Vegetation Carbon Stocks Research Articles

Spatial distribution of vegetation carbon stock among different organs over the Tibetan Plateau: on an intensive field survey

Spatial distribution of vegetation carbon stock among different organs over the Tibetan Plateau: on an intensive field survey

Effects of Grazing on Plant Diversity and Their Carbon Stocks in Different Types of Grasslands

With the drying and warming of the climate and irrational grazing, various types of grasslands in Inner Mongolia have been degraded to different degrees, and different management modes will inevitably affect the plant diversity and vegetation carbon stock of soil grasslands. To clarify the changes and influencing factors of plant diversity and carbon stock in different types of grasslands under different management modes, plant species composition, aboveground biomass, and vegetation carbon were analyzed based on 18 sentinel monitoring stations across three different types of grasslands in Inner Mongolia. The results showed that grazing increased the dominance of typical grassland and desert grassland, whereas meadow grassland decreased, and the evenness index and Shannon Wiener diversity index increased less in meadow grassland and desert grassland. Grazing decreased graminaceous biomass in meadow grassland and typical grassland, whereas it increased in desert grassland. Above-ground vegetation and below-ground root carbon stocks were much higher than those in grazing areas, 1.5 and 1.2 higher, respectively, but vegetation carbon stocks in long-term grazing sites were significantly lower than those in short-term grazing. Further, the structural equations showed that the effects of geographic location, climatic factors, and soil factors on the biomass and vegetation carbon stocks of the three grassland types differed significantly. The results can provide a reference for the ecologically sustainable development of grassland and the optimization of management mode.

Spatio-temporal Dynamics of Land Use/Cover Change and Associated Carbon Stocks in Kanyabaha Wetland in Rukiga District, Uganda

Wetlands play an important ecological function of sequestering atmospheric carbon dioxide and thereby moderating adverse impacts of climate change. It is therefore important to understand the dynamics of carbon stocks in wetland vegetation and soils. This study investigated the spatio-temporal dynamics of aboveground, belowground, and total carbon stocks in Kanyabaha Wetland, located in Rukiga District, Uganda, spanning from 1990 to 2021. Through field sampling and laboratory analysis, aboveground carbon stocks were assessed by harvesting vegetation biomass and converting it to carbon stock using established conversion factors. Soil samples collected at different depths (0-20cm, 20-50cm, 50-100cm) were analyzed for soil organic carbon content to determine belowground carbon stocks. The study reveals variable spatio-temporal patterns of carbon stocks across land use types, with papyrus-dominated areas exhibiting the highest aboveground carbon stocks (49.66 tC/ha), followed by small-scale farmlands (33.73 tC/ha) and tree plantations (23.01 tC/ha). Conversely, built-up areas exhibit the lowest carbon stocks (1.29 tC/ha). Temporal analysis reveals fluctuating patterns in carbon stocks, with increases observed in built-up areas and small-scale farmlands, and decreases in grasslands and tree plantations that could be due to changes in hydrological cycle. Belowground carbon stocks follow similar trends, with papyrus areas maintaining the highest stocks (39.96 tC/ha), particularly at deeper soil depths that exhibit the highest carbon accumulation due to its extensive network of papyrus rhizome. Changes in land use, especially reclamation of the wetlands for farming and settlements affected carbon capture and storage in the wetland ecosystem. These findings highlight the importance of targeted conservation of natural wetlands and sustainable land management strategies in the Kanyabaha Wetland catchment for enhanced carbon sequestration. Further, in depth studies in the variability of carbon stocks due to various eco-climatic factors and anthropogenic activities are necessary to support sustainable wetland land management practices in Uganda.

