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Achieving forest carbon information with higher certainty: A five-part plan

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Abstract

International negotiations on the inclusion of land use activities into an emissions reduction system for the UN Framework Convention on Climate Change (UNFCCC) have been partially hindered by the technical challenges of measuring, reporting, and verifying greenhouse gas (GHG) emissions and the policy issues of leakage, additionality, and permanence. This paper outlines a five-part plan for estimating forest carbon stocks and emissions with the accuracy and certainty needed to support a policy for Reducing Emissions from Deforestation and forest Degradation, forest conservation, sustainable management of forests, and enhancement of forest carbon stocks (the REDD-plus framework considered at the UNFCCC COP-15) in developing countries. The plan is aimed at UNFCCC non-Annex 1 developing countries, but the principles outlined are also applicable to developed (Annex 1) countries. The parts of the plan are: (1) Expand the number of national forest carbon Measuring, Reporting, and Verification (MRV) systems with a priority on tropical developing countries; (2) Implement continuous global forest carbon assessments through the network of national systems; (3) Achieve commitments from national space agencies for the necessary satellite data; (4) Establish agreed-on standards and independent verification processes to ensure robust reporting; and (5) Enhance coordination among international and multilateral organizations.

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... Such estimates will help to determine a potential C gain or loss caused by the conversion between forest and steppe. Furthermore, estimating C stocks is an important initial step for implementing Reduce Emissions from Deforestation and forest Degradation (REDD) programs, which aim to evaluate the economic value of forests as well as environmental values such as C sequestration capacity (Defries et al. 2007;Baker et al. 2010). ...
... Data of total aboveground plant biomass of the steppe was obtained from the previous study conducted in the Dalbay valley (Ariuntsetseg 2003 ...
... Therefore, the real C content in the forest would be much greater than the current estimation. The total aboveground plant biomass of the steppe measured in 2003 was 7.2 ± 0.06 Mg ha -1 for the lower slope and 9.2 ± 0.07 Mg ha -1 for the upper slope (Ariuntsetseg 2003). Using the same value of conversion factor of dry biomass to C content (0.5), steppe contained 3.6-4.6 ...
Thesis
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Northern Mongolia currently sequesters 31 Tg C yr-1 but it may become a carbon source if respiration rates increase due to climate change and overgrazing, or if projected boundary shifts between forest and steppe cause a change in the carbon storage of ecosystems. The objectives of the thesis are to study soil ecosystem response to simulated climate change and grazing, and to assess C stocks in the steppe and forest. Open-top chambers (OTCs) have been frequently used for simulating climate change. However, the pattern of temperature increase by OTCs contradicted the IPCC predictions. An alternative method, open-sided chambers (OSCs), was evaluated based on its effects on abiotic and biotic factors. The results indicated that OSCs manipulated air temperature in a pattern that was predicted by IPCC models, but the overall effect was too small, hence it is not an optimal device. In the subsequent study, OTCs were used to study soil respiration response to experimental warming in three ecosystems. Temperature increase by OTCs had no effect on soil respiration in the steppe but increased soil respiration in the forest (by 0.20 g CO2 m-2 h-1), demonstrating the importance of ecosystem setting. Although warming increased soil respiration, it decreased its temperature sensitivity as well (Q10 = 5.82 in control versus 2.22 in OTC). In addition to OTCs, watering and grazing effects on CO2 effluxes (ecosystem and soil respiration) were studied across the topographical gradients in the steppe. Our results show a robust, positive effect of soil moisture on CO2 effluxes across topography, and the contrasting effects of grazing on CO2 effluxes. Interactive effects of the treatments were minimal. Soil carbon of the forest was the same (8.3 kg C m-2) as the steppe (8.1 kg C m-2) but aboveground carbon in the forest (2.9 kg C m-2) was 3-7 times greater than that in the steppe. In summary, the results show that warming will slightly increase soil respiration in the forest, but in steppe precipitation will have stronger effect on CO2 flux than temperature change. The results also indicated that overgrazing and deforestation could trigger a greater loss of carbon
... Since developing countries could receive up to $20 billion a year in compensation for reducing their forest emissions the UNFCCC [32] recognized that any reported reductions should be "measurable, reportable and verifiable". Consequently, a Measurement, Reporting and Verification (MRV) centre should be established in each country [13]. REDD+ Readiness schemes have been implemented with support from international and national agencies to raise existing national forest monitoring capacities to meet MRV requirements [14]. ...
... The UAF is applied to evaluate government reports submitted to the UNFCCC by twelve developing countries-Brazil, Cambodia, Democratic Republic of the Congo, Costa Rica, Ghana, Indonesia, Laos, Malaysia, Mexico, Nigeria, Peru and Tanzania-which cover all three tropical regions and contain half of total tropical forest area [53]. From the uncertainties associated with estimates of forest carbon fluxes in these reports may be inferred the quality of the Earth observation systems that produced them, in this case, government Measurement, Reporting and Verification (MRV) centres [13]. These reports include: [74], Nigeria [75], Peru [76] and Tanzania [77] ( Tables S3 and S4). ...
Article
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The magnitude of net carbon dioxide emissions resulting from global forest carbon change, and hence the contribution of forests to global climate change, is highly uncertain, owing to the lack of direct measurement by Earth observation and ground data collection. This paper uses a new method to evaluate this uncertainty with greater precision than before. Sources of uncertainty are divided into conceptualization and measurement categories and distributed between the spatial, vertical and temporal dimensions of Earth observation. The method is applied to Forest Reference Emission Level (FREL) reports and National Greenhouse Gas Inventories (NGGIs) submitted to the UN Framework Convention on Climate Change (UNFCCC) by 12 countries containing half of tropical forest area. The two sets of estimates are typical of those to be submitted to the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism of the UNFCCC and the 2023 Global Stocktake of its Paris Agreement, respectively. Assembling the Uncertainty Fingerprint of each estimate shows that Uncertainty Scores are between 10 and 14 for the NGGIs and 5 and 10 for the FREL reports, and so both exceed the threshold of 2 when it is advisable to evaluate uncertainty by standard statistical methods. Conceptualization uncertainties account for 60% of all uncertainties in the NGGIs and 47% in the FREL reports, e.g., there is incomplete coverage of forest carbon fluxes, and limited disaggregation of fluxes between different ecosystem types and forest carbon pools. Of the measurement uncertainties, all FREL reports base forest area estimates on at least medium resolution satellite data, compared with only 3 NGGIs; after REDD+ Readiness schemes, mean area mapping frequency has fallen to 2.3 years in Latin America and 3.0 years in Asia, but only 8.3 years in Africa; and carbon density estimates are based on national forest inventory data in all FREL reports but only 4 NGGIs. The effectiveness of the Global Stocktake and REDD+ monitoring will therefore be constrained by considerable uncertainties, and to reduce these requires a new phase of REDD+ Readiness to ensure more frequent national forest inventories and forest carbon mapping.
... Estimating carbon flux due to afforestation, deforestation, and forest degradation requires quantifying above-ground biomass (AGB), especially over extensive areas of old-growth tropical forests which have high but varied carbon stocks and are threatened by a rapidly changing land-use dynamics in many countries [1]. Precise mapping of AGB in tropical rainforest is a thus major challenge for the success of REDD+ processes [2]. The objectives set by international organizations are very ambitious but they are faced the inability of many tropical countries to produce accurate maps of AGB [3]. ...
... use the tier 1 default value proposed by IPCC-Intergovernmental Panel on Climate Change), whereas precise estimates based on specific spatial data are required (i.e. IPCC tier 2 and tier 3 methods [2]). ...
Article
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Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorre-lation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak auto-correlation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate " wall-to-wall " remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.
