• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.637-647
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• 2019

The detection of crop stress is an important issue for the accurate assessment of yield decline. The photochemical reflectance index (PRI) was developed as a remotely sensed indicator of light use efficiency (LUE). The PRI has been tested in crop stress detection and a number of studies demonstrated the feasibility of using it. However, only few studies have focused on the use of PRI from remote sensing imagery. The monitoring of PRI using drone and satellite is made difficult by the low spectral resolution image captures. In order to estimate PRI from multispectral sensor, we propose a band fusion method using adjacent bands. The method is applied to the drone-based hyperspectral and multispectral imagery and estimated PRI explain 79% of the original PRI. And time series analyses showed that two PRI data (drone-based and SRS sensor) had very similar temporal variations. From these results, PRI from multispectral imagery using band fusion can be used as a new method for evaluation of crop stress.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1265-1274
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• 2021

The analysis of hyperspectral characteristics of materials near the South Han River has been conducted using riverside area measurements by drone installed hyperspectral sensors. Each spectrum reflectance of the riverside materials were compared and analyzed which were consisted of grass, concrete, soil, etc. To verify the drone installed hyperspectral measurements, a ground spectrometer was deployed for field measurements and comparisons for the materials. The comparison results showed that the riverside materials had their unique hyperspectral band characteristics, and the field measurements were similar to the remote sensing data. For the classification of the riverside area, the K-means clustering method and SVM classification method were utilized. The supervised SVM method showed accurate classification of the riverside area than the unsupervised K-means method. Using classification and clustering methods, the inherent spectral characteristic for each material was found to classify the riverside materials of hyperspectral images from drones.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.91-101
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• 2023

In this study, we estimated the concentration of chlorophyll-a (Chl-a) using hyperspectral water surface reflectance in an inland weir (Baekjae weir) and estuarine reservoir (Namyang Reservoir) for monitoring the occurrence of algae in freshwater in South Korea. The hyperspectral reflectance was measured by aircraft in Baekjae Weir (BJW) from 2016 to 2017, and a drone in Namyang Reservoir (NYR) from 2020 to 2021. The 30 reflectance bands (BJW: 400-530, 620-680, 710-730, 760-790 nm, NYR: 400-430, 655-680, 740-800 nm) that were highly related to Chl-a concentration were selected using permutation importance. Artificial neural network based Chl-a estimation model was developed using the selected reflectance in both water bodies. And the performance of the model was evaluated with the coefficient of determination (R²), the root mean square error (RMSE), and the mean absolute error (MAE). The performance evaluation results of the Chl-a estimation model for each watershed was R²: 0.63, 0.82, RMSE: 9.67, 6.99, and MAE: 11.25, 8.48, respectively. The developed Chl-a model of this study may be used as foundation tool for the optimal management of freshwater algal blooms in the future.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.101-111
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• 2022

This study analyzed the relationship between water turbidity and its reflectance, as measured using hyperspectral imaging. First, samples of turbid water were generated in boxes. This was followed by drone-based hyperspectral imaging and analysis of the correlation between the samples' measured turbidity and hyperspectral reflectance. The nine boxes for turbidity measurement were made of black acrylic that absorbed all light turbidity was induced using soil collected near Changhacheon, which causes turbidity in Imha Lake. The results indicate that the reflectance of wavelengths in the near-infrared region followed a pattern of increase with increasing soil content for each box. Analysis of this correlation between the turbidity and average reflectance measured in each box yielded a very high R² value of 0.8702, indicating that reflectance is a suitable proxy for turbidity.

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

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.243-249
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• 2019

The purpose of this study is to analyze the reflectance of bed materials according to changes in the water level using a drone-based hyperspectral sensor. For this purpose, we took hyperspectral images of bed materials such as soil, gravel, cobble, reed, and vegetation to compare and analyze the spectral data of each material. To adjust the water level, we constructed an experimental channel to control the discharge and installed the bed materials within the channel. In this study, we configured 3 cases according to the water level (0.0 m, 0.3 m, 0.6 m). After the imaging process, we used the mean value of 10 points for each bed material as analytical data. According to the analysis, each material showed a similar reflectance by wavelength and the intrinsic reflectance characteristics of each material were shown in the visible and near-infrared region. Also, the deeper the water level, the lower the peak reflectance in the visible and near-infrared region, and the rate of decrease differed depending on the bed material. We expect the intrinsic properties of these bed materials to be used as basic research data to evaluate river environments in the future.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.103-119
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• 2020

Recently, the rapid redeposition and erosion of rivers artificially created by climate change and the Four Rivers Restoration Project is questionable. According to the revised law in Korea, the river management agency will periodically carry out bed changes surveys. However, there are technical limitations in contrast to the trend of increasing spatial coverage, density and narrowing of intervals. National organizations are interest in developing innovative bed changessurvey techniquesfor efficiency. Core of bathymetry survey is to measure the depth of rivers under a variety of river conditions, but that is relatively more risky, time-consuming and expensive compared to conventional ground surveys. To overcome the limitations of traditional technology, echo sounder, which has been mainly used for ocean depth surveying, has been applied to rivers. However, due to various technical limitations, it is still difficult to periodically investigate a wide range of areas. Therefore, technique using the remote sensing has been spotlighted as an alternative, especially showing the possibility of depth measurement using spectral characteristics. In this study, we develop and examine a technique that can measure depth of water using reflectance from spectral characteristics. As a result of applying the technique proposed in thisstudy, it was confirmed that the measured depth and the correlation and error corresponding to 0.986 and 0.053 m were measured in the depth range within 0.95 m. In the future, this study could be applied to the measurement of spatial depth if it is applied to the hyperspectral sensor mounted on the drone.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.54-54
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• 2023

