Search Results (21)

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

Harmonised land evaluation frameworks are essential for sustainable land planning and policy development. Assessing land suitability is crucial for predicting agricultural and forestry potential but also for mitigating land degradation risks. Current land suitability maps in Portugal vary greatly in scale and methodology. This study presents the first nationally consistent framework to produce a harmonised land suitability map for mainland Portugal at a 1:100,000 scale following a recently updated WRB soil map. The latter was obtained by integrating legacy soil data with delineated land units according to soil-forming factors (climate, lithology, and relief). These land units were used to derive key land qualities, subsequently classified into constraint levels. Following FAO land evaluation principles, four land suitability levels for agriculture and forestry were assigned to 125 land units across three representative areas in southern Portugal. Relief and lithology emerged as main drivers of land suitability. Marginal agricultural lands are largely dominant (65.1–78.0%), followed by non-suitable lands (14.8–28.3%). Forestry suitability is mostly confined to moderate (61.5–69.4%) and marginal (30.6–37.4%) classes, reflecting the higher adaptability of forestry systems. High consistency was observed between the derived suitability classes and the latest land use/land cover map of Portugal. The framework enables decision-makers to identify areas suitable for intensive production while safeguarding lands vulnerable to degradation. It also provides a transferable tool for adaptive landscape management and sustainable land allocation, supporting policy development under changing environmental conditions in Mediterranean regions. Full article

Rapid urbanization increasingly threatens urban cultural heritage. While buffer zones are crucial for mitigating external pressures, conventional delineation relies on value-based or geometric rules, overlooking parcel-scale heterogeneous externalities. This study addresses this gap by proposing a parcel-based, risk–value coupling framework that delineates heritage buffer zones and supports differentiated land-use regulations. In this study, “negative-impact risk” is operationalized as a composite proxy of cumulative urban development pressures that may increase the likelihood and potential severity of adverse externalities on heritage settings, rather than a full hazard–exposure–vulnerability risk model. And we construct a multi-source indicator system with 12 parcel-level indicators to characterize negative impact risk and heritage value, and adopt a hybrid weighting strategy integrating an AHP, entropy weighting, and game-theoretic combination to reconcile expert judgement and data-driven heterogeneity. To address uncertainty in multi-criteria evaluation, a cloud model maps indicator sets into discrete management levels. The framework is applied to the Pingjiang Historic District in Suzhou, China, using 121 land parcels as decision units. Results show that the approach identifies spatial risk–value patterns and delineates an operational buffer prioritizing parcels with elevated coupled scores. Compared with a fixed-distance buffer, it achieves greater coverage of high-risk parcels while maintaining a smaller regulatory scope. The parcel classification is then translated into tiered planning controls, including development intensity limits, land-use rules, and monitoring priorities. The framework integrates risk management and heritage conservation to support uncertainty-aware, proactive, and transferable zoning decisions. Full article

Urban green spaces (UGS) are pivotal to urban sustainability, yet their morphology—patch size, shape, and configuration—remains insufficiently linked to institutional drivers. We investigate how land tenure strength shapes UGS morphology across 36 cities in nine countries. Using OpenStreetMap data, we delineate UGS and compute landscape metrics (AREA, PARA, SHAPE, FRAC, PAFRAC) via FRAGSTATS; we develop a composite index of land tenure strength capturing ownership, use-right duration, expropriation compensation, and government land governance capacity. Spearman’s rank correlations indicate a scale-dependent coupling: stronger tenure is significantly associated with micro-scale patterns—smaller patch areas and more complex, irregular boundaries—consistent with fragmented ownership and higher transaction costs, whereas macro-scale indicators (e.g., overall green coverage/connectivity) show weaker sensitivity. These findings clarify an institutional pathway through which property rights intensity influences the physical fabric of urban nature. Policy implications are twofold: in high-intensity contexts, flexible instruments (e.g., transferable development rights, negotiated acquisition, ecological compensation) can maintain network connectivity via embedded, fine-grain interventions; in low-intensity contexts, one-off land assembly can efficiently deliver larger, regular green cores. The results provide evidence-based guidance for aligning green infrastructure design with diverse governance regimes and advancing context-sensitive sustainability planning. Full article

