Statistical methods to map water depth from medium-high resolution multispectral images were easier and more popular than wave spectrum bathymetry or water scattering-based implementation. However, less studies compared the effectiveness of the popular statistical methods for pelagic islands. This study used the Log ratio transform, primary component analysis and independent component analysis methods to retrieve water depth of Pratas Island,using one Landsat 8 Operational Land Imager (OLI) image. Results showed that the Log ratio transformation was not the best method as the proposer suggested. The first primary component and the second independent component are good predictors for absolute water depth ranging from 0 to 20m, while Log Ratio was more sensitive to water depth ranging from 0 to 5m, IC2 was sensitive to water depth between 5 and 10 m.
{"title":"Comparison of three empirical methods for water depth mapping with case study of Pratas Island","authors":"Ailian Chen, Boqin Zhu","doi":"10.1117/12.2204945","DOIUrl":"https://doi.org/10.1117/12.2204945","url":null,"abstract":"Statistical methods to map water depth from medium-high resolution multispectral images were easier and more popular than wave spectrum bathymetry or water scattering-based implementation. However, less studies compared the effectiveness of the popular statistical methods for pelagic islands. This study used the Log ratio transform, primary component analysis and independent component analysis methods to retrieve water depth of Pratas Island,using one Landsat 8 Operational Land Imager (OLI) image. Results showed that the Log ratio transformation was not the best method as the proposer suggested. The first primary component and the second independent component are good predictors for absolute water depth ranging from 0 to 20m, while Log Ratio was more sensitive to water depth ranging from 0 to 5m, IC2 was sensitive to water depth between 5 and 10 m.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127369305","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}
Qian Wang, L. Lei, Da Liu, Min Liu, Xiuchun Qin, B. Sun
CO2 is one of the most important greenhouse gases due to its selective absorption of long wave radiation from the Earth’s surface. In this paper, we use the column average dry air mole fraction of CO2 (XCO2) data from the Japanese GOSAT satellite to conduct a comprehensive and systematic analysis of temporal and spatial distribution of XCO2. This includes: (1) analysis of seasonal change characteristics of XCO2 data; and (2) comparative analysis of the northern and southern hemispheres carbon dioxide concentration at different latitudes. The results show that (1) from 2010 to 2013, atmospheric XCO2 significantly increased each year. The southern hemisphere's annual averages of XCO2 from 2010 to 2012 were 385.2 ppm, 387.3 ppm, and 389.1 ppm, while the average annual values for the northern hemisphere from 2010 to 2012 were 387.8 ppm, 390.0 ppm, and 391.7 ppm. The annual XCO2 in northern and southern hemispheres exhibited growth rates of 1-2 ppm per year. (2) The results show seasonal change trends: winter months displayed higher XCO2. Regarding the global spatial distribution of XCO2, the results show that the total XCO2 in the northern hemisphere is higher than that in the southern hemisphere. (3) The growth of global XCO2 in 2011 and 2012 was 1.9 ppm/yr and 2.1 ppm/yr. These values are in accordance with the growth rates of 1.9 ppm/yr and 2.2 ppm/yr reported in the World Meteorological Organization's greenhouse gas bulletin.
