Search Results (1,782)

To ensure water source security and sustainability of the national major strategic project “South-to-North Water Diversion”, this study aims to evaluate the spatio-temporal evolution characteristics of the ecosystem service value (ESV) in its water source area from 2002 to 2022. This study reveals its changing trends and main influencing factors, and thereby provides scientific support for the ecological protection and management of the water source area. Quantitative assessment of the ESV of the region was carried out using the Equivalence Factor Method (EFM), aiming to provide scientific support for ecological protection and resource management decision-making. In the past 20 years, the ESV has shown an upward trend year by year, increasing by 96%. The regions with the highest ESV were Garzê Prefecture and Aba Prefecture, which increased by 130.3% and 60.6%, respectively. The ESV of Xinlong county, Danba county, Rangtang county, and Daofu county increased 4.8 times, 1.5 times, 12.5 times, and 8.9 times, respectively. In the last two decades, arable land has decreased by 91%, while the proportions of bare land and water have decreased by 84% and 91%, respectively. Grassland had the largest proportion. Forests and grasslands, vital for climate regulation, water cycle management, and biodiversity conservation, have expanded by 74% and 43%, respectively. It can be seen from Moran’s I index values that the dataset as a whole showed a slight positive spatial autocorrelation, which increased from −0.041396 to 0.046377. This study reveals the changing trends in ESV and the main influencing factors, and thereby provides scientific support for the ecological protection and management of the water source area. Full article

In response to the escalating climate crisis, reducing greenhouse gas emissions (GHG) has become a top priority for both the public and private sectors. The pavement industry plays a key role in this transition, offering innovative technologies that minimize environmental impacts without compromising performance. Among these, the incorporation of recycled tire rubber and warm-mix asphalt (WMA) additives represents a promising strategy to reduce energy consumption and resource depletion in road construction. This study conducts a comparative life cycle assessment (LCA) to evaluate the environmental performance of an asphalt pavement incorporating recycled rubber and a WMA additive—referred to as R-W asphalt—against a conventional hot-mix asphalt (HMA) pavement. The analysis follows the ISO 14040/44 standards, covering material production, transport, construction, and maintenance. Two service-life scenarios are considered: one assuming equivalent durability and another with a five-year extension for the R-W pavement. The results demonstrate environmental impact reductions of up to 57%, with average savings ranging from 32% to 52% across key impact categories such as climate change, land use, and resource use. These benefits are primarily attributed to lower production temperatures and extended maintenance intervals. The findings underscore the potential of R-W asphalt as a cleaner engineering solution aligned with circular economy principles and climate mitigation goals. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

Cultivated land quality is a key factor in ensuring sustainable agricultural development. Exploring differences in cultivated land quality under distinct cropping systems is essential for developing targeted improvement strategies. This study takes place in Shenyang City—located in the typical black soil region of Northeast China—as a case area to construct a cultivated land quality evaluation system comprising 13 indicators, including organic matter, effective soil layer thickness, and texture configuration. A minimum data set (MDS) was separately extracted for paddy and upland fields using principal component analysis (PCA) to conduct a comprehensive evaluation of cultivated land quality. Additionally, an obstacle degree model was employed to identify the limiting factors and quantify their impact. The results indicated the following. (1) Both MDSs consisted of seven indicators, among which five were common: ≥10 °C accumulated temperature, available phosphorus, arable layer thickness, irrigation capacity, and organic matter. Parent material and effective soil layer thickness were unique to paddy fields, while landform type and soil texture were unique to upland fields. (2) The cultivated land quality index (CQI) values at the sampling point level showed no significant difference between paddy (0.603) and upland (0.608) fields. However, their spatial distributions diverged significantly; paddy fields were dominated by high-grade land (Grades I and II) clustered in southern areas, whereas uplands were primarily of medium quality (Grades III and IV), with broader spatial coverage. (3) Major constraint factors for paddy fields were effective soil layer thickness (21.07%) and arable layer thickness (22.29%). For upland fields, the dominant constraints were arable layer thickness (27.57%), organic matter (25.40%), and ≥10 °C accumulated temperature (23.28%). Available phosphorus and ≥10 °C accumulated temperature were identified as shared constraint factors affecting quality classification in both systems. In summary, cultivated land quality under different cropping systems is influenced by distinct limiting factors. The construction of cropping-system-specific MDSs effectively improves the efficiency and accuracy of cultivated land quality assessment, offering theoretical and methodological support for land resource management in the black soil regions of China. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

With the emergence of Survey 4.0, the oil and gas (O & G) industry is now considering spatial digital twins during their field design to enhance visualization, efficiency, and safety. O & G companies have already initiated investments in the research and development of spatial digital twins to build digital mining models. Existing studies commonly adopt surveys and case studies as their evaluation approach to validate the feasibility of spatial digital twins and related technologies. However, this approach requires high costs and resources. To address this gap, this study explores the feasibility of the informed argument method within the design science framework. A land survey data model (LSDM)-based digital twin prototype for O & G field design, along with 3D spatial datasets located in Lot 2 on RP108045 at petroleum lease 229 under the Department of Resources, Queensland Government, Australia, was selected as a case for this study. The ISO/IEC 25010 model was adopted as a methodology for this study to evaluate the prototype and Digital Twin Victoria (DTV). It encompasses eight metrics, such as functional suitability, performance efficiency, compatibility, usability, security, reliability, maintainability, and portability. The results generated from this study indicate that the prototype encompasses a standard level of all parameters in the ISO/IEC 25010 model. The key significance of the study is its methodological contribution to evaluating the spatial digital twin models through cost-effective means, particularly under circumstances with strict regulatory requirements and low information accessibility. Full article

