Search Results (377)

Small and medium-sized (SMS) lakes in cold–arid regions are highly sensitive to climate change and play critical roles in regional hydrological and ecological processes. However, their long-term dynamic evolution along aridity–humidity gradients remains insufficiently understood. This study aims to reveal the spatiotemporal variations in SMS lakes on the Inner Mongolia Plateau and clarify their climatic driving mechanisms. Based on Landsat imagery and meteorological data (1984–2021) on the Google Earth Engine (GEE) platform, this study quantified the spatiotemporal variations in SMS lakes and adopted an ecological–geographical zoning framework to characterize lake responses across aridity–humidity gradients. Results indicate that, from 1984 to 2021, the total area of SMS lakes showed an insignificant linear trend but a net increase of 117% (396.50–860.33 km²), while the lake number increased by 155%, with 59 new lakes. The dynamics followed four stages: expansion (1984–1993), fluctuation (1994–2002), low-level stability (2003–2011), and recovery (2012–2021). Notably, recovery levels remained below the pre-2003 peak, with 2003 identified as a critical turning point. Lake numbers responded to climatic stress earlier than area changes. Spatially, lake variations in arid regions were primarily controlled by energy-related factors (e.g., temperature and potential evapotranspiration), while lake changes in semi-humid regions were dominated by precipitation-regulated water availability. Semi-arid regions presented transitional characteristics constrained by both energy and water factors. Although extreme weather events did not dominate long-term lake evolution, they significantly exacerbated short-term lake fluctuations. Overall, the controlling mechanism of SMS lakes shifted from energy limitation to water regulation under ongoing climate warming, highlighting pronounced regional differences in climate–lake interactions. Full article

In the context of dynamic environmental changes, accurate geospatial information is fundamental for evidence-based decision-making in land-use planning. As urban areas undergo rapid structural transformations, characterizing their spatial morphology becomes essential for assessing ecosystem conditions and identifying pressure points within the urban–rural gradient. Drawing on the indicators for ecosystem condition and pressure recommended by the Mapping and Assessment of Ecosystem Services (MAES) framework, reflecting their trends, this study presents a methodology for comprehensive geospatial mapping of urban and peri-urban morphology, using the Functional Urban Area (FUA) of Burgas, Bulgaria, as a case study. The approach enables multi-scale spatial analysis (regional and local), integrates the structure and functions of urban ecosystems, and reveals the spatial heterogeneity of complex socio-economic systems. At the regional level, ecosystems within the FUA were identified using the national land-use/land-cover database. At the local level, within the city of Burgas, urban morphology was classified by combining building and land-cover types into 14 distinct urban morphological zones (local climate zones—LCZs) using high-resolution unmanned aerial vehicle (UAV)-based orthophotos. This precise spatial data allowed for a detailed assessment of the balance between pervious and impervious surfaces within each LCZ. By integrating Google Earth Engine (GEE) data, the appropriate conditions and pressure indicators in the case study are assessed. Regional ecosystem pressure is effectively captured through the spatial distribution of the Final Pressure Index (IPr). Concurrently, the Urban Ecosystem Performance Index (UEPI) highlights sharp spatial polarization, with critical stress concentrated in the industrial and port zones of the urban core. The results provide policy-makers and stakeholders with critical insights into current pressures and environmental changes in urban and peri-urban ecosystems, offering a robust foundation for evidence-based management and climate change adaptation strategies. Full article

Urbanisation and impervious-cover expansion are reshaping riparian landscapes, particularly in mountain cities where steep terrain concentrates development along valley floors. This study examined spatiotemporal land-cover change within the regulated riparian corridors of Thimphu City, Bhutan, over a 25-year period from 1997 to 2022 using Landsat imagery, Random Forest classification and Google Earth Engine. Results show substantial transformation of riparian land cover, with impervious cover increasing from 26.14% to 32.63%, equivalent to an overall increase of 24.83%, while agriculture/barren/low-vegetation declined from 30.59% to 26.01%, equivalent to an overall decrease of 14.98%. A modest increase in detectable vegetation cover was also observed, although this should be interpreted cautiously because the study measured land-cover extent rather than vegetation condition, floristic composition or ecological quality. Classification performance was robust, with overall accuracies ranging from 89.9% to 94.5%, exceeding the commonly accepted 85% benchmark, although uncertainty remains in narrow riparian corridors due to Landsat’s 30 m spatial resolution. Mann–Kendall analysis provided supplementary evidence of monotonic land-cover trends, but the limited number of temporal observations means these results should be interpreted as indicative, rather than definitive. Spatial analysis revealed uneven transformation, with the southern valley recording the greatest increase in impervious cover. These findings demonstrate sustained development pressure within legally regulated riparian buffers and highlight the need for routine spatial monitoring, place-specific buffer management and stronger integration of riparian protection into urban planning. The study provides a quantitative baseline for assessing future riparian land-cover change and supporting more resilient land governance in rapidly urbanising Himalayan mountain cities. Full article

