• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.363-373
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• 2023

Forest biomass surveys are regularly conducted to assess and manage forests as carbon sinks. LiDAR (Light Detection and Ranging), a remote sensing technology, has attracted considerable attention, as it allows for objective acquisition of forest structure information with minimal labor. In this study, we propose a method for estimating overstory and understory biomass in forest stands using backpack laser scanning (BPLS) and unmanned aerial vehicle laser scanning (UAV-LS), and assessed its accuracy. For overstory biomass, we analyzed the accuracy of BPLS and UAV-LS in estimating diameter at breast height (DBH) and tree height. For understory biomass, we developed a multiple regression model for estimating understory biomass using the best combination of vertical structure metrics extracted from the BPLS data. The results indicated that BPLS provided accurate estimations of DBH (R² =0.92), but underestimated tree height (R² =0.63, bias=-5.56 m), whereas UAV-LS showed strong performance in estimating tree height (R² =0.91). For understory biomass, metrics representing the mean height of the points and the point density of the fourth layer were selected to develop the model. The cross-validation result of the understory biomass estimation model showed a coefficient of determination of 0.68. The study findings suggest that the proposed overstory and understory biomass survey methods using BPLS and UAV-LS can effectively replace traditional biomass survey methods.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.15-22
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• 2022

In order to improve the accuracy of the deep learning object detection technique, the effect of magnification rate conditions and seasonal factors on detection accuracy in aerial photographs and drone images was analyzed through experiments. Among the deep learning object detection techniques, Mask R-CNN, which shows fast learning speed and high accuracy, was used to detect the vehicle to be detected in pixel units. Through Seoul's aerial photo service, learning images were captured at different screen magnifications, and the accuracy was analyzed by learning each. According to the experimental results, the higher the magnification level, the higher the mAP average to 60%, 67%, and 75%. When the magnification rates of train and test data of the data set were alternately arranged, low magnification data was arranged as train data, and high magnification data was arranged as test data, showing a difference of more than 20% compared to the opposite case. And in the case of drone images with a seasonal difference with a time difference of 4 months, the results of learning the image data at the same period showed high accuracy with an average of 93%, confirming that seasonal differences also affect learning.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.5
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• pp.961-969
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• 2017

Today, the world is experiencing a variety of natural disasters due to the extreme weather. Drought that occurred throughout Southeast Asia from February to May 2016 is also a form of abnormal climate. As a result of this drought, five countries, including Cambodia, Thailand, Vietnam, Laos and Myanmar, faced food shortages, food shortages, as well as rice yields for export. In this study, remote sensing technique was applied to the vicinity of Tonlé Sap, the largest lake in Southeast Asia, to quantitatively analyze the damage caused by drought. As a result, the change of land cover caused a drastic decrease in the water system (132.582㎢) and greenery (706.937㎢) in February 2016, and the reduced water system and greenery changed to dry land and paddy field. It was also found that the temperature rise of 6℃ ~ 8 ℃ compared to the previous year due to the drought from February to April 2016 due to the change of the surface temperature. And it was found that the function of the lake was deteriorated in April due to continuous drought.

• 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.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.495-504
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• 2008

Evapotranspiration (ET) is an important state variable while simulating daily streamflow in hydrological models. In the estimation of ET, for example, when using FAO Penman Monteith equation, the LAI (Leaf Area Index) value reflecting the conditions of vegetation generally affects considerably. Recently in evaluating the vegetation condition as a fixed quantity, the remotely sensed LAI from MODIS satellite data is available, and the time series values of spatial LAI coupled with land use classes are utilized for ET evaluation. Four years (2001-2004) of MODIS LAI was prepared for the evaluation of Penman Monteith ET in the continuous hydrological model, SLURP (Semi-distributed Land Use-based Runoff Processes). The model was applied for simulating the dam inflow of Chungju watershed ($6661.3km^2$) located in the upstream of Han river basin. For four years (2001-2004) dam inflow data and meteorological data, the model was calibrated and verified using MODIS LAI data. The average Nash-Sutcliffe model efficiency was 0.66. The 4 years watershed average Penman Monteith ETs of deciduous, coniferous, and mixed forest were 639.1, 422.4, and 631.6 mm for average MODIS LAI values of 3.64, 3.50, and 3.63 respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3B
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• pp.285-293
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• 2010

Soil moisture is a key factor to control the exchange of water and energy between the surface and the atmosphere. In recent, many researches for spatial and temporal variability analyses of soil moisture have been conducted. In this study, we analyzed the spatio-temporal variability of soil moisture in Walnut Gulch Experimental Watershed, Arizona, U.S. during the Soil Moisture Experiment 2004 (SMEX04). The spatio-temporal variability analyses were performed to understand sensitivity of five observation sites with precipitation and relationship between mean soil moisture, and its standard deviation and coefficient of variation at the sites, respectively. It was identified that log-normal distribution was superior to replicate soil moisture spatial patterns. In addition, precipitation was identified as a key physical factor to understand spatio-temporal variability of soil moisure based on the temporal stability analysis. Based on current results, higher spatial variability was also observed which was agreed with the results of previous studies. The results from this study should be essential for improvement of the remotely sensed soil moisture retrieval algorithm.