{"title":"Temporal and spatial analysis of global GOSAT XCO2 variations characteristics","authors":"Qian Wang, L. Lei, Da Liu, Min Liu, Xiuchun Qin, B. Sun","doi":"10.1117/12.2204827","DOIUrl":"https://doi.org/10.1117/12.2204827","url":null,"abstract":"CO2 is one of the most important greenhouse gases due to its selective absorption of long wave radiation from the Earth’s surface. In this paper, we use the column average dry air mole fraction of CO2 (XCO2) data from the Japanese GOSAT satellite to conduct a comprehensive and systematic analysis of temporal and spatial distribution of XCO2. This includes: (1) analysis of seasonal change characteristics of XCO2 data; and (2) comparative analysis of the northern and southern hemispheres carbon dioxide concentration at different latitudes. The results show that (1) from 2010 to 2013, atmospheric XCO2 significantly increased each year. The southern hemisphere's annual averages of XCO2 from 2010 to 2012 were 385.2 ppm, 387.3 ppm, and 389.1 ppm, while the average annual values for the northern hemisphere from 2010 to 2012 were 387.8 ppm, 390.0 ppm, and 391.7 ppm. The annual XCO2 in northern and southern hemispheres exhibited growth rates of 1-2 ppm per year. (2) The results show seasonal change trends: winter months displayed higher XCO2. Regarding the global spatial distribution of XCO2, the results show that the total XCO2 in the northern hemisphere is higher than that in the southern hemisphere. (3) The growth of global XCO2 in 2011 and 2012 was 1.9 ppm/yr and 2.1 ppm/yr. These values are in accordance with the growth rates of 1.9 ppm/yr and 2.2 ppm/yr reported in the World Meteorological Organization's greenhouse gas bulletin.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854341","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}
Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation. This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.
土壤水分可通过点测量、水文模型和遥感进行估算。许多研究表明，最有前途的土壤水分方法是将遥感地表土壤水分数据与计算模型相结合。虽然许多研究利用被动微波遥感数据进行粗空间分辨率的土壤水分同化，但利用主动微波遥感观测的研究却很少。本研究开发并测试了一种利用主动微波遥感数据 ASAR（先进合成孔径雷达）进行黑河流域土壤水分预测的同化操作方法。同化方法基于集合卡尔曼滤波器（EnKF）、前向辐射传递模型和分布式水文土壤植被模型（DHSVM）。前向辐射传递模型是一种半经验后向散射模型，用于消除地表粗糙度和植被覆盖对后向散射系数的影响。考虑了地形对土壤水运动的影响以及土壤水的垂直和横向交换。我们在两个基础条件不同的野外地点进行了将主动微波遥感数据（ASAR）观测结果同化到水文模型中的实验。土壤水分地面实况数据是通过野外时域反射仪（TDR）工具收集的，用于评估同化方法。在点式监测点测得的土壤水分的时间演变与基于 EnKF 的模型预测进行了比较。结果表明，通过与 ASAR 观测数据同化，土壤水分的估算得到了改善，基于数据同化的土壤水分可在中等空间分辨率下进行监测。
{"title":"Monitoring soil moisture through assimilation of active microwave remote sensing observation into a hydrologic model","authors":"Qiang Liu, Yingshi Zhao","doi":"10.1117/12.2204954","DOIUrl":"https://doi.org/10.1117/12.2204954","url":null,"abstract":"Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation. This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127683546","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}
Y. Guo, S. Li, X. Wu, Yuan Cheng, L. Wang, T. Liu, G. Zheng
As part of the "High-Resolution Earth Observation System," many major projects are being implemented. The first optical satellite (GF-1) in the high-resolution satellite series has completed in-orbit tests and entered the stage of data acquisition. GF-1 owns high resolution and information of wide field view sensor (WFV sensor) and the panchromatic and multispectral sensor (PMS sensor). In this study, GF-1 WFV sensor data with a resolution of 16 m, integrated with Landsat-8 and RapidEye data were selected to recognize maize in Xuchang using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) method. The results showed that the precision of classification varies greatly among WFV sensors. In particular, WFV3 was of the highest accuracy to identify crops and planting area with accuracy higher than Landsat-8 and close to RapidEye. With regard to WFV1 and WFV4, the application effect was worse and less viable to identify species of complex autumn crops. In brief, the classification accuracy of SVM classifier is better than SAM classifier. It can be also concluded that SVM is more suitable for the identification of crops and planting area of extraction in the study area.
