An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, ...An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.展开更多
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free a...Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.展开更多
Security constrained multi area multi objective dynamic economic dispatch (SCMAMODED) with renewable energy (RE) and all the possible MTDC stability constraints is formulated for the first time. The stability merits o...Security constrained multi area multi objective dynamic economic dispatch (SCMAMODED) with renewable energy (RE) and all the possible MTDC stability constraints is formulated for the first time. The stability merits of multi terminal DC (MTDC) tie lines as compared to the traditional HVAC forms the main objective of this paper. Probabilistic load flow (PLF) is applied to determine the system parameters while the uncertainties are modelled using Scenario Based Method (SBM). The simulation results reveal that with the use of MTDC tie lines, the frequency and voltage stability in the MAMODED with renewable energy sources (RES) are enhanced while keeping the MTDC power exchange interface nodes at secure levels.展开更多
From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Diff...From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Different development subject bears different responsibility.Object system includes farmers engaged in farming,farmer workers,rural unemployed people,rural students,rural left-behind people,and other people in rural areas.Different development object has different features.Development target system includes raising quality of rural human resource,keeping reasonable population size,optimizing structure of rural human resource,and improving vitality of rural human resource,etc.展开更多
基金Project(2009CB226107)supported by the National Basic Research Program of China
文摘An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the CAS Earth Big Data Science Project(No.XDA19060303)the Innovation Project of the State Key Laboratory of Resources and Environmental Information System(No.O88RAA01YA)
文摘Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.
文摘Security constrained multi area multi objective dynamic economic dispatch (SCMAMODED) with renewable energy (RE) and all the possible MTDC stability constraints is formulated for the first time. The stability merits of multi terminal DC (MTDC) tie lines as compared to the traditional HVAC forms the main objective of this paper. Probabilistic load flow (PLF) is applied to determine the system parameters while the uncertainties are modelled using Scenario Based Method (SBM). The simulation results reveal that with the use of MTDC tie lines, the frequency and voltage stability in the MAMODED with renewable energy sources (RES) are enhanced while keeping the MTDC power exchange interface nodes at secure levels.
基金Supported by Project of National Social Science Foundation(09XMZ055)General Program of Scientific Research Project of Guangxi Provincial Department of Education (200911MS104)
文摘From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Different development subject bears different responsibility.Object system includes farmers engaged in farming,farmer workers,rural unemployed people,rural students,rural left-behind people,and other people in rural areas.Different development object has different features.Development target system includes raising quality of rural human resource,keeping reasonable population size,optimizing structure of rural human resource,and improving vitality of rural human resource,etc.
