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Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) can either enhance the areal carbon reserve capacity or exacerbate carbon emission issues, thereby significantly influencing global climate change. Comprehending the impact of LUCC on regional carbon reserve variation holds great significance for regional ecosystem preservation and socioeconomic sustainable development. This study focuses on Liaoning Province, leveraging land use remote sensing data from three periods from 2000 to 2020, natural environmental data and socioeconomic data in conjunction with the Integrated Valuation of Environmental Services and Trade-offs (InVEST) model, and patch-generating land use simulation (PLUS) models. It analyzes the interactive relationship between LUCC and carbon reserves in Liaoning Province between 2000 and 2020 and forecasts the trajectory of carbon reserve changes in Liaoning Province under various scenarios: business as usual, urban development, cropland protection, and ecological protection, all based on LUCC simulations. The findings indicate the following: (1) Over the study period, Liaoning Province experienced significant LUCC characterized primarily by the transformation of farmland to built-up land. Carbon reserves initially declined and later increased due to LUCC changes, resulting in a cumulative increase of 30.52 Tg C. The spatial distribution of carbon reserves was influenced by LUCC, displaying a pattern of spatial aggregation, with higher values in the east and lower values in the west. (2) Across the four simulation scenarios, the spatial pattern of carbon reserves in Liaoning Province continued to exhibit the characteristic spatial aggregation of higher values in the east and lower values in the west. Under the urban development scenario, carbon reserves decreased by 34.56 Tg C tons, representing a 2.45% decrease compared to 2020. Conversely, under the business-as-usual, cultivated land protection, and ecological protection scenarios, carbon reserves displayed a growing tendency, reaching 1449.35 Tg C, 1450.39 Tg C, and 1471.80 Tg C, respectively, with changes of 0.09%, 0.16% and 1.63% compared to 2020. The substantial increase in carbon reserves under the ecological protection scenario primarily stemmed from the significant expansion of woodland and other ecological land areas. In light of these findings, Liaoning Province may consider laying down and strictly executing spatial policies for ecological protection in future land projecting. The PLUS model and InVEST model can help curb the uncontrolled expansion of built-up land, facilitate the increment of ecological land areas, and with effect augment carbon reserves, thereby ensuring the achievement of the "double carbon" target of carbon peak and carbon neutralization. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover Change (LUCC) represents the interaction between human societies and the natural environment. Studies of LUCC simulation allow for the analysis of Land Use and Land Cover (LULC) patterns in a given region. Moreover, these studies enable the simulation of complex future LUCC scenarios by integrating multiple factors. Such studies can provide effective means for optimizing and making decisions about the future patterns of a region. This review conducted a literature search on geographic models and simulations in the Web of Science database. From the literature, we summarized the basic steps of spatiotemporal dynamic simulation of LUCC. The focus was on the current major models, analyzing their characteristics and limitations, and discussing their expanded applications in land use. This review reveals that current research still faces challenges such as data uncertainty, necessitating the advancement of more diverse data and new technologies. Future research can enhance the precision and applicability of studies by improving models and methods, integrating big data and multi-scale data, and employing multi-model coupling and various algorithmic experiments for comparison. This would support the advancement of land use spatiotemporal dynamic simulation research to higher levels. [ABSTRACT FROM AUTHOR]

Watershed ecosystems play a pivotal role in maintaining the global carbon cycle and reducing global warming by serving as vital carbon reservoirs for sustainable ecosystem management. In this study, we based on the "quantity-mechanism-scenario" frameworks, integrate the MCE-CA-Markov and InVEST models to evaluate the spatiotemporal variations of carbon stocks in mid- to high-latitude alpine watersheds in China under historical and future climate scenarios. Additionally, the study employs the Geographic Detector model to explore the driving mechanisms influencing the carbon storage capacity of watershed ecosystems. The results showed that the carbon stock of the watershed increased by about 15.9 Tg from 1980 to 2020. Fractional Vegetation Cover (FVC), Digital Elevation Model (DEM), and Mean Annual Temperature (MAT) had the strongest explanatory power for carbon stocks. Under different climate scenarios, it was found that the SSP2-4.5 scenario had a significant rise in carbon stock from 2020 to 2050, roughly 24.1 Tg. This increase was primarily observed in the southeastern region of the watersheds, with forest and grassland effectively protected. Conversely, according to the SSP5-8.5 scenario, the carbon stock would decrease by about 50.53 Tg with the expansion of cultivated and construction land in the watershed's southwest part. Therefore, given the vulnerability of mid- to high-latitude mountain watersheds, global warming trends continue to pose a greater threat to carbon sequestration in watersheds. Our findings carry important implications for tackling potential ecological threats in mid- to high-latitude watersheds in the Northern Hemisphere and assisting policymakers in creating carbon sequestration plans, as well as for reducing climate change. [ABSTRACT FROM AUTHOR]

In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of "low-carbon human well-being" and evaluation framework; and (3) the proposed framework of "LUCC-CEs-HW," which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the "LUCC-CEs-HW" nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works. [ABSTRACT FROM AUTHOR]

Abstract Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the ‘salt and pepper noise’. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

For the sustainable development of the city, in a study of Tianjin's rapid urbanization, we explore the complex interplay between land use change and the ecosystem carbon cycle from 2000 to 2020. Spatial analysis and profit-loss matrix calculations reveal contrasting ecological impacts: expansion of woodlands and grasslands enhances Net Primary Productivity (NPP) and reduces carbon emissions, while urban construction shows the opposite effect. Over 20 years, Tianjin's urban transformation led to a 16.91 GgC decline in NPP amidst a construction boom. However, post-2015 ecological policy shifts resulted in a significant net carbon uptake of 0.85 Gt, demonstrating the potential of policy interventions in mitigating environmental impacts of urbanization. This study underscores the importance of sustainable urban planning and ecological conservation strategies in highly urbanized settings. [ABSTRACT FROM AUTHOR]

To gain a deeper understanding of the human–land coupling relationship, this study analyzes the coupling relationships with the spatial distribution of construction land from two perspectives: the residential population and the employment population, exploring the similarities and differences in coupling relationships among different subsystems. The Beijing–Tianjin–Hebei region of China is selected as the study area, covering the period from 2000 to 2020. An analytical framework is proposed, encompassing three approaches: coupling analysis based on county-level spatial units; mean center position analysis based on construction land grids; and regression fitting and residual analysis based on homogeneous grid units. The analysis results indicate: (1) the coupling between the employment population and construction land shows a significant advantage; (2) the coupling between the residential population and construction land has improved faster in recent years; (3) factors such as location, development level, and strategic opportunities have an important influence on the spatial and temporal changes in the coupling relationship. The study further discusses the trade-off relationship between different subsystems, key measures to enhance coupling degree, and the application pathways of this analytical framework at various stages of planning. Considering the limitations of industry sector differences, spatial unit precision, and construction land development intensity, this paper also outlines future research directions. [ABSTRACT FROM AUTHOR]

