Quantification Of Carbon Stocks Research Articles

Carbon stock quantification in a floodplain restoration chronosequence along a Mediterranean-montane riparian corridor

Uncertainty in the global carbon (C) budget has been reduced for most stocks, though it remains incomplete by not considering aquatic and transitional zone carbon stocks. A key issue preventing such complete accounting is a lack of available C data within these aquatic and aquatic-terrestrial transitional ecosystems. Concurrently, quantifiable results produced by restoration practices that explicitly target C stock accumulation and sequestration remain inconsistent or undocumented. To support a more complete carbon budget and identify impacts on C stock accumulation from restoration treatment actions, we investigated C stock values in a Mediterranean-montane riparian floodplain system in California, USA. We quantified the C stock in aboveground biomass, large wood, and litter in addition to the C and total nitrogen in the upper soil profile (5 cm) across 23 unique restoration treatments and remnant old-growth forests. Treatments span 40 years of restoration actions along seven river kilometers of the Cosumnes River, and include process-based (limited intervention), assisted (horticultural planting and other intensive restoration activities), hybrid (a combination of process and assisted actions), and remnant (old-growth forests that were not created with restoration actions) sites. Total C values measured up to 1100 Mg ha−1 and averaged 129 Mg ha−1 with biomass contributing the most to individual plot measurements. From 2012 to 2020, biomass C stock measurements showed an average 32 Mg ha−1 increase across all treatments, though treatment specific values varied. While remnant forest plots held the highest average C values across all stocks (336 Mg ha−1), C values of different stocks varied across treatment type. Process-based restoration treatments held more average biomass C (120 Mg ha−1) than hybrid (23 Mg ha−1) or assisted restoration treatments (50 Mg ha−1), while assisted restoration treatments held more average total C in soil and litter (58 Mg ha−1) than hybrid (35 Mg ha−1) and process-based restoration treatments (37 Mg ha−1). Regardless of treatment type, time was a significant factor for all C stock values. These findings support a more inclusive global carbon budget and provide valuable insight into restoration treatment actions that support C stock accumulation.

Mapping of Forest Structural Parameters in Tianshan Mountain Using Bayesian-Random Forest Model, Synthetic Aperture Radar Sentinel-1A, and Sentinel-2 Imagery

The assessment of forest structural parameters is crucial for understanding carbon storage, habitat suitability, and timber stock. However, the labor-intensive and expensive nature of field measurements, coupled with inadequate sample sizes for large-scale modeling, poses challenges. To address the forest structure parameters in the Western Tianshan Mountains, this study used UAV-LiDAR to gather extensive sample data. This approach was enhanced by integrating Sentinel satellite and topographic data and using a Bayesian-Random Forest model to estimate forest canopy height, average height, density, and aboveground biomass (AGB). Validation against independent LiDAR-derived samples confirmed the model’s high accuracy, with coefficients of determination (R2) and root mean square errors (RMSE) indicating strong predictive performance (R2 = 0.63, RMSE = 5.06 m for canopy height; R2 = 0.64, RMSE = 2.88 m for average height; R2 = 0.68, RMSE = 62.84 for density; and R2 = 0.59, RMSE = 29.71 Mg/ha for AGB). Notably, the crucial factors include DEM, Sentinel-1 (VH and VV backscatter in dB), and Sentinel-2 (B6, B8A, and B11 bands). These factors contribute significantly to the modeling of forest structure. This technology aims to expedite and economize forest surveys while augmenting the range of forest parameters, especially in remote and rugged terrains. Using a wealth of UAV-LiDAR data, this outcome surpasses its counterparts’ by providing essential insights for exploring climate change effects on Central Asian forests, facilitating precise carbon stock quantification, and enhancing knowledge of forest ecosystems.

