• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.833-845
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• 2021

Quantification of carbon absorption and understanding the human induced land use changes forms one of the major study with respect to global climatic changes. An attempt study has been made to quantify the carbon absorption by land use changes through remote sensing technology. However, it focused on past carbon absorption changes. So prediction of future carbon absorption changes is insufficient. This study simulated land use change using the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model and predicted future changes in carbon absorption considering climate change scenarios 4.5 and 8.5 of the Representative Concentration Pathways (RCP). Results of this study, in the RCP 4.5 scenarios there predicted to be loss of 7.92% of carbon absorption, but in the RCP 8.5 scenarios was 13.02%. Therefore, the approach used in this study is expected to enable exploration of future carbon absorption change considering other climate change scenarios.

• Journal of Multimedia Information System
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• v.5 no.4
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• pp.235-244
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• 2018

Potential maximum soil moisture retention (S) is a dominant parameter in the Soil Conservation Service (SCS; now called the USDA Natural Resources Conservation Service (NRCS)) runoff Curve Number (CN) method commonly used in hydrologic modeling for event-based flood forecasting (SCS, 1985). Physically, S represents the depth [L] soil could store water through infiltration. The depth of soil moisture retention will vary depending on infiltration from previous rainfall events; an adjustment is usually made using a factor for Antecedent Moisture Conditions (AMCs). Application of the method for continuous simulation of multiple storms has typically involved updating the AMC and S. However, these studies have focused on a time step where S is allowed to vary at daily or longer time scales. While useful for hydrologic events that span multiple days, this temporal resolution is too coarse for short-term applications such as flash flood events. In this study, an approach for deriving a time-variable potential maximum soil moisture retention curve (S-curve) at hourly time-scales is presented. The methodology is applied to the Napa River basin, California. Rainfall events from 2011 to 2012 are used for estimating the event-based S. As a result, we derive an S-curve which is classified into three sections depending on the recovery rate of S for soil moisture conditions ranging from 1) dry, 2) transitional from dry to wet, and 3) wet. The first section is described as gradually increasing recovering S (0.97 mm/hr or 23.28 mm/day), the second section is described as steeply recovering S (2.11 mm/hr or 50.64 mm/day) and the third section is described as gradually decreasing recovery (0.34 mm/hr or 8.16 mm/day). Using the S-curve, we can estimate the hourly change of soil moisture content according to the time duration after rainfall cessation, which is then used to estimate direct runoff for a continuous simulation for flood forecasting.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.4
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• pp.1-14
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• 2019

The 1st Forest Health Management survey was conducted to examine the health of the forests in Korea. However, in order to understand the health of the forests, which account for 63.7% of the total land area in South Korea, it is necessary to comprehensively spatialize the results of the survey beyond the sampling points. In this regard, out of the sample points of the 1st Forest Health Management survey in Gyeongbuk area, 78 spots were selected. For these spots, the species diversity index was selected from the survey sections, and the spatial interpolation method was applied. Inverse distance weighted (IDW), Ordinary Kriging and Ordinary Cokriging were applied as spatial interpolation methods. Ordinary Cokriging was performed by selecting vegetation indices which are highly correlated with species diversity index as a secondary variable. The vegetation indices - Normalized Differential Vegetation Index(NDVI), Leaf Area Index(LAI), Sample Ratio(SR) and Soil Adjusted Vegetation Index(SAVI) - were extracted from Landsat 8 OLI. Verification was performed by the spatial interpolation method with Mean Error(ME) and Root Mean Square Error(RMSE). As a result, Ordinary Cokriging using SR showed the most accurate result with ME value of 0.0000218 and RMSE value of 0.63983. Ordinary Cokriging using SR was proven to be more accurate than Ordinary Kriging, IDW, using one variable. This indicates that the spatial interpolation method using the vegetation indices is more suitable for spatialization of the biodiversity index sample points of 1st Forest Health Management survey.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.530-537
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• 2019

