Search Results (1,518)

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Beautiful Leisure Tourism Villages (BLTVs) represent an effective pathway for advancing high-quality rural industrial development and promoting comprehensive rural revitalization. They are of great significance to enriching new rural business formats and new functions. The analysis is interpreted within an integrated location–distance attenuation framework. Based on the methods of spatial clustering analysis, geographical linkage rate and geographical weighted regression, the spatio-temporal evolution of 1982 BLTVs in China up to 2023 was examined to uncover the underlying driving mechanisms. Findings indicated that (1) a staged expansion in the number of villages across China, with the most pronounced growth occurring between 2014 and 2018, averaged 124 new villages per year; their stage characteristics showed an obvious “unipolar core-bipolar multi-core-bipolar network” development model; (2) the barycenters of villages were all located in Nanyang City of Henan Province; they migrated from east to west, and formed a push and pull migration trend from east to west and then east; (3) the spatial distribution of villages was highly aggregated and demonstrated marked regional heterogeneity, following a south–north and east–west gradient, with the highest concentration in Jiangzhe and the lowest in Ningxia Hui Autonomous Region; and (4) natural ecology, hydrological and climatic conditions, socioeconomic context, transportation accessibility, and resource endowment collectively shaped the spatial layout of villages, exhibiting pronounced spatial variation in the intensity of these driving factors. On the whole, topography, social economy, traffic condition and precipitation condition had greater influences on the spatial distribution of villages in the western than in the eastern part of China. In contrast, the effects of resource endowment and temperature on the spatial distribution of BLTVs were stronger in eastern China than in western China. These findings enhance the theoretical understanding of tourism-oriented rural development by integrating spatio-temporal evolution with a location–distance attenuation perspective and provide differentiated guidance for the sustainable development of BLTVs across regions. Full article

Satellite precipitation products offer great potential for acquiring reliable precipitation data in data-sparse areas, yet they have inherent uncertainties and errors as indirect observations. This study evaluated the accuracy of multi-source satellite precipitation products from daily and precipitation magnitude perspectives and discussed the spatiotemporal variation in their inversion errors. Based on ground rainfall observations, satellite products, and environmental factors, a Transformer-based multi-source precipitation fusion method was proposed, with its effectiveness preliminarily analyzed for daily precipitation in the Jingle River Basin. The main conclusions are as follows: (1) Compared with the observed precipitation data, the GSMaP_Gauge satellite remote sensing precipitation product showed the closest agreement with the observations, ranking first in all indicators except the Probability of Detection (POD). The MSWEP satellite remote sensing precipitation product followed in performance, while the CHIRPS satellite product performed the poorest. Satellite products showed distinct error characteristics across seasons and rainfall intensities, as well as general overestimation of light rain frequency and insufficient heavy rain capture; however, these products also showed better detection capability in flood seasons. Error spatial distribution was consistent with topography, vegetation coverage, and temperature. (2) Verification demonstrated that the Transformer fusion algorithm effectively reduced relative bias and improved correlation with ground data. The scheme which incorporated environmental factors outperformed the other, which only considered precipitation characteristics, achieving higher estimation accuracy and fusion stability. Full article

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number of base stations deployed across substations and distribution networks has led to rising electricity expenditure, making cost-effective energy supply a critical challenge. To reduce the operating costs of base station clusters and enhance the economic efficiency of power supply, this paper proposes a multimodal power consumption optimization method that coordinates wind energy, solar energy, and energy storage based on user interaction behavior. First, considering user interaction characteristics and the complementarity of multiple energy sources, a dual-layer cellular network architecture consisting of macro- and micro-base stations is constructed. This architecture incorporates grid power purchases, wind power generation, and photovoltaic energy. An optimization model is then developed, which includes both equipment operation constraints and energy interaction constraints. Second, the key factors influencing energy consumption are analyzed using operational research methods. The existence of an optimal solution for the energy consumption function is demonstrated based on the Weierstrass optimization theorem. An energy-saving strategy for base stations under user group access is then derived using Karush–Kuhn–Tucker (KKT) conditions. Through spatio-temporal (ST) dynamic analysis, the coupling relationships among wind power, solar energy, energy storage, and grid electricity purchases are quantified. Based on this analysis, a multimodal cost optimization scheme utilizing dynamic bandwidth allocation is proposed. Simulation results demonstrate that, compared with traditional single-source power supply models and representative existing optimization schemes, the proposed multimodal energy scheduling framework can significantly reduce the operating cost of base station clusters while maintaining communication performance. Full article

