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The largest carbon stock in tropical vegetation is in Brazilian Amazonia. In this ~5 million km(2) area, over 750,000 km(2) of forest and ~240,000 km(2) of nonforest vegetation types had been cleared through 2013. We estimate current carbon stocks and cumulative gross carbon loss from clearing of premodern vegetation in Brazil's 'Legal Amazonia' and 'Amazonia biome' regions. Biomass of 'premodern' vegetation (prior to major increases in disturbance beginning in the 1970s) was estimated by matching vegetation classes mapped at a scale of 1 : 250,000 and 29 biomass means from 41 published studies for vegetation types classified as forest (2317 1-ha plots) and as either nonforest or contact zones (1830 plots and subplots of varied size). Total biomass (above and below-ground, dry weight) underwent a gross reduction of 18.3% in Legal Amazonia (13.1 Pg C) and 16.7% in the Amazonia biome (11.2 Pg C) through 2013, excluding carbon loss from the effects of fragmentation, selective logging, fires, mortality induced by recent droughts and clearing of forest regrowth. In spite of the loss of carbon from clearing, large amounts of carbon were stored in stands of remaining vegetation in 2013, equivalent to 149 Mg C ha(-1) when weighted by the total area covered by each vegetation type in Legal Amazonia. Native vegetation in Legal Amazonia in 2013 originally contained 58.6 Pg C, while that in the Amazonia biome contained 56 Pg C. Emissions per unit area from clearing could potentially be larger in the future because previously cleared areas were mainly covered by vegetation with lower mean biomass than the remaining vegetation. Estimates of original biomass are essential for estimating losses to forest degradation. This study offers estimates of cumulative biomass loss, as well as estimates of premodern carbon stocks that have not been represented in recent estimates of deforestation impacts., (© 2014 John Wiley & Sons Ltd.)

Binuang bini (Octomeles sumatrana Miq) is a fast-growing tree with numerous economic benefits, such as the provision of wood for carpentry purposes, building boards, water management, and absorption of carbon dioxide (CO2). Therefore, this tree species has great potential and needs to be included in Reducing Emission from Deforestation and Forest Degradation (REDD)+'s mitigation program to tackle climate change. In its development, REDD+ has made it possible to carry out carbon trading in the world. Therefore, countries capable of performing protective functions and carry out reforestation, afforestation, and restoration, have the opportunity to be involved in world carbon trading. This study aims to determine the moisture content and carbon absorption rate of Binuang bini trees as a first step to regulate the allometric equation using destructive and laboratory analysis. The results show that the water content in the roots, leaves, as well as the base, middle, and tip of the stem were: 73.69%, 68.39%, 65.59%, 61.22%, and 66.26%, respectively. Furthermore, the sample test results indicate a very close relationship between carbon concentration and absorbance in the O. sumatrana tree with a simple linear regression equation: Y = 0.002X + 0.0593 with R2 = 0.9896. Therefore, this regression equation can be used to calculate the carbon concentration sample for the O. sumatrana tree fraction. The carbon content in 3 tree samples with a breast height diameter of 9.24 cm, 10.08 cm, and 11.68 cm was 2,585 kg. 2,913 kg, and 4,654 kg, respectively. In addition, the carbon sequestration for each tree diameter per year is 1.581 kg year-1, 1,782 kg year-1and 2,847 kg year-1, respectively.

Abstract Background The homestead forests of Bangladesh occupy 0.27 million hectares (10% of the total forested area) and have potential to store carbon (C) and conserve biodiversity. Small scale forestry practices, however, are lacking reliable estimation of C stocks and tree species diversity. This may hinder successful implementation of REDD + and similar mechanisms as they concentrate on large-scale forests. This study aimed to estimate the above- and below-ground carbon stocks in homestead forests of Maheshkhali Island in Bangladesh and how tree species diversity and stand structural variation affect these C stocks. We randomly surveyed a total of 239 homestead forests in the hillside, beachside, and inland in 2019. Results Tree biomass C stocks were 48–67% greater in the inland and hillside forests than in the beachside due to significantly greater stand density, basal area, tree diameter. In total we found 52 tree species, but most abundant species in the inland and hillside forests, Mangifera indica, Samanea saman, and Artocarpus heterophyllus stored the most C in tree biomass. Greater tree species richness and diversity index in the inland and hillside forests indicated greater above- and below-ground tree biomass C stocks. An increase in tree species richness and diversity index by one unit was found to increase the tree biomass C stock by 22 and 30 Mg C ha−1, respectively. The total soil C stock was also affected by tree species diversity, stand density, and their interaction with soil properties. Total soil C stocks were greatest (51 Mg ha−1) in the inland forests, having also the greatest stand density and tree species richness. C stock in soil surface was greatest in the hillside forests due to the greatest litterfall, but the average share of litterfall from the total biomass C was only 0.1%. Conclusions Homestead forest ecosystems could store 96 Mg C ha−1 in total, which can contribute to climate change mitigation by generating C credits for small-scale homestead forests owners. Above- and below-ground tree biomass C stocks were found to correlate with tree species diversity, which may also contribute to biodiversity conservation in the REDD + in Bangladesh and countries alike.