Allometric equations and carbon sequestration potential of mango (Mangifera indica) and avocado (Persea americana) in Kenya

Reliable biomass estimates are needed to quantify the climate benefits of fruit trees and include them in landscape restoration and international commitments such as the Nationally Determined Contributions under the Paris Agreements. Fruit trees are widespread in smallholder farming systems, where they provide income, nutrition benefits, and other products and ecosystem services with Mangifera indica (mango) and Persea americana (avocado) being the most popular in East Africa region. However, their contribution to climate change mitigation through carbon sequestration and reduction in greenhouse gas emission has not been explored because of a lack of allometric equations to quantify their biomass accurately. This study developed allometric equations from data collected on 51 mango trees and 40 avocado trees harvested in Makueni and Kiambu Counties in Kenya, respectively. Allometric equation using mean diameter of primary branches (µDPB) or diameter at breast height (DBH) as the predictor variable provided better predictions for mango and avocado, with a low mean absolute percentage error (MAPE) of 28.8 % and 18.4 %, respectively. From an inventory of 23 plots in Makueni (total area = 4.4 ha) and 36 plots in Kiambu (total area = 14.42 ha), aboveground biomass in mango trees was estimated to be 22.3 ± 6.3 Mg ha−1 and 20.6 ± 5.4 Mg ha−1, respectively. Much of the biomass was found in trees located in homesteads and orchards. Allometric equations developed in this study will support the quantification of carbon stocks for projects aimed at generating carbon credits by including fruit trees in the landscape.

Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock.

Species Composition Affects the Accuracy of Stand-Level Biomass Models in Hemiboreal Forests

Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure

The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.

Carbon stocks in a highly fragmented landscape with seasonally dry tropical forest in the Neotropics

Global modeling of carbon storage and sequestration often mischaracterizes unique ecosystems such as the seasonally dry tropical forest of the central region of the Gulf of Mexico, because species diversity is usually underestimated, as is their carbon content. In this study, aboveground and soil carbon stocks were estimated to determine the climate mitigation potential of this highly degraded landscape (<25% of forest cover). Tree species in the study area had carbon content values that were 30%–40% higher than the standard value proposed by the IPCC (i.e., 50%). Tropical oak forest in the region, despite its restricted distribution and low species richness, accounted for the highest mean carbon stocks per unit area. The main factors driving spatial variability in carbon stocks were: maximum precipitation, soil organic matter, clay and silt content. No strong relationship was found between aboveground carbon stocks and soil organic carbon in the study area. Quantification of carbon stocks is an important consideration in the assessment of the conservation value of remnants of native vegetation in human-modified landscapes. This study demonstrates the importance of the highly fragmented tropical dry regions of the Neotropics in maintaining landscape functionality and providing key ecosystem services such as carbon sequestration. Our results also highlight how crucial field-based studies are for strengthening the accuracy of global models. Furthermore, this approach reveals the real contribution of ecosystems that are not commonly taken into account in the mitigation of climate change effects.

USE OF DESTRUCTIVE AND NON-DESTRUCTIVE METHODOLOGIES TO ESTIMATE STEM BIOMASS ACCUMULATION AND CARBON STOCK IN AN EUCALYPTUS FOREST

ABSTRACT Predicting wood biomass and carbon stock contents in planted forests can vary due to limitations associated with the measurement of parameters. Therefore, reducing possible errors generated over biomass and carbon stock quantification is an important step in obtaining reliable data. The study aimed to compare the use of destructive and non-destructive methodologies for predicting biomass and carbon stock in a planted Eucalyptus forest. Scaling was performed on 21 trees and 3 methodologies for carbon stock estimation were compared. For methodology 1, a control sample was harvested, sectioned, weighted in the field, and the carbon stock calculated based on these data. Methodology 2 was also destructive, as trees were harvested, scaled and the carbon stock predicted based on these data. Methodology 3 was non-destructive, as trees were scaled upright with the aid of equipment and the predicted carbon stock was based on these data. Biomass and carbon stock were compared by Test F and no statistical difference was observed. The data were separated according to diametric classes and compared by the Kolmogorov-Smirnov test, and again no significant difference was observed. Furthermore, three equations were generated based on the Schumacher &amp; Hall model and compared by the identity test model and no differences between the methodologies were observed. Thus, both nondestructive and destructive methodologies herein evaluated were effective and showed equal results to the control sample. Moreover, the use of the non-destructive methodology reduces time and cost destined to predicting biomass and carbon stock.

