Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appro...Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both clas展开更多
A new calibration method of L-band radar accuracy using a rotary-wing drone equipped with"GPS"satellite positioning system was proposed.The L-band radar calibration system scheme based on this method was des...A new calibration method of L-band radar accuracy using a rotary-wing drone equipped with"GPS"satellite positioning system was proposed.The L-band radar calibration system scheme based on this method was designed.The theoretical basis required for system realization was studied,and the system calibration method was given.The calibration results referred to the domestic new generation weather radar antenna beam pointing calibration technical indicator(≤0.3°),and its accuracy met relevant business requirement.It show that this method can easily and quickly complete the radar system calibration.Compared with the traditional radar calibration method,it is more convenient,less affected by surrounding environment,and has low requirements on the weather.展开更多
基金This research was supported by a grant from the National Research Foundation of Korea,provided by the Korean government(2017R1A2B4003258).
文摘Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both clas
文摘A new calibration method of L-band radar accuracy using a rotary-wing drone equipped with"GPS"satellite positioning system was proposed.The L-band radar calibration system scheme based on this method was designed.The theoretical basis required for system realization was studied,and the system calibration method was given.The calibration results referred to the domestic new generation weather radar antenna beam pointing calibration technical indicator(≤0.3°),and its accuracy met relevant business requirement.It show that this method can easily and quickly complete the radar system calibration.Compared with the traditional radar calibration method,it is more convenient,less affected by surrounding environment,and has low requirements on the weather.