Regional mangrove vegetation carbon stocks predicted integrating UAV-LiDAR and satellite data

Using satellite RS data predicting mangrove vegetation carbon stock (MVC) is the popular and efficient approach at a large scale to protect mangroves and promote carbon trading. Satellite data have performed poorly in predicting MVC due to saturation issues. UAV-LiDAR data overcomes these limitations by providing detailed structural vegetation information. However, how to cross-scale integration of UAV-LiDAR and satellite RS data and the selection of features and machine learning methods hampered the practitioner in making a lightweight but efficient model to predict the MVC. Our study integrated UAV-LiDAR, Sentinel-1, and Sentinel-2 to extract spectral, structural, and textural features at the regional scale. We estimated the influences of different combinations between three vegetation features and machine learning methods (Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Regression Tree (GBDT), and Extreme Gradient Regression Tree (XGBOOST)) on the results of MVC prediction, and constructed a framework for estimating mangrove vegetation aboveground (ACG) and belowground (BCG) carbon storage in Zhanjiang, the largest mangrove area of China. Our research shows: 1) Compared to using satellite remote sensing (RS), integrating UAV and satellite RS data and fusing multiple vegetation features significantly improved the accuracy of mangrove vegetation carbon stock (MVC) predictions. 2) Structural features, particularly canopy height retrieved from UAV and satellite RS, are essential indicators for predicting MVC. Combined with spectral and structural features, regional MVC was precisely predicted. 3)Although the influence of different machine learning methods on MVC prediction was not significant, XGBOOST demonstrated relatively high precision. We recommend that mangrove practitioners integrate UAV and satellite RS data to predict MVC at a regional scale. Importantly, governments should prioritize the application of UAV-LiDAR in forestry monitoring and establish a long-term mangrove monitoring database to aid in estimating blue carbon resources and promoting blue carbon trading.

Mapping of soil carbon balances changes in the dry tropical forest ecosystem in Pernambuco Brazil

Maps of soil and vegetation carbon stock dynamics resulting from changes in land use in tropical dry areas are still scarce and virtually absent for the Brazilian Northeast region. The few data available were built on a scale that does not allow their use for decision-making and precision farming applications. Based on soil and land use data, we developed a geographical information system to estimate and map carbon balances in the large (86.135 km2) semiarid region of Pernambuco state, Brazil. Maps of carbon stocks for soil and vegetation for the years 2000 and 2016 were created on the scale of 1: 100000, stratified by land use and soil types. In this period, 28% of the area had decreases in soil and vegetation C stocks, 57% had no significant changes and only 13% had increases. Most of the change was associated with converting the open native forest vegetation (Caatinga) into pastures. The net C loss was 291 million Mg, representing an average loss of 2 Mg C ha-1 year-1. Water bodies, urban areas, and other unclassified uses were not accounted for but amounted to only 2% of the area. Overall, the method proved to be a fast and feasible approach to monitoring carbon balances derived from land use changes on a regional scale.

Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data

Accurate aboveground biomass (AGB) estimations over large areas are essential for assessing carbon stocks and forest resources. This study evaluated machine learning approaches for AGB modeling in Pakistan's mountainous region of Diamir district using freely available Sentinel-1 and Sentinel-2 data and 171 field-measured AGB training points. Random Forest, Gradient Tree Boosting, CatBoost, LightGBM, and XGBoost algorithms were implemented and optimized. Models were developed using individual and combined datasets. Sentinel-2 optical data outperformed Sentinel-1 radar data, but the fusion of both sensors achieved the highest accuracy (R2 > 0.7, RMSE = 105.64 Mg/ha, MAE = 85.34 Mg/ha). Tree canopy height was the most informative predictor for this data, besides terrain variables and radar textures. The machine learning models significantly improved AGB estimates compared to traditional regression techniques, and gradient boosters outperformed Random Forest. This research demonstrates the potential of multi-sensor remote sensing data and advanced algorithms for forest biomass mapping in complex terrain, with modeling accuracies reaching root mean squared errors below 90 Mg/ha. The framework provides an effective solution for monitoring biomass using freely available satellite data. Further refinements include integrating higher-resolution optical data and additional field samples for better validation. This study contributes to remote sensing capabilities for assessing vegetation carbon stocks and dynamics.

