• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.213-213
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• 2015

밭에서의 점적 관개를 이용한 노지 재배의 경우 적정 관개 계획 수립을 위해서는 작물 및 토양의 수분 정보에 대한 정확한 파악이 필요하다. 본 연구에서는 밭 토양을 GIS(Geographic Information System)를 통해 격자 형태로 분할하여 작물의 증발산량 및 토양의 수분함량을 모의할 수 있는 격자 기반 토양 물수지 모형을 개발하였다. 본 모형을 통해 작물의 소비수량 및 필요 수량을 파악함으로써 작부기간 중 필요한 관개수량을 제시하는 것이 가능하다. 고도화 기상자료로는 국가농림기상센터에서 운영 중인 고해상도 WRF(Weather Research and Forecasting) 모형에서 생산된 격자 형태의 복사, 온도, 바람, 강수 자료를 사용하였고 고도화 기상자료의 격자 해상도 별로 모의되는 작물 및 토양의 수분 정보 간 비교 및 분석을 실시하였다. 토양 물수지 모형에 입력되는 격자형태의 자료로는 기상, 토성 및 토지이용 자료가 있으며 기상자료의 경우 가로 및 세로의 크기가 각 270, 810, 2430m로 동일한 3가지 경우로 나누어 적용했으며 토성 및 토지이용 자료의 경우 기상 격자의 최소 크기에 맞춰 가로 및 세로의 크기가 각 270m인 격자로 분할하였다. 이와 같은 과정에 의한 모의 결과 각 격자별 작물 증발산량, 토양수분함량 및 관개수량의 일 연별 시계열 자료를 얻을 수 있으며 동시간대 격자별 수문인자 값을 산정하고 위치에 따른 공간적 상호 상관성을 분석하였다. 결과적으로, 고도화 기상자료의 격자 크기에 따른 밭 토양 물수지 분석 결과를 통해 고도화 기상 격자의 규모별 밭 토양 물수지 분석 효용성을 파악하고자 하였다. 더불어, 시험 지역(Test Bed) 선정을 통해 토양수분 및 증발산량을 실측하고 본 모형의 모의 결과와 비교함으로써 검정하는 것을 향후 연구 계획으로 한다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.41-53
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• 2015

In meteorological field, many researchers have tried to develop micro scale weather analysis modules for providing real-time weather information service in the metropolitan area. This effort enables us to cope with various economic and social harms coming from serious change in the micro meteorology of a metropolitan area due to rapid urbanization such as quantitative expansions in its urban activity, growth of population, and building concentration. The accuracy of the micro scale weather analysis modules (MSWAM) directly related to usefulness and quality of the real-time weather information service in the metropolitan area. This paper design a evaluation system along with verification tools that sufficiently accommodate spatio-temporal characteristics of the outputs of the MSWAM. For this we proposes a test for the equality of mean vectors of the output series of the MSWAM and corresponding observed time series by using a spectral analysis technique. As a byproduct, a time series cluster analysis method, using a function of the test statistic as the distance measure, is developed. A real data application is given to demonstrate the utility of the method.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1135-1139
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• 2006

가뭄은 홍수와 함께 인류역사상 가장 큰 재해로 인식되어 있다. 미해양대기청의 발표에 따르면 20세기 최대 자연재해의 상위 5위 안에 4개의 가뭄이 포함되어 있다. 이러한 기록은 가뭄이 동서고금을 막론하고 국가의 흥망성쇠를 좌우할 만큼 막대한 피해를 입혀왔음을 의미한다. 그러나 가뭄의 해석은 가뭄의 정의 자체가 확실하지 않고 서서히 찾아오는 자연재해이기 때문에 그 시작과 끝을 인식하기 어렵다. 아울러 그 진행속도도 굉장히 느리며 또한 장기간에 걸쳐 지속되는 특성을 가지고 있고 시공간적으로 전파된다. 따라서 가뭄의 해석은 굉장히 까다로운 것이라 할 수 있으며 그 해석방법 또한 다양할 수 밖에 없다. 본 연구에서는 우리나라 전역 59개 지점의 표준강수지수(Standard Precipitation Index) 시계열 자료에 대한 공간적 패턴분석과 시간적인 자료확장을 시도하였다. 경험적 직교함수(Emperical Orthogonal function) 해석을 이용하여 자료의 공간적인 패턴을 확인하였고 EOF 해석에서 나타난 EOF Coefficient Time Series를 추계학적 모형에 적용하여 시간적인 자료 확장을 수행하였다. 이렇게 확장된 긴 기간의 자료를 이용하면 재현기간에 대한 평균적인 가뭄심도를 추출할 수 있으며 실제 나타난 사상의 재현기간이 어느 정도인지 평가할 수 있다. 또한 이렇게 나타난 가뭄심도를 강수부족량으로 환산하여 우리나라 대권역별 물부족량을 평가하였다.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1401-1411
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• 2023

