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Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R2 and RMSE of the model based on source III were better than the R2 and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R2 by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R2 of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R2 of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R2 of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. [ABSTRACT FROM AUTHOR]

Carbon (C) losses due to the conversion of natural forests adversely affect the biotic and abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C stock in rubber plantations along elevation gradients is poorly understood. In this study, we investigated biomass and C stock along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. The C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths were measured. A significant negative correlation was observed between AGB, BGB, litter, and total ecosystem C stocks and elevation gradients in both age groups. However, no correlation was observed between the total soil C stock and elevation gradients in 8-year-old rubber plantations, while significant decline was detected in 12-year-old rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha−1 was recorded at 900 m in 8-year-old plantations; whereas, in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha−1 was found at 700 m. The total ecosystem C stock decreased to their lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha−1 and 125.75 Mg C ha−1, respectively. Moreover, total ecosystem C stock significantly decreased from 51.55% to 8.05% and from 42.96% to 11.46% between 700 m and 1100 m, in both 8-year-old and 12-year-old plantations, respectively. Regardless of elevation gradients, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other components contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stock along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass, and C stock capacity decreased above 900 m, and that age and elevation are key factors for biomass and C stock in rubber monoculture plantations. [ABSTRACT FROM AUTHOR]

Global warming is a critical factor driving climate change, impacting every aspect of life on Earth. The escalating concentration of greenhouse gasses in the atmosphere, the primary contributor to global warming, necessitates immediate action through effective climate mitigation strategies. This study aimed to quantify the biomass and blue carbon stocks in the eastern coastal mangrove forests of North Sumatra and Aceh Provinces in Indonesia, focusing on key sites in Langkat, Deli Serdang, Batu Bara, Tanjung Balai, and Aceh Tamiang Regencies. We measured carbon stock in three carbon pools: biomass (above and below ground), necromass, and soil. By analyzing tree stands using parameters such as tree height and diameter at breast height within circular plots (7 m in radius, 125 m apart), we gathered fundamental data on forest structure, species composition, and above- and below-ground biomass. Additionally, we collected soil samples at various points and depths, measuring the amount of wood, stems, or branches (necromass) that fell to or died on the forest floor. Data were collected in plots along a line transect, comprising three transects and six circular plots each. Sixteen diverse mangrove species were found, demonstrating rich mangrove biodiversity. The mangrove forests in the five regencies exhibited significant carbon storage potential, with estimated average above-ground carbon ranging from 96 to 356 MgC/ha and average below-ground carbon from 28 to 153 MgC/ha. The estimated average deadwood carbon varied between 50 and 91 MgC/ha, while soil carbon ranged from 1200 to 2500 MgC/ha. These findings underscore the significant carbon storage potential of these mangrove forests, highlighting their importance to global carbon cycling and climate change mitigation. This research contributes to a broader understanding of mangroves as vital blue carbon ecosystems, emphasizing the necessity of conservation efforts such as forest restoration and rehabilitation to enhance their role in stabilizing coastal areas and improving global climate resilience. [ABSTRACT FROM AUTHOR]

Changes in land use from high-nature-value grasslands to arable fields reduce the organic carbon stock in soil, increasing atmospheric carbon concentrations. Maintaining grasslands through traditional agricultural techniques can mitigate climate change by transferring atmospheric carbon to the soil. Benefits of soil organic carbon sequestration include improved soil properties and enhanced ecosystem services and biodiversity. With Romania's ratification of the Paris Agreement, it is crucial to review climate-related agricultural policies and incentivize carbon sequestration practices in organic soils. This paper presents a soil carbon study in Transylvania's Târnava Mare region, Romania, known for its preserved cultural landscapes. Soil samples were taken at a depth of 60 cm to assess organic carbon pools under grassland and arable land management across three soil classes: Cernisoils, Hidrisoils, and Luvisoils. Several statistical tests were applied to evaluate the most significant drivers of soil organic carbon sequestration including land use, soil class, and soil depth. The results indicate that land management has the largest impact, with grasslands storing 45% more carbon than arable land on average. This finding should be integrated into national climate action plans, prioritizing the preservation of grasslands and sustainable agricultural practices to support soil organic carbon sequestration. [ABSTRACT FROM AUTHOR]

Long-term excessive use of fertilizers and intensive cultivation not only decreases soil organic carbon (SOC) and productivity, but also increases greenhouse gas emissions, which is detrimental to sustainable agricultural development. The purpose of this paper is to identify organic amendments suitable for winter wheat growth in the North China Plain by studying the effects of organic amendments on the economic benefits, carbon emissions, and carbon sequestration for winter wheat fields and to provide a theoretical basis for the wide application of organic amendments in agricultural fields. The two nitrogen rates were N0 (0 kg ha−1) and N240 (240 kg ha−1), and the four organic amendments were straw, manure, mushroom residue (M R), and biochar. The results showed that, compared to N0, N240 significantly increased the yield by 244.1–318.4% and the organic carbon storage by 16.7–30.5%, respectively, but increased the carbon emissions by 29.3–45.5%. In addition, soil carbon stocks increased with all three types of organic amendments compared to the straw amendment, with the biochar treatment being the largest, increasing carbon storage by 13.3–33.6%. In terms of yield and economic benefits, compared to the straw amendment, the manure and biochar amendments increased winter wheat yields by 0.0–1.5% and 4.0–13.3%, respectively, and M R slightly decreased wheat yield; only the economic benefit of the M R amendment was greater than that of the straw amendment, with an increase in economic benefit of 1.3% and 8.2% in the 2021–2022 and 2022–2023 seasons, respectively. Furthermore, according to the net ecosystem productivity (NEP), N0 was the source of CO2, while N240 was a sink of CO2. The TOPSIS results showed that N240 with a mushroom residue amendment could be recommended for increasing soil carbon stocks and economic benefits for winter wheat in the NCP and similar regions. Low-cost M R can increase farmer motivation and improve soil organic carbon, making a big step forward in the spread of organic materials on farmland. [ABSTRACT FROM AUTHOR]

The increase in atmospheric CO2 leads to global warming and ecological environment deterioration. Carbon storage modeling and assessment can promote the sustainable development of the ecological environment. This paper took Xinjiang as the study area, analyzed the spatial and temporal evolution of land use in four periods from 1990 to 2020, explored the spatial relationship of carbon stocks using the InVEST model, and coupled the GMOP model with the PLUS model to carry out multiple scenarios for the future simulation of land use in the study area. We found (1) Over time, the types with an increasing area were mainly impervious and cropland, and the types with a decreasing area were grassland, snow/ice, and barren; spatially, the types were predominantly barren and grassland, with the conversion of grassland to cropland being more evident in the south of Northern Xinjiang and north of Southern Xinjiang. (2) The evolutionary pattern of terrestrial carbon stocks is increasing and then decreasing in time, and the carbon sink areas are concentrated in the Tarim River Basin and the vicinity of the Ili River; spatially, there are differences in the aggregation between the northern, southern, and eastern borders. By analyzing the transfer in and out of various categories in Xinjiang over the past 30 years, it was obtained that the transfer out of grassland reduced the carbon stock by 5757.84 × 104 t, and the transfer out of Barren increased the carbon stock by 8586.12 × 104 t. (3) The land use layout of the sustainable development scenario is optimal under the conditions of satisfying economic and ecological development. The reduction in terrestrial carbon stocks under the 2020–2030 sustainable development scenario is 209.79 × 104 t, which is smaller than the reduction of 830.79 × 104 t in 2010–2020. Land optimization resulted in a lower loss of carbon stocks and a more rational land-use layout. Future planning in Xinjiang should be based on sustainable development scenarios, integrating land resources, and achieving sustainable economic and ecological development. [ABSTRACT FROM AUTHOR]

