Search Results (1,616)

Exploring and predicting the spatiotemporal evolution characteristics and driving forces of carbon storage in typical mountain forest ecosystems under land-use changes is crucial for curbing the effects of climate change and fostering sustainable, eco-friendly growth. The existing literature provides important references for our related studies but further expansion and improvements are needed in some aspects. This study first proposed an integrated framework comprising gray multi-objective optimization (GMOP), Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), the Patch-level Land Use Simulation Model (PLUS), and optimal parameter-based geographical detector (OPGD) models to further expand and improve on existing research. Then, the integrated model was used to analyze the spatial–temporal variation in land-use pattern and carbon storage at the county scale in China’s Daiyun Mountain’s Rim under four scenarios in 2032, and analyze the driving force of spatial differentiation of carbon storage. The results indicated that (1) land-use change primarily involves the mutual transfer among forest, cultivated, and construction land, with approximately 7.2% of the land-use type area undergoing a transition; (2) in 2032, the natural development scenario projects a significant reduction in forest land and an expansion of cultivated, shrub, and construction lands. Conversely, the economic priority, ecological priority, and economic–ecological coordinated scenarios all anticipate a decline in cultivated land area; (3) in 2032, the natural development scenario will see a 2.8 Tg drop in carbon stock compared to 2022. In contrast, the economic priority, ecological priority, and economic–ecological coordinated scenarios are expected to increase carbon storage by 0.29 Tg, 2.62 Tg, and 1.65 Tg, respectively; (4) the spatial differentiation of carbon storage is jointly influenced by various factors, with the annual mean temperature, night light index, elevation, slope, and population density being the key influencing factors. In addition, the influence of natural factors on carbon storage is diminishing, whereas the impact of socioeconomic factors is on the rise. This study deepened, to a certain extent, the research on spatiotemporal dynamics simulation of carbon storage and its driving mechanisms under land-use changes in mountainous forest ecosystems. The results can serve to provide scientific support for carbon balance management and climate adaptation strategies at the county scale while also offering case studies that can inform similar regions around the world. However, several limitations remain, as follows: the singularity of carbon density data, and the research scope being confined to small-scale mountainous forest ecosystems. Future studies could consider collecting continuous annual soil carbon density data and employing land-use simulation models (such as PLUS or CLUMondo) appropriate to the study area’s dimensions. Full article

Driven by the rapid advancement of the economy and urbanization, substantial alterations in land use patterns have taken place, exerting certain impacts on groundwater. This study examines the land use changes in Zhengzhou’s central urban area from 2000 to 2020 and projects these changes to 2030 using the PLUS model. It optimizes the groundwater vulnerability assessment methodology from two key aspects, namely the evaluation indicators and the associated weights, to enhance its suitability for the study area. This study employs a multi-indicator and dual-method validation approach to verify the groundwater vulnerability assessment results, ensuring the accuracy and reliability of the findings. Urban, rural, and construction lands increased significantly, while paddy fields, drylands, and forests decreased. The 2030 prediction suggests a continuation of these trends. The groundwater vulnerability in 2020 correlated strongly with the groundwater quality, particularly with chloride ions (AUC = 0.804, Spearman’s rho = 0.83). The 2030 projection indicates a minimal change in the vulnerability distribution but anticipates an increase in high- and very-high-vulnerability areas, particularly in regions with land use changes, potentially increasing the groundwater contamination risk. This suggests the need for targeted groundwater protection policies to mitigate contamination risks. Full article

Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions (2.5km to 100m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author’s knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases. Full article