Spatial variation of organic carbon sequestration in large lakes and implications for carbon stock quantification

Lakes are recognized as critical zones for carbon transformation and storage, and lacustrine sediments sequestrate considerable amounts of organic carbon (OC). Understanding sedimentation processes and OC burial patterns is crucial to clarifying lakes’ role in global carbon cycling. However, OC sedimentation may be quite spatially heterogeneous within an aquatic system, owing to the differences in OC production and sources, hydrodynamic conditions and underwater topography. The uncertainties in estimating OC sequestration in the world’s large lakes remain poorly constrained. This study takes the test case of two large lakes (50 and 249 km2) with different water depth and trophic status, using a multi-core paleolimnological technique, to identify the spatial variation in OC accumulation and its main influencing factors over the past century. Results of multi-core comparisons revealed similar temporal trends in major organic and nutrient parameters, suggesting coherent processes of whole-lake sedimentary environment changes for each lake. The OC preserved in sediments was primarily of autochthonous origin. However, OC standing stocks varied ∼3-fold spatially, and average OC accumulation rates ranged between 9.5–27.4 g m−2 yr−1 (post–1963 in oligo-mesotrophic deep-lake Lugu) and between 17.4–43.5 g m−2 yr−1 (post–1980 in eutrophic shallow-lake Erhai), respectively. These variations were primarily attributable to the spatial differences in aquatic primary production and terrestrial detritus supply relating to anthropogenic land-use change and phosphorus loading, rather than intra-lake sediment focusing-related transport and redistribution. The single central-core approach from Lugu Lake would overestimate whole-lake OC stock by 32% or underestimate the value by 48%, indicating spatial variability is an important source of uncertainty for OC stock quantification in similar large and/or morphometrically complex waterbodies. Therefore, spatial heterogeneity of OC accumulation in inland waters requires considerable research with well-placed multi-cores to provide a deeper understanding of carbon sequestration patterns and mechanisms.

Biomass and soil carbon stocks in relation to the structure and composition of Chir Pine dominated forests in the lesser Himalayan foothills of Kashmir

Quantification of carbon stocks is critical to evaluate the potential of an ecosystem to mitigate the impact of global climate change in the REDD + scenario. Present research project was designed to gauge the sequestered carbon in Chir Pine (Pinus roxburghii) dominated forests in the foothills of the western Himalayan region of Kashmir which constitute a significant regional carbon pool. The study outcomes revealed an average carbon stock value of 94.3 Mg/ha from which 68 Mg/ha was shared by soil organic carbon (SOC); whereas average biomass carbon share was 26.3 Mg/ha. P. roxburghii was the most noteworthy species containing an average carbon stock value of 5.93 Mg/ha followed by Quercus incana (3.59 Mg/ha) and Olea cuspidata (2.26 Mg/ha). Average forest biomass was chronicled as 52.5 Mg/ha from which tree biomass was 49.34 Mg/ha. Shrub and herb biomass contributed 1.95 and 0.53 Mg/ha correspondingly whereas the Leaf litter and deadwood necromass produced an average of 0.67 Mg/ha. Ordination analysis revealed that the forest carbon stocks were significantly influenced by the species composition, structural attributes as well as anthropogenic disturbances. Current study has resulted in generating a standard baseline dataset of the regional carbon stocks with diverse implications for sustainable carbon management in REDD + perspective.

Quantifying blue carbon for the largest salt marsh in southern British Columbia: implications for regional coastal management

Understanding blue carbon storage and accumulation prior to engineering projects is essential for assessing the potential co-benefit of carbon storage for natural climate solutions. This study collected sediment cores from the western portion of Boundary Bay, Delta, British Columbia (BC), where implementation of a living dike pilot project is planned to alleviate impacts of sea level rise. Total carbon stocks were 10,034 ± 3,148 Mg C for the western 140 ha of marsh, with stocks averaging 83.3 ± 29.3 Mg C/ha (high marsh) and 39.3 ± 24.2 Mg C/ha (low marsh). Carbon accumulation rates (CARs) exhibited substantial variability, ranging from 19.5 to 454 g C/m2/yr (median 70.1 g C/m2/yr). Both stocks and accumulation rates were at least 45% lower than globally averaged estimates, likely due to the shallow depth and dominant vegetation type of the marsh. Despite historical modifications to the marsh, our study indicates that the western marsh has expanded by about 20% since 1930, which we estimate increased carbon stocks by about 1,549-1,698 Mg C. This study’s quantification of carbon stocks and CARs is an important first step towards leveraging the co-benefit of salt marshes for improved management, restoration, and preservation. However, additional data are needed to document the greenhouse gas budgets for carbon accounting purposes, along with exploration of law and policy issues related to carbon stewardship in a multi-jurisdictional coastal environment. We outline subsequent research needed for salt marshes such as Boundary Bay to be included in voluntary carbon markets in British Columbia.