As the scale of the Industrial Internet continues to expand, the number of network connections and data traffic are experiencing explosive growth. Security threats and attack types targeting the Industrial Internet are becoming increasingly complex, rendering traditional firewalls and encryption/decryption technologies inadequate for addressing diverse and sophisticated attack scenarios. Furthermore, traffic characteristics within the Industrial Internet environment exhibit significant asymmetry, such as a highly imbalanced distribution between benign and malicious traffic. To address this challenge, this paper proposes CBiNet—a hybrid deep learning model that integrates a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM). Designed to effectively learn and leverage such asymmetric spatio-temporal patterns, experimental validation demonstrates that the CBiNet model can efficiently tackle complex traffic identification tasks in industrial internet environments. It provides a highly accurate, scalable intrusion detection method for securing industrial internet gateways. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

Background: Static coronal alignment is considered a key of lower limb biomechanics after total knee replacement (TKR); however, its relationship with dynamic foot loading patterns and gait characteristics remains unclear. The primary objective of this prospective study was to investigate whether there is a correlation between dynamic plantar pressures and spatiotemporal parameters of gait and the coronal alignment of the lower limb after unilateral TKR for primary knee osteoarthritis (KOA). Methods: Thirty-two consecutive patients scheduled for TKR were evaluated preoperatively and at six months postoperatively. Changes in plantar pressure distribution and spatiotemporal gait parameters were collected using a multiplatform plantar pressure analysis system (PPAS), while coronal alignment was assessed using the femorotibial angle (FTA). Relationships with preoperative, postoperative, and correction-related alignment measures were examined using non-parametric statistical methods. Results: Dynamic plantar pressures and spatiotemporal gait parameters were not found to be consistently associated with pre- or postoperative values of FTA, respectively. Furthermore, the degree of correction did not appear to influence baropodometric outcomes. Conclusions: Static coronal alignment, as defined by the FTA, was not found to be consistently associated with dynamic plantar pressure patterns or spatiotemporal gait parameters at six months following unilateral TKR in our study population. These findings highlight the potential limitations of using solely static radiographic markers to evaluate complex functional outcomes such as gait. Full article

As a vital ecological security barrier and climate regulator in northwestern China, the spatial patterns and evolving formation mechanisms of ecosystem services within the Qinghai Lake basin hold significant strategic value for ecological conservation and national park development in the region. This study selected land use data during 2000–2020, integrating the equivalent factor method, spatial correlation analysis, and the geodetector approach to systematically investigate the spatial heterogeneity characteristics of ESV in the Qinghai Lake basin and its corresponding driving mechanisms. The results indicate the following: (1) During the period 2000–2020, grassland consistently constituted the primary land cover category within the Qinghai Lake Basin, accounting for over 60% of the total area; water bodies (16.67%) and unused land (16.56%) represented the secondary land use categories. Over this twenty-year period, the total ESV exhibited a slight increasing trend, rising from USD 30.30 × 10⁸ to USD 30.75 × 10⁸, representing a growth of 0.31%. Regulating services constituted the primary component of ESV. The highest contribution to ESV originated from water bodies, with grassland ranking second. (2) ESV displayed a spatial arrangement marked by “high values in the lake center and low values in the surrounding areas” and “higher values in the southeast and lower values in the northwest.” Its spatial correlation exhibits a pronounced positive relationship. The number of units classified as high-high clusters (primarily water bodies at low elevations) and low-low clusters (mainly grasslands and unused land at high elevations) both increased over the study period, indicating a continuous intensification of ESV spatial agglomeration. (3) Results from the geographical detector reveal that both natural and anthropogenic factors collectively drive the spatial variation in ESV, with natural factors exhibiting stronger explanatory capacity. Among these, elevation and temperature are identified as the dominant drivers of ESV spatiotemporal differentiation. The combined effect of two interacting factors surpasses the influence exerted by any single factor in isolation. This research clarifies that the spatial distribution of ESV in the Qinghai Lake Basin, which features “high values in the lake center and low values in the surrounding areas” as well as “higher values in the southeast and lower values in the northwest,” is jointly shaped by the combined control of vertical zonality governed by topographic and climatic factors and the spatial differentiation of human activities. In low-altitude lakeshore zones, ESV rose as a consequence of water body expansion and the enforcement of ecological conservation measures, leading to the emergence of high-value clusters. In contrast, ESV improvement in high-elevation regions remained limited, constrained by fragile natural conditions and minimal human intervention. The insights derived from this research offer a scientific foundation for refining the “one core, four zones, one ring, multiple points” functional zoning framework of the Qinghai Lake National Park, as well as for developing tailored management approaches suited to distinct elevation-based regions. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

