• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.206-209
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• 2012

산불에 영향을 미치는 인자는 일반적으로 산림 연료와 기상 그리고 지형인자로 구분할 수 있다. 이중 지형인자는 경사와 사면의 방향 그리고 해발고, 사면의 길이 등 여러 조건들이 복합적으로 이루어진 산의 형세를 이룬다. 지형 조건 중 경사는 산불 발생 후 확산속도에 영향을 주며 사면의 방향은 태양복사에너지 일사량에 따른 연료의 건조와 연료의 온도 등에 영향을 줌으로써 산불의 발생과 확산에 영향을 준다. 특히 산불이 시작되는 이른 봄철에는 강설 후 사면향에 따라 적설지역과 눈이 녹아 건조한 지역이 사면의 향에 따라 큰 차이를 나타내고 있으며 이로 인해 산불위험 예보시에도 사면향에 따른 산불발생위험을 달리 적용해야 할 필요성이 있다. 이에 본 연구에서는 사면의 방향에 따른 산불위험성을 평가하기 위해 먼저 태양복사에너지의 일사량 분석을 실시하였고 산불사례조사를 통해 사면의 방향에 따라 산불발생 위험과 피해면적을 비교, 분석하였다. 그 결과 태양복사에너지 일사량이 가장 많은 사면의 향인 남사면을 중심으로 산불발생이 높은 것으로 분석되었다.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.19-37
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• 2017

Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS-DNB) data provides a much higher capability for observing and quantifying nighttime light (NTL) brightness in comparison with Defense Meteorological Satellite-Operational Linescan System (DMSP-OLS) data. In South Korea, there is little research on the detection of NTL brightness change using VIIRS-DNB data. This study analyzed the spatial distribution and change of NTL brightness between 2013 and 2016 using VIIRS-DNB data, and detected its spatial relation with possible influencing factors using regression models. The intra-year seasonality of NTL brightness in 2016 was also studied by analyzing the deviation and change clusters, as well as the influencing factors. Results are as follows: 1) The higher value of NTL brightness in 2013 and 2016 is concentrated in Seoul and its surrounding cities, which positively correlated with population density and residential areas, economic land use, and other factors; 2) There is a decreasing trend of NTL brightness from 2013 to 2016, which is obvious in Seoul, with the change of population density and area of industrial buildings as the main influencing factors; 3) Areas in Seoul, and some surrounding areas have high deviation of the intra-year NTL brightness, and 71% of the total areas have their highest NTL brightness in January, February, October, November and December; and 4) Change of NTL brightness between summer and winter demonstrated a significantly positive relation with snow cover area change, and a slightly and significantly negative relation with albedo change.

• Journal of Korea Water Resources Association
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• v.39 no.3 s.164
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• pp.261-274
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• 2006

This study performs a daily long-term runoff analysis for 30 years to forecast medium- and long-term probabilistic reservoir inflows on the Soyang River basin. Snowmelt is computed by Anderson's temperature index snowmelt model and potenetial evaporation is estimated by Penman-combination method to produce input data for a rainfall-runoff model. A semi-distributed TOPMODEL which is composed of hydrologic rainfall-runoff process on the headwater-catchment scale based on the original TOPMODEL and a hydraulic flow routing model to route the catchment outflows using by kinematic wave scheme is used in this study It can be observed that the time variations of the computed snowmelt and potential evaporation are well agreed with indirect observed data such as maximum snow depth and small pan evaporation. Model parameters are calibrated with low-flow(1979), medium-flow(1999), and high-flow(1990) rainfall-runoff events. In the model evaluation, relative volumetric error and correlation coefficient between observed and computed flows are computed to 5.64% and 0.91, respectively. Also, the relative volumetric errors decrease to 17% and 4% during March and April with or without the snowmelt model. It is concluded that the semi-distributed TOPMODEL has well performance and the snowmelt effects for the long-term runoff computation are important on the study area.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.361-365
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• 2007

