• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.413-413
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• 2020

Godunov형 모형을 이용한 홍수모델링에서는 일반적으로 구조적 사각격자나 비구조적 삼각격자가 주로 적용된다. 2차원 수치모형을 이용한 홍수모델링에서 연구유역의 정보가 격자의 노드나 중심에 입력되므로 적용격자의 유형과 생성방법에 따라 모형의 입력자료 오차에 영항을 줄 수 있다. 따라서, 연구유역이 지형 변동성이 심한 지역이거나 흐름형상이나 흐름변동이 심한 구간이라면, 고해상도 격자를 통해 모형의 입력자료 오차를 최소화할 할 수 있다. 본 연구에서는 2가지 유형에 대한 연구를 수행하였다, 첫 번째는 홍수해석을 위한 2차원 모형의 격자형상과 해상도에 따른 홍수위 및 홍수범람범위를 비교·분석하는 연구를 수행하였다. 연구유역은 2000년 10월 29일부터 11월 19일까지 홍수가 발생한 영국의 Severn 강 유역이다. 연구유역의 홍수 모델링을 위한 지형자료는 3m 해상도의 LiDAR(Light Detection And Ranging)를 이용하여 구축하였으며, 격자유형 및 해상도에 따른 2차원 홍수위 및 홍수범람범위를 비교·분석하기 위해서 홍수 발생기간 동안 촬영된 4개(2000년 8월 11, 14, 15, 17일)의 ASAR(Advanced Synthetic Aperture Radar) 영상자료를 활용하였다. 즉, ASAR 영상으로 촬용된 최대범람시기 및 홍수류의 배수기를 활용하여 최대범람범위뿐만 아니라 홍수가 증가하는 시기와 하류단 배수로 인해 홍수가 감소하는 시기를 모두 포함하는 홍수범람범위에 대한 격자유형별 2차원 홍수범람모형의 계산 결과에 대해 비교하였다. 두 번째는 아마존 강 중류유역의 2,500K㎡ 면적에 해당하는 대규모 유역에 대해 SRTM(Shuttle Radar Topography Mission) 지형자료를 이용하여 홍수기와 갈수기에 대해 2차원 모델링을 수행하고 그 결과를 위성자료와 비교하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.321-321
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• 2022

유역 모델링은 유역에 강우의 유출 과정을 재현할 수 있는 과정이다. 과거 유역 모델링은 1차원 수준에서 유출과정을 모의하는데 그쳤으나, 기술이 발전함에 있어 입력하는 매개변수 수가 증가함에 따라 모의값의 신뢰성이 높아지고 있다. 본 연구에서는 다양한 매개변수 중 공간매개변수로써 널리 활용되고 있는 수치표고모델의 신뢰성과 범용성을 확인하고자 한다. 유역모델링 연구에 있어, 수치표고모델 정확성은 결과값의 신뢰성을 좌우하는 중요한 인자 중 하나이다. 수치표고모델이란 실제 지형이 나타내는 표고 정보를 수치화 하여 격자 안에 담은 형태의 파일로 대표적으로 DEM(Digital Elevation Models)과 DSM(Digital Surface Models)로 나눌 수 있다 DEM의 경우 해당 지형의 고도정보만을 담고있으며, DSM은 지표면 상의 나무, 건물 등 포함한 지표면의 고도를 담고 있다. 현재 NASA에서는 전 지구의 30m격자 크기로 SRTM-DSM을 제공하고 있으며, 우리나라 국토지리정보원에서도 90m 격자크기의 DEM을 제공하고 있다. 본 연구에서는 남한강 유역의 수치표고모델을 세 가지 Case로 나눠서 유출량 변화 검토를 진행하였다. 산지가 많은 남한강 유역의 10개의 소유역을 선정하였고, 다음과 같이 3개의 Case를 적용하였다. Case1, DEM 자료를 입력했을 경우, Case2, DSM 자료를 입력했을 경우, Case3 DSM+DEM 자료를 입력했을 경우, 각 Case에 대해 유출량을 산정하였고, 그 결과값을 분석하였다. 해당 유역에 세 가지 Case 모두 유출량의 변화량의 큰 차이를 보이지 않았으며, 공간매개변수 적용에 있어 타당성을 보였다. 따라서 본 연구는 인공위성을 통해 산출된 수치표고모델의 신뢰성을 확인 하였고, 활용가능성을 검토 하였다. 이에 따라 향후 연구에 수치표고모델 적용에 있어 미계측유역에도 활용가능한 연구로 기여할 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.39-39
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• 2022

