Vegetation Carbon Research Articles

Native trees are responsible for the high carbon density in urban natural area forests across eight United States cities

Abstract Urban natural area forests provide cities with crucial ecosystem services, including carbon storage and sequestration. Although previous work from a single city has suggested that urban natural area forests may be carbon‐rich and dominated by native species, it is unclear if that pattern is widespread. Indeed, little is known about the species composition and carbon storage in these green spaces, or how urban natural area forests compared with similar rural forests. Here, we use data collected by the Forests in Cities network to quantify carbon stored in urban natural area forests across 1852 plots in eight of the most populous US cities and examine patterns in woody vegetation carbon storage by tree species composition and size class. We also compare the carbon storage in these urban natural area forests to similar rural forests using USDA FS Forest Inventory and Analysis data. We found that urban natural area forests store between 214 and 267 Mg C ha−1, with the majority (on average 55%) in the above‐ground portion of live trees and shrubs. This carbon was primarily stored by native trees and increased with the proportion of native species, as native trees tended to be larger. The proportion of non‐native species was higher in smaller size classes. Overall, the woody vegetation carbon density in urban natural area forests was higher than similar forest types in rural settings, a phenomenon driven by the presence of more large trees. Synthesis and applications: Together these results demonstrate the importance of urban natural area forests as carbon‐dense ecosystems and havens for native tree species. However, because of the greater number of non‐native species in smaller size classes, these forests may also be at risk of transitioning away from native‐dominated systems. Therefore, greater monitoring efforts and increased management activities such as non‐native species removal will be crucial to maintaining the high carbon storage and health of these ecosystems.

Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing

The carbon sequestration capacity of vegetation is the key to the carbon cycle in terrestrial ecosystems. It is significant to analyze the spatiotemporal variation and influencing factors of vegetation carbon sequestration ability to improve territorial carbon sink and optimize its spatial pattern. However, there is a lack of understanding of the impact of environmental conditions and human activity on the vegetation’s carbon sequestration ability, especially in highly urbanized areas. For example, effective vegetation management methods can enhance vegetation Gross Primary Productivity, while emissions of air pollutants like O3, CO, NO2, and PM2.5 can suppress it. This paper mainly explores the factors influencing vegetation carbon sequestration capacity across different regions of Beijing. Based on remote sensing data and site observation data, this paper analyzed the spatiotemporal variation trend of Annual Gross Primary Production (AGPP) and the influence of environmental factors and human activity factors on GPP in Beijing from 2000 to 2020 by using the Theil−Sen’s slope estimator, Mann−Kendall trend test, and comparing Geographically Weighted Regression method (GWR) and Geographically and Temporally Weighted Regression method (GTWR). GWR is a localized multiple regression technique used to estimate variable relationships that vary spatially. GTWR extends GWR by adding temporal analysis, enabling a comprehensive examination of spatiotemporal data variations. Besides, we used land use cover data to discuss the influence of land use cover change on AGPP. The results showed that the spatial distribution pattern of GPP in Beijing was higher in the northwest and lower in the southeast, and it showed an overall upward trend from 2000 to 2020, with an average annual growth rate of 14.39 g C·m−2·a−1. From 2000 to 2020, excluding the core urban areas, the GPP of 95.8% of Beijing increased, and 10.6% of Beijing showed a trend of significant increase, concentrated in Mentougou, Changping, and Miyun. GPP decreased in 4.1% of the regions in Beijing and decreased significantly in 1.4% of the areas within the sixth ring. The areas where AGPP significantly decreased were concentrated in those where land use types were converted to Residential land (impervious land), while AGPP showed an upward trend in other areas. CO and NO2 are the main driving forces of GPP change in Beijing. O3 and land surface temperature (LST) also exert certain influences, while the impact of precipitation (PRE) is relatively minor. O3 and CO have a positive impact on AGPP as a whole, while LST and NO2 generally exhibit negative impacts. PRE has a positive impact in the central area of Beijing, while it has a negative impact in the peripheral areas. This study further discusses opinions on future urbanization and environmental management policies in Beijing, which will promote the carbon peak and carbon neutrality process of ecological space management in Beijing. Besides, this study was conducted at the urban scale rather than at ecological sites, encompassing a variety of factors that influence vegetation AGPP. Consequently, the results also offer fresh insights into the intricate nexus between human activities, pollutants, and the GPP of vegetation.