Despite Guangxi’s unique ecological diversity and its important role in land-based ecological security and conservation, research on the assessment and prediction of its habitat quality under the influences of rapid urbanization and environmental pressures remains limited. This study systematically analyzes the spatial and temporal dynamics of land use and habitat quality in Guangxi from 2000 to 2020 using the PLUS-InVEST model and simulates future scenarios for 2030. These scenarios include the Natural Development (ND) scenario, Urban Development (UD) scenario, and Cropland and Ecological Protection (CE) scenario. The results indicate the following: (1) Over the past two decades, rapid urban and construction land expansions in Guangxi intensified their negative impact on habitat degradation. Additionally, the disproportionate change between rural settlement land and rural population warrants attention. (2) Although ecological restoration measures have played a positive role in mitigating habitat degradation, their effects have been insufficient to counterbalance the negative impacts of construction land expansion, highlighting the need for balanced land use planning and urbanization policies. (3) The expansion of rural residential areas had a greater impact on regional habitat quality degradation than urban and infrastructure expansion. Moderate urbanization may contribute to habitat quality improvement. (4) The CE scenario shows the most significant improvement in habitat quality (an increase of 0.13%), followed by the UD scenario, which alleviates habitat degradation by reducing pressure on rural land. In contrast, the ND scenario predicts further declines in habitat quality. Furthermore, land use planning, restoration measures, and sustainable development policies are key factors influencing habitat quality changes. These findings emphasize the importance of integrating land use strategies with ecological restoration measures to balance economic growth and biodiversity conservation, especially in rapidly urbanizing regions. Full article

Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yangtze River urban agglomeration is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the “double carbon” plan. Based on three periods of land use data from 2000 to 2020, combined with the InVEST model(Version 3.14.2), the spatiotemporal changes in carbon storage in the urban agglomeration in the middle reaches of the Yangtze River were analyzed. The PLUS model (Version 1.3.5) was used to predict three scenarios of natural development, urban development, and eco-development in the urban agglomeration in the middle reaches of the Yangtze River in 2035 and estimate the carbon storage of the ecosystems under different scenarios, and it used optimal parameter GeoDetectors (Version 4.4.2) to reveal the driving factors affecting the spatiotemporal differentiation of carbon storage. The results show that farmland and construction land area increased and forestland area continued to decrease from 2000 to 2020. Carbon storage decreased by 1 × 10⁶ t, with forestland conversion to farmland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 10⁶ t and 0.32 × 10⁶ t, while it increased by 0.16 × 10⁶ under ecological development. The results of the factor detector showed that the NDVI (Normalized Difference Vegetation Index) had the highest explanatory power on the spatiotemporal variation in carbon storage (q = 0.588), followed by the slope (q = 0.454) and elevation (q = 0.391), and the explanatory power of natural environmental factors on the spatiotemporal variation in of carbon storage was dominant. The interaction detector results showed that the spatiotemporal variation in carbon storage was affected by multiple factors, the interaction intensity between each driving factor was stronger than that of a single factor, and the synergy between the NDVI and slope was the strongest, at q = 0.646. Full article

Identifying the drivers of land use and cover change (LUCC) is crucial for sustainable land management. However, understanding spatial differentiation and conducting inter-regional comparisons of these drivers remains limited, particularly in regions like Malaysia, where complex interactions between human activities and natural conditions pose significant challenges. This study presents a novel analytical framework to examine the spatial variations and complexities of LUCC, specifically addressing the spatiotemporal patterns, driving factors, and pathways of LUCC in Malaysia from 2010 to 2020. Integrating the land use transfer matrix, GeoDetector model, and Structural Equation Modeling (SEM), we reveal a significant expansion of farmland and urban areas alongside a decline in forest cover, with notable regional variations in Malaysia. Human-driven factors, such as population growth and economic development, are identified as the primary forces behind these changes, outweighing the influence of natural conditions. Critically, the interactions among these drivers exert a stronger influence on LUCC dynamics in Malaysia than any single factor alone, suggesting increasingly complex LUCC predictions in the future. This complexity emphasizes the urgency of proactive, multifaceted, and region-specific land management policies to prevent irreversible environmental degradation. By proposing tailored land management strategies for Malaysia’s five subnational regions, this study addresses spatial variations in drivers and climate resilience, offering a strategic blueprint for timely action that can benefit Malaysia and other regions facing similar challenges in sustainable land management. Full article

