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

{"title":"ANALYZING THE RETRIEVAL ACCURACY OF OPTICALLY ACTIVE WATER COMPONENTS FROM SATELLITE DATA UNDER VARYING IMAGE RESOLUTIONS","authors":"F. Sunar, A. Dervisoglu, N. Yagmur, E. Aslan, M. Ozguven","doi":"10.5194/isprs-archives-xlviii-m-1-2023-595-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-595-2023","url":null,"abstract":"Abstract. Water quality monitoring has a key role in maintaining a sustainable ecosystem and environmental health. To ensure consistent monitoring, remote sensing provides regular data acquisition with varying spatial resolutions. However, more accurate, and effective solutions can be achieved by integrating remote sensing data with in-situ measurements. This study investigates the integration of in-situ measurements with satellite data, which have different spectral and spatial resolutions, using linear and exponential regression models for four optically active components in the Gulf of Izmit. In this context, Sentinel-2 (S2) and PlanetScope SuperDove (PS) multispectral images, which were acquired on the same date, were used for the comparative analysis of the accurate mapping of chlorophyll-a (Chl-a), turbidity, Secchi disk depth (SDD) and total suspended matter (TSM) water quality parameters combined with simultaneously collected in-situ measurements. The models were evaluated using validation data, along with visual comparison, to assess their accuracy. The results indicate that, overall, exponential models provide more accurate results than linear models, except for the SDD parameter. Furthermore, models created with S2 data demonstrate better performance in retrieving water quality parameters for Chl-a, turbidity, and TSM, with R2 values of 0.71, 0.84, and 0.91, respectively. The linear model created with PS data stands out in the accurately mapping of SDD parameter. Nevertheless, the spatial distribution of these parameters using both satellite dataset exhibits a similar pattern throughout the gulf, which is under threat from significant terrestrial pollution sources, particularly in the eastern part.\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":"43829794","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":"SPATIAL AND TEMPORAL ANALYSIS OF POLLUTANT GASES IN WESTERN BLACK SEA OF TURKIYE","authors":"D. Arıkan, F. Yildiz","doi":"10.5194/isprs-archives-xlviii-m-1-2023-463-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-463-2023","url":null,"abstract":"Abstract. Environmental pollution, particularly air pollution, is one of the foremost problems we face today. Air pollution has become a global issue that affects not only regional areas but also the entire planet. The increase in the amount and concentration of pollutants or harmful substances in the atmosphere, such as various gases, particulate matter, and water vapor, causes air pollution. The rise in these substances can be due to human activities or natural environmental factors. It is crucial to examine air quality to reduce the harm inflicted on living and non-living entities. In this study, the spatial and temporal analysis of air pollutants (CO, NO2, UV_AER) in the Western Black Sea region was conducted using the Sentinel-5 TROPOMI satellite sent to monitor climate change and air quality. The Google Earth Engine platform was used to obtain the data. Monthly pollution maps were created for the year 2022, and the primary sources of pollutants were analysed. As a result, it was observed that pollutants changed on a monthly and seasonal basis, and areas with high pollutant concentrations in the region were identified. Mining, industrial activities, transportation networks, and domestic activities were determined to be the primary sources of air pollution in the study area.","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":"44532694","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 URBAN LAND USE CHANGE USING REMOTE SENSING AND DIFFERENT CHANGE DETECTION TECHNIQUES: THE CASE OF ANKARA PROVINCE","authors":"M. Gurbuz, A. Çilek","doi":"10.5194/isprs-archives-xlviii-m-1-2023-515-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-515-2023","url":null,"abstract":"Abstract. This study aims to use remote sensing techniques to map the urban region of Ankara from the past to the present, assessing the nature, magnitude and direction of changes within the area, including the transformation of LULC classes and explaining the driving forces behind these transformations. The study encompasses three stages. Firstly, Landsat 7 ETM+ images from 2000 and Sentinel-2 satellite images from 2020 were obtained for Ankara city and surroundings through the Google Earth Engine (GEE) platform. Image classification was conducted for both 2000 and 2020 using 'Blue', 'Green', 'Red', 'Vegetation Red Edge1', 'Vegetation Red Edge2', 'Vegetation Red Edge3', 'NIR', 'Vegetation Red Edge4', 'Water vapour', ' SWIR1', 'SWIR2' bands, as well as 'NDWI', 'NDVI', 'NDBI' indices on the GEE platform. LULC was classified using the Random Forest (RF) classifier, which included six classes: urban area, forest, water surfaces, open areas, agricultural areas and roads. Secondly, the LULC maps of the 2000 and 2020 images were classified using RF. The study employed the 'Categorical Change, Pixel Value Change and Time Series Change' methods to determine the transformations between LULC categories. Specifically, the urban change within the study area increased by 70% between 2000 and 2020. Over the past 20 years, from 2000 to 2020, the urban areas in Ankara expanded by 170%. Consequently, accurately determining the nature, magnitude and direction of urban development using remote sensing data offers valuable baseline information for various disciplines related to spatial planning at local and national scales.