The evaluation of ecosystem service value (ESV) is crucial for decision making in regional sustainable development. The close relationship between ecosystem services and land use/cover change (LUCC) is well acknowledged. However, the impact of the mutual transformation among different land use types on the temporal and spatial differences in the ESV is still unclear. To fulfill this gap, this study evaluates the ESV in the Wuhan Urban Agglomerations based on LUCC, taking the spatiotemporal characteristics into consideration. The results show that (1) The land use structure in the Wuhan Urban Agglomerations has undergone great changes from 2012 to 2021, and the area of cultivated land converted to forest land is the largest, which may be related to policies such as returning farmland to forests. (2) The total amount of ESV shows a downward trend, and the spatial distribution of ESV is "low in the west and high in the central and eastern regions", which may be related to the natural factors in study area. (3) The spatial distribution of ESV in the study area will remain unchanged in the future. However, the transformation among land use types may exacerbate the reduction in the total ESV, which will have an adverse impact on the ecological environment and sustainable development of the region. This study initiates a more comprehensive framework to better reflect the real scenario of ESV, which will hopefully provide a reference for regional sustainable development. [ABSTRACT FROM AUTHOR]

As a fundamental element of global carbon storage, the storage carbon in terrestrial ecosystem is significant for climate change mitigation. Land use/cover change (LUCC) is a main impact element of ecosystems' carbon storage. Evaluating the relation between land use change and carbon storage is vital for lowering global carbon emissions. Taking Hainan Island as an example, this paper employs the InVEST as well as the CA-Markov models to assess and predict how different land use affects carbon storage in various situations from 2000 to 2020 and from 2030 to 2050 on Hainan Island. The influence factors, together with driving mechanisms of carbon storage spatial distribution are quantitatively analyzed as well in this paper. The results demonstrate that, from 2000 to 2020, Hainan Island's net increase in built land was 605.49 km2, representing a growth rate of 77.05%. Over the last 20 years, Hainan Island's carbon storage and density have decreased by 5.90 Tg and 1.75 Mg/hm2, respectively. The sharp rise in built land mainly makes the carbon storage decline. From 2030 to 2050, land use changes on Hainan Island are expected to result in differing degrees of carbon storage loss in various scenarios. In 2050, Hainan Island's carbon storage will decline by 17.36 Tg in the Natural Development Scenario (NDS), 13.61 Tg in the Farmland Protection Scenario (FPS), and 8.06 Tg in the Ecological Protection Scenario (EPS) compared to 2020. The EPS can efficiently maintain carbon sequestration capability, but it cannot effectively prevent cropland area loss. Regarding the carbon storage's spatial distribution, Hainan Island generally exhibits a pattern of high carbon storages in the low and middle carbon storages in the surrounding areas. Areas with high value are primarily located in Hainan Island's central and southern mountainous areas, whereas areas with low value are primarily located in surrounding areas with lower elevations, primarily encompassing built land and cropland. Geographic detection presented the spatial differentiation of carbon storage in Hainan Island is mainly influenced by factors like slope, land use intensity, and DEM, as well as its interaction with other factors is significantly strengthened (p < 0.05). Under the strategic framework of the "carbon peaking and carbon neutrality" goal and the national ecological civilization pilot zone, it is imperative to carefully consider scenarios for ecological protection and farmland protection, adopt ecological regulation models with spatial differentiation, and implement land use policies to improve ecosystem stability, which will contribute to carbon storage loss reduction and ensure food and ecological security. [ABSTRACT FROM AUTHOR]

Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the main factor leading to changes in ecosystem carbon storage. Understanding the impact of LUCC on regional carbon storage changes is crucial for protecting regional ecosystems and promoting sustainable socio-economic development. This paper selects Shanxi province as the study area and explores the spatial and temporal evolution characteristics of carbon storage in Shanxi province based on the InVEST model and univariate spatial autocorrelation. The results show that the total carbon storage in Shanxi Province in 2000, 2010, and 2020 is 513.51 × 104 t C, 513.46 × 104 t C, and 509.29 × 104 t C, respectively. High carbon storage areas are distributed in forest and grassland land types, while low carbon storage areas are widely distributed in building land in urban metropolitan areas. Shanxi Province is mainly dominated by farmland, which has decreased by 3448.60 km2 in the past 20 years. Grassland has decreased by 1588.31 km2 and the area of building land has increased by 4205.73 km2. Due to the influence of carbon conversion among different land use types, the total carbon storage loss of Shanxi Province in the past 20 years was 4.21 × 104 t C. The transfer of farmland resulted in an increase in carbon stock of 14.46 × 104 t C. The transfer of grassland resulted in an increase of 17.15 × 104 t C, while the transfer of forest resulted in a decrease of 41.44 × 104 t C. The increase in land use types with low carbon density and the decrease in land use types with high carbon density led to the decrease in carbon storage in Shanxi Province. Furthermore, social factors were more likely to influence the carbon storage than natural factors, and the influence of social factors was often negative. On this basis, regional development countermeasures were proposed for the current situation of carbon storage in Shanxi Province and provide a scientific basis for Shanxi Province to achieve the carbon neutrality target. [ABSTRACT FROM AUTHOR]

The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics. Plain Language Summary: With climate change and intensifying human activities, the water cycle has been changed. Runoff is an essential component of the water cycle, and its response to changing environments has drawn broad attention. This study aims to quantify the contributions of climate change and human activities to runoff changes in the upper Yangtze River (UYR) and its four sub‐watersheds over the past half‐century. According to the results, human activities and climate change have been responsible for approximately 51.2% and 48.8% of the observed runoff changes in UYR, respectively. It suggests that both human activities and climate change have played significant roles, and their contributions are remarkably comparable. In addition, the impacts of human activities on watershed environment changes are analyzed. The results suggest that increased settlement area and industrial water use are the dominant influencing factors. Key Points: Human activities and climate change have made comparable contributions to the runoff reduction in the upper Yangtze RiverWater use, land use/cover change, vegetation change, and dam construction together explain 22% of the watershed environment changesHuman settlements and increased industrial water use have been the principal factors contributing to the changes in watershed environment [ABSTRACT FROM AUTHOR]