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article

Global warming and landscape fragmentation significantly affect the spatial distribution pattern of bamboo forests. This study used high-resolution data and an optimized MaxEnt model to predict the distribution of Phyllostachys edulis in China under current and future climatic conditions in three climate scenarios (SSP126, SSP370, SSP585), and analyzed its land use landscape fragmentation using landscape indices. The results indicate that Phyllostachys edulis currently has potentially suitable habitats majorly distributed in East China, Southwest China, and Central South China. The precipitation of the driest month (BIO14) and the precipitation seasonality (BIO15) are the key environmental factors affecting the distribution of Phyllostachys edulis. In the next three scenarios, the adaptive distribution area of Phyllostachys edulis is generally expanding. With an increase in CO₂ concentration, the adaptive distribution of Phyllostachys edulis in the 2050s migrates towards the southeast direction, and in the 2070s, the suitable habitat of Phyllostachys edulis migrates northward. In the suitable habitat area of Phyllostachys edulis, cropland and forests are the main land use types. With the passage of time, the proportion of forest area in the landscape pattern of the high-suitability area for Phyllostachys edulis continues to increase. Under SSP370 and SSP585 scenarios, the cropland in the Phyllostachys edulis high-suitability area gradually becomes fragmented, leading to a decrease in the distribution of cropland. In addition, it is expected that the landscape of high-suitability areas will become more fragmented and the quality of the landscape will decline in the future. This research provides a scientific basis for understanding the response of Phyllostachys edulis to climate change, and also provides theoretical guidance and data support for the management and planning of bamboo forest ecosystems, which will help in managing bamboo forest resources rationally and balancing carbon sequestration and biodiversity conservation. Full article

Miscanthus (Miscanthus × giganteus) and Willow (Salix spp.) are promising bioenergy crops due to their high biomass yields and adaptability to diverse climatic conditions. This study applies the MiscanFor/SalixFor models to assess the sustainability of these crops under current and future climate scenarios, focusing on biomass productivity, carbon intensity (CI), and energy use efficiency (EUE). Under present conditions, both crops show high productivity in tropical and subtropical regions, with Miscanthus generally outperforming Willow. Productivity declines in less favourable climates, emphasising the crops’ sensitivity to environmental factors at the regional scale. The average productivity for Miscanthus and Willow was 19.9 t/ha and 10.4 t/ha, respectively. Future climate scenarios (A1F1, representing world markets and fossil-fuel-intensive, and B1, representing global sustainability) project significant shifts, with northern and central regions becoming more viable for cultivation due to warmer temperatures and extended growing seasons. However, southern and arid regions may experience reduced productivity, reflecting the uneven impacts of climate change. Miscanthus and Willow are predicted to show productivity declines of 15% and 8% and 12% and 7% under A1F1 and B1, respectively. CI analysis reveals substantial spatial variability, with higher values in industrialised and temperate regions due to intensive agricultural practices. Future scenarios indicate increased CI in northern latitudes due to intensified land use, while certain Southern Hemisphere regions may stabilise or reduce CI through mitigation strategies. Under climate change, CI for Miscanthus is projected to increase by over 100%, while Willow shows an increase of 64% and 57% for A1F1 and B1, respectively. EUE patterns suggest that both crops perform optimally in tropical and subtropical climates. Miscanthus shows a slight advantage in EUE, though Willow demonstrates greater adaptability in temperate regions. Climate change is expected to reduce EUE for Miscanthus by 10% and 7% and for Willow by 9% and 6%. This study underscores the need for region-specific strategies to optimise the sustainability of bioenergy crops under changing climate conditions. Full article

Timely and accurate land use information in open-pit mines is essential for environmental monitoring, ecological restoration planning, and promoting sustainable progress in mining regions. This study used high-resolution unmanned aerial vehicle (UAV) imagery, combined with object-oriented methods, optimal segmentation algorithms, and machine learning algorithms, to develop an efficient and practical method for classifying land use in open-pit mines. First, six land use categories were identified: stope, restoration area, building, vegetation area, arterial road, and waters. To achieve optimal scale segmentation, an image segmentation quality evaluation index is developed, emphasizing both high intra-object homogeneity and high inter-object heterogeneity. Second, spectral, index, texture, and spatial features are identified through out-of-bag (OOB) error of random forest and recursive feature elimination (RFE) to create an optimal multi-feature fusion combination. Finally, the classification of open-pit mines was executed by leveraging the optimal feature combination, employing the random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) classifiers in a comparative analysis. The experimental results indicated that classification of appropriate scale image segmentation can extract more accurate land use information. Feature selection effectively reduces model redundancy and improves classification accuracy, with spectral features having the most significant effect. The RF algorithm outperformed SVM and KNN, demonstrating superior handling of high-dimensional feature combinations. It achieves the highest overall accuracy (OA) of 90.77%, with the lowest misclassification and omission errors and the highest classification accuracy. The disaggregated data facilitate effective monitoring of ecological changes in open-pit mining areas, support the development of mining plans, and help predict the quality and heterogeneity of raw clay in some areas. Full article

