Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820262
Jing Wang, Kun Yu, Miao Tian, Zhiming Wang
Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of key phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. With China Remote Sensing career advancement, a large number of independent researches and development satellites have launched. Among a new generation of middle to high resolution satellites, HJ-1 stands out. It sets fine spatial resolution (30 m), multi-spectral and high temporal resolution (2-day for constellation) with 360 km swath in a fusion technology with strategic significance. The time-series vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and the 2-band Enhanced Vege-tation Index (EVI2) are widely used in the studies of crop land classification, plant productivity, phenology, and crop growth monitoring. It has been shown that VIs values are relatively insensitive to the differences in angular viewing factors and atmospheric disturbances and thereby can be used as a benchmark for direct comparison between sensors. In order to explore the adaptability of Chinese HJ-1 images in rice phenological parameters extraction, two widely used VIs, NDVI and EVI2, were adopted to minimize the influence of environmental factors and the intrinsic difference among the sensor. Savitzky-Golay (S-G) filters were applied to construct continuous VI profiles per pixel. Before phenological parameters extraction, the planting area of single-cropped rice was estimated using a stepwise classification strategy. Divided by the heading date, the growth phases of single-cropped rice can be classified into vegetative growth and reproductive growth. Because the maximum VI usually appears around the heading date, we defined the heading date as the date of the maximum VI on the VI profile. In general, the rice fields are flooded before transplanting and the VI of rice fields decreases during this period and then increases after rice planting. Therefore, we defined the transplanting date of rice as the minimal point along the VI profile. Due to the etiolation and senescence of the rice leaves, the VI decreases after the heading, and the maturation date of rice is identified by the maximum slope method. The results were validated with the field survey data collected by the local agro-meteorological station. The results showed that, compared with NDVI, EVI2 was more stable. Compared with the observed phenological data of the single-cropped rice, the VI time-series had a low root mean square error (RMSE), and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where comple
{"title":"Estimation of rice key phenology date using Chinese HJ-1 vegetation index time-series images","authors":"Jing Wang, Kun Yu, Miao Tian, Zhiming Wang","doi":"10.1109/Agro-Geoinformatics.2019.8820262","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820262","url":null,"abstract":"Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of key phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. With China Remote Sensing career advancement, a large number of independent researches and development satellites have launched. Among a new generation of middle to high resolution satellites, HJ-1 stands out. It sets fine spatial resolution (30 m), multi-spectral and high temporal resolution (2-day for constellation) with 360 km swath in a fusion technology with strategic significance. The time-series vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI) and the 2-band Enhanced Vege-tation Index (EVI2) are widely used in the studies of crop land classification, plant productivity, phenology, and crop growth monitoring. It has been shown that VIs values are relatively insensitive to the differences in angular viewing factors and atmospheric disturbances and thereby can be used as a benchmark for direct comparison between sensors. In order to explore the adaptability of Chinese HJ-1 images in rice phenological parameters extraction, two widely used VIs, NDVI and EVI2, were adopted to minimize the influence of environmental factors and the intrinsic difference among the sensor. Savitzky-Golay (S-G) filters were applied to construct continuous VI profiles per pixel. Before phenological parameters extraction, the planting area of single-cropped rice was estimated using a stepwise classification strategy. Divided by the heading date, the growth phases of single-cropped rice can be classified into vegetative growth and reproductive growth. Because the maximum VI usually appears around the heading date, we defined the heading date as the date of the maximum VI on the VI profile. In general, the rice fields are flooded before transplanting and the VI of rice fields decreases during this period and then increases after rice planting. Therefore, we defined the transplanting date of rice as the minimal point along the VI profile. Due to the etiolation and senescence of the rice leaves, the VI decreases after the heading, and the maturation date of rice is identified by the maximum slope method. The results were validated with the field survey data collected by the local agro-meteorological station. The results showed that, compared with NDVI, EVI2 was more stable. Compared with the observed phenological data of the single-cropped rice, the VI time-series had a low root mean square error (RMSE), and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where comple","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121221462","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820638
Tian Tian, Dingran Wang, Xiaojuan Zhao
Forest is the largest terrestrial ecosystem on the earth. Quantitative evaluation of the impacts of the land surface slope on forest spatial distributions is of great significance for a deeper understanding of functions and stability of forest ecosystem, scientific planning and rational management of forest resources. The superposition analysis of map of vegetation and digital elevation model (DEM) is an effective method to study impacts of the land surface slope on forest spatial distributions. In the past time, the data of land surface slope was mainly obtained by field measurement which had some problems of time-consuming, labor-intensive and high investigation cost. With the development of space technology, DEM data can be used to obtain land surface slope data rapidly and efficiently which has been widely used in digital forestry construction. However, studies on the influence of land surface slope on forest spatial distribution by using DEM data are rare at home and abroad. Dali City of Yunnan Province was selected as the research area in this study. The contour line vector is used to establish DEM of this area, then collect the slope data and divide the slope grades into five groups: flat slopegentle slopemoderate slopesteep slope and sharp slope. Supervised classification and visual interpretation were executed to interpret and classify the map of vegetation of Dali. By putting map of forest distribution and DEM togetherthe relationship between forest distribution and slope was analyzed and the trend of forest spatial distribution was found out. The results showed that Dali city is relatively flat and the terrain is complex and diverse. The woodland has a large area distribution in Dali City, which is related to the monsoon climate of the subtropical plateau in Dali. The shrub forests were mainly distributed on moderate slope and steep slope, and the coniferous forests were mainly distributed on gentle slope and moderate slope. As the slope changes, the distribution of shrubberies increases and decreases sharply, indicating that shrubberies are highly dependent on slope. Coniferous forests have a large area distribution at each grade, indicating that they are less dependent on slope. In general, with the increase of slope, both forest types showed a trend of increasing first and then decreasing, indicating that the surface slope has an impact on the spatial distribution of forests. This study can offer reference to the rational and scientific management of forest resources.