Enunciating the carbon storage across various types of forests is a precondition for comprehending forest ecosystem carbon cycling. However, previous studies regarding forest carbon storage were primarily conducted in the general temperature zones, with a limited understanding of carbon storage in different forest types within climate transition zones. In this study, we employed biomass models to explore the carbon storage in three types of natural secondary forests (Pinus armandii forest, Quercus aliena forest, and Q. aliena–P. armandii mixed forest) in the transition zone between the northern subtropical and warm temperate regions of China. Furthermore, we used the variance decomposition analysis and random forest model to determine the key factors influencing carbon storage in three types of natural secondary forests. Our results indicated that the carbon storage of wood and soil layers in the Q. aliena–P. armandii mixed forest was significantly higher than that in the P. armandii and Q. aliena forests. Total carbon storage was ranked as follows: Q. aliena–P. armandii mixed forest (266.09 t/ha) > P. armandii forest (222.89 t/ha) > Q. aliena forest (212.46 t/ha). Our results also revealed that carbon storage of the wood layer was jointly regulated by environmental factors, plant physiological characteristics, and soil properties, while soil carbon storage was mainly affected by soil properties. These results highlight the significant advantages of mixed conifer–broadleaf forests in carbon storage, emphasizing the importance of mixed natural secondary forests in carbon cycling and ecosystem services. This study provides scientific evidence for enhancing forest carbon sink functions and developing forest conservation and management policies to combat climate change. [ABSTRACT FROM AUTHOR]

In recent years, the "dual carbon" issue has become a major focus of the international community. Changes in land use driven by anthropogenic activities have a profound impact on ecosystem structure and carbon cycling. This study quantitatively assesses the spatiotemporal changes in cropland occupation and supplementation in the Pearl River Delta from 2000 to 2020 using the InVEST model, analyzing the spatial clustering of carbon storage changes caused by variations in cropland area. The PLUS model was employed to simulate land-use patterns and the spatial distribution of carbon storage in four future development scenarios. The results indicate the following: (1) From 2000 to 2020, the net change rate of cropland area in the Pearl River Delta was −0.81%, with a decrease of 16.49 km2 in cropland area, primarily converted to built-up land and forest land. (2) Carbon storage in the Pearl River Delta exhibited a pattern of lower values in the center and higher values in the periphery. The terrestrial ecosystem carbon storage in the Pearl River Delta was 534.62 × 106 t in 2000, 518.60 × 106 t in 2010, and 512.57 × 106 t in 2020, showing an overall decreasing trend. The conversion of cropland and forest land was the main reason for the decline in total regional carbon storage. (3) The area of carbon sequestration lost due to cropland occupation was significantly greater than the area of carbon loss compensated by new cropland, indicating an imbalance in the quality of cropland occupation and supplementation as a crucial factor contributing to regional carbon loss. (4) Under the ecological priority scenario, the expansion of built-up land and the reduction in ecological land such as cropland and forest land were effectively controlled, resulting in the minimal loss of carbon storage. The soil organic carbon pool of cropland is the most active carbon pool in terrestrial ecosystems and has a significant impact on carbon storage. Clarifying the relationship between "cropland protection measures–land use changes–ecosystem carbon storage" will improve cropland protection policies, provide references for regional carbon sequestration enhancement, and support sustainable socio-economic development. [ABSTRACT FROM AUTHOR]

Mixed forests created by incorporating nitrogen-fixing tree species offer enhanced ecological advantages compared with forests consisting of only one type of tree species. These benefits include habitat rehabilitation and the promotion of biodiversity. Nevertheless, the impact of introducing nitrogen-fixing tree species on ecosystem carbon (C), nitrogen (N), and phosphorus (P) sequestration and storage capacity in the Loess Plateau of China remains inadequately explored. To examine changes in the sequestration and storage capacity of ecosystem C, N, and P, the mixed plantations of P. tabulaeformis and H. rhamnoides (HrPt) were selected as the research object, and the pure plantations of H. rhamnoides (Hr) and P. tabulaeformis (Pt) were selected as the control. The results indicated that in comparison to the pure forest, the ecosystem in HrPt significantly increased C and N stocks but decreased P stocks. In addition, C, N, and P stocks in the soil layer accounted for more than 60% of the C, N, and P stocks in the pure and mixed forest ecosystems compared with the vegetation layer. Moreover, HrPt significantly improved ecosystem C and N sequestration rates relative to the pure forest but decreased P sequestration rates. Furthermore, the soil physicochemical properties can be inferred from the redundancy analysis showing 66.79% and 0.06% in Hr, 44.84% and 0.06% in Pt, as well as 44.28% and 0.04% in HrPt, respectively. In conclusion, compared with the pure forest, the introduction of N-fixing tree species was more conducive to the accumulation of C and N. The results offer substantial significance for the scientific guidance of vegetation restoration in degraded landscapes and the stewardship of mixed forests in the Loess Hilly Region, providing essential data for nutrient storage in ecosystems. [ABSTRACT FROM AUTHOR]

Carbon stocks provide information that is essential for analyzing the role of forests in global climate mitigation, yet they are highly vulnerable to wildfires in Mediterranean ecosystems. These carbon stocks' exposure to fire is usually estimated from specific allometric equations relating tree height and diameter to the overall amount of aboveground carbon storage. Assessments of vulnerability to fire additionally allow for specific fire resistance (bark thickness, crown basal height) and post-fire recovery traits (cone mass for regeneration, and fine branches or leaves mass for flammability) to be accounted for. These traits are usually considered as static, and their temporal dynamic is rarely assessed, thus preventing a full assessment of carbon stocks' vulnerability and subsequent cascading effects. This study aimed to measure the pools of carbon stocks of individual trees varying between 30 and 96 years old in the Djbel Mansour Aleppo pine (Pinus halepensis) forest in semi-arid central Tunisia in the southern range of its distribution to fit a sigmoid equation of the carbon pools and traits recovery according to age as a vulnerability framework. Allometric equations were then developed to establish the relationships between fire vulnerability traits and dendrometric independent variables (diameter at breast height, height, and live crown length) for further use in regional vulnerability assessments. The total carbon stocks in trees varied from 29.05 Mg C ha−1 to 92.47 Mg C ha−1. The soil organic carbon stock (SOC) at a maximum soil depth of 0–40 cm varied from 31.67 Mg C ha−1 to 115.67 Mg C ha−1 at a soil depth of 0–70 cm. We could identify an increasing resistance related to increasing bark thickness and basal crown height with age, and enhanced regeneration capacity after 25 years of age with increasing cone biomass, converging toward increasing vulnerability and potential cascading effects under shorter interval fires. These results should be considered for rigorous forest carbon sequestration assessment under increasing fire hazards due to climate and social changes in the region. [ABSTRACT FROM AUTHOR]

Roots play a fundamental role in forest ecosystems, but obtaining samples from deep layers remains a challenging process due to the methodological and financial efforts required. In our quest to understand the dynamics of Eucalyptus roots, we raise three fundamental questions. First, we inquire about the average extent of the roots of two contrasting Eucalyptus genotypes. Next, we explore the factors that directly influence the growth and depth of these roots, addressing elements such as soil type, climate, and water availability. Lastly, we investigate how the variation in Eucalyptus species may impact root growth patterns, biomass, and carbon stock. In this study, we observed that the maximum root depth increased by an average of 20% when genotypes were grown on sites with higher water availability (wet site). E. urophylla stands had a higher biomass and carbon stock (5.7 Mg C ha−1) of fine roots when cultivated on dry sites (annual rainfall~727 mm) than the wet sites (annual rainfall~1590 mm). In E. grandis × E. camaldulensis stands, no significant differences were observed in the stock of fine root biomass (3.2 Mg C ha−1) between the studied environments. Our results demonstrated that genotypes with greater drought tolerance (E. grandis × E. camaldulensis) tend to maintain higher stocks of fine root biomass (3.2–6.3 Mg ha−1) compared to those classified as plastic (E. urophylla), regardless of the edaphoclimatic conditions of the cultivation site. Finally, our research helps understand how Eucalyptus trees adapt to their environment, aiding sustainable forest management and climate change mitigation. We also provide a practical tool to estimate underground biomass, assisting forest managers and policymakers in ensuring long-term forest sustainability. [ABSTRACT FROM AUTHOR]