In recent years, human activities have caused significant changes in the underlying surface and affected the processes of rainstorms and floods. It is of great practical significance for water resources management to understand the mechanism of the rainstorm and flood process under the changing conditions of land use and cover change (LUCC) and grasp the change law of the rainstorm and flood. Given this, this study aims to take the flood simulation of the Kuye River basin in the Loess Plateau as an example, selecting the measured flood data for the last 60 years (1960–2018) for statistical analysis and then illustrating the effect of the LUCC on the storm flooding using the distributed time varying gain hydrological model (DTVGM). The primary outcomes are as follows: (1) The statistical results show that the four rainstorm indicators of the annual maximum one-hour precipitation, the annual maximum precipitation, the number of days with ≥ 25 mm precipitation, and the number of days with ≥ 50 mm precipitation have no apparent increasing trend during the study period. (2) During the study period, the most significant annual flood peak discharge and the frequency of heavy and moderate floods in the basin decreased significantly (p < 0.01). (3) By constructing the DTVGM model, it was found that the flood flow showed a significant reduction during the simulation period, with an attenuation rate of about 40%, subject to changes in the underlying surface conditions. Discussing the impacts of changes in the LUCC on the Kuye River basin flood processes will enable better formulation of water resources management policies. This integrated research methodology is expected to serve as a reference for other river basins and help build a more sustainable water resource management system for rational utilization and adequate protection of water resources. [ABSTRACT FROM AUTHOR]

Land use profoundly impacts the sustainable development of the ecological environment. Optimizing land use patterns is a vital approach to mitigate climate change and achieve carbon neutrality. Using Shandong Province as a case study, this research evaluates the impacts of land use and land cover change (LUCC) on regional carbon storage and emissions. Employing a coupled PLUS–InVEST–GM(1,1) model, simulations were conducted for scenarios including the natural scenario (NS), cropland protection scenario (CPS), high-speed development scenario (HDS), and low-carbon scenario (LCS), to assess LUCC and changes in carbon storage and emissions from 2030 to 2060 under these scenarios. The findings indicate that due to the expansion of construction land and significant declines in arable and grassland areas, carbon emissions increased by 40,436.44 × 104 t over a 20-year period, while carbon storage decreased by 4881.13 × 104 t. Notably, forests contributed the most to carbon sequestration, while construction land emerged as the primary source of carbon emissions. Simulating four scenarios demonstrates that measures such as protecting cropland, expanding forest, grassland, and aquatic areas, controlling construction land expansion, and promoting intensive development positively affect emission reductions and carbon sequestration in Shandong. These findings underscore the importance of rational planning of land use patterns, which can enhance contributions to carbon neutrality by harmonizing the relationships among cropland protection, ecological conservation, and economic development. [ABSTRACT FROM AUTHOR]

Carbon storage plays a vital role in the provision of ecosystem services, and the alteration of land use has a profound influence on the carbon storage capacity of ecosystems. Therefore, in this study, two scenarios of natural evolution scenario (NES) and ecological conservation scenario (ECS) were set up based on the development of Mohe City,China. Meanwhile, a coupled model of LUCC and ecosystem carbon storage was established using Logistic-CA-Markov and InVEST models, as well as optimal parameters_based geographical Detector and GeoDa, to predict the distribution and change of ecosystem carbon storage based on LUCC in the future. The effects of different influencing factors on the spatial differentiation of carbon storage were also explored. The results show that: (1) From 1980 to 2020, the land use type in Mohe City was dominated by the forest and its area decreased; the area of wetland increased. From 2020 to 2040, ecological benefits will be shown under the ECS, with an increase in forest and a slower expansion of built-up. (2) Carbon loss in Mohe City from 1980 to 2020 was 4.04Tg. Under the ECS carbon storage increased slightly by 0.2Tg. Soil carbon storage was the main carbon pool in Mohe City, and forest was the largest contributor. The carbon storage of Mohe city in 2030 and 2040 has a strong positive spatial correlation. Hot spots in more than a cold spots area, the high value area is concentrated in the east, low concentrated in urban areas. (3) Apart from LUCC, mean annual precipitation was the most significant factors affecting the spatial differentiation variability of carbon storage. The interactions of mean annual precipitation and population density with other factors exhibit a non-linear enhancement,which had a coefficient of 21.91%. This study contributes to a deeper understanding of the relationship between LUCC and carbon storage. [ABSTRACT FROM AUTHOR]

Studying the spatio-temporal changes and driving mechanisms of vegetation's net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m−2·a−1. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m−2·a−1, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation's dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed's water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study's results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed. [ABSTRACT FROM AUTHOR]

For the past several decades, climate change has been driving vegetation dynamics in arid regions worldwide. This study investigates vegetation dynamics and their links to climate from 1990 to 2020 in Xinjiang, China, using data from 30-m resolution land use and cover change, remote sensing, and climate reanalysis. Our approach encompasses a range of analytical techniques, including transfer matrix analysis, modeling, correlation, regression, and trend analysis. During the study period, there were major vegetation conversions from grassland to forestland in the mountains, and from cropland to grassland in the plains. Climate change emerged as an important trigger of these changes, as evidenced by the increase in net primary productivity in most vegetation types, except for cropland-grassland and grassland-cropland conversions. Precipitation and soil moisture were the most influential climatic factors, contributing 15.1% and 15.2%, respectively, to natural vegetation changes. The study also found that evapotranspiration serves as a key mechanism for moisture dissipation in the hydrological cycle of vegetation dynamics. The interplay between precipitation, soil moisture, and evapotranspiration is a critical pattern of climatic influence that shapes vegetation dynamics across zones of intersection, increase, decrease, and change. These insights are invaluable for informing vegetation conservation and development strategies in Xinjiang and other similar environments facing climate-driven ecological transitions. [ABSTRACT FROM AUTHOR]

Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 106 t, 700.33 × 106 t, 697.22 × 106 t, 696.03 × 106 t, and 691.21 × 106 t, respectively. This represents changes of 3.69 × 106 t, 1.69 × 106 t, −1.49 × 106 t, −2.68 × 106 t, and −7.43 × 106 t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.

Quantitative understanding of drought risks on arid region is vital in drought risk mitigation and ecological restoration. However, no study has been conducted on the drought risk variance between different shared socioeconomic pathway (SSP) scenarios and China’s dual carbon goals scenario on arid region. This study evaluates drought risks of Loess Plateau between the year of 1980–2100 based on the Coupled Model Intercomparison Project phase 6 data sets and the United Nations International Strategy for Disaster Reduction drought risk assessment framework. The average values of the drought risk index in four scenarios during the projection period (2030–2100) are 0.29 (±0.026) for the low carbon emission scenario, 0.257 (±0.019) for the China’s dual carbon target scenario, 0.232 (±0.057) for the medium carbon emission scenario, and 0.248 (±0.059) for the high carbon emission scenario, which are higher than the historical period (i.e. 1980: 0.210; 2020: 0.215). Accordingly, the Loess Plateau will have severe drought risks in the future. In the zone of increased drought risk indices on the Loess Plateau, the areas of woodland and croplands increased, whereas the area of grasslands increased in the zone of decreased drought risk indices. China’s dual carbon goals (i.e. SSP1-2.6) scenario has the smallest degree of variability in drought risk, which is the most reliable pathway of across the four scenarios. We emphasize the potential of spatial planning based on regional characteristics and creating ecological buffer zones for endemic biome habitats to address the uncertainty of drought risk in arid and semi-arid regions like the Loess Plateau.

