• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.15-26
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• 2017

The decrease of green space according to the urbanization has caused many environmental problems as the destruction of habitat, air pollution, heat island effect. With interest growing in natural view recently, proper management of evergreen tree which is lived even the winter season has been on the rise importantly. This study analyzed the distribution area of evergreen tree using vegetation index based on unmanned aerial vehicle (UAV). Firstly, RGB and NIR+RG camera were loaded in fixed-wing UAV and image mosaic was achieved using GCPs based on Pix4d SW. And normalized differences vegetation index (NDVI) and soil adjusted vegetation index (SAVI) was calculated by band math function from acquired ortho mosaic image. validation points were applied to evaluate accuracy of the distribution of evergreen tree for each range value and analysis showed that kappa coefficient marked the highest as 0.822 and 0.816 respectively in "NDVI > 0.5" and "SAVI > 0.7". The area of evergreen tree in "NDVI > 0.5" and "SAVI > 0.7" was $11,824m^2$ and $15,648m^2$ respectively, that was ratio of 4.8% and 6.3% compared to total area. It was judged that UAV could supply the latest and high resolution information to vegetation works as urban environment, air pollution, climate change, and heat island effect.

• The Korean Journal of Air & Space Law and Policy
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• v.28 no.2
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• pp.37-61
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• 2013

In modern international law, the absence of legal definition regarding drone(Unmanned Aerial Vehicle) has made legal scholars work on an typical analogy between aircraft codified in the international document and drone. The wording of the Convention on International Civil Aviation is limited to two categories of aircraft, such as civil aircraft and state aircraft, whereas military aircraft is not legally defined. As such it is, the current practices of the State regarding the drone flight over foreign territory have proven a hypothese that drone is being deemed as military aircraft. Principal usage of drone lies in reconnaissance and surveillance mission as well as so-called targeted killing, which is prohibited if the killing is treacherous. Claimed war against terrorism, however, is providing a legal rationale that targeted killing is not treacherous, and that the targeted person is not civilian but combatant. In such context, armed attack of drone is deemed legal and justified. Consequently, such attack is legal in the general context of the war. The rules that govern targeting do not turn on the type of weapon system used, and there is no prohibition under the laws of war on the use of technologically advanced weapons systems in armed conflict so long as they are employed in conformity with applicable laws of war. Drones may present interesting new challenges because of their sophistication and the technological advantage they convey to their operators.

• The Korean Journal of Air & Space Law and Policy
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• v.34 no.1
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• pp.235-268
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• 2019

Under the Aviation Safety Act of Korea, any person who intends to operate a drone is required to follow the operational conditions listed below, unless approved by the Minister of Land, Infrastructure, Transport and Tourism; (i) Operation of drones in the daytime, (ii) Operation of drones within Visual Line of Sight, (iii) Maintenance of a certain operating distance between drones and persons or properties on the ground/ water surface, (iv) Do not operate drones over event sites where many people gather, (v) Do not transport hazardous materials such as explosives by drone, (vi) Do not drop any objects from drones. Requirements stated in "Airspace in which Flights are Prohibited" and "Operational Limitations" are not applied to flights for search and rescue operations by public organizations in case of accidents and disasters. This paper analyzes legal issues as to definition and regulations of drones in Korean Aviation Safety Act. This paper, also, offers some implications and suggestions for regulations of drones under Korean Aviation Safety Act by comparing the regulations of drones in Japanese Civil Aeronautics Act.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.10
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• pp.791-799
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• 2020

Launched in October 2019, Northrop Grumman's MEV-1 was the world's first unmanned mission demonstrating the practical feasibility of on-orbit servicing. Although the concept of on-orbit servicing was proposed several decades ago, it has been developed to various mission concepts providing services such as orbit change, station keeping, propellant and equipment supply, upgrade, repair, on-orbit assembly and production, and space debris removal. The historical success of MEV-1 is expected to expand the market of on-orbit servicing for government agencies and commercial sectors worldwide. The on-orbit servicing essentially requires the utilization of a highly propellant efficient electric propulsion system due to the nature of the mission. In this study, the space mission analysis for a simple on-orbit mission involving Hall thruster is conducted, which is life extension mission for geostationary orbit satellites. In order to analyze the mission, design space exploration for various Hall thruster design variable combinations is performed. The values of design variables and operational parameters of Hall thruster suitable for the mission are proposed through design space analysis and optimization, and mission performance is derived. In addition, the direction of further improvement for the current on-orbit mission analysis process and space mission analysis involving Hall thruster is reviewed.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.611-624
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• 2018

The percentage of moisture content in rice before harvest is crucial to reduce the economic loss in terms of yield, quality and drying cost. This paper discusses the application of artificial neural network (ANN) in developing a reliable prediction model using the low altitude fixed-wing unmanned air vehicle (UAV) based reflectance value of green, red, and NIR and statistical moisture content data. A comparison between the actual statistical data and the predicted data was performed to evaluate the performance of the model. The correlation coefficient (R) is 0.862 and the mean absolute percentage error (MAPE) is 0.914% indicate a very good accuracy of the model to predict the moisture content in rice before harvest. The model predicted values are matched well with the measured values($R^2=0.743$, and Nash-Sutcliffe Efficiency = 0.730). The model results are very promising and show the reliable potential to predict moisture content with the error of prediction less than 7%. This model might be potentially helpful for the rice production system in the field of precision agriculture (PA).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.9-18
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• 2019

The intensity and the number of days of high temperature occurrence are also high and record heat occurred. In addition, the global warming phenomenon is intensifying globally, and especially in South Korea, the urban heat island phenomenon is also occurring due to rapid urbanization due to rapid industrial development. As the temperature of the city rises, it causes problems such as the comfort of the residential living and the cooling load. In this study, the cool roof performance is evaluated according to the roof color to reduce these problems. Unlike previous studies, UAV(Unmanned Aerial Vehicle) thermal infrared camera was used to obtain the surface temperature (white, grey, green, blue, brown, black) according to the rooftop color by remote sensing technique. As a result, the surface temperature of white color was $11{\sim}20^{\circ}C$ lower than other colors. Also air conditioning temperature of white color was $1.5{\sim}4.4^{\circ}C$ lower than other colors and the digital thermometer of white color was about $1.5{\sim}3.5^{\circ}C$ lower than other colors. It was confirmed that the white cool roof performance is the best, and the UAV and the thermal infrared camera can confirm the cool roof performa.