REDD+ is moving beyond readiness and piloting into real implementation, where measured and verified emission reductions can result in payments to countries or subnational governments [...]

Background and Methods: Degradation of forests in developing countries results from multiple activities and is perceived to be a key source of greenhouse gas emissions, yet there are not reliable methodologies to measure and monitor emissions from all degrading activities. Therefore, there is limited knowledge of the actual extent of emissions from forest degradation. Degradation can be either in the forest interior, with a repeatable defined pattern within areas of forest, as with timber harvest, or on the forest edge and immediately bounding areas of deforestation. Forest edge degradation is especially challenging to capture with remote sensing or to predict from proxy factors. This paper addresses forest edge degradation and: (1) proposes a low cost methodology for assessing forest edge degradation surrounding deforestation; (2) using the method, provides estimates of gross carbon emissions from forest degradation surrounding and caused by alluvial mining in Guyana, and (3) compares emissions from mining degradation with other sources of forest greenhouse gas emissions. To estimate carbon emissions from forest degradation associated with mining in Guyana, 100 m buffers were located around polygons pre-mapped as mining deforestation, and within these buffers rectangular transects were established. Researchers collected ground data to produce estimates of the biomass damaged as a result of mining activities to apply to the buffer area around the mining deforestation. Results: The proposed method to estimate emissions from forest edge degradation was successfully piloted in Guyana, where 61% of the transects lost 10 Mg C ha−1 or less in trees from mining damage and 46% of these transects lost 1 Mg C ha−1 or less. Seventy percent of the damaged stems and 60% of carbon loss occurred in the first 50 m of the transects. The median loss in carbon stock from mining damage was 2.2 Mg C ha−1 (95% confidence interval: 0.0–10.2 Mg C ha−1). The carbon loss from mining degradation represented 1.0% of mean total aboveground carbon stocks, with emissions from mining degradation equivalent to ~2% of all emissions from forest change in Guyana. Conclusions: Gross carbon emissions from forest degradation around mining sites are of little significance regardless of persistence and potential forest recovery. The development of cost- and time-effective buffers around deforestation provides a sound approach to estimating carbon emissions from forest degradation adjacent to deforestation including surrounding mining. This simple approach provides a low-cost method that can be replicated anywhere to derive forest degradation estimates.

Completeness is an important element for Reducing Emissions from Deforestation and forest Degradation (REDD+) accounting to ensure transparency and accountability. However, including a full accounting for all emission sources in a REDD+ program is often resource-intensive and cost-prohibitive, especially considering that some emission sources comprise far less than 10% of total emissions and are thus considered insignificant according to Intergovernmental Panel on Climate Change (IPCC) guidance. This is evident in forest reference emission level (FREL)/forest reference level (FRL) submissions to the United Nations Framework Convention on Climate Change (UNFCCC). Of the 50 countries that have submitted FRELs to date, only half of them include degradation in their FRELs even though degradation is often a significant source of emissions. Half of the countries that do include degradation use satellite imagery without necessarily specifying degrading activities or separating anthropogenic activities. Guyana provides an example of an approach that enables inclusion of all emission sources while considering the significance of each when developing an accounting approach. Since submitting its FREL in 2014, Guyana has made stepwise improvements to its emission estimates so that the country is now able to report on all deforestation and degradation activities resulting in emissions, whether significant or not. Based on the example of Guyana’s efforts, the authors recommend a simple approach to move towards complete accounting in a cost-effective manner. This approach can be scaled to other countries with other activities that results in greenhouse gas emissions from deforestation and forest degradation. Such complete accounting allows for higher accountability in REDD+ systems and can lead to greater effectiveness in reducing emissions.

In the Lower Mekong River Basin (LMB), deforestation rates are some of the highest in the world as land is converted primarily into intensive agriculture and plantations. While this has been a key for the region's economic development, rural populations dependent on the freshwater water resources that support their fishing and agriculture industries are increasingly vulnerable to the impacts of flood, drought and non-point source pollution. Impacts of deforestation on ecosystem services (ES) including hydrological ES that control the availability and quality of fresh water across the landscape, regulating floods and droughts, soil erosion and non-point source pollution are known. Despite this understanding at the hillslope level, few studies have been able to quantify the impact of wide-scale deforestation on larger tropical watersheds. This study introduces a new methodology to quantify the impact of deforestation on water-based ES in the LMB with a focus on Cambodia by combining spatial datasets on forest loss from remote sensing and spatially-explicit hydrological modeling. Numerous global and regional remote sensing products are synthesized to develop detailed land use change maps for 2001 to 2013 for the LMB, which are then used as inputs into a hydrological model to develop unique spatial datasets that map ES changes due to deforestation across the LMB. The results point to a clear correlation between forest loss and surface runoff, with a weaker but upward trending relationship between forest loss and sediment yield. This resulted in increased river discharge for 17 of the 22 watersheds, and increased sediment for all 22 watersheds. While there is considerable variability between watersheds, these results could be helpful for prioritizing interventions to decrease deforestation by highlighting which areas have experienced the greatest change in water-based ES provision. These results are also presented in a web-based platform called the Watershed Ecosystem Service Tool.

