• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.100-119
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• 2020

During recent years, the heat environment and particulate matter (PM₁₀) have become serious environmental problems, as increases in heat waves due to rising global temperature interact with weakening atmospheric wind speeds. There exist urban heat islands and urban pollution islands with higher temperatures and air pollution concentrations than other areas. However, few studies have examined these issues together because of a lack of micro-scale data, which can be constructed from spatial data. Today, with the help of satellite images and big data collected by private telecommunication companies, detailed spatial distribution analyses are possible. Therefore, this study aimed to examine the spatial distribution patterns of urban heat islands and urban pollution islands within Busan Metropolitan City and to compare the distributions of the two phenomena. In this study, the land surface temperature of Landsat 8 satellite images, air temperature and particulate matter concentration data derived from a private automated meteorological observation system were gridded in 30m × 30m units, and spatial analysis was performed. Analysis showed that simultaneous zones of urban heat islands and urban pollution islands included some vulnerable residential areas and industrial areas. The political migration areas such as Seo-dong and Bansong-dong, representative vulnerable residential areas in Busan, were included in the co-occurring areas. The areas have a high density of buildings and poor ventilation, most of whose residents are vulnerable to heat waves and air pollution; thus, these areas must be considered first when establishing related policies. In the industrial areas included in the co-occurring areas, concrete or asphalt concrete-based impervious surfaces accounted for an absolute majority, and not only was the proportion of vegetation insufficient, there was also considerable vehicular traffic. A hot-spot analysis examining the reliability of the analysis confirmed that more than 99.96% of the regions corresponded to hot-spot areas at a 99% confidence level.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.295-300
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• 2018

Net radiation is the total amount of radiation energy used as a heat flux for the Earth's energy cycle, and net radiation from the surface is an important factor in areas such as hydrology, climate, meteorological studies and agriculture. It is very important to monitoring the net radiation through remote sensing to be able to understand the trend of heat island and urbanization phenomenon. However, net radiation estimation using only remote sensing data is generally causes difference in accuracy depending on cloud. Therefore, in this paper, we retrieved and monitored high resolution surface net radiation at 1 hour interval in Eunpyeong New Town where urbanization using Communication, Ocean and Meteorological Satellite (COMS), Landsat-8 satellite and Computational Fluid Dynamics (CFD) model data reflecting the difference in building height. We compared the observed and estimated net radiation at the flux tower. As a result, estimated net radiation was similar trend to the observed net radiation as a whole and it had the accuracy of RMSE $54.29Wm^{-2}$ and Bias $27.42Wm^{-2}$. In addition, the calculated net radiation showed well the meteorological conditions such as precipitation, and showed the characteristics of net radiation for the vegetation and artificial area in the spatial distribution.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.167-178
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• 2020

KOMPSAT-3A images have been used in various kinds of applications, since its launch in 2015. However, there were limits to scientific analysis and application extensions of these data, such as vegetation index estimation, because no tool was developed to obtain the surface reflectance required for analysis of the actual land environment. The surface reflectance is a product of performing an absolute atmospheric correction or calibration. The objective of this study is to quantitatively verify the accuracy of top-of-atmosphere reflectance and surface reflectance of KOMPSAT-3A images produced from the OTB open-source extension program, performing the cross-validation with those provided by a site measurement data of RadCalNet, an international Calibration/Validation (Cal/Val) portal. Besides, surface reflectance was obtained from Landsat-8 OLI images in the same site and applied together to the cross-validation process. According to the experiment, it is proven that the top-of-atmosphere reflectance of KOMPSAT-3A images differs by up to ± 0.02 in the range of 0.00 to 1.00 compared to the mean value of the RadCalNet data corresponding to the same spectral band. Surface reflectance in KOMPSAT-3A images also showed a high degree of consistency with RadCalNet data representing the difference of 0.02 to 0.04. These results are expected to be applicable to generate the value-added products of KOMPSAT-3A images as analysisready data (ARD). The tools applied in thisstudy and the research scheme can be extended as the new implementation of each sensor model to new types of multispectral images of compact advanced satellites (CAS) for land, agriculture, and forestry and the verification method, respectively.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.695-706
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• 2022