This study analyzed the CO2 capture potential for the Parque Natural Regional Serrania de las Quinchas buffer area in Colombia. For this purpose, multitemporal analysis for land covering for the years 1989, 2000, 2006, 2011, 2017 and 2021 was performed using the normalized difference vegetation index (NDVI) for each cover and land cover (LC) methodology. In the same way, aboveground biomass (AGB) was measured for representative parcels by measuring tree diameters and heights and applying adequate allometric models; carbon content in soils was measured too. The results showed that carbon content in soils is higher than that in aboveground biomass. Average values for the tree parcels were above 2 times the value recorded for average tropical humid forests, and one of them had a value 7 times this value. A very interesting potential for existing forest recovery was found for this area. Strategies for this include the development of sustainable practices, land use management, biodiversity preservation and the participation and leading of the local communities. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of forest carbon stock is important for analyzing the carbon cycle. In order to obtain forest carbon stock efficiently, this paper utilizes airborne LiDAR data to research the applicability of different feature screening methods in combination with machine learning in the carbon stock estimation model. First, Spearman's Correlation Coefficient (SCC) and Extreme Gradient Boosting tree (XGBoost) were used to screen out the variables that were extracted via Airborne LiDAR with a higher correlation with carbon stock. Then, Bagging, K-nearest neighbor (KNN), and Random Forest (RF) were used to construct the carbon stock estimation model. The results show that the height statistical variable is more strongly correlated with carbon stocks than the density statistical variables are. RF is more suitable for the construction of the carbon stock estimation model compared to the instance-based KNN algorithm. Furthermore, the combination of the XGBoost algorithm and the RF algorithm performs best, with an R2 of 0.85 and an MSE of 10.74 on the training set and an R2 of 0.53 and an MSE of 21.81 on the testing set. This study demonstrates the effectiveness of statistical feature screening methods and Random Forest for carbon stock estimation model construction. The XGBoost algorithm has a wider applicability for feature screening. [ABSTRACT FROM AUTHOR]

Grasslands in China cover an extensive area and rank second globally. They constitute the second-largest carbon reservoir in China after forests, holding about 8% of the total carbon stock of the world's grassland ecosystems. This study focuses on the grasslands of Wuchuan County, Inner Mongolia Autonomous Region of Northern China. This study compares the carbon stock and density characteristics across different communities based on plot survey and GIS vector data. It also anticipates the region's carbon sequestration potential using biomass-to-carbon conversion and extrapolation methods. The findings indicate that (1) the total carbon stock in the study area is 1805.65 × 104 tons with an average carbon density of 77.50 t/ha. The distribution of carbon density and stock follows a pattern: soil layer > herbaceous layer > litter layer; (2) the Stipa krylovii + Leymus chinensis community exhibits the highest carbon stock and density; (3) there is a positive correlation between herbaceous carbon density and NPP (Net Primary Productivity) values in the study area; and (4) the overall carbon stock in the region is projected to increase, with growth rates accelerating annually. These results contribute to our understanding of the formation, turnover, stability maintenance, and regulation mechanisms of grassland soil organic carbon. Furthermore, they hold significant implications for enhancing the carbon sequestration capacity of ecosystems. [ABSTRACT FROM AUTHOR]

The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological–climatic and anthropogenic–technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations. [ABSTRACT FROM AUTHOR]

Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area. [ABSTRACT FROM AUTHOR]

Lowland tropical rainforests provide an abundance of biodiversity as well as dynamic and stable ecosystems. These forests include tropical peat forests in various locations and forest types that have vegetation structure and composition characteristics, and carbon stocks that still need to be explored more deeply. Research on the structure and the composition of the vegetation and carbon stock in the old secondary peat forest was carried out in the protected areas of Bukit Batu, Riau Province, and Muara Merang, South Sumatra Province. Based on a 1-hectare permanent plot established in Bukit Batu and Muara Merang, 25 subplots of 20 m by 20 m were established in each location for measurement purposes. The results showed that Bukit Batu and Muara Merang had 42 and 36 species belonging to 26 and 20 families, respectively. Bukit Batu had a species diversity index (H') of 2.93, and the dominant tree species were Palaquium xanthochymum with an importance value index (IVI) = 66.27%, Eugenia sp. (IVI = 32.76%), and Litsea sp. (IVI = 18.39%). The Muara Merang location had a species diversity index (H') of 2.82, and the dominant tree species were Eugenia sp. (IVI = 60.88%), Alseodaphne insignis (IVI = 26.34%), and Adenanthera pavonina (IVI = 22.11%). In Bukit Batu, forest stands with a diameter of ≥10 cm contained a biomass of 178.10 tonnes/ha and carbon stock of 83.70 tonnes C/ha, which is equal to 307.20 tonnes CO2/ha. Meanwhile, in Muara Merang, it was 190.41 tonnes/ha and 89.49 tonnes C/ha, which is equal to 328.44 tonnes CO2/ha. This research, especially that in Bukit Batu, Riau Province, enriches the data and information available to date and is very useful in supporting restoration practices in Riau Province's Giam Siak Kecil Biosphere Reserve, which was designated by UNESCO as part of the Man and Biosphere Program. [ABSTRACT FROM AUTHOR]

This study examines the impact of silvicultural and land-use management practices on carbon sequestration in peri-urban forest ecosystems, with a particular focus on human-induced carbon dynamics. The study area's complex profile spans from a compact native forest to varying degrees of fragmentation. This included areas undergoing secondary succession forest without silvicultural interventions (No-SI) alongside sites subjected to high-intensity (High-SI) and low-intensity silvicultural interventions (Low-SI). The research assessed carbon stocks and sequestration in different carbon pools (living biomass, dead organic matter and soil) using field data, allometric equations and laboratory analysis. Findings reveal a significant correlation between the intensity of anthropogenic interventions and variations in carbon stocks. The CASMOFOR model facilitated the reconstruction of carbon stock and carbon-stock change dynamics over four decades (1980–2022), showing disparities in carbon storage capabilities linked to the structural characteristics of the sites. The Low-SI site had the highest carbon stock in all carbon pools (378 tonnes C ha−1), which is more than double compared to High-SI (161 tonnes C ha−1) or No-SI sites (134 tonnes C ha−1). However, the secondary succession forest (No-SI) demonstrated the highest annual carbon stock change (4.4 tonnes C ha−1 year−1), two times higher than the Low-SI mature stand (2.2 tonnes C ha−1 year−1), emphasising the resilience of forest ecosystems to recover and sustain carbon sequestration capacities after harvesting if forest land use remains unchanged. The study underscores the significant importance of anthropogenic interventions on carbon dynamics, especially for living tree biomass, which has consequences in enhancing carbon sequestration and contributing to emission reduction targets. [ABSTRACT FROM AUTHOR]

The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by "low in the southeast, high in the north, and medium in the center". (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations. [ABSTRACT FROM AUTHOR]

Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes. [ABSTRACT FROM AUTHOR]

Forests face a variety of threats in the modern era. Agroforestry systems, both traditional and introduced, have a tremendous capacity for providing sustainable resources and combating the impact of global climate change. Indigenous agroforestry and forest land-use systems are important reservoirs for biodiversity conservation and ecosystem services, providing a potential contribution to livelihood security for rural communities. This study aimed to assess the tree diversity and carbon stock of agroforestry and adjoining forests along altitudinal gradients, ranging between 700 and 2200 masl (i.e., lower, middle, and upper altitudes) by laying sample plots randomly of a size of 20 × 20 m2. In the forest land-use system, the maximum Importance Value Index (IVI) included Dalbergia sissoo (71.10), Pyrus pashia (76.78), and Pinus roxburghii (79.69) at the upper, middle, and lower elevations, respectively, whereas, in the agroforestry land-use system, the IVI reported for Ficus semicordata was 43.05 at the upper, while for Grewia optiva it was at 53.82 at the middle and 59.33 at the lower altitudes. The below-ground biomass density (AGBD) was recorded as 1023.48 t ha−1 (lower), 242.92 t ha−1 (middle), and 1099.35 t ha−1(upper), while in the agroforestry land-use system, the AGBD was 353.48 t ha−1 (lower), 404.32 t ha−1 (middle), and 373.23 t ha−1 (upper). The total carbon density (TCD) values recorded were 630.57, 167.32, and 784.00 t ha−1 in forest land-use systems, and 227.46, 343.23, and 252.47 in agroforestry land-use systems for lower, middle, and upper altitudes, respectively. The Margalef's Index values for agroforestry and forests ranged from 2.39 to 2.85 and 1.12 to 1.30, respectively. Soil organic carbon (SOC) stock recorded 45.32, 58.92, and 51.13 Mg C ha−1 for agroforestry and 61.73, 42.65, and 71.08 Mg C ha−1 for forest in lower, middle and upper elevations, respectively. The study suggests that selecting land use patterns can be an effective management system for tree species at different elevations for carbon storage, helping to mitigate climate change and aiding in sustainable management of ecosystems in the Garhwal Himalayas. [ABSTRACT FROM AUTHOR]

