Remote Sensing Applications-Society and Environment最新文献

{"title":"Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)","authors":"Seyed Mohsen Mousavi","doi":"10.1016/j.rsase.2024.101406","DOIUrl":"10.1016/j.rsase.2024.101406","url":null,"abstract":"<div><div>Methane (CH₄) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH₄ concentration (XCH₄) at both national and international levels. This study investigates the correlation between atmospheric XCH₄ concentrations (XCH₄) and key influencing factors to identify the primary sources and sinks of CH₄ across the Middle East (ME). Initially, XCH₄ data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH₄ across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH₄ and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH₄ variations. The results reveal significant spatial and temporal variations in XCH₄ concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH₄, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH₄ concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH₄ and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH₄.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101406"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720418","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":"Examining the nexus of social vulnerability, land cover dynamics, and heat exposure in Reno, Nevada, USA","authors":"Consolata Wangechi Macharia,&nbsp;Lawrence Kiage","doi":"10.1016/j.rsase.2024.101400","DOIUrl":"10.1016/j.rsase.2024.101400","url":null,"abstract":"<div><div>Intense heat is a persistent urban challenge whose impacts are detrimental to human health. Heat-related effects disproportionately impact underserved populations. Modification of urban landscapes through increased imperviousness intensifies surface temperatures, leading to heightened heat exposure risks. While climate adaptation efforts have advanced, they are inadequate in addressing the uncertainties of climate change and the long-term risks of climate-related hazards. In addition, despite the numerous heat vulnerability studies across U.S. cities, the City of Reno is largely understudied. To address these gaps, the research examined the relationship between the spatiotemporal patterns of social vulnerability, changes in biophysical properties, and the heat hazard in Reno, Nevada. We utilized CDC census data to map the Social Vulnerability Index (SVI) and Landsat satellite data from 1990 to 2023 to analyze Land Surface Temperature (LST) trends for a temporal comparative study of heat patterns. Additionally, we employed the Normalized Difference Vegetation Index (NDVI) for vegetation extent. The zonal statistics tool helped assess the influence of different land use features on surface temperatures. The results showed that regions identified as social vulnerability hotspots often coincided with areas highly exposed to extreme temperatures and vice versa. Our findings also revealed an extension of heat vulnerability hotspots from the urban core to suburban regions. We observed a decline in mean LST values in regions covered by vegetation and a rise in mean surface temperatures in regions encompassed with imperviousness. These findings underscore the need for increased vegetation for heat mitigation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101400"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720417","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":"Integrated multi-satellite data and machine learning approach in mapping the successional stages of forest types in a tropical montane forest","authors":"Richard Dein D. Altarez ,&nbsp;Armando Apan ,&nbsp;Tek Maraseni","doi":"10.1016/j.rsase.2024.101407","DOIUrl":"10.1016/j.rsase.2024.101407","url":null,"abstract":"<div><div>Understanding the successional stages in tropical montane forests (TMF) is crucial for its conservation and management. This study integrated Sentinel-1, Sentinel-2, InSAR, GEDI, and machine learning to map the categorical successional stages of different forest types in a Philippines’ TMF. Field data collected from December 2022 to January 2023 were used to create and validate successional stages models. Sentinel-1 interferogram, unwrapped interferogram, and coherence exhibited moderate positive correlations with canopy height (r = 0.43). Incorporating GEDI with InSAR to predict canopy height yielded less accurate predictions (r = −0.20 to 0.04; RMSE = 12–13 m). Results show that canopy height, a widely accepted attribute for forest structure, appears secondary to other biophysical variables. Integrating optical, radar, and auxiliary variables achieved an overall accuracy of 79.56% and a kappa value of 75.74%. Feature importance analysis using Random Forest enhanced the overall accuracy (84.22%) and kappa value (81.19%). The integration of multi-satellite data with machine learning has proven effective for studying TMFs successional stages. Elevation emerged as the most significant predictor of forest type distribution, with mature and young pine forests dominating lower elevation (700–1,400m) and mossy forests dominating above 1,400m. Given the observed disturbances, the study underscores the need for robust conservation strategies and sustainable TMF management. Future research should focus on time-series analyses of successional stages, further optimization of machine learning models, and integrating additional data sources, such as LiDAR, to enhance canopy height predictions and forest monitoring efforts. The findings also provide valuable knowledge applicable to TMFs globally, supporting informed conservation and policies intended to protect biodiversity.