticleGoogle Scholar
  6. Geist HJ, Lambin EF: Proximate causes and underlying driving forces of tropical deforestation. Bioscience 2002, 52: 143–150. 10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2View ArticleGoogle Scholar
  7. UNFCCC: Draft conclusions for Agenda Item 6: Reducing Emissions From Deforestation in Developing Countries UNFCCC/COP-11 Draft Decision. 2005.http://unfccc.int/resource/docs/2005/cop11/eng/l02.pdf Available at:Google Scholar
  8. UNFCCC: Outcome of the work of the Ad Hoc Working Group on long-term Cooperative Action under the Convention, UNFCCC-COP16 Draft Decision CP.16. 2010.http://unfccc.int/files/meetings/cop_16/application/pdf/cop16_lca.pdf available at:Google Scholar
  9. Griscom B, Shoch D, Stanley B, Cortez R, Virgilio N: Sensitivity of amounts and distribution of tropical forest carbon credits depending on baseline rules. Environmental Science & Policy 2009, 12: 897–911. 10.1016/j.envsci.2009.07.008View ArticleGoogle Scholar
  10. GCP: The Little REDD Book: A guide to governmental and non-governmental proposals for reducing emissions from deforestation and degradation. Oxford: Global Canopy Programme; 2008.Google Scholar
  11. DeFries RS, Houghton RA, Hansen MC, Field CB, Skole DL, Townshend J: Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s. Proceedings of the National Academy of Sciences 2002, 99: 14256–14261. 10.1073/pnas.182560099View ArticleGoogle Scholar
  12. Le Quere C, Raupach MR, Canadell JG, Marland G, Bopp L, Ciais P, Conway TJ, Doney SC, Feely R, Foster P, Friedlingstein P, Gurney K, Houghtoh RA, House JI, Huntingford C, Levy P, Lomas MR, Majkut J, Metzl N, Ometto JP, Peters GP, Prentice IC, Randerson JT, Running SW, Sarmiento JL, Schuster U, Sitch S, Takahashi T, Viovy N, van der Werf GR, et al.: Trends in the sources and sinks of carbon dioxide. Nature Geoscience 2009. doi:10.1038/NGEO1689 doi:10.1038/NGEO1689Google Scholar
  13. van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, DeFries RS, Jin Y, van Leeuwen TT: Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry & Physics 2010, 10: 11707–11735. 10.5194/acp-10-11707-2010View ArticleGoogle Scholar
  14. Hirsch AI, Little WS, Houghton RA, Scott NA, White JD: The net carbon flux due to deforestation and forest re-growth in the Brazilian Amazon: analysis using a process-based model. Global Change Biology 2004, 10: 908–924. 10.1111/j.1529-8817.2003.00765.xView ArticleGoogle Scholar
  15. Houghton RA, Skole DL, Nobre CA, Hackler JL, Lawrence KT, Chomentowski WH: Annual Fluxes of carbon from deforestation and regrowth in the Brazilian Amazon. Nature 2000, 403: 301–304. 10.1038/35002062View ArticleGoogle Scholar
  16. Ramankutty N, Gibbs HK, Achard F, DeFries RS, Foley JA, Houghton RA: Challenges to estimating carbon emissions from tropical deforestation. Global Change Biology 2007, 13: 51–66. 10.1111/j.1365-2486.2006.01272.xView ArticleGoogle Scholar
  17. Potter C, Genovese VB, Klooster S, Bobo M, Torregrosa A: Biomass burning losses of carbon estimated from ecosystem modeling and satellite data analysis for the Brazilian Amazon region. Atmospheric Environment 2001, 35: 1773–1781. 10.1016/S1352-2310(00)00459-3View ArticleGoogle Scholar
  18. Fearnside PM: Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Climatic Change 1997, 35: 321–360. 10.1023/A:1005336724350View ArticleGoogle Scholar
  19. Loarie SR, Asner GP, Field CB: Boosted carbon emissions from Amazon deforestation. Geophysical Research Letters 2009, 36: L14810. doi:14810.11029/12009GL037526 doi:14810.11029/12009GL037526View ArticleGoogle Scholar
  20. IPCC: Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. Japan: IGES; 2006.Google Scholar
  21. GOFC-GOLD: Reducing greenhouse gas emissions from deforestation and degradation in developing countires: a sourcebook of methods and procedures for monitoring, measuring, and reporting, GOFC-GOLD Report version COP14–2. Alberta, Canada: Natural Resources Canada; 2009.Google Scholar
