• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.503-514
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• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1221-1229
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• 2020

In urgent situations such as disasters and accidents, rapid data acquisition and processing is required. Therefore, in this study, a rapid geocoding method according to EOP (Exterior Orientation Parameter) correction was proposed through pattern analysis of the initial UAV image information. As a result, in the research area with a total flight length of 1.3 km and a width of 0.102 ㎢, the generation time of geocoding images took about 5 to 10 seconds per image, showing a position error of about 8.51 m. It is believed that the use of the rapid geocoding method proposed in this study will help provide basic data for on-site monitoring and decision-making in emergency situations such as disasters and accidents.

• Journal of the Korean Institute of Rural Architecture
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• v.23 no.4
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• pp.1-12
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• 2021

Recently, the use of unmanned aerial vehicles (UAVs) is increasing in the field of land information acquisition and terrain exploration through high-altitude aerial photography. High-altitude aerial photography is suitable for large-scale geographic information collection, but has the disadvantage that it is difficult to accurately collect small-scale geographic information. Therefore, this study used low-altitude UAV to monitor changes in small rural spaces around rural resources, and the results are as follows. First, the low-altitude aerial imagery had a very high spatial resolution, so it was effective in reading and analyzing topographic features. Second, an area with a large number of aerial images and a complex topography had a large amount of point clouds to be extracted, and the number of point clouds affects the three-dimensional quality of rural space. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. In this study, the possibility of rural space analysis of low-altitude UAV was verified through aerial photography and analysis, and the effect of 3D mapping on rural space monitoring was visually analyzed. If data acquired by low-altitude UAV are used in various forms such as GIS analysis and topographic map production it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.63-68
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• 2018

The establishment of Ground Control Points (GCPs) in UAV-Photogrammetry is a working process that requires the most time and expenditure. Recently, the rapid developments of navigation sensors and communication technologies have enabled Unmanned Aerial Vehicles (UAVs) to conduct photogrammetric mapping without using GCP because of the availability of new methods such as RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) technology. In this study, an experiment was conducted to evaluate the potential of RTK-UAV mapping with no GCPs compared to that of non RTK-UAV mapping. The positioning accuracy results produced by images obtained simultaneously from the two different types of UAVs were compared and analyzed. One was a RTK-UAV without GCPs and the other was a non RTK-UAV with different numbers of GCPs. The images were taken with a Canon IXUS 127 camera (focal length 4.3mm, pixel size $1.3{\mu}m$) at a flying height of approximately 160m, corresponding to a nominal GSD of approximately 4.7cm. As a result, the RMSE (planimetric/vertical) of positional accuracy according to the number of GCPs by the non-RTK method was 4.8cm/8.2cm with 5 GCPs, 5.4cm/10.3cm with 4 GCPs, and 6.2cm/12.0cm with 3 GCPs. In the case of non RTK-UAV photogrammetry with no GCP, the positioning accuracy was decreased greatly to approximately 112.9 cm and 204.6 cm in the horizontal and vertical coordinates, respectively. On the other hand, in the case of the RTK method with no ground control point, the errors in the planimetric and vertical position coordinates were reduced remarkably to 13.1cm and 15.7cm, respectively, compared to the non-RTK method. Overall, UAV photogrammetry supported by RTK-GPS technology, enabling precise positioning without a control point, is expected to be useful in the field of spatial information in the future.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.352-352
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• 2017