In this paper we propose a novel approach to representation of the 3D reconstruction problem by employing a concept of system function that is defined as the ratio of the output to the input signal. Akin to determination of system function (or system response), this paper determines system function by choosing (or defining) appropriate input and output signals. In other words, the 3D reconstruction using structured circular light patterns is reformulated as determination of system function from input and output signals. This paper introduces two algorithms for the reconstruction. The one defines the input and output signals as projected circular light patterns and the images overlaid with the patterns and captured by camera, respectively. The other one defines input and output signals as 3D coordinates of the object surface and the image captured by camera. The first one leads to the problem as identifying the system function and the second one leads to the problem as estimation of an input signal employing concept of modulation-demodulation theory. This paper substantiate the proposed approach by providing experimental results.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.608-616
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• 2021

Purpose: Safety inspections by existing personnel have been limited in evaluation and data securing due to concerns about the safety of technicians or difficulty in accessing them, and are becoming a bigger problem as the number of maintenance targets increases due to the aging of facilities. As drone technology develops, it is possible to ensure the safety of personnel, secure visual data, and diagnose quickly, and use it is increasing as safety inspection of facilities by drones was introduced recently. In order to further enhance utilization, it is considered necessary to base a consideration standard for facility appearance investigation by drones, and in this paper, research was conducted on dams. Method: To calculate the quality, existing domestic safety inspection and drone-related consideration standards were investigated, and procedures related to safety inspection using drones were compared and analyzed to review work procedures and construction types. In addition, empirical data were collected through drone photography and elevation image production for the actual dam. Result: Work types for safety inspection of facilities using drones were derived, and empirical survey results were collected for two dams according to work types. The existing guidelines were applied for the adjustment ratios for each structural type and standard of the facility, and if a meteorological reference point survey was necessary, the unmanned aerial vehicle survey of the construction work standard was applied. Conclusion: The finer the GSD in appearance investigation using drones, the greater the number of photographs taken, and the concept of adjustment cost was applied as a correction to calculate the consideration standard. In addition, it was found that the problem of maximum GSD indicating limitations should be considered in order to maintain the safe distance.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1879-1887
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• 2022

In this study, a method of estimating the velocity and altitude of aircrafts photographed in a KOMPSAT-3 satellite was proposed. In the proposed method, parallax effect, which is a time offset between bands due to the photographing method of the KOMPSAT-3 satellite, the structure of the sensor, and the movement of the satellite's orbit, was calculated, and in this process, trucks running on the highway were used. In addition, the actual direction and the direction by parallax effect of the aircraft were calculated using the coordinates of the aircraft in the image, and the attitude information of the KOMPSAT-3 satellite was calculated using metadata to estimate the velocity and altitude of the aircraft. The estimated value through the proposed method was compared with the actual value, automatic dependent surveillance-broadcast (ADS-B), and the error rate was calculated here. As a result, it was confirmed that the velocity and altitude error rate of large aircraft (I1, I3, S2) were lower than that of light aircraft (I2, S2), and the estimated velocity and altitude were relatively high in large aircraft using the proposed method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.87-96
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• 2022

The traffic vulnerable, including elderly pedestrians, have a relatively low walking speed and slow cognitive response time due to reduced physical ability. Although a smart crossing system has been developed and operated to improve problem, it is difficult to operate a signal that reflects the appropriate walking speed for each pedestrian. In this study, a neural network model and a multiple regression model-based traversing speed estimation model were developed using image information collected in an area with a high percentage of traffic vulnerability. to support the provision of optimal walking signals according to real-time traffic weakness. actual traffic data collected from the urban traffic network of Paju-si, Gyeonggi-do were used. The performance of the model was evaluated through seven selected indicators, including correlation coefficient and mean absolute error. The multiple linear regression model had a correlation coefficient of 0.652 and 0.182; the neural network model had a correlation coefficient of 0.823 and 0.105. The neural network model showed higher predictive power.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.373-380
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• 2023

