Search Results (2,733)

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000–2024 were downscaled to 30 m × 30 m. The active layer thickness (ALT)–ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m × 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 °C and −0.1 °C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and −0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000–2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change. Full article

In-depth exploration of the spatial heterogeneity patterns of urban carbon emissions holds significant scientific importance for regional sustainable development. However, few scholars have examined the spatiotemporal characteristics of county-level carbon emissions in Inner Mongolia. This study focuses on the three major cities of Hohhot, Baotou, and Ordos in Inner Mongolia. By integrating NPP-VIIRS nighttime light data, the CLCD (China Land Cover Dataset) dataset, and statistical yearbooks, it quantifies county-level carbon emissions and establishes a spatiotemporal analysis framework of urban morphology–carbon emissions from 2013 to 2021. Six morphological indicators—Class Area (CA), Landscape Shape Index (LSI), Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Patch Density (PD), and Interspersion Juxtaposition Index (IJI)—are employed to represent urban scale, complexity, centrality, compactness, fragmentation, and adjacency, respectively, and their impacts on regional carbon emissions are examined. Using a geographically and temporally weighted regression (GTWR) model, the results indicate the following: (1) from 2013 to 2021, The high-value areas of carbon emissions in the three cities show a clustered distribution centered on the urban districts. The total carbon emissions increased from 20,670 (10⁴ t/CO₂) to 37,788 (10⁴ t/CO₂). The overall spatial pattern exhibits a north-to-south increasing gradient, and most areas are projected to experience accelerated carbon emission growth in the future; (2) the global Moran’s I values were all greater than zero and passed the significance tests, indicating that carbon emissions exhibit clustering characteristics; (3) the GTWR analysis revealed significant spatiotemporal heterogeneity in influencing factors, with different cities exhibiting varying directions and strengths of influence at different development stages. The ranking of influencing factors by degree of impact is: CA > LSI > COHESION > LPI > IJI > PD. This study explores urban carbon emissions and their heterogeneity from both temporal and spatial dimensions, providing a novel, more detailed regional perspective for urban carbon emission analysis. The findings enrich research on carbon emissions in Inner Mongolia and offer theoretical support for regional carbon reduction strategies. Full article

Vegetation phenology (VP) is a crucial biological indicator for monitoring terrestrial ecosystems and global climate change. However, VP monitoring using traditional remote sensing vegetation indices has significant limitations in precise analysis. Furthermore, most studies have overlooked the distinction between stable and short-term VP in relation to climate change and have failed to clearly identify the seasonal variation in the impact of climatic factors on stable VP (SVP). This study compared the accuracy of solar-induced chlorophyll fluorescence (SIF) and three traditional vegetation indices (e.g., Normalized Difference Vegetation Index) for estimating SVP in China, using ground-based data for validation. Additionally, this study employs Sen’s slope, the Mann–Kendall (MK) test, and the Hurst index to reveal the spatiotemporal evolution of the Start of Season (SOS), End of Season (EOS), and Length of Growing Season (LOS) over the past two decades. Partial correlation analysis and random forest importance evaluation are used to accurately identify the key climatic drivers of SVP across different climate zones and to assess the seasonal contributions of climate to SVP. The results indicate that (1) phenological metrics derived from SIF data showed the strongest correlation coefficients with ground-based observations, with all correlation coefficients (R) exceeding 0.69 and an average of 0.75. (2) The spatial distribution of SVP in China has revealed three primary spatial patterns: the Tibetan Plateau, and regions north and south of the Qinling–Huaihe Line. From arid, cold-to-warm, and humid regions, the rate of SOS advancement gradually increases; EOS transitions from earlier to nearly unchanged; and the rate of LOS delay increases accordingly. (3) The spring climate primarily drives the advancement of SOS across China, contributing up to 70%, with temperatures generally having a negative effect on SOS (r = −0.53, p < 0.05). In contrast, EOS is regulated and more complex, with the vapor pressure deficit exerting a dual ‘limitation–promotion’ effect in autumn (r = −0.39, p < 0.05) and summer (r = 0.77, p < 0.05). This study contributes to a deeper scientific understanding of the interannual variability in SVP under seasonal climate change. Full article