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

Context: Habitat quality (HQ) is vital for achieving sustainable regional development. Understanding the spatial patterns and temporal evolution of HQ in the context of land use–cover change (LUCC) is crucial for promoting ecological civilisation and high-quality growth, especially for regions with rapid economic development. Aims: In order to analyse the impact of LUCC on habitat quality in the coastal areas with frequent human activities. Methods: This study focused on the Greater Bay Area around Hangzhou Bay and analysed the HQ on the basis of LUCC data during 2010–2020 by using the InVEST model and spatial autocorrelation analysis. Additionally, land-use data for multiple scenarios in 2030 were predicted using the PLUS model, and the variations in land use and HQ in the study region during 2020–2030 were assessed. Key results: During 2010–2020, the construction land in the region expanded by 1932.79 km2, primarily at the expense of cropland and water areas. The mean HQ values were 0.6287, 0.6181 and 0.6037 for 2010, 2015 and 2020 respectively, indicating a continuous decline. Spatially, HQ exhibited strong clustering during this period. However, there was a clear trend of fragmentation and reduction in 'high–high' cluster areas along the coast, mostly owing to the transformation of water areas and wetlands into construction land and cropland. In the projected scenarios (natural development, ND; economic development, ED; crop protection, CP; and ecological protection, EP) for 2030, the mean HQ values are estimated to be 0.5881, 0.5837, 0.5915 and 0.5965 respectively. Compared with 2020, there will be a certain decrease in HQ, with the EP scenario showing the lowest decrease of 0.0052. Conclusions: The HQ changes were closely linked to LUCC, the construction-land expansion was the main cause of HQ destruction in the Greater Bay Area around Hangzhou Bay. To alleviate the trend of declining HQ, it is essential to select appropriate development scenarios for each city in the region and coordinate the development of the cities. Implications: These findings provide valuable insights for promoting sustainable economic growth in the Greater Bay Area around Hangzhou Bay. Land use–cover change affects habitat quality and sustainable growth. This study predicted 2030 land-use scenarios with the PLUS model and assessed habitat quality from 2010 to 2030 with InVEST in the Greater Bay Area around Hangzhou Bay. Limiting construction, protecting farmland and expanding green spaces are essential for improving habitat quality. Findings have provided insights for sustainable growth. This article belongs to the collection Ecological Monitoring and Assessment of Freshwater Ecosystems: New Trends and Future Challenges. [ABSTRACT FROM AUTHOR]

Land use changes modify soil properties, including aggregate structure, and thus, profoundly affect soil quality and health. However, the effects of land use changes originating from alpine grassland on soil aggregates and aggregate-associated organic carbon have received little attention. Soil aggregate fraction, aggregate-associated organic carbon and relevant influencing factors were determined at 0–20, 20–40 cm soil layers for alpine grassland, cropland and abandoned land in the eastern Qinghai–Tibet Plateau (QTP), and their relationships were analyzed by partial least square regression (PLSR). Results showed the following: (1) conversion from alpine grassland to cropland resulted in a significant decline macroaggregate fraction (R0.25), mean weight diameter (MWD), mean weight diameter (GMD), soil organic carbon (SOC), and microaggregate-associated SOC; (2) almost all aggregate stability indexes, SOC, and aggregate-associated SOCs were significantly positively correlated with silt and glomalin, suggesting that the binding of fine particles (silt) with the organic cementing agent (glomalin) was probably a key mechanism of SOC formation and aggregate stability in the studied region; (3) compared with biotic factors such as SOC, glomalin and root biomass, abiotic factors including silt and sand can better predict aggregate stability and SOC fraction using the PLSR model. The above results indicated that the conversion of alpine grassland to other land use types in high altitude areas would destroy soil structure and decrease soil organic carbon content, and then reduce soil quality. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics. [ABSTRACT FROM AUTHOR]

Low-impact developments (LIDs) have emerged as effective strategies for mitigating the adverse impacts of urbanization on river environments. This study aims to enhance river environment quality by examining the effects of LIDs and land use/cover change (LUCC) in the context of river environment and hydrological conditions. Using the Stormwater Management Model (SWMM) in an urban river setting, the study investigates the impact of LIDs on urban river water volume. An analysis of river runoff quality and quantity is conducted, followed by the development of an optimal river water regulation scheme through a multi-objective ecological scheduling model. The results reveal that the incorporation of LIDs can substantially decrease river runoff yield for varying recurrence periods of design rainstorms. Consequently, the flood peak reduction rate ranged from 10% to 18%, and the flood volume experienced a reduction of 10–29% in the study area. The combination of river water regulation, LIDs and LUCC leads to a decrease in river water volume within the lower river channel by up to 47% especially in a typical dry year and dry season, accompanied by a decline in river flow velocity and water self-purification capacity. A risk-based multi-objective stochastic optimization model is employed to ensure sustainable management of urban river runoff in terms of both quantity and quality. This research contributes to the advancement of knowledge in sustainable basin management practices and offers practical insights for policymakers involved in the management of water resources and environmental conservation in semi-arid basins. [ABSTRACT FROM AUTHOR]

Rice terraces are crucial for agriculture in China's southern mountainous regions. Land use and land cover change (LUCC) in these areas impact hydrology, but our understanding is limited. In this study, we applied the hydrological assessment tool SWAT to a selected small watershed in the rice terrace-dense area of central Hunan province, China. This study covered four LUCC periods over the past 40 years and was simulated at annual and monthly scales. The results showed that from 1980 to 2020, the areas of paddy fields and drylands decreased by 4.19% and 5.76%, respectively, while the areas of forests and construction land increased by 1.14% and 92.48%, respectively. During the past period, LUCC led to a decrease of 1.77% and 2.49% in runoff and infiltration, respectively, and an increase of 1.03% in ET. However, the water yield remained almost unchanged, indicating that the rice terrace plays a positive role in maintaining stable watershed water yield under LUCC. The results of the sub-basin analysis indicated that the transformation of paddy fields and forests directly determines the trend of hydrological changes. Land use type had the most significant impact on the runoff of the rice-terrace watershed, with the proportions of paddy fields and forests being the most influential factors. Paddy fields significantly influenced the stability of runoff at the watershed scale, suggesting that a high paddy field ratio doesn't ensure sustainability. This study offers valuable insights for managing small watershed terraces, land use planning, and achieving sustainable development in the watershed. [ABSTRACT FROM AUTHOR]