... Many publications provide guidance on monitoring carbon benefits from forestry activities. These include: Baker et al. (2010); Diaz and Delaney (2011); FAO (2013); GOFC-GOLD (2011); Harris et al. (2012); Herold and Johns (2007); Herold and Skutsch (2011);Hodgman et al. (2012); MacDicken (1997); Muraya and Baraka (2010); Pearson et al. (2005aPearson et al. ( , 2005bPearson et al. ( , 2007Pearson et al. ( , 2012 including UNDP and the World Bank, started a project costing in excess of US$10 million aimed at providing a cost-effective, user-friendly and scientifically rigorous methodology for modelling, measuring and monitoring carbon and GHG mitigation benefits in projects dealing with natural resources in all climate zones and land-use systems. ...
... Several documents provide guidance on monitoring carbon benefits from forestry activities (Baker et al. 2010;Diaz and Delaney 2011;FAO 2013;Harris et al. 2012;Herold and Skutsch 2011;Hodgman et al. 2012;Herold and Johns 2007;Muraya and Baraka 2010;Pearson et al. 2005aPearson et al. , 2005bPearson et al. , 2012Pearson et al. , 2007Petrokofsky et al. 2012;Ravindranath and Ostwald 2007;Rombold 2003;UN-REDD Programme 2013a;Walker et al. 2012;Watson 2009;Zhang et al. 2012). This guide builds on this existing knowledge and proposes an approach that is in line with existing UNFCCC decisions. ...
Book
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ITTO Projects is to provide basic knowledge and techniques on the quantification of carbon benefits in forest-related projects. The guide will help forest managers to: • calculate the potential carbon benefits of their projects; • determine which existing climate-change mitigation framework to use; and • understand the specific requirements and challenges of the various frameworks and accounting mechanisms. The guide also sets out a method for the voluntary monitoring and reporting of carbon benefits arising specifically from ITTO projects. It provides added value to existing technical guidance on accounting for carbon benefits by offering a comparison of existing accounting mechanisms.
... Future changes in the underlying mechanisms that affect the production of biomass may increase, decrease, or reverse the current terrestrial sink (Houghton 2007). (Baker et al. 2010). The significant uncertainties related to global biomass must ultimately be addressed by improving estimates at the national level (Baker et al. 2010). ...
... (Baker et al. 2010). The significant uncertainties related to global biomass must ultimately be addressed by improving estimates at the national level (Baker et al. 2010). Improving national estimates necessitates addressing identified critical gaps in biomass estimation relevant to the specific country. ...
Thesis
Accurate estimates of tree biomass are necessary in order to realize climate change mitigation strategies such as large-scale carbon accounts of sources and sinks through time and biomass stocks for bioenergy. Biomass is also an important surrogate to evaluate the status of biodiversity, freshwater, and soil resources. Improving the estimation of biomass for each of these purposes begins with improving the estimate at the level of the individual tree and ends with that estimate scaled-up to the appropriate scale. This thesis sought to address specific knowledge gaps related to biomass estimation in Norway by improving individual tree biomass estimation through four peer-reviewed papers. In Paper I, single-tree allometric birch biomass functions were derived for total aboveground and component biomass. In Paper II, single-tree allometric birch biomass functions were derived for belowground and whole tree biomass. In Paper III, the uncertainty due to the vertical variation in dry weight to fresh weight ratio on the national birch stem biomass stock estimate was estimated for the first time. In Paper IV, extracted root system volume and 3D structure was estimated with a terrestrial laser scanner and quantitative structure modeling cylinder fitting. The derived allometric functions from Papers I and II are the best available for estimating birch biomass stock and stock change in Norway. The uncertainty due to the vertical variation in dry weight to fresh weight ratio from Paper III had a minimal effect on the national stem biomass estimate, but should be considered in future national biomass uncertainty estimates. Scanned root systems reconstructed with quantitative structure models provided accurate root volume estimates and 3D root system structure. The four papers have effectively improved biomass estimation in Norway and could be used to improve biomass estimation elsewhere.
... Adicionalmente, a estas conversaciones, deben realizarse estimaciones precisas para aumentar la exactitud en diferentes temáticas: identificar con mayor certidumbre las zonas de cambios en la cubierta vegetal, mayor precisión en el cálculo de la existencia inicial de carbono, su crecimiento y en las pérdidas por degradación, entre otros procesos (Houghton 2005, Baker et al., 2010. Estas estimaciones, se apoyan en la creciente disponibilidad de datos e información mundial (Ramankutty et al., 2006) como los datos procedentes de imágenes de satélite, que combinándolos con medidas de campo, proporcionan un elemento clave, en la determinación de la pérdida de la cubierta forestal y en consecuencia, en las estimaciones de carbono. ...
Conference Paper
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In this research, a comparative analysis of the methods used nowadays to monitor deforestation nationally, regionally, and globally has been done. The goal is to know which one of them adapts better to the tropical environments of Venezuela, and recommend its use in this ecosystem both in the assessment of forest cover loss and the estimation of CO2 emissions, framed into REDD+ strategies. For this study, Caparo Forest Reserve has been selected. This is a low land forests area, with a high dynamic of loss processes. The comparative analysis used multispectral data from Landsat ETM+ 2007 and 2009, and was validated using panchromatic data from SPOT 4 and 5. The results show that forest mapping in 2007 and 2009 was better classified using FRA-RSS - TREES 3 method (Global Precision (GP) of 87.7% and kappa index of 0.72). The second best was CLASlite method (GP 85.3% and kappa 0.70), then PRODES (GP 84.9% and 0.69 kappa), and FSI (GP 84.5% and 0.67 kappa). However, in the validation of deforestation mapping PRODES method gave the best results both in the confusion matrix, and in the linear regression analysis (GP 88.82% and kappa 0.32, 0.49 R2).
... For example, one of the major challenges for successful implementation of the international forest carbon policy-reducing emissions from deforestation and forest degradation (REDD+), and through conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries-is to estimate national-level net changes in carbon stocks that would occur without policy implementation (De Sy et al. 2012). Remotely sensed data can potentially address this challenge by providing a means to estimate baseline forest conditions in a spatially explicit manner, departures from which can be employed to assess current and historical trends of deforestation and forest degradation (Baker et al. 2010). ...
Article
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Unique among earth observation programs, the Landsat program has provided continuous earth observation data for the past 41 years. Landsat data are systematically collected and archived following a global acquisition strategy. The provision of robust data products for free since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat time series (LTS) for multitemporal characterizations. The science and applications capacity has developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of particular relevance to management and scientific activities is ensured in the short term. In particular, forest monitoring benefits from LTS, whereby a baseline of conditions can be interrogated for both abrupt and gradual changes and attributed to different drivers. Such benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, as well as sophisticated image processing approaches. In this review, we present the status of remote sensing of forests and forest dynamics using LTS, including issues related to the sensors, data availability, data preprocessing, variables used in LTS, analysis approaches, and validation issues.
... For measuring activity data, there is general consensus that satellite remote sensing is the most practical way to establish baseline deforestation rates against which future rates of change can be monitored (Achard et al 2010, Baker et al 2010. Many developing countries lack the resources and expertise to use remote sensing to evaluate forest extent and change, and some do not have national land-cover map available, so widespread technical capacity building efforts are needed. ...
Article
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Countries participating in climate change mitigation via the United Nations Framework Convention on Climate Change reducing emissions from deforestation and forest degradation mechanism are required to establish national forest monitoring systems. The design of national forest monitoring system includes provision of transparent, consistent and accurate estimates of emissions and removals from forests, while also taking into account national circumstances and capabilities. One key component of these systems lies in satellite remote sensing approaches and techniques to determine baseline data on forest loss against which future rates of change can be evaluated. Advances in approaches meeting these criteria for measuring, reporting and verification purposes are therefore of tremendous interest. A robust example advancing such approaches, focused on Peru, is provided in the recent paper of Potapov et al (2014 Environ. Res. Lett. 9 124012).