{"title":"Maize recognition and accuracy evaluation with GF-1 WFV sensor data","authors":"Y. Guo, S. Li, X. Wu, Yuan Cheng, L. Wang, T. Liu, G. Zheng","doi":"10.1117/12.2204722","DOIUrl":"https://doi.org/10.1117/12.2204722","url":null,"abstract":"As part of the \"High-Resolution Earth Observation System,\" many major projects are being implemented. The first optical satellite (GF-1) in the high-resolution satellite series has completed in-orbit tests and entered the stage of data acquisition. GF-1 owns high resolution and information of wide field view sensor (WFV sensor) and the panchromatic and multispectral sensor (PMS sensor). In this study, GF-1 WFV sensor data with a resolution of 16 m, integrated with Landsat-8 and RapidEye data were selected to recognize maize in Xuchang using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) method. The results showed that the precision of classification varies greatly among WFV sensors. In particular, WFV3 was of the highest accuracy to identify crops and planting area with accuracy higher than Landsat-8 and close to RapidEye. With regard to WFV1 and WFV4, the application effect was worse and less viable to identify species of complex autumn crops. In brief, the classification accuracy of SVM classifier is better than SAM classifier. It can be also concluded that SVM is more suitable for the identification of crops and planting area of extraction in the study area.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121950622","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}
Based on the development of classification algorithm applied in monitoring spatio-temporal dynamic changes of coal-- mining areas, several improvements were made on feature space and classification model in this paper. There were two innovations in our study: 1) During building the feature spaces, a new index for extracting information about mining area was created, which can classify mining area and settlements efficiently; 2) a special ticket-voting SVM algorithm with wavelet kernel function was proposed, which provides higher classification accuracy than other traditional classifiers via the secondary classification. Here we took the northeast plain of Pei county in Xuzhou city as a studying region, applying the proposed method to implement the classification by using the image of multi-temporal TM/ETM from the year of 1987 to 2013. How to carry on deep analysis combined with various non-spatial data is much more significant. Then we studied the rules of dynamic changes of land use/cover and further analyzed their driving factors by combining RS interpretation with GIS spatial analysis techniques. In this study, image recognition technology was applied to the problems of environmental change in coal mining area. These explanations provide some valuable supports for human to recognize and deal with the conflicts between economic development and environmental protection in coal mining areas.
{"title":"A study on monitoring land use/cover change of mining area based on ticket-voting SVM classification","authors":"Yi Lin, Jie Yu, Min Ying, M. Shen","doi":"10.1117/12.2204811","DOIUrl":"https://doi.org/10.1117/12.2204811","url":null,"abstract":"Based on the development of classification algorithm applied in monitoring spatio-temporal dynamic changes of coal-- mining areas, several improvements were made on feature space and classification model in this paper. There were two innovations in our study: 1) During building the feature spaces, a new index for extracting information about mining area was created, which can classify mining area and settlements efficiently; 2) a special ticket-voting SVM algorithm with wavelet kernel function was proposed, which provides higher classification accuracy than other traditional classifiers via the secondary classification. Here we took the northeast plain of Pei county in Xuzhou city as a studying region, applying the proposed method to implement the classification by using the image of multi-temporal TM/ETM from the year of 1987 to 2013. How to carry on deep analysis combined with various non-spatial data is much more significant. Then we studied the rules of dynamic changes of land use/cover and further analyzed their driving factors by combining RS interpretation with GIS spatial analysis techniques. In this study, image recognition technology was applied to the problems of environmental change in coal mining area. These explanations provide some valuable supports for human to recognize and deal with the conflicts between economic development and environmental protection in coal mining areas.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"28 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128274335","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}
Regional river basins, transboundary rivers in particular, are shared water resources among multiple users. The tempo-spatial distribution and utilization potentials of water resources in these river basins have a great influence on the economic layout and the social development of all the interested parties in these basins. However, due to the characteristics of cross borders and multi-users in these regions, especially across border regions, basic data is relatively scarce and inconsistent, which bring difficulties in basin water resources management. Facing the basic data requirements in regional river management, the overall technical framework for remote sensing monitoring and data service system in China’s regional river basins was designed in the paper, with a remote sensing driven distributed basin hydrologic model developed and integrated within the frame. This prototype system is able to extract most of the model required land surface data by multi-sources and multi-temporal remote sensing images, to run a distributed basin hydrological simulation model, to carry out various scenario analysis, and to provide data services to decision makers.
