Estimates Of Carbon Stocks Research Articles

Unraveling the link between sediment acoustics and organic carbon

The top meter of marine sediments is estimated to store a total of 2322 Pg of carbon [Atwood et al., 2020. Front. Mar. Sci., vol. 7 p.165], which is twice that of terrestrial soils. Physical disturbance and remineralization of these carbon stocks could further accelerate climate change. Rapid and accurate quantification of sediment carbon stocks can (1) better inform carbon budgets and management of human activities in the ocean to minimize carbon remineralization and (2) monitor changes in sediment carbon stocks due to anthropogenic and natural disturbances. Sediment acoustics, which have been linked with sediment total organic carbon/content in mud banks, seagrass beds, and estuarine environments, can be measured in situ and offer a scalable solution toward rapid estimation of carbon stocks. However, before this modality can be applied broadly, a fundamental understanding between sediment acoustic properties and sediment organic/inorganic constituents must be realized. Here, direct ex situ measurements of sediment acoustic properties, sediment organic carbon, and grain size distribution will be compiled and compared across a variety of sediment types and locations from our own measurements and from published datasets. The development of a non-site-specific constitutive-based relationship will be discussed. [Work supported in part by ONR.]

Carbon stock of Agoro Agu Central Forest reserve, in Lamwo district, Northern Uganda

Terrestrial ecosystems such as forest landscapes provide critical ecosystem services such as carbon sequestration, fundamental to people, society and the global climate change discourse. Just like moist forest, dry Afromontane forests too present a high carbon sequestration potential. However, Uganda has since not undertaken carbon stock inventories for these conservation areas, especially with dry Afromontane forests like Agoro-agu central forest reserve (CFR). So, their potential to capture and store carbon is yet to be understood in Uganda. This study was carried out to estimate carbon stock of Agoro-agu CFR, for its potential in climate change mitigation. A stratified sampling design was used, where 65 sample plots were established. Nested, fixed area circular sample plots with sub-plots of varying radii for tree height, diameter measurements and soil sampling, were used. The mean total carbon stock of Agoro-agu CFR was estimated at 606.7 Mg C ha−1, for which 409, 72, 124 and 0.24 Mg C ha−1 was stored as above ground carbon, below ground carbon, soil organic carbon and carbon in litter herbs and grass respectively. The study illustrates the carbon sequestration potential of the forest reserve for any results-based payment projects for climate change mitigation. This is mainly due to the interventions through collaborative forest management arrangements and the pro-poor reducing emissions from Deforestation and forest Degradation (REDD+) pilot project in the landscape. This however, calls for more multi-stakeholders’ collaboration from direct resource users to national level to enhance forest conservation and reduce forest degradation for sustainable resource benefits and other ecosystem services.

Carbon Stock Assessment Of Mangrove Vegetation In Anambas Islands Marine Tourism Park, Indonesia

Global climate change can increase carbon dioxide (CO2) emissions in the environment. The mangrove ecosystem is one of the main ecosystems that have a role in mitigating climate change as carbon sinks and stores. Mangroves absorb CO2 during photosynthesis, then convert it into carbohydrates by storing it in biomass. This study aims to determine the mangrove ecosystem’s total biomass, carbon stock, and CO2 sequestration in Anambas islands marine tourism park. The novelty of this research is conducted in conservation areas with minimal potential for land loss so that they can be used as monitoring in the following years. In addition, carbon stock assessment is carried out using allometric equations to obtain Above Ground Biomass (AGB) and Below Ground Biomass (BGB) values so as not to damage the existing mangrove ecosystem. The assessment of total biomass and carbon stock was carried out at 12 stations around Siantan and Jemaja islands using the line transect method. The species of mangrove and diameter breast high (DBH) were measured in each plot with a transect size of 10x10 m. The biomass calculation was carried out to provide information on carbon stock and converted it to get the data of Carbon stocks and estimates of CO2 sequestration. The results showed that the mangrove ecosystem in the Anambas Islands Marine Tourism Park has a biomass of 574.73 tons/ha, an estimated carbon stock of 270.12 ton/ha, and CO2 sequestration of 990.45 ton/ha. The mangrove ecosystem spread over the Anambas Islands Marine Tourism Park is 766.32 Ha, so that it can store carbon reserves of 206,998.39 Ton C. As a conservation area, it is necessary to be sustainably managed to protect the existence of the mangrove ecosystem and increase the carbon stock in the future. Climate change mitigation may be achieved by reducing carbon emission levels and maintaining the mangrove ecosystem services as carbon sinks and sequestration.