Over the past three decades, the Yangtze River Delta has undergone a rapid urbanization phenomenon, resulting in pronounced urban sprawl that has significantly impacted regional sustainable development and air quality. This study constructs an urban sprawl index based on nighttime light data spanning 2000–2020 and employs exploratory spatio-temporal analysis, panel data models, and spatial econometric models to examine the evolution of urban sprawl and its effects on PM_2.5 concentrations. The results reveal four key findings: (1) Urban sprawl is spatially heterogeneous, exhibiting a ‘high in the centre-east, low in the north-west’ pattern, with high-intensity sprawl expanding from the central region towards the north-west and south-west; (2) The dominant growth pattern is characterized by relatively rapid expansion. The global Moran’s I index fluctuates between 0.428 and 0.214, indicating a gradual decline in the global clustering effect of urban sprawl. Meanwhile, the share of local high–high agglomeration zones decreases to 21.9%, whereas low–low zones increase to 24.3%; (3) Spatio-temporal transitions of urban sprawl show strong spatial dependence while overall relocation exhibits inertia; (4) Before the implementation of the Ten Key Measures for Air Pollution Prevention and Control in 2013, urban sprawl significantly intensified PM_2.5 pollution. Following the policy, this relationship notably reversed, with sprawl exhibiting pollution-mitigating effects in certain regions. The spatial diffusion of pollution is evident, as urban sprawl influences air quality through both local development and inter-regional interactions. This study provides an in-depth analysis of the spatio-temporal evolution of urban sprawl and establishes a framework to examine the interactive mechanisms between urban expansion and air pollution, thereby broadening perspectives on atmospheric pollution research and offering scientific and policy guidance for sustainable land use and air quality management in the Yangtze River Delta. Full article

Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article

Mosquito community composition can differ spatially and temporally within the same wetlands. Understanding this spatiotemporal variation is crucial, particularly in wetlands of conservation importance. Here, we examine the diversity and community composition of Culicidae (Diptera) across four sites within Fetzara Lake, a large Ramsar site in Northeast Algeria. For two years, we conducted monthly field surveys across four sites (Northeast, Northwest, Southeast, and Southwest) from April 2021 to March 2023. During these surveys, we used ovitraps to sample mosquitoes and assess species richness as well as alpha and beta diversity. We identified seven mosquito species (Aedes aegypti, Ae. albopictus, Ae. geniculatus, An. labranchiae, Culex perexiguus, Cx. pipiens, and Cs. longiareolata). There was a clear dominance of Culex pipiens (Usutu and West Nile virus vector), which accounted for 74.3% of all samples, whereas Aedes aegypti was the least abundant (<1%). Species richness varied between five and six across sites. The Shannon index and beta diversity revealed significant variation in species diversity across sites and seasons, likely driven by local differences in environmental conditions. This study emphasizes the importance of local variation in environmental conditions in shaping ecological communities in space and time. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

The interactive coupling mechanism between ecosystem service value (ESV) and urbanization has emerged as a critical research focus in ecological security and sustainable development. This study quantifies the ESV of prefecture-level cities by leveraging remote sensing data and socioeconomic statistics from the Yangtze River Delta (YRD) region spanning 2006—2020. It constructs a multidimensional evaluation index system for urbanization. We systematically assess both systems’ spatiotemporal evolution and interactions by employing entropy weighting, comprehensive indexing, and coupling coordination models. Furthermore, Geo-detectors and Geographical and Temporal Weighted Regression (GTWR) models are applied to identify driving factors influencing their coordinated development. Key findings include (1) the total amount of ESV in the YRD exhibits a fluctuating decline, primarily due to a steady increase in urbanization levels; (2) the coordination degree between ESV and urbanization demonstrates phased growth, transitioning to a “basic coordination” stage post-2009; (3) spatially, coordination patterns follow a “core–periphery” hierarchy, marked by radial diffusion and gradient disparities, with most cities being of the ESV-guidance type; (4) GTWR analysis reveals spatiotemporal heterogeneity in driving factors, ranked by intensity as Normalized Difference Vegetation Index (NDVI) > Economic density (ECON) > Degree of openness (OPEN) > Scientific and technological level (TECH) > Industrial structure upgrading index (ISUI) > Government investment efforts (GOV). This study advances methodological frameworks for analyzing ecosystem–urbanization interactions in metropolitan regions, while offering empirical support for ecological planning, dynamic redline adjustments, and territorial spatial optimization in the YRD, particularly within the Ecological Green Integrated Development Demonstration Zone. Full article