In response to the global ecological and agricultural challenges posed by coastal saline-alkali areas, this study focuses on Dongying City as a representative region, aiming to develop a high-precision soil salinity prediction mapping method that integrates multi-source remote sensing data with machine learning techniques. Utilizing the SCORPAN model framework, we systematically combined diverse remote sensing datasets and innovatively established nine distinct strategies for soil salinity prediction. We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. The results reveal that among the models evaluated across the nine strategies, the SVR model demonstrated the highest accuracy, followed by RF. Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. In summary, this research successfully developed a comprehensive, high-resolution soil salinity mapping framework for the Dongying region by integrating multi-source remote sensing data and employing diverse predictive strategies alongside machine learning models. The findings highlight the potential of Vegetation Type Factors to enhance large-scale soil salinity monitoring, providing robust scientific evidence and technical support for sustainable land resource management, agricultural optimization, ecological protection, efficient water resource utilization, and policy formulation. Full article

Population growth and land use change often have significant environmental impacts, affecting biodiversity, water supply, agricultural production, and other resources. Future scenario models can provide a better understanding of these changes, helping planners and the public understand the consequences of choices regarding development density, land use, and conservation. This study presents a model that has been used to identify alternative future scenarios for Florida considering future population growth and land use. It includes two scenarios: a “Sprawl” scenario reflecting a continuation of current development patterns and a “Conservation” scenario with higher densities, redevelopment, and more land protection. The study incorporates sea level rise scenarios for both 2040 and 2070. Results show that the Sprawl scenario could lead to 3.5 million acres of new developed land and 1.8 million acres of lost agricultural land by 2070 in Florida. In contrast, the Conservation scenario for 2070 results in 1.3 million fewer acres of developed land and 5 million more acres of protected natural land, showing that it is possible to accommodate future population growth while reducing impacts to agricultural and conservation priorities in Florida. Although this is by no means a “prediction” of future Florida, it has been useful as a tool for evaluating potential future land use scenarios and is a model that may be more broadly applied by other locations and users. Full article

In the stage of high-quality development, the functional coordination between transportation systems and territorial space is a key issue for improving urban spatial efficiency. This paper breaks through the traditional volume-to-capacity ratio analysis paradigm and innovatively integrates the “production-living-ecological space” theory. By introducing an improved accessibility evaluation model and developing a coordination measurement algorithm, a three-dimensional evaluation mechanism covering development potential assessment, service efficiency diagnosis, and resource allocation optimization is established. Empirical research indicates that the improved accessibility indicators can precisely identify the transportation location value of regional functional cores, while the composite coordination indicators can deconstruct the spatiotemporal matching characteristics of “transportation facilities—spatial functions,” providing a dual decision-making basis for the redevelopment of existing space. This measurement system innovatively realizes the integration of planning transmission mechanisms with multi-scale application scenarios, guiding both overall spatial planning and urban renewal area re-optimization. The methodology, applied to the urban villages of Guangzhou, can significantly increase land utilization intensity and value. The research results offer a technical tool for cross-scale collaboration in land space planning reforms and provide theoretical innovations and practical guidance for the value reconstruction of existing spaces under the context of new urbanization. Full article

Guadua amplexifolia J. Presl is a Neotropical bamboo native to southern Mexico through Central America to Colombia, where it thrives in riparian zones of the San Jorge River basin. Despite its ecological and socio-economic importance, its environmental dynamics and provision of ecosystem services remain poorly understood. This study (1) quantifies spatial and temporal land use/cover changes in the municipality of Montelíbano between 2002 and 2022 and (2) evaluates the ecosystem services that local communities derive from in 2002, 2012, and 2022, and they were classified in QGIS using G. amplexifolia. We applied a supervised classification of Landsat imagery (2002, 2012, 2022) in QGIS, achieving 85% overall accuracy and a Cohen’s Kappa of 0.82 (n = 45 reference points). For the social assessment, we held participatory workshops and conducted semi-structured interviews with artisans, fishers, authorities, and NGO representatives; responses were manually coded to extract key themes. The results show a 12% decline in total vegetated area from 2002 to 2012, followed by an 8% recovery by 2022, with bamboo-dominated stands following a similar pattern. Communities identified raw material provision (87% of mentions), climate regulation (82%), and cultural–recreational benefits (58%) as the most important services provided by G. amplexifolia. This is the first integrated assessment of G. amplexifolia’s landscape dynamics and community-valued services in the San Jorge basin, highlighting its dual function as a renewable resource and a natural safeguard against environmental risks. Our findings offer targeted recommendations for management practices and land use policies to support the species’ conservation and sustainable utilization. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article