Vegetation and carbon sink response to water level changes in a seasonal lake wetland

Water level fluctuations are among the main factors affecting the development of wetland vegetation communities, carbon sinks, and ecological processes. Hongze Lake is a typical seasonal lake wetland in the Huaihe River Basin. Its water levels have experienced substantial fluctuations because of climate change, as well as gate and dam regulations. In this study, long-term cloud-free remote sensing images of water body area, net plant productivity (NPP), gross primary productivity (GPP), and Fractional vegetation cover (FVC) of the wetlands of Hongze Lake were obtained from multiple satellites by Google Earth Engine (GEE) from 2006 to 2023. The trends in FVC were analyzed using a combined Theil-Sen estimator and Mann-Kendall (MK) test. Linear regression was employed to analyze the correlation between the area of water bodies and that of different degrees of FVC. Additionally, annual frequencies of various water levels were constructed to explore their association with GPP, NPP, and FVC.The results showed that water level fluctuations significantly influence the spatial and temporal patterns of wetland vegetation cover and carbon sinks, with a significant correlation (P<0.05) between water levels and vegetation distribution. Following extensive restoration efforts, the carbon sink capacity of the Hongze Lake wetland has increased. However, it is essential to consider the carbon sink capacity in areas with low vegetation cover, for the lakeshore zone with a higher inundation frequency and low vegetation cover had a lower carbon sink capacity. These findings provide a scientific basis for the establishment of carbon sink enhancement initiatives, restoration programs, and policies to improve the ecological value of wetland ecosystem conservation areas.

Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change.

Quantitative Analysis of Human Activities and Climatic Change in Grassland Ecosystems in the Qinghai–Tibet Plateau

Net primary production (NPP) serves as a critical proxy for monitoring changes in the global capacity for vegetation carbon sequestration. The assessment of the factors (i.e., human activities and climate changes) influencing NPP is of great value for the study of terrestrial systems. To investigate the influence of factors on grassland NPP, the ecologically vulnerable Qinghai–Tibet Plateau region was considered an appropriate study area for the period from 2000 to 2020. We innovated the use of the RICI index to quantitatively represent human activities and analyzed the effects of RICI and climatic factors on grassland NPP using the geographical detector. In addition, the future NPP was predicted through the integration of two modeling approaches: The Patch-Generating Land Use Simulation (PLUS) model and the Carnegie–Ames–Stanford Approach (CASA) model. The assessment revealed that the expanded grassland contributed 7.55 × 104 Gg C (Gg = 109 g) to the total NPP, whereas the deterioration of grassland resulted in a decline of 1.06 × 105 Gg C. The climatic factor was identified as the dominant factor in grassland restoration, representing 70.85% of the total NPP, as well as the dominant factor in grassland degradation, representing 92.54% of the total NPP. By subdividing the climate change and human activity factors into sub-factors and detecting them with a geographical detector, the results show that climate change and anthropogenic factors have significant ability to explain geographic variation in NPP to a considerable extent, and the effect on NPP is greater when the factors interact. The q-values of the Relative Impact Contribution Index (RICI) and the RICI of the land use change NPP are consistently greater than 0.6, with the RICI of the human management practices NPP and the evapotranspiration remaining at approximately 0.5. The analysis of the interaction between climate and human activity factors reveals an average impact of greater than 0.8. By 2030, the NPP of the natural development scenario, economic development scenario (ED), and ecological protection scenario (EP) show a decreasing trend due to climate change, the dominant factor, causing them to decrease. Human activities play a role in the improvement. The EP indicates a positive expansion in the growth rate of forests, water, and wetlands, while the ED reveals rapid urbanization. It is notable that this is accompanied by a temporary suspension of urban greening.

Impacts of Bedrocks on Vegetation Carbons in Typical Karst Areas: A Case Study in Puding County, Southwest China