Estimation of Aboveground Biomass of Individual Trees by Backpack LiDAR Based on Parameter-Optimized Quantitative Structural Models (AdQSM)

Forest aboveground biomass (AGB) plays a key role in assessing forest productivity. In this study, we extracted individual tree structural parameters using backpack LiDAR, assessed their accuracy using terrestrial laser scanning (TLS) data and field measurements as reference values, and reconstructed 3D models of trees based on parameter-optimized quantitative structural models (AdQSM). The individual tree AGB was estimated based on individual tree volumes obtained from the tree model reconstruction, combined with the basic wood density values of specific tree species. In addition, the AGB calculated using the allometric biomass models was validated to explore the feasibility of nondestructive estimation of individual tree AGB by backpack LiDAR. We found that (1) the backpack LiDAR point cloud extracted individual tree diameter at breast height (DBH) with high accuracy. In contrast, the accuracy of the tree height extraction was low; (2) the optimal parameter values of the AdQSM reconstruction models for Larix gmelinii and Betula platyphylla were HS = 0.4 m and HS = 0.6 m, respectively; (3) the individual tree AGB estimated based on the backpack LiDAR and AdQSM fit well with the reference values. Our study confirms that backpack LiDAR can nondestructively estimate individual tree AGB, which can provide a reliable basis for further forest resource management and carbon stock estimation.

Estimasi Simpanan Karbon Hutan Mangrove Menggunakan Citra (SAR) Synthetic Aperture Radar Sentinel -1 di Taman Hutan Raya Ngurah Rai,Bali


 
 
 
 Taman Hutan Raya (Tahura) Ngurah Rai, Bali, is recorded to have a mangrove forest covering an area of 1,373.5 ha, which plays a role in absorbing carbon from the atmosphere. Carbon is stored in the form of biomass. The bigger the biomass, the bigger the carbon stock in it. Direct measurement of biomass in the field requires an expensive method, a relatively long time, and narrow area coverage. Nowadays, the weakness of measuring carbon stocks directly in the field can be overcome by remote sensing approaches, especially estimations from satellite imagery combined with geographic information systems (SIG). One of the images that can be used to estimate carbon stock is the Sentinel-1 Synthetic Aperture Radar (SAR) image. The aim of this research is to estimate biomass and carbon stocks based on SAR backscatter distribution. The method used is the analysis of satellite imagery combined with field data. Field data collection and above-ground biomass (AGB) calculations were carried out by taking 30 plots with a plot size of 10 x 10 m. The results showed that there was a correlation between the backscatter value of SAR images and AGB from field measurements (r = 0.59 and the AGB estimation model y = 57.42x + 1223.8, where y is biomass and x is backscatter), and the coefficient of determination R2 = 0.36. The estimation model developed next is used to calculate the overall AGB, which is then used to estimate the overall aboveground carbon storage.
 
 
 

Estimating Settlement carbon stock and density using an inventory approach and quantifying their variation by land use and parcel size

A lack of very-high resolution land-cover data and in-situ carbon sampling in Settlement areas has limited the quantification of terrestrial carbon in Canadian Settlements and elsewhere. Without those data, it is difficult to quantify Settlement area terrestrial carbon for United Nations Framework Convention on Climate Change reporting within the Land Use, Land Use Change, and Forestry sector. The presented research takes a step toward filling this gap by first classifying Settlement land cover at a very-high resolution (<=80 cm, 93% overall accuracy). Then, with those data, an inventory approach is used to estimate carbon stocks based on local data, standard rulesets from the IPCC, and practice-based assumptions. Guided by FAIR principles, our approach is operationalized and available in a Jupyter Notebook for distribution, use, and extension by others. Results found that the study Settlement comprised 30% tree cover and 18% turfgrass. When analyzed by parcel size, carbon densities varied little for parcels less than 1.6 ha (3.9–4.2 kg C m-2), but then increased with larger parcels up to 6.6 Gg C m-2 for parcels > 8.1 ha. Among different land uses, industrial, commercial, and transportation had the lowest carbon densities (2.4–2.8 kg C m-2), followed by high, medium, and low-density residential (3.6, 4.0, 8.9 kg C m-2, respectively) with low-density residential almost achieving carbon densities in protected and recreation areas (10.2 kg C m-2). Our results suggest that land use is a stronger driver of carbon-storage relative to parcel size, but their combination best represents the variation in carbon-storage in low-density residential land use. Currently, only carbon change in urban trees and deforestation around urban centres is reported in Canada’s National GHG Inventory Report. The present research quantifying carbon densities provides an analysis that could inform carbon change resulting from other land-use conversions and improve deforestation estimates by better defining the final state of a land-use change.

Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa

Woody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R2 (RMSE) = 0.39 (36.04%), and in 2015, R2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.

Estimation of biomass and carbon storage potential in agroforestry systems of north western Himalayas, India

The estimation of carbon stock in agroforestry is crucial for curbing the global climate crisis. In this study, biomass production and carbon storage potential of the prevalent agroforestry systems along an altitudinal gradient in the Chamba district of Himachal Pradesh was carried out during 2019–2021. Agroforestry practices were categorized into eight land use systems viz., agrisilviculture, agrihorticulture, agrosilvohorticulture, agrohortisilviculture, hortiagriculture, silvopastoral, pastoralsilviculture and hortipastoral systems and were divided into four altitudinal ranges viz., <1000 m above sea level (m asl; Z-I), 1000–1500 m asl (Z-II), 1500–2500 m asl (Z-III) and > 2500 m asl (Z-IV). Results revealed that biomass production and total carbon storage (vegetation + soil) were significantly affected by the land use type along the altitudinal gradient. Among the different agroforestry systems, maximum aboveground biomass (47.48 Mg ha−1) and belowground biomass (12.20 Mg ha−1) were recorded for agrohortisilviculture. Biomass production was positively correlated with altitudes and increased with an increase in altitudes. Soil carbon density was the maximum (35.77 Mg ha−1) under silvopastoral showing a decreasing trend along the altitudinal gradient. The total carbon storage potential was observed the maximum (59.76 Mg ha−1) under silvopastoral system followed by agrohortisilvicuture > agrosilvohorticulture > agrisilviculture > pastoralsilviculture > hortiagriculture > hortipastoral > agrihorticulture. Along the elevation gradient, total carbon stock ranged from 43.99 to 51.13 Mg ha−1 with the maximum carbon stock corresponding to Z-III and the minimum for Z-IV. This study offers a useful approach for selecting different land use systems and vegetation patterns to maximize thecarbon stocks in a cost-effective manner, which will further help to mitigate global warming issues in other fragile Himalayan catchments.

Deep SOC stock dynamics under contrasting management systems: Is the EPIC model ready for carbon farming implementation?

Process-based models have been recognized as cost-effective tools to assess carbon farming mechanisms through quantifying the C fluxes in the agroecosystems. A result-based approach is suggested however the wide variability of agricultural environments makes further model implementation necessary to limit the uncertainty of the results, especially on deep soil organic carbon (SOC) stock estimation and stratification and in agroecosystems characterized by a shallow water table. In this study, a comprehensive soil and crop dataset collected over a seven-year period from different pedoclimatic conditions across the Veneto Region (NE Italy) was used for EPIC model calibration and validation of SOC stock dynamics. Experimental data included yields from several crops (corn, winter and spring wheat, rapeseed, and soybean), continuous monitoring of soil water content, and SOC stocks (1872 total samples within the 0–50 cm soil profile) under conventional, cover crop and conservation agriculture systems. Modelling was performed by testing two N-C sub-models (CENTURY and PHOENIX), which differentiated in terms of mineralization/immobilization rates.Results showed that the procedure was able to obtain parameters valuable for most of the management system and pedoclimatic condition, reproducing well the tested variables. The EPIC model acceptably captured soil water dynamics (Nash-Sutcliffe coefficient – NSE – was up to 0.26), especially in the topsoil. Furthermore, simulation of weed—crop competition in conservation agriculture strongly contributed to properly explain the variability in crop production among the contrasting agricultural systems (R2 ranged from 0.51 to 0.71). Likewise, EPIC skillfully simulated SOC stocks within the 0–50-cm profile regardless of the sub-model used (NSE was up to 0.64). Moreover, the model acceptably captured the profile SOC stratification among the different management practices. This study highlights the EPIC robustness for predicting SOC stocks and assessing with high accuracy carbon farming results.