Refined GEDI Level-2A Database Construction: Focused on Gyeonggi Province, Republic of Korea

The Global Ecosystem Dynamics Investigation (GEDI), a full-waveform light detection and ranging system, translated the energy into a continuous waveform and recorded the signals chronologically for enabling geometric analysis of the vertical structure of vegetation. The National Aeronautics and Space Administration has used the land, vegetation, and ice sensor (LVIS) airborne laser altimeter system to measure terrain, tree heights, and vegetation carbon stocks in designated areas. The effectiveness of the collected LVIS data has been proven in mapping forest structures and biomass in tropical and temperate systems. Based on the successful achievements of LVIS, the GEDI aimed to establish a global range of forest data needed to analyze and predict the carbon cycle and climate change. The GEDI was launched aboard the SpaceX-16 in 2018 and successfully attached to the International Space Station (ISS) for a 2-year mission, but now extended until March 2023. Since being mounted on the ISS, GEDI measured over 10 billion cloud-free surface observations within the range of 51.6°N to 51.6°S. In this paper, GEDI mission is introduced, and the process of downloading, refining the GEDI level-2A product focused on Gyeonggi Province is outlined.

Bomb radiocarbon evidence for strong global carbon uptake and turnover in terrestrial vegetation.

Vegetation and soils are taking up approximately 30% of anthropogenic carbon dioxide emissions because of small imbalances in large gross carbon exchanges from productivity and turnover that are poorly constrained. We combined a new budget of radiocarbon produced by nuclear bomb testing in the 1960s with model simulations to evaluate carbon cycling in terrestrial vegetation. We found that most state-of-the-art vegetation models used in the Coupled Model Intercomparison Project underestimated the radiocarbon accumulation in vegetation biomass. Our findings, combined with constraints on vegetation carbon stocks and productivity trends, imply that net primary productivity is likely at least 80 petagrams of carbon per year presently, compared with the 43 to 76 petagrams per year predicted by current models. Storage of anthropogenic carbon in terrestrial vegetation is likely more short-lived and vulnerable than previously predicted.

Comparing trends of crop and pasture in future land-use scenarios for climate change mitigation

Revegetation of former agricultural land is a key option for climate change mitigation and nature conservation. Expansion and abandonment of agricultural land is typically influenced by trends in diets and agricultural intensification, which are two key parameters in the Shared Socioeconomic Pathways (SSPs). Datasets mapping future land dynamics under different SSPs and climate change mitigation targets stem from different scenario assumptions, land data and modelling frameworks. This study aims to determine the role that these three factors play in the estimates of the evolution of cropland and pastureland in future SSPs under different climate scenarios from four main datasets largely used in the climate and land surface studies. The datasets largely agree with the representation of cropland at present-day conditions, but the identification of pastureland is ambiguous and shows large discrepancies due to the lack of a unique land-use category. Differences occur with future projections, even for the same SSP and climate target. Accounting for CO2 sequestration from revegetation of abandoned agricultural land and CO2 emissions from forest clearance due to agricultural expansion shows a net reduction in vegetation carbon stock for most SSPs considered, except SSP1. However, different datasets give differences in estimates, even when representative of the same scenario. With SSP1, the cumulative increase in carbon stock until 2050 is 3.3 GtC for one dataset, and more than double for another. Our study calls for a common classification system with improved detection of pastureland to harmonize projections and reduce variability of outcomes in environmental studies.

