• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.101-119
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• 2022

Existing algal bloom monitoring is based on field sampling, and there is a limit to understanding the spatial distribution of algal blooms, such as the occurrence and spread of algae, due to local investigations. In this study, algal bloom monitoring was performed using an unmanned aerial vehicle and multispectral sensor, and data on the distribution of algae were provided. For the algal bloom monitoring site, data were acquired from the Mulgeum·Mae-ri site located in the lower part of the Nakdong River, which is the areas with frequent algal bloom. The Chlorophyll-a(Chl-a) value of field-collected samples and the Chl-a estimation formula derived from the correlation between the spectral indices were comparatively analyzed. As a result, among the spectral indices, Maximum Chlorophyll Index (MCI) showed the highest statistical significance(R²=0.91, RMSE=8.1mg/m³). As a result of mapping the distribution of algae by applying MCI to the image of August 05, 2021 with the highest Chl-a concentration, the river area was 1.7km², the Warning area among the indicators of the algal bloom warning system was 1.03km²(60.56%) and the Algal Bloom area occupied 0.67km²(39.43%). In addition, as a result of calculating the number of occurrence days in the area corresponding to the "Warning" in the images during the study period (July 01, 2021~November 01, 2021), the Chl-a concentration above the "Warning" level was observed in the entire river section from 12 to 19 times. The algal bloom monitoring method proposed in this study can supplement the limitations of the existing algal bloom warning system and can be used to provide information on a point-by-point basis as well as information on a spatial range of the algal bloom warning area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.1
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• pp.61-72
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• 2019

With the diversification of research using UAV(Unmanned Aerial Vehicle)s, the possibility of remote sensing research for urban green spaces is increasing. UAVs can be used as an investigation method to monitor the successful construction of the park and the planting of vegetation since its creation. This study was carried out to investigate UAVs utilization of urban green space monitoring in Dosol Square. It was photographed three times on May 21, July 13, and September 16, 2018 using DJI Phantom3 pro, Inspire2, and Parrot Sequoia multispectral camera. Orthographic images were overlaid on the planting plan of the site and the construction results were checked, the change of vitality of the plantation area was analyzed by NDVI(Normalized Difference Vegetation Index) and SAVI(Soil Adjusted Vegetation Index). As a result, it was confirmed that the UAVs are very effective for surveying the view of the urban green space after the construction and recording the results, which can be grasped quantitatively by overlaying the planting plan map. UAVs are more likely to be used in terms of monitoring vegetation vitality. It is interpreted that SAVI is better than NDVI in the green space just after composition. Chionanthus retusus and Pinus strobus were analyzed for their low level of vitality, and partially damaged and their vitality was lowered. In addition, there was difficulty in grass planting area and flower garden due to drainage and summer drought problems. In the future, it is expected that orthoimage and multispectral data using UAVs will be useful in the early vegetation monitoring and management field of urban green spaces.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.271-277
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• 2012

In this paper, we propose a dynamic tree formation protocol for multiple UAV which is gathering data or accomplishing a mission such as disaster monitoring, environment monitoring, and disaster relief. Especilly, we designed Hop-LQI Weight algorithm to form optimal tree in wireless dynamic environment applying situation of radio signal attenuation over distance and implemented our algorithm in MSP 430 K-mote sensor platform using TinyOS codes. We verified performance of our algorithm by comparing average link setup time by the number of nodes with minimum LQI, link cost calculation method in wireless communication.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.6
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• pp.731-742
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• 2016

