Remote Sensing Applications-Society and Environment最新文献

{"title":"Spatial multi-criteria decision analysis on underground water harvesting using PV water pumping system","authors":"Amirali Mahjoob ,&nbsp;Younes Noorollahi ,&nbsp;Hossein Yousefi ,&nbsp;Andreas Ulbig","doi":"10.1016/j.rsase.2025.101602","DOIUrl":"10.1016/j.rsase.2025.101602","url":null,"abstract":"<div><div>In Iran, groundwater resources are essential for agricultural irrigation, but their sustainable use requires efficient energy solutions. This study introduces an innovative approach for evaluating the feasibility of photovoltaic water pumping systems (PVWPS) using Geographic Information System (GIS) and Analytical Hierarchy Process (AHP). By integrating spatial analysis and multi-criteria decision-making, the methodology identifies suitable locations for PVWPS implementation across Iran. The study incorporates hydrological, solar, and land-use data to generate a comprehensive suitability map, addressing energy demand, solar radiation, well characteristics, and land constraints. Results indicate that approximately 12.1 % of related lands are suitable for PVWPS, with the northwestern and southwestern regions being the most favorable due to optimal water and solar conditions. The study highlights the potential of PVWPS as a sustainable solution for irrigation in arid regions and offers a scalable framework for similar analyses globally. This research underscores the need for targeted investments and policies to promote renewable energy in agriculture, particularly in regions with high groundwater dependency.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101602"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231697","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-2 vs. PlanetScope: Comparison and combination for tree species classification in two central European forest ecosystems","authors":"Ardalan Daryaei ,&nbsp;Michael Lechner ,&nbsp;Anna Iglseder ,&nbsp;Lars T. Waser ,&nbsp;Markus Immitzer","doi":"10.1016/j.rsase.2025.101617","DOIUrl":"10.1016/j.rsase.2025.101617","url":null,"abstract":"<div><div>The increasing rate of species extinction and declining environmental conditions necessitate a comprehensive understanding of habitats, including tree species diversity, which is a critical factor influencing forest ecosystem functions. Traditional methods of acquiring information on tree species, like forest inventories and field-based approaches, are often time-intensive, costly, and impractical for large-scale applications, making remote sensing a feasible alternative. This study compared and combined two multispectral remote sensing datasets, including Sentinel-2 (S2) and PlanetScope (PS), for tree species classification in two Austrian forest ecosystems: the riparian forests of the National Park Donau-Auen (NPDA), where nine tree species were distinguished, and the forests of the Biosphere Reserve Wienerwald (BRWW) where 12 species were investigated. Mono-temporal and multi-temporal data from S2 and PS were analyzed individually and in combination (S2 + PS). A robust reference dataset (835 samples in NPDA and 1283 in BRWW) and a Random Forest algorithm with recursive feature selection were used for classifications. When comparing mono-temporal datasets, S2 consistently outperformed PS, achieving the highest overall accuracies of 63.7 % for NPDA and 70.6 % for BRWW, compared to 58.1 % and 57.4 % with PS. Using multi-temporal S2 data further enhanced classification accuracy, reaching 78.3 % for NPDA and 83.3 % for BRWW, while multi-temporal PS data achieved 74.4 % and 77.7 %, respectively. Combining datasets in NPDA demonstrates an improvement of 1.8 and 5.7 percentage points compared to the sole use of S2 and PS multi-temporal data, respectively. In BRWW, the improvement was 1.3 and 6.9 percentage points. Classification accuracies were higher in BRWW, likely due to its larger reference dataset and the inclusion of more phenologically and morphologically distinct tree species. Overall, this study highlighted the superior performance of S2, particularly in mono-temporal analyses, the added value of combining S2 and PS datasets, and the well-known advantages of using multi-temporal datasets. Notably, the study fairly distinguished between three closely related Poplar species, including <em>Populus alba</em>, <em>Populus × canadensis</em>, and <em>Populus nigra,</em> in riparian forests of NPDA, which is also of great interest from a nature conservation perspective. The outputs of this study can provide helpful information for new satellite missions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101617"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281013","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":"Dynamic vertical land motion driven by climate and humans in the metropolitan Taipei Basin","authors":"Pei-Chin Wu ,&nbsp;Meng (Matt) Wei ,&nbsp;Jyr-Ching Hu ,&nbsp;Steven D'Hondt ,&nbsp;Hsin Tung ,&nbsp;Shao-Hung Lin ,&nbsp;Christopher J. Russoniello","doi":"10.1016/j.rsase.2025.101622","DOIUrl":"10.1016/j.rsase.2025.101622","url":null,"abstract":"<div><div>This study investigates basin-wide vertical ground motion in metropolitan Taipei between 2009 and 2023 using geodetic data. Despite seasonal variations of ∼1 cm, we observed two major subsidence episodes: a 2-cm subsidence during 2009–2012 followed by complete recovery within a year, and a 2.5-cm subsidence during 2018–2022 