The ecosystem services of forests, such as the water conservation function, are the combined results of diverse processes, and the modification of one part of a forest affects each ecosystem service separately via complex processes. It is necessary to develop an ecosystem service assessment model for various tree species to ensure proper forest management. In this study, a model to evaluate three ecosystem services, namely, the water supply, wood supply, and carbon sink, for various tree species in Japan is developed using many observation data from the previous literature. The integrated evaluation model consists of the forest model, hydrological model, and carbon stock assessment model. The forest model consists of the forest growth model and LAI estimation model, based on allometry. The results of the simulations for the major tree species yield the following findings: (1) Water supply varies with tree species but decreases until about 40 years of age, after which it is near constant. (2) Although beech has a larger LAI than needleleaf forests, water supply is not significantly different. (3) Broadleaf forests are more affected by thinning than needleleaf forests and tend to receive increased water supply as a result of processes such as thinning. This study enabled the evaluation of water conservation function in watersheds containing various tree species. [ABSTRACT FROM AUTHOR]

In this study, the biomass expansion factor (BEF) method was combined with the tree growth function in order to obtain a more accurate growth function of tree species through the fitting of different growth functions to tree growth, and to determine the characteristics of the forest carbon stock as well as the carbon sink potential of Huangshan National Forest Park (HNFP) in China. The carbon sink potential of each tree species and the integrated influencing factors, such as the stand and soil, were directly represented by structural equation modelling (SEM) to clarify the size and path of each influencing factor against the carbon sink potential. The results showed the following: (1) the logistic growth function fitting results for the seven major tree species in HNFP were better than those from the Richard–Chapman growth function, and the R2 was greater than 0.90. (2) In 2014, the total carbon stock of the forest in HNFP was approximately 9.59 × 105 t, and the pattern of carbon density, in general, was higher in the central region and the northeastern region and lower in the northern and southern regions, while the distribution of carbon density was lower in the northern and southern regions. The carbon density pattern generally showed a higher distribution in the central and northeastern regions and a lower distribution in the northern and southern regions; most of the high-carbon-density areas were distributed in blocks, while the low-carbon-density areas were distributed sporadically. (3) The total carbon sink of the forest in HNFP was 8.26 × 103 t in 2014–2015, and due to the large age structure of the regional tree species, the carbon sinks of each tree species and the total carbon sink of HNFP showed a projected downward trend from 2014 to 2060. (4) For different tree species, the influencing factors on carbon sink potential are not the same, and the main influence factors involve slope position, slope, altitude, soil thickness, etc. This study identified the carbon stock and carbon sink values of the forest in HNFP, and the factors affecting the carbon sink potential obtained by SEM can provide a basis for the selection of new afforestation sites in the region as well as new ideas and methods to achieve peak carbon and carbon neutrality both regionally and nationally in the future. [ABSTRACT FROM AUTHOR]

Forest carbon sinks play an important role in climate change mitigation and adaptation. The Chinese government has demonstrated its proactive approach to addressing climate change by setting development targets for low-carbon transformation and making solemn commitments to peak carbon emissions before 2030 and to achieve carbon neutrality by 2060. In this article, the Chinese Government's Work Reports and Five-Year Plans are used to construct an index named China's Climate Change Policy Attention ( C C P A ), which reflects the government's emphasis on climate change and forestry in China. This article aims to examine the impact of C C P A , the adjusted Climate Policy Uncertainty ( C P U ) index from the U.S., and the Economic Policy Uncertainty ( E P U ) index from China on the Chinese forest carbon stocks ( F C S ) comprehensively. On this basis, we are interested in clarifying the channels by which C C P A promotes the growth of forest carbon sequestration in China. Specifically, panel data from 30 provinces in China from 2000 to 2017 are used for empirical analysis, and the following results are obtained: (1) The baseline regression indicates that C C P A significantly promotes the growth of F C S , while C P U or E P U inhibits its growth. (2) The interactive regression shows that the effect of C C P A on the growth of F C S can mitigate the inhibiting impact of C P U or E P U . (3) Mediation analysis documents that C C P A promotes F C S growth by increasing the forest tending area, expanding the existing forest area, increasing renewable energy consumption, and improving green investment. (4) Heterogeneity analysis reveals a clear differentiation in the effect of C C P A on F C S under different situations. Finally, policy implications are proposed based on the results. This article is expected to provide a theoretical basis for the Chinese government to develop relevant policies from the perspective of promoting F C S growth. [ABSTRACT FROM AUTHOR]

The purpose of this article was to assess the participation of young soils of ash dump forest communities in carbon sequestration by soils of southern taiga forests, considering the physico-chemical properties of the ash substrate and forest litter (pH, TOC, TN, content of P and K mobile compounds, and exchangeable Ca and Mg ions). It was revealed that on three Middle Ural ash dumps (composed of fly ash from various brown coals) over 50–60 years, forest communities spontaneously formed according to the zonal type (with the dominance of Betula pendula Roth and Populus tremula L.) with poorly differentiated young soils—technosols. For the first time, as a result of using an integrated approach to assess the direction of forest ecosystem formation on fly ash dumps, a tendency to increase carbon stocks in technogenic soils that have not reached the level of zonal soils was revealed, as well as the dependence of C accumulation on some physico-chemical characteristics of ash was established. Carbon stocks in Technosols are on average equal to 44 t/ha but vary significantly. It was shown that there is a medium negative relationship between the content and stocks of organic carbon in soils formed on a technogenic substrate and the content of mobile phosphorus compounds in them (the correlation coefficient is −0.58 and −0.53, respectively). The average carbon stocks in the litter of technosols, which is the main source of organic carbon in forest soils, are 3.2 t/ha. It was revealed that the carbon stocks in the litter are most influenced by the content of exchangeable calcium cations and magnesium in it (the correlation coefficients are −0.68 and −0.69, respectively). Any correlation between the studied litter parameters and carbon accumulation in the soils of ash dumps was found. The study revealed that the carbon stocks in the technosols of ash dump forest communities are two times less than the carbon stocks in the zonal forest soils of the Middle Ural southern taiga. The stocks of this element in the litter of young soils are equal to 1/3 of the litter of zonal soils. The composition of the humus substance system formed in the soils of forest areas of ash dumps and zonal soils is similar. The results of this study can serve to fill gaps in the knowledge about carbon sequestration by soils and aim to draw attention to forest communities of technogenic ecosystems to consider the contribution of their components to carbon sequestration. [ABSTRACT FROM AUTHOR]

Mangrove forests play an important role in coastal areas from an ecological perspective, being able to store large amounts of carbon through sequestration and inhibiting climate change processes by absorbing CO2 in the atmosphere. In recent years, there have been changes in the land cover of converted and degraded mangrove forests which have resulted in the release of carbon and an imbalance in soil structure, which in turn cause a flux of CO2 into the atmosphere. This research was conducted at the Karang Gading and Langkat Timur Laut Wildlife Reserve (KGLTLWR) in North Sumatra, Indonesia. The study focused on six different land covers, namely natural forests, restoration, mixed agriculture, paddy fields, oil palm plantation, and ponds. This study aimed to measure the total carbon stock of mangrove forests that have been converted to other land covers and estimate the level of CO2 flux in the area. A total of three transects and six plots for each land cover were used in this study; for tree biomass, a non-destructive method was used by recording every DBH > 5 cm, and for soil carbon, drilling was carried out, which was divided into five depths in each plot. CO2 flux was measured using an Eosense Eosgp CO2 sensor with the static closed chamber method. The highest carbon stock was found at 308.09 Mg ha−1 in natural forest, while the lowest 3.22 Mg ha−1 was found in mixed agriculture. The highest soil carbon was found at 423.59 MgC ha−1 in natural forest, while the lowest 50.44 MgC ha−1 was found in mixed agriculture dry land. The highest average CO2 flux value of 1362.24 mgCO2 m2 h−1 was found in mangrove restoration and the lowest in ponds was 123.03 mgCO2 m2 h−1. Overall, the research results inform how much carbon stock is lost when converted to other land covers so that it can be used as a reference for policy makers to provide future management of mangrove forests and develop mitigation measurements to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