Forest carbon stock and biomass estimation in West Central India using two allometric models

The west central Indian forest ecosystems strongly contribute to livelihood of local human communities as well as climate change mitigation. However, due to the lack of accurate biomass equations and predictive tools, the contribution of these forests towards carbon stock and sequestration remains poorly recognized. At present due to some developmental and mining activities these forests face huge anthropogenic pressure.Therefore, the objective of the present study was to estimate the biomass and carbon stock of forest tree species of west central India to provide baseline information. It will also be useful for measuring the changes after the intervention as well as for future management and conservation.The non-destructive allometric equation approach was adopted to estimate the biomass, carbon stock and carbon dioxide equivalent from total 28 plots of 0.1 ha at seven sites of dry deciduous forests of west central India. In the present study, the results of biomass and carbon stock were calculated by employing two different allometric models.The model with tree diameter and wood specific gravity gave higher estimations of total plant biomass (123.39 – 65.86 Mg ha−1) and carbon stock (58.61—31.28 Mg ha−1) as compared with model having only tree diameter (108.84 – 55.91 Mg ha−1) and (51.70 -26.55 Mg ha−1), respectively.We conclude that the allometric model with tree diameter and wood specific gravity was found best suited for these forests. The results also showed the positive relation between tree diversity, basal area and aboveground biomass and carbon stock.

Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R2 =0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R2 = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability.

Estimation of the Aboveground Carbon Stock in Kanonge Local Forest of Kaputa District in Northern Province, Zambia

Purpose: The majority of district-level carbon stock estimates for the local forests are obsolete and incomplete. Because of this, decisions made by policymakers have an impact on forest regions. This study establishes some baseline information for estimates of the above-ground carbon stocks in the nearby Kanonge forest.&#x0D; Methodology: The use of a sequential explanatory design allowed the quantitative data to be collected and analysed first, and the qualitative data to be collected and analysed later to explain why the variables identified in the first phase are significant predictors of the aboveground carbon stocks. As a result, 15 sample plots were used in the forest, 286 households were sampled on villages within 5km radius to the forest, 8 key informants were sampled from Forestry Department, 8 from Ministry of Agriculture, 10 village head persons and 10 forest users. The quantitative data was collected using forest inventories and household surveys while qualitative data was collected using key informants’ interviews. The allometric equations were used to analyse forest inventory data, logistic regression was used to analyse household survey data and the key informant interviews were analysed using thematic analysis.&#x0D; Findings: The results show that, the above ground biomass and carbon stock were 276.19 ± 7.41 and 155.76 ± 4.18 t ha-1 respectively. The logistic regression results shows that Forest products use, farm size, household size and forest clearing where the significant predictors of the reduction of the aboveground carbon stocks. The results further reveals that there is inadequate intersectoral coordination at district and local level.&#x0D; Unique Contribution to Theory, Practice and Policy: Further study should be done to find strategies to sustain carbon stocks in the forest while maintaining the use of the forest by the local communities. This study provides accurate estimates of forest and tree carbon stocks and supports in the development of the district integrated plan in accordance with decentralized policy. This information can therefore be used at the district and federal levels in various carbon programs like REDD+, offering the district the chance to participate in the fight against climate change and global warming.

ABOVEGROUND BIOMASS AND CARBON STOCK ESTIMATION OF FALCATA THROUGH THE SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 IMAGES

Abstract. Estimates of aboveground biomass (AGB) in forests have been made in the context of climate change mitigation. There were limited studies about Falcata aboveground biomass and carbon stock estimation using the traditional method; however, that is time-intensive and expensive. Hence, this study was executed to utilize remote sensing and assess the potential of the synergistic use of Sentinel-1 and Sentinel-2 images in estimating the AGB and carbon stock of Falcata. The methodology consists of boundary demarcation of Falcata plantations in Butuan City, development of aboveground biomass models, and estimation and mapping of aboveground biomass and carbon stock. Among the developed models, the model which is the combination of Sentinel-1 and Sentinel-2 has the highest coefficient of determination (R2) of 0.632 and lowest Root Mean Square Error (RMSE) of 1.94 ton/pixel and was found to perform best in predicting the AGB and carbon stock of 4-year-old Falcata and performed poorly in 1-year-old Falcata. Nevertheless, its overall R2 and RMSE have proven that the model is moderately good and acceptable in predicting AGB of all stand ages of Falcata and, indirectly, the carbon stock. This study demonstrates that combining satellites generates a robust and more accurate AGB and Carbon Stock model than the models derived from the individual satellites.