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101407"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142748003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of the global operational geostationary meteorological satellites","authors":"Ram Kumar Giri ,&nbsp;Satya Prakash ,&nbsp;Ramashray Yadav ,&nbsp;Nitesh Kaushik ,&nbsp;Munn Vinayak Shukla ,&nbsp;P.K. Thapliyal ,&nbsp;K.C. Saikrishnan","doi":"10.1016/j.rsase.2024.101403","DOIUrl":"10.1016/j.rsase.2024.101403","url":null,"abstract":"<div><div>Geostationary meteorological satellite data and products are proven to be indispensable in operational weather monitoring and forecasting for various sectorial applications and disaster risk reduction due to their large spatial coverage and spatio-temporally consistent availability. The meteorological instruments such as imager or radiometer and atmospheric sounder onboard these satellites have gone through incremental advancement in terms of accuracy, stability, and resolutions. In addition, new meteorological instruments such as lightning detection and ocean monitoring payloads have been developed in the recent decades. This paper reviews brief history of the global operational geostationary meteorological satellites and onboard meteorological instruments. The capability of currently available operational geostationary meteorological satellites is also highlighted. In order to prepare a global climate data record of geostationary satellite observations, well-calibrated data are essentially required from each operational satellite. The calibration exercises taken up by several satellite agencies under the Global Space-based Inter-Calibration System, and development of global and regional long-term inter-calibrated geostationary climate data records are briefly discussed. Moreover, expected meteorological instruments onboard the proposed next-generation geostationary satellites from different satellite agencies across the globe are summarized.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101403"},"PeriodicalIF":3.8,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703690","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":"Analysis of spatiotemporal surface water variability and drought conditions using remote sensing indices in the Kagera River Sub-Basin, Tanzania","authors":"Nickson Tibangayuka ,&nbsp;Deogratias M.M. Mulungu ,&nbsp;Fides Izdori","doi":"10.1016/j.rsase.2024.101405","DOIUrl":"10.1016/j.rsase.2024.101405","url":null,"abstract":"<div><div>Drought is one of the major challenges affecting water resources, agriculture, and ecosystem resilience in the sub-Saharan region. This study analyzed the spatial and temporal variation of surface water and drought conditions in the Kagera sub-basin using remote sensing indices: the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI). The analysis covered the period from 1985 to 2020 at 5-year intervals. The Standardized Precipitation Index (SPI) was utilized to assess rainfall anomalies, which were then compared with surface water variability and drought intensity indicated by remote-sensing indices. The SPI revealed multiple instances of extreme and severe drought, with higher frequencies observed in the 3-month and 6-month SPI compared to the 12-month SPI. The NDWI revealed significant spatial and temporal variations in surface water area in the Kagera sub-basin. In general, surface water area showed a mixed trend, decreasing from 660 km² in 1985 to 632 km² in 2000, and then gradually increasing to 698 km² in 2020. Additionally, the NDWI exhibited a strong correlation with 3-month and 6-month SPI but a weaker correlation with 12-month SPI. On the other hand, the NDVI indicated significant variations in drought conditions, with areas experiencing severe drought ranging between 446 km² and 1892 km². These severe drought events were prevalent from 1990 to 2000. The results also indicated a strong correlation between drought extent and intensity extracted from NDVI and rainfall anomalies, with SPI-3 and SPI-6 showing stronger correlations compared to SPI-12. Moreover, the SAVI results were consistent with those of NDVI, suggesting that the soil brightness effect on the NDVI is not significant in the sub-basin. In contrast, NDMI indicated that severe drought areas generally increased over the analyzed years and exhibited a weak correlation with SPI for all time scales. These findings contribute valuable insights that are important for decision-makers in managing surface water resources and implementing proactive and targeted environmental conservation measures to enhance ecosystem resilience in the Kagera sub-basin.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101405"},"PeriodicalIF":3.