An accurate estimation of vegetation carbon pools and their carbon sequestration potential is significant in global carbon cycle research but the existing estimations are still insufficient and largely uncertain. Here, we estimated the vegetation carbon density, carbon stocks, and carbon sequestration potential under three main bedrock types (limestone, dolomite, and non-carbonate) in Puding County, Guizhou Province, Southwestern China. The data used here included high-resolution vegetation maps of Puding, data from 274 sample plots, and the carbon contents measured previously in adjacent areas. The land area ratio of natural vegetation at an early stage (namely, grassland and shrub, excluding artificial forests and cultivated land) in carbonate rock areas is significantly larger than that in non-carbonate areas. The average existing carbon densities of vegetation in the non-carbonate, limestone, and dolomite areas were 31.59 ± 7.43, 16.75 ± 4.12, and 8.26 ± 2.45 Mg·ha−1, respectively, while their existing carbon stocks were 752.37 ± 172.85, 855.69 ± 210.65, and 208.49 ± 61.82 Gg, respectively. The maximum vegetation carbon densities of mature forests in the three bedrock types were 156.49 ± 12.92, 130.27 ± 6.05, and 117.41 ± 30.03 Mg·ha−1, respectively. Then, their average vegetation carbon sequestration potentials were 56.07 ± 23.06, 70.13 ± 11.39, and 59.11 ± 33.00 Mg·ha−1, respectively. In other words, vegetation carbon stocks in the non-carbonate, limestone, and dolomite areas increased by 1.34 ± 0.42, 3.58 ± 0.48, and 1.49 ± 0.51 Tg, respectively, after continuous evolution to mature forests. In conclusion, the potential growth of carbon density for karst vegetation is slightly higher than that of non-karst vegetation, despite its lower existing carbon density. Additionally, natural vegetation has a greater potential for carbon sequestration than plantations on all three bedrock types.

Principles for satellite monitoring of vegetation carbon uptake

Principles for satellite monitoring of vegetation carbon uptake

Knowledge‐Based Deep Learning to Predict Vegetation Carbon, Nitrogen and Phosphorus Densities in China’s Shrublands

AbstractAccurate estimations of carbon (C), nitrogen (N), and phosphorus (P) densities in shrublands are pivotal for assessing terrestrial ecosystem carbon sequestration. Combining in‐situ investigations and machine learning facilitates large‐scale patterns mapping, however, which often overlooks underlying ecological regulations. Here we utilize data from 1,122 survey plots across China's shrublands and develop a novel knowledge‐based deep learning framework that integrates a structural equation model (SEM) to elucidate mechanisms and construct an artificial neural network (ANN) based on these causal relationships. Results show that biomass allocation to different organs follows allometric regulations and that N and P concentrations maintain a degree of stoichiometric homeostasis following biological stoichiometry theory. This insight guides the construction of our ANN, which outperforms both SEM and other prevalent machine learning methods. By leveraging ecological theories to inform model construction, our framework not only enhances prediction accuracy and explainability but also provides a methodological blueprint for ecological research.

Effects of Restoration Strategies on Soil Health after Lycium chinense Removal in the Qaidam Basin

Ecological restoration of arid land plays a pivotal role in maintaining ecological sustainability and enhancing the resilience of local communities. As an ecologically significant arid land, the Qinghai Qaidam Basin has been severely impacted by human activities such as the widespread planting of Lycium chinense, leading to considerable degradation of vegetation and soil carbon and nutrients. Currently, this vital area is undergoing extensive ecological restoration through the employment of a variety of strategies, but the impact remains inadequately understood. This study seeks to compare the effects of different restoration strategies in the Qinghai Qaidam Basin, focusing on soil properties across five scenarios: a controlled desert area, natural restoration after the removal of L. chinense, continued planting of L. chinense, restoration through the planting of Haloxylon ammodendron, and mixed reseeding after four years of restoration. Our findings indicate that mixed reseeding significantly improved soil water storage to 4.26%, especially in the deep soil layer. The planting of H. ammodendron strategy efficiently reduced soil pH in such an alkaline environment. Soil nutrients, including total nitrogen (TN), total phosphorus (TP), and total potassium (TK), were predominantly concentrated in the top soil layer, with reduced concentrations observed in the medium and deep soil layers. Although soil organic matter remained relatively stable across all restoration strategies, its content was notably lower in the deeper layers. Overall, mixed reseeding proved to be the most efficient strategy for enhancing soil water retention and nutrient levels. In contrast, despite achieving high vegetative coverage to 62.6%, planting of L. chinense was less ecologically beneficial due to its extensive irrigation requirements and adverse effects on soil structure. These findings suggest that restoring degraded areas to an ideal ecological state cannot be achieved within a few years, underscoring the importance of sustained restoration efforts. This study offers valuable insights and practical guidance for the ecological restoration of arid lands, contributing to the development of sustainable land management practices in similar regions.

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

Climate feedback from plant physiological responses to increasing atmospheric CO2 in Earth system models.