Exploring the mechanisms that drive land use and cover change (LUCC) is essential for informing the formulation and implementation of effective policies aimed at optimizing land use patterns. In this study, we examined the spatial and temporal patterns of LUCC within the Lancang–Mekong River Basin (LMRB) using Globeland30 data for the years 2000, 2010, and 2020. Firstly, we analyzed the quantitative characteristics of LUCC within the LMRB in terms of the value of change and rate of change. Additionally, we investigated the converting characteristics of LUCC within the LMRB by employing land use transition matrices and land use transition probability matrices. Furthermore, we depicted the spatial distribution of LUCC within the LMRB through land use mapping and statistical analysis. The results indicate a substantial decline in forests, coupled with a notable expansion in cultivated land. Given the vital role of forests as carbon sinks, reforestation can enhance ecological services and address challenges related to climate change. Converting cultivated land to forests is an effective human intervention promoting forest transition. This study applies binary logistic models to explore the mechanisms that influence the conversion from cultivated land to forests. The results reveal that slopes ranging from 5° to 15° have the lowest probability of conversion, whereas distances between the cultivated land and the nearest tourist attraction ranging from 9 km to 18 km have the highest probability. Moreover, the conversion process is positively associated with traffic conditions and significantly influenced by human interventions. Within the study area, China, Laos, and Myanmar show a tendency to convert cultivated land into natural LULC types, while Cambodia, Thailand, and Vietnam tend to encroach on cultivated land and expand artificial surfaces. Promoting ecological restoration in the LMRB requires cooperation among these countries. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) constitutes a critical component of carbon reservoirs within terrestrial ecosystems. The ramifications of urban land use transitions on SOC dynamics, particularly in rapidly urbanizing regions such as Shanghai, remain insufficiently elucidated. This investigation synergizes a predictive land use change model (Logistic-CA-Markov) with an ecosystem service quantification framework (InVEST), aiming to delineate the interplay between SOC variability and Land Use and Land Cover Change (LUCC) under natural development and ecological protection scenarios. Empirical observations from 2010 to 2020 reveal a contraction in Shanghai's agricultural land of 34,912.76 hectares, juxtaposed with an expansion of urban built-up areas of 36,048.24 hectares. Projections for 2030 under an ecological protection scenario indicate a moderated urban sprawl, reducing built-up area expansion by 13,518 hectares relative to the natural development scenario. Notably, the net carbon sequestration capacity of Shanghai is anticipated to diminish by approximately 0.418 million tons between 2020 and 2030. This trend is observed under both considered scenarios, forecasting a cumulative reduction in SOC stocks exceeding 1 million tons by 2030. The natural development pathway portends a more pronounced and accelerated depletion of SOC reserves. Although ecological conservation measures show the potential to decelerate this loss, they appear insufficient to reverse the ongoing decline in SOC stocks. This study advocates for strategic urban planning interventions focused on constraining the growth of building densities and augmenting the preservation and management of eco-lands. Such measures are imperative for bolstering Shanghai's carbon sequestration capacity. [ABSTRACT FROM AUTHOR]

Abstract The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics.

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

In light of international climate agreements and the Sustainable Development Goals (SDGs), there is a growing need to enhance the understanding of the linkages among land use/cover change (LUCC) and its carbon effects (CEs), as well as human well-being (HW). While existing studies have primarily focused on the impacts of LUCC on CEs or ecosystem services, there remains a gap in systematically elucidating the complex relationships among LUCC, CEs, and HW. This paper presents a comprehensive review of the nexus between land-based CEs and HW, examining: (1) the correlation between LUCC and CEs, encompassing methodologies for investigating LUCC CEs; (2) the association between CEs and HW, introducing the concept of “low-carbon human well-being” and evaluation framework; and (3) the proposed framework of “LUCC-CEs-HW,” which delves into the intricate connections among three elements. The study identifies research gaps and outlines potential future directions, including assessments of LUCC CEs and low-carbon HW, exploration of the “LUCC-CEs-HW” nexus, and the development of standardized measurement approaches. Key opportunities for further investigation include establishing a unified evaluation index system and developing scalable methods. This paper elucidates the relationships among LUCC, CEs, and HW, offering insights for future works.

The Sanggan River Basin in the loess hill and gully area is the primary water source for the establishment of the capital water conservation function area and the ecological environment support zone. Against the backdrop of global warming, population growth, and accelerated urbanization, water consumption in the Sanggan River Basin has persistently increased in recent years, leading to a series of issues, such as river pollution, surface streamflow reduction, groundwater over-exploitation, soil erosion, and decreased vegetation coverage. In this research, we employed the DMC approach, SWAT model, InVEST model, and CA-Markov model to quantitatively analyze the correlation between alterations in streamflow and sediment within this area and three influential factors: climate variations, LUCC, and other human interventions. Furthermore, we clarified the relative contributions of climate factor elements, land-use types, and human activities to streamflow and sediment in this region. The findings indicate a decline in the annual streamflow and sediment quantities observed within this region from 1960 to 2020, with reduction rates of 1.27 × 108 m3/10 a and 129.07 × 104 t/10 a, respectively, and an abrupt change year in 1983 and 1982. Compared to the annual streamflow, the annual sediment volume demonstrates more substantial variation. Based on the constructed model, three scenario simulation periods: P0 (1962–1981/1982), P1 (1983/1982–1999), and P2 (2000–2020) were set. Compared with P0, the respective contribution rates of climatic variation, land use, and other human intervention to streamflow (sediment volume) were 15.247% (19.601%), −0.03% (−1.349%), and 84.783% (81.748%) in P1. In P2, the contribution rates of these three factors on streamflow (sediment volume) were 9.160% (9.128%), 0.211% (3.053%), and 90.629% (84.818%), respectively. Through a quantitative analysis of climatic factors on streamflow and sediment, we found a positive correlation between precipitation and both streamflow and sediment. Additionally, there is an inverse relationship between temperature and streamflow, but streamflow will increase when temperature rises by 10%. Under three different future land-use scenarios, the variations in streamflow and sediment exhibited as ecological protection scenario < urban expansion scenario < natural development scenario. Changes in streamflow in this region are primarily caused by human intervention that alters the underlying surface. The increase in check dams and silted land could lead the runoff and sediment to decrease. Moreover, compared with the increase in industrial and agricultural water consumption, the increase in urban water consumption is the main factor for human water consumption. [ABSTRACT FROM AUTHOR]