Allocation Patterns and Temporal Dynamics of Chinese Fir Biomass in Hunan Province, China

How trees allocate their biomass among different components has important implications for their survival and growth and ecosystem carbon cycling. Data on the distribution pattern and dynamics of tree biomass are essential for fully exploiting forest carbon sequestration potential and achieving the goal of carbon neutralization. However, there has not been enough research to-date on tree biomass spatial allocation and temporal dynamics in different site qualities at specific tree species scales. This study aimed to evaluate the biomass allocation patterns within tree components of Chinese fir and to examine how they are affected by tree age and site quality. A total of 87 trees were destructively sampled and measured for stem, branch, leaf, bark and root biomass. The biomass proportion difference of tree components in different age stages (8-40 years) was analysed, and the influence process of tree age and site quality on biomass allocation was examined. Our results indicate that the biomass allocation varied with tree age and was also affected by site quality. Stem biomass accounted for the largest proportion of total tree biomass, followed by leaf, root, branch and bark biomass in young forests, and it was followed by root, bark, branch and leaf biomass in other age groups. The biomass proportion of each component all nonlinearly changed with tree age. The proportion of stem biomass increased with increasing tree age, and the biomass proportion of branches and leaves decreased with increasing tree age. The proportion of root biomass first increased and then decreased with tree age, while the bark biomass proportion first decreased and then increased with increasing tree age. Site quality had a positive effect on the biomass proportion of stems but a negative effect on the biomass proportion of branches and bark. The interaction of tree age and site quality also had a significant effect on the proportion of stem biomass as well as root biomass. Therefore, to obtain accurate estimates of Chinese fir forest biomass and carbon stocks, age-specific changes and the influence of site conditions on it need to be considered.

Korelasi indeks keanekaragaman dan kerapatan tegakan dengan simpanan karbon mangrove Estuari Perancak

Land use change is a huge threat for mangrove ecosystems,which are known for their high carbon sequestration and storage capacity.Vegetation restoration efforts are often undertaken, but fail to restore optimal ecosystem carbon sequestration. The mangrove forest of Perancak Estuary with a history of restoration project was made the subject of this research. The objectives include: (i) estimation of mangrove biomass and sediment carbon stock; (ii) comparison of restored, mixed and natural mangroves’ total carbon stock; (iii) correlational analysis between stand density and diversity indices with ecosystem carbon stock. Nine sampling points were determined within three mangrove categories (mixed, natural, restored). Stand characteristics and diameter at breast height (DBH) were measured to allometrically estimate biomass carbon. Sediment carbon was analyzed with Loss on Ignition (LOI) method. Correlational analysis was done with Pearson’s correlation coefficient. Total ecosystem carbon stock is 4472,93 tonnes ha-1 (biomass C: 4046,31 tonnes ha-1; sediment C: 426,62 tonnes ha-1). Highest carbon stock value was found on restored mangroves due to high contribution of sediment C offsetting its low biomass C. Lowest carbon stock value was found on natural mangroves due to decreased root biomass production and increased decomposition due to change in tidal regimes. There is a strong positive correlation between stand density and biomass carbon. Simpson index of diversity has a stronger (though non significant) correlation with biomass carbon than Shannon-Wiener index.

Enabling soil carbon farming: presentation of a robust, affordable, and scalable method for soil carbon stock assessment

The main hurdle in instrumentalizing agricultural soils to sequester atmospheric carbon is the development of methods to measure soil carbon stocks which are robust, scalable, and widely applicable. Our objective is to develop an approach that can help overcome these hurdles. In this paper, we present the Wageningen Soil Carbon STOck pRotocol (SoilCASTOR). SoilCASTOR uses a novel approach fusing satellite data, direct proximal sensing-based soil measurements, and machine learning to yield soil carbon stock estimates. The method has been tested and applied in the USA on fields with agricultural land use. Results show that the estimates are precise and repeatable and that the approach could be rapidly scalable. The precision of farm C stocks is below 5% enabling detection of soil organic carbon changes desired for the 4 per 1000 initiative. The assessment can be done robustly with as few as 0.5 sample per hectare for farms varying from 20 to 150 hectares. These findings could enable the structural implementation of carbon farming.