Modeling the carbon dynamics of ecosystem in a typical permafrost area

Climate change poses mounting threats to fragile alpine ecosystem worldwide. Quantifying changes in carbon stocks in response to the shifting climate was important for developing climate change mitigation and adaptation strategies. This study utilized a process-based land model (Community Land Model 5.0) to analyze spatiotemporal variations in vegetation carbon stock (VCS) and soil organic carbon stock (SOCS) across a typical permafrost area - Qinghai Province, China, from 2000 to 2018. Multiple potential factors influencing carbon stocks dynamics were analyzed, including climate, vegetation, soil hydrothermal status, and soil properties. The results indicated that provincial vegetation carbon storage was 0.22 PgC (0.32 kg/m2) and soil organic carbon pool was 9.12 PgC (13.03 kg/m2). VCS showed a mild increase while SOCS exhibited fluctuating uptrends during this period. Higher carbon stocks were observed in forest (21.74 kg/m2) and alpine meadow (18.08 kg/m2) compared to alpine steppes (9.63 kg/m2). Over 90 % of the carbon was stored in the 0-30 cm topsoil layer. The contribution rates of soil carbon in the 30-60 cm and 60-100 cm soil layers were significantly small, despite increasing stocks across all depths. Solar radiation, temperature, and NDVI emerged as primary influential factors for overall carbon stocks, exhibiting noticeable spatial variability. For SOCS at different depths, the normalized differential vegetation index (NDVI) was the foremost predictor of landscape-level carbon distributions, which explained 52.8 % of SOCS variability in shallow layers (0-30 cm) but dropped to just 12.97 % at the depth of 30-60 cm. However, the dominance of NDVI diminished along the soil depth gradients, superseded by radiation and precipitation. Additionally, with an increase in soil depth, the influence of inherent soil properties also increased. This simulation provided crucial insights for landscape-scale carbon responses to climate change, and offered valuable reference for other climate change-sensitive areas in terms of ecosystem carbon management.

Carbon stock, density, and biomass of seagrass in South Bangka, Indonesia

Abstract. Supratman O, Farhaby AM, Henri, Septiyati MU, Adi W. 2024. Carbon stock, density, and biomass of seagrass in South Bangka, Indonesia. Biodiversitas 25: 1736-1742. As a component of the carbon ecosystem, seagrass meadows play a crucial role in preventing climatic change. The execution of Indonesian programs pertaining to carbon economic value, carbon trading, and other endeavors aimed at attaining the Nationally Determined Contribution (NDC) requires estimated data on seagrass carbon stocks. The present study investigates the seagrass density, vegetation carbon stock, and sediment carbon stock of South Bangka, Indonesia. The investigation is carried out in the following stages: i) data collection on-site, ii) laboratory sample analysis, and iii) data analysis. The accumulation of data on-site utilizes a square transect method, which includes the gathering of sediment, sample biomass, and density information. The carbon composition of sediment and seagrass vegetation is ascertained through laboratory analysis employing the Loss of Ignition (LOI) method. The investigation uncovered eight species of seagrass in South Bangka. Cymondocea rotundata, Enhalus acoroides, Halophila ovalis, Halodule pinifolia, Halodule uninervis, Oceana serrulata, Siringodium isotifolium, and Thalassia Hemprichii are the identified species of seagrass. In South Bangka, the combined carbon stock of seagrass (above and below ground carbon) varies between 0.049±0.057 and 1.323±1.581 Mg C Ha-1. The range of sediment carbon is 110.53±42.07 Mg Corg Ha-1 extrapolated to a depth of 1 m. The results of this study suggest that the seagrass meadows in South Bangka are characterized by a significant species diversity and effectively sequester carbon in the form of biomass and sediment. This supports the National Climate Change Mitigation Program for Blue Carbon Ecosystems, an initiative that seeks to mitigate the adverse effects of climate change by utilizing the carbon sequestration capabilities of mangrove forests, seagrass meadows, and tidal marshes.

The Dynamics of Vegetation Structure, Composition and Carbon Stock in Peatland Ecosystem of Old Secondary Forest in Riau and South Sumatra Provinces

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.

Carbon stock increase during post-agricultural succession in central France: no change of the superficial soil stock and high variability within forest stages

Forest development following agricultural abandonment concerns extensive areas including the Massif Central region of France where this study was undertaken. This land-use and land-cover change is expected to have effects on biodiversity and ecosystem services, including an increase of carbon sequestration—a major concern in the face of climate change. Nevertheless, uncertainties about carbon stock changes during successions are remaining, especially as to the total stock and the contribution of the different carbon pools. Our work contributes to this field by studying carbon stocks in multiple plots of different successional stages. We measured and estimated carbon stocks in aboveground and belowground vegetation, deadwood, litter and superficial soil, and surveyed plant communities and plot conditions (slope, aspect, soil characteristics). The average total carbon stock increased along the succession from 70.60 at stage 0 to 314.19 tC ha−1 at stage 5. However, the total carbon stocks at the young forest stage (abandoned for 74 years maximum) and the older forest stage (forested for at least 74 years) were not significantly different, and probably reflected strong local heterogeneity in the older forest stage. An increase of the carbon stock was found in all pools, except the soil pool that did not vary significantly between the successional stages. The aboveground carbon stock was found strongly related to the woody species cover, especially the macrophanerophyte cover. This case study supports the view that the succession dynamics of former agricultural plots participates in carbon sequestration, sometimes with great local variations.