Due to abnormal climate phenomena and climate change in Korea, overgrowth of algae in rivers and reservoirs occurs frequently. Algae in rivers are classified into green algae, blue-green algae, diatom, and other types, and some species of blue-green algae cause problems due to odor and the discharge of toxic substances. In Korea, an algae alert system is in place, and it is issued based on the number of harmful blue-green algae cells. Thus, measuring harmful blue-green algal blooms is very important, and currently, the analysis method of algae involves taking field samples and determining the cell count of green algae, blue-green algae, and diatoms through algal microscopy, which takes a lot of time. Recently, the analysis of algae concentration through Phycocyanin, an alternative indicator for the number of harmful algae cells, has been conducted through remote sensing. However, research on the analysis of the number of blue-green algae cells is currently insufficient. In this study, we water samples for algal analyses were collected from river and counted the number of blue-green algae cells using algae microscopy. We also obtained the Phycocyanin concentration using an optical sensor and acquired algae spectra through a hyperspectral sensor. Based on this, we calculated the equation for estimating blue-green algae cell counts and estimated the number of blue-green algae cells.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.246-246
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• 2021

본 연구는 강우 발생시 유량을 추정하는 것에 목적이 있다. 이를 위해 본 연구는 선행연구의 모형 개발방법론에서 벗어나 딥러닝 알고리즘 중 하나인 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 유량을 추정하였다. 합성곱 신경망은 일반적으로 분류 문제 (classification)을 해결하기 위한 목적으로 개발되었기 때문에 불특정 연속변수인 유량을 모의하기에는 적합하지 않다. 이를 위해 본 연구에서는 합성곱 신경망의 완전 연결층 (Fully connected layer)를 개선하여 연속변수를 모의할 수 있도록 개선하였다. 대부분 합성곱 신경망은 RGB (red, green, blue) 사진 (photograph)을 이용하여 해당 사진이 나타내는 것을 예측하는 목적으로 사용하지만, 본 연구의 경우 일반 RGB 사진을 이용하여 유출량을 예측하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이를 위해 본 연구에서는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는 수문학적 이미지는 입력자료로 활용했다. 합성곱 신경망의 구조는 Convolution Layer와 Pulling Layer가 5회 반복하는 구조로 설정하고, 이후 Flatten Layer, 2개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 다시 1개의 Dense Layer가 이어지는 구조로 설계하였다. 마지막 Dense Layer의 활성화 함수는 분류모형에 이용되는 softmax 또는 sigmoid 함수를 대신하여 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 이와 함께 각 층의 활성화 함수는 정규화 선형함수 (ReLu)를 이용하였으며, 모형의 학습 평가 및 검정을 판단하기 위해 MSE 및 MAE를 사용했다. 또한, 모형평가는 NSE와 RMSE를 이용하였다. 그 결과, 모형의 학습 평가에 대한 MSE는 11.629.8 m3/s에서 118.6 m3/s로, MAE는 25.4 m3/s에서 4.7 m3/s로 감소하였으며, 모형의 검정에 대한 MSE는 1,997.9 m3/s에서 527.9 m3/s로, MAE는 21.5 m3/s에서 9.4 m3/s로 감소한 것으로 나타났다. 또한, 모형평가를 위한 NSE는 0.7, RMSE는 27.0 m3/s로 나타나, 본 연구의 모형은 양호(moderate)한 것으로 판단하였다. 이에, 본 연구를 통해 제시된 방법론에 기반을 두어 CNN 모형 구조의 확장과 수문학적 이미지의 개선 또는 새로운 이미지 개발 등을 추진할 경우 모형의 예측 성능이 향상될 수 있는 여지가 있으며, 원격탐사 분야나, 위성 영상을 이용한 전 지구적 또는 광역 단위의 실시간 유량 모의 분야 등으로의 응용이 가능할 것으로 기대된다.