Confronted with increasing global food demands, diminishing arable land, and climate volatility, controlled-environment agriculture with advanced red and far-red LED lighting can enhance photosynthesis and optimize plant growth. This investigation reports the generation of a Mn⁴⁺/Nd³⁺ co-doped Y₂SiO₅ phosphor with a Nd³⁺ concentration ranging from 0.1 to 2.5 mol% via a solid-state synthesis method, aiming to enhance red and far-red emission for plant cultivation LEDs. For the Y₂SiO₅:Mn⁴⁺ (1 mol%), Nd³⁺ (2 mol%) phosphor, the phase integrity, nanostructured morphology, elemental mapping, and vibrational characteristics were examined using XRD, Rietveld analysis, FTIR, SEM, and EDX. Nd³⁺ ions act as near-infrared excitation mediators, ensuring efficient Nd³⁺ → Mn⁴⁺ energy transfer upon 808 nm excitation, and this leads to pronounced red photoluminescence from Mn⁴⁺ ions that covers the range of 640–710 nm, exhibiting strong emission peaks centered at 650nm, 663nm, and 685nm, coinciding with the absorption band of phytochromes and chlorophyll. The optimal emission intensity was accomplished for a Nd³⁺ doping concentration of 2 mol%, beyond which concentration quenching occurred. The material produced a strong, concentrated deep red emission with CIE coordinates near (0.73, 0.27) and a high color purity of 98.96%, making it well-suited for photosynthetic activation. A phosphor-integrated red pc-LED was fabricated, and Tulsi plants were grown under this LED during the winter in Meghalaya, a period critical for plant growth due to the low ambient light. Over a 30-day period, the plants exhibited enhanced height and leaf development, demonstrating the practical potential of Mn⁴⁺/Nd³⁺ co-doped Y₂SiO₅ for energy-efficient, wavelength-optimized horticultural lighting. Full article

Urban agglomerations underpin regional economic growth and sustainability transitions, yet the spatial heterogeneity and drivers of land use intensity (LUI) remain insufficiently resolved in inland settings. This study develops a high-resolution framework—combining a 1 km hexagonal grid with XGBoost-SHAP—to (i) map subsystem-specific LUI evolution, (ii) identify dominant drivers and nonlinear thresholds, and (iii) inform differentiated, sustainable land governance in the Guanzhong Plain Urban Agglomeration (GPUA) over 2000–2020. Composite LUI indices were constructed for human settlement (HS), cropland (CS), and forest (FS) subsystems; eleven natural, socioeconomic, urban–rural, and locational variables served as candidate drivers. The results show marked redistributions across subsystems. In HS, the share of low-intensity cells declined (86.54% to 83.18%) as that of medium- (12.10% to 14.26%) and high-intensity ones (1.22% to 2.56%) increased, forming a continuous high-intensity corridor between Xi’an and Xianyang by 2020. CS shifted toward medium-intensity (32.53% to 50.57%) with the contraction of high-intensity cells (26.62% to 14.53%), evidencing strong dynamism (55.1% net intensification; 38.5% net decline). FS transitioned to low-intensity dominance by 2020 (59.12%), with stability and delayed growth concentrated in conserved mountainous zones. Urban–rural gradients were distinct: HS rose by >20% (relative to 2000) in cores but remained low and stable in rural areas (mean < 0.20); CS peaked and stayed stable at fringes (mean ≈ 0.60); FS shifted from an inverse gradient (2000–2010) to core-area recovery by 2020. Explainable machine learning revealed inverted U-shaped relationships for HS (per capita GDP) and CS (population density) and a unimodal peak for FS with respect to distance to urban centers; model performance was strong (HS R² up to 0.82) with robust validation. Policy recommendations are subsystem-specific: enforce growth boundaries and prioritize infill/polycentric networks (HS); pair farmland redlines with precision agriculture (CS); and maintain ecological redlines with differentiated conservation and afforestation (FS). The framework offers transferable, data-driven evidence for calibrating thresholds and sequencing interventions to reconcile land use intensification with ecological integrity in rapidly urbanizing contexts. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

Climate change drives land use intensity changes in Xinjiang, a typical inland arid region. There are relatively few studies on the changes in land use intensity under future climate change. For this purpose, this study adopts the Patch-level Land Use Simulation (PLUS) model and the Markov chain model, combined with shared socioeconomic pathways (SSPs). This study uses the PLUS model to make projections of land use/land cover (LULC) in Xinjiang under different climate scenarios for 2025–2060, constructs a land use intensity atlas to visualize regional spatial patterns, and analyzes the driving factors. The results show that under the SSP126 scenario, the cropland area decreases sharply while the forest, grassland, and water area expand rapidly. However, under the SSP245 and SSP585 scenarios, this trend is obviously reversed; the cropland area expands quickly, and the area of grassland and water decreases. In addition, under the SSP126 scenario, the management and control of LULC are strict, and it may be significantly affected by the conversion of cropland to forest, and the change of forest is relatively active. Under the SSP585 scenario, productivity increases, which may exacerbate the use of constructed land, and the change of constructed land is relatively active. Land use intensity may not significantly promote changes in land type proportions in the region. Population density and GDP are key drivers of land use intensity, showing relatively significant spatial heterogeneity. This study conducts research on the trend of LULC changes under different future climate scenarios, providing data support for the sustainable development of LULC and helping the government formulate different policies to cope with future LULC changes. Full article