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial ‘heat bank’ that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task. Full article

This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration. Full article

Recent above-normal temperatures, which exacerbated the impacts of precipitation deficits, are recognized as the primary driver of droughts in the Upper Colorado River Basin (UCRB), USA. This research aims to enhance drought prediction models by addressing structural changes in non-stationary temperature time series and minimizing drought misclassification through the ES-CBS-SVR model, which integrates ESSVR and CBS-SVR. The research investigates whether this coupling improves prediction accuracy. Furthermore, the model’s performance will be tested in a region distinct from those originally used to evaluate its generalizability and effectiveness in forecasting drought conditions. We used a change point detection technique to divide the non-stationary time series into stationary subsets. To minimize the chances of drought mis-categorization, category-based scoring was used in ES-CBS-SVR. In this study, we tested and compared the ES-CBS-SVR and SVR models in the Upper Colorado River Basin (UCRB) using data from the Global Land Data Assimilation System (GLDAS), where the periods 1950–2004 and 2005–2014 were used for training and testing, respectively. The results indicated that ES-CBS-SVR outperformed SVR consistently across of the drought indices used in this study in a higher portion of the UCRB. This is mainly attributed to variable hyperparameters (regularization constant and tube size) used in ES-CBS-SVR to deal with structural changes in the data. Overall, our analysis demonstrated that the ES-CBS-SVR can predict drought more accurately than traditional SVR in a warming climate. Full article

The Yellow River Basin serves as a critical ecological barrier in China. However, it has increasingly faced severe ecological and environmental challenges, with soil erosion and overgrazing being particularly prominent issues. As an important region in the middle and lower reaches of the Yellow River, the Jinan section of the Yellow River Basin is similarly affected by these problems, posing significant threats to the stability and sustainability of its ecosystems. To scientifically identify areas severely impacted by soil erosion and systematically quantify the effects of climate change on vegetation coverage within the Yellow River Basin, this study focuses on the Jinan section. By analyzing the spatio-temporal evolution patterns of the Normalized Difference Vegetation Index (NDVI), this research aims to explore the driving mechanisms behind these changes and further predict the future spatial distribution of NDVI, providing theoretical support and practical guidance for regional ecological conservation and sustainable development. This study employed the slope trend analysis method to examine the spatio-temporal variation characteristics of NDVI in the Jinan section of the Yellow River Basin from 2008 to 2022 and utilized the FLUS model to predict the spatial distribution of NDVI in 2025. The Optimal Parameters-based Geographical Detector (OPGD) model was applied to systematically analyze the impacts of four key driving factors—precipitation (PRE), temperature (TEM), population density (POP), and gross domestic product (GDP) on vegetation coverage. Finally, correlation and lag effect analyses were conducted to investigate the relationships between NDVI and TEM as well as NDVI and PRE. The research results indicate the following: (1) from 2008 to 2022, the NDVI values during the growing season in the Jinan section of the Yellow River Basin exhibited a significant increasing trend. This growth suggests a continuous improvement in regional vegetation coverage, likely influenced by the combined effects of natural and anthropogenic factors. (2) The FLUS model predicts that, by 2025, the proportion of high-density NDVI areas will rise to 55.35%, reflecting the potential for further optimization of vegetation coverage under appropriate management. (3) POP had a particularly significant impact on vegetation coverage, and its interaction with TEM, PRE, and GDP generated an amplified combined effect, indicating the dominant role of the synergy between socioeconomic and climatic factors in regional vegetation dynamics. (4) NDVI exhibited a significant positive correlation with both temperature and precipitation, further demonstrating that climatic conditions were key drivers of vegetation coverage changes. (5) In urban areas, NDVI showed a certain time lag in response to changes in precipitation and temperature, whereas this lag effect was not significant in suburban and mountainous areas, highlighting the regulatory role of human activities and land use patterns on vegetation dynamics in different regions. These findings not only reveal the driving mechanisms and influencing factors behind vegetation coverage changes but also provide critical data support for ecological protection and economic development planning in the Yellow River Basin, contributing to the coordinated advancement of ecological environment construction and economic growth. Full article