How does energy utilization affect rural sustainability development in traditional villages? Re-examination from the coupling coordination degree of atmosphere-ecology-socioeconomics system

In the face of global environmental challenges and China's commitment to achieving carbon neutrality, understanding the impact of energy transition in rural areas, particularly in traditional villages, becomes crucial for sustainable development. Based on 703 traditional villages in Hunan Province from 2005 to 2020, this study conducts a comprehensive spatiotemporal analysis of energy utilization, and analyzes the influence of energy utilization on the coupling coordination of the Atmosphere-Ecology-Socioeconomics System. The findings reveal a fluctuating trend in traditional villages' coupling coordination degree (CCD), particularly higher in western Hunan Province, and a notable reduction in regional disparities over time. The traditional villages' atmospheric conditions are better than the social and ecological conditions, which reflects the importance of improving socioeconomics and ecological subsystems for coupling coordination development. The study identifies that all seven energy utilization factors have positive effects on CCD. Forest vegetation carbon stocks and total energy consumption are critical drivers of CCD, highlighting spatial heterogeneity in their impacts on the CCD across different regions. Additionally, from 2005 to 2020, the effects of Forest Understory Vegetation Carbon Stocks on coupling coordination changed from widespread to localized, while Total Energy Consumption's impact was consistently localized. This paper reevaluates the role of energy management in enhancing the quality of traditional village habitats and offers valuable insights into environmental management and sustainable development in traditional villages, providing compelling evidence for policymakers.

Estimating carbon sequestration potential and optimizing management strategies for Moso bamboo (Phyllostachys pubescens) forests using machine learning

Estimating the carbon sequestration potential of Moso bamboo (Phyllostachys pubescens) forests and optimizing management strategies play pivotal roles in enhancing quality and promoting sustainable development. However, there is a lack of methods to simulate changes in carbon sequestration capacity in Moso bamboo forests and to screen and optimize the best management measures based on long-term time series data from fixed-sample fine surveys. Therefore, this study utilized continuous survey data and climate data from fixed sample plots in Zhejiang Province spanning from 2004 to 2019. By comparing four different algorithms, namely random forest, support vector machine, XGBoost, and BP neural network, to construct aboveground carbon stock models for Moso bamboo forests. The ultimate goal was to identify the optimal algorithmic model. Additionally, the key driving parameters for future carbon stocks were considered and future aboveground carbon stocks were predicted in Moso bamboo forests. Then formulated an optimal management strategy based on these predictions. The results indicated that the carbon stock model constructed using the XGBoost algorithm, with an R2 of 0.9895 and root mean square error of 0.1059, achieved the best performance and was considered the optimal algorithmic model. The most influential driving parameters for vegetation carbon stocks in Moso bamboo forests were found to be mean age, mean diameter at breast height, and mean culm density. Under optimal management measures, which involve no harvesting of 1–3 du bamboo, 30% harvesting of 4 du bamboo, and 80% harvesting of bamboo aged 5 du and above. Our predictions show that aboveground carbon stocks in Moso bamboo forests in Zhejiang Province will peak at 36.25 ± 8.47 Tg C in 2046 and remain stable from 2046 to 2060. Conversely, degradation is detrimental to the long-term maintenance of carbon sequestration capacity in Moso bamboo forests, resulting in a peak aboveground carbon stock of 29.50 ± 7.49 Tg C in 2033, followed by a continuous decline. This study underscores the significant influence of estimating carbon sequestration potential and optimizing management decisions on enhancing and sustaining the carbon sequestration capacity of Moso bamboo forests.