... More direct methods to measure changes in forest carbon at different scales continue to be developed using a variety of remote sensing technologies, including combinations of optical, light detection and ranging (LiDAR), and radar data coupled with in situ field measurements [6,[9][10][11][12]. LiDAR provides the best estimates of above-ground biomass [13,14] but is not available with 'wall-to-wall' coverage over large areas. ...
Article
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Emissions of carbon from tropical deforestation and degradation currently account for 12-15% of total anthropogenic carbon emissions each year, and Reducing Emissions from Deforestation and Forest Degradation (REDD; including REDD+) is poised to be the primary international mechanism with the potential to reduce these emissions. This article provides a brief summary of the scientific research that led to REDD, and that continues to help refine and resolve issues of effectiveness, efficiency and equitability for a REDD mechanism. However, REDD deals only with tropical forests and there are other regions, ecosystems and processes that govern the sources and sinks of carbon in terrestrial ecosystems. Ongoing research will reveal which of these other flows of carbon are most important, and which of them might present further opportunities to reduce emissions (or enhance sinks) through environmental policy mechanisms, as well as how they might do this.
... Robust systems for measuring, assessing, and reporting key forest parameters, e.g. biomass, carbon, are needed to define adequate management practices and policies to address the challenge of sustainable management of forest resources and to strengthen forest-based climate change mitigation (Baker et al., 2010;Bernier and Schoene, 2009;Liu and Han, 2009;Thürig and Kaufmann, 2010). A spaceborne lidar that acquires samples of vegetation height and canopy closure measurements, used alone or in combination with optical and radar imagery, appears as the most promising way to estimate aboveground forest biomass and carbon at a global scale. ...
Article
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Assessing forest aboveground biomass at global scale is crucial to address the challenge of sustainable management of forest resources and to strengthen forest-based climate change mitigation. To achieve this goal relying on spaceborne lidar missions is acknowledged to be a highly relevant solution. However, if this is taken as a given from the measurement point of view, the premise that spaceborne observation is the most suitable solution to provide information for sustainable management of forest resources is worth discussing. In this paper we suggest to take a fresh look at measurement processes designed to support the monitoring of Earth resources. We discuss the sustainability of Earth observation from space considering (1) issues that call into question the assumption that Earth-orbiting platform will always be available to the civilian remote sensing community and (2) issues concerning environmental impacts of space activity on the Earth. This leads us to suggest some actions that could help to design future observation systems in a more sustainable way in order to strengthen the capacity of measurement processes to meet their stated functional goal, i.e. sustainable management of forest resources.
... As a contribution towards national-level Measurement, Reporting and Verification (MRV) systems and policy, as well as reporting to the UNFCCC, a global carbon observing system will need to have the necessary spatial resolution to accommodate, where possible, the constraints that national governments have in terms of the scales and temporal P. Ciais et al.: Current systematic carbon-cycle observations 3569 aggregation of their emissions estimates (Baker et al., 2010). National governments also need to regularly provide access to dedicated United Nations (UN) review panels and build systems, which are sufficiently transparent in terms of models and observational data. ...
Article
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A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.
... In many countries, however, large uncertainties still remain surrounding the emission estimates that arise from inadequate data on carbon density in local forests and unreliable estimates of national deforestation rates (Baccini et al. 2012). Unfavorable policy environments and technical challenges of measuring, reporting, and verifying GHG emissions from forests also remain critical in many developing countries (Harris et al. 2012;Baker et al. 2010). Developing countries that wish to participate in REDD+ programs need to construct a reference emissions level, which can be used as a benchmark for estimating emission reductions to be achieved by local REDD+ projects (Harris et al. 2012). ...
Article
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In tropical developing countries, reducing emissions from deforestation and forest degradation (REDD+) is becoming an important mechanism for conserving forests and protecting biodiversity. A key prerequisite for any successful REDD+ project, however, is obtaining baseline estimates of carbon in forest ecosystems. Using available published data, we provide here a new and more reliable estimate of carbon in Bangladesh forest ecosystems, along with their geo-spatial distribution. Our study reveals great variability in carbon density in different forests and higher carbon stock in the mangrove ecosystems, followed by in hill forests and in inland Sal (Shorea robusta) forests in the country. Due to its coverage, degraded nature, and diverse stakeholder engagement, the hill forests of Bangladesh can be used to obtain maximum REDD+ benefits. Further research on carbon and biodiversity in under-represented forest ecosystems using a commonly accepted protocol is essential for the establishment of successful REDD+ projects and for the protection of the country’s degraded forests and for addressing declining levels of biodiversity.
... Several studies showed that Tier 1 does not adequately represent national circumstances and may have uncertainties of up to ±70% from the mean (Meridian institute, 2009). At least for significant pools such as AGB and BGB, reporting should be done at higher Tiers which use allometric equations or models that are specific for the biomes and tree species in the country and have lower uncertainties (Wertz-Kanounnikoff et al., 2008;Baker et al., 2010;GOFC-GOLD, 2014). ...
Article
Monitoring of forest cover and forest functions provides information necessary to support policies and decisions to conserve, protect and sustainably manage forests. Especially in the tropics where forests are declining at a rapid rate, national forest monitoring systems capable of reliably estimating forest cover, forest cover change and carbon stock change are of vital importance. As a large number of tropical countries had limited capacity in the past to implement such a system, capacity building efforts are now ongoing to strengthen the technical and political skillsets necessary to implement national forest monitoring at institutional levels. This paper assesses the current status and recent changes in national forest monitoring and reporting capacities in 99 tropical countries, using the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA) 2015 data, complemented with FRA 2010 and FRA 2005 data. Three indicators "Forest area change monitoring and remote sensing capacities", "Forest inventory capacities" and "Carbon pool reporting capacities" were used to assess the countries' capacities for the years 2005, 2010 and 2015 and the change in capacities between 2005-2010 and 2010-2015. Forest area change monitoring and remote sensing capacities improved considerably between 2005 and 2015. The total tropical forest area that is monitored with good to very good forest area change monitoring and remote sensing capacities increased from 69% in 2005 to 83% in 2015. This corresponds to 1435. million. ha in 2005 and 1699. million. ha in 2015. This effect is related to more free and open remote sensing data and availability of techniques to improve forest area change monitoring. The total tropical forest area that is monitored with good to very good forest inventory capacities increased from 38% in 2005 to 66% in 2015. This corresponds to 785. million. ha in 2005 and 1350. million. ha in 2015. Carbon pool reporting capacities did not show as much improvement and the majority of countries still report at Tier 1 level. This indicates the need for greater emphasis on producing accurate emission factors at Tier 2 or Tier 3 level and improved greenhouse gases reporting. It is further shown that there was a positive adjustment in the net change in forest area where countries with lower capacities in the past had the tendency to overestimate the area of forest loss. The results emphasized the effectiveness of capacity building programmes (such as those by FAO and REDD+ readiness) but also the need for continued capacity development efforts. It is important for countries to maintain their forest monitoring system and update their inventories on a regular basis. This will further improve accuracy and reliability of data and information on forest resources and will provide countries with the necessary input to refine policies and decisions and to further improve forest management.
... FRAs rely heavily on information supplied by governments in response to FAO questionnaires, and the lack of up to date and comprehensive national forest inventories in developing countries on which these responses are based has raised concerns about the accuracy of the resulting statistics on forest area change (Grainger, 2008). It has also led to proposals for improving global forest monitoring for REDD+ by making better use of satellite images (Baker et al., 2010;Grainger and Obersteiner, 2011). ...