{"title":"Impacts of the Land Surface Slope on Forest Spatial Distributions","authors":"Tian Tian, Dingran Wang, Xiaojuan Zhao","doi":"10.1109/Agro-Geoinformatics.2019.8820638","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820638","url":null,"abstract":"Forest is the largest terrestrial ecosystem on the earth. Quantitative evaluation of the impacts of the land surface slope on forest spatial distributions is of great significance for a deeper understanding of functions and stability of forest ecosystem, scientific planning and rational management of forest resources. The superposition analysis of map of vegetation and digital elevation model (DEM) is an effective method to study impacts of the land surface slope on forest spatial distributions. In the past time, the data of land surface slope was mainly obtained by field measurement which had some problems of time-consuming, labor-intensive and high investigation cost. With the development of space technology, DEM data can be used to obtain land surface slope data rapidly and efficiently which has been widely used in digital forestry construction. However, studies on the influence of land surface slope on forest spatial distribution by using DEM data are rare at home and abroad. Dali City of Yunnan Province was selected as the research area in this study. The contour line vector is used to establish DEM of this area, then collect the slope data and divide the slope grades into five groups: flat slopegentle slopemoderate slopesteep slope and sharp slope. Supervised classification and visual interpretation were executed to interpret and classify the map of vegetation of Dali. By putting map of forest distribution and DEM togetherthe relationship between forest distribution and slope was analyzed and the trend of forest spatial distribution was found out. The results showed that Dali city is relatively flat and the terrain is complex and diverse. The woodland has a large area distribution in Dali City, which is related to the monsoon climate of the subtropical plateau in Dali. The shrub forests were mainly distributed on moderate slope and steep slope, and the coniferous forests were mainly distributed on gentle slope and moderate slope. As the slope changes, the distribution of shrubberies increases and decreases sharply, indicating that shrubberies are highly dependent on slope. Coniferous forests have a large area distribution at each grade, indicating that they are less dependent on slope. In general, with the increase of slope, both forest types showed a trend of increasing first and then decreasing, indicating that the surface slope has an impact on the spatial distribution of forests. This study can offer reference to the rational and scientific management of forest resources.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379166","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820245
J. Mohite, S. Sawant, Mariappan Sakkan, Praveen Shivalli, Krishnaiah Kodimela, S. Pappula
Rice is a staple food across the majority of the world’s population that is expected to exceed 9 billion by 2050 and will require approximately 60% more food. In season and accurate information on the spatiotemporal distribution of rice cultivation, phenology across the region and spatial distribution of yield is of significant importance. This information is used by various stakeholders such as government, policymakers, insurance companies, and agri-input companies. Methods involving manual surveys for developing spatial crop yield are constrained by short harvest window and availability of the skilled human resource. Estimation of regional crop yield with precision and accuracy requires the use of high-resolution remote sensing data. The key contribution of this study is the spatial estimation of rice yield by assimilation of parameters derived from Synthetic Aperture RADAR (SAR) data from Sentinel-1 satellite into a process-based Oryza crop growth simulation model. The study has been carried out in four districts of coastal Andhra Pradesh, India viz., Guntur, Krishna, East Godavari and West Godavari during monsoon season locally called Kharif (mid-Jun. to midDec.) 2018. In the study area, rice is transplanted during mid-Jun to Aug. end and harvested from Oct. to mid-Dec. months. The methodology for in-season regional rice area estimation using random forest classifier has been described in our previous work. This study provides insights into the estimation of rice crop phenology and Leaf Area Index (LAI) using early time series of Sentinel-1 SAR observations. The rice phenology parameter such as Start of the Season (SoS) is estimated using Sentinel-1 SAR time series available during Jun.-Sept. 2018. The pixel-wise SoS estimation method comprises finding the local minima from the time series and image compositing. Total of six different SoS estimates is considered to cover early and late transplanted areas. The equation presented in literature has been used to estimate LAI from VH backscatter. Further, to facilitate the compute-intensive crop growth simulation task and cover maximum variation, the estimated LAI was categorized into five classes. Other datasets required for crop growth simulation such as weather was obtained from NOAA. A lookup table based approach was used wherein yield simulations were generated considering five SoS classes, five LAI classes, and four weather combinations. The total of 120 yield simulations were finally mapped to each pixel’s SoS, LAI, and weather categories. The plot-wise crop yield data for fifty-two (52) plots was collected for independent validation of yield estimates. The comparison of simulated and actual yield showed Normalized Root Mean Squared Value (NRMSE) of 9.21%. The overall agreement between actual and simulated yield is 83-89%. The results showed that spatialization of crop growth simulation for yield estimation using remote sensing observations provides fairly accurate yield estimates. Also, it