The ecological security pattern plays a crucial role in maintaining ecosystem health and ensuring ecological security. The establishment of the ecological security pattern in Mudanjiang City can provide a scientific basis and effective support for stabilizing the ecological environment, mitigating regional human–land conflicts, and rational land- use planning. This paper utilizes the theory of constructing an ecological security pattern using a source-resistance plane-corridor node to grade the importance of source areas based on the connectivity index. It combines morphological spatial pattern analysis and PLUS model to generate and identify the present value of 2022 in Mudanjiang City, as well as predict eight land types and seven landscape types under three development scenarios by 2032. A transfer matrix and transfer-intensity map are introduced to explore the structural characteristics of landscape transfer, while four fragmentation indexes are combined with principal component analysis and the coefficient of variation method to form comprehensive fragmentation indexes for different classes. Finally, based on constructing the ecological security pattern of Mudanjiang City in 2022, an analysis method is developed that establishes logical connections between land-use structure, a comprehensive fragmentation of land types, landscape transformation mechanism, and the importance of ecological sources. The results are as follows: (1) In Mudanjiang City, 23 ecological source areas, 65 corridors, and 66 ecological nodes were extracted. The overall ecological security pattern shows a “U” shape with openings to the northeast. (2) The cumulative weight of economic and social factors on the ecological resistance surface in Mudanjiang City reached 51.36%. (3) The response between the comprehensive fragmentation degree of forest land and the importance of primary and tertiary source areas was highly significant, with R values reaching 0.9675 and −0.8746, respectively. The comparative study comprehensively showed that the best scenario for the sustainable development of the ecological security pattern in the future is an ecological priority scenario, where the tertiary source area with the smallest area proportion but strongest disturbance fluctuation becomes a key area affecting connectivity and overall ecological security pattern in Mudanjiang City. Full article

To control and improve the phenomena of rocky desertification and soil erosion in karst landform areas, which are caused by a series of human factors that include social and economic development and human activities, China has successively introduced many policies, resulting in spatial and temporal changes in the landscape pattern of the southern karst area. In this study, land use transfer intensity maps, the grid method, the sample line method, the semivariogram method, and the Spearman analysis method are used to explore the spatial and temporal evolutions in surface runoff as responses to landscape pattern and policy factors in karst landform area. Therefore, this study provides theoretical and policy support for improving the regional landscape structure, optimizing the landscape layout, introducing regional policies, reducing surface runoff, and alleviating soil erosion. The results show that the best scale for the study of landscape patterns in the southern karst area is 3000 m. Forests are the land type that make up the highest proportion in the southern karst area, and they have the strongest interception capacity for surface runoff. The spatial and temporal distributions of the surface runoff are significantly different, and urban expansion has led to an increase in impervious runoff year over year. Runoff is positively correlated with the Shannon diversity index (SHDI), patch density (PD), and landscape shape index (LSI). The stronger the landscape heterogeneity, the more runoff. DIVISION is positively correlated with forest runoff and negatively correlated with other land types. The higher is the degree of aggregation of impervious patches, the higher the regional runoff rate. The more dispersed the forest patches are, the smaller the area proportion, and the greater the runoff. In addition, policy factors have a significant impact on surface runoff. Full article

The study of Land Use and Landscape Patterns (LULPs) changes and their ecological quality effects in Haikou city under the background of the Hainan Free Trade Port Plan (HFTPP) helps to promote coordinated development between cities and the environment. To date, most research on ecological quality has focused on areas with extremely fragile ecology and/or is related to LULP analysis. There are few studies in the literature focusing on the impact of high-intensity human activities caused by relevant policies on urban LULPs. The purpose of this research was to design a framework that monitors urban ecological security by considering the effect of the developing free trade port. The proposed framework was constructed by integrating multi-temporal Sentinel-2 remote sensing images, night light remote sensing data, digital elevation model (DEM) data, and spectral index features such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), bare soil index (BSI), and normalized intertidal mangrove index (NIMI), as well as analytical approaches such as the land use transfer matrix, land use dynamic degree, land use degree and transfer matrix, land use gravity center measurement, and landscape pattern index. The framework takes advantage of the Google Earth Engine (GEE) cloud platform and was applied to a highly developed Haikou city, the capital of Hainan province. Maps of brightness (SBI), greenness (GVI), and humidity (WET) were created annually from 2016 to 2021, enabling detailed ecological environment quality evaluation and analysis. The advantages of this study are (1) reliable land cover results obtained automatically and quickly; (2) the strong objectivity of the quantitative research on landscape patterns and land use; and (3) deep integration with free trade port policies. Through the research on the ecological quality problems caused by the change in LULP in the study area, the research results show that, from 2016 to 2021, the spatial distribution of land use and landscape pattern in Haikou city had been constantly changing; the area of construction land has decreased, with most of it having been converted into forest land and farmland; the gravity center of the building land has moved to the northwest; the degree of landscape fragmentation has decreased and the heterogeneity of landscape distribution has increased; the free trade port policies have promoted Haikou’s economic development and ecological civilization construction; and finally, Haikou’s ecological environmental quality has improved significantly. Full article