하천유역조사는 관련 법률의 규정에 의해 물관리정책의 수립에 필요한 기초정보를 제공하는 것을 목적으로 기본현황, 이수, 치수 환경생태 등 유역관리에 필요한 주요 조사항목을 대상으로 수행되고 있다. 조사방법 중 원격탐사자료 활용한 조사는 드론 모니터링 영상 및 위성영상자료를 이용해 댐·제방과 같은 치수 시설물의 안전관리, 수질 모니터링, 하천지형조사, 하상변동조사 등에 활용되고 있다. 최근에는 일반 RGB 영상뿐만 아니라 수백개의 분광밴드를 포함한 초분광영상을 이용한 하천조사 연구가 이루어지고 있다. 초분광영상은 분광해상도가 높아 다항목 조사에 활용할 수 있다는 장점이 있지만, 많은 양의 분광정보를 포함하고 있기 때문에 초기 수집 자료의 용량이 너무 크고, 분석을 위한 전처리 과정이 까다롭다는 단점이 있다. 반면, 10개 이하 밴드의 분광정보를 수집하는 다분광영상은 2개 밴드를 이용해 정규식생지수(NDVI)를 즉각적으로 모니터링할 수 있고, 작물의 생육현황 등을 분석할 수 있어 농업 및 산림분야에서 널리 활용되고 있다. 초분광영상을 이용한 수심산정 연구는 최적 밴드비 탐색 기법(OBRA)을 활용해 측정수심과 상관관계가 높은 밴드비를 이용해 수심맵을 구축하는 방식이 활용되어왔다. 본 연구에서는 기존의 초분광영상을 활용한 수심산정기법을 다분광영상에 적용하여 분광밴드수가 축소된(경량화된) 자료를 활용한 수심산정 가능성을 확인하기 위해 동일한 현장에서 초분광과 다분광 두가지 영상을 촬영하였으며, 각각 수심맵을 구축해 하천분야에서 다분광영상의 활용도를 평가하였다. 또한, 기존의 OBRA의 한계를 개선하기 위해 가우시안 혼합 모델(GMM; Gaussian Mixture Model)을 활용해 영상을 군집화하여 수심산정 정확도를 개선하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.159-159
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• 2020

하천환경조사는 하천의 전반적인 특성을 조사 분석하는 것으로 하천환경 조사결과는 하천관련사업의 기초자료로 사용된다. 하천환경조사의 기초조사에서는 현장답사를 통해 하천의 특성을 대략적으로 판단하고 하천 전구간의 물리적 구조와 식생의 분포, 중요 서식처 정보를 포함하는 RCS 지도(River Corridor Survey)를 작성한다. 기초조사를 위해서는 하천 전 구간에 대한 현장답사가 필요하기 때문에 많은 시간, 비용 그리고 인력이 필요하고, 육안 또는 사진을 통한 스케치로 이루어져 조사 결과가 정성적이고 작업자의 경험이나 능력에 따라 결과가 좌우된다는 한계가 있다. 따라서 하천환경조사를 좀 더 간편하고 과학적이며 경제적으로 조사하기 위해 최근 드론 영상을 이용한 조사 기술 개발에 대한 연구들이 증가하고 있다. 하지만 드론을 이용한 하천환경조사의 대부분은 RGB 영상을 이용하기 때문에 정밀한 하천환경 변화를 정량적으로 분석하는데 한계가 있다. 이를 극복하기 위한 대안으로 사람이 감지할 수 있는 빛의 영역 뿐 아니라 자외선과 적외선 영역의 분광특성을 이용하여 하천환경의 특성을 세밀하게 분류하는 것이 가능한 초분광센서를 드론에 탑재하여 하천환경을 조사하기 위한 기초 연구들이 시작되고 있다. 본 연구에서는 line scan 방식의 초분광센서를 드론에 탑재하여 초분광영상을 촬영하기 위한 드론 시스템을 구성하였고, 하나의 사진과 같이 초분광영상을 제작하기 위해 다양한 기하보정 기술을 적용하여 최적의 기하보정 방법을 제시하였다. 이를 위해 초분광영상의 기하보정은 각각의 초분광영상의 GCP와 대응점을 이용한 2차원 변환 방법 및 비선형 변환 방법을 적용하여 보정을 수행하였으며, 각 방법에 따른 정사보정 영상의 위치정확도를 검증하였다. 연구 결과 드론 기반의 초분광영상 촬영 및 기하 보정 방법을 제시하였다. 향후 하천환경조사 뿐만 아니라 다양한 분야의 원격탐사에 초분광영상을 활용하는데 도움이 될 것으로 기대한다.