Article
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The area of land covered by forest and trees is an important indicator of environmental condition. This study presents and analyses results from the Global Forest Resources Assessment 2015 (FRA 2015) of the Food and Agriculture Organization of the United Nations. FRA 2015 was based on responses to surveys by individual countries using a common reporting framework, agreed definitions and reporting standards. Results indicated that total forest area declined by 3%, from 4128 M ha in 1990 to 3999 M ha in 2015. The annual rate of net forest loss halved from 7.3 M ha y−1 in the 1990s to 3.3 M ha y−1 between 2010 and 2015. Natural forest area declined from 3961 M ha to 3721 M ha between 1990 and 2015, while planted forest (including rubber plantations) increased from 168 M ha to 278 M ha. From 2010 to 2015, tropical forest area declined at a rate of 5.5 M ha y−1 – only 58% of the rate in the 1990s – while temperate forest area expanded at a rate of 2.2 M ha y−1. Boreal and sub-tropical forest areas showed little net change. Forest area expanded in Europe, North America, the Caribbean, East Asia, and Western-Central Asia, but declined in Central America, South America, South and Southeast Asia and all three regions in Africa. Analysis indicates that, between 1990 and 2015, 13 tropical countries may have either passed through their forest transitions from net forest loss to net forest expansion, or continued along the path of forest expansion that follows these transitions. Comparing FRA 2015 statistics with the findings of global and pan-tropical remote-sensing forest area surveys was challenging, due to differences in assessment periods, the definitions of forest and remote sensing methods. More investment in national and global forest monitoring is needed to provide better support for international initiatives to increase sustainable forest management and reduce forest loss, particularly in tropical countries.
... Thus, it is necessary to use satellite data to examine the trend of greenhouse gases over Peninsular Malaysia. Satellite remote sensing is one of the most effective approaches to monitor the distributions of greenhouse gases in global scale with very high spatial temporal resolution (Baker et al., 2010). It provides an alternative way to evaluate the influence of human anthropogenic activity on the climate change. ...
Article
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Carbon dioxide (CO<sub align="right"> 2 </sub>) is the most important anthropogenic greenhouse gas contributing to global climate change. The aim of this study was to assess the seasonal variations of CO<sub align="right"> 2 </sub> concentrations in Peninsular Malaysia from January 2003 to December 2009 using level 3 of the CO<sub align="right"> 2 </sub> column WFMD version 2.1 retrieved from SCIAMACHY satellite. The analysis for five locations showed that CO<sub align="right"> 2 </sub> increased by approximately 15 ppm from 2003 to 2009. As the northeastern monsoon prevails, cold air outbreaks from Siberia are high and spread to the equatorial region in the form of northeasterly cold surge winds around the low-level anticyclones over South East Asia. Inversely, the air masses from southwesterly contribute to long range air pollution, including CO<sub align="right"> 2 </sub> due to transportation of pollutants by wind during southwestern monsoon associated with biomass burning in Sumatra, Indonesia.
... Satellite remote sensing is one of the most effective approaches for monitoring the distributions of greenhouse gases on a global scale at very high spatial and temporal resolution (Baker et al., 2010), and provides an alternative for evaluating the influence of human anthropogenic activity on climate change. The free, downloadable data from the satellite scanning imaging absorption spectrometer for atmospheric chartography (SCIAMACHY) onboard the ENVISAT can be used to observe the Earth's greenhouse gas concentrations (Tan et al., 2014b). ...
Article
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This study aims to predict monthly columnar ozone in Peninsular Malaysia based on concentrations of several atmospheric gases. Data pertaining to five atmospheric gases (CO2, O3, CH4, NO2, and H2O vapor) were retrieved by satellite scanning imaging absorption spectrometry for atmospheric chartography from 2003 to 2008 and used to develop a model to predict columnar ozone in Peninsular Malaysia. Analyses of the northeast monsoon (NEM) and the southwest monsoon (SWM) seasons were conducted separately. Based on the Pearson correlation matrices, columnar ozone was negatively correlated with H2O vapor but positively correlated with CO2 and NO2 during both the NEM and SWM seasons from 2003 to 2008. This result was expected because NO2 is a precursor of ozone. Therefore, an increase in columnar ozone concentration is associated with an increase in NO2 but a decrease in H2O vapor. In the NEM season, columnar ozone was negatively correlated with H2O (–0.847), NO2 (0.754), and CO2 (0.477); columnar ozone was also negatively but weakly correlated with CH4 (–0.035). In the SWM season, columnar ozone was highly positively correlated with NO2 (0.855), CO2 (0.572), and CH4 (0.321) and also highly negatively correlated with H2O (–0.832). Both multiple regression and principal component analyses were used to predict the columnar ozone value in Peninsular Malaysia. We obtained the best-fitting regression equations for the columnar ozone data using four independent variables. Our results show approximately the same R value (≈ 0.83) for both the NEM and SWM seasons.
... REDD strategies have emerged to focus on how we can manage the changes in forest practices to avoid and reduce C0 2 emissions (DeFries eta!. 2007, Baker et al. 201 0). Although C sequestration strategies associated with REDD consider a hierarchical structure of decision making (Baker et al. 2010), the importance, impact, and feedback of decisions on interregional C flow is not well developed or understood. In general, regional decision making is usually aimed at the well-being of communities , states, or nations, whereas local decision making is aimed at private progression including strong property rights and livelihood considerations (Tschakert eta!. 2008). ...
Chapter
Terrestrial ecosystems provide a number of key services to society that are linked to carbon (C) cycle processes, a few of which include controlling food and fiber production, basic building materials, energy sources, and soil water holding capacity. Human societies have developed a number of land-use practices to enhance biological C processes and increase the delivery of many ecosystem services. However, some of the modifications have led to unintended degradation of land systems in ways that have reduced the natural capacity of ecosystems to maintain a range of supporting, provisioning, and regulating services. As society strives to sustain key ecosystem services while attempting to meet the challenge of a growing human population and manage for climate change, new and sustainable land-use strategies must play a role. Sustainable management practices – those that maintain the provision of ecosystem services at or from a location – should be a main component of any land-use strategy if we are to successfully deal with global environmental challenges. Society is now demanding much more from land-use systems to achieve multiple goals. Multiple ecosystems services are being required from these systems – to provide food, environments for maintaining biodiversity, and production of energy products, and for preventing pollutants from entering the air and waterways. Developing land-system practices and policies that consider the long-term dynamics of C cycling among competing ecosystem services will provide a framework to develop more sustainable land management.
... The GHGI contains estimates of GHG emissions and CO 2 removals reported in the Common Reporting Format (CRF) tables, and a description of methods and data used in the National Inventory Report (NIR). The quantitative information in these GHGIs represents a fundamental support for any national or international climate policy [1,2], while solutions for land-related estimates in developing countries are searched for [3] or independent estimates are provided [4,5]. ...
Article
Reporting national greenhouses gas inventories for the Land Use, Land Use Change and Forestry (LULUCF) under the United Nations Framework Convention for Climate Change (UNFCCC) has been driving significant development of national data. Lands classification and definitions for C pools are very different across the 28 EU member states, as are the methods used for data collection and processing. Even when definitions or sampling methods were in substance the same, specifications were-different. However, member states' inventories are assumed to be fully comparable and consistent with reporting principles. For forest, data collection rely on forest inventory repeated measurements (both statistical sampling and stand-wise), while for most important contributor, the biomass, there is no preference for “gain-loss” or “stock-change” method. For cropland and grasslands, operational records information is equally important to statistical sampling and aerial photography sources for area estimation, while default emission factors are used less and less as they are being replaced by country-specific data. The obvious trend is the move toward statistical sampling covering all land categories within national territory and slight increasing use of models. Such heterogeneity requires a better harmonization of data collection and processing as to increase the credibility of the EU GHG inventory.
