Remote Sensing Applications-Society and Environment最新文献

{"title":"Detecting sinkholes and land surface movement in post-mining regions using multi-source remote sensing data","authors":"Sebastian Walczak,&nbsp;Wojciech T. Witkowski,&nbsp;Tomasz Stoch,&nbsp;Artur Guzy","doi":"10.1016/j.rsase.2025.101560","DOIUrl":"10.1016/j.rsase.2025.101560","url":null,"abstract":"<div><div>This study investigated vertical land movement and sinkhole formation in the 40 km² abandoned “Siersza” mine area, Poland, by integrating multi-source remote sensing data and precise levelling measurements. First, publicly available InSAR products from the European Ground Motion Service (EGMS) were used to characterize broad-scale uplift and residual subsidence following mine closure. The EGMS “Ortho” and “Calibrated” datasets showed close agreement in regions with coherent scatterers, although the Calibrated data offered a higher spatial density. Second, airborne laser scanning (ALS) provided fine-scale insights into localized terrain changes, enabling the detection of around 60 % of documented sinkholes at a 0.90 probability threshold when compared to a field inventory. In contrast, comparisons with precise levelling revealed strong correlations (approximately 0.90–0.97) between EGMS and levelling data, but minimal concordance with ALS-derived displacements, likely due to the short-term, localized signals captured by ALS. A consistent offset of roughly 7 mm/year emerged between the satellite-derived and levelling measurements, indicating that some nominally stable geodetic reference points may be moving. Finally, the spatial distribution of vertical land movements was analysed concerning sinkhole centroids, showing that deformations become more variable within 200–300 m, reflecting overlapping regional uplift and localized subsidence linked to sinkhole formation. Overall, these findings highlight the importance of combining InSAR and ALS data for effective detection of early sinkhole-related hazards and emphasize the need to verify reference benchmark stability in post-mining environments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101560"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ocean subsurface temperature prediction using an improved hybrid model combining ensemble empirical mode decomposition and deep multi-layer perceptron (EEMD-MLP-DL)","authors":"A.R. Malavika ,&nbsp;Maya L. Pai ,&nbsp;Kavya Johny","doi":"10.1016/j.rsase.2025.101556","DOIUrl":"10.1016/j.rsase.2025.101556","url":null,"abstract":"<div><div>Ocean Subsurface Temperature (ST) has emerged as a critical factor in understanding global climate change. The penetration of warming signals from the oceanic surface to the deeper layers of oceans necessitates the development of prompt and effective predictive strategies for climate modelling. Recognizing the critical role of ocean ST in climate across the globe, the paper employs a hybrid approach integrating Ensemble Empirical Mode Decomposition (EEMD) and deep Multi-Layer Perceptron (MLP) to predict the ST at different depths. The study utilizes both ocean and atmospheric parameters like sea surface temperature, humidity, pressure, wind speed and heat fluxes, providing a comprehensive framework for assessing the intricate relationships between ocean layers and the atmosphere. The paper compares two methodologies: EMD with MLP of Single Layer (EMD-MLP-SL) and the proposed model EEMD with MLP of Deep Layers (EEMD-MLP-DL) for predicting the ST in the Arabian Sea for depths ranging from 5m to 967m. The results highlight the improved predictive capabilities of the proposed EEMD-MLP-DL methodology, achieving up to 95 % accuracy at 5m depth and maintaining robust performance at every depth, in contrast to the EMD-MLP-SL model. This paper highlights the importance of multifaceted approaches in oceanographic modelling and emphasizes the inclusion of more oceanic and atmospheric factors in understanding climate variability.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101556"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using remote sensing to assess vegetation dynamics in a hyper-arid region: The Arava valley as a case study","authors":"Ariel Mordechai Meroz ,&nbsp;He Yin ,&nbsp;Noam Levin","doi":"10.1016/j.rsase.2025.101550","DOIUrl":"10.1016/j.rsase.2025.101550","url":null,"abstract":"<div><div>Hyper-arid areas are characterized by high evaporation rates, low levels of precipitation, and significant intra-annual variation in both the quantity and timing of rainfall. While harsh desert conditions pose considerable challenges, the resilience of local vegetation indicates remarkable adaptation strategies. This study aimed to evaluate the response of vegetation cover to fluctuating rainfall amounts typical to the hyper-arid environment, using the Arava Valley (Israel/Jordan) as a case study. We analyzed a long-term time series (1984–2022) of monthly rainfall records to examine overall trends and identify distinct dry (drought) and wet periods, using the Standardized Precipitation Index (SPI). We used the Normalized Difference Vegetation Index (NDVI) derived from Landsat satellite imagery to quantify and monitor vegetation cover and its annual dynamics, and constructed proxies for perennial and annual vegetation based on their yearly phenological cycles. Our results revealed no clear statistical long-term trend