Understanding the relationship between land use and carbon storage is vital for achieving sustainable development goals. However, our understanding of how carbon storage develops under land policy planning is still incomplete. In this study, a comprehensive framework that integrates Gray Multi-objective Optimization Programming (GMOP), the Patch-generating Land Use Simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models is introduced to evaluate land use dynamics and ecosystem services. Two scenarios have been established to estimate Land Use and Land Cover Change (LUCC) patterns in the Hexi Corridor by 2035: the business-as-usual (BAU) scenario, developed based on historical trends, and the ecological conservation scenario (ECS), optimized with multiple policy objectives. The results show the following: (1) From 2000 to 2020, the predominant land use type in the Hexi Corridor was unutilized land, with LUCC mainly involving the transformation of unutilized land to grass land. (2) Carbon storage in the Hexi Corridor increased by approximately 9.05 × 106 t from 2000 to 2020 due to LUCC, characterized by higher levels in the south and lower levels in the north. (3) The areas of grass land and arable land are expected to continue increasing until 2035, while the extent of unutilized land is projected to decrease. The ECS is poised to create a balance between ecological protection and economic development. (4) By 2035, both the BAU scenario and ECS estimate an increase in the carbon storage of the Hexi Corridor, with the ECS expected to result in the most significant gains. These research findings provide valuable insights for administrators and researchers, guiding more rational land use planning and ecological restoration policies to achieve carbon peaking and neutrality. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) plays a key role in water cycle and energy balance, and the construction of large areas of vegetation in the Yellow River Basin (YRB) will cause an increase in regional ET and changes in water use in the basin. Based on GLEAM evapotranspiration products, meteorological stations and land use data, this study used slope trend analysis, Mann-Kendall (M-K) test and partial correlation analysis to analyse changes in evapotranspiration and water use in the YRB before and after reforestation from 1980 to 2019. The results showed that the annual average ET of the YRB from 1980 to 2019 ranged from 363 to 447 mm with an average change rate of 10.2 mm/10a. The actual evapotranspiration/potential evapotranspiration (ET/PET) growth rate of the YRB decreased after the Grain for Green Project (GGP), and the actual evapotranspiration/precipitation (ET/P) decreased extremely significantly. Water use efficiency in the YRB was significantly reduced, and grassland was subjected to greater water stress. This study provides scientific support for future water balance, landscape restoration, ecological protection and quality development of the YRB. [ABSTRACT FROM AUTHOR]

The construction of low-carbon cities is an optimal means to balance the competing interests of economic growth and carbon emission reduction. This study focuses on the optimization of land use patterns with a low carbon orientation, taking the Chengdu-Chongqing Economic Circle (CCEC), the fourth-largest economic growth pole in China, as an example. The panel data regression analysis is carried out to identify the dynamic correlations between the landscape changes and the carbon emission induced by land use and land cover change (LICE) of each city, each year, for the last 20 years. The results show that the CCEC has witnessed a 142.85% increase in carbon emissions during the period studied, with the growth of built-up land contributing 94% of total carbon emissions from 2000 to 2020. By constructing the panel regression model, this study finds that the intensity of carbon emissions increases significantly as the urban built-up land area and the agglomeration of artificial structures increase. The conversion of cropland, which dominates the landscape pattern, to built-up land has led to further fragmentation of the landscape pattern and a reduction in LPI, thus increasing carbon emissions. And a more complex regional landscape pattern will have a positive impact on carbon emission reduction. Based on the above findings, suggestions are articulated for carbon emission reduction. [ABSTRACT FROM AUTHOR]

With a series of redevelopment activities, such as land consolidation and urban renewal, many cities in China have experienced land de-urbanization phenomena. These include the conversion of construction land into green spaces (such as parks, forests, and lawns), blue spaces (such as rivers, lakes, and wetlands), and farmland. However, there is currently limited research on diverse land de-urbanization types and pathways. This study focuses on investigating the land de-urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) from 2014 to September 2023 using the Continuous Change Detection and Classification (CCDC) method. The results demonstrate that the GBA experienced 72.74 square kilometers of de-urbanization during the study period, primarily through the conversion of construction land to land with low plant coverage, including grassland and farmland. There were significant differences in the quantity and spatial agglomeration of de-urbanization between cities and within individual cities. Temporally, de-urbanization predominantly occurred in the period of 2016 to 2021, with a sharp decline in 2022. The temporal changes were significantly influenced by urban renewal policies and the impact of the COVID-19 pandemic. In terms of spatial clustering characteristics, the de-urbanization process in the GBA exhibited spatial agglomeration but was primarily characterized by low-level clustering. This study also examines the correlations between de-urbanization and factors including location and the stage of urbanization. The analysis showed that de-urbanization within cities tended to concentrate near the main urban roads within a range of 10–30 km from city centers. The trend of de-urbanization followed a pattern that is consistent with the Northam curve, where de-urbanization tends to increase during the rapid urbanization phase and decline as urbanization reaches a mature stage. Overall, this study provides valuable insights for the redevelopment of construction land within the context of ecological civilization construction. It also offers suggestions for urban land development and redevelopment in metropolitan areas. [ABSTRACT FROM AUTHOR]

New urbanization often leads to land-use and land-cover change (LUCC), which inevitably affects ecosystem services (ESs). Although it is traditionally believed that urbanization reduces ecosystem services, some studies have shown that reasonable urban development facilitates ecosystem conservation. Previous studies have focused on the impacts of urbanization on either LUCC or ESs, with fewer dynamic assessments of the coordination of the three. Taking China's contiguous poor areas (CPAs) as an example, this study applied coupling coordination, path analysis, and a multiscale geographically weighted regression (MGWR) model to identify the dynamic relationship among urbanization, land use, and the environment and then predicted their coupling coordination under shared socioeconomic pathways (SSP-RCP) in 2035 using the Patch Generation Land Use Simulation (PLUS) and a random forest model. The results of the study show that (1) urbanization, land-use change, and environmental loads in China's CPAs showed an inconsistent upward trend. There was a slight overall decrease in ESs before 2013, which was consistent with the early stage of the Environmental Kuznets Curve (EKC); after that time, they showed different characteristics. (2) From 2000 to 2018, the coupling coordination degree of CPAs decreased slightly due to urbanization, geographic factors, and grassland and unused land. LUCC was essential to maintaining the system balance. The SN (southern contiguous poverty area) was at a basic level of coordination, while the other regions showed a moderate imbalance. (3) According to scenario projections, the degree of coupling coordination in all regions will increase by 2035. Environmental prioritization and sustainable routes are the best options for CPAs' development. The SN is more stable, while the WN (western contiguous poverty area) has the lowest coupling coordination. (4) Environmentally friendly urbanization should be carried out with land management tailored to local conditions. Measures that could be recommended include establishing ecological pilot zones in SN areas, prioritizing the protection of grassland ecosystems in WN areas, and promoting intensive land use in the NN (northern contiguous poverty area). The present study offers a novel perspective on the interplay between the economy and the environment at the county level and achieves predictive coupling coordination through the integration of PLUS and random forest models. This investigation of coordinated urbanization–LUCC–ES development in CPAs yields valuable insights for enhancing environmental and economic well-being in similar regions within China, as well as globally. [ABSTRACT FROM AUTHOR]