{"title":"Spatialization of rice crop yield using Sentinel-1 SAR and Oryza Crop Growth Simulation Model","authors":"J. Mohite, S. Sawant, Mariappan Sakkan, Praveen Shivalli, Krishnaiah Kodimela, S. Pappula","doi":"10.1109/Agro-Geoinformatics.2019.8820245","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820245","url":null,"abstract":"Rice is a staple food across the majority of the world’s population that is expected to exceed 9 billion by 2050 and will require approximately 60% more food. In season and accurate information on the spatiotemporal distribution of rice cultivation, phenology across the region and spatial distribution of yield is of significant importance. This information is used by various stakeholders such as government, policymakers, insurance companies, and agri-input companies. Methods involving manual surveys for developing spatial crop yield are constrained by short harvest window and availability of the skilled human resource. Estimation of regional crop yield with precision and accuracy requires the use of high-resolution remote sensing data. The key contribution of this study is the spatial estimation of rice yield by assimilation of parameters derived from Synthetic Aperture RADAR (SAR) data from Sentinel-1 satellite into a process-based Oryza crop growth simulation model. The study has been carried out in four districts of coastal Andhra Pradesh, India viz., Guntur, Krishna, East Godavari and West Godavari during monsoon season locally called Kharif (mid-Jun. to midDec.) 2018. In the study area, rice is transplanted during mid-Jun to Aug. end and harvested from Oct. to mid-Dec. months. The methodology for in-season regional rice area estimation using random forest classifier has been described in our previous work. This study provides insights into the estimation of rice crop phenology and Leaf Area Index (LAI) using early time series of Sentinel-1 SAR observations. The rice phenology parameter such as Start of the Season (SoS) is estimated using Sentinel-1 SAR time series available during Jun.-Sept. 2018. The pixel-wise SoS estimation method comprises finding the local minima from the time series and image compositing. Total of six different SoS estimates is considered to cover early and late transplanted areas. The equation presented in literature has been used to estimate LAI from VH backscatter. Further, to facilitate the compute-intensive crop growth simulation task and cover maximum variation, the estimated LAI was categorized into five classes. Other datasets required for crop growth simulation such as weather was obtained from NOAA. A lookup table based approach was used wherein yield simulations were generated considering five SoS classes, five LAI classes, and four weather combinations. The total of 120 yield simulations were finally mapped to each pixel’s SoS, LAI, and weather categories. The plot-wise crop yield data for fifty-two (52) plots was collected for independent validation of yield estimates. The comparison of simulated and actual yield showed Normalized Root Mean Squared Value (NRMSE) of 9.21%. The overall agreement between actual and simulated yield is 83-89%. The results showed that spatialization of crop growth simulation for yield estimation using remote sensing observations provides fairly accurate yield estimates. Also, it","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124270389","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820253
Jiahui Sheng, Peng Rao, Hongliang Ma
Soil moisture (SM) plays a vital role in regulating the feedback between the terrestrial water, carbon, and energy cycles. However, the passive microwave SM product can hardly satisfy many applications, owing to their coarse spatial resolution. In this study, a random forest (RF) -based downscaling approach was applied to downscale the FY3B L2 soil moisture data from 25 -km to 1 -km, synergistically using the optical and thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS). The RF algorithm used various surface variables to construct the SM relationship model, such as surface temperature, leaf area index, albedo, water index, vegetation index, and elevation, comparing with the widely used polynomial-based relationship model. The correlation coefficient (R) and the root-mean-square deviation (RMSD) of RF-based method reached 0.93 and 0.051 m3/m3, respectively. Four blends of data were used to retrieve the downscaled SM through the RF-based downscaling method. The downscaling results were validated by the in-situ soil moisture from REMEDHUS network. The temporal changing pattern of the downscaled SM was assessed with the precipitation time series. This study suggests that the RF-based downscaling method can characterize the variation of SM and is helpful to improve accuracy of the passive microwave SM product.
