Search Results (284)

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article

International efforts are underway to implement carbon neutrality policies in rapidly changing climate conditions. This situation has strongly demanded the discovery of novel carbon sinks. The Salix genus has attracted attention as a promising carbon sink owing to its rapid growth and efficient use as a biofuel in short-rotation cultivation. The present study aims to derive an allometric equation and conduct stem analysis as fundamental tools for estimating net primary productivity (NPP) in Salix pierotii Miq. stand, which is increasingly acknowledged as an important emerging carbon sink. The allometric equations derived showed a high explanatory rate and fitness (R² ranged from 0.74 to 0.99). The allometric equations between DBH and stem volume and biomass derived in the process of stem analysis also showed a high explanatory rate and fitness (R² ranged from 0.87 to 0.94). The NPPs calculated based on the allometric equation derived and stem analysis were 11.87 tonC∙ha⁻¹∙yr⁻¹ and 15.70 tonC∙ha⁻¹∙yr⁻¹, respectively. These results show that the S. pierotii community, recognized as the representative riparian vegetation, could play an important role as a carbon sink. In this context, an assessment of the carbon absorption capacity of riparian vegetation such as willow communities could contribute significantly to achieving carbon neutrality goals. Full article

The transition zone between the Qinghai–Tibet and Loess Plateaus is an important ecological functional area and carbon (C) reservoir in China. Studying the main drivers of C density changes in forest ecosystems is crucial to enhance the C sink potential of those ecosystems in ecologically fragile regions. In this study, four stand types at different succession stages in the transition zone of Xinglong Mountain were selected as the study objective. The C densities of the ecosystem, vegetation, plant debris, and soil of each stand type were estimated, and the related driving factors were quantified. The results showed that the forest ecosystem C density continuously increased significantly with natural succession (381.23 Mg/hm² to 466.88 Mg/hm²), indicating that the ecosystem has a high potential for C sequestration with progressive forest succession. The increase in ecosystem C density was mainly contributed to by the vegetation C density, which was jointly affected by the vegetation characteristics (C sink, mean diameter at breast height, mean tree height), litter C/N (nitrogen), and surface soil C/N, with factors explaining 95.1% of the variation in vegetation C density, while the net effect of vegetation characteristics was the strongest (13.9%). Overall, this study provides a new insight for understanding the C cycle mechanism in ecologically fragile areas and further improves the theoretical framework for understanding the C sink function of forest ecosystems. Full article

The accurate and large-scale mapping of seagrass meadows is essential, as these meadows form primary habitats for marine organisms and large sinks for blue carbon. Image data available for mapping these habitats are often scarce or are acquired through multiple surveys and instruments, resulting in images of varying spatial and spectral characteristics. This study presents an unsupervised domain adaptation (UDA) strategy that combines histogram-matching with the transformer-based SegFormer model to address these challenges. Unoccupied aerial vehicle (UAV)-derived imagery (3-cm resolution) was used for training, while orthophotos from airplane surveys (12.5-cm resolution) served as the target domain. The method was evaluated across three Danish estuaries (Horsens Fjord, Skive Fjord, and Lovns Broad) using one-to-one, leave-one-out, and all-to-one histogram matching strategies. The highest performance was observed at Skive Fjord, achieving an F1-score/IoU = 0.52/0.48 for the leave-one-out test, corresponding to 68% of the benchmark model that was trained on both domains. These results demonstrate the potential of this lightweight UDA approach to generalization across spatial, temporal, and resolution domains, enabling the cost-effective and scalable mapping of submerged vegetation in data-scarce environments. This study also sheds light on contrast as a significant property of target domains that impacts image segmentation. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

Reclaiming coastal wetlands for agricultural purposes has led to intensified farming activities, which are anticipated to affect greenhouse gas (GHG) flux processes within coastal wetland ecosystems. However, how greenhouse gas exchanges respond to variations in agricultural reclamation activities across different years remains uncertain. To address this knowledge gap, this study characterized dynamic exchanges within the soil–plant–atmosphere continuum by employing continuous monitoring across four representative coastal wetland soil–vegetation systems in Jiangsu, China. The results show the carbon dioxide (CO₂) and nitrous oxide (N₂O) flux exchanges between the system and the atmosphere and soil–vegetation carbon pools, which revealed the drivers of carbon dynamics in the coastal wetland system. The four study sites, converted from coastal wetlands to agricultural lands at different times (years), generally act as CO₂ sinks and N₂O sources. Higher levels of CO₂ sequestration occur as the age of reclamation rises. In terms of time scale, crops lands were found to be CO₂ sinks during the growing period but became CO₂ sources during the crop fallow period. Although the temporal trend of the N₂O flux was generally smooth, reclaimed farmlands acted as net sources of N₂O, particularly during the crop-growing period. The RDA and PLS-PM models illustrate that soil salinity, acidity, and hydrothermal conditions were the key drivers affecting the magnitude of the GHG flux exchanges under reclamation. This study demonstrates that GHG emissions from reclaimed wetlands can be effectively regulated through science-based land management, calling for prioritized attention to post-development practices rather than blanket restrictions on coastal exploitation. Full article