Carbon stock quantification holds vital significance in evaluating the climate change mitigation potential and carbon management of forest ecosystems. The current study was designed to quantify the biomass carbon stocks in the lesser Himalayan subtropical broadleaf forests of the Kashmir region. Primary data about the structural attributes and species composition of the local forests was collected through quadrat-based sampling followed by the application of allometric equations for the estimation of forest biomass. The biomass carbon stocks were calculated as 135.2 Mg ha-1 ranging from a maximum of 226.64 Mg ha-1 to a minimum of 11.83 Mg ha-1. The tree layer contributed a biomass carbon content of 134.67 Mg ha-1 making up to 99% share in the total forest biomass as compared to the shrub and herb layers with a very low biomass carbon value of 0.37 Mg ha-1 and 0.17 Mg ha-1 respectively. Dalbergia sissoo was recorded as the most dominant tree species with a biomass carbon stock value of 40.70 Mg ha-1 followed by Mallotus philippensis (30.09 Mg ha-1) and Ficus palmata (20.11 Mg ha-1). Principal Component Analysis revealed that the variations in the local carbon stocks were significantly correlated with the distribution pattern of the dominant tree species. Generalized Linear models showed a strong affinity of biomass carbon reserves with the structural attributes of the forest stands. This study generated a standard scientific dataset of the local biomass carbon stocks in the subtropical broadleaf forests with dynamic implications in sustainable forestry and carbon pool management in the region. [ABSTRACT FROM AUTHOR]

Background: The homestead forests of Bangladesh occupy 0.27 million hectares (10% of the total forested area) and have potential to store carbon (C) and conserve biodiversity. Small scale forestry practices, however, are lacking reliable estimation of C stocks and tree species diversity. This may hinder successful implementation of REDD + and similar mechanisms as they concentrate on large-scale forests. This study aimed to estimate the above- and below-ground carbon stocks in homestead forests of Maheshkhali Island in Bangladesh and how tree species diversity and stand structural variation affect these C stocks. We randomly surveyed a total of 239 homestead forests in the hillside, beachside, and inland in 2019. Results: Tree biomass C stocks were 48–67% greater in the inland and hillside forests than in the beachside due to significantly greater stand density, basal area, tree diameter. In total we found 52 tree species, but most abundant species in the inland and hillside forests, Mangifera indica, Samanea saman, and Artocarpus heterophyllus stored the most C in tree biomass. Greater tree species richness and diversity index in the inland and hillside forests indicated greater above- and below-ground tree biomass C stocks. An increase in tree species richness and diversity index by one unit was found to increase the tree biomass C stock by 22 and 30 Mg C ha−1, respectively. The total soil C stock was also affected by tree species diversity, stand density, and their interaction with soil properties. Total soil C stocks were greatest (51 Mg ha−1) in the inland forests, having also the greatest stand density and tree species richness. C stock in soil surface was greatest in the hillside forests due to the greatest litterfall, but the average share of litterfall from the total biomass C was only 0.1%. Conclusions: Homestead forest ecosystems could store 96 Mg C ha−1 in total, which can contribute to climate change mitigation by generating C credits for small-scale homestead forests owners. Above- and below-ground tree biomass C stocks were found to correlate with tree species diversity, which may also contribute to biodiversity conservation in the REDD + in Bangladesh and countries alike. [ABSTRACT FROM AUTHOR]

Tropical forests play a key role in global carbon cycle. Reducing Emissions from Deforestation and forest Degradation (REDD+) program requires reliable mechanisms for Monitoring, Reporting and Verification (MRV). In this regard, new methods must be developed using updated technologies to assess carbon stocks. The combination of LiDAR technology and in situ forest networks allows the estimation of biomass with high resolution in low data environments, such as tropical countries. However, the evaluation of current LiDAR methods of biomass inventory, and the development of new methodologies to reduce uncertainty and increase accuracy, is still needed. Our aim is to evaluate new methodologies of spatially explicit LiDAR biomass inventories based on local and general plot-aggregate allometry. For this purpose, 25 field plots were inventoried, covering the structural and ecological variability of Poás Volcano National Park (Costa Rica). Important differences were detected in the estimation of aboveground biomass (92.74 t ha-1 considering the mean value of plot sample) depending on the chosen tree allometry. We validated the general aboveground biomass plot-aggregate allometry proposed by Asner & Mascaro (2014) in our study area, and we fitted two specific models for Poás forests. Both locals and general models depend on LiDAR top-of-canopy height (TCH), basal area (BA) and wood density. Small deviations in the wood density plot sample (0.60 ± 0.05) indicated that a single wood density constant value could be used throughout the study area. A BA-TCH origin forced linear model was fitted to estimate basal area, as suggested by the general methodology. Poás forest has a larger biomass density for the same THC compared to the rest of the forests previously studied, and shows that the BA-TCH relationship might have different trends in each life zone. Our results confirm that the general plot-aggregate methodology can be easily and reliably applied as aboveground biomass in a new area could be estimated by only measuring BA in field plots to obtain a local BA-TCH regression. For both local and general methods, the estimation of BA is critical. Therefore, the definition of precise basal area field measurement procedures is decisive to achieve reliable results in future studies.

Abstract Background Recent studies have shown that fragmentation is an increasing threat to global forests, which has major impacts on biodiversity and the important ecosystem services provided by forested landscapes. Several tools have been developed to evaluate global patterns of fragmentation, which have potential applications for REDD+. We study how canopy height and above ground biomass (AGB) change across several categories of forest edges determined by fragmentation analysis. We use Democratic Republic of Congo (DRC) as an example. Results An analysis of variance of different edge widths and airborne estimated canopy height found that canopy heights were significantly different in forest edges at a distance of 100 m from the nonforest edge. Biomass was significantly different between fragmentation classes at an edge distance of 300 m. Core forest types were found to have significantly higher canopy height and greater AGB than forest edges and patches, where height and biomass decrease significantly as the level of fragmentation increases. A change analysis shows that deforestation and degradation are increasing over time and biomass loss associated with degradation account for at least one quarter of total loss. We estimate that about 80 % of primary forests are intact, which decreases 3.5 % over the 15 year study period, as primary forest is either deforested or transitioned to forest edge. While the carbon loss per hectare is lower than that of deforestation, degradation potentially affects up to three times more area than deforestation alone. Conclusions When defining forest degradation by decreased biomass without any loss in forest area, assessing transitions of core forest to edges over time can contribute an important element to REDD+MRV systems. The estimation of changes between different forest fragmentation types and their associated biomass loss can provide an estimate of degradation carbon emission factors. Forest degradation and emissions due to fragmentation are often underestimated and should comprise an essential component of MRV systems.