New allometric equations for quantifying tree biomass and carbon sequestration in seasonally dry secondary forest in northern Thailand

As tropical deforestation and forest degradation accelerate, carbon-credit trading could provide a financial incentive to preserve and regenerate forests. Since carbon trading relies on the accurate quantification of carbon stocks, allometric equations are urgently needed to derive above-ground dry biomass (AGB) from easily measured variables. Few allometric equations have been published for regenerating secondary forests in Southeast Asia. This study established new allometric equations, to determine AGB and carbon in regenerating secondary forests on shifting cultivation fallows in northern Thailand. To develop the equations, data were collected from 78 trees (136 felled individual stems, including coppices) ranging in size from 1 to 32.9 cm diameter at breast height (D). The dependent variable was AGB. The independent variables were D, tree height (H) and wood density (WD). Wood density varied significantly among species (p < 0.05). Consequently, including WD in the function D2H × WD predicted AGB most accurately, compared with other functions that excluded it. Mean carbon concentration in stems, branches, and leaves was 44.84% ± 1.63 of dry mass, but it varied significantly among tree species (p < 0.05). The new allometric equation revealed that tree ABG was highest in secondary forest, followed by the 7-year-fallow and the 4-year-fallow: 105.3, 38.3 and 10.3 Mg ha−1, respectively, while above-ground carbon was 47.7, 17.4, and 4.6 Mg C ha−1, respectively. Natural regeneration accumulated carbon slower than reported for active restoration, suggesting that managed restoration is preferable to passive regeneration on fallows in northern Thailand. The allometric equations, derived from this study, can be used to accurately determine tree ABG and carbon storage in regenerating secondary forest, with higher precision than has hitherto been possible, thus satisfying the monitoring requirements of REDD+ and other carbon-trading schemes.

Quantifying Contemporary Organic Carbon Stocks of the Baltic Sea Ecosystem

The identification of carbon pools and the quantification of carbon stocks is necessary to (1) track changes in ecosystem dynamics, (2) inform science-based ecosystem and blue-carbon management, and (3) evaluate ecosystem and food web models. However, estimates of organic carbon stocks in marine ecosystems are incomplete or inconsistent. Therefore, we provide a first consistent estimate of relevant organic carbon stocks of a distinct marine ecosystem- the Baltic Sea. We estimate its contemporary standing stocks of 18 non-living and living organic carbon pools using data from literature and open-access databases. In contrast to existing data, our estimates are valid for the entire Baltic Sea, include necessary pools and are verifiable, as we describe data sources, methods and the associated uncertainties in detail to allow reproduction and critical evaluation. The total organic carbon (TOC) in the Baltic Sea ecosystem amounts to 1,050 ± 90 gC/m2 (440 ± 40 Mt). The non-living stocks account for about 98.8% and the living stocks for 1.2% of the TOC. Our estimates indicate that benthos has the highest living organic carbon stock and that the stock of particulate organic carbon (POC) has been underestimated in some previous studies. In addition, we find a partially inverted biomass distribution with a higher stock of primary consumers than primary producers. Our estimates provide a baseline of the size and distribution of the organic carbon in the Baltic Sea for the current period. Analyses of inorganic carbon stocks and the interplay between inorganic and organic stocks must follow to further define the baseline of total carbon stocks in the Baltic Sea.

When a definition makes the difference: operative issues about tree height measures from RPAS-derived CHMs

Tree height (H) survey is a fundamental step in forest mensuration. The error affecting tree height measure, necessarily influences the correspondent tree estimates. The remotely survey of vegetation using PHODAR (PHOtogrammetric Detection And Ranging) or LiDAR (Light Detection And Ranging) techniques generates very high-density point clouds, that result into Canopy Height Models (CHMs) having GSD (Ground Sampling Distance) of few centimetres. This GSD value potentially allows to survey single crown apexes, which, from a forestry point of view, do not represent the actual tree height. Apex height value, in fact, does not represent the prevailing dendrometric height (PDH) but the maximum tree value. In this study we propose a new approach aimed at measuring dendrometric height by PHODAR derived CHM, taking care about this issue. The proposed method defines a correcting factor (found equal to 95% percentile of CHM values distribution within a given crown) for the tree height extraction from CHM based on the PDH concept. The method could be implemented to single crown approach in forest parameters extraction algorithms permitting more reliable results, especially in terms of tree volume and related estimations (e.g., carbon stock quantification, allometric models).