최근 전 지구적인 기후변화로 인하여, 극한기후일수 증가, 이상기후 등의 환경문제가 심화되고 있으며, 이는 이·치수 측면에서 물관리 정책 수립 등의 어려움을 가중시키고 있다. 더욱이 우리나라는 산악지형이 많고 수계형태가 복잡한 지형적 특성과 여름철에 연강수량이 집중되는 계절적 특성을 지니고 있어 수자원의 효율적인 관리가 어려운 실정이다. 연구 대상 유역은 DMZ 이북의 미계측 유역을 포함한 북한강 전체유역을 대상으로 하였으며, 주요 댐 유역별로 세분하여 6개 댐유역(화천댐, 춘천댐, 소양강댐, 의암댐, 청평댐, 팔당댐)에서 홍수량 분석을 실시하였다. 이때 상류의 미계측 유역을 분석하기 위해 격자기반으로 매개변수의 물리적인 계산이 가능한 분포형 모형인 GSSHA 모형을 활용하였다. 또한 온실가스 저감 정책의 실현 여부에 따른 저탄소 및 고탄소 기후변화 시나리오를, 미래 전·중·후반기의 기간별로 적용하여, 현재를 포함한 7가지 시나리오를 반영하였다. 연구결과, 미래 전반기에서는 홍수량이 다소 감소하는 것으로 나타났으며 미래 중반기 및 후반기에서는 증가하는 것으로 분석되었다. 소유역별 분석 결과를 종합하면, 탄소 배출 농도에 따른 평균 홍수량 변화율은 저탄소 시나리오에서는 -1.03 %에서 +4.01 %, 고탄소 시나리오에서는 -4.54 %에서 +17.73 %로 나타났다. 저탄소와 고탄소 시나리오를 비교하면 홍수량 변화율 차이는 미래 기간 및 소유역 마다 상이하지만, 최소 359 %에서 최대 527 %까지 차이를 보였다. 따라서 인류의 탄소저감 노력은 기후변화 자체를 막을 수는 없으나, 그 영향을 최대 5배 이상 감소할 수 있다는 결론을 도출하였다. 본 연구는 북한강 유역의 미래 기간별 확률홍수량 예측값 및 수문특성의 변화 전망을 주요 댐 유역에서 정량적으로 제시하였다. 이에 따라 본 연구가 향후 기후변화에 대비한 이·치수 정책 마련 및 접경지역의 재난예방에 기여할 수 있을 것으로 기대된다.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.409-420
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• 2012

Since the Industrial Revolution, CO2 levels have been increasing with climate change. In this study, Analyze time-series changes in snow cover quantitatively and predict the vanishing point of snow cover statistically using remote sensing. The study area is Mt. Kilimanjaro, Tanzania. 23 image data of Landsat-5 TM and Landsat-7 ETM+, spanning the 27 years from June 1984 to July 2011, were acquired. For this study, first, atmospheric correction was performed on each image using the COST atmospheric correction model. Second, the snow cover area was extracted using the NDSI (Normalized Difference Snow Index) algorithm. Third, the minimum height of snow cover was determined using SRTM DEM. Finally, the vanishing point of snow cover was predicted using the trend line of a linear function. Analysis was divided using a total of 23 images and 17 images during the dry season. Results show that snow cover area decreased by approximately $6.47km^2$ from $9.01km^2$ to $2.54km^2$, equivalent to a 73% reduction. The minimum height of snow cover increased by approximately 290 m, from 4,603 m to 4,893 m. Using the trend line result shows that the snow cover area decreased by approximately $0.342km^2$ in the dry season and $0.421km^2$ overall each year. In contrast, the annual increase in the minimum height of snow cover was approximately 9.848 m in the dry season and 11.251 m overall. Based on this analysis of vanishing point, there will be no snow cover 2020 at 95% confidence interval. This study can be used to monitor global climate change by providing the change in snow cover area and reference data when studying this area or similar areas in future research.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.139-147
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• 2012

Mongolia's solar-meteorological resources map has been developed using satellite data and reanalysis data. Solar radiation was calculated using solar radiation model, in which the input data were satellite data from SRTM, TERA, AQUA, AURA and MTSAT-1R satellites and the reanalysis data from NCEP/NCAR. The calculated results are validated by the DSWRF (Downward Short-Wave Radiation Flux) from NCEP/NCAR reanalysis. Mongolia is composed of mountainous region in the western area and desert or semi-arid region in middle and southern parts of the country. South-central area comprises inside the continent with a clear day and less rainfall, and irradiation is higher than other regions on the same latitude. The western mountain region is reached a lot of solar energy due to high elevation but the area is covered with snow (high albedo) throughout the year. The snow cover is a cause of false detection from the cloud detection algorithm of satellite data. Eventually clearness index and solar radiation are underestimated. And southern region has high total precipitable water and aerosol optical depth, but high solar radiation reaches the surface as it is located on the relatively lower latitude. When calculated solar radiation is validated by DSWRF from NCEP/NCAR reanalysis, monthly mean solar radiation is 547.59 MJ which is approximately 2.89 MJ higher than DSWRF. The correlation coefficient between calculation and reanalysis data is 0.99 and the RMSE (Root Mean Square Error) is 6.17 MJ. It turned out to be highest correlation (r=0.94) in October, and lowest correlation (r=0.62) in March considering the error of cloud detection with melting and yellow sand.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.155-163
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• 2010