Reducing carbon emissions from deforestation and degradation (REDD) requires detailed insight into how the forest biomass is measured and distributed. Studies so far have estimated forest biomass stocks using rough assumptions and unreliable data. High-resolution data and robust methods are required to capture the spatial variability of forest biomass with sufficient precision. Here we aim to improve on previous approaches by using radar satellite ALOS PALSAR (25-m resolution) and optical Landsat-derived tree cover (30-m resolution) observations to estimate forest biomass stocks in Madagascar, for the years 2007–2010. The radar signal and in situ biomass were highly correlated ( R 2  = 0.71) and the root mean square error was 30% (for biomass ranging from 0 to 500 t/ha). Using our map at 25-m resolution for the entire island of Madagascar, we estimated the total above-ground forest carbon for the four years 2007, 2008, 2009 and 2010 to be 1.1173 ± 0.0304, 1.1029 ± 0.0303, 1.0916 ± 0.0301 and 1.0773 ± 0.0298 PgC, respectively. Carbon stocks were found to have decreased constantly over this period due to anthropogenic deforestation and likely also to climate change. The results are expected to serve as a more accurate benchmark for monitoring progress on REDD and to provide strong supports for current and future spaceborne missions such as ALOS-2, SAOCOM and BIOMASS. [ABSTRACT FROM AUTHOR]

Accurate estimates of forest biomass are increasingly important in relation to sequestration of carbon by forest trees. Satellite remote sensing is a useful tool for biomass estimation and monitoring of forest ecological processes. Microwave synthetic aperture radar (SAR) can increase the accuracy of estimations of forest biomass in comparison to optical remote sensing, due to the unique capacities of SAR, including high penetrability, volumetric scattering, interaction with surface roughness, and dielectric property. We studied the potential of multi-polarized C-band Radarsat-2, a SAR technology, with HH, HV and VV polarization for estimating biomass of moist tropical Indian forest. Backscatter values are correlated with field-based biomass values and are regressed to generate models for estimating biomass. HH polarization provided maximum information regarding tree biomass. A coefficient of determination of 0.49 was calculated for HH polarized C-band image with in situ measurements. An exponential model was proved to be best suited for estimating forest biomass. Correlation of 0.62 and RMSE of 24.6 t ha−1 were calculated for the relationship between estimated and predicted biomass values for the best fit model. The average absolute accuracy of the model was 61%, while Willmott’s index of agreement was 0.87. Results suggest that most of the biomass of the area ranged within 70 t ha−1 a probably due to the saturation of C-band around 60-70 t ha−1 for tropical forests. [ABSTRACT FROM AUTHOR]

Brazil’s Amazonian protected areas play an important role in maintaining the environmental services of the region, including Amazonia’s role in regional and global climate. These protected areas face threats both from deforestation and from degradation of standing forest. Preserving carbon stocks in protected areas is important both because of the climatic benefit of avoiding greenhouse gas emissions and because of the potential to provide a monetary value that contributes to supporting local human populations in ways that maintain rather than destroy the forest. REDD+ represents one potential mechanism for maintaining these areas. A variety of legal threats to protected areas in Brazilian Amazonia has arisen, leading to concern over the future of these areas and their role as a bulwark against regional climate change. [ABSTRACT FROM AUTHOR]

By 2014 approximately 2.2 million km (~43%) of Brazil's Legal Amazonia region had been incorporated into an extensive network of 718 protected areas, which are comprised by 372 indigenous lands, 313 federal, state and municipal (county) conservation units, and 33 Maroon territories ( Quilombos). Although protected areas occupy vast expanses in Amazonia, their importance as carbon reserves needs to be better understood. In this study, we estimate the total carbon in 2014 held in protected areas in Brazil's 'Legal Amazonia' and 'Amazonia biome' regions, and the carbon loss in the portions of these protected areas that were cleared by 2014. In 2014, a total of 33.4 Pg C or 57.0% of all carbon stored in Legal Amazonia was held in protected areas and 32.7 Pg C or 58.5% of all the carbon stored in the Amazonia biome was held in protected areas. By 2014, carbon lost due to clearing in protected areas in Legal Amazonia and the Amazonia biome totaled, respectively, 0.787 (or 2.3%) and 0.702 (or 2.1%) Pg C if one assumes that previously each protected area was entirely covered by native vegetation. If the protection of these areas is effective, about half of the carbon in Brazilian Amazonia will be maintained. Carbon in protected areas has strategic value for environmental conservation and for mitigation of climate change because these areas are under lower risk of being emitted to the atmosphere than carbon stored in vegetation located outside of protected areas, although the effectiveness of protected areas varies. [ABSTRACT FROM AUTHOR]