Plant physiological responses to increasing atmospheric CO2 concentration (iCO2), including enhanced photosynthesis and reduced stomatal conductance, impact regional and global climate. Here, I describe recent advances in understanding these effects through Earth system models (ESMs). Idealized simulations of a 1% annual iCO2 show that despite fertilization, CO2 physiological forcing contributes to 10% of warming and at least 30% of future precipitation decline in Amazonia. This reduces aboveground vegetation carbon storage and triggers positive carbon-climate feedback. ESM simulations indicate that reduced transpiration and increased heat stress from iCO2 could amplify meteorological drought and wildfire risks. Understanding these climate feedbacks is essential for improving carbon accounting in natural climate solutions, such as avoiding deforestation and reforestation, as iCO2 complicates assessing their climate benefits.

Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century.

Enhanced relationship between seasonal soil moisture droughts and vegetation under climate change over China

Climate change could intensify seasonal soil moisture droughts and subsequently degrade vegetation, but it can also facilitate vegetation growth with the rising temperature and CO2 concentrations. Therefore, the change of the vegetation-drought relationship under climate change remains elusive. Here, we investigate the effects of vegetation greening (i.e., increasing leaf area index (LAI)) on seasonal soil moisture drought trends by performing a set of land surface model simulations with the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), as well as the impact of seasonal soil moisture drought change on vegetation productivity via a statistical fitting model. Our results indicate that both the impact of increasing LAI on exacerbating seasonal droughts and the impact of increasing seasonal droughts on reducing vegetation productivity have been enhanced over China during 1961–2018. Specifically, increased LAI accounts for 48 % of the rising drought frequency through escalated evapotranspiration losses, with more pronounced effects in northern semiarid regions. The decrease in vegetation stomatal conductance caused by rising CO2 concentrations limits transpiration and alleviates 11 % of the increase in drought frequency, but it cannot fully offset the drought aggravation induced by increased vegetation water consumption under climate warming. Meanwhile, increased drought frequency and intensity have reduced the upward trend in vegetation productivity by 5 % over China, and by 8 % in water-limited northern regions. Our study shows the relationship between seasonal soil moisture droughts and vegetation over China has strengthened, indicating that vegetation greening will lead to increased water shortages and further impact vegetation growth and carbon sequestration. This challenges water resources management and environmental sustainability, especially in semiarid regions.

Estimation of carbon emissions in various clustered regions of China based on OCO-2 satellite XCO2 data and random forest modelling

Atmospheric carbon dioxide (CO2) stands as one of the most important greenhouse gasses, with steadily increasing concentrations attributable to human activities. In the pursuit of reaching peak carbon and carbon neutrality goals, it is essential to quantify carbon emissions and evaluate carbon reduction strategies. To establish a high-precision observation with full time series and spatial coverage, a spatio-temporal interpolation method was developed to obtain XCO2 data over mainland China at a resolution of 0.5° × 0.5° for the years 2015–2021. An east-west gradient, higher levels in the east and lower levels in the west, was observed, exhibiting a seasonal pattern of elevation in spring and reduction in summer. Subsequently, the research area is classified into seven clusters based on time-series XCO2 anomalies (ΔXCO2) and ODIAC (Open Source Data Inventory of Anthropogenic Carbon Dioxide) carbon emission data. This classification aims to emphasize the differentiation of spatial heterogeneity in carbon emissions and the results highlight that regions with high ΔXCO2 reflect higher carbon emission. Finally, the carbon emissions of each cluster were estimated by using a random forest model individually yielding an R2 of approximately 0.6. For assessing the variables influencing carbon emission predictions, the importance of each variable was calculated. Specifically, NightTime Lighting data (NTL), representing human production activities, emerged as a crucial variable influencing carbon emission predictions in most clusters. In comparison, Gross Primary Productivity (GPP) is considered a more critical variable in Southwest China (SWC), primarily owing to the intricate vegetation carbon sink system in this region. Temperature (T) emerges as a key variable influencing the estimation of carbon emissions in certain developed cities in Eastern China (EC), driven by the urban heat island effect which amplifies energy consumption, modifies land use, and impacts urban systems, influencing the spatial patterns of carbon emissions. Carbon emissions in different characteristic regions was quantified by establishing machine learning models with remote sensing data, which can provide new insights and support for refined carbon monitoring and management strategy.