Satellite-derived vegetation indices and climate data from 2018 to 2024 were analysed to quantify smallholder agricultural responses to climate variability in two rural villages in the Eastern Cape, South Africa. Using Google Earth Engine, R programming 4.4.0, and ArcGIS Pro 3.6, the study assessed spatiotemporal trends in vegetation condition in relation to bioclimatic variables and plot-scale land ownership. The results showed an overall accuracy of 96%, with producer and user accuracies at 79% and 85%, respectively, and a kappa coefficient of 0.95. Time-series analysis revealed a trend of decreasing rainfall and increasing temperatures across the study area, accompanied by elevated Plant Senescence Reflectance Index (PSRI > 0.294) values indicative of advanced vegetation stress. Spatial analysis showed that valley areas exhibited higher moisture accumulation potential and aligned with drainage networks, reflecting enhanced soil moisture retention relative to surrounding terrain. These findings demonstrate the strong influence of topography-mediated water availability on vegetation health in rain-fed smallholder systems. In accordance with the Sustainable Development Goals, the study stresses the importance of gender equity in combating climate change and achieving food security, highlighting the value of integrating multi-scale remote sensing and climate data to identify localised agricultural vulnerability, and underscores the importance of gender-responsive, climate-aware land management strategies to support food security under changing environmental conditions. By situating smallholder agriculture within a land system science framework, the study advances understanding of how topography-mediated soil moisture retention, climate variability, and gendered land governance jointly shape land system trajectories in communal tenure settings. Full article

This study assessed the spatiotemporal dynamics of eco-environmental quality (EEQ) in Sonid Left Banner from 2000 to 2025, using Landsat imagery and the remote sensing ecological index (RSEI) via Google Earth Engine. Theil–Sen slope, Mann–Kendall test, Hurst exponent, and Pearson correlation analysis were used to analyze trends and their associations with climatic and anthropogenic factors. Results showed that EEQ exhibited an overall improving trend, with a mean RSEI of 0.270 and an annual increase of 0.0022 a⁻¹, though it remained at a fair grade with a spatial pattern of “regionally poor but locally improved.” Hurst exponent analysis has indicated that 75.35% of the study area will sustain improvement, while 17.03% faces continuous degradation risk. Climatic factors showed the strongest associations with RSEI: precipitation (r = 0.329) and humidity (r = 0.313) showed the strongest positive correlations, with a distinct north–south spatial gradient in their association patterns; temperature (r = 0.272) showed bidirectional correlation patterns; and wind speed (r = −0.197) was the primary negative correlated factor. Human activity intensity (HAI) was negatively correlated with RSEI (r = −0.128), with 7.8% of high-intensity development areas showing significant degradation. These findings reveal that moisture availability establishes the ecological baseline in semi-arid grasslands, while human activities modulate ecosystem change, informing targeted ecological restoration. Full article