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703692","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":"Assessing drivers of vegetation fire occurrence in Zimbabwe - Insights from Maxent modelling and historical data analysis","authors":"Upenyu Mupfiga ,&nbsp;Onisimo Mutanga ,&nbsp;Timothy Dube","doi":"10.1016/j.rsase.2024.101404","DOIUrl":"10.1016/j.rsase.2024.101404","url":null,"abstract":"<div><div>Vegetation fires are known to profoundly impact ecosystem structure and composition, posing threats to ecosystem stability and human safety. In Zimbabwe, uncontrolled fires have been recurrent, yet a rigorous analysis of the key drivers is still lacking. Previous studies in Zimbabwe have predominantly focused on spatio-temporal dynamics of the occurrence of vegetation fire, leaving a gap in understanding the underlying drivers. Accurate prediction of fire occurrence and identification of the major drivers is imperative for effective fire management strategies. The study employs the Maxent model, a machine-learning approach, to analyze historical MODIS fire data alongside bioclimatic, topographic, anthropogenic, and vegetation variables, to assess the likelihood of fire occurrence in Zimbabwe. The research also aims to elucidate the major factors that influence fire occurrence within the region. The independent contributions of predictor variables to the model's goodness of fit are evaluated using a jackknife test, while model accuracy is assessed using the AUC (area under the receiver operating characteristic curve). Results indicate that elevation, precipitation seasonality, temperature annual range and human footprint emerge as the major factors influencing fire occurrence in Zimbabwe. The model demonstrates an acceptable accuracy, with an average AUC of 0.77. This study underscores the utility of the Maxent model in elucidating the contributions of various environmental factors to vegetation fire occurrence. Moreover, the ability of the model to predict the probability of fire occurrence offers valuable insights for fire managers, facilitating the assessment of the spatial vulnerability of vegetation to fire occurrence. Overall, this research contributes to an improved understanding of the drivers of vegetation fires in Zimbabwe and provides a practical tool for enhancing fire management efforts in the region and beyond.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101404"},"PeriodicalIF":3.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel spatio-temporal vision transformer model for improving wetland mapping using multi-seasonal sentinel data","authors":"Mohammad Marjani ,&nbsp;Fariba Mohammadimanesh ,&nbsp;Masoud Mahdianpari ,&nbsp;Eric W. Gill","doi":"10.1016/j.rsase.2024.101401","DOIUrl":"10.1016/j.rsase.2024.101401","url":null,"abstract":"<div><div>Wetlands mapping using remote sensing data is a challenging task due to the spectral similarity of wetlands, the fragmented nature of these landscapes, and seasonal variations in wetlands. To address these limitations, this study proposes a novel spatio-temporal vision transformer (ST-ViT) model for an accurate wetland classification using seasonal data. The ST-ViT model was trained using multi-seasonal Sentinel-1 (S1) and Sentinel-2 (S2) data acquired during the spring, summer, and fall of 2020 in a study area located in Newfoundland and Labrador, Canada. The performance of the ST-ViT model was evaluated against the validation dataset, achieving an overall accuracy (OA) of 0.950 and F1-score (F1) of 0.934, outperforming other deep learning models such as random forest (RF), hybrid spectral network (HybridSN), etc. The model demonstrated strong classification capabilities among most wetland classes, with some challenges in distinguishing between spectrally similar classes like bogs and fens. Moreover, the integration of spatio-temporal features enabled the reduction of feature mixing between wetland classes, particularly during different seasons. The ST-ViT model provides an accurate wetland distribution map in different seasons, supporting critical decision-making processes related to wetland conservation and environmental monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101401"},"PeriodicalIF":3.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703691","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":"The Palu-Koro fault behaviour monitoring associated with the 2018 Palu earthquake based on the multi-temporal planetscope and Landsat 8 satellite images","authors":"Bondan Galih Dewanto ,&nbsp;Calvin Wijaya ,&nbsp;Ramadhan Priadi","doi":"10.1016/j.rsase.2024.101397","DOIUrl":"10.1016/j.rsase.2024.101397","url":null,"abstract":"<div><div>The Palu-Koro Fault on Sulawesi Island possesses an extensive record of earthquake-related activity, notably the Palu earthquake on September 28, 2018, which was particularly destructive. This study investigates the evolution of this fault by using high-resolution PlanetScope and Landsat 8 Operational Land Imager/Therma Infrared Sensor (OLI/TIRS) images. By investigating the interseismic, coseismic, and postseismic stages of the earthquake's habits, this paper aims to obtain an in-depth understanding of its behavior. The coseismic displacement analysis, which was carried out alongside the optical image correlation technique, indicated major displacements throughout the Palu-Koro Fault, with the largest displacement of roughly 7 