... Para quantificar a biomassa, uma alternativa é utilizar as informações do Inventário Florestal (IF) que permite atender a diversas demandas de informações utilizadas como base para tomada de decisões estratégicas, tanto público quanto privadas (BAKER et al., 2010). E que vão desde por exemplo a análises de apuração de ciclagem de nutrientes (TIAGO, 2014), passando pela mensuração de dados para o setor energético e que, de acordo com Silveira (2010), esses dados colaboram para o desenvolvimento do mercado florestal e pesquisas referentes ao sequestro de carbono. ...
Article
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O setor de florestas plantadas desponta como alternativa viável para mitigação dos gases de efeito estufa. Nesse sentido, a mensuração das estimativas de biomassa e carbono contribuem para amenização dos impactos antrópicos e permite a adaptação sustentável do manejo florestal. Assim, este trabalho objetiva estimar a biomassa e o estoque de carbono em nível acima do solo da vegetação arbórea em floresta plantada de Eucaliptus grandis, localizada entre os municípios de Borebi e Iaras, no Estado de São Paulo. Foi utilizado o Inventário Florestal, de dezembro de 2016, no qual os indivíduos apresentaram idades entre 4 anos e 2 meses a 8 anos, com espaçamento de plantio inicial de 3,8 m x 2,1 m. A área experimental trata-se de seis fazendas, distribuídas em 141 talhões e com 258 pontos amostrais, totalizando 119.498 indivíduos vivos. O conjunto de dados explorado neste trabalho possui como variáveis a altura média da planta, o diâmetro médio da altura do peito, biomassa de volume comercial, e dióxido de carbono equivalente, sendo que as duas últimas foram estimadas por meio de equações alométricas. Para o cálculo da estimativa da biomassa utilizou-se o modelo de regressão de Schumacher & Hall, com fator de expansão de Massa Específica Básica para o gênero Eucaliptus. O crescimento da biomassa no E. grandis pode ser explicado pelo modelo linearizado de Schumacher & Hall com um coeficiente de determinação de 98,94%, evidenciando que este trabalho tem grande potencial para auxiliar no cálculo de estimativa do estoque de biomassa e carbono em floresta plantada.
... To obtain accurate estimates of emissions from land use changes in the tropics several components must be estimated accurately, in particular: the area of forest cover changes, the initial carbon stocks (above and below ground biomass and soil organic matter) in forests before deforestation or forest degradation and the processes of changes in the carbon stocks within forests caused by deforestation, gains from growth and losses from degradation and other processes (Ramankutty et al., 2007). (Baker et al., 2010). Data on forest carbon stocks at both continental and global scales is improving (Saatchi et al., 2011). ...
Chapter
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There are existing and emerging financial incentives for land managers to adopt practices that contribute to climate change mitigation. Maximizing terrestrial carbon sequestration while minimizing greenhouse gas (GHG) emissions, then documenting and rewarding outcomes requires the ability to deliver the following functions: 1. Estimate the total biophysical and feasible potential for terrestrial mitigation (through avoided emissions and sequestration) for all lands; 2. Measure and monitor terrestrial carbon (based on land area and carbon density) for different land classes at multiple scales (and aggregate project-scale and national carbon accounting data to produce global estimates); and 3. Set reference emission and sequestration levels and comply with standards.
... Informal engagement of negotiators with technical experts and other stakeholders has helped move the technical agenda and increase buy-in for REDD. However, capacities to use tools and technical measures as well as the practical aspects of establishing monitoring systems have posed challenges [28]. ...
Article
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Reducing Emissions from Deforestation and Degradation (REDD) has gained significant policy momentum as an international mechanism for global climate change mitigation. The mobilization of funding, technical activity and institutional engagement for REDD has been relatively quick and widespread. The policy and technical lessons learned over the evolution of REDD are not yet widely understood, nor have they been widely integrated into efforts aimed at enabling and incentivizing agricultural mitigation. Within the UN Framework Convention on Climate Change, there are opportunities to include agricultural mitigation through the ad hoc working groups and technical work programs. To create the policy space and operational feasibility necessary for an international mechanism for agricultural mitigation, parallel advancement is needed on developing a shared vision, tackling high-priority analysis, coordinating efforts among stakeholders and getting money to flow from donor governments, foundations and industry.
... Tropical forest ecosystems are an important pool of carbon sink, which partially regulates the exchanges flux of atmospheric CO 2 (Baker et al. 2010, Pechanec et al. 2018). However, the changes of tropical land use impact the biodiversity and this natural carbon cycle (Mendoza-Ponce et al. 2018). ...
Article
Forest ecosystems are important in the carbon storage process. Thus, the objective was to investigate the effectiveness of the Simulated Annealing meta-heuristic analysis for selecting variables to maximize the accuracy of the aboveground carbon prediction at the tree level. We used data from uneven-aged forests located in the Rio Grande Basin - Minas Gerais, Brazil, where 227 trees had their carbon stock measured. The classic Spurr linear model, stepwise linear regression and pan-tropical coverage, Random Forest (RF), and the hybrid SARF method (Simulated Annealing and Random Forest) were used to estimate the carbon stock from the selection of variables for the different compartments of the tree (total, stem, branch, and leaf). The SARF consisted of the metaheuristic to select the variables to be used in the RF. These methods were evaluated by the root mean square error (RMSE), coefficient of determination (R²), and residual graph. As a result, the pan-tropical equation demonstrated superior performance than the Spurr model due to its greater homogeneity of residues. The stepwise technique reduced the number of variables and the error of the estimates, mainly for the validation set. SARF showed better adjustments than RF, as it reduced in on average 99.2% of the number of variables and 9% of the error of estimates considering all compartments. In general, variables such as volume, basic wood density, canopy projection area, diameter at 0%, diameter at breast height, height, and latitude contributed strongly to the carbon independent of the tree compartment. Among the methods, SARF is an alternative to the traditional method, as it can extract accurate information from a large data set.
... Guidelines for accounting for the climate change impacts of biogenic CO 2 emissions are being developed (US Environmental Protection Agency, 2012); however, such initiatives are limited by a lack of appropriate data reflecting the complexity and variability in biomass carbon cycling (e.g. Baker et al., 2010). Improving understanding of the full emissions implications of bioenergy systems, and the associated impact on climate change, is necessary to inform energy policy design. ...
Article
Conventional cost-effectiveness calculations ignore the implications of greenhouse gas (GHG) emissions timing and thus may not properly inform decision-makers in the efficient allocation of resources to mitigate climate change. To begin to address this disconnect with climate change science, we modify the conventional cost-effectiveness approach to account for emissions timing. GHG emissions flows occurring over time are translated into an ‘Equivalent Present Emission’ based on radiative forcing, enabling a comparison of system costs and emissions on a consistent present time basis. We apply this ‘Present Cost-Effectiveness’ method to case studies of biomass-based electricity generation (biomass co-firing with coal, biomass cogeneration) to evaluate implications of forest carbon trade-offs on the cost-effectiveness of emission reductions. Bioenergy production from forest biomass can reduce forest carbon stocks, an immediate emissions source that contributes to atmospheric greenhouse gases. Forest carbon impacts thereby lessen emission reductions in the near-term relative to the assumption of biomass ‘carbon neutrality’, resulting in higher costs of emission reductions when emissions timing is considered. In contrast, conventional cost-effectiveness approaches implicitly evaluate strategies over an infinite analytical time horizon, underestimating nearer term emissions reduction costs and failing to identify pathways that can most efficiently contribute to climate change mitigation objectives over shorter time spans (e.g. up to 100 years). While providing only a simple representation of the climate change implications of emissions timing, the Present Cost-Effectiveness method provides a straightforward approach to assessing the cost-effectiveness of emission reductions associated with any climate change mitigation strategy where future GHG reductions require significant initial capital investment or increase near-term emissions. Timing is a critical factor in determining the attractiveness of any investment; accounting for emissions timing can better inform decisions related to the merit of alternative resource uses to meet near-, mid-, and long-term climate change mitigation objectives.