Medium and high-resolution optical satellites have proven their effectiveness in detecting wildfire areas. However, smoke plumes generated by wildfire scatter visible light incidents on the surface, thereby interrupting accurate monitoring of the area where wildfire occurs. Therefore, a technology to extract smoke in advance is required. Deep learning technology is expected to improve the accuracy of smoke extraction, but the lack of training datasets limits the application. However, for clouds, which have a similar property of scattering visible light, a large amount of training datasets has been accumulated. The purpose of this study is to develop a smoke extraction technique using deep learning, and the limits due to the lack of datasets were overcome by using a cloud dataset on transfer learning. To check the effectiveness of transfer learning, a small-scale smoke extraction training set was made, and the smoke extraction performance was compared before and after applying transfer learning using a public cloud dataset. As a result, not only the performance in the visible light wavelength band was enhanced but also in the near infrared (NIR) and short-wave infrared (SWIR). Through the results of this study, it is expected that the lack of datasets, which is a critical limit for using deep learning on smoke extraction, can be solved, and therefore, through the advancement of smoke extraction technology, it will be possible to present an advantage in monitoring wildfires.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.203-203
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• 2011

최근 들어 기후변화에 따른 강우패턴이 바뀌고 각종 하천개발이나 토목공사, 농경지, 경작지 등의 객토 등으로 인해 매년 탁수의 발생이 크게 증가하고 있는 추세이다. 특히 여름철 집중강우의 영향에 따라 상류지역 하천에서 발생하는 부유물질은 호수로 유입되어 장기간 체류하며 심각한 오염원으로서 수중생태계에 치명적인 영향을 주고 있다. 또한 하천과 호수의 상류지역의 농경지나 경작지에서 발생된 부유물질에는 과도한 비료의 사용으로 입자표면에 많은 인을 포함하고 있어 호수 수질악화 및 부영양화의 직접적 요인이 되고 있다. 이에 따라 세계 각국에서는 부유물질은 오염원뿐 아니라 생태계에 영향을 주는 인자로서 엄격히 규제하고 있으며, 특히 농업지역이 많은 하천에 대해서는 유역전체를 대상으로 부유물질에 대한 총량관리를 적용하고 있다. 그러나 우리나라의 경우 하천 수질기준 1급수의 부유물질 농도는 25 mg/l 로서 이는 선진국과 유사한 기준이나 실질적으로 규제가 어려운 실정이다. 수환경에서의 부유물질이란 수체 내 존재하는 유기성, 무기성 물질로써 입자 지름이 2mm 이하의 물에 용해되지 않는 물질을 말하는 것으로, 물의 탁도를 유발시키는 원인이 되며 빛을 차단하여 수생태계에 악영향을 초래한다. 국내 132개 하천을 대상으로 부유물질의 농도와 어류의 종 다양성간 상관성을 조사한 결과, 부유물질의 농도가 15 ~ 20 mg/l 이상에서 종 다양도는 1.0 이하로 급감하는 경향을 보였다(최재석 등, 2004). 한편, 대청호는 1975년부터 1980년에 걸쳐 건립된 저수 면적 $72.8km^2$, 저수량 15억톤의 인공호수로 우리나라 3번째 규모의 인공호수이다. 특히, 대전 및 청주지역의 식수는 물론, 생활용수 및 공업용수를 공급하는 중요한 수자원으로서 부유물질에 대한 모니터링 및 관리가 시급하나 저수 용량이 크고 체류시간이 길어 여름철 부영양화가 매년 반복되고 있다. 따라서 본 연구에서는 부유물질의 농도 변화에 따른 분광반사 특성을 조사하고, 이를 대청호의 Landsat 위성영상에 적용하여 대청호 내 부유물질의 농도변화를 추정하였다. 이와 함께 부유물질 농도 변화에 따른 탁수 환경 모니터링에 원격탐사 기법이 효과적임을 제시하고자 하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.423-423
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• 2023