Forest landscape restoration (FLR) has been adopted by governments and practitioners across the globe to mitigate and adapt to climate change and restore ecological functions across degraded landscapes. However, the extent to which these activities capture CO2 with associated climate mitigation impacts are poorly known, especially in geographies where data on biomass growth of restored forests are limited or do not exist. To fill this gap, we developed biomass accumulation rates for a set of FLR activities (natural regeneration, planted forests and woodlots, agroforestry, and mangrove restoration) across the globe and global CO2 removal rates with corresponding confidence intervals, grouped by FLR activity and region/climate.

Countries are required to generate baselines of carbon emissions, or Forest Reference Emission Levels, for implementing REDD+ under the United Nations Framework Convention on Climate Change and to access results-based payments. Developing these baselines requires accurate maps of carbon stocks and historical deforestation. Global remote sensing products provide low-cost solutions for this information, but there has been little validation of these products at national scales. This study compares the ability of currently available products obtained from remote sensing data to deliver estimates of deforestation and associated carbon emissions in Guinea-Bissau, a West African country encompassing the climate and vegetation gradients that are typical of sub-Saharan Africa. We show that disagreements in estimates of deforestation are striking, and this variation leads to high uncertainty in derived emissions. For Guinea-Bissau, we suggest that higher temporal resolution of remote sensing products is required to reduce this uncertainty by overcoming current limitations in differentiating deforestation from seasonality. In contrast, existing datasets of carbon stocks show better agreement, and contribute much less to the variation in estimated emissions. We conclude that using global datasets based on Earth Observation data is a cost-effective solution to make REDD+ operational, but deforestation maps in particular should be derived carefully and their uncertainty assessed.

Timber harvest from tropical regions generates seven billion dollars annually in exports and is estimated to occur across 20% of the area of remaining tropical forests. This timber harvesting is estimated to account for more than one in eight of all greenhouse gas emissions from tropical forests. Yet there is currently no means to independently estimate extracted volumes and associated greenhouse gas emissions. In this study, we built upon an earlier paper that used an automated algorithm applied to LiDAR to accurately identify area of timber harvest impact in the categories of roads/decks, skid trails and gaps. This algorithm was applied to 2014 harvest areas in four concessions in Kalimantan, Indonesia. In two of these concessions, total harvested timber volumes and greenhouse gas emissions were measured and calculated in the field using data from 188 harvested and extracted trees. In order to relate remote sensing data with the estimated extracted volumes, we calculated factors that linked extracted timber volumes with greenhouse gas emissions, and applied three different regression equations. The parameters of the most accurate equation were the areas of roads, skid trails and gaps, explaining 87% of the variation in the data. For situations where rivers are used in place of roads for extracting timber and for instances of non-mechanized, often illegal logging, a second equation was created in which only skid trail and gap attribute data were used, and in this equation 86% of the variation was accounted. The final equation, intended for use in scenarios where LiDAR data are not available but moderate resolution imagery could be used, associated length of roads only with extracted volumes. In this case, 78% of the variation was explained. Application of the first equation permitted estimation of extracted volumes and associated greenhouse gas emissions from two additional logging concessions. We discuss the application of these equations to areas that have been identified as illegal logging concessions, and propose that these may be applied to larger regions across the country. These equations offer a way to estimate volumes of timber extraction when no ground data is available, and to calculate greenhouse gas emissions associated with extracted volumes, providing a simple methodology useful across forested tropical countries.

Selective logging has an impact on the global carbon cycle, as well as on the forest micro-climate, and longer-term changes in erosion, soil and nutrient cycling, and fire susceptibility. Our ability to quantify these impacts is dependent on methods and tools that accurately identify the extent and features of logging activity. LiDAR-based measurements of these features offers significant promise. Here, we present a set of algorithms for automated detection and mapping of critical features associated with logging – roads/decks, skid trails, and gaps – using commercial airborne LiDAR data as input. The automated algorithm was applied to commercial LiDAR data collected over two logging concessions in Kalimantan, Indonesia in 2014. The algorithm results were compared to measurements of the logging features collected in the field soon after logging was complete. The automated algorithm-mapped road/deck and skid trail features match closely with features measured in the field, with agreement levels ranging from 69% to 99% when adjusting for GPS location error. The algorithm performed most poorly with gaps, which, by their nature, are variable due to the unpredictable impact of tree fall versus the linear and regular features directly created by mechanical means. Overall, the automated algorithm performs well and offers significant promise as a generalizable tool useful to efficiently and accurately capture the effects of selective logging, including the potential to distinguish reduced impact logging from conventional logging.