in rainfall amounts; however, we identified transitions between wet and dry sub-periods occurring in clusters spanning several years. Vegetation cover aligned with rainfall patterns; no distinct long-term trend was seen but clear declines in vegetation cover and subsequent recoveries corresponded to rainfall amounts. When assessing vegetation responsiveness to the fluctuating conditions, we identified a time lag of two to four years between the response of annual and perennial vegetation during transitions between contrasting sub-periods. The year-to-year correlation between rainfall and yearly vegetation cover was strongest when averaging rainfall over two consecutive years for annual vegetation cover (∼0.45–0.65), and three to four consecutive years for perennial vegetation cover (∼0.52–0.79), highlighting the significant influence of past years' conditions on yearly vegetation cover. By integrating long-term remote sensing satellite imagery and climatic records, we were able to uncover the complexity of rainfall-vegetation dynamics and the remarkable resilience of natural desert vegetation in the extreme conditions of hyper-arid environments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101550"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Method for assessing volumetric solar potential within urban street canyons","authors":"Teresa Santos ,&nbsp;Márcia Matias ,&nbsp;Jorge Rocha ,&nbsp;Killian Lobato","doi":"10.1016/j.rsase.2025.101564","DOIUrl":"10.1016/j.rsase.2025.101564","url":null,"abstract":"<div><div>While rooftops have been extensively studied for their photovoltaic (PV) potential, the volumetric space between buildings remains largely unexplored. This study introduces a replicable framework to quantify solar radiation within this unoccupied urban volume. The methodology leverages widely available city-scale datasets (e.g., LIDAR data, elevation contours, and building footprints) and accessible software to generate virtual surfaces at incremental heights between buildings. These surfaces serve as the basis for calculating solar insolation at 30-min intervals. The approach is demonstrated using neighbourhoods with differing urban morphologies to showcase its applicability across various contexts. This framework produces detailed insolation maps, revealing how volumetric solar radiation varies with urban form and time of year. The use of city-scale datasets makes this approach particularly suited for planning at the urban scale, enabling urban planners to identify optimal locations for PV installations, enhance urban thermal comfort, and improve street luminosity. The primary contribution of this study lies in the accessibility and generalizability of the methodology, which can be applied to support urban design decisions where solar insolation is a critical factor. By addressing the underexplored volumetric solar potential, this study provides actionable tools for advancing urban sustainability.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101564"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatially explicit assessment of carbon storage and sequestration in forest ecosystems","authors":"Bruna Almeida ,&nbsp;Luís Monteiro ,&nbsp;Rafaela Tiengo ,&nbsp;Artur Gil ,&nbsp;Pedro Cabral","doi":"10.1016/j.rsase.2025.101544","DOIUrl":"10.1016/j.rsase.2025.101544","url":null,"abstract":"<div><div>Forests play an important role in the global carbon cycle, making accurate assessments of carbon dynamics essential for effective forest management and climate change mitigation strategies. This research examines the spatiotemporal patterns of carbon storage and sequestration (CSS) in forests' aboveground biomass using satellite data, machine learning (Support Vector Machines), carbon modelling and spatial statistics. The methodology follows a two-step classification process: (i) binary forest classification and (ii) forest type classification, mapping seven forest types within two main categories - Broadleaves (<em>Quercus suber, Quercus ilex, Eucalyptus</em> sp., and other species) and Coniferous (<em>Pinus pinaster, Pinus pinea,</em> and other species). We analyzed the relationship between forest type and CSS at the Nomenclature of Territorial Units for Statistics (NUTS) III level and identified spatial clusters, outliers, and hot and cold spots of carbon sequestration at the municipal level across mainland Portugal. The broadleaved category demonstrated the highest classification accuracy in both years, decreasing slightly from 90.3 % in 2018 to 89 % in 2022, while the Coniferous group had the lowest accuracy, declining from 84.1 % in 2018 to 83.6 % in 2022. Anselin's Local Moran's I identified clusters of carbon sequestration, while the Getis-Ord Gi analysis confirmed these findings, revealing statistically significant hotspots of carbon sequestration in the northern and central regions and cold spots in the southern region. By providing insights at the sub-regional and municipal levels, this study offers a robust framework to support sustainable forest management and climate change mitigation strategies. Moreover, it can assist decision-makers in prioritizing natural capital, and developing nature-based solutions to tackle climate change and biodiversity loss.