The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences最新文献

{"title":"AEROSOL OPTICAL DEPTH FROM SPECTRAL DIRECT NORMAL IRRADIANCE MEASUREMENTS IN MONTEVIDEO, URUGUAY","authors":"P. Russo, A. Laguarda, G. Abal, L. Doppler","doi":"10.5194/isprs-archives-xlviii-m-1-2023-565-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-565-2023","url":null,"abstract":"Abstract. Aerosols are liquid or solid particles with diameters between 2.5 and 10 µm suspended in the lower layers of the atmosphere. Aerosol Optical Depth (AOD) is a relevant parameter that quantifies their concentration in the atmosphere. It is usually estimated from sun photometer measurements at specific wavelengths. The objective of this work is to implement a simple inversion algorithm to retrieve AOD at six different wavelengths (340, 380, 440, 500, 675 and 870 nm) using solar direct normal spectral irradiance ground measurements from a relatively low cost collimated spectroradiometer (EKO MS-711) at a low-altitude site in Montevideo, Uruguay. The results obtained are compared with AERONET products for the same site, including AOD and Angström coefficient. The results of AOD for all wavelengths show a consistent negative mean bias (MBD, unitless), between −0.005 and −0.015, and dispersion (RMSD, unitless) between 0.021 and 0.015 (to be compared to a mean reference AOD of 0.097). These metrics improve considerably for very clear days, MBD up to ± 0.001 and RMSD under 0.007 (to be compared to a mean reference AOD of 0.058). These results are considered to be a first step in implementing the methodology and acquiring local knowledge about AOD retrievals using relatively simple instrumentation.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48065131","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":"GLCM FEATURES FOR LEARNING FLOODED VEGETATION FROM SENTINEL-1 AND SENTINEL-2 DATA","authors":"B. Tavus","doi":"10.5194/isprs-archives-xlviii-m-1-2023-601-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-601-2023","url":null,"abstract":"Abstract. Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47248856","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":"BISTATIC SCATTERING CHARACTERISTICS OF A WIND PARK TURBINE DERIVED FROM AN UAV-MOUNTED RECEIVER RECORDING C-BAND WEATHER RADAR SIGNALS","authors":"E. Çolak, B. V. Patel, A. Vyas, R. Zichner, M. Chandra","doi":"10.5194/isprs-archives-xlviii-m-1-2023-485-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-485-2023","url":null,"abstract":"Abstract. As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48274292","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":"POINT-WISE CLASSIFICATION OF HIGH-DENSITY UAV-LIDAR DATA USING GRADIENT BOOSTING MACHINES","authors":"E. Sevgen, S. Abdikan","doi":"10.5194/isprs-archives-xlviii-m-1-2023-587-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-587-2023","url":null,"abstract":"Abstract. Point-wise classification of 3D point clouds is a challenging task in point cloud processing, whereas, in particular, its application to high-density point clouds needs special attention because a large number of point clouds affect computational efficiency negatively. Although deep learning based models have been gaining popularity in recent years and have reached state-of-the-art results in accuracy for point-wise classification, their requirements of the high number of training samples and computational resources make those models inefficient for high-density 3D point clouds. However, traditional machine learning classifiers require less training samples, so they are capable of reducing computational requirements, even considering the latest machine learning classifiers, particularly in ensemble learning of gradient boosting machines, the results can compete with deep learning models. In this study, we are studying the point-wise classification of high-density UAV LiDAR data and focusing on efficient feature extraction and a recent state-of-the-art gradient boosting machine learning classifier, LightGBM. Our proposed framework includes the following steps: at first, we are using point cloud sampling for creating sub-sampled point clouds, then we are calculating the features based on those scales implemented on GPU. Finally, we are using the LightGBM classifier for training and testing. For the evaluation of our framework, we used a publicly available benchmark dataset, Hessigheim 3D. According to the results, we achieved an overall accuracy of 87.59% and an average F1 score of 75.92%. Our framework has promising results and scores closer to deep learning models. However, more distinctive features are required to obtain more accurate results.