The forests of Kalimantan are under severe pressure from extensive land use activities dominated by logging, palm oil plantations, and peatland fires. To implement the forest moratorium for mitigating greenhouse gas emissions, Indonesia's government requires information on the carbon stored in forests, including intact, degraded, secondary, and peat swamp forests. We developed a hybrid approach of producing a wall-to-wall map of the aboveground biomass (AGB) of intact and degraded forests of Kalimantan at 1 ha grid cells by combining field inventory plots, airborne lidar samples, and satellite radar and optical imagery. More than 110 000 ha of lidar data were acquired to systematically capture variations of forest structure and more than 104 field plots to develop lidar-biomass models. The lidar measurements were converted into biomass using models developed for 66 439 ha of drylands and 44 250 ha of wetland forests. By combining the AGB map with the national land cover map, we found that 22.3 Mha (106 ha) of forest remain on drylands ranging in biomass from 357.2 ± 12.3 Mgha−1 in relatively intact forests to 134.2 ± 6.1 Mgha−1 in severely degraded forests. The remaining peat swamp forests are heterogeneous in coverage and degradation level, extending over 3.62 Mha and having an average AGB of 211.8 ± 12.7 Mgha−1. Emission factors calculated from aboveground biomass only suggest that the carbon storage potential of more than 15 Mha of degraded and secondary dryland forests will be about 1.1 PgC.

BACKGROUND:The degradation of forests in developing countries, particularly those within tropical and subtropical latitudes, is perceived to be an important contributor to global greenhouse gas emissions. However, the impacts of forest degradation are understudied and poorly understood, largely because international emission reduction programs have focused on deforestation, which is easier to detect and thus more readily monitored. To better understand and seize opportunities for addressing climate change it will be essential to improve knowledge of greenhouse gas emissions from forest degradation.RESULTS:Here we provide a consistent estimation of forest degradation emissions between 2005 and 2010 across 74 developing countries covering 2.2 billion hectares of forests. We estimated annual emissions of 2.1 billion tons of carbon dioxide, of which 53% were derived from timber harvest, 30% from woodfuel harvest and 17% from forest fire. These percentages differed by region: timber harvest was as high as 69% in South and Central America and just 31% in Africa; woodfuel harvest was 35% in Asia, and just 10% in South and Central America; and fire ranged from 33% in Africa to only 5% in Asia. Of the total emissions from deforestation and forest degradation, forest degradation accounted for 25%. In 28 of the 74 countries, emissions from forest degradation exceeded those from deforestation.CONCLUSIONS:The results of this study clearly demonstrate the importance of accounting greenhouse gases from forest degradation by human activities. The scale of emissions presented indicates that the exclusion of forest degradation from national and international GHG accounting is distorting. This work helps identify where emissions are likely significant, but policy developments are needed to guide when and how accounting should be undertaken. Furthermore, ongoing research is needed to create and enhance cost-effective accounting approaches.

Locating terrestrial sources and sinks of carbon (C) will be critical to developing strategies that contribute to the climate change mitigation goals of the Paris Agreement. Here we present spatially resolved estimates of net C change across United States (US) forest lands between 2006 and 2010 and attribute them to natural and anthropogenic processes.

A new prototype wireless data acquisition system has been developed with the intended application to read-out instrumentation systems having thousands of channels. The data acquisition and control is based on a compliant implementation of 802.11 based hardware and protocols. Our case study is for a large detector containing photomultiplier tubes. The front-end circuitry, including a high-voltage power supply is powered wirelessly thus creating an all-wireless detector readout. The bench marked performance of the prototype system and how a large scale implementation of the system might be realized are discussed.