Optimization of vegetation carbon content parameters and their application in carbon storage estimation in China

As a key parameter for C pool and flux assessments, vegetation carbon (C) content can be used in ecological models to predict climate-induced changes in the C sequestration capacity of vegetation. However, the differences in methods for upscaling C content from the organ to the community scale and their impact on regional C stock estimates have been ignored. Based on a comprehensive community structure survey of 72 typical natural ecosystems in China and 27,905 measured samples of plant organs (leaves, twigs, trunks, and roots), we first quantified the differences among scaling-up methods for vegetation C content. These methods included the community or dominant species-weighted mean, geometric mean, arithmetic mean, and traditional empirical coefficients (45 % and 50 %), and their impact on C storage estimation at the regional scale. Comparing the accuracy, variability, and response patterns of the different scaling-up methods, the dominant C species biomass-weighted mean (CDWM) method had the highest similarity to the community-weighted C mean (CCWM) method. Concerning vegetation C storage estimation in China's natural terrestrial ecosystems, the relative errors of the other methods ranged from −2.6 % to 8.22 % compared with that of the CCWM method (18.39 Pg C). The empirical coefficients had the highest uncertainty, with a 45 % empirical coefficient underestimating the vegetation C stock by 2.60 %, and a 50 % empirical coefficient overestimating it by 8.22 %. The CDWM method proposed here has high reliability for C storage estimation (overestimated by only 0.44 %), making it a preferable sampling and scaling-up method for regional C content and stock assessment. Additionally, our study provided the C content of plant organs for China's provinces and typical vegetation types based on the CCWM, which could be used for regional C stock assessment and C cycle models.

Assessing and explaining rising global carbon sink capacity in karst ecosystems

Global karst vegetation is crucial for carbon sequestration and biodiversity conservation. However, research on the carbon sink capacity of vegetation and its potential driving mechanisms across different geographic types of global karst remains scant. This research delineates the trend characteristics and differences of net primary productivity (NPP), heterotrophic respiration (Rh), and net ecosystem productivity (NEP) across various bedrock and climatic zones within global karst landscapes. Extreme Gradient Boosting (XGBoost), Shapley Additive Explanations (SHAP), Neural Network fitting, and Partial Least Squares Structural Equation Modeling were utilized to investigate the nonlinear impacts and underlying mechanisms exerted by the explanatory variables on NPP, Rh, and NEP. The findings indicate that from 1981 to 2019, the rates of increase in NPP (2.02 gC·m⁻2 yr⁻1), Rh (1.47 gC·m⁻2 yr⁻1), and NEP (0.54 gC·m⁻2 yr⁻1) were marginally higher in global karst regions compared to non-karst areas. Additionally, when contrasted with various bedrock types, the differences in mean values and trends of these three indicators were more pronounced across different climatic zones of karst areas. XGBoost effectively captures the nonlinear relationship between NEP and its explanatory variables, while SHAP provides a detailed explanation of the contribution and interaction of these variables. The leaf area index (LAI) was the major contributor to NEP. Additionally, the nonlinear effects of individual critical drivers on NEP varied across different aridity gradients. The cascade effect of explanatory variables on NEP follows a pathway where geography affects climate, climate change subsequently alters vegetation characteristics, and these changes in vegetation directly drive NPP and Rh, ultimately leading to an indirect impact on NEP. This impact pathway exhibits minimal difference across different bedrock and climate types. This study enhances our comprehension of the role of vegetation carbon sinks and their underlying mechanisms across various bedrock and climate types in global karst regions.

A multi-objective optimization framework for regional land-use allocation: Fully utilizing terrestrial vegetation to mitigate carbon emissions

Human-induced changes in land use and land cover (LULC) considerably impact the global carbon cycle. Rational LULC optimization with full utilization of terrestrial vegetation facilitates reconciling carbon reduction and socio-economic development. To this end, this study develops a multi-objective framework for low-carbon LULC spatial optimization that incorporates carbon suitability, historical suitability, and socio-economic needs. The framework is applied to Hubei Province, China. The key findings include: (1) Considering spatial variations in vegetation carbon sinks could lead to remarkable carbon reductions of 2.75 × 106 t C by 2030 and 7.68 × 106 t C by 2060. It would also lower carbon emissions per unit of gross domestic product (GDP) and enhance carbon sequestration per unit of vegetation by 2030, signifying enhanced carbon reduction efficiency. (2) Integrating carbon sequestration potential and historical suitability can considerably minimize emissions while sustaining socio-economic growth. Our method, compared with strategies based solely on historical development laws, can reduce carbon emissions by approximately 1.3–1.6 × 106 t C·a−1. (3) We also propose targeted low-carbon development suggestions for different LULCs. This study provides optimal spatial LULC allocations and strategic advice for regional LULC planning aimed at low-carbon development, demonstrating the feasibility of reconciling the tension between carbon reduction and socio-economic growth at the regional scale.