{"title":"Spatial Downscaling of the FY3B Soil Moisture Using Random Forest Regression","authors":"Jiahui Sheng, Peng Rao, Hongliang Ma","doi":"10.1109/Agro-Geoinformatics.2019.8820253","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820253","url":null,"abstract":"Soil moisture (SM) plays a vital role in regulating the feedback between the terrestrial water, carbon, and energy cycles. However, the passive microwave SM product can hardly satisfy many applications, owing to their coarse spatial resolution. In this study, a random forest (RF) -based downscaling approach was applied to downscale the FY3B L2 soil moisture data from 25 -km to 1 -km, synergistically using the optical and thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS). The RF algorithm used various surface variables to construct the SM relationship model, such as surface temperature, leaf area index, albedo, water index, vegetation index, and elevation, comparing with the widely used polynomial-based relationship model. The correlation coefficient (R) and the root-mean-square deviation (RMSD) of RF-based method reached 0.93 and 0.051 m3/m3, respectively. Four blends of data were used to retrieve the downscaled SM through the RF-based downscaling method. The downscaling results were validated by the in-situ soil moisture from REMEDHUS network. The temporal changing pattern of the downscaled SM was assessed with the precipitation time series. This study suggests that the RF-based downscaling method can characterize the variation of SM and is helpful to improve accuracy of the passive microwave SM product.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121965039","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820645
Ying Zhang, Di Wang, Qingbo Zhou
Wetlands and oceans, forests are the three major ecosystems and it is one of the most important living environment of human being. Wetland change has a great impact on climate change, biodiversity, economic development, etc. It is of great significance for wetland conservation and management to study the change of wetland landscape pattern and the causes of the change. At present, the research on wetland landscape pattern change mainly focuses on the spatial-temporal law and driving force of wetland landscape change, but the relationship between landscape pattern index and driving factor needs to be further studied. In this paper, the Shengjin Lake wetland in Chizhou City, Anhui Province, was selected as the research object. Using the Landsat TM remote sensing data from 1993, 2000, 2008 and 2016, the maximum likelihood classification was used to extract the wetland. The landscape evaluation indexes such as patch density, maximum patch index, incense density evenness index, dominance degree and aggregation degree were selected to study the temporal and spatial changes of wetland area in this area by using landscape pattern index analysis method. The main causes of wetland change were also analyzed. The results show (1) The change of wetland area showed an upward trend from 1993 to 2000. The wetland area decreased continuously in 2000-2008 and 20082016, and the decline was smaller in 2008-2016. The wetland landscape area in 2000 was the largest, and the area was 144.973 km2. (2) From 1993 to 2016, the area of wetland and the largest patch in Shengjin Lake area first increased and then decreased, and the maximum patch index reached 10.55% in 2008. The shape of the patch was complicated, and the degree of human disturbance gradually increased. The landscape dominance of Shengjin Lake wetland is higher, but it shows the trend of decreasing first and then rising. (3) The influence of man-made interference on the area connectivity of the landscape wetland in the Lijin Lake area is weakened firstly, the influence of manmade interference on the shape of the wetland patch is large, and the main cause of the change of the wetland landscape area in the Shengjin Lake area is also the main reason. It is found that the wetland area of Shengjin Lake has been decreasing continuously since 2000, which is mainly influenced by human activities. What needs to be studied is the mechanism of interaction between landscape pattern and man-made disturbance in this area. It provides important basis for protection and management of Shengjin Lake wetland.
{"title":"Landscape Pattern Change of Shengjin Lake Watland from 1993 to 2016 and its Response to Human Disturbance","authors":"Ying Zhang, Di Wang, Qingbo Zhou","doi":"10.1109/Agro-Geoinformatics.2019.8820645","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820645","url":null,"abstract":"Wetlands and oceans, forests are the three major ecosystems and it is one of the most important living environment of human being. Wetland change has a great impact on climate change, biodiversity, economic development, etc. It is of great significance for wetland conservation and management to study the change of wetland landscape pattern and the causes of the change. At present, the research on wetland landscape pattern change mainly focuses on the spatial-temporal law and driving force of wetland landscape change, but the relationship between landscape pattern index and driving factor needs to be further studied. In this paper, the Shengjin Lake wetland in Chizhou City, Anhui Province, was selected as the research object. Using the Landsat TM remote sensing data from 1993, 2000, 2008 and 2016, the maximum likelihood classification was used to extract the wetland. The landscape evaluation indexes such as patch density, maximum patch index, incense density evenness index, dominance degree and aggregation degree were selected to study the temporal and spatial changes of wetland area in this area by using landscape pattern index analysis method. The main causes of wetland change were also analyzed. The results show (1) The change of wetland area showed an upward trend from 1993 to 2000. The wetland area decreased continuously in 2000-2008 and 20082016, and the decline was smaller in 2008-2016. The wetland landscape area in 2000 was the largest, and the area was 144.973 km2. (2) From 1993 to 2016, the area of wetland and the largest patch in Shengjin Lake area first increased and then decreased, and the maximum patch index reached 10.55% in 2008. The shape of the patch was complicated, and the degree of human disturbance gradually increased. The landscape dominance of Shengjin Lake wetland is higher, but it shows the trend of decreasing first and then rising. (3) The influence of man-made interference on the area connectivity of the landscape wetland in the Lijin Lake area is weakened firstly, the influence of manmade interference on the shape of the wetland patch is large, and the main cause of the change of the wetland landscape area in the Shengjin Lake area is also the main reason. It is found that the wetland area of Shengjin Lake has been decreasing continuously since 2000, which is mainly influenced by human activities. What needs to be studied is the mechanism of interaction between landscape pattern and man-made disturbance in this area. It provides important basis for protection and management of Shengjin Lake wetland.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690452","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820212