  22. Houghton RA: Aboveground forest biomass and the global carbon balance. Global Change Biology 2005, 11: 945–958. 10.1111/j.1365-2486.2005.00955.xView ArticleGoogle Scholar
  23. Avitabile V, Herold M, Henry M, Schmullius C: Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda. Carbon Balance and Management 2011, 6: 7. 10.1186/1750-0680-6-7View ArticleGoogle Scholar
  24. Morton DC, DeFries RS, Randerson JT, Giglio L, Schroeder W, Van der Werf GR: Agricultural intensification increases deforestation fire activity in Amazonia. Global Change Biology 2008, 14: 2262–2275. 10.1111/j.1365-2486.2008.01652.xView ArticleGoogle Scholar
  25. van der Werf GR, Morton DC, DeFries RS, Giglio L, Randerson JT, Collatz GJ, Kasibhatla PS: Estimates of fire emissions from an active deforestation region in the southern Amazon based on satellite data and biogeochemical modelling. Biogeosciences 2009, 6: 235–249. 10.5194/bg-6-235-2009View ArticleGoogle Scholar
  26. Kohl M, Baldauf T, Plugge D, Krug J: Reduced emissions from deforestation and forest degradation (REDD): a climate change mitigation strategy on a critical track. Carbon Balance and Management 2009., 4: doi:10.1186/1750–0680–1184–1110 doi:10.1186/1750-0680-1184-1110Google Scholar
  27. Grassi G, Monni S, Federici S, Achard F, Mollicone D: Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates. Environmental Research Letters 2007., 3: doi:10.1088/1748–9326/1083/1083/035005 doi:10.1088/1748-9326/1083/1083/035005Google Scholar
  28. Le Quere C, Raupach MR, Canadell JG, Marland G, Bopp L, Ciais P, Conway TJ, Doney SC, Feely R, Foster P, Friedlingstein P, Gurney K, Houghtoh RA, House JI, Huntingford C, Levy P, Lomas MR, Majkut J, Metzl N, Ometto JP, Peters GP, Prentice IC, Randerson JT, Running SW, Sarmiento JL, Schuster U, Sitch S, Takahashi T, Viovy N, van der Werf GR, et al.: Trends in the sources and sinks of carbon dioxide. Nature Geoscience 2009, 2: 831–836. 10.1038/ngeo689View ArticleGoogle Scholar
  29. Pelletier J, Ramankutty N, Potvin C: Diagnosing the uncertainty and detectability of emission reductions for REDD+ under current capabilities: an example for Panama. Environmental Research Letters 2011, 6: 024005. 10.1088/1748-9326/6/2/024005View ArticleGoogle Scholar
  30. Pelletier J, Kirby KR, Potvin C: Significance of carbon stock uncertainties on emission reductions from deforestation and forest degradation in developing countries. Forest Policy and Economics 2010. doi:10.1016/j.forpo.2010.1005.1005 doi:10.1016/j.forpo.2010.1005.1005Google Scholar
  31. Hansen MC, Stehman SV, Potapov PV, Loveland TR, Townshend J, DeFries RS, Pittman KW, Arunarwati B, Stolle F, Steininger MK, Carroll M, DiMiceli C: Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proceedings of the National Academy of Sciences 2008, 105: 9439–9444. 10.1073/pnas.0804042105View ArticleGoogle Scholar
  32. SEMA-MT: Sistema Integrado de Monitoramento e Licenciamento Ambiental de Mato Grosso (SIMLAM). Secretario de Estado do Meio Ambiente-Mato Grosso 2008.Google Scholar
  33. INPE: Projeto PRODES: Monitoramento da floresta Amazônica Brasileira por satélite. Instituto Nacional de Pesquisas Espaciais; 2008.Google Scholar
  34. DeFries RS, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B, Souza CM Jr: Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science & Policy 2007, 10: 385–394. 10.1016/j.envsci.2007.01.010View ArticleGoogle Scholar
  35. Goward S, Arvidson T, Williams D, Faundeen J, Irons J, Franks S: Historical record of Landsat global coverage: Mission operations, NSLRSDA, and international cooperator stations. Photogrammetric Engineering & Remote Sensing 2006, 72: 1155–1169.View ArticleGoogle Scholar
  36. Chambers JQ, Asner GP, Morton DC, Anderson LO, Saatchi SS, Espirito-Santo FdB, Palace M, Souza CM Jr: Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution 2007, 22: 414–423. 10.1016/j.tree.2007.05.001View ArticleGoogle Scholar