This study is essential for medium- and long-term land-use management, as land-use patterns directly influence local economic and social development. Geographic Information System (GIS) techniques are fundamental tools for analyzing a wide range of geomorphological processes, including relief fragmentation density, relief energy, soil texture, slope gradient, and slope orientation. The present research focuses on the Pesceana river basin in the Southern Carpathians, Romania. It addresses three main objectives: (1) to analyze land-use dynamics derived from CORINE Land Cover (CLC) data between 1990 and 2018, along with the long-term distribution of the Normalized Difference Vegetation Index (NDVI) for the period 2000–2025; (2) to evaluate the basin’s natural potential byintegrating topographic data (contour lines and profiles) with relief fragmentation density, relief energy, vegetation cover, soil texture, slope gradient, aspect, the Stream Power Index (SPI), and the Topographic Wetness Index (TWI); and (3) to assess the spatial distribution of habitat types, characteristic plant associations, and soil properties obtained through field investigations. For the first two research objectives, ArcGIS v. 10.7.2 served as the main tool for geospatial processing. For the third, field data were essential for geolocating soil samples and defining vegetation types across the entire 247 km² area. The spatiotemporal analysis from 1990 to 2018 reveals a landscape in which deciduous forests clearly dominate; they expanded from an initial area of 80 km² in 1990 to over 90 km² in 2012–2018. This increase, together with agricultural expansion, is reflected in the NDVI values after 2000, which show a sharp increase in vegetation density. Interestingly, other categories—such as water bodies, natural grasslands, and industrial areas—barely changed, each consistently representing less than 1 km² throughout the study period. These findings emphasize the importance of land-use/land-cover (LULC) data within the applied GIS model, which enhances the spatial characterization of geomorphological processes—such as vegetation distribution, soil texture, slope morphology, and relief fragmentation density. This integration allows a realistic assessment of the physical–geographic, landscape, and pedological conditions of the river basin. Full article

Exploring the spatiotemporal evolution of building stock in African countries is of great significance for understanding the urbanization process, regional development disparities, and sustainable development pathways in the Global South. Integrating long-term (1975–2020), 100 m resolution building stock data for Tanzania with multi-source environmental and socioeconomic datasets, this study employed GIS spatial analysis techniques—including optimized hotspot analysis, standard deviational ellipse, and geographical detector—to investigate the spatiotemporal evolution characteristics and influencing factors of building differentiation. The results indicate that over the 45-year period, Tanzania’s building stock underwent rapid expansion, with a 3.83-fold increase in volume and a 4.93-fold increase in area, while the average height decreased continuously by 1.04 m. This growth was predominantly driven by the expansion of residential buildings. The spatial distribution of buildings exhibited a “north-dense, south-sparse” pattern with agglomeration along traffic axes. During 1975–1990, building growth hotspots were concentrated in western and southern regions, shifting to areas surrounding Lake Victoria and central administrative centers during 2005–2020. In contrast, coldspots expanded progressively from northern, northeastern regions and Zanzibar Island to parts of the southern and eastern coasts. The building distribution consistently maintained a northwest–southeast spatial orientation, with increasingly prominent directional characteristics; the centroid of building distribution moved more than 90 km northwestward, and the agglomeration intensity continued to increase. Socioeconomic factors—including population density, road network density, and GDP density—have a significantly stronger influence on building distribution than natural factors. Among natural factors, only river network density exhibits a significant effect, while constraints such as slope and terrain relief are relatively insignificant. Full article