Estimation of fuel loads and carbon stocks of forest floor in endemic Dalmatian black pine forests

Estimation of forest floor loading is important for many forest management applications, especially those related to fire management and carbon balance. We quantified the physical properties (depth, fuel load, bulk density) and carbon stocks of endemic Dalmatian black pine (Pinus nigra J.F. Arnold subsp. dalmatica [Vis.] Franco) forest floor layers. We also examined how these properties differ with stand age and layer. Forest floor depths ranged from 1.5 cm to 11.5 cm and forest floor fuel (FFF) loads ranged from 11.9 Mg ha-1 in the young stand to 197.3 Mg ha-1 in the old stand. Forest floor carbon (FFC) stocks ranged from 6.4 Mg C ha-1 in the young stand to 85.8 Mg C ha-1 in the old stand. We developed regression equations that can be used to convert the investigated forest floor depth into load in each layer individually and across all layers. These equations, together with the organic carbon (OC) concentration determined here for individual forest floor layers, simplify quantification of carbon stocks in the forest floor. Bulk density (BD) values reported here can also be used to convert depth measurements to loads for each layer and the entire forest floor. The results presented here are suitable for rapid estimation of FFF loads and FFC stocks based solely on forest floor depth, without the need to sample and analyze large amounts of forest floor fuels. Similarly, spatial distribution in FFF loads and carbon stocks can be assessed simply by measuring forest floor depths.

Models for estimating aboveground biomass of four dryland woody species in Burkina Faso, West Africa

Reliable estimates of carbon stocks for drylands are constrained by a limited number of biomass models for predicting accurately the biomass of dominant tree and shrub species. In this study, 119 trees with a diameter at breast height (dbh) between 3.82 and 45.48 cm were destructively sampled to evaluate aboveground biomass (AGB) allocation and prediction for four dryland species (Senegalia dudgeonii, Senegalia gourmaensis, Vachellia nilotica and Vachellia tortilis) commonly found in the Sudanian and Sahelian zones of Burkina Faso. Biomass allocation to leaf, branch and stem was examined using a classmometric analysis. Allometric equations relating biomass to dbh alone, and to dbh in combination with height and/or crown area were developed with and without consideration of the additivity property. Branches stored the largest (up to 90%) proportion of AGB. For three of the species, the branch mass fraction increased with increasing tree size at the expense of stem and leaf mass fractions. The statistical evaluations of the selected additive and non-additive models were satisfactory with no severe deficiencies. Diagnostics of model performance revealed that non-additive models performed better than additive models. The biomass models developed can be used for improved quantification of carbon stock and dynamics in dry savanna ecosystems.

Quantification of carbon stock under different land use regimes of Chitwan district, Nepal

Carbon sequestration in terrestrial ecosystems is gaining a global attention, including Nepal, to address the issues of climate change. Since, the quantification of carbon stock under different land use systems with focus on both biomass and soil profile is lacking, objective of this paper is to quantify carbon stock in biomass and in soil profile under different land use regimes, namely community forest, leasehold forest and agricultural land of Chitwan district. The carbon stock in biomass was calculated using the standard allometric equations, and Dry Combustion Method was used to determine the Soil Organic Carbon (SOC). The carbon content in above ground tree biomass (AGTB) was found to be higher (81.25 t/ha) in community forest than in leasehold forest (80.09 t/ha). The carbon stock in above ground sapling biomass (AGSB) was calculated only for the community forest, and was found to be 3. 67 t/ha. Similarly, the density of leaf litter, herbs and grasses (LHG) was also found to be higher (9. 25 t/ha) in the community forest in comparison to leasehold forest (6.45 t/ha). Further, the root carbon stock density was also higher (16.25 t/ha) in the community forest than in the leasehold forest (16.02 t/ha). However, the SOC density was highest in the agricultural land (73.42t/ha) followed by the community forest (66.38 t/ha)and the leasehold forest (52. 62 t/ha). Overall, the carbon stock was highest in the community forest (176.8 t/ha) then in leasehold forest (155.18 t/ha) followed by the agricultural land (73.42 t/ha). Hence, this study shows that well managed community forest can contribute significantly in offsetting global carbon emission.