Dysregulated microRNA (miR) expression has emerged as a potential prognostic tool in head and neck squamous cell carcinoma (HNSCC), but the clinical value of miR-34a remains unclear. This systematic review, meta-analysis, and trial sequential analysis (TSA) evaluated the association between tumor tissue miR-34a expression and survival outcomes in HNSCC. Following a protocol registered in PROSPERO (n. CRD420251238772), PubMed/MEDLINE, Scopus, ScienceDirect, CENTRAL, Google Scholar, and grey literature sources were searched for studies reporting overall survival (OS) or disease-free survival (DFS) stratified by miR-34a expression in HNSCC or its subsites. Hazard ratios (HRs) were extracted directly or reconstructed from Kaplan–Meier (KM) curves using the Tierney method, supported by a dedicated Python application (KM2HR). Four retrospective studies, corresponding to six study/site-specific cohorts and 318 patients, met the inclusion criteria. For OS (four cohorts), the fixed-effects model yielded a pooled HR of 2.25 (95% CI 1.48–3.41) for low versus high miR-34a expression, indicating worse survival in the low-expression group. However, the random-effects model attenuated the association (HR 1.32, 95% CI 0.32–5.54), with substantial heterogeneity (I² ≈ 77%). For DFS (two studies), the fixed-effects model suggested poorer outcomes with low miR-34a (HR 2.92, 95% CI 1.24–6.88), whereas the random-effects model reversed the direction of effect with extremely wide confidence intervals (HR 0.19, 95% CI ≈ 0.00–129.34; I² = 91%). TSA for OS (accrued information size 225 patients; estimated power ≈66%) crossed the monitoring boundary but did not reach the a priori information size, supporting only a tentative signal. A bioinformatic exploration of the TCGA HNSCC cohort (n = 522) showed a non-significant trend towards worse OS with low miR-34a (HR 1.24, 95% CI 0.93–1.65) and was excluded from pooling. Overall, low tumor miR-34a expression appears to be associated with poorer OS, but the evidence is limited by retrospective design, small sample size, and marked heterogeneity. miR-34a is a promising biomarker for prognostic stratification in HNSCC, yet larger, prospective, site-specific studies with standardized assays, pre-defined cut-offs, and appropriate adjustment for HPV status and clinical covariates are required before clinical implementation can be recommended. Full article

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R² = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance. Full article

Background/Objectives: Regular physical exercise is strongly recommended for individuals with type 1 diabetes mellitus (T1DM) because of its beneficial effects on cardiovascular fitness, insulin sensitivity, metabolic control, and overall health. Nevertheless, participation in physical activity remains limited, largely due to the fear of exercise-induced hypoglycemia and glycemic instability. Glycemic responses to exercise in T1DM are influenced by the interaction between exercise modality, circulating insulin levels, nutritional status, and diabetes technologies. Continuous aerobic exercise, resistance training, high-intensity interval exercise, and mixed intermittent activities elicit distinct metabolic and hormonal responses, resulting in heterogeneous glycemic trajectories. This narrative review aimed to provide a clinically oriented synthesis of the physiological mechanisms underlying exercise-related glycemic fluctuations in T1DM and to discuss integrated insulin- and carbohydrate-based strategies to support safer participation in physical activity in the context of modern diabetes technologies. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, and complementary searches in Google Scholar to identify experimental studies, observational studies, systematic reviews, consensus statements, and clinical guidelines focused on exercise-related glycemic responses in individuals with T1DM. Only articles published in English were considered. Evidence was selected and synthesized according to relevance to exercise modality, insulin therapy strategies, carbohydrate management, and diabetes technologies, including continuous glucose monitoring, continuous subcutaneous insulin infusion, and automated insulin delivery systems. The final narrative synthesis was based on 44 selected studies, reviews, consensus statements, and guidance documents considered most relevant to the objectives of this narrative review. Results: Available evidence indicates that continuous moderate-intensity aerobic exercise is most consistently associated with progressive glucose declines and increased risk of hypoglycemia, particularly when performed in the presence of elevated insulin on board. In contrast, resistance exercise and short-duration high-intensity or anaerobic exercise more frequently induce stable glycemia or transient hyperglycemia through adrenergic stimulation and increased hepatic glucose output. Mixed and intermittent exercise modalities often produce more variable responses depending on exercise sequencing, nutritional status, and insulin exposure. Across studies, integrated adjustment of basal and prandial insulin doses together with individualized carbohydrate supplementation emerged as the most effective strategy to reduce exercise-related glycemic instability. Continuous glucose monitoring and insulin pump technologies improved glucose trend awareness and management flexibility; however, physical exercise remains a challenging condition for current automated insulin delivery algorithms and still requires active user-driven decision-making. Conclusions: Exercise management in T1DM should be based on an individualized interpretation of exercise modality, glucose trends, insulin exposure, and nutritional context rather than on fixed glucose thresholds alone. Combining anticipatory insulin adjustments, tailored carbohydrate strategies, and appropriate use of diabetes technologies may substantially reduce glycemic variability and improve confidence toward physical activity participation. Structured education and individualized clinical guidance remain essential to translate physiological knowledge into effective real-world exercise management. Full article