... Remote sensing techniques have many advantages, in biomass estimation, over traditional field measurement methods and provide the potential to estimate biomass at different scales. However, there is still a need for improving the accuracy of biomass estimates [93,94]. Based on the adjacent time OLI, ETM+ images, and 2014 NFI data, we used the RF model and 10-fold cross validation method to determine the effects of different pretreatments on the uncertainty of biomass estimation using remote sensing techniques implemented with Landsat series satellites (OLI, ETM+). ...
... Remote sensing techniques have many advantages, in biomass estimation, over traditional field measurement methods and provide the potential to estimate biomass at different scales. However, there is still a need for improving the accuracy of biomass estimates [93,94]. Based on the adjacent time OLI, ETM+ images, and 2014 NFI data, we used the RF model and 10-fold cross validation method to determine the effects of different pretreatments on the uncertainty of biomass estimation using remote sensing techniques implemented with Landsat series satellites (OLI, ETM+). ...
Article
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The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM+), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R2) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10-fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM+ images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM+. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM+ increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM+) and terrain correction (1.00% for OLI, 1.67% for ETM+) significantly reduced uncertainty for OLI and ETM+, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM+. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.
... Given the above, this paper describes a dataset that diminishes the knowledge gap on ρ of a considerable amount of species of the subtropical Atlantic Forest, aiming to reduce the uncertainty in forest biomass estimates [18] and in studies on functional ecology [1,13,14], among other demands. The disclosed dataset is composed of three data tables. ...
Article
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Wood density ( ρ ) is a trait involved in forest biomass estimates, forest ecology, prediction of stand stability, wood science, and engineering. Regardless of its importance, data on ρ are scarce for a substantial number of species of the vast Atlantic Forest phytogeographic domain. Given that, the present paper describes a dataset composed of three data tables: (i) determinations of ρ (kg m−3) for 153 species growing in three forest types within the subtropical Atlantic Forest, based on wood samples collected throughout the state of Santa Catarina, southern Brazil; (ii) a list of 719 tree/shrub species observed by a state-level forest inventory and a ρ value assigned to each one of them based on local determinations and on a global database; (iii) the means and standard deviations of ρ for 477 permanent sample plots located in the subtropical Atlantic Forest, covering ∼95,000 km2. The mean ρ over the 153 sampled species is 538.6 kg m−3 (standard deviation = 120.5 kg m−3), and the mean ρ per sample plot, considering the three forest types, is 525.0 kg m−3 (standard error = 1.8 kg m−3). The described dataset has potential to underpin studies on forest biomass, forest ecology, alternative uses of timber resources, as well as to enlarge the coverage of global datasets.
... The prospect of inexpensive emissions reductions and the potential for financial transfers in the region of US$30bn/year to developing countries (the 'development dividend') has meant that REDD has garnered significant international commitment and is now seen as a priority for negotiators (Corbera et al., 2010;Baker et al., 2010). The increasingly broad scope of REDD+ has contributed to significant support for the scheme, with proponents labelling it a 'win-win', capable of delivering a reduction in deforestation and contributing towards sustainable economic development and poverty alleviation. ...
Thesis
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Mineral resources are essential to the functioning and wellbeing of human societies. There is mounting concern, however, about the environmental degradation and social impacts typically resulting from mineral extraction. As a result, the mining industry is increasingly embracing the sustainability agenda, that is, pursuing development which ostensibly balances economic, social and environmental interests. In recent years, escalating anxiety over climate change in particular has propelled forest conservation to the top of the sustainability agenda which, in the case of mining, has increased attention on the loss of forest cover associated with activities, the success of reclamation and the manifold social conflicts often associated with resource-use. The hegemonic neoliberal approach to environmental governance has led to a burgeoning of strategies to manage forests using carbon finance as a conduit for investment. Although these schemes purportedly facilitate the mitigation of carbon emissions on a global scale while simultaneously delivering economic benefits to poor local communities, there is apprehension regarding the prospect of projects being implemented in contexts in which the dynamics of resource-use are not adequately understood. Cross-sectoral issues are among the concerns which have yet to receive sufficient attention. The purpose of this thesis is to broaden understanding of the interactions between the poorly articulated and understood relationship between mining, forests, climate change and development. Using the case of Ghana, where conflicts and trade-offs between mining and forests proliferate, an interdisciplinary and exploratory approach is taken to investigate the impact of mining on forest carbon stocks, survey the perspectives and influence of key stakeholders on mining-forest conflicts, and determine how these cross-sectoral issues are governed. Findings reveal that public and policy discourse on mining in forest areas focuses on formal activities in forest reserves and the relative success of reclamation. An examination of carbon stocks under different land-uses shows that reclamation does not completely restore carbon stocks to levels found in forests, but that it can restore approximately 10% of carbon on decadal timescales. This underscores the limitations of pursuing a purely technocratic approach to policy-making: although science is a necessary component of sound governance it is it not sufficient per se. The results further demonstrate the potential for carbon-finance to support reclamation activities in both the large- and small-scale mining sectors. In addition to factors affecting the reclamation of mine sites in forested areas, there are a host of other cross-sectoral interactions which are important determinants of the outcomes associated with mining and forestry management. The thesis provides a nuanced analysis of the complexity of governance arrangements, highlighting in particular how a combination of private interests and traditional authorities serve to subjugate state sovereignty over natural resources. This is particularly evident at local levels where conflicts between fragmented government institutions, mining companies, traditional authorities and communities are more tangible than at the national level where debates revolve around trade-offs. This has been exacerbated by decentralisation reforms which have, inter alia, marginalised traditional authorities and their central role in resource governance, especially in the small-scale mining sector. Not surprisingly, in light of this analysis, donor-led state-centric interventions have had limited impact on the actual governance of cross-sectoral conflicts. Creating space for, and supporting, the deliberation of foundational governance issues, especially the role of traditional authorities, is essential if nascent carbon-based approaches to forest management are to improve environmental and development outcomes in Ghana and elsewhere in sub-Saharan Africa.
... International obligations require signatory countries to report and monitor greenhouse gas balances from forests [56]. Many carbon models use the same data that is used by forest management agencies and the forest industry to estimate forest carbon for reporting purposes [57]. ...
Article
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Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.
... impacts of subsequent disturbances [4]. Forest biomass maps are also a critical tool for measuring, reporting, and verifying forest carbon stocks [5]. Programs such as Reducing Emissions from Deforestation and forest Degradation (REDD+) and California cap-and-trade seek to mitigate rising greenhouse gas concentrations by storing carbon in forests. ...
Article
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Background: Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation—errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285). Results: Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30–75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25–70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE] = 50%). This is likely due to low-sample size (10–20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg; % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg; % RMSE = 165%). Conclusions: Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.
... Deforestation accounts for 10-25% of global carbon release (Houghton 2005), while regrowth of forest area in the next four decades is potentially able to capture 8.48 Pg of carbon (Chazdon et al. 2016). Furthermore, baseline information on these vegetation cover change in the national level is needed especially in the Monitoring, Reporting, and Verification (MRV) stages to provide the pathway to achieving sustainable development at the national level (Baker et al. 2010). Therefore, it is important to provide an accurate estimation of vegetation cover change. ...