Climate and land-use changes have been contributing to the increase in the occurrence of extreme wildfires, shifting fire regimes and driving desertification, particularly in Mediterranean-climate regions. However, few studies have researched the influence of land use/cover on fire regimes and carbon storage at the broad national scale. To address this gap, we used spatially explicit data from annual burned areas in mainland Portugal to build a typology of fire regimes based on the accumulated burned area and its temporal concentration (Gini Index) between 1984 and 2019. This typology was then combined with carbon stock data and different landscapes to explore relationships between landscape types and two important ecosystem services: wildfire reduction and carbon stock. Multivariate analyses were performed on these data and the results revealed a strong relationship between landscapes dominated by maritime pine and eucalypt plantations and highly hazardous fire regimes, which in turn hold the highest carbon stocks. Shrubland and mixed landscapes were associated with low carbon stocks and less hazardous fire regimes. Specialized agricultural landscapes, as well as mixed native forests and mixed agroforestry landscapes, were the least associated with wildfires. In the case of agricultural landscapes, however, this good wildfire performance is achieved at the cost of the poorest carbon stock, whereas native forests and agroforestry landscapes strike the best trade-off between carbon stock and fire regime. Our findings support how nature-based solutions promoting wildfire mitigation and carbon stock ecosystem services may prevent and revert land degradation harming Mediterranean regions. [ABSTRACT FROM AUTHOR]

Wetlands provide important ecosystem services, such as water conservation, biodiversity protection, and carbon sequestration. The Zoige alpine wetland is the largest high-altitude swamp in the world and plays a critical role in regional ecological balance and climate change. However, little is known about the fate of its soil organic carbon (SOC) storage. In this study, we estimated the degradation status of the wetland over the past 35 years and used machine learning to investigate the dynamics and driving factors of SOC at different soil depths of the Zoige wetland in 1985, 2000, and 2020. We also simulated the future SOC balance under different scenarios. The results showed that the area of Zoige wetland has degraded by 378.71 km2 in the past 35 years. Increased precipitation and solar radiation offset the adverse effects of global warming, making the soil act as a carbon sink in the past 35 years. The total SOC storage of the wetland soils in 1985, 2000, and 2020 was estimated to be 2.03 Pg, 2.05 Pg, and 2.21 Pg, respectively, with 46.95% of SOC distributed in the subsoil layers. Climate change was the most important driving factor controlling the SOC storage of the Zoige wetland, explaining 51.33% of the SOC changes in the soil. Temperature change was always the most important factor controlling wetland SOC, and precipitation had a greater impact on the topsoil. Under the temperature control targets of 1.5 °C and 2 °C, the SOC pool of the Zoige wetland will decrease by 60.21 Tg C and 69.19 Tg C, respectively. Under scenarios of a 10% and 20% increase in precipitation, the wetland soil will accumulate an additional 46.53 Tg C and 118.89 Tg C, respectively. The study results provide important references for the sustainable management of the Zoige wetland under the background of global climate change. [ABSTRACT FROM AUTHOR]

Forest management decisions affect carbon stock and rates of sequestration. One subject of debate is the rotation age that will optimize sequestration over extended periods. Some argue that shorter rotations facilitate greater sequestration rates due to the accelerated growth rates of younger trees compared to mature or old-growth trees. Others maintain that frequent harvesting will not allow forest carbon to rebound after each subsequent rotation, and thus more extended periods between clearcutting is the superior choice. These contrasting viewpoints are mirrored regarding the impact of thinning treatments, in that either thinning will enhance forest carbon uptake by facilitating improved and sustained r growth of residual trees or removing any above-ground biomass will outweigh the yields. This study aims to compare the different suites of management decisions and identify practical combinations of rotation ages and thinning applications that will optimize carbon sequestration while meeting other objectives over a 240-year projection timeframe. Stand development under different harvest rotations and thinning specifications was modeled using a Forest Vegetation Simulator (FVS). We found that site productivity was the primary determinant in stand-above-ground carbon dynamics under various management scenarios. Thus, the optimal rotation age/thinning treatment combinations differed between site classes. High productivity stands were estimated to sequester the most above-ground live carbon with 60-year rotations with a low-intensity thin at age 40. Moderately productive stands performed the best with 80-year rotations when two low-intensity thinning treatments were applied between harvests. For high and moderate productivity stands, estimates of gross carbon increased when two low or moderate-intensity thinning treatments were applied within 80- or 120-year rotations. High-intensity thinning treatments reduced total carbon sequestered over the 240-year projection timeframe for all productivity levels and rotation ages, except for low productivity stands under 120-year rotations. [ABSTRACT FROM AUTHOR]

Food production in sandy soils has evolved significantly, most notably through the advent of integrated crop–livestock systems (ICLSs). ICLSs increase soil cover, which maintains soil moisture and sequesters carbon (C). Here we investigate the influence of ICLSs on soil physical, chemical, and biochemical properties, and grain yield (GY) in tropical sandy soils in short-time. We compared seven ICLSs in two consecutive crops seasons (with soybean or maize as cash crops) in southeastern Brazil. These were (1) corn + Urochloa brizantha cv. BRS Paiaguás—soybean (ICL-Paiaguás); (2) corn + U. brizantha cv. BRS Piatã—soybean; (3) corn + U. ruziziensis—soybean; (4) corn–soybean under conventional tillage (CT) as a negative control; (5) corn–soybean under no-tillage (NT) as a positive control; (6) Paiaguás grass—continuous grazing (Perennial Paiaguás); (7) and Piatã grass—continuous grazing (Perennial Piatã). Soybean and corn GY data, soil physical and chemical attributes, and soil enzymatic activity were subjected to descriptive statistics and multivariate analysis. CT and NT shared high loadings of H + Al, Al, and soil temperature and low loadings of soil pH, SOM physical and chemical fractions, cationic exchange capacity, and arylsulfatase activity. ICL-Paiaguás and Perennial Piatã had a similarly high loading of total N, humin, total organic carbon, and mineral-associated carbon stocks. The fulvic acid fraction was the most sensitive to C accumulation in the sandy soil under ICLSs. Soil water and thermal regimes were limiting in both CT and NT. The study not only confirms the capacity of conservation mechanisms to enhance soil-based ecosystem functions, but it also highlights the potential of ICLSs to aid sustainable food production even in the context of tropical sandy soils, which frequently receive limited attention in intensive agricultural practices. [ABSTRACT FROM AUTHOR]

Uncertainties continue to prevail in the potential of natural forests and plantations in carbon stock assessment. The present study was carried out to assess the carbon stock in natural and plantation forests of Manipur using geo-informatics in Imphal East and West districts. The integrated approach of geospatial technology, along with field inventory based data, was used in spatial modeling of biomass carbon of selected natural and plantation forests. The stand density was similar for both LNG and TRS forests (680 individuals ha−1) and lowest for KHP forest (640 individuals ha−1). Paulownia fortunei (770 individuals ha−1) showed highest density among tree species while Tectona grandis (54.07 m2 ha−1) followed by Gmelina arborea (42.18 m2 ha−1) had higher basal area compared to other tree species. The soil moisture content (%) in the natural forest ranged from 19.13 ± 0.47 to 26.9 ± 0.26%. The soil moisture content in the plantation forest ranged from 19.16 ± 0.98 to 25.83 ± 0.06%. The bulk density of natural forests ranged from 1.27 g cm−3 to 1.37 g cm−3 while for plantation forests it ranged from 1.18 g cm−3 to 1.34 g cm−3. Among the studied sites of natural forest, TRS forest had both the highest AGBC value of 132.74 t ha−1 as well as the BGBC value of 38.49 t ha−1. Similarly, among the plantations, T. grandis plantation showed the highest AGBC (193 t ha−1) and BGBC (55.97 t ha−1). On the other hand, Tharosibi forest and T. grandis plantation had the highest total carbon stock for natural and plantation forest with values of 274.824 t ha−1 and 390.88 t ha−1, respectively. The total above-ground carbon stock estimated for the natural forest of KHP, LNG and TRS were 109.60 t ha−1, 79.49 t ha−1 and 132.74 t ha−1, respectively. On the other hand, the estimated total above-ground carbon stock in plantation of GA, PD, PF and TG were 62.93 t ha−1 62.81 t ha−1, 45.85 t ha−1 and 193.82 t ha−1. In the present study, the relationship with the biomass was observed to be better in SAVI compared to NDVI and TVI. The linear regression analysis performed to determine the relationship between the estimated and predicted biomass resulted in a correlation coefficient of R2 = 0.85 for the present study area, which is an indication of a good relationship between the estimated and predicted biomass. [ABSTRACT FROM AUTHOR]

