• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.345-345
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• 2020

우리나라는 물산업 강국 도약을 위한 글로벌 경쟁력을 갖추기 위해 2015년 세계물포럼 이후 복합 수재해 대응과 능동적 하천관리 분야가 중요시되고 있다. 기존의 하천관리는 수위관측소 및 하도계획을 중심으로 이루어져왔으나 최근에는 항공측량, 드론 등 원격관리시스템에 기반을 둔 하천 통로에 대한 입체적인 3차원 관리로의 인식 전환이 고려되는 추세이다. 대부분 수위 및 하도를 중심으로 한 조사방식은 계측장비, 인력, 시간 등 많은 소요비용이 필요하게 되어 급변하는 하천 공간 조사 기술로 제한적 요소가 많아 현재의 하천조사 시스템으로는 하천관리에 필요한 데이터를 환경변화에 맞추어 신속하고 정확하게 취득하기가 어려운 실정으로 하천관리 고도화를 위해 필요한 자료를 적기에 신속하고 효율적으로 구축할 수 있는 기술 개발 필요한 실정이다. 사회 환경의 발달로 급변하는 환경에 맞는 적정한 하천관리를 위해서는 유역 및 하천에 대한 수문정보 생산 및 하상변동조사가 필요하고 첨단기술(ICT)을 활용하여 조사의 효율성, 안전성 및 정확성을 높일 필요성이 있다. 한편, 하천유역조사는 하천유역에 대한 다양한 정보 수집을 목표로 하나 그간 유역조사에 대한 체계적인 연구가 이루어지지 않아 수요자의 요구 자료 제공이 미흡한 실정이다. 최근 수심, 하상, 하천재료 등 하천법에 규정된 측량은 현장 직접계측을 통해 실시되고 있으며 측량의 공간적·시간적 범위의 변동으로 기존 방식의 비용이 급증하게 되어 신기술을 통한 효율화가 필요한 실정이다. 현재 하천조사를 위해 위성 및 유인항공기 등 다양한 방법이 활용되고 있으나 고빙용을 수반하고 위성영상은 공간해상도가 낮아 폭이 좁은 하천에 적용하는데 한계가 있다. 또한, 폭이 좁고 길게 형성된 하천의 특성상 항공측량 보다 드론이 효율적으로 드론에 레이저 광선으로 지형을 측량하는 장비를 탑재해 3차원 지형을 측량하는 방법이 유리하다. 따라서 본 연구에서는 동진강 상류 하천을 대상으로 드론을 활용할 경우 투입인력 및 소요시간 절감, 장비 및 인력 진입 불가지역에 대한 정보획득, 높은 공간해상도, 항공측량 대비 경제성이 높은 것으로 판단되어 드론을 활용하여 하천지형 모니터링을 실시하여 유역관리 및 수질모델링에 필요한 지도 생성 및 유역의 공간 정보를 획득하여 향후 유역관리 모형의 기초자료로 활용하고자 하였다.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.495-505
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• 2023

The Musan mine, situated in Musan County, Hamgyong Province, North Korea, stands as a prominent open-pit iron mine on the Korean Peninsula. This study focuses on estimating the mining and dumping activities within the Musan mine area by analyzing digital elevation model (DEM) changes. To calculate the long-term volume changes in the Musan mine, we digitized and converted the 1:200,000-scale third topographic map of the Joseon published in 1918 and compared with interferometric synthetic aperture radar (InSAR) DEMs, including Shuttle Radar Topography Mission DEM (2000) and Copernicus DEM (2011-2015). The findings reveal that over a century, Musan mine yielded around 1.37 billion tons of iron ore, while approximately 1.06 billion tons of waste rock were dumped. This study is particularly significant as it utilizes a historical topographic map predating the full-scale development of Musan mine to estimate a century's mining production and waste rock deposition. It is expected that this research provides valuable insights for future investigation of surface change of North Korea where the acquisition of in situ data remains challenging.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.31-42
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• 2015

The objective of this study is to estimate the flood flow of inaccessible regions using satellite-derived rainfall and global geographic data. This study focuses on Dongsingun, an area located upstream of the Cheongcheon River in North Korea. The IFAS model was used to estimate flood flow. The model was calibrated in the Gap Stream watershed in South Korea and verified for the Byeongsung Stream watershed in the Nakdong River basin. Satellite-derived rainfalls for North Korea was revised using ground gauge data. Analysis results using CMORPH and GSMaP_NRT showed $4,886m^3/s$ and $5,718m^3/s$ respectively. In future studies, hydrological analysis in unmeasured and inaccessible regions will be carried out by applying more rainfall events.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1477-1482
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• 2023

This study examines marine heat wave (MHW) in the Northeast Asia region from 2012 to 2021, utilizing geostationary satellite Communication, Ocean, and Meteorological Satellite (COMS)/Meteorological Imager sensor (MI) and GEO-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager sensor (AMI) Sea Surface Temperature (SST) data. Our analysis has identified an increasing trend in the frequency and intensity of MHW events, especially post-2018, with the year 2020 marked by significantly prolonged and intense events. The statistical validation using Optimal Interpolation (OI) SST data and satellite SST data through T-test assessment confirmed a significant rise in sea surface temperatures, suggesting that these changes are a direct consequence of climate change, rather than random variations. The findings revealed in this study serve the necessity for ongoing monitoring and more granular analysis to inform long-term responses to climate change. As the region is characterized by complex topography and diverse climatic conditions, the insights provided by this research are critical for understanding the localized impacts of global climate dynamics.