Urban airports are typically large infrastructures with significant cultural, economic, and ecological impacts; meanwhile, abandoned airports are common worldwide. However, there is limited knowledge regarding transformation strategies for the renewal of abandoned airports and their surrounding regions in historically and culturally rich areas. We use Beijing's Nanyuan Airport as a case study, combining the historic urban landscape approach, land use and land cover change, and counterfactual simulations of land use patterns to construct a comprehensive analytical framework. Our framework was used to analyze the long-term land use patterns of the study area, determine its value, and improve perception from a macro- and multi-perspective. We discovered that the traditional knowledge and planning systems in the study area have largely disappeared, but Nanyuan Airport's impact on the surrounding land use patterns is unique and significant. By considering the characteristics and mechanisms of land use in the study area, we aimed to find a balance point between the historical context and future potential. As such, we propose optimized recommendations with the theme of connection and development engines. Our findings supplement the planning knowledge of relevant areas and provide a springboard for interdisciplinary research in landscape planning. [ABSTRACT FROM AUTHOR]

Quantitatively revealing the response of carbon stocks to land use change (LUCC) and analyzing the vulnerability of ecosystem carbon stock (ECS) services are of great significance for maintaining the carbon cycle and ecological security. For this study, China's Guizhou Province was the study area. Land use data in 2000, 2010, and 2020 were selected to explore the impacts of LUCC on carbon stocks in multiple scenarios by combining the PLUS and InVEST models and then analyzing the vulnerability of ECS services. The results show that forest land plays an important role in improving ECS services in karst plateau mountainous areas. In 2000–2020, forest land expansion offset the carbon stock reduced by the expansion of built-up land, greatly improving the regional ECS function. Following the natural trend (NT), the total carbon stock in Guizhou Province will decrease by 1.86 Tg; however, under ecological protection (EP) measures, the ECS service performs a positive function for LUCC. Focusing on socioeconomic development (ED) will increase the vulnerability of the regional ECS service. In the future, the forest land area size should be increased, and built-up land should be restricted to better improve the service function of ECS in karst plateau mountainous areas. [ABSTRACT FROM AUTHOR]

Carbon storage plays a vital role in the provision of ecosystem services, and the alteration of land use has a profound influence on the carbon storage capacity of ecosystems. Therefore, in this study, two scenarios of natural evolution scenario (NES) and ecological conservation scenario (ECS) were set up based on the development of Mohe City,China. Meanwhile, a coupled model of LUCC and ecosystem carbon storage was established using Logistic-CA-Markov and InVEST models, as well as optimal parameters_based geographical Detector and GeoDa, to predict the distribution and change of ecosystem carbon storage based on LUCC in the future. The effects of different influencing factors on the spatial differentiation of carbon storage were also explored. The results show that: (1) From 1980 to 2020, the land use type in Mohe City was dominated by the forest and its area decreased; the area of wetland increased. From 2020 to 2040, ecological benefits will be shown under the ECS, with an increase in forest and a slower expansion of built-up. (2) Carbon loss in Mohe City from 1980 to 2020 was 4.04Tg. Under the ECS carbon storage increased slightly by 0.2Tg. Soil carbon storage was the main carbon pool in Mohe City, and forest was the largest contributor. The carbon storage of Mohe city in 2030 and 2040 has a strong positive spatial correlation. Hot spots in more than a cold spots area, the high value area is concentrated in the east, low concentrated in urban areas. (3) Apart from LUCC, mean annual precipitation was the most significant factors affecting the spatial differentiation variability of carbon storage. The interactions of mean annual precipitation and population density with other factors exhibit a non-linear enhancement,which had a coefficient of 21.91%. This study contributes to a deeper understanding of the relationship between LUCC and carbon storage.

The evaluation of ecosystem service value (ESV) is crucial for decision making in regional sustainable development. The close relationship between ecosystem services and land use/cover change (LUCC) is well acknowledged. However, the impact of the mutual transformation among different land use types on the temporal and spatial differences in the ESV is still unclear. To fulfill this gap, this study evaluates the ESV in the Wuhan Urban Agglomerations based on LUCC, taking the spatiotemporal characteristics into consideration. The results show that (1) The land use structure in the Wuhan Urban Agglomerations has undergone great changes from 2012 to 2021, and the area of cultivated land converted to forest land is the largest, which may be related to policies such as returning farmland to forests. (2) The total amount of ESV shows a downward trend, and the spatial distribution of ESV is “low in the west and high in the central and eastern regions”, which may be related to the natural factors in study area. (3) The spatial distribution of ESV in the study area will remain unchanged in the future. However, the transformation among land use types may exacerbate the reduction in the total ESV, which will have an adverse impact on the ecological environment and sustainable development of the region. This study initiates a more comprehensive framework to better reflect the real scenario of ESV, which will hopefully provide a reference for regional sustainable development.

Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the main factor leading to changes in ecosystem carbon storage. Understanding the impact of LUCC on regional carbon storage changes is crucial for protecting regional ecosystems and promoting sustainable socio-economic development. This paper selects Shanxi province as the study area and explores the spatial and temporal evolution characteristics of carbon storage in Shanxi province based on the InVEST model and univariate spatial autocorrelation. The results show that the total carbon storage in Shanxi Province in 2000, 2010, and 2020 is 513.51 × 104 t C, 513.46 × 104 t C, and 509.29 × 104 t C, respectively. High carbon storage areas are distributed in forest and grassland land types, while low carbon storage areas are widely distributed in building land in urban metropolitan areas. Shanxi Province is mainly dominated by farmland, which has decreased by 3448.60 km2 in the past 20 years. Grassland has decreased by 1588.31 km2 and the area of building land has increased by 4205.73 km2. Due to the influence of carbon conversion among different land use types, the total carbon storage loss of Shanxi Province in the past 20 years was 4.21 × 104 t C. The transfer of farmland resulted in an increase in carbon stock of 14.46 × 104 t C. The transfer of grassland resulted in an increase of 17.15 × 104 t C, while the transfer of forest resulted in a decrease of 41.44 × 104 t C. The increase in land use types with low carbon density and the decrease in land use types with high carbon density led to the decrease in carbon storage in Shanxi Province. Furthermore, social factors were more likely to influence the carbon storage than natural factors, and the influence of social factors was often negative. On this basis, regional development countermeasures were proposed for the current situation of carbon storage in Shanxi Province and provide a scientific basis for Shanxi Province to achieve the carbon neutrality target.