The past researches on flood inundation simulation mainly focused on development of numerical models based on unstructured mesh networks to improve model performances. However, despite the accurate simulation results, such models are not suitable for real-time flood inundation forecasting due to a huge computational burden in terms of geographic data processing. In addition, even though various types of vector and raster data are available to be compatible with flood inundation models for post-processes such as flood hazard mapping and flood inundation risk analysis, the unstructured mesh-based models are not effective to fully use such information due to data incommensurability. Therefore, this study aims to develop a raster-based two-dimensional inundation model; it guarantees computational efficiency because of direct application of DEM for flood inundation modeling and also has a good compatibility with various types of raster data, compared to a commercial model such as FLUMEN. We applied the model to simulate the BaekSan levee break in the Nam river during a flood period from August 10 to 13, 2002. The simulation results showed a good agreement with the field-surveyed inundation area and were also very similar with results from the FLUMEN. Moreover, the model provided physically-acceptable velocity vectors with respect to inundating and returning flows due to the difference of water level between channel and lowland.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.501-510
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• 2014

Volcanic eruptions alone may lead to serious natural disasters, but the associated release of water from a caldera lake may be equally damaging. There is both historical and geological evidence of the past eruptions of Mt. Baekdusan, and the volcano, which has not erupted for over 100 years, has recently shown signs of reawakening. Action is required if we are to limit the social, political, cultural, and economic damage of any future eruption. This study aims to identify the area that would be inundated following a volcanic flood from the Cheon-Ji caldera lake that lies within Mt. Baekdusan. A scenario-based numerical analysis was performed to generate a flood hydrograph, and the parameters required were selected following a consideration of historical records from other volcanoes. The amount of water at the outer rim as a function of time was used as an upper boundary condition for the downstream routing process for a period of 10 days. Data from the USGS were used to generate a DEM with a resolution of 100 m, and remotely sensed satellite data from the moderate-resolution imaging spectroradiometer (MODIS) were used to show land cover and use. The simulation was generated using the software FLO-2D and was superposed on the remotely sensed map. The results show that the inundation area would cover about 80% of the urban area near Erdaobaihezhen assuming a 10 m/hr collapse rate, and 98% of the area would be flooded assuming a 100 m/hr collapse rate.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.89-99
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• 2015

Recently, our construction industry is actively participating in numerous city planning projects in the third world countries. Considering the current depression of domestic real estate market, the emerging foreign demands could certainly provide substantial opportunities for the domestic industry to overcome the trough. For the field planners dealing with such foreign projects, though, the immediate problem is the lack of public statistics and geographic information to perform spatial analyses and/or prepare master plans. This study, in this context, tries to simulate a process to construct a digitized basemap of the case area, 'Bab Ezzouar,' in Algeria of Northern Africa. The area is a typical municipality that lacks the IT databases. To overcome the data shortage, the study uses the satellite map tiles so as to digitize the roads and building structures. It then estimates the block-wise populations based on the building image interpolation as well as the supplementary field survey data. The topographic TINs are also built by the SRTM (Shuttle Radar Topography Mission) digital elevation maps so that the three-dimensional configuration of the structures and terrains are rendered to check the urban scenery and skylines.

• Journal of Environmental Health Sciences
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• v.47 no.3
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• pp.267-278
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• 2021

Objectives: Using atmospheric dispersion representative models (AERMOD and CALPUFF), the emissions characteristics of each model were compared and analyzed in ready-mixed concrete manufacturing facilities that generate a large amount of particulate matter (PM-10, PM-2.5). Methods: The target facilities were the ready-mixed concrete manufacturing facilities (Siheung RMC, Goyang RMC, Ganggin RMC) and modeling for each facility was performed by dividing it into construction and operation times. The predicted points for each target facility were selected as 8-12ea (Siheung RMC 10, Goyang RMC 8, and Gangjin RMC 12ea) based on an area within a two-kilometer radius of each project district. The terrain input data was SRTM-3 (January-December 2019). The meteorological input data was divided into surface weather and upper layer weather data, and weather data near the same facility as the target facility was used. The predicted results were presented as a 24-hour average concentration and an annual average concentration. Results: First, overall, CALPUFF showed a tendency to predict higher concentrations than AERMOD. Second, there was almost no difference in the concentration between the two models in non-complex terrain such as in mountainous areas, but in complex terrain, CALPUFF predicted higher concentrations than AERMOD. This is believed to be because CALPUFF better reflected topographic characteristics. Third, both CALPUFF and AERMOD predicted lower concentrations during operation (85.2-99.7%) than during construction, and annual average concentrations (76.4-99.9%) lower than those at 24 hours. Fourth, in the ready-mixed concrete manufacturing facility, PM-10 concentration (about 40 ㎍/m³) was predicted to be higher than PM-2.5 (about 24 ㎍/m³). Conclusions: In complex terrain such as mountainous areas, CALPUFF predicted higher concentrations than AERMOD, which is thought to be because CALPUFF better reflected topographic characteristics. In the future, it is recommended that CALPUFF be used in complex terrain and AERMOD be used in other areas to save modeling time. In a ready-mixed concrete facility, PM-10, which has a relatively large particle size, is generated more than PM-2.5 due to the raw materials used and manufacturing characteristics.