Biodiversity conservation is often considered to be an important co-benefit of REDD+ and other mechanisms aiming to increase carbon in biomass and soil to mitigate climate change. This reasoning is based on the assumption that the level of biodiversity and ecosystem carbon are positively correlated. Firstly, however, studies have shown both positive and negative relationships. Secondly, incentives for additional ecosystem carbon do not trigger random or all potential changes in land-use, but often concentrate on one or a few specific changes that could have an opposite effect than the general trend indicates. Therefore, it is important to study biodiversity impacts of plausible measures to increase carbon. We obtained land-use scenarios on pathways to increase carbon based on 97 face-to-face interviews of local land-use experts in twelve landscapes in seven countries and five continents. We then conducted another set of face-to-face interviews with biodiversity experts yielding 2963 estimations concerning the value of land-use classes for 264 taxa of fauna and flora in these landscapes. We found positive carbon to biodiversity relationships in ten of the twelve landscapes. The biodiversity impacts of measures to increase carbon were positive in eleven of the twelve landscapes. Our results indicate that a random land-use change that increases biodiversity is also likely to increase carbon and vice versa.

Airborne laser scanning (ALS) has been proposed as a reliable remote sensing technique for supporting biomass and carbon stock estimation under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in developing countries (UN-REDD). Under the United Nations Framework Convention on Climate Change (UNFCCC), developing countries can receive financial benefits from REDD + activities upon the implementation of reliable measuring, verification, and reporting mechanisms. As a UN-REDD country, Tanzania has implemented the National Forestry Resources Monitoring and Assessment (NAFORMA) program as a cost-efficient solution for providing an appropriate level of precision required for sustainable forest management practices, and for international reporting on carbon pools and carbon pools change estimates at national and regional scales. The main objective of the study was to investigate various design- and model-based sampling strategies incorporating ALS measurements for estimation of aboveground biomass (AGB) in miombo woodlands. The field data consisted of 65 clusters containing 513 circular NAFORMA ground plots located in Liwale District (15,867 km 2 ), southeastern Tanzania, on which the aboveground tree biomass was estimated using locally developed allometric models. The ALS measurements were acquired along 32 parallel flight lines oriented in the east–west direction, covering nearly 26% of Liwale District. The flight lines were spaced 5 km apart and were distributed over the ground plots. Compared to the uncertainty (standard error) of the field-based estimate (4.79 Mg ha − 1 ), the uncertainties of the ALS-assisted AGB estimates were consistently lower, varying from 1.73 Mg ha − 1 up to 2.52 Mg ha − 1 under two-stage cluster sampling, and 1.96 Mg ha − 1 for double sampling with regression estimation. Finally, strengths and shortcomings of using the NAFORMA inventory for ALS-assisted biomass estimation were discussed, underlining the implications of the field inventory design. Importantly, this study reveals the difficulty of accommodating a double sampling for stratification design, which was employed for NAFORMA, with an ALS survey having the flight lines systematically positioned over the landscape. [ABSTRACT FROM AUTHOR]

Background: Recent studies have shown that fragmentation is an increasing threat to global forests, which has major impacts on biodiversity and the important ecosystem services provided by forested landscapes. Several tools have been developed to evaluate global patterns of fragmentation, which have potential applications for REDD+. We study how canopy height and above ground biomass (AGB) change across several categories of forest edges determined by fragmentation analysis. We use Democratic Republic of Congo (DRC) as an example. Results: An analysis of variance of different edge widths and airborne estimated canopy height found that canopy heights were significantly different in forest edges at a distance of 100 m from the nonforest edge. Biomass was significantly different between fragmentation classes at an edge distance of 300 m. Core forest types were found to have significantly higher canopy height and greater AGB than forest edges and patches, where height and biomass decrease significantly as the level of fragmentation increases. A change analysis shows that deforestation and degradation are increasing over time and biomass loss associated with degradation account for at least one quarter of total loss. We estimate that about 80 % of primary forests are intact, which decreases 3.5 % over the 15 year study period, as primary forest is either deforested or transitioned to forest edge. While the carbon loss per hectare is lower than that of deforestation, degradation potentially affects up to three times more area than deforestation alone. Conclusions: When defining forest degradation by decreased biomass without any loss in forest area, assessing transitions of core forest to edges over time can contribute an important element to REDD+MRV systems. The estimation of changes between different forest fragmentation types and their associated biomass loss can provide an estimate of degradation carbon emission factors. Forest degradation and emissions due to fragmentation are often underestimated and should comprise an essential component of MRV systems. [ABSTRACT FROM AUTHOR]

Background: The success of satellites in mapping deforestation has been invaluable for improving our understanding of the impacts and nature of land cover change and carbon balance. However, current satellite approaches struggle to quantify the intensity of forest disturbance, i.e. whether the average rate of biomass loss for a region arises from heavy disturbance focused in a few locations, or the less severe disturbance of a wider area. The ability to distinguish between these, very different, disturbance regimes remains critical for forest managers and ecologists. Results: We put forward a framework for describing all intensities of forest disturbance, from deforestation, to widespread low intensity disturbance. By grouping satellite observations into ensembles with a common disturbance regime, the framework is able to mitigate the impacts of poor signal-to-noise ratio that limits current satellite observations. Using an observation system simulation experiment we demonstrate that the framework can be applied to provide estimates of the mean biomass loss rate, as well as distinguish the intensity of the disturbance. The approach is robust despite the large random and systematic errors typical of biomass maps derived from radar. The best accuracies are achieved with ensembles of ≥1600 pixels (≥1 km² with 25 by 25 m pixels). Summary: The framework we describe provides a novel way to describe and quantify the intensity of forest disturbance, which could help to provide information on the causes of both natural and anthropogenic forest loss--such information is vital for effective forest and climate policy formulation. [ABSTRACT FROM AUTHOR]