Research Purpose: This study investigates the role of digital investor behavior, measured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Daily data from 2020 to 2025 were collected, covering both crisis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frameworks, with Max Drawdown employed as a complementary risk metric to capture extreme market movements. Findings: Digital investor attention shows a predictive association with volatility, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predictive performance. The EGARCH model successfully captured the asymmetric impact of negative shocks (γ < 0, p < 0.05), while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress, achieving an R² of 95.36%. Scientific value: This study positions digital attention as a complementary variable that improves forecasting, moving beyond conventional price-based models in volatility modeling; by integrating Google Trends with technical analysis, the research introduces a hybrid forecasting framework that can be adapted to other emerging markets. Practical Implications: The findings offer practical value for policymakers and investors. Regulators can use digital attention measures as early warning signals to anticipate volatility, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools. Full article

This study presents a controlled empirical and comparative analysis of existing data augmentation techniques for text generation in Turkish, a morphologically rich, low-resource language. A collection of 265 Turkish reading passages for Grades 4 and 5 was augmented using four techniques: paraphrasing with GPT-3.5-turbo (Generative Pre-trained Transformer 3.5 Turbo), back translation (Turkish–English–Turkish and Turkish–French–Turkish) via Google Translate, synonym replacement via GPT-3.5-turbo, and random insertion via GPT-3.5-turbo. Human evaluators assessed the fluency, coherence, grammaticality, logical flow, and naturalness of the augmented datasets. Each augmented dataset, along with the original, was then used to fine-tune a Turkish GPT-2-medium model, which was evaluated using automatic metrics such as BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR (Metric for Evaluation of Translation with Explicit ORdering), chrF (CHaRacter-level F-score), BERTScore (Bidirectional Encoder Representations from Transformers Score), and cosine similarity. According to the human evaluation of the original and augmented datasets, the original texts received the highest ratings, followed by those generated through random insertion, paraphrasing, synonym replacement, and back translation variants, with cosine similarity results between original and augmented texts showing a comparable trend; however, the differences between methods were generally small. The results from text generation indicate that models trained on the original dataset generally achieved slightly higher performance across evaluation metrics compared to those trained on augmented datasets. Among the augmented methods, synonym replacement showed marginally better performance, followed by back translation, random insertion, and paraphrasing; however, the differences between methods were small and not statistically significant. Full article

The Souss-Massa basin, one of Morocco’s major agricultural regions, is increasingly affected by water scarcity and climatic stress. However, the long-term interactions between hydro-climatic change and farmers’ cropping system adjustments remain insufficiently documented. This study analyzes hydro-climatic trends and agricultural transformations over the period 1995–2021. The methodology combines statistical trend analysis of meteorological data (Mann–Kendall test and Sen’s slope estimator), diachronic land use/land cover mapping using Google Earth Engine, Crop Water Stress Index (CWSI) assessment, and groundwater piezometric analysis. Results reveal declining and highly variable precipitation, together with a significant warming trend reaching +0.116 °C/year. In parallel, cultivated cereal areas (rainfed and irrigated) declined, while irrigated forage crops expanded, particularly Berseem/Maize. Despite increasing aridity, CWSI results indicate maintained crop vigor in irrigated areas, suggesting growing dependence on groundwater extraction. These findings highlight an ongoing agricultural transition that increases pressure on already vulnerable water resources and underscores the need for integrated climate adaptation and groundwater management strategies in the basin. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article