Belize Blue Carbon: Establishing a national carbon stock estimate for mangrove ecosystems

Mangrove ecosystems are among the most economically and ecologically valuable marine environments in the world. Mangroves are effective at long-term carbon storage within their sediments and are estimated to hold 12 billion metric tons of carbon worldwide. These ecosystems are therefore vitally important for carbon sequestration and, by extension, climate change mitigation. As part of the Paris Agreement, participating countries agree to provide plans to reduce their carbon emissions, or nationally determined contributions (NDCs). However, despite mangroves being recognized as important nature-based solutions, many countries still lack national data on carbon stocks and must use global or regional averages, which may not be sufficiently accurate. Here, we present the national carbon stock estimate of mangrove ecosystems for the NDC of Belize, acquired through a collaborative approach involving government agencies and NGOs. We conducted a comprehensive sampling of mangroves across the country, including a range of mangrove ecotypes. The mean total ecosystem carbon stock (TECS) for the nation was 444.1 ± 21.0 Mg C ha−1, with 74.4 ± 6.2 Mg C ha−1 in biomass stocks, and 369.7 ± 17.7 Mg C ha−1 in sediment stocks. Combining these data with a recent mapping effort, we provide the first national comprehensive mangrove carbon stock estimate of 25.7 Tg C. The national mean from this study varies from previous global analyses, which can under- or overestimate TECS by as much as 0.6 Tg C and 16.5 Tg C, respectively, depending on the study. These data supported the NDC update of Belize, and can be used to inform the country's mangrove protection and restoration commitments. The collaborative approach of this work should serve as a blueprint for other countries seeking to conserve natural blue carbon sinks as a strategy to achieve their climate targets.

Advance methodological approaches for carbon stock estimation in forest ecosystems.

The forests are a key player in maintaining ecological balance on the earth. They not only conserve biodiversity, reduce soil erosion, and protect watersheds but also promote the above and below-ground ecosystem services. Forests are known as air cleaners on the planet and play a significant role in mitigating greenhouse gas (GHG) emissions into the atmosphere. As per programs launched in the Conference of Parties (COP) 26, there is a need to promote policies and programs to reduce the atmospheric carbon (C) through the forest ecosystem; it is because forests can capture the atmospheric CO2 for a long time and help to achieve the goals of net-zero emission CO2 on the earth. Therefore, there is an urgent need to know the advanced technological approaches for estimating C stock in forest ecosystems. Hence, the present article is aimed at providing a comprehensive protocol for the four C stock estimation approaches. An effort has also been made to compare these methods. This review suggests that tree allometry is the most common method used for the quantification of C stock, but this method has certain limitations. However, the review shows that accurate results can be produced by a combination of two or more methods. We have also analyzed the results of 42 research studies conducted for C stock assessment along with the factors determining the amount of C in different types of forests. The C stock in vegetation is affected by temporal and spatial variation, plantation age, land use, cropping pattern, management practices and elevation, etc. Nevertheless, the available results have a large degree of uncertainty mainly due to the limitations of the methods used. The review supports the conclusion that the uncertainty in C stock measurements can be addressed by the integration of the above-mentioned methods.

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane‐dominated African savannah dry forest

AbstractThis study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L‐band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north‐western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite‐derived metrics, a two‐pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands.

SYSTEMATIC STUDY OF CARBON SEQUESTRATION OF MANGROVE FORESTS AT RAIGAD DISTRICT COAST, MAHARASHTRA, INDIA

Mangrove forests are highly productive ecosystems with carbon sequestration rate almost equivalent to tropical green forests. Mangroves distribute proportionally more carbon below ground and possess higher below to above ground carbon mass ratio as compared to any other forests (Kathiresen, 2004). Mangrove is amongst the most carbon rich biomes containing more than 950 tC/H. The said study overlooks destructive and non-destructive methods of the carbon sequestration. It is conducted as a comparative assessment of the carbon sequestration potential of mangrove and non-mangrove regions. By means of methodology of calculating Soil Bulk Density (gcm -3) and Total Organic Carbon (TOC) in mangroves soils (El Wakeel and Riley, 1956) and Carbon Sequestration Rate (CSR) is estimated based on the sediment rates and TOC content of the soil (Duan Xiaonan et al (2008). The systematic assessment conducted at West Coast of Maharashtra; shows mangrove region with Avicinnia marina species and in non–mangrove region total carbon in soil (%) recorded 17.15% and 4.52 percent respectively. Total Soil Carbon Stock recorded is 431 million g / Ha at mangrove region and 105.7 million g / Ha at non mangrove region. After methodical estimation of biomass and carbon stock of mangroves (English et.al, 1997 and Kaufman and Donato, 2012), total carbon stock of mangrove stand was recorded 393.14 mg/Ha and that of non-mangrove stand was recorded to be 91.7 mg/Ha. It shows that mangroves absorb almost 315.14 percent more carbon than non-mangrove region. In other words, carbon stock in Avicinnia marina stand found to be almost 3 times more than non-mangrove zone.