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

The lower reaches of the Minjiang River are located in an area of land-sea-air interaction. It is not only a sensitive area concerning environmental evolution, but also a hotspot for archaeological research on the southeast coast of China. Exploring the scope of ancient human activities and the evolution of land-use patterns is of great significance for understanding the development of human-land relations. By generating various cost surfaces of sites through GIS cost distance analysis and combining with the site catchment theory and method, we comprehensively analyzed the human activity range and land-use pattern in every cultural period from the Neolithic to the Bronze Age in the lower reaches of the Minjiang River and reconstructed the potential population in each period of the region. The results show that: (1) the area of site catchment, available and actually developed land increased from the Keqiutou to Huangtulun culture period in the lower reaches of the Minjiang River. Chronologically, the area of the site catchment continues to grow from 212 km2 to 4,858 km2, the accessible land area increased from 261 km2 to 7,599 km2, and the land area that was actually used by ancient humans continuously increased from 173 km2 to 3,914 km2. (2) With the development process of culture, the land-use intensity of prehistoric humans in the region first increased and then decreased by 81.58%, 92.95%, 87.99%, 79.33% and 80.57%, respectively, and the degree of land development was reduced, which may be related to the development and progress of productive forces. The frequency of ancient human exchanges can be represented by the ratio of the number of sites in the overlapping site catchment to the total number of sites in the same period (87.50%, 60.00%, 66.67%, 92.54%, and 97.81%, respectively). (3) The reconstruction results showed that the potential population in the area continued to expand, with substantial population growth in the lower Tanshishan-Tanshishan and Huangguashan-Huangtulun transitional periods. It was inferred that there was a relationship between population pressure and culture succession.

Study Region: The Fenhe River Basin (FRB) is an important tributary of the Yellow River Basin and locate in a semi-arid and semi-humid region in northwest China. Over the past four decades, significant anthropogenic activities have resulted in significant land use and land cover change (LUCC) that have markedly impacted hydrological processes in FRB.Study Focus: To analyze land use changes under multiple historical land use scenarios from LU1980 to LU2015, and to establish the Water and Energy transfer Processes in Large basins (WEP-L) model to quantify the influences of LUCC on hydrological processes, particularly on baseflow.New hydrological insights for the region: LUCC from LU1980 to LU2015 resulted in a 14.5 % increase in water yield (WY) and a 2.7 % and 1.4 % decrease in baseflow (BF) and evapotranspiration, respectively. Urbanization led to a significant increase in WY and a reduction in BF, exhibiting strong spatial gradients between urban centers and their peripheries. Increases in forestland decreased WY, while ET showed no significant change due to large absolute values and moisture limitations. Furthermore, the response of BF to forest cover exhibits spatial variation. A negative correlation between BF and forest cover was evident in upstream, whereas no significant correlation was observed in downstream. The findings of this study offer valuable insights for land use planning and sustainable watershed management in semi-arid semi-humid region.

Study region: Kaidu River Basin, an arid region of China Study focus: Water resources are scarce and unstable. Adverse changes in blue-green water resources can disrupt water balance and availability in arid regions. Therefore, it is crucial to effectively assess and predict future blue-green water resources in arid areas. This study utilizes the Soil and Water Assessment Tool (SWAT), Patch-generating Land Use Simulation (PLUS), and Global Circulation Models (GCMs) to analyze the interactive impacts of climate and land use/cover changes (LUCC) on blue-green water in arid basin from 1990 to 2050. New hydrological insights for the region: This work identified key factors influencing blue-green water changes, including forests, grasslands, and snowmelt. In the historical scenarios, the Kaidu River basin exhibited significant interannual variations in blue water and precipitation. In the future scenarios, blue water increases (+3.6%), and green water decreases (−1.8%) under SSP245 (medium emissions). Under SSP585 (high emissions), blue water decreases (−2.4%), and green water increases (+1.8%). Under SSP126 (low emissions), both blue water (+8.5%) and green water (+0.73%) show an increasing trend. The change in snowmelt is the main reason for the first peak of blue water in April May. Under historical and future snowmelt conditions, blue water shows a significant upward or downward trend with the amount of snowmelt. The findings provided practical hints for the enhancement of the management and allocation of precious water resources.

Ramsar Site No. 1148 (ORDOS National Nature Reserve, ONNR) serves as a habitat for the rare bird Larus Relictus globally. The gulls disappeared in 2006 and returned in 2019. Why did this happen, and how did it respond to changes in the wetland ecosystem? We developed a remote sensing technology-enabled comprehensive wetland ecological assessment framework for typical inland saline marshes (ONNR) on the Mongolia-Xinjiang Plateau. That is, by integrating natural indicators (Normalized Vegetation Index, NDVI; Temperature and precipitation), anthropogenic indicators (land use and land cover change, LUCC) were used to identify the change characteristics of wetland water indicators (normalized difference water index, NDWI), and verified by statistical analysis and field survey data. It has the advantages of (1) observing water dynamic in wetlands over nearly half a century, (2) observing changes in atmospheric precipitation and surface recharge, and (3) calculating most indices from remote sensing data from 1973 to 2021. Study results showed that: (i) the climate in the inflow area is a strong response to regional climate change, with an evident warming trend. (ii) The area of the lake basin reduced from 14.545 km2 in 1998 to just 0.134 km2 in 2015, causing a decline of 99.08 % in its total area. (iii) During 1973–1999, changes in the water area of the wetland were predominantly driven by climate change, whereas both climate change and anthropological factors influenced the area from 2000 to 2021 (Sig. = 0.007). This study reveals the typical water environment issues of lakes and wetlands confronted in the arid regions of northern China. It helps to provide dataset and decision support for the management and conservation of wetland resources and biodiversity.

To systematically explore land use/cover change (LUCC) trends and driving mechanisms at the large watershed scale under the background of climate change and rapid urbanization. Taking the lower reaches of the Yangtze River (LRYR) as the research object, based on the land use remote sensing monitoring data from 1980 to 2020, the spatial and temporal evolution characteristics of LUCC in LRYR were analyzed by adopting the methods of land use dynamics degree (LUDD) and hotspot analysis and used geospatial detectors to quantitatively assess the intensity of the role of the land use change drivers in the LRYR. The results show that: 1) The land-use types in LRYR were dominated by arable land and woodland, accounting for more than 70% of the total area. During the study period, the construction land area increased by 11,835 km2, and became the third largest land-use type in LRYR after 2010 and formed a typical urban contiguous zone along the route from Nanjing to Shanghai. 2) The land use degree comprehensive index (LUDCI) in LRYR at each stage is 270.91, 270.88, 272.22, 272.72, 274.00, 275.57, 276.93 and 280.37, respectively. LUCC in LRYR has become more dramatic, and there is significant spatial heterogeneity. Shanghai has always been a hot area of LUCC and Huangshan and Chizhou are cold spots of LUCC. 3) The driving mechanism of LUCC in LRYR can be divided into three stages. In these three stages, secondary industry output value, precipitation, and elevation are always important factors affecting LUCC. The interaction between driving factors is significant. The strongest interaction is the output value of the secondary industry ∩ precipitation, the interaction reaches above 0.6. The research results are of great significance for promoting the sustainable development of land use in this region.