Xiaomei Zheng, P. Song, Yingying Li, Kangyu Zhang, Huijuan Zhang, Li Liu, Jingfeng Huang
Locusta migratoria manilensis is one of the major migratory locusts in China which prefers phragmites australis (Cav.) Trin.ex Steudel (here after called reed). Locust damage is one of the major agricultural pests in the world which has a serious impact on agricultural production. With the development of optical remote sensing techniques, detection of plant diseases and pests by measurements of canopy spectra has been implemented on wheat, barely leaves, cotton, etc. However, rare studies have been focused on reed, especially on estimation of loss component caused by locust until now. Therefore, the objective of this study was to investigate hyperspectral characteristics of reed from ground level canopy spectral data by ASD FieldSpec® 3 Spectroradiometer and to establish loss estimation models based on a field simulated L. m. manilensis damage experiment. Up to now, Kenli District of Dongying City is an important region of locust monitoring and prevention in China. Therefore, we carried out the simulated damage experiment during July 2017 in Kenli district, Dongying city, Shangdong province of China. The simulated locust damage experiment was based on six simulated locust density levels and three different damage durations. According to the experiment schedule, hyperspectral field data were obtained in four times and corresponding aboveground biomass (AGB) were cut immediately after each of the three damage durations. Loss estimation models were based on 40 sample points between loss component of selected vegetation indices (including RVI, NDVI, GNDVI SAVI) and dry weight loss of green leaf of reed. The results indicated that: 1) After L. m. manilensis damage, reed canopy reflectance decreased in near infrared region whereas the gap between visible light and near infrared region was narrowed. Also, the more serious the damage, the more serious the decline of near infrared region. The near infrared region was more sensitive to locust damage extent than visible light region. 2) Models based on four selected loss component of vegetation indices ($Delta, Delta, Delta, Delta$) all had good correlations with dry weight loss of reed green leaf with their R$^{2,}$ ranging from 0.60 to 0.74. Among these models, the model based on $Delta$ and $Delta$ performed better with being 0.74 and 0.72 respectively. Assessment on the loss estimation models were conducted by additional 20 sample points. The assessment results also indicated that $Delta$ and $Delta$ produced a higher estimation accuracy with the RMSE being 14.3 g/m2 and 14.2 g/m2 respectively on dry weight loss of green leaf. Therefore, the result concluded that loss component of NDVI and GNDVI can further improve the results and be the optimal choice for loss estimation after locust damage.
{"title":"Monitoring Locusta migratoria manilensis damage using ground level hyperspectral data","authors":"Xiaomei Zheng, P. Song, Yingying Li, Kangyu Zhang, Huijuan Zhang, Li Liu, Jingfeng Huang","doi":"10.1109/Agro-Geoinformatics.2019.8820212","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820212","url":null,"abstract":"Locusta migratoria manilensis is one of the major migratory locusts in China which prefers phragmites australis (Cav.) Trin.ex Steudel (here after called reed). Locust damage is one of the major agricultural pests in the world which has a serious impact on agricultural production. With the development of optical remote sensing techniques, detection of plant diseases and pests by measurements of canopy spectra has been implemented on wheat, barely leaves, cotton, etc. However, rare studies have been focused on reed, especially on estimation of loss component caused by locust until now. Therefore, the objective of this study was to investigate hyperspectral characteristics of reed from ground level canopy spectral data by ASD FieldSpec® 3 Spectroradiometer and to establish loss estimation models based on a field simulated L. m. manilensis damage experiment. Up to now, Kenli District of Dongying City is an important region of locust monitoring and prevention in China. Therefore, we carried out the simulated damage experiment during July 2017 in Kenli district, Dongying city, Shangdong province of China. The simulated locust damage experiment was based on six simulated locust density levels and three different damage durations. According to the experiment schedule, hyperspectral field data were obtained in four times and corresponding aboveground biomass (AGB) were cut immediately after each of the three damage durations. Loss estimation models were based on 40 sample points between loss component of selected vegetation indices (including RVI, NDVI, GNDVI SAVI) and dry weight loss of green leaf of reed. The results indicated that: 1) After L. m. manilensis damage, reed canopy reflectance decreased in near infrared region whereas the gap between visible light and near infrared region was narrowed. Also, the more serious the damage, the more serious the decline of near infrared region. The near infrared region was more sensitive to locust damage extent than visible light region. 2) Models based on four selected loss component of vegetation indices ($Delta, Delta, Delta, Delta$) all had good correlations with dry weight loss of reed green leaf with their R$^{2,}$ ranging from 0.60 to 0.74. Among these models, the model based on $Delta$ and $Delta$ performed better with being 0.74 and 0.72 respectively. Assessment on the loss estimation models were conducted by additional 20 sample points. The assessment results also indicated that $Delta$ and $Delta$ produced a higher estimation accuracy with the RMSE being 14.3 g/m2 and 14.2 g/m2 respectively on dry weight loss of green leaf. Therefore, the result concluded that loss component of NDVI and GNDVI can further improve the results and be the optimal choice for loss estimation after locust damage.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122531197","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820426
Jiaxing Chen, Xiaoyu Zhang, Guorong Huang