\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":"46833097","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":"DETECTION OF DISCOMFORT INDEX WITH REMOTE SENSING TECHNOLOGY: THE CASE OF ANTALYA PROVINCE","authors":"M. Şahingöz, S. Berberoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-573-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-573-2023","url":null,"abstract":"Abstract. Thermal adaptation and thermal comfort indices are critical in determining the thermal comfort of the outdoor environment. They also play an essential role in research on heat stress, an environmental threat that can affect individuals' productivity, health and even survival. Urban growth and the resulting expansion of impervious surfaces affect the thermal characteristics of a landscape by raising Land Surface Temperatures (LST). The resulting warming can lead to thermal discomfort, the prevalence of heat-related health problems, air pollution, increased water use and energy demand for air conditioning, among others. Recently, efforts to understand the effects of urbanization and landscape changes on indoor and outdoor temperatures have increased significantly. Together with remote sensing technology, this study aims to understand human heat stress, and geographic information system (GIS) is a tool used in the research. In the estimation of heat stress, besides temperature, physiological status, environmental impact and relative humidity factors are also important. The discomfort index (DI) is a heat stress indicator proposed by Thom (1959), which expresses the contribution of air temperature and relative humidity to human thermal comfort. The discomfort index proposed by Thom (1959) was calculated as DI=0.5Ta+0.5Tw (Ta: dry bulb temperature, Tw: wet bulb temperature) modified by SOHAR, Adar and Laky (1963). In the study, the dry bulb temperature, assumed to be equal to the air temperature, was taken monthly from MODIS LST data at 1km resolution. Relative humidity was produced by interpolating 73 meteorological data in the study area at 1km resolution. Wet bulb temperature is difficult to measure, so it was calculated from dry bulb temperature and relative humidity data so that the discomfort index as a measure of heat stress in the study area was calculated with a resolution of 1 km. The discomfort index was calculated monthly and annually and classified according to Thom's 4 comfort classes. According to the calculation results, Antalya's average discomfort index value for the whole year is 24.9 °C, indicating that Antalya is a moderately comfortable place. This value varies monthly, especially in April and October when the heat stress is the highest.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70624599","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":"DEEP LEARNING BASED AERIAL IMAGERY CLASSIFICATION FOR TREE SPECIES IDENTIFICATION","authors":"O. Bayrak, F. Erdem, M. Uzar","doi":"10.5194/isprs-archives-xlviii-m-1-2023-471-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-471-2023","url":null,"abstract":"Abstract. Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively.\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":"41841249","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":"MODELING LAND DEGRADATION USING REMOTE SENSING DATA: THE CASE OF SEYHAN BASIN","authors":"T. Akın, S. Berberoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-449-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-449-2023","url":null,"abstract":"Abstract. Land degradation is a global barrier to ecological, economic and sustainable developments. Climate change, natural disasters, human activities may result changes in soil organic carbon content, land productivity and land use/cover. Climate change is accelerating and expanding these degraded areas. If land destruction is not minimized, cause increasing population, inappropriate land use, climate change and rapid depletion of natural resources etc. in the coming years. It is estimated that land degradation and desertification will be the most important environmental problems. Mapping of land degradation using remote sensing techniques; determining sensitive areas for land degradation and taking protection measures; sustainable management of natural resources, ensuring sustainable agricultural production, etc. are the key factors. This study was conducted in the Seyhan basin, which is suffer from soil loss processes, changes in land cover and land use. These indicators are; trends in land productivity dynamics, land cover change and change of soil organic carbon stocks. The data set utilized to reveal the land degradation was including; 1 km resolution Land Productivity from JRC GLOBAL (1999–2013) and 250 m resolution NDVI from MOD13Q1 (2000–2015), Land Cover ESA CCI's with 300 m resolution LC (2000–2015), SOC stock from LUCAS (JRC) with 250 m resolution, 2000–2018 data from CORINE. The land degradation of the Seyhan basin was mapped using the specified land degradation indicators together with the One Out All Out (1OAO) rule.