Regional management of the urban thermal environment is essential for sustainable development. However, both the surface urban heat island (SUHI) spatiotemporal patterns and driving mechanisms across humid–arid regions remain uncertain. Therefore, 329 cities from various humid–arid regions were selected to investigate the interannual, seasonal, and diurnal distribution characteristics of SUHIs across regions. By constructing six-dimensional influencing factors and using CatBoost-SHAP and SEM methods, the contributions and action pathways of these factors to SUHIs were analyzed across humid–arid regions. The influence mechanisms, differences in feature importance, and similarities and discrepancies in action pathways were thoroughly examined. The findings are as follows: 1. During the day, higher SUHII values occur in humid and semihumid regions, exceeding those in arid and semiarid regions by 1.521 and 0.921, respectively. At night, arid and semiarid regions exhibit UHI effects (SUHII > 0). The SUHI distribution across humid–arid regions demonstrates seasonal variations. 2. ΔSA and ΔNDVI are stable dominant influencing factors across all regions. The contribution rank varies along the humid–arid region: Pollution factors are more important in arid and semiarid regions, whereas surface features and 2D/3D dominate in humid and semihumid regions at night. 3. SUHI regulation by influencing factors across humid–arid regions follows both similar paths and regional variations. This study reveals the SUHI distribution across humid–arid regions and provides reference data for regional thermal environment management. Full article

The persistent expansion in the intensity and scope of human disturbance has become a key driver of global biodiversity loss, affecting wildlife behavior and population stability across multiple dimensions. As a characteristic symbiotic assemblage in the subalpine forest ecosystems of Sichuan, the giant panda (Ailuropoda melanoleuca), red panda (Ailurus fulgens), and forest musk deer (Moschus berezovskii) exhibit significant research value in their responses to human disturbance. However, existing studies lack systematic analysis of multiple disturbances within the same protected area. This study was conducted in the Sichuan Liziping National Nature Reserve, where infrared camera traps were deployed using a kilometer-grid layout. By integrating spatiotemporal pattern analysis and Generalized Additive Models (GAM), we investigated the characteristics of human disturbance and the response strategies of the three species within their habitats. The results show that: (1) A total of seven types of human disturbance were identified in the reserve, with the top three by frequency being cattle disturbance, goat disturbance, and walking disturbance; (2) Temporally, summer and winter were high-occurrence seasons for disturbance, with peaks around 12:00–14:00, while the giant panda exhibited a bimodal diurnal activity pattern (10:00–12:00, 14:00–16:00), the red panda peaked mainly at 8:00–10:00, and the forest musk deer preferred crepuscular and nocturnal activity—all three species displayed activity rhythms that temporally avoided peak disturbance periods; (3) Spatially, giant pandas were sparsely distributed, red pandas showed aggregated distribution, and forest musk deer exhibited a multi-core distribution, with the core distribution areas of each species spatially segregated from high-disturbance zones; (4) GAM analysis revealed that the red panda responded most significantly to disturbance, the giant panda showed marginal significance, and the forest musk deer showed no significant response. This study systematically elucidates the spatiotemporal differences in responses to multiple human disturbances among three sympatric species within the same landscape, providing a scientific basis for the management of human activities, habitat optimization, and synergistic biodiversity conservation in protected areas. It holds practical significance for promoting harmonious coexistence between human and wildlife. Full article

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions. Full article