Due to high turbidity, high primary productivity, complex hydrodynamic conditions, and tidal effects, monitoring the dynamic changes of diffuse attenuation coefficient is of great significance for underwater light detection and laser observation of underwater topography and landforms in the Yangtze Estuary and adjacent waters. In this study, the $K_{d} (490)$ inversion in the Yangtze Estuary and its adjacent sea areas is carried out based on GOCI data. The research focused on the various characteristics and influencing factors of $K_{d} (490)$ in the Yangtze Estuary and its adjacent sea areas during half tidal period. The feasibility of laser sounding in the sea areas is evaluated according to the variation characteristics of $K_{d} (490)$. The result indicates that:1) The Yangtze Estuary and its adjacent waters are typical type II water. The maximum suspended sediment content can be rapidly reduced from 1000 mg/L in Hangzhou Bay to below 10 mg/L. Therefore, the piecewise diffuse attenuation coefficient inversion algorithm is suitable for the study area;2) The inversion results suggest that the $K_{d} (490)$ of the Yangtze Estuary and its adjacent sea areas vary in the range of $0.10 pm 0.02 {mathrm {m}}^{-1}-2.8 pm 0.2 {mathrm {m}}^{-1}$, and increases in the inner estuary and then decreases with the decrease of offshore distance. The $K_{d} (490)$ values of the Yangtze Estuary and its adjacent sea areas are generally lower in low tide period than in high tide period. The suspended sediment concentration and the tidal level are important factors affecting $K_{d} (490)$ values in the low tide period; 3) During the low tide period, the detectable laser depth in the Yangtze Estuary and Hangzhou Bay is from less than 5 m to 30 m, which is more suitable for LiDAR observation at the lowest tide level. It can be seen that GOCI’s daily resolution of 8 scenes per hour can realize the dynamic change monitoring of $K_{d} (490)$. The research provides technical support for the further development of airborne LiDAR detection.
{"title":"Diffuse Attenuation Coefficient Inversion for the Yangtze Estuary and Its Adjacent Sea Areas on the GOCI Images and Application in the Preevaluation of Airborne Laser Bathymetry","authors":"Jiaxing Chen, Xiaoyu Zhang, Guorong Huang","doi":"10.1109/Agro-Geoinformatics.2019.8820426","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820426","url":null,"abstract":"Due to high turbidity, high primary productivity, complex hydrodynamic conditions, and tidal effects, monitoring the dynamic changes of diffuse attenuation coefficient is of great significance for underwater light detection and laser observation of underwater topography and landforms in the Yangtze Estuary and adjacent waters. In this study, the $K_{d} (490)$ inversion in the Yangtze Estuary and its adjacent sea areas is carried out based on GOCI data. The research focused on the various characteristics and influencing factors of $K_{d} (490)$ in the Yangtze Estuary and its adjacent sea areas during half tidal period. The feasibility of laser sounding in the sea areas is evaluated according to the variation characteristics of $K_{d} (490)$. The result indicates that:1) The Yangtze Estuary and its adjacent waters are typical type II water. The maximum suspended sediment content can be rapidly reduced from 1000 mg/L in Hangzhou Bay to below 10 mg/L. Therefore, the piecewise diffuse attenuation coefficient inversion algorithm is suitable for the study area;2) The inversion results suggest that the $K_{d} (490)$ of the Yangtze Estuary and its adjacent sea areas vary in the range of $0.10 pm 0.02 {mathrm {m}}^{-1}-2.8 pm 0.2 {mathrm {m}}^{-1}$, and increases in the inner estuary and then decreases with the decrease of offshore distance. The $K_{d} (490)$ values of the Yangtze Estuary and its adjacent sea areas are generally lower in low tide period than in high tide period. The suspended sediment concentration and the tidal level are important factors affecting $K_{d} (490)$ values in the low tide period; 3) During the low tide period, the detectable laser depth in the Yangtze Estuary and Hangzhou Bay is from less than 5 m to 30 m, which is more suitable for LiDAR observation at the lowest tide level. It can be seen that GOCI’s daily resolution of 8 scenes per hour can realize the dynamic change monitoring of $K_{d} (490)$. The research provides technical support for the further development of airborne LiDAR detection.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126295858","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820436
Weidan Wang, Li Sun, Zhiyuan Pei, Yuanyuan Chen, Xiaomei Zhang
A standardized precipitation evapotranspiration Index (SPEI), combining the advantage of standard precipitation index (SPI) and palmer drought severity index (PDSI), is computed at different time scales (1, 3, 6 months) in Jilin Province, based on monthly precipitation and temperature data, got after preprocessing of China surface climatological data daily data set provided by National Meteorological Information Center. The temporal and spatial characteristics of drought in growing season were analyzed using linear trend analysis, Mann-Kendall trend test, Mann-Kendall abrupt test, and spatial interpolation. The results showed that from 1968 to 2017, the SPEI decreased with a rate of 0.109 10 a-1 approximately based on SPEI-6 in October, indicating that there is drying trends in Jilin Province. However, inter-annual drought fluctuates, the pattern of wet-dry-wet-dry during this period is identified, and is associated with three turning year points of 1975, 1985, and 1995. Through using SEPI-3 to analyze seasonal variation, we find that the trend of aridification in autumn is significant. The SEPI-1 decreased in growing season, from April to October, too. Monthly SPEI (SPEI-1) demonstrates that the total number of droughts was the highest in October, September takes second place, nevertheless, mild drought in the two months is more than others. July is the month with the most moderate drought, and far more than in any other month. Severe drought in June happens more frequently, and the situation is like the moderate drought in July. Extreme drought is relatively less, about 12 times every month in these 50 years. Spatial distribution of drought in the district was heterogeneous and complexity. Totally, the western region was the most seriously affected area, with the highest drought frequency, especially along the southwest administrative line and separate region of the southeast. SPEI of six-month scale in October shows that extreme drought infrequently, only in the southeast and southwest of the individual areas; severe drought mainly distributes in the western region, especially Songyuan, Qianan, Changling, Siping and so on; Western such as Daan, Baicheng, Tongyu, North Central Changchun, Jiaohe, Wangqing etc., is where moderate drought happen more frequently; most of the area has experienced mild drought, and it happened more frequently along the southwest provincial boundaries. The results of this study may provide a scientific basis for early drought prediction and risk management of water resources and agricultural production in Jilin Province.