Road construction demonstrably accelerates deforestation rates in tropical forests, but its consequences for forest degradation remain less clear. We estimated a series of forest value metrics including components of biodiversity, carbon stocks, and timber and non-timber forest product resources, along the recently paved Inter-Oceanic Highway (IOH) integrating Brazil and Peru along the Bolivian border. We installed 69 vegetation plots in intact terra firme forests representative of local community holdings near and far from the IOH, and we characterized 15 components of forest value for each plot. We observed strong geographic gradients in forest value components across the region, with increases from west to east in aboveground biomass and in the abundance of timber and non-timber forest product trees and regeneration. Plots in communities in Pando, Bolivia, where the IOH remains in part unpaved, had the highest aboveground biomass, standing timber volumes and Brazil nut tree density. In contrast, communities in Madre de Dios, Peru, where settlements and unpaved portions of the IOH have existed for decades, and in Acre, Brazil, where paving of the IOH has been underway for more than a decade, were more degraded. Seven of the fifteen forest value components we measured increased with increasing distance from the IOH, although the magnitude of these effects was weak. Landscape scale remote sensing analyses showed much stronger effects of road proximity on deforestation. We suggest that remote sensing techniques including canopy spectral signatures might be calibrated to characterize multiple components of forest value, so that we can estimate landscape scale impacts of infrastructure developments on both deforestation and forest degradation in tropical regions. [ABSTRACT FROM AUTHOR]

Although mangroves are recognized by high capacity of carbon storage and sequestration, few studies have been dedicated to determine the monetary value of this ecosystem service. Accordingly, the aim of this study is to assign monetary values to this service in a protected area (Southeastern Brazil). This economic valuation was performed considering preexisting estimates of carbon storage and sequestration in the aboveground biomass of these forests and average transaction values of carbon credits. The mean values of the service of carbon sequestration varied according to the physiographic type from 19.00 ± 10.00 US$ ha−1 yr−1(basin forests, high intertidal) to 82.28 ± 32.10 US$ ha−1 yr−1(fringe forests, low intertidal). Considering the area occupied by each physiographic type, the service of carbon sequestration may be worth up to 455,827 US$ yr−1. In regard to carbon storage, 3,477,041 US$ are stored in these forests, and values between 104,311 and 208,622 US$ ha−1 yr−1can be considered as the annual maintenance cost of this service. The income generated by future projects for the maintenance of carbon-related functions may represent a major advance for the conservation of this ecosystem. [ABSTRACT FROM PUBLISHER]

Tropical forests play a vital role in the global carbon cycle, but the amount of carbon they contain and its spatial distribution remain uncertain. Recent studies suggest that once tree height is accounted for in biomass calculations, in addition to diameter and wood density, carbon stock estimates are reduced in many areas. However, it is possible that larger crown sizes might offset the reduction in biomass estimates in some forests where tree heights are lower because even comparatively short trees develop large, well-lit crowns in or above the forest canopy. While current allometric models and theory focus on diameter, wood density, and height, the influence of crown size and structure has not been well studied. To test the extent to which accounting for crown parameters can improve biomass estimates, we harvested and weighed 51 trees (11-169 cm diameter) in southwestern Amazonia where no direct biomass measurements have been made. The trees in our study had nearly half of total aboveground biomass in the branches (44% ± 2% [mean ± SE]), demonstrating the importance of accounting for tree crowns. Consistent with our predictions, key pantropical equations that include height, but do not account for crown dimensions, underestimated the sum total biomass of all 51 trees by 11% to 14%, primarily due to substantial underestimates of many of the largest trees. In our models, including crown radius greatly improves performance and reduces error, especially for the largest trees. In addition, over the full data set, crown radius explained more variation in aboveground biomass (10.5%) than height (6.0%). Crown form is also important: Trees with a monopodial architectural type are estimated to have 21-44% less mass than trees with other growth patterns. Our analysis suggests that accounting for crown allometry would substantially improve the accuracy of tropical estimates of tree biomass and its distribution in primary and degraded forests. [ABSTRACT FROM AUTHOR]

Understanding spatial variation in carbon storage in natural habitats is critical for climate change mitigation efforts such as REDD. Terrestrial forests are being mapped with increasing accuracy, but the distribution of ``blue carbon'' in marine ecosystems remains poorly understood. We reviewed the literature to obtain field data on carbon storage and fluxes in mangroves world-wide. Using this material we developed a climate-based model for potential mangrove above-ground biomass (AGB) with almost four times the explanatory power of the only previous published model. From this model, we present the first ever global map of potential mangrove AGB and estimate a total global mangrove AGB of 2.83 Pg, with an average of 184.8 t ha−1. Data on other carbon stocks and fluxes confirm the importance of mangroves in carbon accounting. The map highlights the high variability in mangrove AGB and indicates areas that should be prioritised for mangrove conservation and restoration. [ABSTRACT FROM AUTHOR]