Large trees are keystone ecological structures that sustain biodiversity and ecosystem services, particularly in human-altered landscapes. However, their persistence is increasingly threatened by land-use change, urban expansion, and inadequate monitoring. This study develops and validates a scalable, automated framework for monitoring the loss of large individual trees using satellite image time series and breakpoint detection. We compared four spectral indices (SIs): Enhanced Vegetation Index 2–EVI2; Normalized Burn Ratio–NBR; Normalized Difference Red Edge–NDRE, and the Normalized Difference Vegetation Index–NDVI derived from Sentinel-2 imagery (2015–2025) for 691 georeferenced trees in Lousada, northern Portugal. Data were accessed and processed in Google Earth Engine and analyzed using a custom R-based workflow, including cloud masking, gap-filling, temporal interpolation, upper-envelope smoothing, deseasonalization, and break detection. Five breakpoint detection algorithms were compared: BFAST, energy-divisive, linear regression of structural changes, wild-binary segmentation, and change point models. Detected breakpoints were subsequently post-validated to determine whether they were associated with declines in SIs, using three pre-/post-breakpoint methods: comparisons of short- and long-term medians and a randomized trend analysis. As a baseline, these algorithms/post-validation logic were compared against the Continuous Change Detection and Classification (CCDC) approach. The results indicate moderate but consistent break detection performance, with a maximum balanced accuracy of 73% (for EVI2 or NDVI and using the energy-divisive algorithm coupled with the long-term median post-validator) under conservative validation criteria and high specificity for surviving trees. CCDC ranked comparatively lower at 62%. Algorithm performance varied substantially, with the energy-divisive providing the most conservative detection and the wild-binary segmentation yielding higher sensitivity. Performance was further influenced by tree structural attributes and species identity, with larger, taller and isolated trees, as well as particular genera, showing higher detection accuracy, with genus Eucalyptus, Tilia and Celtis yielding top performance results (79–65%) and Quercus, Castanea and Platanus the lowest (62–60%). By integrating satellite observations with large-tree inventory data from the Green Giants citizen science project, this study demonstrates the potential of decentralized, Earth observation-based monitoring to support tree-level loss assessments in fragmented landscapes. The proposed framework provides a transferable foundation for wide-scale monitoring of large trees in peri-urban and mixed-use environments. Full article

Net primary production (NPP) is a key indicator of the terrestrial carbon cycle, and its response to disturbance and subsequent recovery is important for understanding regional carbon sink dynamics. Conventional region-based statistical approaches have limitations in capturing localized heterogeneous changes. In this study, a typical ecologically fragile region on the northwestern Sichuan Plateau was selected as the study area. Using the Google Earth Engine (GEE) platform, Landsat time-series imagery (2001–2020) and MOD17A3HGF NPP data were integrated. The LandTrendr algorithm was applied to identify vegetation disturbance patches, and two representative disturbance years (2008 and 2014) were selected for long-term analysis. Trend analysis, coefficient of variation, and the Hurst exponent were used to characterize the spatiotemporal dynamics and stability of NPP in disturbed areas. The results show that: (1) NPP declined after disturbance and then exhibited a recovery trend, with significant spatial heterogeneity in recovery rates; (2) recovery trajectories differed between disturbance years, indicating combined effects of disturbance intensity and environmental conditions; and (3) Hurst exponent analysis suggests that although recovery trends are persistent in most areas, some disturbed patches show potential instability. This study establishes an analytical framework integrating disturbance detection and recovery tracking, which improves the representation of NPP dynamics in heterogeneous regions and provides a basis for assessing ecosystem recovery and carbon sink dynamics. Full article

Surface water extent (SWE) is a key indicator of the regional water balance in dryland environments. However, the hydrological processes regulating SWE responses remain poorly constrained. Focusing on the Mu Us Sandy Land (MUSL), this study integrates multi-source remote sensing and hydrological datasets to investigate the long-term evolution of SWE and, critically, to distinguish the hydrological linkages between SWE dynamics and water storage variability in endorheic and exorheic regions during 1987–2024. An improved water extraction method was implemented on the Google Earth Engine platform, and SWE dynamics were interpreted within a water-balance framework supported by attribution analysis using machine learning. The results show that total SWE exhibited a significant increasing trend (7.95 km² yr⁻¹, p < 0.05) during 1987–2024, primarily driven by permanent SWE, while fundamentally different hydrological regimes governed SWE evolution. In the endorheic basin, SWE exhibited strong co-variation with subsurface water storage, with soil moisture and groundwater storage changes occurring concurrently with SWE changes. In contrast, no synchronous increase in SWE with groundwater storage was observed in the exorheic region. Instead, SWE expansion was mainly associated with accelerated groundwater storage depletion and reservoir construction. These contrasting patterns indicated that SWE dynamics in the endorheic basin were primarily controlled by subsurface water storage, whereas in exorheic regions they were largely driven by human-induced water redistribution rather than increases in total water storage. These findings highlight the importance of integrated surface–subsurface water management for sustaining long-term water security under climate change and increasing human water regulation. Full article