\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":"43929895","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 EARTH OBSERVATION TO SUPPORT FIRST AID RESPONSE IN CRISIS SITUATIONS– LESSONS LEARNED FROM THE EARTHQUAKE IN TÜRKIYE/SYRIA (2023)","authors":"A. Schneibel, M. Gähler, M. Halbgewachs, R. Berger, J. Brauchle, M. Gessner, V. Gstaiger, D. Hein, C. Henry, N. Merkle, D. Klein","doi":"10.5194/isprs-archives-xlviii-m-1-2023-579-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-579-2023","url":null,"abstract":"Abstract. In the early morning hours on Tuesday, February 6, 2023, the southern part of Türkiye was struck by two large and several smaller earthquakes, causing destruction and casualties over a remarkably large area. In such cases, quick response and well-informed coordination is a key factor to successful first aid responses since damage and the number of people buried or in need often remain unclear in the hours after the disaster. The German Aerospace Center (DLR) responded to the earthquake by rapidly providing a number of information products, all above very high-resolution imagery in an easy-to-use web-based application. Next to satellite and drone imagery, damage information and pre-disaster imagery were provided to the users. Drone imagery was acquired in person for Kirikhan, a city in the south of the disaster area. Access to the viewer was granted to authorized users from public authorities, humanitarian aid organisations, and research institutes. Furthermore, DLR generated information products in the fields of settlement pattern, AI based damage assessment and tectonic movements. These data, as scientifically significant as they are, were not part of the web viewer. Within this paper, the reasons will be assessed as well as the general workflow of the activation. The paper will also discuss what steps need to be taken to ensure research outcomes being integrated into information products for users in future and how to prepare for the next disaster to still ensure a quick response but with an enriched product suite.\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":"41967316","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":"SPATIO-TEMPORAL ANALYSIS OF THE EFFECTS OF URBAN GROWTH ON URBAN HEAT ISLAND: CASE OF KONYA, TURKIYE","authors":"H. B. Akdeniz","doi":"10.5194/isprs-archives-xlviii-m-1-2023-441-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-441-2023","url":null,"abstract":"Abstract. The increase in impermeable surfaces within the urban areas contributes to local and regional-scale climate changes. This phenomenon, called \"Urban Heat Island,\" is observed as the temperature in urban areas is higher than rural areas and natural landscape areas on the urban fringe. In recent years, advances in remote sensing and geographic information system technologies have enabled the urban heat island effect to be determined more quickly, economically, and accurately. In this study, the rapidly increasing urbanization in Konya, Türkiye and the resulting urban heat island effect have been analyzed. The study consists of four steps. In the first step, land surface temperatures for 1990 and 2022 of Konya city center were determined using the thermal band of Landsat-5 TM and Landsat-8 OLI satellite images. Then, satellite images were classified using the maximum likelihood method to determine land use and land cover in Konya. The effects of land use types and urban growth on urban heat island were examined. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-Up Index (NDBI) analyses were examined the statistical relationships between land surface temperature. The last step, the urban heat island effects of different types of regions in the city center of Konya were determined based on their urban form, texture, structure, landscape, and planning strategy. As a result of the study, measures that can be taken especially in spatial planning and design policies have been identified to reduce and prevent the urban heat island in Konya.\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":"42440429","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 COMPREHENSIVE ANALYSIS OF THE SPATIO-TEMPORAL VARIATION OF SATELLITE-BASED AEROSOL OPTICAL DEPTH IN MARMARA REGION OF TURKIYE DURING 2000–2021","authors":"P. Ettehadi Osgouei, S. Kaya","doi":"10.5194/isprs-archives-xlviii-m-1-2023-509-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-509-2023","url":null,"abstract":"Abstract. This study investigates the spatiotemporal variability of the aerosol optical depth (AOD) in the atmosphere over the Marmara region, Turkiye. Long-term satellite observations from MODIS MAIAC AOD data spanning the period from 2000 to 2021 are utilized. Examining the temporal variations in AOD in the Marmara region, it is observed that AOD reaches its peak during spring (May) and summer (August) months, while lower AOD values are observed in winter. Specifically, between August and December, there is a significant decline in monthly mean AOD which is majorly due to particulate removal from the atmosphere via precipitation scavenging. The findings reveal that the inter-annual variability of monthly AOD variations in the Marmara region is primarily influenced by temporary Saharan dust transportation with highest deviations from 22 year averaged AOD in late winters and early springs. The findings from the analysis of seasonal spatial variation of high AOD values revealed that the high AOD area is largest in the summer with about 54% of the total area and then spring (45%) and autumn (26%). Winter has the lowest HVA with 17% of the total area. The seasonal percentage rates of HVA are due to atmospheric conditions and aerosol sources. Larger HVA in summer is due to the increase of farming practices and biomass residue burnings combined with high moisture absorption effects and high temperature. The heating-specific emissions are the main source of anthropogenic emissions over the high AOD areas during the autumn and winter and aerosols are concentrated over the urbanized centres and industrialized zones.\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":"45393007","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":"CYCLE-GAN BASED FEATURE TRANSLATION FOR OPTICAL-SAR DATA IN BURNED AREA MAPPING","authors":"E. Çolak, F. Sunar","doi":"10.5194/isprs-archives-xlviii-m-1-2023-491-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-491-2023","url":null,"abstract":"Abstract. For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas.\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":"49434847","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}