Research on Leaf Area Index Inversion Based on LESS 3D Radiative Transfer Model and Machine Learning Algorithms

The Leaf Area Index (LAI) is a critical parameter that sheds light on the composition and function of forest ecosystems. Its efficient and rapid measurement is essential for simulating and estimating ecological activities such as vegetation productivity, water cycle, and carbon balance. In this study, we propose to combine high-resolution GF-6 2 m satellite images with the LESS three-dimensional RTM and employ different machine learning algorithms, including Random Forest, BP Neural Network, and XGBoost, to achieve LAI inversion for forest stands. By reconstructing real forest stand scenarios in the LESS model, we simulated reflectance data in blue, green, red, and near-infrared bands, as well as LAI data, and fused some real data as inputs to train the machine learning models. Subsequently, we used the remaining measured LAI data for validation and prediction to achieve LAI inversion. Among the three machine learning algorithms, Random Forest gave the highest performance, with an R2 of 0.6164 and an RMSE of 0.4109, while the BP Neural Network performed inefficiently (R2 = 0.4022, RMSE = 0.5407). Therefore, we ultimately employed the Random Forest algorithm to perform LAI inversion and generated LAI inversion spatial distribution maps, achieving an innovative, efficient, and reliable method for forest stand LAI inversion.

Spatiotemporal evolution and driving factors of China's carbon footprint pressure: Based on vegetation carbon sequestration and LMDI decomposition

Evaluating carbon footprint pressure (CFP) scientifically and quantifying the environmental impact of economic activities precisely are crucial for informing policies on carbon emission reduction and achieving carbon neutrality in China. This study revealed China's CFP from 2005 to 2021 using data sets of fossil fuel carbon emissions and vegetation carbon sequestration from remote sensing. We investigated the drivers of CFP using LMDI decomposition. Additionally, we used the Tapio model to examine the decoupling of CFP and economic growth. The findings indicate that China's CFP has increased over time and shows a positive spatial correlation. Rapid economic growth and significant population expansion are the primary drivers of the surge in CFP. Conversely, optimizing energy structure, reducing energy intensity, and enhancing ecological quality are key to alleviating the rising pressure on China's carbon footprint. Moreover, we observe a weak decoupling of China's CFP and economic growth. Notably, it is more challenging to achieve an ideal decoupling state in the western region than in the central and eastern regions.

Carbon Sequestration Capacity after Ecological Restoration of Open-Pit Mines: A Case Study in Yangtze River Basin, Jurong City, Jiangsu Province

Open-pit mining seriously damages the original vegetation community and soil layer and disturbs the carbon cycle of vegetation and soil, causing instability in the mining ecosystem and decrease in the carbon sequestration capacity of the mining area. With the deepening of environmental awareness and the influence of related policies, the ecological restoration of open-pit mines has been promoted. The mining ecosystem is distinct owing to the disperse distribution of mines and small scale of single mines. However, the carbon sequestration capability of mines after ecological restoration has not been clearly evaluated. Therefore, this study evaluated the carbon sequestration capacity of restoration mines, taking the mines of the Yangtze River Basin in Jurong City, Jiangsu Province as the research objects. Firstly, the visual effects of the vegetation and soil in their current status were determined through field investigation, the methods for sampling and data collection for the vegetation and soil were selected, and the specific laboratory tests such as the vegetation carbon content and soil organic carbon were clarified. Meanwhile, the evaluation system consisting of three aspects and nine evaluation indexes was established by using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). The process of evaluation included the following: the establishment of the judgment matrix, calculation of the index weight, determination of the membership function, and establishment of the fuzzy membership matrix. Finally, the evaluation results of the restoration mines were determined with the ‘excellent, good, normal and poor’ grade classification according to the evaluation standards for each index proposed considering the data of the field investigation and laboratory tests. The results indicated that (1) the evaluation results of the mines’ carbon sequestration capacity were of excellent and good grade at a proportion of 62.5% and 37.5%, which was in line with the field investigation results and demonstrated the carbon sequestration capacity of all the restored mines was effectively improved; and (2) the weights of the criterion layer were ranked as system stability > vegetation > soil with the largest value of 0.547, indicating the stability of the system is the main factor in the carbon sequestration capacity of the mines and the sustainability of the vegetation community and the stability of soil fixation on the slope. The proposed evaluation system effectively evaluates the short-term carbon sequestration capability of the restoration mining system according to the visual effects and the laboratory testing results, objectively reflecting the carbon sequestration capacity via qualitative assessment and quantitative analysis. The evaluation method is relatively applicable and reliable for restoration mines and can provide a reference for similar ecological restoration engineering.