  37. Alves DS, Morton DC, Batistella M, Roberts DA, Souza CM Jr: The changing rates and patterns of deforestation and land use in Brazilian Amazonia. In Amazonia and global change. Edited by: Keller M, Gash J, Silva Dias P. Stockholm: International Geosphere-Biosphere Programme (IGBP); 2009.Google Scholar
  38. Morton DC, DeFries RS, Shimabukuro YE, Anderson LO, del bon Espírito-Santo F, Hansen MC, Carroll M: Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data. Earth Interactions 2005, 9: 22.View ArticleGoogle Scholar
  39. Asner GP, Keller M, Pereira R, Zweede JC: Remote sensing of selective logging in Amazonia: assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis. Remote Sensing of Environment 2002, 80: 483–486. 10.1016/S0034-4257(01)00326-1View ArticleGoogle Scholar
  40. Goetz S, Baccini A, Laporte N, Johns T, Walker W, Kellndorfer J, Houghtoh RA, Sun M: Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management 2010., 4: doi:10.1186/1750–0680–1184–1182 doi:10.1186/1750-0680-1184-1182Google Scholar
  41. Baccini A, Laporte N, Goetz SJ, Sun M, Dong G: A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters 2008., 3: doi:10.1088/1748–9326/1083/1084/045011 doi:10.1088/1748-9326/1083/1084/045011Google Scholar
  42. Asner GP, Powell GVN, Mascaro J, Knapp DE, Clark JK, Jacobson J, Kennedy-Bowdoin T, Balaji A, Paez-Acosta G, Victoria E, Secada L, Valqui M, Hughes RF: High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Science 2010. doi: 10.1073/pnas.1004875107 doi: 10.1073/pnas.1004875107Google Scholar
  43. Helmer EH, Lefsky MA, Roberts DA: Biomass accumulation rates in Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. Journal of Applied Remote Sensing 2009., 3: doi: 10.1117/1111.3082116 doi: 10.1117/1111.3082116Google Scholar
  44. Asner G: Tropical forest carbon assessment: integrating satellite and airborne mapping. Environmental Research Letters 2009., 4: doi:10.1088/1748–9326/1084/1083/034009 doi:10.1088/1748-9326/1084/1083/034009Google Scholar
  45. Baker TR, Phillips OL, Malhi Y, Almeida S, Arroyo L, Di Fiore A, Erwin T, Higuchi N, Killeen TJ, Laurance SG, Laurance WF, Lewis SL, Monteagudo A, Neill DA, Vargas PN, Pitman NCA, Silva JNM, Martinez RV: Increasing biomass in Amazon forest plots. Philisophical Transactions of the Royal Society of London B 2004, 359: 353–365. 10.1098/rstb.2003.1422View ArticleGoogle Scholar
  46. Haugaasen T, Barlow J, Peres CA: Surface wildfires in central Amazonia: short-term impact on forest structure and carbon loss. Forest Ecology and Management 2003, 179: 321–331. 10.1016/S0378-1127(02)00548-0View ArticleGoogle Scholar
  47. Asner GP, Keller M, Pereira R, Zweede JC, Silva JN: Canopy damage and recovery following selective logging in an Amazon forest: Integrating field and satellite studies. Ecological Applications 2004, 14: 280–298. 10.1890/01-6019View ArticleGoogle Scholar
  48. Morton DC, DeFries RS, Nagol J, Souza CM Jr, Kasischke ES, Hurtt GC, Dubayah R: Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sensing of Environment 2011, 115: 1706–1720. 10.1016/j.rse.2011.03.002View ArticleGoogle Scholar
  49. Asner GP, Broadbent EN, Oliveira PJC, Keller M, Knapp DE, Silva JN: Condition and fate of logged forests in the Brazilian Amazon. Proceedings of the National Academy of Sciences 2006, 103: 12947–12950. 10.1073/pnas.0604093103View ArticleGoogle Scholar
  50. Kennedy RE, Cohen WB, Schroeder TA: Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment 2007, 110: 370–386. 10.1016/j.rse.2007.03.010View ArticleGoogle Scholar
  51. Lula da Silva LI, Mantega G, Rossi WG, Jorge M, Zimmermann MP, Rezende SM, Teixeira IMV: Presidential Decree No. 7,390 (Decreto N° 7.390, de 9 de dezembro de 2010). 2010.http://www6.senado.gov.br/ available atGoogle Scholar