Article
Massive deforestation in Indonesia drives the need for proper monitoring using appropriate technology and method. The continuing mission of Landsat sensor extends the observation to almost 30 years back, initiating the ability to monitor the dynamics of vegetation intensively. By taking the advantage of the Landsat archive, advanced semi-automatic classification method, namely ClasLite developed by Asner et al. (J Appl Remote Sens 3:33543–33543, 2009) and a new end-product of 30 m Global Forest Cover Change cover (GFC) datasets developed by (Hansen et al. in Science 342:850–853, 2013a), offered the ability to easily monitor deforestation and forest degradation with little or few knowledge of mapping. This study aims to assess the performance of these newly available products of GFC and the ClasLite method against the traditional pixel-based supervised classification of minimum distance to mean (MD), maximum likelihood (ML), spectral angle mapper (SAM), and random forest (RF). Visual image interpretation of pan-sharpened Landsat was carried out to measure the accuracy of each final map. Result demonstrated that GFC and CLaslite performance has 3 to 18% higher overall accuracy for mapping vegetation cover change compared with the conventional supervised analysis using MD, ML, SAM, and RF with ClasLite as the most accurate method with 78.14 ± 2%. Further adjustment of the cover change map of GFC by using forest extent from ClasLite was able to increase the accuracy of the original GFC data by 10%. Therefore, GFC and ClasLite ensure the ability to monitor vegetation cover change accurately in a simple manner.
... Los países involucrados con REDD+ (Reducción de las emisiones derivadas de la deforestación, la degradación forestal, la conservación de las reservas forestales de carbono, el manejo forestal sostenible y el mejoramiento de los almacenes de carbono) enfrentan la dificultad de producir mapas precisos de biomasa aérea para reportar estimaciones considerando el nivel 1 sugerido por el Panel Intergubernamental de Cambio Climático (IPCC) (Romijn et al., 2012). Sin embargo, en bosques manejados, se requieren estimaciones precisas que consideren datos espaciales específicos (nivel 2 y nivel 3) (Baker et al., 2010;Guitet et al., 2015). Las principales técnicas de mapeo se basan en: (a) la interpolación espacial; (b) Modelos determinísticos y (c) percepción remota (Guitet et al., 2015). ...
... For example, machine learning techniques have been applied to historical remote sensing datasets of forests and deforestation with high-resolution data from recent years, leading to significant improvements in the proxies for the calculation of above-ground and below-ground biomass, as well as in the identification of correlational signals of forest conservation with concepts of "additionality" (i.e., additional carbon sequestration) and "leakage" (i.e., emissions that result from sequestration efforts) (Meyers, 1999;Baker et al., 2010;Cowie et al., 2012). Such landuse based emissions are frequently contributors to uncertainty in emissions inventories, and the use of EO data has been discussed in policy communities for helping primarily to provide spatiotemporal data with sufficient resolution and enhanced accuracy. ...
Article
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Climate change has been called “the defining challenge of our age” and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.
... The Intergovernmental Panel on Climate Change (IPCC) provides guidance for producing unbiased GHG inventories that are reported in a transparent way, and are complete, comparable, consistent over time, and accurate (collectively, these are referred to as the TCCCA principles) (IPCC 2003(IPCC , 2006a(IPCC , 2013. There has been growing focus on improving the accuracy of LULUCF GHG inventories by investing in data collection, including mapping land cover and land conversion, and measuring corresponding GHG fluxes (Baker et al 2010, GOFC-GOLD 2016. However, the underlying reasons for differences among GHG flux estimates go beyond documented challenges with respect to input data measurement and accuracy (Pongratz et al 2014). ...
Article
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This study examines underlying reasons for differences among land-based greenhouse gas flux estimates in Indonesia, where six national inventories reported average emissions of between 0.4 and 1.1 Gt CO2e yr⁻¹ over the 2000–2012 period. The large range among estimates is only somewhat smaller than Indonesia's GHG mitigation commitment. To determine the reasons for these differences, we compared input data and estimation methods, including the definitions and assumptions used for setting accounting boundaries, including emitting activities, incorporating fluxes from various carbon pools, and handling legacy fluxes. We also tested the sensitivity of methodological differences by generating our own reference emissions estimate and iteratively modifying individual components of the inventory. We found that the largest changes stem from the inclusion of legacy GHG emissions due to peat drainage (which increased emissions by at least +94% compared to the reference), methane emissions due to peat fires (+35%), and GHG emissions from belowground biomass and necromass carbon pools (+61%), modifications to assumptions of the mass of fuel burnt in peat fire events (+88%), and accounting for regrowth following a deforestation event (−31%). These differences cumulatively explain more than half of the observed difference among inventory estimates. Understanding the various approaches to emissions estimation, and how these influence the magnitude of component GHG fluxes, is an important first step towards reconciling GHG inventories. The Indonesian government's success in achieving its mitigation goal will depend on its ability to measure progress and evaluate the effectiveness of abatement actions, for which reliable harmonized greenhouse gas inventories are an essential foundation.
... RS satellite data can address the challenges of assessing forest conditions and evaluating the existing historical trends of forest degradation and deforestation [7]. The information of LU/LC changes is essential for various management and decision-making activities [59]. ...
Article
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The change detection (CD) methods explore the potential of remote sensing (RS) spatial datasets in various land use/land cover (LU/LC) applications. These methods are used to analyze the LU/LC dynamics using various high and medium-resolution multi-spectral remote sensing satellite datasets (Landsat-TM/ETM+/OLI, IRS LISS-3 & 4, Sentinel-2, SPOT, and ASTER). The study’s objective is to summarize multiple changes in the last two decades in land use applications at the regional and international levels using traditional and advance change detection methods. Mapping of LU/LC dynamics at regional and global scales is essential for various land use applications (vegetation monitoring, crop cultivation monitoring, urban planning, landslide, and socio-economic dynamics). The review study showed that machine learning and deep learning techniques play an essential role in classification and change detection applications. The deep learning methods more effectively identify the changes in LU/LC (due to human activities and natural phenomena) than other traditional methods. The present study analyzes the conventional and advanced methods of change detection methods and various challenges and problems facing during the change detection.
... FRAs rely heavily on information supplied by governments in response to FAO question- naires, and the lack of up to date and comprehensive national for- est inventories in developing countries on which these responses are based has raised concerns about the accuracy of the resulting statistics on forest area change (Grainger, 2008). It has also led to proposals for improving global forest monitoring for REDD+ by making better use of satellite images ( Baker et al., 2010;Grainger and Obersteiner, 2011). ...
Article
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... This is highly dependent on available resources, and will vary by country. Just because the technology is available, this does not automatically translate into its operational use [6]. The ideal scenario comprises (1) a commitment from space agencies to systematic acquisition of appropriate and free or low-cost satellite data over all forested areas, (2) the outcomes of R&D to be integrated into training materials and capacity building initiatives, (3) donor support and understanding of the science behind the reporting and what is realistic and achievable, and (4) Government support for sustainable MRV programs and national forest inventory. ...
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Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.
... About 75% of these emissions occur in developing countries where forests are under tremendous pressure due to a variety of social and economic factors. This crucial role of forests in climate change makes it imperative to include forest-related climate actions in international agreements (Baker et al. 2010). ...