Through this study, we established equations for estimating the standing tree carbon stock based on 24 tree species in multiple size classes in a case study at the Ngao Demonstration Forest (NDF) in northern Thailand. Four hundred thirty-nine wood samples from trees in mixed deciduous forest (MDF), dry dipterocarp forest (DDF), and dry evergreen forest (DEF) were collected using non-destructive methods to estimate aboveground carbon equations through statistical regression. The equations were established based on four criteria: (1) the coefficient of determination (R2), (2) standard error of estimate (SE), (3) F-value, and (4) significant value (p-value, α ≤ 0.05). The aboveground carbon stock (C) equations for standing trees in the MDF was C = 0.0199DBH2.1887H0.5825, for DDF was C = 0.0145DBH2.1435H0.748, for DEF was C = 0.0167DBH2.1423H0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH2.1625H0.6614, where DBH is tree diameter at breast height, and H is tree total height. The aboveground carbon stock in the DDF, MDF, and DEF was 142, 53.02, and 12 tons/ha, respectively, and the estimated aboveground carbon stock in the Mae Huad sector at the NDF was 61 tons/ha. [ABSTRACT FROM AUTHOR]

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks. [ABSTRACT FROM AUTHOR]

The mitigation of global warming and conservation of biodiversity are two significant environmental challenges today. Estimating and comparing forest carbon stock and plant diversity under different management approaches provide insight into the choice of management approaches for carbon and plant diversity management. We investigated the variation in carbon stock and diversity of plant species in two forest managements under different approaches: the Kakrebihar protection forest (PF) and Sano Surkhet community forest (CF) in Karnali Province, Surkhet, Nepal. In total, 63 sample plots (30 plots in PF and 33 plots in CF) were laid out systematically across the forests. Dendrometric measurements were carried out for trees, poles, and saplings, and representative leaf litter and herb samples were collected. Soil samples were taken at 10 cm, 20 cm, and 30 cm depths using a soil auger. The existing tree volume equations of tree species of interest were used to estimate tree volume, and species-specific wood density and conversion factors were used to obtain total biomass and carbon content. Soil samples were analyzed using the Walkley-Black method to determine soil organic carbon. PF had higher carbon stock, plant species richness, and abundance at the landscape level than CF; however, the scenario differed at the plot level. At the plot level, PF had significantly higher total carbon stock and biomass carbon stock than CF. However, PF and CF were statistically indistinguishable in term of soil carbon stock. At the plot level, PF and CF were statistically indistinguishable regarding richness, Simpson diversity, and Shannon diversity, but PF had significantly higher plant abundance than CF. In conclusion, the value of PF for carbon stock and plant diversity surpassed those of CF. This study suggests that PF might be a better strategy to enhance carbon stock in forests and maintain habitat for various plant species. [ABSTRACT FROM AUTHOR]

Both forest management—especially forest tending—and afforestation help to enhance the carbon sequestration of forest vegetation. However, with limited resources, appropriate measures need to be selected to increase the vegetation carbon sinks based on regional endowments. This study aimed to assess the differences in the effects and costs of afforestation and forest tending on vegetation carbon sequestration and to help select suitable afforestation and forest tending areas. In this paper, we used panel fixed effects models to analyze the effects of afforestation and forest tending on vegetation carbon sequestration and conducted a regional heterogeneity analysis to identify suitable afforestation and tending areas. Our results show that the vegetation carbon sequestration capacity of forest tending is 4.48 times higher than that of afforestation, and there is obvious spatial heterogeneity in the effects of afforestation and forest tending on vegetation carbon sequestration. Specifically, the marginal contribution of afforestation is higher than that of tending in northwest and southwest China, whereas the marginal contribution of tending is greater in other regions. Additionally, the afforestation cost for vegetation carbon sequestration is 44.44 times higher than that of tending. Therefore, the management of existing forests must be enhanced, especially in northeastern, southern, and northern China. Similar to the northwest and southwest regions of China, there is still a need to emphasize the use of suitable space for afforestation. [ABSTRACT FROM AUTHOR]

Bottomland Hardwood Forests (BHFs) are commonly acknowledged worldwide for their vast carbon sequestration potential and carbon storage capacity. However, the paucity of forest carbon stock data from BHFs along the Lower Mississippi Alluvial Valley (LMAV) in Northeast Louisiana is an existing knowledge gap in understanding the carbon sequestration and storage dynamics across the region. This study was carried out in the Russell Sage Wildlife Management Area (RSWMA) in Northeast Louisiana using a protocol modified from the Terrestrial Carbon Observations Protocol for Vegetation Sampling. Comprehensive analyses of carbon stocks in trees, woody shrubs and seedlings, herbaceous vegetation, downed woody debris, leaf litter, and soil were carried out to quantify the carbon stored in each ecosystem component. Trees accounted for a carbon stock of 132.4 Mg C ha−1, approximately 99% of the total stock for the area. Woody shrubs and seedlings and leaf litter stored 0.4% (0.62 Mg C ha−1) and 0.3% (0.4 Mg C ha−1), respectively. Considering the sparse understory in a BHF, the carbon stored per hectare is comparable to other temperate forests in the conterminous United States. These findings highlight the importance of the BHF ecosystem in carbon storage and their overall role in regional and global ecosystem management in light of climate change. [ABSTRACT FROM AUTHOR]

The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach's reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated two approaches for C stock prediction using two new generation multispectral remote sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical paradigms handle uncertainty. Information on C stock spectral prediction in the form of NDVI, SAVI, and EVI derived from multispectral remote sensing platforms, Landsat-8 and Sentinel-2, was used to build Bayesian and frequentist-based C stock predictive models in the sampled plantation forest ecosystem. Sentinel-2-based C stock predictive models outperform their Landsat-8 counterparts using both the Bayesian and frequentist inference approaches. However, the Bayesian-based Sentinel-2 C stock predictive model ( R M S E   =   0.17   M g C h a − 1 ) is more accurate than its frequentist-based Sentinel-2 ( R M S E = 1.19  M g C h a − 1 ) C stock equivalent. The Sentinel-2 frequentist-based C stock predictive model gave the C stock prediction range of  1   ≤   M g C h a − 1   ≤   290 , whilst the Sentinel-2 Bayesian-based C stock predictive model resulted in the prediction range of  1   ≤   M g C h a − 1   ≤   285 . However, both the Bayesian and frequentist C stock predictive models built with the Landsat-8 sensor overpredicted the sampled C stock because the range of predicted values fell outside the range of the observed C stock values. As a result, we recommend and conclude that the Bayesian-based C stock prediction method, when it is combined with high-quality remote sensing data such as that of Sentinel-2, is an effective inferential statistical methodology for reporting C stock in managed plantation forest ecosystems. [ABSTRACT FROM AUTHOR]