As the global population continues to rise, the impact of urbanization on land utilization and ecosystems are growing more pronounced, particularly within the expanding area of Asia. The land use/land change (LULC) brought by urban expansion directly impacts plant growth and ecological productivity, altering the carbon cycle and climate regulation functions of the region. This research focuses on Harbin City as a case study, employing an enhanced version of the Carnegie–Ames–Stanford Approach (CASA) model to analyze the spatial–temporal variations in vegetation Net Primary Productivity (NPP) across the area from 2000 to 2020. The findings indicate that Net Primary Productivity (NPP) in Harbin exhibited notable interannual variability and spatial heterogeneity. From 2000 to 2005, a decline in NPP was observed across 60.75% of the area. This reduction was predominantly concentrated in the central and eastern areas of the city, where forested landscapes are the dominant feature. In contrast, from 2010 to 2015, 92.12% of the region saw an increase in NPP, closely related to the overall improvement in NPP across all land-use types. Land-use change significantly influenced NPP dynamics. Between 2000 and 2005, 54.26% of NPP increases stemmed from the transition of farmland into forest, highlighting the effectiveness of the “conversion of farmland back to forests” policy. From 2005 to 2010, 98.6% of the area experienced NPP decline, mainly due to forest and cropland degradation, especially the unstable carbon sink function of forest ecosystems. Between 2010 and 2015, NPP improved across 96.86% of the area, driven by forest productivity recovery and better agricultural management. These results demonstrate the profound and lasting impact of land-use transitions on the spatiotemporal dynamics of NPP. Full article

Understanding the interplay between climate change, landscape patterns, and carbon sequestration is critical for sustainable ecosystem management. This study investigates the spatiotemporal evolution of vegetation Net Primary Productivity (NPP) and landscape patterns in Inner Mongolia, China, from 2000 to 2020, and evaluates their implications for carbon sink capacity under climate change. Using remote sensing data, meteorological records, and landscape metrics (CONTAG, SPLIT, IJI), we quantified the relationships between vegetation productivity, landscape connectivity, and fragmentation. Results reveal a northeast-to-southwest gradient in NPP, with high values concentrated in forested regions of the Greater Khingan Range and low values in arid western deserts. Over two decades, NPP increased by 73% in high-productivity zones, driven by rising temperatures and ecological restoration policies. Landscape aggregation (CONTAG) and patch connectivity showed strong positive correlations with NPP, while higher fragmentation values (SPLIT, IJI) negatively impacted carbon sequestration. Climate factors, particularly precipitation variability, emerged as critical drivers of NPP fluctuations, with human activities amplifying regional disparities. We propose targeted strategies—enhancing landscape connectivity, regional differentiation management, and optimizing patch structure—to bolster climate-resilient carbon sinks. These findings underscore the necessity of integrating climate-adaptive landscape planning into regional carbon neutrality frameworks, offering feasible alternatives for mitigating climate impacts in ecologically vulnerable regions. Full article

Food security and environmental quality related to food production are global issues that need urgent solutions. Proteins are crucial for diets, and demand is growing for innovative and more environmentally sustainable sources of protein, like vegetables, microorganisms, and insects, and lab-grown food that can meet nutritional and environmental goals. This study analyzes a time series to assess the sustainability of different protein sources by evaluating their effects on emissions of greenhouse gases and the use of agricultural land while accounting for the carbon sink potential across the supply chain. The study also explores future trends in global protein sources, emphasizing shellfish as a key to achieving food security from both nutritional and environmental perspectives. By reviewing terrestrial livestock, farmed seafood, vegetal proteins, and alternative sources like insects and cultured cells, the study assesses sustainability, food security potential, and challenges from nutritional, environmental, and consumer viewpoints. We conclude that shellfish aquaculture, particularly oysters, mussels, clams, and scallops, has significant potential in enhancing food security, fostering sustainable protein consumption, reducing land use, and contributing to climate change mitigation by sequestering significant amounts of atmospheric carbon. Full article