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Forests are important carbon pools as they provide pathway to mitigate climate change. Quantification of forest carbon has gained momentum after Paris Agreement in 2015. This information is a prerequisite for REDD+ implementation and carbon trading. Temperate and subtropical mountain systems of Khyber Pakhtunkhwa province host about one third of Pakistan’s 4.51 million ha forests. Present study estimated forest carbon stocks in the Khyber Pakhtunkhwa province of Pakistan. The data was collected from 449 sites in different forests across the province using a stratified cluster sampling technique. Total carbon stock in the forests of the province was estimated at 144.71 million tons with an average of 127.66 ± 9.32 t/ha. Aboveground carbon stock was 68.15 million tons accounting for 48% of the total forest carbon stock of the province. Further, belowground biomass and litter accounted for 10% and 1% respectively. The mean aboveground carbon stock was 59.98 ± 4.26 t/ha. The highest aboveground carbon stock was found in dry temperate forests (99.41 t/ha) followed by moist temperate (85.04 t/ha). Overall, temperate forests have aboveground carbon stock of 90.52 t/ha. Temperate and subtropical forests of Pakistan with high carbon densities have ample potential for reducing forest sector emissions. Therefore, forests of Khyber Pakhtunkhwa province having substantial carbon stocks must be conserved for climate change mitigation. Present study provides a framework for carbon stock assessments in other temperate and subtropical regions of the world.
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A recently completed research program (TREES) employing the global imaging capabilities of Earth-observing satellites provides updated information on the status of the world's humid tropical forest cover. Between 1990 and 1997, 5.8 1.4 million hectares of humid tropical forest were lost each year, with a further 2.3 0.7 million hectares of forest visibly degraded. These figures indicate that the global net rate of change in forest cover for the humid tropics is 23% lower than the generally accepted rate. This result affects the calculation of carbon fluxes in the global budget and means that the terrestrial sink is smaller than previously inferred.
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This paper reviews the technical capabilities for monitoring deforestation from a pan-tropical perspective in response to the United Nations Framework Convention on Climate Change (UNFCCC) process, which is studying the technical issues surrounding the ability to reduce greenhouse gas emissions from deforestation in developing countries. The successful implementation of such policies requires effective forest monitoring systems that are reproducible, provide consistent results, meet standards for mapping accuracy, and can be implemented from national to pan-tropical levels. Remotely sensed data, supported by ground observations, are crucial to such efforts. Recent developments in global to regional monitoring of forests can contribute to reducing the uncertainties in estimates of emissions from deforestation. Monitoring systems at national levels in developing countries can also benefit from pan-tropical and regional observations, mainly by identifying hot spots of change and prioritizing areas for monitoring at finer spatial scales. A pan-tropical perspective is also required to ensure consistency between different national monitoring systems. Data sources already exist to determine baseline periods in the 1990s as historical reference points. Key requirements for implementing such monitoring programs, both at pan-tropical and at national scales, are international commitment of resources to increase capacity, coordination of observations to ensure pan-tropical coverage, access to free or low-cost data, and standardized, consensus protocols for data interpretation and analysis.
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Large-scale carbon mapping is needed to support the UNFCCC program to reduce deforestation and forest degradation (REDD). Managers of forested land can potentially increase their carbon credits via detailed monitoring of forest cover, loss and gain (hectares), and periodic estimates of changes in forest carbon density (tons ha−1). Satellites provide an opportunity to monitor changes in forest carbon caused by deforestation and degradation, but only after initial carbon densities have been assessed. New airborne approaches, especially light detection and ranging (LiDAR), provide a means to estimate forest carbon density over large areas, which greatly assists in the development of practical baselines. Here I present an integrated satellite–airborne mapping approach that supports high-resolution carbon stock assessment and monitoring in tropical forest regions. The approach yields a spatially resolved, regional state-of-the-forest carbon baseline, followed by high-resolution monitoring of forest cover and disturbance to estimate carbon emissions. Rapid advances and decreasing costs in the satellite and airborne mapping sectors are already making high-resolution carbon stock and emissions assessments viable anywhere in the world.
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Reducing carbon emissions from deforestation and degradation in developing countries is of central importance in efforts to combat climate change. Key scientific challenges must be addressed to prevent any policy roadblocks. Foremost among the challenges is quantifying nations' carbon emissions from deforestation and forest degradation, which requires information on forest clearing and carbon storage. Here we review a range of methods available to estimate national-level forest carbon stocks in developing countries. While there are no practical methods to directly measure all forest carbon stocks across a country, both ground-based and remote-sensing measurements of forest attributes can be converted into estimates of national carbon stocks using allometric relationships. Here we synthesize, map and update prominent forest biomass carbon databases to create the first complete set of national-level forest carbon stock estimates. These forest carbon estimates expand on the default values recommended by the Intergovernmental Panel on Climate Change's National Greenhouse Gas Inventory Guidelines and provide a range of globally consistent estimates.
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Deforestation and forest degradation account for between 7% and 30% of total anthropogenic carbon emissions [Canadell et al., 2007; Denman et al., 2007]. This wide range of values results from three major uncertainties: rates of deforestation, carbon stocks (biomass and soils) in forests prior to deforestation, and changes in carbon stocks within forests (i.e., both increases from growth and decreases from degradation). Historically, rates of deforestation and reforestation, together with estimates of forest biomass, have been used to calculate the net flux of carbon between terrestrial ecosystems and the atmosphere [Woodwell et al., 1983; Detwiler and Hall, 1988; Hall and Uhlig, 1991; Fearnside, 2000; DeFries et al., 2002; Achard et al., 2004; Houghton, 2003]. This net flux is the difference between the sinks of carbon in growing and recovering forests and the sources from burning and decay associated with deforestation. Satellite imagery, particularly from Landsat, has long been used to sample deforestation rates [DeFries et al., 2002; Achard et al., 2004; Skole and Tucker, 1993; Hansen et al., 2008]. Obtaining estimates of biomass, reforestation, and forest growth and degradation, however, has proven more difficult.
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Forest biomass and its change over time have been measured at both local and large scales, an example for the latter being forest greenhouse gas inventories. Currently used methodologies to obtain stock change estimates for large forest areas are mostly based on forest inventory information as well as various factors, referred to as biomass factors, or biomass equations, which transform diameter, height or volume data into biomass estimates. However, while forest inventories usually apply statistically sound sampling and can provide representative estimates for large forest areas, the biomass factors or equations used are, in most cases, not representative, because they are based on local studies. Moreover, their application is controversial due to the inconsistent or inappropriate use of definitions involved. There is no standardized terminology of the various factors, and the use of terms and definitions is often confusing. The present contribution aims at systematically summarizing the main types of biomass factors (BF) and biomass equations (BE) and providing guidance on how to proceed when selecting, developing and applying proper factors or equations to be used in forest biomass estimation. The contribution builds on the guidance given by the IPCC (Good practice guidance for land use, land-use change and forestry, 2003) and suggests that proper application and reporting of biomass factors and equations and transparent and consistent reporting of forest carbon inventories are needed in both scientific literature and the greenhouse gas inventory reports of countries.
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Efforts to control climate change require the stabilization of atmospheric CO2 concentrations. This can only be achieved through a drastic reduction of global CO2 emissions. Yet fossil fuel emissions increased by 29% between 2000 and 2008, in conjunction with increased contributions from emerging economies, from the production and international trade of goods and services, and from the use of coal as a fuel source. In contrast, emissions from land-use changes were nearly constant. Between 1959 and 2008, 43% of each year's CO2 emissions remained in the atmosphere on average; the rest was absorbed by carbon sinks on land and in the oceans. In the past 50 years, the fraction of CO2 emissions that remains in the atmosphere each year has likely increased, from about 40% to 45%, and models suggest that this trend was caused by a decrease in the uptake of CO2 by the carbon sinks in response to climate change and variability. Changes in the CO2 sinks are highly uncertain, but they could have a significant influence on future atmospheric CO2 levels. It is therefore crucial to reduce the uncertainties.