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41274383","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 FEATURE-BASED DEEP LEARNING APPROACH FOR THE EXTRACTION OF GROUND POINTS FROM 3D POINT CLOUDS","authors":"Y. Dogan, A. O. Ok","doi":"10.5194/isprs-archives-xlviii-m-1-2023-503-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-503-2023","url":null,"abstract":"Abstract. Extracting ground points from 3D point clouds is important for sustainable development goals, infrastructure planning, disaster management, and more. However, the irregularity and complexity of the data make it challenging. Deep learning techniques, particularly end-to-end and non-end-to-end approaches, have shown promise for 3D point cloud segmentation and classification, but both require a comprehensive understanding of the features and their relationship to the problem. This paper presents a study on the filtering of 3D LiDAR point clouds into ground and non-ground points using a non-end-to-end deep learning approach. The aim of this research is to investigate the effectiveness of utilizing geometric features and a binary classifier-based deep learning model in accurately classifying point clouds. The publicly available ACT benchmark datasets were employed for training, validation, and testing purposes. The study utilized a k-fold cross-validation method to address the limited availability of training data. The results demonstrated highly satisfactory performance, with validation averages reaching 96.83% for the divided Dataset-1 and an accuracy of 97% for the test set. Furthermore, an independent dataset, Dataset-2, was used to evaluate the generalizability of the trained model, achieving an accuracy of 93%. These findings highlight the potential of the proposed non-end-to-end approach to filtering point cloud data and its applicability in various domains such as DEM and DTM production, city modeling, urban planning, and disaster management. Moreover, this study emphasizes the need for accurate data to achieve sustainable development goals, positioning the proposed approach as a viable option in various studies.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41684538","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":"OPTIMIZED ECOLOGICAL NETWORK APPROACH OF HIGHLY URBANIZED CITIES: THE CASE OF ADANA CITY","authors":"G. Kurt, M. Külahlıoğlu, S. Berberoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-553-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-553-2023","url":null,"abstract":"Abstract. One of the most significant challenges in urban areas, where the process of rapid urban expansion takes place, is the loss of agricultural lands and natural habitats. The conversion of these areas into residential and commercial zones leads to a decline in urban biodiversity and the progressive loss of vital habitat areas. Analyzing habitat connectivity and conducting landscape measurements provide valuable insights for the development of land use and management strategies, enhancing our understanding of the spatial structure of the landscape, and directing conservation efforts. Incorporating measures such as green corridors and landscape connection networks into urban planning management becomes crucial in order to mitigate the adverse effects of habitat fragmentation and enhance ecosystem resilience within cities. Remote sensing techniques offer opportunities to create habitat connectivity models that enable the quantitative and qualitative identification of fragmented habitat patches. These models serve as tools to evaluate the effectiveness of conservation measures and monitor the potential impacts of future land use changes on habitat networks. Within this context, an optimized approach to habitat connectivity is presented, aiming to contribute to landscape planning and ecological-based studies in a city with undergoing rapid urbanization like Adana. By identifying degraded areas and introducing new habitat patches, a significant improvement in the connectivity of the habitat network has been observed. The findings indicated that the addition of new habitat patches to degraded areas can substantially enhance the city's overall habitat connectivity.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48415941","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":"IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS","authors":"Ç. H. Karaman, Z. Akyurek","doi":"10.5194/isprs-archives-xlviii-m-1-2023-537-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-537-2023","url":null,"abstract":"Abstract. In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43389880","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":"MULTI-HAZARD SUSCEPTIBILITY ASSESSMENT WITH HYBRID MACHINE LEARNING METHODS FOR TUT REGION (ADIYAMAN, TURKIYE)","authors":"G. Karakas, S. Kocaman, C. Gokceoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-529-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-529-2023","url":null,"abstract":"Abstract. Recent Kahramanmaras earthquakes (Mw 7.7 and 7.6) occurred on 6 February 2023 have shown the importance of site selection for settlements and infrastructure considering the fact that multiple hazards may affect the same area and even interact with each other. The Kahramanmaras earthquakes triggered several landslides, which also increased the level of destruction. Here, we implemented a multi-hazard susceptibility assessment approach for Tut town of Golbasi, Adiyaman