자연재난은 발생원인에 따라 바람, 강우, 적설, 파고등으로 구분할 수 있다. 이러한 재난원인은 자연현상의 일부로 발생하였으나, 경제가 발전함에 따라 과거에 비해 재해에 안전한 사회를 요구하게 되어 재해저감을 위한 투자와 방재정책 개발의 필요성이 증대되고 있다. 기존에는 자연재난을 저감시키기 위하여 연최대치 강우를 통계 분석하고 강우-유출관계를 이용하여 장래에 발생할 홍수량을 예측하여 자연재난을 저감할 수 있도록 설계하는 것이 일반적인 재난저감계획 수립으로 간주되었다. 그러나, 재해예방을 위해서는 과거에 발생한 재난의 지역적 특성을 분석하여 재난발생 위험과 피해규모를 파악함으로써 재난계획 수립의 기초자료로 활용할 필요성이 있다. 이러한 자연재난에 대한 대책수립은 국가차원에서 필요하며 이를 위해서는 지역별 안전도 평가의 필요성이 증가하게 된다. 그러나 이러한 연구를 수행하기 위해서는 방대한 자료를 바탕으로 풍수해 발생특성을 분석하는 연구와 지자체 또는 지역별 재난피해저감 능력을 수치적으로 나타낼 필요성이 있다. 따라서, 공학적인 면뿐만 아니라 행정적인 면을 동시에 고려하는 학제간 연구가 필요하다. 본 연구에서는 우리나라에서 주로 발생하는 풍수해에 의한 자연재난 특성을 파악하고 재난발생 확률을 고려한 재난피해규모와 재난피해 저감능력을 비교하여 전국 234개 지자체별 지역 및 시설물별 안전도를 평가하고자 한다. 과거 10년간 재해연보에 수록된 지자체별 피해현황을 지자체별 특성을 고려하여 분석, 지자체별 연평균 피해규모를 소방방재청의 지역별 안전도 지침서에 의거, 10등급으로 분석하였다. 또한, 지자체별로 투자우선순위 및 방재예산편성의 효율성 극대화를 위해 지자체별 시설물별 피해현황을 분석하는 기법을 개발하여 지자체별 시설물별 안전도 진단지표를 설정하였다. 분석된 결과는 지자체별 시설물별 재해저감을 위한 풍수해저감 종합계획 재난보험제도 도입등의 기초자료로 적용될 수 있다.로 나타났다. 이는 두 흐름에 의해 와(vortex)가 크게 형성되어 하상의 세굴에 영향을 미치기 때문으로 판단되었다.보다 본질부가 차지하는 면적이 월등히 적고 제1차 및 제2차섬유가 차지하는 면적이 많았다. 따라서 고섬유함량인 대마의 품종개량에 있어서는 가능한 한 본질부가 차지하는 면적은 축소시키고 제1차 및 제2차섬유가 차지하는 면적은 증대시켜야 할 것으로 본다.우리 나라 수도의 작기는 앞으로 당기는 것이 좋다고 고찰된다. 6. 우리 나라의 현행 수도작기로 본 기온 및 일조조건은 수도의 분얼전기에 대해서는 호조건하에 놓여 있으나, 분얼후기인 7월 중ㆍ하순 경의 일조부족과 고온다습조건은 병해, 특히 도열병의 유발원인이 되고 있다. 7. 우리 나라의 현행수도작기로 본 전국각지의 수도의 출수기는 모두 일조시간이 적은 부적당한 시기에 처해 있다. 8. 출수후 40일간의 평균기온에 의한 적산온도 88$0^{\circ}C$의 출현기일은 수원에서 8월 23일이었고, 년간편차를 고려한 안전출수기일은 8월 19일로서 적산온도면에서는 관행 출수기일은 약간 늦다고 보았다. 9. 등열기의 평균기온에 의한 적산온도는 현행 수도작기로서는 최종한계시기에 놓여 있으며, 평균기온의 년간편차와 우리 나라의 최저기온이 낮은 점을 고려할 때, 현행출수기는 다소 늦은 것으로 보았다. 10. 생육단계별의 수도체내의 질소함량은 영양생장기의 질소함량이 과다하였으며, 출수 이후에 영양조락을 여하히 방지하느냐가 문제된다고 보았다. 11. 수리불안전답 및 천수답이 차지하는 전답면적의 비율은 차차 감소되고 있는데, 이와 전체 10a당 수량의 증가율과의 상관계수를 산출하였는데, 수리불안전답과의 상관계수 (4)는 +0.525였으며, 천수답과는 r=+0.832, 그리고 수리불안전답과 천수답을 합계한 것과의 상관계수 (r)는 +0.841로서 후2자와는 고도의 정(+) 상관을 보여 천수답이 차지하는 면적비율이 작

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.20-20
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• 2017

최근 기후변화 영향에 따라 전 세계적으로 인명피해 및 재산피해를 유발하는 자연재난이 지속적으로 증가하고 있으며, 그로 인한 자연재해의 규모가 점점 더 커지고 있다. 실제로 우리나라에서도 지난 1994 년에서 2013 년까지 지난 20 년간 자연재해에 의한 피해액은 12조 3천억 원으로 집계되었으며, 이 중 강우와 태풍에 의한 피해가 85 % 이고, 대설에 의한 피해는 약 13 % 로 자연재해 중 대부분의 피해는 강우 및 태풍에서 발생하지만, 폭설에 의한 피해도 적지 않은 것으로 나타났다. 이에 따라, 정확한 예측을 위해 신뢰도 높은 자료 구축을 통한 대설피해 예측에 관한 연구가 필요한 시점이다. 본 연구에서는 대설피해액 예측을 위해 우리나라의 63개 기상 관측소에서 관측한 적설심 자료 및 기상관측 자료와 사회 경제 자료 총 11개를 대설피해 예측을 위한 입력변수로 선정하고, 이를 기상관측소가 속한 도시의 면적에 따라 3개의 지역으로 구분하였다. 주성분분석을 활용하여 선정된 입력변수들을 4개의 주성분으로 구분하고, 인공신경망 및 다중선형 회귀 모형을 구성하여 각 지역별 대설피해 예측의 오차를 분석하였다. 적용결과, 인공신경망 모형을 이용한 대설피해 예측의 수정결정계수는 22.8 %~48.2 %를 나타냈고, 다중선형회귀 모형의 수정결정 계수는 9.2 %~39.7% 로 나타났다. 그러므로 인공신경망 모형이 다중회귀 모형보다 선택된 입력자료를 활용하여 대설피해를 예측하는 목적으로 조금 더 우수한 결과를 나타내었다. 향후 자료를 보완 및 모형의 고도화를 통해 보다 정확한 대설피해 예측 함수 개발이 가능할 것으로 기대된다.

