• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1607-1622
• /
• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.27 no.2
• /
• pp.119-125
• /
• 2017

The mud flat in Korea is the geographical feature generated from the sediment of rivers of Korea and China and it is the important topography for pollution purification and fishing industry. The mud flat is difficult to access such that it requires the aerial survey for the high-resolution spatial information of the area. In this study we used drones instead of the conventional aerial and remote sensing approaches which have shortcomings of costs and revisit times. We carried out GPS-based control point survey, temporal image acquisition using drones, bundle adjustment, stereo image processing for DSM and ortho photo generation, followed by co-registration between the spatio-temporal information.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.5
• /
• pp.97-102
• /
• 2017

Recently deep learning techniques such as convolutional neural networks (CNN) have been introduced to classify high-resolution remote sensing data. In this paper, we investigated the possibility of applying CNN to crop classification of farmland images captured by drones. The farming area was divided into seven classes: rice field, sweet potato, red pepper, corn, sesame leaf, fruit tree, and vinyl greenhouse. We performed image pre-processing and normalization to apply CNN, and the accuracy of image classification was more than 98%. With the output of this study, it is expected that the transition from the existing image classification methods to the deep learning based image classification methods will be facilitated in a fast manner, and the possibility of success can be confirmed.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.496-496
• /
• 2017

위성을 이용한 원격탐사 기술이 발전하고 다양한 산출물이 나타남에 따라 수자원 및 하천관리에서의 원격탐사기술의 활용의 폭이 넓어지고 있다. 특히, 위성에서 관측할 수 있는 다양한 정보들을 이용하여 수자원 및 하천관리에 사용하려하는 연구가 활발히 진행되고 있으며, 기본적인 가시영상 이외에도 적외영상, 초분광영상, 수위정보, 레이더 반사도 등을 활용하여 수문량을 추정하려는 시도가 이루어져왔다. 원격탐사의 대표적인 장비인 위성은 광범위한 정보를 쉽게 취득할 수 있지만 위성마다 탑재된 센서에 따라 획득할 수 있는 자료가 서로 다르고, 산출물의 시공간 해상도에 따라 자료의 질이 결정된다. 본 연구에서는 원격탐사영상을 이용한 하천유량추정기법을 수립하기 위해 통제된 실험하천 규모에서 드론을 이용하였다. 실험은 대한민국 안동에 위치한 한국건설기술연구원 하천실험센터에서 수행되었으며, DJI Phantom 3 standard 드론을 활용하여 영상을 획득하였다. 하천유량추정의 방법론은 운동량 방정식과 Manning 유속공식을 활용한 하폭기반 유량추정 기법을 수립하였다. 1차 실험은 하천유량을 증가시키고 감소시키는 동시에 드론을 이용하여 하천을 촬영함으로써 하폭의 변화와 동시에 유량의 변화를 추정할 수 있는지 확인하였다. 2차 실험에서는 배수효과가 존재하는 조건에서 드론영상을 이용한 하천유량을 산정하고 보정계수를 산정하였다. 본 연구에서 수립된 하천유량추정기법은 위성영상을 이용한 하천유량 추정에 활용할 수 있으리라 기대한다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.229-229
• /
• 2020

최근 광학이미지 및 레이저를 활용한 계측 기술이 발전됨에 따라서 수공학 분야의 지리정보시스템(Geographic Information System, GIS) 및 원격탐사(Remote Sensing, RS) 기술의 활용 분야가 증가하고 있다. 광범위한 하천공간의 조사결과를 가시화하고 품질 및 활용성 제고를 위하여 GIS 및 RS 기술과 같은 고도화된 조사기술 및 장비의 도입하고 많이 활용하고 있다. 우리나라의 하천은 지형학적 특성으로 하천연장이 길고 접근성이 매우 불리하다. 하천정비 사업을 통하여 하천에 근접하게 접근할 수 있는 조건이 있다 하더라도 교목이나 지형지물에 의하여 시야 확보가 어려운 조사구간이 많다. 하천공간조사를 위한 드론 활용의 장점은 차량 및 도보로 접근이 어려운 조사대상에 접근하여 다양한 각도로 촬영하여 조사 목적에 부합하는 성과물 획득이 가능하다. 한국수자원조사기술원은 하도특성(평면, 사행 및 종횡단 조사)을 사전에 하천기본계획 보고서에서 곡률반경과 만곡도, 유심편향축, 최심하상경사 및 평균하상경사 등을 수집하고 현장에서 드론을 활용하여 접근조사가 어려운 항목에 대하여 드론을 적극적으로 활용하여 조사하고 있다. 드론을 활용할 경우 인력 및 소요시간이 대폭 절감되며 조사목적에 부합하는 다양한 조사 성과물을 얻을 수 있은 장점이 있다. 최종적으로 촬영된 영상자료는 넓은 범위의 하천공간을 고해상도 수치 지도화가 가능해 하천특성을 정량적으로 평가할 수 있을 것으로 판단된다.