The focus of land-use related efforts in developing countries to reduce carbon emissions has been on slowing deforestation, yet international agreements are to reduce emissions from both deforestation and forest degradation (REDD). The second ‘D’ is poorly understood and accounted for a number of technical and policy reasons. Here we introduce a complete accounting method for estimating emission factors from selective timber harvesting, a substantial form of forest degradation in many tropical developing countries. The method accounts separately for emissions from the extracted log, from incidental damage to the surrounding forest, and from logging infrastructure, and emissions are expressed as units of carbon per cubic meter of timber extracted to allow for simple application to timber harvesting statistics. We applied the method in six tropical countries (Belize, Bolivia, Brazil, Guyana, Indonesia, and Republic of Congo), resulting in total emission factors of 0.99−2.33 Mg C m−3. In all cases, emissions were dominated by damage to surrounding vegetation and the infrastructure rather than the logs themselves, and total emissions represented about 3–15% of the biomass carbon stocks of the associated unlogged forests. We then combined the emission factors with country level logging statistics for nine key timber producing countries represented by our study areas to gain an understanding of the order of magnitude of emissions from degradation compared to those recently reported for deforestation in the same countries. For the nine countries included, emissions from logging were on average equivalent to about 12% of those from deforestation. For those nine countries with relatively low emissions from deforestation, emissions from logging were equivalent to half or more of those from deforestation, whereas for those countries with the highest emissions from deforestation, emissions from logging were equivalent to <10% of those from deforestation. Understanding how to account emissions and the magnitude of each emissions source resulting from tropical timber harvesting practices helps identify where there are opportunities to reduce emissions from the second ‘D’ in REDD.

Guinea-Bissau includes large extensions of mangroves ( Avicennia germinans (L.) L., Rhizophora mangle L., Laguncularia racemosa (L.) C.F. Gaertn, Conocarpus erectus L., Rhizophora racemosa G. Mey, Rhizophora harrisonii Leechm, and Machaerium lunatum (L.f.) Ducke). These wetland forests are among the most carbon (C)-rich ecosystems in the tropics, but deforestation processes may be contributing by as much as 10 % of carbon emissions from the global forest sector. Therefore, avoiding mangrove deforestation can contribute to mitigation of climate change in addition to preserving the many other vital services that these ecosystems provide. The main objective of this study is to analyze the extent to which the revenues generated by the C retained in standing mangroves can cover the cost of avoiding their clearance in Guinea-Bissau. Moreover, this study aims at demonstrating the feasibility of producing a spatially explicit national emissions baseline in a country where data, technology, and capacity are still mostly absent. It also discusses the requirements and costs of implementing the measuring, reporting and verification system needed to make C payments a reality. The analysis relies on both the quantification of C stock dynamics in mangroves and the calculation of expected returns from avoiding their clearance, given necessary investment and C prices in the market. Methodologies based on science and Earth observation technology are used to fulfil the first requirement and economic projections are applied to fulfil the second. In this paper, previously unavailable quantitative data on the deforestation trends and the biomass content of the mangroves of Guinea-Bissau are provided at national and local levels, and compared to values presented for other regions. Additionally, a national mangrove C reference emissions level (REL) is established and an assessment of the mitigation potential of mangroves, nationally and locally, is performed. The main conclusion of the analysis presented is that, if the price of avoided carbon dioxide (CO 2 ) emissions is above the United State Dollars (USD) 6.69 to USD 7.20/t range, and governance risk can be contained, it is possible to delineate cost-effective activities to avoid deforestation of mangroves and promote climate change mitigation activities in Guinea-Bissau using C revenues alone.

This sourcebook is designed to be a guide for developing and implementing land use, land-use change and forestry (LULUCF) projects for the BioCarbon Fund of the World Bank that meet the requirements for the Clean Development Mechanism (CDM) of the Kyoto Protocol. Only project types and carbon pools that are eligible for credit under the CDM during the first commitment period (2008-2012) are covered. With its user-friendly format, the sourcebook introduces readers to the CDM processes and requirements, and provides methods and procedures to produce accurate and precise estimates of changes in carbon stocks. The sourcebook is not designed as a primer on field measurement tech-niques, although guidance is given. The sourcebook is intended as an addition to the Intergovernmental Panel on Climate Change (IPCC) good practice guidance on land use, land-use change and forestry (2003), providing additional explanation, clarification and enhanced methodologies. It is designed to be used alongside the good practice guidance.

There is general consensus that carbon (C) sequestration projects in forests are a relatively low cost option for mitigating climate change, but most studies on the subject have assumed that transaction costs are negligible. The objectives of the study were to examine transaction costs for forest C sequestration projects and to determine the significance of the costs based on economic analyses. Here we examine four case studies of active C sequestration projects being implemented in tropical countries and developed for the C market. The results from the case studies were then used with a dynamic forest and land use economic model to investigate how transaction costs affect the efficiency and cost of forest C projects globally. In the case studies transaction costs ranged from 0.38 to 27 million US dollars ($0.09 to $7.71/t CO 2 ) or 0.3 to 270 % of anticipated income depending principally on the price of C and project size. The three largest cost categories were insurance (under the voluntary market; 41–89 % of total costs), monitoring (3–42 %) and regulatory approval (8–50 %). The global analysis indicated that most existing estimates of marginal costs of C sequestration are underestimated by up to 30 % because transaction costs were not included.