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101544"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Remote Sensing and machine learning for dynamic burn probability mapping in data-limited contexts","authors":"Diego Díaz-Vázquez ,&nbsp;Luis Fernando Casillas-García ,&nbsp;Alejandro Garcia- Gonzalez ,&nbsp;Sergio Humberto Graf Montero ,&nbsp;José Isaac Márquez Rubio ,&nbsp;Juan José Llamas Llamas ,&nbsp;Misael Sebastian Gradilla Hernandez","doi":"10.1016/j.rsase.2025.101554","DOIUrl":"10.1016/j.rsase.2025.101554","url":null,"abstract":"<div><div>Effective Burn probability mapping is crucial for proactive fire management and enhancing firefighting efficiency. Typically, these maps rely on static variables like topography, vegetation density, and fuel availability. Dynamic data sources such as remote sensing data offer precise, easy-access information for structuring dynamic Burn probability assessment tools. This study introduces a remote sensing-based Burn probability prediction model tailored for the State of Jalisco, Mexico, leveraging satellite data and machine learning algorithms (Logistic regression, Random Forest, XGBoost) to support public policy development. The model utilizes multispectral datasets, local geographic information, and algorithms such as logistic regression and random forest to identify high-risk wildfire areas. All evaluated parameters presented significant differences between the Fire-Affected and Non-Fire-Affected groups. Both NDVI and NDWI presented strong correlations to the presence of fire events, with smaller dispersion values for Fire-Affected entries within the dataset compared to Non-Fire-Affected entries, indicating high potential for its use as predictor of Burn probability. The model delivers a robust decision support system by integrating climatic, topographical, and anthropogenic factors. The XGBoost model incorporating nine parameters, identified as the best-performing by a recursive feature elimination analysis, presented an AUC value of 0.96 and a Sensitivity of 0.9333. Our findings highlight that this approach effectively identifies high-risk areas, aiding in targeted policy interventions and resource allocation to mitigate wildfire impacts, and offering a low-cost alternative for Burn probability monitoring in developing countries and resource-restricted areas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101554"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing the dynamics of aboveground carbon stock in intertidal seagrass meadows using Sentinel-2 time-series imagery","authors":"Pramaditya Wicaksono ,&nbsp;Amanda Maishella ,&nbsp;Ramadhan","doi":"10.1016/j.rsase.2025.101552","DOIUrl":"10.1016/j.rsase.2025.101552","url":null,"abstract":"<div><div>One of the challenges associated with the monitoring of seagrass meadows is the seasonal variability in percent cover, which is closely linked to the aboveground biomass carbon stock (AGC). To gain a comprehensive understanding of seagrass dynamics, it is essential to obtain spatial and temporal information on seagrass AGC. The most effective approach for mapping the dynamics of seagrass AGC is remote sensing; however, limited robustness of the mapping model limits their applicability across different locations. To address this issue, we developed a robust model for mapping seagrass AGC, with the objective of capturing the dynamics of seagrass AGC in intertidal seagrass meadows. Using seagrass field data and assuming that pure seagrass and sand pixels have 100 % and 0 % seagrass cover, respectively, we trained stepwise, machine learning (random forest, support vector machine, and multivariate adaptive regression spline), and deep learning (dense neural network) regression models to convert Sentinel-2 reflectance into seagrass AGC. The accuracy of the models was evaluated at multiple sites with available field data, and the results demonstrated an RMSE ranging from 6.28 to 13.97 g C m⁻² and a correlation coefficient between 0.69 and 0.83. Overall, the SVM regression model exhibited the highest accuracy. The SVM model was subsequently applied to 13 seagrass sites across Indonesia over a 36-month period, revealing consistent and recurring monthly and bimonthly AGC patterns. The majority of seagrass meadows exhibited their highest AGC during the May–June period and their lowest during the September–October period. This study also represents the first time-series mapping of seagrass AGC in Indonesia on a monthly and bimonthly basis, marking a significant advancement in understanding seagrass's potential as a blue carbon sink. Additionally, to achieve more accurate assessments of seagrass changes, it is crucial to account for the monthly and seasonal dynamics in seagrass growth patterns.