Understanding the relationship between land use and opium production is critical for monitoring the dynamics of poppy cultivation and developing an effective counter narcotics policy in Afghanistan. However, mapping agricultural land accurately and rapidly is challenging, as current methods require resource-intensive and time consuming manual image-interpretation. Deep convolutional neural nets have been shown to greatly reduce the manual effort in mapping agriculture from satellite imagery but require large amounts of densely labelled training data for model training. Here we develop a generalised model using past images and labels from different medium resolution satellite sensors for fully automatic agricultural land classification using the latest medium resolution satellite imagery. The model (FCN-8) is first trained on Disaster Monitoring Constellation (DMC) satellite images from 2007 to 2009. The effect of shape, texture and spectral features on model performance are investigated along with normalisation in order to standardise input medium resolution imagery from DMC, Landsat-5, Landsat-8, and Sentinel-2 for transfer learning between sensors and across years. Textural features make the highest contribution to overall accuracy (∼73%) while the effect of shape is minimal. The model accuracy on new images, with no additional training, is comparable to visual image interpretation (overall > 95%, user accuracy > 91%, producer accuracy > 85%, and frequency weighted intersection over union > 67%). The model is robust and was used to map agriculture from archive images (1990) and can be used in other areas with similar landscapes. The model can be updated by fine tuning using smaller, sparsely labelled datasets in the future. The generalised model was used to map the change in agricultural area in Helmand Province, showing the expansion of agricultural land into former desert areas. Training generalised deep learning models using data from both new and long-term EO programmes, with little or no requirement for fine tuning, is an exciting opportunity for automating image classification across datasets and through time that can improve our understanding of the environment. Full article

The Bayech basin is located in southwestern Tunisia, a highly prone region to flooding risks. The Bayech basin is characterized by wadis that adopt a wide, sometimes ill-defined bed, often intersected by low-lying areas, resulting in a semi-endoreismo, greatly disrupting the flow regimes. The Bayech basin drains the slopes of the Nementchas and Tebessa mountains in Algeria, collecting water from the Medjen Bel Abbes plain in its middle course before crossing the Gafsa djebls chain at the Gafsa gap. In this basin, flooding is generally caused by high-intensity storms and is often relatively limited in extent. The slope shape and soil type can promote rapid surface runoff during intense rainfall. Therefore, the purpose of creating a web application, labeled ClimInonda, is to respond to a critical need of readily available information on climatic, environmental, and land use data collected in this basin and its morphometric characteristics using recent methods. The application consists of three essential components: the front-end, back-end, and database. The front-end focuses on the user interface, allowing users to interact with the application’s features. It communicates with the back-end through Hypertext Transfer Protocol requests for data processing and retrieval. The back-end handles the server-side operations, processes requests, and provides responses by retrieving data from the database. The database stores and manages the application’s data, ensuring integrity and efficient access. This modular architecture ensures a user-friendly interface, seamless data processing, and reliable data storage. Visualizations can include different types of data, such as satellite imagery, weather data, and terrain data, and can be displayed using various techniques, such as heat maps, contour maps, and 3D models, by providing easy-to-understand visualizations. ClimInonda is an application developed to expand upon existing platforms by providing a suite of exploratory data analysis features, including the ability to calculate the total precipitation depth recorded for any period, interpolate the annual recurrence interval for rainfall events, etc. A simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedback was received. There is clear evidence that ClimInonda would provide the necessary basis for informed decision making by stakeholders and development agencies in arid and semi-arid Tunisia. Full article

The study of the systematic stability of land use change is essential for regulating land use results and layout. This article took Jinan, China, as an example, and used the land transfer matrix to calculate the changing area and intensity based on remote sensing image maps and land use status maps, and then used the intensity analysis method to compare the changing intensity with the average intensity at three levels: interval level, land category level, and transition level. The systematicity and stability of land use changes from 2005 to 2018 in Jinan were analyzed using intensity analysis. The results showed that the intensity of land use change in Jinan led to a rapid change pattern from 2005 to 2010 and a slow change pattern from 2010 to 2018. The occupation of cultivated land by construction land in Jinan showed high activity, while the transition process of cultivated land to construction land and other land categories showed a steady, systematic change pattern, other land categories showed different trends and intensities of change, and the transition of forest land and other land categories showed stability in time scale. The results showed that the changes in construction land were mainly due to external influences, showing a systematic non-steady change pattern. Full article

Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs’ activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the ‘Lieberoser Heide’ in northeastern Germany, is home to the country’s largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands. Full article