Recently, the South Korean government encourages the cultivation of upland crops in the paddy field to maintain an adequate level of rice production and then to balance the demand and supply of rice. This is mainly because the rice consumption per capita per year has continued to decline from 135 kg in 1979 to 61.9 kg in 2016, although the rice production was relatively stable. As a result, the rice overproduction became a big social problem. As a part of that, various upland crops such as soybean, maize, minor cereals and forage crops are planted in the paddy field 10 years ago. The cultivation of these crops may settle the problem of short supply and mass import of the crops to some extent. However, a systematic remote observation of upland crops in the paddy field is very scarce. This study investigated the cultivation status of upland crops and any changes of crop harvesting in the paddy field by using an unmanned aerial vehicle (UAV). Also, we analyzed the kind of upland crops and cultivation area in the paddy field by utilizing time series observation images. A fixed wing UAV is used for the investigation. This is because it is easy to use the flight operation and to control flight management software, and it can automatically cope with various emergency states such as a strong wind and battery discharge. The material of UAV is expanded polypropylene, which has an advantage of less equipment damage and risk during takeoff and landing. We acquired observed images in Buljeong-myeon, Goesan-gun, Chungcheongbuk-do, South Korea by using fixed wing UAV in 2015 and 2016. The total investigated area reaches 6,045 ha, and among them the agricultural area was 1,377 ha. For the next step, we created an orthoimage from all images taken using Pix 4D mapper program. According to the results of image analyses in 2015, the paddy field covered total 577 ha (75.9%) with crop plant. The cultivation area of beans, ginseng, maize, tobacco and peach was 256 ha (36.6%), 63 ha (9.2%), 37 ha (5.4%), 31 ha (4.5%) and 27 ha (3.8), respectively. And in 2016, the total covered area was 586 ha (77.1%), and it was comprised of 253 ha (35.5%), 88 ha (12.3%), 29 ha (4.1%), 22 ha (3.1%) and 32 ha (4.5%) in the same order. In this study, we focused on identifying the paddy field which was converted to the cultivation of upland crops by using UAV. And, it has been indicated that the cultivation area of rice decreased from 141 ha in 2015 to 127 ha in 2016, although that of ginseng increased by 25 ha. As a result, it is expected that a lot of paddy field could be replaced by high-income crops such as ginseng and fruit tree (peach) instead of relative low-income rice. More specific and widespread research on the remote sensing in the paddy field needs to be done.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.393-406
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• 2018

Every year in the ocean, various accidents occur frequently and illegal fishing is rampant. Moreover, their size and frequency are also increasing. In order to reduce losses of life or property caused by these, it is necessary to have a means to perform remote monitoring quickly. As an effective platform of such monitoring means, an Unmanned Aerial Vehicle (UAV) is receiving the spotlight. In these situations where marine accidents or illegal fishing occur, main targets of monitoring are ships. In this study, we propose a UAV based ship monitoring system and suggest a method of determining ship positions using UAV multi-sensory data. In the proposed method, firstly, the position and attitude of individual images are determined by using the pre-performed system calibration results and GPS/INS data obtained at the time when images were acquired. In addition, after the ship being detected automatically or semi-automatically from the individual images, the absolute coordinates of the detected ships are determined. The proposed method was applied to actual data measured at 200 m, 350 m, and 500 m altitude, the ship position can be determined with accuracy of 4.068 m, 8.916 m, and 13.734 m, respectively. According to the minimum standard of a hydrographical survey, the ship positioning results of 200 m and 350 m data satisfy grade S and the results of 500 m data do grade 1a, where the accuracy is required for positioning the coastline and topography less significant to navigation order. Therefore, it is expected that the proposed method can be effectively used for various purposes of marine monitoring or surveying.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.905-923
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• 2018

In order to generate high-resolution products using UAV images, it is necessary to analyze the sharpness of the themselves measured through image analysis. When images that have unclear sharpness of UAV are used in the production, they can have a great influence on operations such as acquisition and mapping of accurate three-dimensional information using UAV. GRD (Ground Resolved Distance) has been used as an indicator of image clarity. GRD is defined as the minimum distance between two identifiable objects in an image and is used as a concept against the GSD (Ground Sampling Distance), which is a spatial sample interval. In this study, GRD is extracted by analyzing the edge target without visual analysis. In particular, GRD to GSD ratio (GRD/GSD), or GRD expressed in pixels, is used as an index for evaluation the relative image sharpness. In this paper, GRD is calculated by analyzing edge targets at various altitudes in various shooting environments using a rotary wing. Using GRD/GSD, it was possible to identify images whose sharpness was significantly lowered, and the appropriateness of the image as an image clarity index was confirmed.

• Journal of KIBIM
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• v.6 no.1
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• pp.1-8
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• 2016

This paper provides a method for generating flight path of Unmanned Aerial Vehicle (UAV) that is intended to be used in visual inspection of dome-type nuclear containment building. The method basically employs 3-D model to extract accurate location coordinates. Two basic route patterns that provide guide lines in defining moving locations were defined for each side wall and dome section of the containment. The route patterns support sequential capturing of images as well. In addition, several simple equations and an algorithm for calculation of the moving location on the route were developed on the basis of 3-D geometric characteristics of the containment building. A prototype computer program has been implemented to validate the proposed method, and a case study shows the method can visualize covering area in 3-D model as well.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.17-23
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• 2016

This paper presents the implementation of altitude and attitude measurement algorithm using stereo camera for an unmanned aerial vehicle (UAV). Depth images are generated by calibrating the stereo cameras, and converted into 3D point cloud data. By applying a plane fitting algorithm to the resultant point cloud, altitude from ground level, and roll and pitch angles are extracted. To verify the performance, experimental results are provided by comparing with those of the motion caption system.