In promoting the “dual-carbon goals” and sustainable development strategy, analyzing the spatio-temporal response mechanism of landscape patterns to carbon emissions is a critical foundation for achieving carbon emission reductions. However, existing research primarily targets urbanized zones or individual ecosystem types, often overlooking how landscape pattern affects carbon emissions across entire watersheds. This research examines spatial–temporal characteristics of carbon emissions and landscape patterns in China’s Yellow River Basin, utilizing Kernel Density Estimation, Moran’s I, and landscape indices. The Geographically and Temporally Weighted Regression model is used to analyze the impact of landscape patterns and their spatial–temporal changes, and recommendations for sustainable low-carbon development planning are made accordingly. The findings indicate the following: (1) The overall carbon emissions show a spatial pattern of “low upstream, high midstream and medium downstream”, with obvious spatial clustering characteristics. (2) The degree of fragmentation in the upstream area decreases, and the aggregation and heterogeneity increase; the landscape fragmentation in the midstream area increases, the aggregation decreases, and the diversity increases; the landscape pattern in the downstream area is generally stable, and the diversity increases. (3) The number of patches, staggered adjacency index, separation index, connectivity index and modified Simpson’s evenness index are positively correlated with carbon emissions; landscape area, patch density, maximum number of patches, and average shape index are negatively correlated with carbon emissions; the distribution of areas positively or negatively correlated with average patch area is more balanced, while the spread index shows a nonlinear relationship. (4) The effects of landscape pattern indices on carbon emissions exhibit substantial spatial heterogeneity. For example, the negative impact of landscape area expands upstream, patch density maintains a strengthened negative effect downstream, and the diversity index shifts from negative to positive in the upper reaches but remains stable downstream. This study offers scientific foundation and data support for optimizing landscape patterns and promoting low-carbon sustainable development in the basin, aiding in the establishment of carbon reduction strategies. Full article

Under the increasing pressures of land-based pollution and intensive coastal development, marine ecosystems are facing unprecedented challenges, highlighting the urgent need for enhanced protection and management of marine environmental quality. This study examines the spatiotemporal distribution and pollution risks of seven potentially toxic elements (Hg, Cd, Pb, Cr, As, Zn, and Cu) in the coastal waters of Bohai Bay, China, based on monitoring data collected from 2020 to 2023. Results show a significant decline in annual average concentrations of Pb (from 3.23 ± 1.11 μg/L to 0.10 ± 0.06 μg/L) and Hg (from 0.05 ± 0.02 μg/L to 0.01 ± 0.00 μg/L), reflecting effective pollution control measures. In contrast, Cu concentrations nearly doubled, rising from 0.90 ± 0.50 μg/L in 2020 to 1.98 ± 0.42 μg/L in 2023, while Zn exhibited a “V”-shaped fluctuation over the study period. Spatially, Zn, Pb, and Hg displayed pronounced clustering patterns, with coefficients of variation (CV) of 1.04, 1.49, and 1.17, respectively. The Pollution Load Index (PLI) decreased from 1.82 in 2020 to 0.94 in 2023, indicating an overall improvement in ecological quality. However, the Risk Index (RI) reached a maximum of 672.5 at Site 11 in 2020, with Hg and Cd contributing 49.6% and 22.7% to the total risk, respectively. Health risk assessment revealed non-carcinogenic risks (Hi) below the safety threshold (Hi < 1) across all sites. In contrast, carcinogenic risks (CR) ranged from 5.7 × 10⁻⁴ to 9.1 × 10⁻⁴, approaching the acceptable upper limit of 10⁻³, primarily due to dermal exposure to Hg and the high toxicity of Cd. Principal Component Analysis (PCA) suggested familiar sources for Hg, Pb, and Zn, whereas As appeared to originate from distinct pathways. Overall, this study establishes an integrated “pollution–ecological–health” assessment framework, offering scientific support for targeted pollution prevention and zonal management strategies in coastal environments. Full article