Tropical forests are both important stores of carbon and among the most biodiverse ecosystems on the planet. Reducing emissions from deforestation and degradation (REDD) schemes are designed to mitigate the impacts of climate change, by conserving tropical forests threatened by deforestation or degradation. REDD schemes also have the potential to contribute significantly to biodiversity conservation efforts within tropical forests, however biodiversity conservation and carbon sequestration need to be aligned more closely for this potential to be realized. This paper analyses the relationship between tree species diversity and above-ground biomass (AGB) derived from 1-ha tree plots in Central African rainforests. There was a weakly significant correlation between tree biomass and tree species diversity (r = 0.21, p = 0.03), and a significantly higher mean species diversity in plots with larger AGB estimates (M = 44.38 species in the top eight plots, compared to M = 35.22 in the lower eight plots). In these Central African plots, the relationship between tree species diversity and AGB appeared to be highly variable; nonetheless, high species diversity may often be related to higher biomass and, in such cases, REDD schemes may enhance biodiversity by targeting species diverse forests. [ABSTRACT FROM PUBLISHER]

Forest management can result in net losses of carbon stock. To quantify the impact of the management it is important to assess losses or gains of carbon, as well as the sustainability of the management system. This study quantified the impact management under a Small-Scale Sustainable Forest Management Plan, which is a recently created category of authorized management for small managers in the state of Amazonas, Brazil. Impact was quantified on the number of individuals, the biomass of natural regeneration, and the damage to the remaining trees 2 months after logging. The impact of these changes on carbon stock was estimated. The study was carried out in the Uatumã Sustainable Development Reserve, Amazonas, Brazil, where two areas of small-scale forest management and one control were evaluated. Average total carbon stock previous to logging was estimated at 161.25 ± 9.66 MgC ha−1. Two months after logging, reductions were found of 3% in one managed area (MA1) and 8.3% in the other (MA3), including the carbon stock from the harvested timber. For each harvested tree, the logging caused damage to 12 trees in MA1 and four trees in MA3. The reductions in carbon stock and number of trees damaged per harvested tree were less than the reductions found for higher impact forest management and other experiences in community forest management. No significant alteration was found in the carbon stock of natural regeneration. However, there was an increase in the number of individuals, both in the logged areas and in their respective control areas. [ABSTRACT FROM PUBLISHER]

Highlights: [•] D2H is the best-fit variable to estimate the above-ground biomass for trees. [•] Our allometric models showed substantial differences in biomass among forest types. [•] All mixed species combined/select dominant species can be used to establish the allometric models. [•] Biomass accumulation in the study area generally increased with fallow age, with a large contribution of bamboo in fallows. [ABSTRACT FROM AUTHOR]

Performance of the above ground (dry) biomass estimation with the medium resolution optical (ALOS AVNIR) data and radar (ALOS PALSAR) data was evaluated on a tropical forest site in Lao PDR (Laos). The average biomass of ground reference plots was relatively low, 78 t/ha, due to strong anthropogenic influence in most of the study area. The biomass estimates were computed using linear regression analysis and the Probability method that combines unsupervised clustering and fuzzy estimation. The predictions were validated with independent field plot data. [ABSTRACT FROM PUBLISHER]

Abstract: Estimating biomass of terrestrial vegetation is not only a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD). The accuracy of biomass estimates, and rate of biomass change, is not only important in the context of carbon markets emerging under REDD, but also for characterizing uncertainty in estimates of carbon cycling and the global carbon budget. There is particular interest in mapping biomass so that carbon stocks and stock changes can be monitored consistently across a range of scales – from relatively small projects (tens of hectares) to national or continental scales – but also so that other benefits of forest conservation can be factored into decision making (e.g. biodiversity and habitat corridors). We conducted an analysis of reported biomass accuracy estimates from more than 70 refereed articles using different remote sensing platforms (airborne and spaceborne) and sensor types (optical, radar, and lidar), with a particular focus on lidar since those papers reported the lowest errors when used in a synergistic manner with other coincident multi-sensor measurements. We show systematic differences in accuracy between different types of lidar systems flown on different platforms but, perhaps more importantly, differences between forest types (biomes) and plot sizes used for field calibration and assessment. We discuss these findings in relation to monitoring, reporting and verification under REDD, and also in the context of more systematic assessment of factors that influence accuracy and error estimation. [Copyright &y& Elsevier]

Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model ( r = 0.80, RMSE = 27.6 Mg C ha). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy. [ABSTRACT FROM AUTHOR]

Information on changes in forest structure and composition is required for informed, adaptive management and conservation. As the collection of such information requires field studies that are expensive, difficult, and time consuming, the prioritization of metrics can be of significant value. This study evaluates a number of metrics used to assess changes in forest structure and composition for a set of 59 forests in five countries – Kenya, India, Nepal, Uganda and USA. Changes in tree density are significantly positively correlated with changes in species richness, and changes in sapling/shrub density are significantly positively correlated with changes in species richness. Thus, rapid assessments of tree density change can be used to prioritize locations where there may be rapid deterioration in tree diversity, where the collection of detailed information on changes in species composition may be prioritized. Changes in tree density do not reflect changes in shrub and sapling density. The shrub and sapling layer appears to respond differently to human or natural disturbances compared to the tree layer, and may require separate assessment. Changes in tree DBH and tree height are not completely congruent, indicating that measurements of DBH and height may be required to accurately estimate changes in above ground carbon storage over time, for programs such as REDD that provide payment for carbon sequestration services. [Copyright &y& Elsevier]

The article discusses a study which investigates the use of trees according to its size the estimation of biomass and carbon stock in tropical forests in Madagascar. The study proposed Chave's or Brown's generic empirical models to estimate forest biomass and carbon in the forests. Result shows that Chave's model was the only generic model that could give the accurate tree biomass with estimates from the regional allometric models.