{"title":"Framework design for remote sensing monitoring and data service system of regional river basins","authors":"J. Fu, Jingxuan Lu, Z. Pang","doi":"10.1117/12.2204860","DOIUrl":"https://doi.org/10.1117/12.2204860","url":null,"abstract":"Regional river basins, transboundary rivers in particular, are shared water resources among multiple users. The tempo-spatial distribution and utilization potentials of water resources in these river basins have a great influence on the economic layout and the social development of all the interested parties in these basins. However, due to the characteristics of cross borders and multi-users in these regions, especially across border regions, basic data is relatively scarce and inconsistent, which bring difficulties in basin water resources management. Facing the basic data requirements in regional river management, the overall technical framework for remote sensing monitoring and data service system in China’s regional river basins was designed in the paper, with a remote sensing driven distributed basin hydrologic model developed and integrated within the frame. This prototype system is able to extract most of the model required land surface data by multi-sources and multi-temporal remote sensing images, to run a distributed basin hydrological simulation model, to carry out various scenario analysis, and to provide data services to decision makers.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127054108","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}
Yue Xu, Jinwei Chen, Yue-ting Chen, Zhi-hai Xu, H. Feng, Qi Li
This paper establishes a geometric model of multi-band mosaic imaging from the same orbit by agile satellites, and introduces a self-write simulation software. Geometric parameters of each band are calculated based on the attitude control ability of the satellite and the mission requirements. Considering the different ground resolution and the imaging angle of each band, two new concepts, Gradient Entropy and Structure Similarity Parameter are presented. These two values are used to evaluate the change of image quality caused by agility, and help to estimate the effect of the mission. By building the geometric model and calculating the agile information with the program, we propose a new approach of forward analysis of agile imaging, which helps users evaluate the image degradation.
{"title":"Geometric simulation analysis of multi-band mosaic imaging from the same orbit by agile satellites","authors":"Yue Xu, Jinwei Chen, Yue-ting Chen, Zhi-hai Xu, H. Feng, Qi Li","doi":"10.1117/12.2204808","DOIUrl":"https://doi.org/10.1117/12.2204808","url":null,"abstract":"This paper establishes a geometric model of multi-band mosaic imaging from the same orbit by agile satellites, and introduces a self-write simulation software. Geometric parameters of each band are calculated based on the attitude control ability of the satellite and the mission requirements. Considering the different ground resolution and the imaging angle of each band, two new concepts, Gradient Entropy and Structure Similarity Parameter are presented. These two values are used to evaluate the change of image quality caused by agility, and help to estimate the effect of the mission. By building the geometric model and calculating the agile information with the program, we propose a new approach of forward analysis of agile imaging, which helps users evaluate the image degradation.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131277410","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}
Da Liu, L. Lei, Min Liu, Lijie Guo, Qian Wang, Nian Bie
Satellite observations and model simulations are of two important data sources to study atmospheric carbon dioxide concentration. For analyzing and evaluating the bias correction method of ACOS dry-air column averaged CO2 (Xco2) product, the GEOS-Chem Xco2 simulations are selected according to observing time and locations of the ACOS product. The GEOS-Chem simulations of CO2 profiles are transformed to Xco2 data by convolving with satellite averaging kernels and pressure weighting functions. The GEOS-Chem Xco2 data are then compared with both bias uncorrected and bias corrected satellite retrievals of ACOS. The comparisons show that the bias uncorrected ACOS retrievals are on average 1.12ppm higher than the model Xco2 data, while the corrected ACOS retrievals are only on average 0.06ppm lower than the model Xco2 data. By assuming consistency between model Xco2 simulations and true atmospheric Xco2, this study indicates that the bias can be obvious decreased through the bias correction method, and the correction is effective and necessary for satellite Xco2 retrievals.