• Journal of Environmental Science International
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• v.31 no.10
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• pp.815-824
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• 2022

It is necessary to understand the amount of snowfall and area of snow cover of Mt. Halla to ensure the safety of mountaineers and to protect the ecosystem of Mt. Halla against climate change. However, there are not enough related studies and observation posts for monitoring snow load. Therefore, to supplement the insufficient data, this study proposes an analysis of snow load and snow cover using normalized-difference snow index. Using the images obtained from the Sentinel2 satellite, the normalized-difference snow index image of Mt. Halla could be acquired. This was examined together with the meteorological data obtained from the existing observatory to analyze the change in snow cover for the years 2020 and 2021. The normalized-difference snow index images showed a smaller snow pixel number in 2021 than that in 2020. This study concluded that 2021 may have been warmer than 2020. In the future, it will be necessary to continuously monitor the amount of snow and the snow-covered area of Mt. Halla using the normalized-difference snow index image analysis method.

• 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.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.

• Journal of Bio-Environment Control
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• v.22 no.1
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• pp.27-33
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• 2013

In order to provide basic data for the development of a controlled environment cultivation system and standardization of the structures, structural status and improvement methods were investigated for the fruit tree greenhouses of grape, pear, and peach. The greenhouses for citrus and grape cultivation are increasing while pear and persimmon greenhouses are gradually decreasing due to the advance of storage facilities. In the future, greenhouse cultivation will expand for the fruit trees which are more effective in cultivation under rain shelter and are low in storage capability. Fruit tree greenhouses were mostly complying with standards of farm supply type models except for a pear greenhouse and a large single-span peach greenhouse. It showed that there was no greenhouse specialized in each species of fruit tree. Frame members of the fruit tree greenhouses were mostly complying with standards of the farm supply type model or the disaster tolerance type model published by MIFAFF and RDA. In most cases, the concrete foundations were used. The pear greenhouse built with the column of larger cross section than the disaster tolerance type. The pear greenhouse had also a special type of foundation with the steel plate welded at the bottom of columns and buried in the ground. As the results of the structural safety analysis of the fruit tree greenhouses, the grape greenhouses in Gimcheon and Cheonan and the peach greenhouses in Namwon and Cheonan appeared to be vulnerable for snow load whereas the peach greenhouse in Namwon was not safe enough to withstand wind load. The peach greenhouse converted from a vegetable growing facility turned out to be unsafe for both snow and wind loads. Considering the shape, height and planting space of fruit tree, the appropriate size of greenhouses was suggested that the grape greenhouse be 7.0~8.0 m wide and 2.5~2.8 m high for eaves, while 6.0~7.0 m wide and 3.0~3.3 m of eaves height for the pear and peach greenhouses.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.26-67
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• 2020

Synthetic Aperture Radar(SAR) is able to photograph the earth's surface regardless of weather conditions, day and night. Because of its possibility to search for hydrological factors such as soil moisture and groundwater, and its importance is gradually increasing in the field of water resources. SAR began to be mounted on satellites in the 1970s, and about 15 or more satellites were launched as of 2020, which around 10 satellites will be launched within the next 5 years. Recently, various types of SAR technologies such as enhancement of observation width and resolution, multiple polarization and multiple frequencies, and diversification of observation angles were being developed and utilized. In this paper, a brief history of the SAR system, as well as studies for estimating soil moisture and hydrological components were investigated. Up to now hydrological components that can be estimated using SAR satellites include soil moisture, subsurface groundwater discharge, precipitation, snow cover area, leaf area index(LAI), and normalized difference vegetation index(NDVI) and among them, soil moisture is being studied in 17 countries in South Korea, North America, Europe, and India by using the physical model, the IEM(Integral Equation Model) and the artificial intelligence-based ANN(Artificial Neural Network). RADARSAT-1, ENVISAT, ASAR, and ERS-1/2 were the most widely used satellite, but the operation has ended, and utilization of RADARSAT-2, Sentinel-1, and SMAP, which are currently in operation, is gradually increasing. Since Korea is developing a medium-sized satellite for water resources and water disasters equipped with C-band SAR with the goal of launching in 2025, various hydrological components estimation researches using SAR are expected to be active.