Background: Accurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar. Results: We found that elevation and the fraction of photosynthetic vegetation (PV) cover, analyzed throughout forests of widely varying structure and condition, account for 27-67% of the spatial variation in ACD. This finding facilitated spatial extrapolation of LiDAR-based carbon estimates to a total of 2,372,680 ha using satellite data. Remote, humid sub-montane forests harbored the highest carbon densities, while ACD was suppressed in dry spiny forests and in montane humid ecosystems, as well as in most lowland areas with heightened human activity. Independent of human activity, aboveground carbon stocks were subject to strong physiographic controls expressed through variation in tropical forest canopy structure measured using airborne LiDAR. Conclusions: High-resolution mapping of carbon stocks is possible in remote regions, with or without human activity, and thus carbon monitoring can be brought to highly endangered Malagasy forests as a climate-change mitigation and biological conservation strategy. [ABSTRACT FROM AUTHOR]

Abstract: Madagascar is currently developing a policy and strategies to enhance the sustainable management of its natural resources, encouraged by United Nations Framework Convention on Climate Change (UNFCCC) and REDD. To set up a sustainable financing scheme methodologies have to be provided that estimate, prevent and mitigate leakage, develop national and regional baselines, and estimate carbon benefits. With this research study this challenge was tried to be addressed by analysing a lowland rainforest in the Analanjirofo region in the district of Soanierana Ivongo, North East of Madagascar. For two distinguished forest degradation stages: “low degraded forest” and “degraded forest” aboveground biomass and carbon stock was assessed. The corresponding rates of carbon within those two classes were calculated and linked to a multi-temporal set of SPOT satellite data acquired in 1991, 2004 and 2009. Deforestation and particularly degradation and the related carbon stock developments were analysed. With the assessed data for the 3years 1991, 2004 and 2009 it was possible to model a baseline and to develop a forest prediction for 2020 for Analanjirofo region in the district of Soanierana Ivongo. These results, developed applying robust methods, may provide important spatial information regarding the priorities in planning and implementation of future REDD+ activities in the area. [Copyright &y& Elsevier]

Abstract: Satellite L-band synthetic aperture radar backscatter data from 1996 and 2007 (from JERS-1 and ALOS PALSAR respectively), were used with field data collected in 2007 and a back-calibration method to produce biomass maps of a 15000km2 forest–savanna ecotone region of central Cameroon. The relationship between the radar backscatter and aboveground biomass (AGB) was strong (r 2 =0.86 for ALOS HV to biomass plots, r 2 =0.95 relating ALOS-derived biomass for 40 suspected unchanged regions to JERS-1 HH). The root mean square error (RMSE) associated with AGB estimation varied from ~25% for AGB<100Mgha−1 to ~40% for AGB>100Mgha−1 for the ALOS HV data. Change detection showed a significant loss of AGB over high biomass forests, due to suspected deforestation and degradation, and significant biomass gains along the forest–savanna boundary, particularly in areas of low population density. Analysis of the errors involved showed that radar data can detect changes in broad AGB class in forest–savanna transition areas with an accuracy >95%. However, quantitative assessment of changes in AGB in Mgha−1 at a pixel level will require radar images from sensors with similar characteristics collecting data from the same season over multiple years. [Copyright &y& Elsevier]

Abstract: As in many other developing countries, the state government of Acre, Brazil, is developing a program for compensating forest holders (such as communities of rubber tappers and indigenous peoples as well as small, medium and large private land holders) reducing their emission of atmospheric heat-trapping gases by not deforesting. We describe and then apply to Acre a method for estimating carbon stocks by land cover type. We then compare the results of our simple method, which is based on vegetation mapping and ground-based samples, with other more technically demanding methods based on remote sensing. We estimated total biomass carbon stocks by multiplying the measured above-ground biomass of trees>10cm DBH in each of 18 forest types and published estimates for non-forest areas, as determined by measurement of 44 plots throughout the state (ranging from 1 to 10ha each), by land-cover area estimated using a geographical information system. State-wide, we estimated average above-ground biomass in forested areas to be 246±90Mgha−1; dense forest showed highest (322±20Mgha−1) and oligotrophic dwarf forest (campinarana) the lowest biomass (20±30Mgha−1). The two most widespread forest types in Acre, open canopy forests dominated by either palms and bamboo (for which ground-based data are scant), support an estimated 246±44 and 224±50Mgha−1 of above-ground biomass, respectively. We calculate the total above-ground biomass of the 163,000km2 State of Acre to be 3.6±0.8Pg (non-forest biomass included). This estimate is very similar to two others generated using much more technologically demanding methods, but all three methods, regardless of sophistication, suffer from lack of field data. [Copyright &y& Elsevier]

Abstract: In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (>100t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r²=0.53 with an RMSE of 79t/ha. The model is valid up to 307t/ha with an accuracy requirement of 50t/ha and up to 614t/ha with an accuracy requirement of 100t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types. [Copyright &y& Elsevier]

Abstract: The development of tree allometric equations is crucial to accurate forest carbon assessment. However, very few allometric equations exist for sub-Saharan Africa and as a result generalized allometric equations, often established for forests in other continents, are used by default. The objectives of this study were (1) to propose a sampling methodology and calculation procedures to assess biomass for tropical tree species of contrasted tree shapes in Africa, (2) to identify factors affecting within and between trees wood density, (3) to propose an allometric model that integrates these factors and (4) to evaluate the reliability of using generalized allometric equations in this type of forests. Models were developed to predict wood density and phytomass of the trees based on the harvesting of 42 trees from 16 species, representing three guild status in the wet evergreen forest of Boi Tano in Ghana. Results indicated that the wood density was highly influenced by the tree species, guild status, size of the tree and pith to bark distance. Dry mass of a tree was influenced by diameter at breast height, crown diameter and wood density. The wood density depends on the position of the wood within the tree and the guild status considered. The use of generalized allometric models in literature is limited by the specific climate zone, the consideration of tree height and species specific wood density. In considering those factors, using generalized allometric equations could result in an error of 3%. Further research should better consider the bigger trees and the influence of the topography and ecosystem history. [Copyright &y& Elsevier]