Estimation of vegetation density using NDVI and carbon stock in the Utilization Zone of Gunung Halimun Salak National Park

Forestry and Other Land Uses (FOLU) is one of the critical sectors in low carbon and climate resilience development. Gunung Halimun Salak National Park (GHSNP) can potentially mitigate climate change by increasing carbon stocks. This study aimed to estimate vegetation density using the Normalized Difference Vegetation Index (NDVI), estimate biomass and aboveground carbon stocks, and analyze the relationships of NDVI with vegetation diversity and carbon stocks in the utilization zone of GHSNP. The method used was vegetation analysis and carbon estimation using allometric and destructive testing for seedling and understory levels. The total plots were 15 (a plot size of 50 × 50 m). NDVI was categorized into three classes, specifically class 1 (0,147 - 0,276), class 2 (0,276 - 0,321), and class 3 (0,321 - 0,493). The utilization zone of GHSNP consists of 56 plant species from 40 families. The research location was dominated by Pinus merkusii. The average biomass and carbon concentrations were 131,14 Mg/ha and 61,64 Mg C/ha, respectively. Carbon concentration strongly correlated with the basal area (r = 95,2%). The NDVI value strongly correlated with the number of individual trees per hectare. NDVI can be used as an approach to estimating forest cover.

Global patterns of tree wood density.

Wood density is a fundamental property related to tree biomechanics and hydraulic function while playing a crucial role in assessing vegetation carbon stocks by linking volumetric retrieval and a mass estimate. This study provides a high-resolution map of the global distribution of tree wood density at the 0.01° (~1 km) spatial resolution, derived from four decision trees machine learning models using a global database of 28,822 tree-level wood density measurements. An ensemble of four top-performing models combined with eight cross-validation strategies shows great consistency, providing wood density patterns with pronounced spatial heterogeneity. The global pattern shows lower wood density values in northern and northwestern Europe, Canadian forest regions and slightly higher values in Siberia forests, western United States, and southern China. In contrast, tropical regions, especially wet tropical areas, exhibit high wood density. Climatic predictors explain 49%-63% of spatial variations, followed by vegetation characteristics (25%-31%) and edaphic properties (11%-16%). Notably, leaf type (evergreen vs. deciduous) and leaf habit type (broadleaved vs. needleleaved) are the most dominant individual features among all selected predictive covariates. Wood density tends to be higher for angiosperm broadleaf trees compared to gymnosperm needleleaf trees, particularly for evergreen species. The distributions of wood density categorized by leaf types and leaf habit types have good agreement with the features observed in wood density measurements. This global map quantifying wood density distribution can help improve accurate predictions of forest carbon stocks, providing deeper insights into ecosystem functioning and carbon cycling such as forest vulnerability to hydraulic and thermal stresses in the context of future climate change.

Enhancement effects of mangrove restoration on blue carbon storage in Qinzhou Bay

IntroductionMangroves are the main carbon sinks in tropical regions and have high capabilities for carbon sequestration. Protection and restoration of mangroves are necessary to reduce carbon emissions and fight climate change. While the Qinzhou Bay as the main area of national mangrove restoration plan in the future, studies on its carbon pools, especially assessment of the carbon sink enhancement effect of restored mangroves along forest chronosequence, are lacking.MethodsThis study aimed to quantify the changes in restored mangrove soil carbon stock, vegetation and root carbon stocks along the forest age sequence in Qinzhou Bay through field survey.ResultsThe results revealed that the carbon stocks of vegetation and roots significantly increased with the developing forest age. Only in the soil layer above 30 cm, the soil carbon storage apparently increased with the developing forest age in non-cofferdam area, and then decreased slowly after reaching the peak (at 6 ~ 8 years). Moreover, the soil carbon storage of mangroves was greater in the cofferdam area than in the non-cofferdam area.DiscussionThis implied that the cofferdam restoration efforts may be more effective in enhancing blue carbon storage, during the initial stages of the restoration process. The results of this study suggested that mangrove restoration has substantial potential capacity in carbon storage and nutrient cycling, providing a reference for the protection and restoration efforts concerning mangroves.