with subsequent 2-cm rebound. Analysis of rainfall data, groundwater levels, and construction activities reveals that these deformation patterns correlate with both climate factors (drought and rainfall) and human activities (major construction projects). On the first order, the basin-wide deformation are controlled by climate, subsiding in dry years and uplift in wet years. However, areas undergoing intensive redevelopment exhibited 0.5–1 cm greater net subsidence than surrounding regions, indicating irreversible deformation in these zones due to the combined effects of construction-related groundwater extraction, increased surface loading from new buildings, and soil compaction during development activities. These findings highlight the complex interplay between natural and anthropogenic factors in urban ground deformation and emphasize the need for integrated water-management strategies in the context of climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101622"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281014","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}

{"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":"Traditional agroecosystems for urban temperature regulation: a remote sensing analysis of an historical palm grove","authors":"Ignacio Melendez-Pastor","doi":"10.1016/j.rsase.2025.101569","DOIUrl":"10.1016/j.rsase.2025.101569","url":null,"abstract":"<div><div>The current expansion of urban areas means that an ever-increasing population is affected by urban heat islands (UHI). Different strategies have been developed to mitigate the effects of UHI, such as the implementation of new urban green areas. However, before the expansion of green areas, it was common to see agroecosystems that have been systematically transformed into built-up areas. Fortunately, there are still traditional agroecosystems, such as the World Heritage Palm Grove (WHPG) of Elche (Spain), whose effect on urban temperature regulation requires evaluation. A time series of satellite remote sensing images was used to analyse the dynamics of land surface temperature (LST). Different statistical procedures (e.g., Kruskall-Wallis test, Friedman test) were used to determine the temperature attenuation effect throughout the year by the diverse land covers and green areas. Significant differences in LST between the agroecosystem conserved within the WHPG and the rest of the city were observed, with their cooling effect extending several hectometers around its perimeter. It was shown that the date palm grove and its traditional irrigation system have a significant regulatory effect on the LST and, consequently, on the attenuation of heat islands. This study highlights the need to conserve or regenerate traditional agroecosystems within cities, since in addition to being adapted for centuries to existing environmental conditions, they provide numerous ecosystem services and improve natural temperature regulation in urban environments. The traditional agroecosystem of the Elche Palm Grove has a significant thermal regulation capacity and is highly adapted to the limited water resources typical of semi-arid areas.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101569"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881738","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":"Ratoon rice mapping based on Sentinel-1 and Sentinel-2 imagery","authors":"Vidya Nahdhiyatul Fikriyah ,&nbsp;Roshanak Darvishzadeh ,&nbsp;Alice Laborte ,&nbsp;Andrew Nelson","doi":"10.1016/j.rsase.2025.101592","DOIUrl":"10.1016/j.rsase.2025.101592","url":null,"abstract":"<div><div>Rice ratooning has gained increasing interest in Asia as a way to boost rice production by allowing two rice harvests from a single growing season. Accurate mapping of this practice can improve rice production estimates. However, current efforts have mainly relied on optical sensors, which are limited by cloud cover, especially during the wet season when ratooning is common. This study systematically assessed the use of optical Sentinel-2, Synthetic Aperture Radar (SAR) Sentinel-1 data and their combination to map ratoon rice crops. Field data were collected in four provinces of the Philippines in 2018–19. Backscatter intensity from Sentinel-1, spectral information, and six commonly used vegetation indices (VIs) from Sentinel-2 were analysed using the Mann-Whitney <em>U</em> significance test to examine differences between the main and ratoon rice crops. Next, we compared the classification performance of decision tree (DT), support vector machine (SVM), and random forest (RF) classifiers. Results show that ratoon and main rice crop significantly differed in VV and VH polarisations, red edge and near-infrared bands, and all VIs. The highest accuracy was achieved with selected features in an RF classifier (overall accuracy of 92 %), compared to SVM (87 %) and DT (81 %). Classification using features from both Sentinel-1 and 2 consistently yielded higher accuracy than using features from one sensor alone. The total planting of ratoon rice was estimated at approximately 223 km² (±4 % of the wet season rice area). This study demonstrates the value of combining SAR Sentinel-1 and optical Sentinel-2 for ratoon rice mapping.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"38 ","pages":"Article 101592"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099064","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}