Forest loss and degradation in the tropics contribute 6-17% of all greenhouse gas emissions. Protected areas cover 217.2 million ha (19.6%) of the world's humid tropical forests and contain c. 70.3 petagrams of carbon (Pg C) in biomass and soil to 1 m depth. Between 2000 and 2005, we estimate that 1.75 million ha of forest were lost from protected areas in humid tropical forests, causing the emission of 0.25-0.33 Pg C. Protected areas lost about half as much carbon as the same area of unprotected forest. We estimate that the reduction of these carbon emissions from ongoing deforestation in protected sites in humid tropical forests could be valued at USD 6,200-7,400 million depending on the land use after clearance. This is >1.5 times the estimated spending on protected area management in these regions. Improving management of protected areas to retain forest cover better may be an important, although certainly not sufficient, component of an overall strategy for reducing emissions from deforestation and forest degradation (REDD). [ABSTRACT FROM AUTHOR]

Papua New Guinea (PNG) has become the focus of climate change mitigation initiatives such as reducing emissions from deforestation and forest degradation, but defensible estimates of forest carbon are lacking. Here we present a methodology for estimating aboveground forest carbon, and apply it to a large Permanent Sample Plot system maintained by Papua New Guinea Forest Research Institute. We report the first estimates of forest carbon in lowland tropical forest in PNG. Average aboveground carbon in stems >10 cm diam. for 115 selectively harvested 1-ha plots in lowland tropical forest was 66.3±3.5 Mg C/ha (95% CI) while for 10 primary forest plots the average was 106.3±16.2 Mg C/ha. We applied ratios based on field observations, in-country studies, and the literature to estimate unmeasured pools of aboveground carbon (stems <10 cm diam., fine litter and coarse woody debris). Total aboveground carbon was estimated at 90.2 and 120.8 Mg C/ha in selectively harvested and primary lowland forest, respectively. Our estimate for primary tropical forest is lower than biome averages for tropical equatorial forest, and we hypothesize that frequent disturbances from fire, frost, landslides, and agriculture are limiting carbon stock development. The methodology and estimates presented here will assist the PNG government in its preparedness for mitigation initiatives, are of interest to communities that are seeking to participate in voluntary carbon markets, and will encourage transparency and consistency in the estimation of forest carbon. [ABSTRACT FROM AUTHOR]

Aim Tropical forests have been recognized as important global carbon sinks and sources. However, many uncertainties about the spatial distribution of live tree above-ground biomass (AGB) remain, mostly due to limited availability of AGB field data. Recent studies in the Amazon have already shown the importance of large sample size for accurate AGB gradient analysis. Here we use a large stem density, basal area, community wood density and AGB dataset to study and explain their spatial patterns in an Asian tropical forest. Location Borneo, Southeast Asia. Methods We combined stem density, basal area, community wood density and AGB data from 83 locations in Borneo with an environmental database containing elevation, climate and soil variables. The Akaike information criterion was used to select models and environmental variables that best explained the observed values of stem density, basal area, community wood density and AGB. These models were used to extrapolate these parameters across Borneo. Results We found that wood density, stem density, basal area and AGB respond significantly, but differentially, to the environment. AGB was only correlated with basal area, but not with stem density and community wood specific gravity. Main conclusions Unlike results from Amazonian forests, soil fertility was an important positive correlate for AGB in Borneo while community wood density, which is a main driver of AGB in the Neotropics, did not correlate with AGB in Borneo. Also, Borneo's average AGB of 457.1 Mg ha−1 was c. 60% higher than the Amazonian average of 288.6 Mg ha−1. We find evidence that this difference might be partly explained by the high density of large wind-dispersed Dipterocarpaceae in Borneo, which need to be tall and emergent to disperse their seeds. Our results emphasize the importance of Bornean forests as carbon sinks and sources due to their high carbon storage capacity. [ABSTRACT FROM AUTHOR]

Although allometric equations can be used to accurately estimate biomass and/or carbon stock in forest ecosystems, few have been developed for logged-over tropical rainforests in Southeast Asia. We developed allometric relationships between tree size variables (stem diameter at breast height (dbh) and tree height) and leaf, branch, stem and total above-ground biomass in two logged-over tropical rainforests with different soil conditions in Sarawak, Malaysia. The study sites were originally classified as mainly lowland dipterocarp forest and have been selectively logged in the past 20 years. In total, 30 individuals from 27 species were harvested to measure above-ground parts. The correlation coefficients for the allometric relationships obtained for total above-ground biomass as a function of dbh had high values (0.99), although the relationships for leaf biomass had a relatively low coefficient (0.83). We also found relatively high coefficients for allometric relationships between tree height and plant-part biomass, ranging from 0.82 to 0.97. Moreover, there were no differences for allometric equations of total above-ground biomass between study sites. A comparison of equations of above-ground biomass in various previously reported tropical rainforests and pan-tropic general equations imply that our allometric equations differ largely from the equations for tropical primary forests, early successional secondary forest, and even for the general models. Therefore, choosing the biomass estimation models for above-ground biomass in the logged-over forests of Southeast Asia requires careful consideration of their suitability. [ABSTRACT FROM AUTHOR]