As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate on primary productivity require further investigation. Integrating multi-source remote sensing data (2000–2020), meteorological observations with the Standardized Precipitation Evapotranspiration Index (SPEI) and an improved CASA model, this study systematically investigates spatiotemporal patterns of vegetation net primary productivity (NPP) responses to extreme drought events while quantifying vegetation coverage’s regulatory effects on ecosystem drought sensitivity. Among drought events identified using a three-dimensional clustering algorithm, high-intensity droughts caused an average NPP loss of 23.2 gC·m⁻² across the basin. Notably, artificial irrigation practices in the Hetao irrigation district significantly mitigated NPP reduction to −9.03 gC·m⁻². Large-scale afforestation projects increased the NDVI at a rate of 3.45 × 10⁻⁴ month⁻¹, with a contribution rate of 78%, but soil moisture competition from high-density vegetation reduced carbon-sink benefits. However, mixed forest structural optimization in the Three-North Shelterbelt Forest Program core area achieved local carbon-sink gains, demonstrating that vegetation configuration alleviates water competition pressure. Drought amplified the suppressive effect of afforestation through stomatal conductance-photosynthesis coupling mechanisms, causing additional NPP losses of 7.45–31.00 gC·m⁻², yet the April–July 2008 event exhibited reversed suppression effects due to immature artificial communities during the 2000–2004 baseline period. Our work elucidates nonlinear vegetation-climate interactions affecting carbon sequestration in semi-arid ecosystems, providing critical insights for optimizing ecological restoration strategies and climate-adaptive management in the Yellow River Basin. Full article

Research was conducted in Gannan Prefecture, China, to better understand the characteristics of carbon emissions and sequestration in areas dominated by animal husbandry. The emission factor method was used to calculate and analyze changes in carbon emissions from 2009 to 2024. The region’s average annual carbon emissions from animal husbandry are 774,286 t C-eq (2,839,049 t CO₂eq), with enteric emissions from cattle being the biggest contributor. However, as the number of locally raised cattle and sheep has decreased, carbon emissions have gradually fallen at an average annual rate of −1.0%. The annual average total carbon sequestration of vegetation in the region is 6,861,535 t C-eq, and the carbon content in underground biomass is higher than that in aboveground biomass, making it the main contributor to grassland carbon sequestration. Carbon sequestration from grassland vegetation is greater than the carbon emissions from animal husbandry, which means that the entire production system is currently a carbon sink. Meanwhile, the analysis of land-use carbon sequestration found that the annual average total sequestration by forests and grasslands over the same time period was 752,327 t C-eq, and sequestration is increasing at an annual rate of 1.4%, primarily driven by the progressive expansion of forested areas. Although the regional carbon emissions from animal husbandry are lower than the carbon sequestration, developing a science-based animal husbandry plan aligned with regional ecological thresholds, continuing to implement grass–livestock balance management measures, and preventing livestock numbers from exceeding their ecological carrying capacity remain critical to promoting sustainable coordination between livestock economies and ecological conservation. Full article

Land use and land cover changes are critical drivers of terrestrial carbon stock dynamics, as they alter native vegetation and land-based production activities. Scenario-based simulation of land use and carbon stock evolution offer valuable insights into the carbon sink potential of different development strategies and support low-carbon land planning. We focus on the Dianchi Basin, integrating a Markov-PLUS land use simulation with the InVEST carbon assessment model to examine carbon stock changes from 2000 to 2030 under three scenarios: natural development and cropland and ecological protections. Results indicate that from 2000 to 2020, the region experienced significant urbanization, with cropland decreasing and forest land expanding. Forests contributed the most to the total carbon storage, followed by cropland. The total carbon stock initially increased but experienced a marked decline from 2010 to 2020, aa trend expected to continue, largely attributable to the transformation of cropland and grassland into construction land, as well as the conversion of forest into cropland. By 2030, carbon stock trajectories would vary across scenarios. Both the natural development and cropland protection scenarios resulted in carbon loss, whereas the ecological protection scenario increased carbon storage and reversed the declining trend. Spatially, carbon stock distribution in the basin exhibits strong heterogeneity, with higher values in the periphery and lower values in the urban center. We reveal the spatio-temporal characteristics of carbon stock change and the carbon consequences of land use policies, providing scientific evidence to support land use restructuring, carbon sink enhancement, and regional carbon emission reduction under the dual-carbon goals of China. Full article