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101552"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using satellite imagery to track the development of the green belt of Astana, Kazakhstan: A remote sensing perspective on artificial forestry development","authors":"Erin Driscoll ,&nbsp;Jorge Portugues Fernandez del Castillo ,&nbsp;Dana Bazarkulova ,&nbsp;Daniel Hicks ,&nbsp;Kirsten de Beurs","doi":"10.1016/j.rsase.2025.101543","DOIUrl":"10.1016/j.rsase.2025.101543","url":null,"abstract":"<div><div>The Astana green belt is an artificial forestry project initiated in 1999 to mitigate harsh climatic conditions and improve the local microclimate around Kazakhstan's capital. As part of the master plan of Astana, fields, or “patches” of green belt tree rows were designated for development around the periphery of the city. Using remote sensing techniques, we tracked the spatial and temporal development of the green belt patches over time, from initiation of the forestry efforts until present day. Simultaneously, we assess the effectiveness of these methods in capturing large-scale planned urban forest dynamics and explore how remote sensing can enhance our understanding of the long-term development and management practices of such projects. A temporal segmentation method was applied to identify initial forestry development in each green belt patch. Our findings show continuous planting efforts throughout the study period, resulting in significant greenery expansion. The spatial design was strategic, beginning with a central ring near the city and expanding outward, with planting directions of the tree rows optimized to counter prevailing winds and enhance windbreak functionality. No major areas of vegetation failures were observed. Notably, the current green belt has exceeded the boundaries outlined in the original master plan, indicating a broader scope of development. A preliminary investigation of winter land surface temperature (LST) change in the study area shows overall warming, with more pronounced temperature increases in some of the densely clustered plantations within the green belt.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101543"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case study of the effect of permafrost peat on fires in the Arctic using Sentinel-5P data","authors":"Margit Aun ,&nbsp;Jan-Peter George ,&nbsp;Kerstin Stebel","doi":"10.1016/j.rsase.2025.101540","DOIUrl":"10.1016/j.rsase.2025.101540","url":null,"abstract":"<div><div>Sentinel-5P data was used to conduct a case study of possible differences between permafrost peat and other types of wildfires in the Arctic. Ten fires from Canada, Russia, and Sweden were chosen from 2018 to 2023, with different permafrost peat fractions from 0 to 92 %. Concentrations and various ratios of CH₄, CO, NO₂, SO₂, aerosol index, and layer height above and in proximity to the starting locations of the fires were investigated to find the effect of permafrost peat on the fire emissions. We found higher CH₄ values for fires with higher than 50 % permafrost peat fraction and higher NO₂ concentrations for fires with the lowest permafrost peat fraction. Among other ratios, we also looked at CH₄/CO and CO/NO₂ ratios as indicators of peat presence. No statistically significant correlation with peat fraction was found in the first case, and in the latter case, there was not enough data available to draw any conclusions. Relying on our results and previous studies, we see the potential of using the concentrations and composition of the atmosphere above the fires as an indicator of the fire type. Due to the complicated conditions of the Arctic with high cloud cover and large variability in the fires (intensity, area, length, fuel types), a larger scale study is needed as a next step.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101540"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentinel-1 time-series SAR interferometry for understanding tropical peat surface oscillation","authors":"Yuta Izumi ,&nbsp;Wataru Takeuchi ,&nbsp;Albertus Sulaiman ,&nbsp;Joko Widodo ,&nbsp;Awaluddin Awaluddin ,&nbsp;Osamu Kozan ,&nbsp;Qoriatu Zahro","doi":"10.1016/j.rsase.2025.101541","DOIUrl":"10.1016/j.rsase.2025.101541","url":null,"abstract":"<div><div>Rapid degradation of tropical peatlands in Southeast Asia, driven by land conversion and drainage, has led to severe subsidence, forest fires, and carbon emissions, prompting restoration efforts to raise groundwater levels (GWL). Monitoring peatland surface displacement, including irreversible long-term subsidence and reversible oscillations, is crucial for assessing peat conditions and hydrology. Studies have shown peat surface oscillation (PSO) dynamics vary with peat degradation, highlighting their potential as indicators of restoration progress. This study explores the feasibility of large-scale PSO analysis in tropical peatlands in Kalimantan using a series of spaceborne synthetic aperture radar (SAR) data. We applied time-series interferometric SAR (TInSAR) analysis to three years of Sentinel-1 C-band SAR data to derive displacement time-series across the study area. The displacement data were further decomposed into long-term and short-term components using Seasonal-Trend decomposition based on Loess (STL) to estimate PSO. The estimated PSO was then compared with in-situ GWL data to analyze their relationship and reveal the oscillation coefficient, defined as the slope of this relationship. Our results revealed a statistically significant linear relationship between PSO and GWL dynamics, with correlation coefficients ranging from 0.23 to 0.8. The derived oscillation coefficients at in-situ locations indicated that peat elevation change accounted for 2.8 %–8.3 % of GWL variation. Additionally, the PSO amplitude was found to be greater in degraded peatlands than in less degraded ones. These findings highlight the potential of spaceborne SAR data to enhance understanding of PSO mechanisms and support effective evaluations of peatland restoration efforts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101541"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}