• Journal of Aerospace System Engineering
• /
• v.18 no.4
• /
• pp.27-33
• /
• 2024

This study deals with the demonstration of precision agriculture technology that can predict the health status of crops by analyzing the vegetation index (NDVI) using a drone equipped with a multi-spectral camera and an EO/IR camera. The multi-spectral camera measures crop reflectance to determine the vegetation index, while the EO/IR camera detects temperature changes in crops to evaluate water stress and health status. Data from this study can improve agricultural productivity and optimize the use of chemical fertilizers and pesticides. Moreover, integrating object recognition technology in the future could turn precision agriculture into a vital alternative for enhancing the sustainability of agriculture.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1433-1435
• /
• 2003

Very decisive progress was made in advancing fundamental POL-IN-SAR theory and algorithm development during the past decade. This was accomplished with the aid of airborne & shuttle platforms supporting single -to-multi-band multi-modal POL-SAR and also some POL-IN-SAR sensor systems, which will be compared and assessed with the aim of establishing the hitherto not completed but required missions such as tomographic and holographic imaging. Because the operation of airborne test-beds is extremely expensive, aircraft platforms are not suited for routine monitoring missions which is better accomplished with the use drones or UAVs. Such unmanned aerial vehicles were developed for defense applications, however lacking the sophistic ation of implementing advanced forefront POL-IN-SAR technology. This shortcoming will be thoroughly scrutinized resulting in the finding that we do now need to develop most rapidly POL-IN-SAR drone-platform technology especially for environmental stress-change monitoring with a great variance of applications beginning with flood, bush/forest-fire to tectonic-stress (earth-quake to volcanic eruptions) for real-short-time hazard mitigation. However, for routine global monitoring purposes of the terrestrial covers neither airborne sensor implementation - aircraft and/or drones - are sufficient; and there -fore multi-modal and multi-band space-borne POL-IN-SAR space-shuttle and satellite sensor technology needs to be further advanced at a much more rapid phase. The existing ENVISAT with the forthcoming ALOSPALSAR, RADARSAT-2, and the TERRASAT will be compared, demonstrating that at this phase of development the fully polarimetric and polarimetric-interferometric modes of operation must be viewed and treated as preliminary algorithm verification support modes and at this phase of development are still not to be viewed as routine modes.

• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.623-634
• /
• 2022

Due to river maintenance projects such as the creation of hydrophilic areas around rivers and the Four Rivers Project, the flow characteristics of rivers are continuously changing, and the risk of water quality accidents due to the inflow of various pollutants is increasing. In the event of a water quality accident, it is necessary to minimize the effect on the downstream side by predicting the concentration and arrival time of pollutants in consideration of the flow characteristics of the river. In order to track the behavior of these pollutants, it is necessary to calculate the diffusion coefficient and dispersion coefficient for each section of the river. Among them, the dispersion coefficient is used to analyze the diffusion range of soluble pollutants. Existing experimental research cases for tracking the behavior of pollutants require a lot of manpower and cost, and it is difficult to obtain spatially high-resolution data due to limited equipment operation. Recently, research on tracking contaminants using RGB drones has been conducted, but RGB images also have a limitation in that spectral information is limitedly collected. In this study, to supplement the limitations of existing studies, a hyperspectral sensor was mounted on a remote sensing platform using a drone to collect temporally and spatially higher-resolution data than conventional contact measurement. Using the collected spatio-temporal hyperspectral images, the tracer concentration was calculated and the transverse dispersion coefficient was derived. It is expected that by overcoming the limitations of the drone platform through future research and upgrading the dispersion coefficient calculation technology, it will be possible to detect various pollutants leaking into the water system, and to detect changes in various water quality items and river factors.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1731-1738
• /
• 2021

The objective of this study was to extract individual trees and tree heights using UAV drone images. The study site was Gongju national university experiment forest, located in Yesan-gun, Chungcheongnam-do. The thinning intensity study sites consisted of 40% thinning, 20% thinning, 10% thinning and control. The image was filmed by using the "Mavic Pro 2" model of DJI company, and the altitude of the photo shoot was set at 80% of the overlay between 180m pictures. In order to prevent image distortion, a ground reference point was installed and the end lap and side lap were set to 80%. Tree heights were extracted using Digital Surface Model (DSM) and Digital Terrain Model (DTM), and individual trees were split and extracted using object-based analysis. As a result of individual tree extraction, thinning 40% stands showed the highest extraction rate of 109.1%, while thinning 20% showed 87.1%, thinning 10% showed 63.5%, and control sites showed 56.0% of accuracy. As a result of tree height extraction, thinning 40% showed 1.43m error compared with field survey data, while thinning 20% showed 1.73 m, thinning 10% showed 1.88 m, and control sites showed the largest error of 2.22 m.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1669-1683
• /
• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.