and its surroundings. Over 600 landslides were triggered in the area by the earthquakes. In addition, the region is prone to flooding and a devastating one occurred on March 15, 2023 after heavy rains. In this study, we employed co-seismic landslide inventory for landslide susceptibility assessment with random forest. Regarding flood susceptibility, a modified analytical hierarchical process was utilized based on expert opinion on factor importance. The earthquake hazard probability distribution was obtained from a distance-based interpolation of Arias intensity values. We utilized Mamdani Fuzzy Inference System for producing a multi-hazard susceptibility map from univariate maps of earthquake, landslide and flood. The result shows that the selected methods for each type of susceptibility map was suitable and the output of the study can be utilized for the site selection in Tut region, which is a crucial subject due to the need of new construction sites after the earthquakes.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42653961","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 THE IMPACT OF BEET WEBWORM MOTHS ON SUNFLOWER FIELDS USING MULTITEMPORAL SENTINEL-2 SATELLITE IMAGERY AND VEGETATION INDICES","authors":"S. Kara, B. Maden, B. Ercan, F. Sunar, T. Aysal, O. Saglam","doi":"10.5194/isprs-archives-xlviii-m-1-2023-521-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-521-2023","url":null,"abstract":"Abstract. Remote sensing technology plays a crucial role in detecting and monitoring environmental issues, offering the ability to monitor large areas, diagnose problems early, and facilitate accurate interventions. By integrating in-situ data with qualitative measurements obtained from satellite images, comprehensive insights can be obtained, and statistical inferences can be established. This study focuses on analyzing the damages caused by beet webworm moths (Loxostege sticticalis) in sunflower fields located in the Ortaca neighborhood of Tekirdağ province in Thrace region, utilizing Sentinel-2 satellite images and in-situ data collected from the sunflower fields in Ortaca. The relationship between different spectral indices, such as the Enhanced Vegetation Index, Chlorophyll Index Green, and spectral transformation techniques like Tasseled Cap Greenness, derived from Sentinel-2 satellite images, and the observed damage rates in various sunflower fields' in-situ data was investigated. The results revealed a negative correlation between the variables, highlighting EVI as the most effective indicator of damage among the plant indices. Leveraging these findings, a damage map was generated using EVI, enabling visual interpretation of the damage status in other sunflower fields within the study area. These findings offer valuable insights into the impact of pests on sunflower crops, despite the accuracy evaluation results falling below the desired level, with an overall accuracy of 75% and a Kappa accuracy of 65%, attributed to the limited availability of in-situ data.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47226172","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":"MONITORING THE SLOWLY DEVELOPING LANDSLIDE WITH THE INSAR TECHNIQUE IN SAMSUN PROVINCE, NORTHERN TURKEY","authors":"S. Coşkun, Ç. Bayık, S. Abdikan, T. Gorum, F. Balik Sanli","doi":"10.5194/isprs-archives-xlviii-m-1-2023-497-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-497-2023","url":null,"abstract":"Abstract. Landslides are prominent natural events with high destructive power. Since they affect large areas, it is important to monitor the areas they cover and analyse their movement. Remote sensing data and image processing techniques have been used to monitor landslides in different areas. Synthetic aperture radar (SAR) data, particularly with the Interferometric SAR (InSAR) method, is used to determine the velocity vector of the surface motion. This study aims to detect the landslide movements in Samsun, located in the north of Turkey, using persistent scattering InSAR method. Archived Copernicus Sentinel-1 satellite images taken between 2017 and 2022 were used in both descending and ascending directions. The results revealed surface movements in the direction of the line of sight, ranging between −6 and 6 mm/year in the study area. Persistent Scatterer (PS) points were identified mainly in human structures such as roads, coasts, ports, and golf courses, especially in settlements. While some regions exhibited similar movements in both descending and ascending results, opposite movements were observed in some regions. The results produced in both descending and ascending directions were used together and decomposed into horizontal and vertical deformation components. It was observed that the western coastal part experienced approximately 4.5 cm/year vertical deformation, while the central part there is more significant horizontal deformation, reaching up to approximately 6 cm/year.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48322335","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}