  52. UNFCCC: Report of the Conference of the Parties on its seventh session, held at Marrakesh from 29 October to 10 November 2001. UNFCCC; Marrakesh, Morrocco 2001.Google Scholar
  53. IPCC: IPCC Report on Definitions and Methodological Options to Inventory Emissions from Direct Human-induced Degradation of Forests and Devegetation of Other Vegetation Types. In Edited by: Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D, Ipiatti R, Buendia L, Miwa K, T N. 2003, 30. Intergovernmental Panel on Climate Change (IPCC); 30Google Scholar
  54. DNPM: Projeto RADAMBRASIL: Levantamento de Recursos Naturais, vols. 1–23, Ministério das Minas e Energia, Departamento Nacional de Produção Mineral. Rio de Janeiro, Brazil; 1973–1983.Google Scholar
  55. Hansen MC, DeFries RS, Townshend J, Sohlberg R, Dimiceli C, Carroll M: Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment 2002, 83: 303–319. 10.1016/S0034-4257(02)00079-2View ArticleGoogle Scholar
  56. Skole DL, Tucker C: Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988. Science 1993, 260: 1905–1910. 10.1126/science.260.5116.1905View ArticleGoogle Scholar
  57. FAO: Global forest resources assessment 2005: Progress towards sustainable forest managment. Rome: Food and Agriculture Organization of the United Nations; 2006.Google Scholar
  58. Câmara G, Valeriano DM, Soares JV: Metodologio para o cálculo da taxa anual de desmatamento na Amazônia Legal. São José dos Campos, SP, Brazil: Instituto Nacional de Pesquisas Espaciais (INPE); 2005:22. 22 22Google Scholar
  59. Costa A, Souza CM Jr: Comparação entre imagens Landsat ETM+ e MODIS/Terra para detecção de incrementos de desmatamento na região do Baixo Acre. Revista Brasileira de Cartografia 2005., 52: No. 2 No. 2Google Scholar
  60. INPE: Sistema DETER: Detecção de Desmatamento em Tempo Real. Instituto Nacional de Pesquisas Espaciais; 2006.Google Scholar
  61. Souza CM Jr, Roberts DA, Cochrane MA: Combining spectral and spatial information to map canopy damage from selective logging and forest fires. Remote Sensing of Environment 2005, 98: 329–343. 10.1016/j.rse.2005.07.013View ArticleGoogle Scholar
  62. Matricardi EAT, Skole DL, Cochrane M, Qi J, Chomentowski WH: Monitoring selective logging in tropical evergreen forests using Landsat: mutlitemporal regional analyses in Mato Grosso, Brazil. Earth Interactions 2005, 9: 1–24.View ArticleGoogle Scholar
  63. Alencar A, Nepstad DC, Vera Diaz MdC: Forest understory fire in the Brazilian Amazon in ENSO and non-ENSO years: Area burned and committed carbon emissions. Earth Interactions 2006, 10: Paper 10–006.View ArticleGoogle Scholar
  64. Matricardi EAT, Skole DL, Pedlowski MA, Chomentowski W, Feranandes LC: Assessment of tropical forest degradation by selective logging and fire using landsat imagery. Remote Sensing of Environment 2010, 114: 1117–1129. 10.1016/j.rse.2010.01.001View ArticleGoogle Scholar
  65. Fearnside PM: Amazonian deforestation and global warming: carbon stocks in vegetation replacing Brazil's Amazon forest. Forest Ecology and Management 1996, 80: 21–34. 10.1016/0378-1127(95)03647-4View ArticleGoogle Scholar
  66. Morton DC, Shimabukuro YE, Freitas R, Arai E, DeFries RS: Secondary forest dynamics and Cerradão loss in Mato Grosso during 2001–2005 from MODIS phenology time series. XIII Simpósio Brasileiro de Sensoriamento Remoto; Florianopolis, SC Brasil 2007, 8.Google Scholar
  67. Carreiras JMB, Pereira JMC, Campagnolo ML, Shimabukuro YE: Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sensing of Environment 2006, 101: 283–298. 10.1016/j.rse.2005.12.017View ArticleGoogle Scholar
  68. Almeida C: Estimative da área e do tempo de permanência da vegetação secundária na Amazônia Legal por meio de imagens Landsat/TM. Institudo Nacional de Pesquisas Espaciais (INPE); 2008.Google Scholar