Tropical deforestation drivers and associated carbon emission factors derived from remote sensing data

Reducing emissions from deforestation and forest degradation, and enhancing carbon stocks (REDD+) is a crucial component of global climate change mitigation. Remote sensing can provide continuous and spatially explicit above-ground biomass (AGB) estimates, which can be valuable for the quantification of carbon stocks and emission factors (EFs). Unfortunately, there is little information on the fate of the land following tropical deforestation and of the associated carbon stock. This study quantified post-deforestation land use across the tropics for the period 1990–2000. This dataset was then combined with a pan-tropical AGB map at 30 m resolution to refine EFs from forest conversion by matching deforestation areas with their carbon stock before and after clearing and to assess spatial dynamics of EFs by follow-up land use. In Latin America, pasture was the most common follow-up land use (72%), with large-scale cropland (11%) a distant second. In Africa deforestation was often followed by small-scale cropping (61%) with a smaller role for pasture (15%). In Asia, small-scale cropland was the dominant agricultural follow-up land use (35%), closely followed by tree crops (28%). Deforestation often occurred in forests with lower than average carbon stocks. EFs showed high spatial variation within eco-zones and countries. While our EFs are only representative for the studied time period, our results show that EFs are mainly determined by the initial forest carbon stock. The estimates of the fraction of carbon lost were less dependent on initial forest biomass, which offers opportunities for REDD+ countries to use these fractions in combination with recent good quality national forest biomass maps or inventory data to quantify emissions from specific forest conversions. Our study highlights that the co-location of data on forest loss, biomass and fate of the land provides more insight into the spatial dynamics of land-use change and can help in attributing carbon emissions to human activities.

Analysis of carbon stock density in protected and non- protected areas of Guinea Savanna in Niger State, Nigeria

Quantification of carbon stock has gained major attention in international climate change mitigation and adaptation negotiations. However, poor knowledge of the quantity of carbon stock in respective ecosystems is one major challenge in estimating carbon stock in developing countries. This study is aimed at estimating and comparing carbon stock density of protected (forest reserve) and non-protected (parkland) areas of guinea savanna in Niger state. The research methodology includes field survey inventory, biometric measurements and laboratory analysis. At each of the 45 sampling plot locations, Carbon stock was measured from six pools viz above ground tree, undergrowth, dead wood, litter, root and soil. within a quadrat sampling plot of 500 m 2 . Four fixed size square frames encompassing 1m 2 was used for the undergrowth (shrubs and grasses) and litter. Composite soil samples (for organic carbon) and undisturbed samples (for bulk density) were taken from each of the 1m 2 subplot quadrants at two depths (0-15cm and 15-30cm). Soil organic carbon concentration was estimated in the laboratory using Walkley-Black method. The findings of the study revealed that in terms of carbon stock in respective pools, in both protected and non-protected areas, soil pool was the highest, followed by tree and undergrowth; while litter, dead wood and root were the least in carbon stock. The average aggregate carbon stock density in the protected area is 118.2 Mg ha -1 which is greater than 69.3 Mg ha -1 recorded in the non- protected area. It was also observed that there is significant difference in carbon stock density between the forest reserve and parkland study sites where t (43) = 18.34, p < 0.001). The study concludes that if savannas were to be protected from fire, grazing and anthropogenic disturbances, most of them would accumulate substantial carbon and the sink would be larger; with a view to mitigating climate change effect. Keywords: Carbon stock, climate change, density, guinea savanna, mitigation

Getting forest science to policy discourse: a theory-based outcome assessment of a global research programme

This paper presents an assessment of the outcomes of research carried out under the Sustainable Wetlands Adaptation and Mitigation Programme (SWAMP). SWAMP aimed to inform national and international climate policy and practice by developing tools and methods to quantify greenhouse gas (GHG) emissions, carbon stocks and flux in tropical wetlands due to land use, land-use change and forestry (LULUCF). This assessment modelled SWAMP's intended outcomes as a theory of change (ToC) and used qualitative methods to test the ToC and to evaluate whether and how the outcomes were achieved. It found that SWAMP research has helped raise academic and policy interest in wetlands, mangroves and peat forests as carbon reservoirs, and that SWAMP's recommendations informed policy discourse and supported the development of technical guidance and strategies of sustainable wetland management. However, the research had a weak effect on international and Indonesian climate change policies compared to other factors. The Paris Agreement and Indonesia's nationally determined contribution (NDC) do not include the quantification of carbon stocks from mangroves, which are not all located in the forest areas. Knowledge translation was achieved through a variety of mechanisms, with direct engagement identified as particularly important. The outcome evaluation approach proved useful as a way of conceptualising and organising the analysis of research impact on development outcomes.