The potential to sequester carbon by tree species in tropical regions such as Bangladesh is promising in regard to carbon sequestration (CS) potentiality and reducing CO2 emissions. This study focuses on perennial tree species within 488 hectares of Bangladesh Agricultural University (BAU) to assess the CS and to produce a C stock map for BAU. To compute the green and dry weight, weight of C and CO2 sequestration in the tree, a simplified methodology from the National Computational Science Institute of the Shodor Education Foundation was applied. A total of 27,543 trees comprising 424 species were taken into consideration, dividing the whole study area into four segments. B. ceiba and L. acidissima received the maximum and minimum green, dry, and C weight values. The topmost five carbon stock accumulating trees are M. longifolium (264,768 kg yr−1), S. mahagoni (257,290), A. lebbeck (118,310), M. indica (78,906), and T. grandis (51,744) whilst A. lebbeck is the major C stock accumulating tree within BAU. The top five CS potential are found for B. ceiba (181 kg), A. columnaris (139 kg), S. siamea (116 kg), F. elastica (113 kg), and F. religiosa (83 kg). To reveal the prospects of tree species in Bangladesh for emission reduction, the CS potential could be incorporated with the C trading scheme of the CDM (clean development mechanism) of the Kyoto Protocol. [ABSTRACT FROM AUTHOR]

Several agroforestry systems prevail in different agro-ecological zones of Pakistan, and cover a remarkable area of 19.3 million hectares. They not only play an important role in slowing down CO2 emissions, but also contribute to mitigating climate change. However, in many regions, the relevant effect of agroforestry systems on overall carbon (C) stock and their reliance on various factors are quite unidentified. This study was planned to assess the biomass accumulation and C stocks of different commonly practiced agroforestry systems (boundary, bund, scattered, agri-horticulture) and their constituent land use types (tree + cropland) through a non-destructive approach (allometric equations) in a semi-arid region of Punjab, Pakistan. The results showed that the highest plant biomass (87.12 t ha−1) increased by 46%, 17%, 78%, and 339%, and C stock (42.77 t ha−1) increased by 49.51%, 20%, 82%, and 361% in the boundary planting system compared to the bund, scattered, agri-horti and sole cropland, respectively. The soil organic carbon (SOC) stock at all three depths, 0–15 cm, 15–30 cm & 30–45 cm, was found in the following order: boundary planting system > bund planting system > agri-horti system > scattered planting system > agricultural system, with a maximum in the boundary planting system and minimum in the sole cropping system at all three depths. Overall, the total C stock of the ecosystem's vegetation + soil C (0–30 cm) in the forested area was 275 t ha−1, equating to 37 t ha−1 in the agricultural system alone. Our results highlighted that agroforestry systems have the highest potential for C sequestration. We suggest that research and investment in agroforestry systems can be a successful way for Pakistan to achieve some of its climate change mitigation goals. [ABSTRACT FROM AUTHOR]

Developing appropriate tools to understand and protect ecosystems and the services they provide is of unprecedented importance. This work describes the activity performed by ISPRA for the mapping of the types of ecosystems and the evaluation of their related ecosystem services, to meet the needs of the "ecosystem extent account" and "ecosystem services physical account" activities envisaged by the SEEA-EA framework. A map of the types of ecosystems is proposed, obtained by integrating the main Copernicus data with the ISPRA National Land Consumption Map, according to the MAES (Mapping and Assessment of Ecosystems and their Services) classification system. The crop production and carbon stock values for 2018 were then calculated and aggregated with respect to each ecosystem. The ecosystem accounting was based on the land cover map produced by ISPRA integrating, according to an EAGLE compliant classification system, the same Copernicus and National input data used for mapping the types of ecosystems. The analysis shows the importance of an integrated reading of the main monitoring tools and the advantages in terms of compatibility and comparability, with a view to enhancing the potential of Copernicus land monitoring instruments also in the context of ecosystem accounting activities. [ABSTRACT FROM AUTHOR]

Mangroves play a crucial role in maintaining the stability of coastal regions, particularly in the face of climate change. To gain insight into associations between climate change and mangroves, we conducted bibliometric research on the global indexed database of the Web of Knowledge, Core Collection. A total of 4458 literature were analyzed based on bibliometric information and article metadata through a scientometric analysis of citation analysis as well as a cluster analysis. Results suggest that coastal countries such as the USA, Australia, China, India, and Brazil are showing the recent influential mangrove-related keywords such as blue carbon and carbon stock. Interestingly, the "carbon stock", "Saudi Arabia", "range expansion" and "nature-based flood risk mitigation" is among the top cluster networks in the field of climate change and mangrove forest. The present research is expected to attract potential leaders in research, government, civil society, and business to advance progress towards mangrove sustainability in the changing climate meaningfully. [ABSTRACT FROM AUTHOR]

The objective of this study was to improve cacao (Theobroma cacao) production through its association with a woody species, such as Ricinodendron heudelotii, in western Côte d'Ivoire. To do this, a design of two transects (10 m and 20 m) was installed around the species in 5 localities (Buyo, Duekoué, Guéyo, San-Pedro, and Soubré). The diameter at the breast height of the tree, the height, the number of fruits, the above biomass, and its carbon stock were measured. Results showed morphological variability of R. heudelotii according to the localities. The presence of the species within cacao trees reduces the rate of pod rot, stabilizes the rate of pods eaten away, and increases the biomass production and the carbon storage of cacao trees. The distance between the two species had no impact on the vigor and the yield of cacao trees. However, cacao density in the 10 m line was reduced compared to that of the 20 m. Therefore, the integration of R. heudelotii into cacao agroforestry systems could improve cacao production. That is why the reduction of cacao density, as well as the use of more suitable varieties of cacao, should be considered for the sustainability of this system. [ABSTRACT FROM AUTHOR]

This study examines the soil environment of eight charcoal kiln platforms and the neighboring soil in Czech and Bohemian low-altitude forest stands. Both mixed and undisturbed soil samples were used to assess the hydrophysical soil properties, nutrient content, cation exchange capacity, enzyme activity, and soil active carbon content, while soil color, stoniness, root density, and horizon thickness were estimated in the field. Charcoal-rich horizons had high total organic carbon concentrations and total nitrogen content (about 150% and 40% higher than in the organomineral horizons of the control plot, respectively), with total carbon stocks being higher than those in neighboring forest soils. Fine root density was highest in the charcoal-rich horizons, encouraged by high soil porosity, aeration, and favorable chemical properties. Enzyme group activity differed between individual soil horizons and kiln and control plots, with depolymerization enzyme activity highest in charcoal-rich horizons and humification enzyme activity highest in mineral horizons. Phosphatase, chitinase, and phenoloxidase activity were highest in charcoal-rich horizons, while β-glucosidase activity remained similar across horizons. After long-term abandonment, kiln sites shift from inhospitable sites to localized hotspots for plant and microbial growth, having more favorable physical, enzymatic, and chemical soil properties than the surrounding areas. This study confirmed that kiln production platforms act as microhabitat hotspots, also providing information on a wide spectrum of soil properties linked with soil microorganisms and root growth. [ABSTRACT FROM AUTHOR]

Dead wood, including coarse woody debris, CWD, and fine woody debris, FWD, plays a substantial role in forest ecosystem functioning. However, the amount and dynamics of dead wood in the forests of Northern Eurasia are poorly understood. The aim of this study was to develop a spatially distributed modelling system (limited to the territories of the former Soviet Union) to assess the amount and structure of dead wood by its components (including snags, logs, stumps, and the dry branches of living trees) based on the most comprehensive database of field measurements to date. The system is intended to be used to assess the dead wood volume and the amount of dead wood in carbon units as part of the carbon budget calculation of forests at different scales. It is presented using multi-dimensional regression equations of dead wood expansion factors (DWEF)—the ratio of the dead wood component volume to the growing stock volume of the stands. The system can be also used for the accounting of dead wood stock and its dynamics in national greenhouse gas inventories and UNFCCC reporting. The system's accuracy is satisfactory for the average level of disturbance regimes but it may require corrections for regions with accelerated disturbance regimes. [ABSTRACT FROM AUTHOR]