습지 생태계는 탄소저장고, 대기 온·습도 조절 등의 기능을 수행하는 만큼 면밀한 관리가 요구된다. 습지의 규모와 생태계는 밀접한 연관성을 가지므로 그 규모를 우선적으로 파악할 필요가 있으며, 이를 위해 지표면의 상태를 산지, 습지, 수역 등의 항목으로 구분한 토지피복지도가 고려될 수 있다. 현재, 환경부에서 운영 중인 환경공간정보서비스(https://egis.me.go.kr/)에서는 각각 30 m, 5 m, 1 m의 공간 해상도와 7, 22, 41가지 분류 항목을 갖는 대분류, 중분류, 세분류로 구분된 토지피복지도를 제공하며 이러한 자료들은 모두 1년 이상의 시간 해상도를 갖는다. 습지의 경우, 계절에 따른 환경 변화로 인한 규모의 변동성이 크게 나타날 수 있기 때문에 1년 이하의 시간 해상도를 갖는 고품질 토지피복 분류 정보가 요구된다. 따라서 본 연구에서는 기존 자료의 낮은 시간 해상도 보완을 목표로, 1개월과 30 m의 시·공간 해상도를 갖는 토지피복지도를 구축하기 위한 방법론을 제안하고자 한다. 이를 위해 Landsat-8 등과 같은 다양한 인공위성 자료를 수집하고, Support Vector Machine 등과 같은 머신러닝 기법을 적용하였다. 최종적으로 습지보전법에서 지정한 습지보호지역 중 내륙습지 26개소를 대상으로, 본 연구로부터 산출된 토지피복지도를 기존 환경공간정보서비스 내 대분류 토지피복지도와 비교·평가하였다.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.1
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• pp.1-12
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• 2017

This study has been carried out for the purpose of predicting the potential habitat sites of invasive alien plants in the DMZ and providing the basic data for decision-making in managing the future DMZ natural environment. From 2007 to 2015, this study collected the data for the advent of Ambrosia trifida var. trifida through fieldwork around the DMZ area, and simulated the potential distribution area of Ambrosia trifida var. trifida using Maxent model among the models of species distributions. As a result, it showed that the potential distribution area of the Ambrosia trifida var. trifida was concentrated in the western DMZ with relatively low altitude and scanty in the central east regions with relatively high elevation and forest cover rate. Because the invasive alien vegetation is a significant threatening factor in the agriculture and restoration of ecology and it costs a lot to restore the area already invaded by invasive alien vegetation, advance precautions are necessary to prevent biological invasions. It is expected that it is possible to predict the disturbed ecosystems through this study for the efficient land use within DMZ in the future and to apply this study in setting up the areas for the development and conservation within the DMZ.

• Journal of Korean Society of Forest Science
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• v.101 no.4
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• pp.547-557
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• 2012

This study purposed to estimate the forest biomass using k-Nearest Neighbor (k-NN) algorithm. Multiple data sources were used for the analysis such as forest type map, field survey data and Landsat TM data. The accuracy of forest biomass was evaluated with the forest stratification, horizontal reference area (HRA) and spatial filtering. Forests were divided into 3 types such as conifers, broadleaved, and Korean pine (Pinus koriansis) forests. The applied radii of HRA were 4 km, 5 km and 10 km, respectively. The estimated biomass and mean bias for conifers forest was 222 t/ha and 1.8 t/ha when the value of k=8, the radius of HRA was 4 km, and $5{\times}5$ modal was filtered. The estimated forest biomass of Korean pine was 245 t/ha when the value of k=8, the radius of HRA was 4km. The estimated mean biomass and mean bias for broadleaved forests were 251 t/ha and -1.6 t/ha, respectively, when the value of k=6, the radius of HRA was 10 km. The estimated total forest biomass by k-NN method was 799,000t and 237 t/ha. The estimated mean biomass by ${\kappa}NN$method was about 1t/ha more than that of filed survey data.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.