Spatiotemporal variation of vegetation carbon stocks and its driving factors in the Urumqi River Basin

Vegetation carbon (VC) is a key indicator of the carbon cycle in terrestrial ecosystems, and the study of spatial and temporal changes in VC stocks at the watershed scale can better reveal the impacts of natural and human activities on VC stocks in watersheds, which will help in monitoring watershed ecological environments and to realize sustainable development in watersheds and regions. To reveal the spatial and temporal changes of the VC stock in the Urumqi River Basin, a typical arid zone basin, from 2001 to 2020, the spatial and temporal distribution characteristics of multi-year VC stocks in the Urumqi River Basin were estimated based on the Pixel Information Expert Engine platform, with MODIS (MOD16A17) as the main data source, and by combining methods such as trend analysis and the Hurst index, and analyzing the impacts of different factors on the VC stock by using a geo-probe to quantify the effects of the different factors on the VC stock. The results were as follows: (1) VC in the Urumqi River Basin has continuously increased in the last 20 years, the carbon stock in the whole basin has decreased sequentially from upstream to downstream, and there is expected to be a general trend of continuous improvement in VC in the basin in the future. (2) In terms of land use, the VC stock was largest in forest, followed by grassland, cropland, construction land, unutilized land, and finally water bodies. The VC of forest fluctuated and changed between 0 and 180 t C·km−2, while the carbon stocks of other land-use types fluctuated and changed between 0 and 90 t C·km−2. (3) Regarding the effects of topographic changes in the Urumqi River Basin on VC stocks, elevation and slope had the most obvious influence, with the VC stock rising with elevation showing an increasing trend, and the carbon stock fluctuating on different slopes. On flat land and with a slope direction of north, northeast, east, or southeast, the carbon stock increased; while with a slope direction of south, southwest, west, or northwest, the carbon stock decreased. (4) The interaction of temperature, land-surface temperature, land-use and land-cover change, and topography in the Urumqi River Basin has had a pronounced effect on VC over the last 20 years. The results are a valuable reference for further understanding and research on VC stocks and their mechanisms of influence in arid zones more broadly, as well as being of great significance for vegetation conservation and restoration efforts in the Urumqi River Basin specifically.

Thinning increases forest ecosystem carbon stocks

Thinning, a major measure of forest management, is known to alter carbon sequestration by affecting the forest microclimate and soil environmental conditions. However, how thinning effect on forest ecosystem carbon stocks have presented inconsistent results. Therefore, we conducted 1776 pairs of thinning experiment observations worldwide to explore how thinning affects forest vegetation and soil carbon stocks. The results showed that overall thinning significantly increased total tree aboveground biomass carbon stocks (AGCtotal, +23.9%, +1.3 Mg ha-1 yr-1), understory vegetation carbon stocks (UVC, +68.3%, 0.12 Mg ha-1 yr-1), and soil organic carbon stocks (SOCstocks, +4.8%, 1.0 Mg ha-1 yr-1) at the global, but exhibited significantly decreased carbon stocks of litterfall (LBC, –9.6%, –0.03 Mg ha-1 yr-1). Belowground biomass carbon stocks remained unchanged under thinning. The sensitivity of carbon sequestration to thinning gradually disappeared after 6 years, except for AGCtotal, as they were jointly subject to stand density and thinning intensity and not just recovery ages. The response to UVC was more positive in medium-density (1500 – 3000 trees ha-1) and moderately thinned (30 – 50%) stands, and the thinning effects on UVC and SOCstocks were more noticeable in the subtropical and temperate zones. In summary, thinning enhanced forest ecosystem carbon sinks (+2.4 Mg ha-1 yr-1) but reduced carbon stocks in low-density stands (≤ 1500 trees ha-1). Soil moisture content (SMC, +6.6%) and bulk density (BD, –1.5%) dynamics after thinning were important factors regulating vegetation growth and soil carbon cycling processes.