Carbon stock assessment in various ecosystems is vital for monitoring the health of these ecosystems and national accounting for the United Nations convention on climate change. The influence of various anthropogenic drivers on carbon stock in different ecosystems has not been examined comprehensively. This study aims to determine the impact of anthropogenic pressures (lopping, cutting, grazing) on soil physico-chemical properties and carbon stock in four temperate broadleaf forests dominated by different species of oak, viz., Banj oak (Quercus leucotrichophora), Rianj oak (Quercus lanuginosa), Moru oak (Quercus floribunda) and Kharsu oak (Quercus semecarpifolia) along an elevation gradient from 1700–3000 m asl in Gori valley, western Himalaya. Biomass data were collected from 120 quadrats of 10 × 10 m size at three distinct altitudes (4 forest sites × 3 altitudes × 10 quadrats) and analysed for carbon stock, whereas soil samples were randomly collected in triplicate from three depths of each altitude of the forest site and further analysed for their physico-chemical properties. A total of 767 individual trees with a diameter of ≥31 cm were measured at twelve sites and standing biomass was estimated following the growing stock volume equations. Mean carbon stock was highest in Moru oak (396.6 ± 29.5 Mg C ha−1) and lowest in Banj oak forest (189.3 ± 48.6 Mg C ha−1). We also found soil to be the largest pool of forest carbon (43.0–59.7%) followed by aboveground biomass (31.5–45.0%), belowground biomass (8.4–11.7%) and litter (0.4–0.5%). The basal area showed significant effect on altitude and carbon stock, whereas disturbance showed significant (p < 0.05) negative correlation with the total carbon stock. Soil nitrogen exhibited a significant positive correlation (R2 = 0.60) with the basal area, indicating that nitrogen enhances tree growth and forest carbon stock. However, anthropogenic disturbance showed a significant negative impact on the basal area, soil nutrients and carbon stock of oak forests. This concludes that forest structure, anthropogenic pressure and soil parameters contribute to the carbon stock of the area. Considering the significance of these overexploited oak forests, it is recommended to conserve the old-growth forest species in the study area, since they have the highest carbon accumulation potential. [ABSTRACT FROM AUTHOR]

Forests play an important role in combating the challenges posed by changing climate through sequestering carbon in their living biomasses and the soil. Tropical forests, which harbour a large number of species, are anticipated to play a great role in this regard due to the favourable growing environments. However, there is limited knowledge of the variability in carbon stock among land use types and its relationship with biodiversity. Therefore, this study assessed the variability in storing the different carbon pools among natural forest, woodland and khat plantation land use types. It also explored the relationship between biodiversity and carbon storage in the different carbon pools. Plant inventory and sample collection were undertaken following standard methods. In addition, soil samples were taken at three depth profile classes of 0–30 cm (top layer), 30–60 cm (middle layer) and 60–100 cm (bottom layer). Results of the study revealed that there was no statistically significant relationship between biodiversity and total biomass carbon, soil organic carbon or total carbon stock at a 95% level of confidence. The results indicated that the natural forest had the highest plant biomass (456.93 Mg ha−1) followed by woodland (19.78 Mg ha−1) and khat plantation (2.46 Mg ha−1). Consequently, the total carbon stock estimate of the natural forest (366.47 Mg ha−1) was significantly larger than that of the woodland (141.85 Mg ha−1) and khat plantation (125.86 Mg ha−1). The variation in total carbon stock among land use types arises from the variation in the total biomass carbon stock. The study results also revealed that soil organic carbon stock decreased with soil depth in all the land-use types. The findings of this study have implication of improving topsoil management in monoculture crops such as khat plantation and conserving natural forests for enhancing carbon sequestration potentials. [ABSTRACT FROM AUTHOR]

Biochar (BC) soil amendments could partially counteract soil carbon (C) stock decrease in broad-leaved forests in Italy; however, its effects on the growth of representative tree species—Fagus sylvatica L. and Quercus cerris L.—has not yet been addressed. We examine whether seed germination and growth of these species are affected by addition of BC obtained from deciduous broadleaf trees. Seeds were left to germinate in greenhouse conditions under three different BC amendments: 0% (control), 10% and 20% (v/v). Seedlings were then subjected to controlled conditions under the same BC percentage. Biochar effects on seed germination were assessed measuring germination time and percentage, while effects on photosynthesis were assessed using leaf chlorophyll content (mg/m2) and photosynthetic efficiency (FV/FM). Plant growth was estimated by recording leaf number, longest leaf length and plant height. Biochar treatments had no negative effects on germination and early growth stage of the two species. Positive effects were found on the chlorophyll content of both species (ca. +8%) regardless of the treatment and on the leaf number (+30%), leaf length (+14%) and plant height (+48%) of Q. cerris (only with 10% BC). Biochar applications seem, therefore, a suitable method for increasing broad-leaved forest C stock in Italy. [ABSTRACT FROM AUTHOR]

As wood products in use store carbon and can contribute to reducing the concentration of atmospheric CO2, the improved and enhanced use of wood products can be a successful measure in climate change mitigation. This study estimates the amount of carbon stored in the Hungarian harvested wood product (HWP) pool and the CO2 emissions and removals of the pool. According to our results, the total carbon stock of the Hungarian HWP pool is continuously increasing. We estimated the total carbon stock of the HWP pool to be 17,306 kt C in the year 2020. Our results show that the HWP pool in Hungary is a carbon sink in most parts of the time series, with some years where it turns to a source of emissions. We carried out a simple projection up to 2070, assuming a constant inflow for the projected years that is equal to the average inflow of the last five historic years. This resulted in a decreasing trend in CO2 removals, with removals already very close to zero in 2070. We concluded that in order to achieve significant future carbon sinks in the HWP pool technological improvements are needed, such as increasing the lifetime of the wood products and expanding the carbon storage capacity of wood products by reusing and recycling wood in a cascade system. [ABSTRACT FROM AUTHOR]

Climate change requires mitigation actions, mainly preventive, by reducing greenhouse gas emissions; however, carbon sequestration is a complementary measure. Although short-term carbon sequestration can be somewhat effective, it is really interesting when it is permanent. Sequestration calculates the carbon removed from the atmosphere over a period, while the stock expresses the cumulative carbon of a forest. Sequestration and stock are closely related, but ecosystem service valuation often focuses on the former, which can discourage forest maintenance. This study analyzes carbon sequestration and storage in four pine forests located in central Spain, comparing its valuation for different equivalence times, a period considered sufficient to compensate for the emission of one ton of CO2 into the atmosphere. Equilibrium equivalence times were calculated for each forest, defined as the period in which carbon sequestration and stock payments are equal; values ranged from 33 to 101 years, with significant correlations with commercial volume and carbon stock. Equivalence times of 30–50 years are reasonable in Mediterranean forest stands with moderate growth and density, while in dense mature stands this time should increase to 50–100 years. Valuing carbon stocks and paying for them in a sustained manner over time promotes sustainable forest management, while the sale of sequestration credits may generate a speculative "greenwashing" market. In addition, payments for stocks can be applied to any forest stand and not only to new plantations. Carbon stock valuation is a win–win strategy for climate change mitigation, sustainable forest management, and rural development. [ABSTRACT FROM AUTHOR]

This research estimates the carbon stock of the subtropical broad-leaved evergreen scrub forest of Lehtrar, a revenue estate of Kotli Sattian, Rawalpindi, Punjab, Pakistan. A total of six nested co-centric plots of 17.84 m2 each were laid out in the forest, having two sub-plots of 5.64 m2 and 1 m2 each, for shrubs and litter, respectively. Stem density, tree height, diameter at breast height (DBH), total tree biomass, and total carbon stock were calculated. In each plot, parameters like latitude, longitude, aspect, slope, elevation, tree count, etc., were catalogued. The carbon value was calculated in pools such as aboveground biomass (AGB), belowground biomass (BGB), litter, shrubs, etc. The tree height was measured using Abney's level and the diameter at breast height (DBH) with diameter tape, while factors such as volume, shrub mass, litter mass, total tree biomass, and total carbon stock were calculated by using standard formulas. Results showed Olea ferrugineae to be the most abundant tree species in the study area, followed by Acacia modesta. The total average DBH and height were calculated as 17.03 and 16.79, respectively, with the species Dalbergia sissoo having the greatest DBH value. The mean carbon stock came out to be 47.75 tons/ha, with plot number 3 having the highest value of carbon stock, owing to the greatest stem count. The results of the study were significant and reflected a rich stem density, rich biomass, and an adequate carbon stocking capacity. The scrub forests of the study area, being important carbon sinks, are prone to deforestation and forest degradation activities that need to be controlled by using proper forest management practices to keep their carbon sequestration ability intact, as suggested under various reducing emissions from deforestation and forest degradation (REDD initiatives of UNFCCC. [ABSTRACT FROM AUTHOR]