Recent technological advance in UAV (Unmanned Aerial Vehicle) technology offers new opportunities for assessing crop situation using UAV imagery. The objective of this study was to assess if reflectance and NDVI derived from consumer-grade cameras mounted on UAVs are useful for crop condition monitoring. This study was conducted using a fixed-wing UAV(Ebee) with Cannon S110 camera from March 2015 to March 2016 in the experiment field of National Institute of Agricultural Sciences. Results were compared with ground-based recordings obtained from consumer-grade cameras and ground multi-spectral sensors. The relationship between raw digital numbers (DNs) of UAV images and measured calibration tarp reflectance was quadratic. Surface (lawn grass, stairs, and soybean cultivation area) reflectance obtained from UAV images was not similar to reflectance measured by ground-based sensors. But NDVI based on UAV imagery was similar to NDVI calculated by ground-based sensors.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.409-421
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• 2017

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.829-842
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• 2018

Unmanned Aerial Vehicle (UAV) is diversely utilized in our lives such as daily hobbies, specialized video image taking and disaster prevention activities. New ways of UAV application have been explored recently such as UAV-based delivery. However, most UAV systems are being utilized in a passive form such as real-time video image monitoring, filmed image ground analysis and storage. For more proactive UAV utilization, there should be higher-performance UAV and large-capacity memory than those presently utilized. Against this backdrop, this study described the general matters on proactive software platform and high-performance UAV hardware for real-time target tracking; implemented research on its design and implementation, and described its implementation method. Moreover, in its established platform, this study measured and analyzed the core-specific CPU consumption.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.384-385
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• 2016

본 연구에서는 최근 어류 폐사, 상수원 오염 등의 피해를 발생시키고 있는 녹조를 대상으로 위성영상을 이용한 발생 유무와 분포를 분석하고자 하였다. 녹조는 엽록소를 가지고 광합성을 하므로 식생과 매우 유사한 분광특성을 가진다. 이는 위성영상에서 제공하는 근적외 정보로부터 정규식생지수를 산출하고 그 변화를 분석함으로써 녹조 발생 유무를 식별해낼 수 있음을 의미한다. 연구 대상지역인 대청호는 올해 첫 조류경보가 발령된 수역으로 8월~10월 사이 상류지역을 중심으로 녹조가 발생하였다. 본 연구에서는 Landsat-8 위성영상을 이용하여 대청호에서 발생한 녹조분포를 분석하고, 그 중 높은 농도의 녹조가 발생한 추소리를 직접 방문하여 Unmanned Aerial Vehicle(UAV) 자료를 취득하였다. UAV 촬영 영상을 통해 추소리 수역에 녹조가 다량 발생한 것을 확인할 수 있었다. 향후에는 고해상도 위성영상인 플래닛스코프 위성영상을 추가적으로 활용함으로써 녹조 모니터링의 정확성과 적시성을 확보할 예정이다.

• Journal of KIBIM
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• v.13 no.4
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• pp.64-73
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• 2023

This study introduces a method of estimating the 3D coordinates of structural damage from the detection results of visual inspection provided in 2D image coordinates using sensing data of UAV and 3D shape information of BIM. This estimation process takes place in a virtual space and utilizes the BIM model, so it is possible to immediately identify which member of the structure the estimated location corresponds to. Difference from conventional structural damage localization methods that require 3D scanning or additional sensor attachment, it is a method that can be applied locally and rapidly. Measurement accuracy was calculated through the distance difference between the measured position measured by TLS (Terrestrial Laser Scanner) and the estimated position calculated by the method proposed in this study, which can determine the applicability of this study and the direction of future research.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.10-20
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• 2019

Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.382-392
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• 2020

The use of unmanned aerial vehicle (UAV) have been regarded as a promising technique in both military and civilian applications. Nevertheless, due to the lack of relevant and regulations and laws, the misuse of illegal drones poses a serious threat to social security. In this paper, aiming at deriving the three-dimension optimal surveillance trajectories for group monitoring drones, we develop a group trajectory planner based on the particle swarm optimization and updating mechanism. Together, to evaluate the trajectories generated by proposed trajectory planner, we propose a group-objectives fitness function in accordance with energy consumption, flight risk. The simulation results validate that the group trajectories generated by proposed trajectory planner can preferentially visit important areas while obtaining low energy consumption and minimum flying risk value in various practical situations.