  69. Lucas RM, Honzak M, Curran PJ, Foody GM, Milne R, Brown T, Amaral S: Mapping the regional extent of tropical forest regeneration stages in the Brazilian Legal Amazon using NOAA AVHRR data. International Journal of Remote Sensing 2000, 21: 2855–2881. 10.1080/01431160050121285View ArticleGoogle Scholar
  70. Houghton RA, Lawrence KT, Hackler JL, Brown S: The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 2001, 7: 731–746. 10.1046/j.1365-2486.2001.00426.xView ArticleGoogle Scholar
  71. Malhi Y, Phillips OL, Lloyd J, Baker TR, Wright J, Arroyo AS, Frederiksen T, Grace J, Higuchi N, Killeen TJ, Laurance WF, Leano C, Lewis P, Meir P, Monteagudo A, Neill DA, Nunez Vargas P, Panfil S, Patino S, Pitman N, Quesada CA, Rudas A, Salomão R, Saleska SR, Silva JNM, Silveira M, Sombroek W, Valencia R, Vasquez Martinez R, Vieira IC, et al.: An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR). Journal of Vegetation Science 2002, 13: 439–450. 10.1111/j.1654-1103.2002.tb02068.xView ArticleGoogle Scholar
  72. Chave J, Andalo C, Brown S, Cairns M, Chambers JQ, Eamus D, Folster H, Fromard F, Higuchi N, Kira T, Lescure J, BW N, Ogawa H, Puig H, Riera B, Yamakura T: Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 2005, 145: 87–99. 10.1007/s00442-005-0100-xView ArticleGoogle Scholar
  73. Nogueira EM, Fearnside PM, Nelson BW, Barbosa RI, Keizer EW: Estimates of forest biomass in the Brazilian Amazon: New allometric equations and adjustments to biomass from wood-volume inventories. Forest Ecology and Management 2008, 256: 1853–1867. 10.1016/j.foreco.2008.07.022View ArticleGoogle Scholar
  74. Malhi Y, Wood D, Baker TR, Wright J, Phillips OL, Cochrane T, Meir P, Chave J, Killeen TJ, Laurance SG, Laurance WF, Vargas PN, Pitman NCA, Quesada CA, Salomão R, Silva JN, Lezama AT, Terborgh J, Martínez RV, Vinceti B: The regional variation of aboveground live biomass in old-growth Amazonian forests. Global Change Biology 2006, 12: 1–32. 10.1111/j.1365-2486.2005.001080.xView ArticleGoogle Scholar
  75. Saatchi SS, Houghton RA, Dos Santos Alvalá R, Soares JV, Yu Y: Distribution of aboveground live biomass in the Amazon basin. Global Change Biology 2007, 13: 816–837. 10.1111/j.1365-2486.2007.01323.xView ArticleGoogle Scholar
  76. Sales MH, Souza CM Jr, Kyriakidis PC, Roberts DA, Vidal E: Improving spatial distribution estimation of forest biomass with geostatistics: A case study for Rondônia, Brazil. Ecological Modelling 2007, 2005: 221–230.View ArticleGoogle Scholar
  77. Fisher JI, Hurtt GC, Thomas RQ, Chambers JQ: Clustered disturbances lead to bias in large-scale estimates based on forest sample plots. Ecology Letters 2008, 11: 554–563. 10.1111/j.1461-0248.2008.01169.xView ArticleGoogle Scholar
  78. Hurtt GC, Frolking SE, Fearon MG, Moore B, Shevliakova E, Malyshev S, Pacala SW, Houghton RA: The underpinnings of land-use history: three centuries of global gridded land-use transitions, wood-harvest activity, and resulting secondary lands. Global Change Biology 2006, 12: 1–22. 10.1111/j.1365-2486.2005.001080.xView ArticleGoogle Scholar
  79. Lefsky MA, Harding DL, Keller M, Cohen WB, Carabajal CC, Espirito-Santo FdB, Hunter MO, de Oliveira R Jr: Estimates of forest canopy height and aboveground biomass unsing ICESat. Geophysical Research Letters 2005, 32: LS22S02.View ArticleGoogle Scholar
  80. Ruesch A, Gibbs HK: New IPCC Tier1 Global Biomass Carbon Map For the Year 2000, Available online from the Carbon Dioxide Information Analysis Center.Oak Ridge National Laboratory, Oak Ridge, Tennessee; 2008. [http://cdiac.ornl.gov]Google Scholar
  81. Brown S, Lugo A: Aboveground biomass estimates for tropical forests of the Brazilian Amazon. Interciência 1992, 17: 8–18.Google Scholar
  82. Gibbs HK, Brown S, Niles JO, Foley JA: Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2007., 2: doi:10.1088/1748–9326/1082/1084/045023 doi:10.1088/1748-9326/1082/1084/045023Google Scholar

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