m. To ensure the accuracy of the results, internal verification standards, such as a reliability criterion of &gt;30% and a mean structural similarity index (MSSIM) of 1, were used. Landsat 8 imagery was processed using the land surface temperature method to enhance the understanding of the earthquake phases. Prior to the earthquake, the results suggested a rise in temperature, which peaked during the coseismic phase and decreased progressively during the postseismic phase. Intriguingly, the temperature behavior revealed the possibility of using information from remote sensing as an alternative approach to identify the fault distribution in Palu City. Overall, this study demonstrates the utility of remote sensing data for analyzing the dynamics of the Palu-Koro Fault and understanding each stage of the 2018 Palu earthquake. By integrating high-resolution satellite imagery with sophisticated image processing techniques, this paper provides crucial insights into earthquake activity and its impact in this area.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101397"},"PeriodicalIF":3.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703693","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":"Shedding light on local development: Unveiling spatial dynamics from infrastructure implementation through nighttime lights in the Nacala corridor, Mozambique","authors":"Ricardo Gellert Paris ,&nbsp;Andreas Rienow","doi":"10.1016/j.rsase.2024.101388","DOIUrl":"10.1016/j.rsase.2024.101388","url":null,"abstract":"<div><div>The increased use of nighttime lights (NTL) to assess infrastructure implementation and socioeconomic development highlights the potential of this open data source, often used as a proxy indicator of economic dynamics. Many studies focus on supra-national levels and the quantification of light emissions, generating assumptions regarding development. However, fewer studies address the characterization of socio-spatial dynamics at the local level. This research analyses the Nacala corridor in Mozambique, aiming to challenge the assumption that increasing NTL levels equals local development. We qualify and contextualize the types of activities identified by nighttime light anomalies. Using data cubes with 10-year seasonal NTL emissions, we identified anomalies in the time series of 17 out of 74 settlements and subsequently analyzed them with very high-resolution images. Among these settlements, we identified soil extraction, quarrying, or industries in 13 cases. Finally, we compared the results with household surveys indicating that during the period, the population had no significant increase in access to energy. We conclude that the NTL time series can effectively portray infrastructure-driven activities, such as surface mining and industry, in the context of the Corridor. However, the assumption that local development is linked with an increase in NTL in non-urbanized areas can be misleading without qualitative analysis. The activities that are the source of radiance can be illicit, not socially adopted, economically concentrated, and/or environmentally harmful.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101388"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High spatial–temporal image fusion model for retrieving aerosol optical depth based on top-of-atmosphere reflectance","authors":"Chih-Yuan Huang ,&nbsp;Hsuan-Chi Ho ,&nbsp;Tang-Huang Lin","doi":"10.1016/j.rsase.2024.101402","DOIUrl":"10.1016/j.rsase.2024.101402","url":null,"abstract":"<div><div>With the growth in industrialization and urban development, air pollution has become an increasing serious health concern. Although ground stations can effectively monitor air quality, they generally observe only located phenomena and limited in the spatial distribution. Remote-sensing approaches have thus been employed by many scholars for air quality monitoring in an entire region. However, no single satellite equips with sufficient spatial and temporal resolutions for detecting rapidly changing local phenomena, such as air quality variations. A top-of-atmosphere reflectance–based spatial–temporal image fusion model (TOA-STFM) is proposed in this paper to solve this problem. The proposed TOA-STFM is modified based on the spatial–temporal adaptive reflectance fusion model (STARFM) and yields fused images in which atmospheric properties are retained. A key process in the TOA-STFM is blurring effect adjustment (BEA), which is performed to match the atmospheric effects caused by aerosols in images with different spatial resolutions. The feasibility of fusing Himawari-8 images with SPOT-6 images was evaluated in this study. We used the proposed model to extract aerosol optical depths (AODs) from images produced by fusing Himawari-8 and SPOT-6 images and compared the extracted AODs with corresponding in-situ observations made by the AErosol RObotic NETwork (AERONET). The AOD relative errors of the proposed TOA-STFM were 2.3%–7.6%, which is a significant improvement comparing to a relative error of 8.4%–13.5% from Himawari-8 images and existing AOD products.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101402"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702971","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}