As the global climate continues to warm, continuous high-temperature heat waves have a significant impact on urban socio-economic and sustainable development. Based on the relationship equation between the WBGT index and labor productivity, this paper estimates the economic losses caused by high temperature in 278 cities in China, and investigates the spatial-temporal evolution of the urban economic losses. The main conclusions are as follows: (1) Nationally, the secondary industry experiences the highest average economic losses. Cities in southern, eastern, and central China exhibit the greatest vulnerability, necessitating prioritized climate adaptation planning. (2) Temporally, the average economic losses due to high temperature in Chinese cities from 2010 to 2020 showed a fluctuating upward trend, increasing from 1.343 billion yuan in 2010 to 5.557 billion yuan in 2020. The national average WBGT index increased by 1.69 °C between 2010 and 2020. For every 1 °C increase in the WBGT index, the national economic loss is projected to increase by 0.249 billion yuan. (3) Spatially, areas with high average economic losses were predominantly concentrated in eastern regions, whereas western and central regions exhibited relatively lower losses. The northeastern region recorded the lowest average economic losses. (4) The center of economic loss shifted southwestward from Huangshi City (2010) to Jiujiang City (2020), with an overall migration distance of 143.37 km. The migration velocity exhibited a decelerating trend. This study aims to provide insights for formulating differentiated regional climate adaptation policies and advancing the development of sustainable cities that are resilient to high temperatures while balancing social equity and economic stability. Full article

Exploring the interactions and coupling effects in the Population–Land–Industry–Facility (PLIF) system can help maximize resource allocation and promote the synergistic development of systems. This study constructs an index system for the PLIF system in Hebei Province, employing coupling coordination degree and spatial autocorrelation methods to investigate the spatio-temporal evolution of the system’s coordination. Furthermore, grey relational analysis is employed to examine the key factors influencing the coordination degree of the system. The results show the following: (1) The development levels of each subsystem and the overall development level of the PLIF system in Hebei Province have generally increased, but the overall level remains relatively low. (2) The PLIF system in Hebei Province exhibits a pattern of “low in the north and high in the south, high in the east and low in the west”, with most counties in a barely coordinated state and a generally high degree of coupling. (3) The main factors affecting the coordinated development of the PLIF system include population density, the proportion of the tertiary industry, and the degree of non-agriculturalization of rural labor force. The research results of this paper provide a reference for promoting the coordinated development of population, land, industry, and facilities in Hebei Province and facilitating the sustainable development of the region. Full article

Background/Objectives: Mobility screening is standard practice in hospitalized geriatric patients, but clinical assessments alone may not fully capture functional capacity and related risks. This study aimed to describe the physical performance (gait analysis, postural stability and regulation) and clinical–functional status (e.g., Tinetti [TIN], Barthel Index [BI]) in geriatric inpatients, and to explore associations between measures from different domains. Methods: Fifty-five geriatric inpatients (mean age: 84.3 ± 5.47 years, range: 71–97; 49% female) underwent spatiotemporal gait analysis (inertial sensor system/RehaGait) and posturography (Interactive Balance System). Clinical assessments included TIN, BI, Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Clinical Frailty Scale (CFS), and Numeric Rating Scale (NRS). Gait and postural data were compared with age-, sex-, and height-adjusted reference values. Results: Clinical data indicated a low fall risk (TIN: 24), moderate functional independence (BI: 54), and moderate frailty (CFS: 5). Deviations from reference values were more frequent in gait parameters (18/50%) than in postural parameters (6/17%), with postural stability consistently reduced. The largest differences for the geriatric patients compared with the reference gait data were found for stride length, walking speed, double and single support, roll-off angle, and landing angle. TIN showed the strongest correlation with walking speed (r = 0.47, 95% CI: 0.22–0.67), a relationship unaffected by gender (partial r = 0.52). Conclusions: Gait assessment revealed greater performance deficits than postural measures in this cohort. Full article

Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R² of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions. Full article