{"title":"Effectiveness analysis of ACOS-Xco2 bias correction method with GEOS-Chem model results","authors":"Da Liu, L. Lei, Min Liu, Lijie Guo, Qian Wang, Nian Bie","doi":"10.1117/12.2204821","DOIUrl":"https://doi.org/10.1117/12.2204821","url":null,"abstract":"Satellite observations and model simulations are of two important data sources to study atmospheric carbon dioxide concentration. For analyzing and evaluating the bias correction method of ACOS dry-air column averaged CO2 (Xco2) product, the GEOS-Chem Xco2 simulations are selected according to observing time and locations of the ACOS product. The GEOS-Chem simulations of CO2 profiles are transformed to Xco2 data by convolving with satellite averaging kernels and pressure weighting functions. The GEOS-Chem Xco2 data are then compared with both bias uncorrected and bias corrected satellite retrievals of ACOS. The comparisons show that the bias uncorrected ACOS retrievals are on average 1.12ppm higher than the model Xco2 data, while the corrected ACOS retrievals are only on average 0.06ppm lower than the model Xco2 data. By assuming consistency between model Xco2 simulations and true atmospheric Xco2, this study indicates that the bias can be obvious decreased through the bias correction method, and the correction is effective and necessary for satellite Xco2 retrievals.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114651296","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}
Ping Huang, Jingxuan Lu, Delong Li, W. Song, W. Qu
Satellite remote sensing with a larger spatial coverage and high temporal resolution makes it possible to monitor precipitation distribution under extreme rainfall events. In this paper, the heavy rainstorm that occurred in Beijing on 21, July in 2012 was monitored using the TRMM and Fengyun precipitation data. Results indicate that: (1) these two kinds of satellite precipitation data are in good agreement with ground observed precipitation data, having a correlation coefficient of 0.9390 and 0.9846 and an underestimation of 14.42% and 19.86% respectively; (2) The moving track of this extreme rainstorm can be well detected, with the storm center and a heavy rain belt moving from southwest to northeast found; (3) 15 minutes interval between the two satellite data makes them complement each other, which enables the temporal frequency of the monitoring data further increased so as to get construction of the rainstorm processes improved.
{"title":"Remote sensing monitoring of the 2012 Beijing extreme rainstorm event","authors":"Ping Huang, Jingxuan Lu, Delong Li, W. Song, W. Qu","doi":"10.1117/12.2204720","DOIUrl":"https://doi.org/10.1117/12.2204720","url":null,"abstract":"Satellite remote sensing with a larger spatial coverage and high temporal resolution makes it possible to monitor precipitation distribution under extreme rainfall events. In this paper, the heavy rainstorm that occurred in Beijing on 21, July in 2012 was monitored using the TRMM and Fengyun precipitation data. Results indicate that: (1) these two kinds of satellite precipitation data are in good agreement with ground observed precipitation data, having a correlation coefficient of 0.9390 and 0.9846 and an underestimation of 14.42% and 19.86% respectively; (2) The moving track of this extreme rainstorm can be well detected, with the storm center and a heavy rain belt moving from southwest to northeast found; (3) 15 minutes interval between the two satellite data makes them complement each other, which enables the temporal frequency of the monitoring data further increased so as to get construction of the rainstorm processes improved.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128003889","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}
In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 ㎡.Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.
{"title":"A rapid extraction of landslide disaster information research based on GF-1 image","authors":"Sai Wang, Suning Xu, Li Peng, Zhiyi Wang, Na Wang","doi":"10.1117/12.2204784","DOIUrl":"https://doi.org/10.1117/12.2204784","url":null,"abstract":"In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 ㎡.Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.","PeriodicalId":340728,"journal":{"name":"China Symposium on Remote Sensing","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128753619","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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