{"title":"Analysis of Temporal and Spatial Variation of Growing Season Drought in Jiling Province Based on Standardized Precipitation Evapotranspiration Index","authors":"Weidan Wang, Li Sun, Zhiyuan Pei, Yuanyuan Chen, Xiaomei Zhang","doi":"10.1109/Agro-Geoinformatics.2019.8820436","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820436","url":null,"abstract":"A standardized precipitation evapotranspiration Index (SPEI), combining the advantage of standard precipitation index (SPI) and palmer drought severity index (PDSI), is computed at different time scales (1, 3, 6 months) in Jilin Province, based on monthly precipitation and temperature data, got after preprocessing of China surface climatological data daily data set provided by National Meteorological Information Center. The temporal and spatial characteristics of drought in growing season were analyzed using linear trend analysis, Mann-Kendall trend test, Mann-Kendall abrupt test, and spatial interpolation. The results showed that from 1968 to 2017, the SPEI decreased with a rate of 0.109 10 a-1 approximately based on SPEI-6 in October, indicating that there is drying trends in Jilin Province. However, inter-annual drought fluctuates, the pattern of wet-dry-wet-dry during this period is identified, and is associated with three turning year points of 1975, 1985, and 1995. Through using SEPI-3 to analyze seasonal variation, we find that the trend of aridification in autumn is significant. The SEPI-1 decreased in growing season, from April to October, too. Monthly SPEI (SPEI-1) demonstrates that the total number of droughts was the highest in October, September takes second place, nevertheless, mild drought in the two months is more than others. July is the month with the most moderate drought, and far more than in any other month. Severe drought in June happens more frequently, and the situation is like the moderate drought in July. Extreme drought is relatively less, about 12 times every month in these 50 years. Spatial distribution of drought in the district was heterogeneous and complexity. Totally, the western region was the most seriously affected area, with the highest drought frequency, especially along the southwest administrative line and separate region of the southeast. SPEI of six-month scale in October shows that extreme drought infrequently, only in the southeast and southwest of the individual areas; severe drought mainly distributes in the western region, especially Songyuan, Qianan, Changling, Siping and so on; Western such as Daan, Baicheng, Tongyu, North Central Changchun, Jiaohe, Wangqing etc., is where moderate drought happen more frequently; most of the area has experienced mild drought, and it happened more frequently along the southwest provincial boundaries. The results of this study may provide a scientific basis for early drought prediction and risk management of water resources and agricultural production in Jilin Province.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116150","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820609
Taha Gorji, Aylin Yildirim, N. Hamzehpour, Elif Sertel, A. Tanik
Land degradation by salinity is one of the main environmental hazards threatening soil sustainability especially in arid and semi-arid regions of the world characterized by low precipitation and high evaporation. Geo-statistical approaches and remote sensing (RS) techniques have provided fast, accurate and economic prediction and mapping of soil salinity within the last two decades. Obtaining multi-temporal data via satellite images in different spatial domains with various scales is one of the key developments of monitoring spatial variability of soil salinity. In addition, geo-statistical methods have the capability of producing prediction surfaces from limited sample data. This study aims to map spatial distribution of soil salinity in the selected pilot area which is located in the western part of Urmia Lake Basin, Iran, by applying geo-statistical methods. A kriging based map and three different co-kriging based maps were produced using electrical conductivity (EC) measurements as primary variable and three different soil salinity index values as secondary variable. Three soil salinity indices were created by using Sentinel-2A image that were acquired in the same date of field measurements to generate 3 various soil salinity prediction maps. Salinity maps obtained from geo-statistical methods were compared and validated to understand the performance of these approaches for soil salinity prediction. The results of this study demonstrated that co-kriging can provide promising estimation of spatial variability of soil salinity especially when there is relevant and abundant set of secondary data derived from satellite images.
{"title":"Characterizing the spatial variability of soil salinity in Lake Urmia Basin by applying geo-statistical methods","authors":"Taha Gorji, Aylin Yildirim, N. Hamzehpour, Elif Sertel, A. Tanik","doi":"10.1109/Agro-Geoinformatics.2019.8820609","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820609","url":null,"abstract":"Land degradation by salinity is one of the main environmental hazards threatening soil sustainability especially in arid and semi-arid regions of the world characterized by low precipitation and high evaporation. Geo-statistical approaches and remote sensing (RS) techniques have provided fast, accurate and economic prediction and mapping of soil salinity within the last two decades. Obtaining multi-temporal data via satellite images in different spatial domains with various scales is one of the key developments of monitoring spatial variability of soil salinity. In addition, geo-statistical methods have the capability of producing prediction surfaces from limited sample data. This study aims to map spatial distribution of soil salinity in the selected pilot area which is located in the western part of Urmia Lake Basin, Iran, by applying geo-statistical methods. A kriging based map and three different co-kriging based maps were produced using electrical conductivity (EC) measurements as primary variable and three different soil salinity index values as secondary variable. Three soil salinity indices were created by using Sentinel-2A image that were acquired in the same date of field measurements to generate 3 various soil salinity prediction maps. Salinity maps obtained from geo-statistical methods were compared and validated to understand the performance of these approaches for soil salinity prediction. The results of this study demonstrated that co-kriging can provide promising estimation of spatial variability of soil salinity especially when there is relevant and abundant set of secondary data derived from satellite images.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133759930","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}
Pub Date : 2019-07-01DOI: 10.1109/Agro-Geoinformatics.2019.8820259
Liang Liang, Di Geng, Ting Huang, L. Di, Li Lin, Ziheng Sun
Drought is one of the most serious natural disasters. In this study, based on NOAA/AVHRR meteorological satellite data from 1981 to 2015, the spatial and temporal characteristics of spring drought in China were analyzed by using vegetation status index (VCI) as drought monitoring index, frequency analysis, trend rate analysis, anomaly index analysis and Mann-Kendall mutation test. The results show that China is a high-incidence area of spring drought, but most of the areas are mainly light and moderate drought. The heavy drought areas are concentrated in southern Tibet, Sichuan Basin, Tarim Basin and the surrounding areas of Qaidam Basin. The frequency of drought is obviously different in different regions. The frequency of spring drought is relatively high in the northern and southern regions which are greatly affected by monsoon. The frequency of spring drought is relatively low in the northwest and Qinghai-Tibet regions which are less affected by monsoon, except in Xinjiang, northern Inner Mongolia and southern Tibet. During 1981 - 2015, the spring VCI in all parts of China showed an overall upward trend, that is, drought in most regions tended to ease. Moreover, the trend was a wavelike increasing trend rather than a one-way change and could be divided into 4 phases: 1) a slow increasing phase from 1981-1990, 2) an intensive fluctuating phase from 1991-2000, 3) a steady increasing phase from 2001-2010, and 4) a slow decreasing phase after 2010. Mann-Kendall analysis further suggested that the VCI sequence of the Spring Festival in China was on the rise, and the changes in the south, northwest and Qinghai-Tibet regions reached significant levels. The time point of mutation in the South was 2000, and that in the northwest and Qinghai-Tibet regions was 1992.
{"title":"VCI-based Analysis of Spatio-temporal Variations of Spring Drought in China from 1981 to 2015","authors":"Liang Liang, Di Geng, Ting Huang, L. Di, Li Lin, Ziheng Sun","doi":"10.1109/Agro-Geoinformatics.2019.8820259","DOIUrl":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820259","url":null,"abstract":"Drought is one of the most serious natural disasters. In this study, based on NOAA/AVHRR meteorological satellite data from 1981 to 2015, the spatial and temporal characteristics of spring drought in China were analyzed by using vegetation status index (VCI) as drought monitoring index, frequency analysis, trend rate analysis, anomaly index analysis and Mann-Kendall mutation test. The results show that China is a high-incidence area of spring drought, but most of the areas are mainly light and moderate drought. The heavy drought areas are concentrated in southern Tibet, Sichuan Basin, Tarim Basin and the surrounding areas of Qaidam Basin. The frequency of drought is obviously different in different regions. The frequency of spring drought is relatively high in the northern and southern regions which are greatly affected by monsoon. The frequency of spring drought is relatively low in the northwest and Qinghai-Tibet regions which are less affected by monsoon, except in Xinjiang, northern Inner Mongolia and southern Tibet. During 1981 - 2015, the spring VCI in all parts of China showed an overall upward trend, that is, drought in most regions tended to ease. Moreover, the trend was a wavelike increasing trend rather than a one-way change and could be divided into 4 phases: 1) a slow increasing phase from 1981-1990, 2) an intensive fluctuating phase from 1991-2000, 3) a steady increasing phase from 2001-2010, and 4) a slow decreasing phase after 2010. Mann-Kendall analysis further suggested that the VCI sequence of the Spring Festival in China was on the rise, and the changes in the south, northwest and Qinghai-Tibet regions reached significant levels. The time point of mutation in the South was 2000, and that in the northwest and Qinghai-Tibet regions was 1992.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129294621","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}
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