In the face of escalating utilization of the marine spatial domain, conflicts have emerged among stakeholders, necessitating effective management strategies beyond conventional government permits and regulations. Particularly within the domain of aquaculture, operational oversight relies on a localized licensing system, posing challenges in accurately assessing the prevailing circumstances. This research employs synthetic aperture radar (SAR) imagery as a tool to monitor coastal aquaculture fish farms, aimed at enhancing insights into management protocols. Leveraging Sentinel-1A imagery and time series SAR data integration, a superimposition technique is utilized, facilitating noise reduction while retaining crucial information regarding smaller-scale facilities, such as fish farms. Through analysis of VH polarization data, a detection overall accuracy of approximately 88% for coastal fish farms was achieved. The findings of this study offer potential applications in the continuous monitoring of aquaculture farms in correspondence with seasonal variations in aquaculture yields, thereby proposing frameworks for the establishment of effective management cycles for marine space utilization.

• Spatial Information Research
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• v.20 no.3
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• pp.39-50
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• 2012

The objective of this study is the quantitative analysis of climate change effects by performing several statistical analyses with hydrometeorological data sets for past 30 years in Geum river watershed. Temperature, precipitation, relative humidity data sets were collected from eight observation stations for 37 years(1973~2009) in Geum river watershed. River level data was collected from Gongju and Gyuam gauge stations for 36 years(1973~2008) considering rating curve credibility problems and future long-term runoff modeling. Annual and seasonal year-to-year variation of hydrometeorological components were analyzed by calculating the average, standard deviation, skewness, and coefficient of variation. The results show precipitation has the strongest variability. Run test, Turning point test, and Anderson Exact test were performed to check if there is randomness in the data sets. Temperature and precipitation data have randomness and relative humidity and river level data have regularity. Groundwater level data has both aspects(randomness and regularity). Linear regression and Mann-Kendal test were performed for trend test. Temperature is increasing yearly and seasonally and precipitation is increasing in summer. Relative humidity is obviously decreasing. The results of this study can be used for the evaluation of the effects of climate change on water resources and the establishment of future water resources management technique development plan.

• Journal of the Korean Regional Science Association
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• v.34 no.4
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• pp.19-34
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• 2018

The purpose of this study is to analyze the seasonal issues using the local newspaper articles with the keyword beach from 2004 to 2017. Topic modeling and Time series regression analysis based on open source programs were performed for analysis. Topic modeling results showed 35 topics in spring, 47 topics in summer, 36 topics in autumn and 35 topics in winter. The common themes were 'beaches', 'festivals and events', 'accident and environmental issues', 'tourism', 'development and sale', 'administration and policy' and 'weather'. Time series regression analysis showed in the spring, 5 Hot-Topics and 2 Cold-Topic were found out of the 35 topics. In the summer, 6 Hot-Topics and 3 Cold-Topic were found out of the 47 topics. In the autumn, 4 Hot-Topics and 3 Cold-Topic were found out of the 36 topics. In the winter, 3 Hot-Topics and 3 Cold-Topic were found out of the 35 topics. And for each season, topics that do not fall into the Hot-Topic and Cold-Topic are classified as Neutral-Topic. In this study if seasonal uses are different such as beaches are deemed that seasonal topic modeling for analysis of regional issues will yield more useful results and enable detailed diagnosis.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.1-16
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• 2022

Areas developed through land reclamation projects have huge economic advantages in terms of supplying lands that can be used for farmlands, urban areas and etc., however have relatively small areas of grasslands and densely located buildings compared to inland cities. Hence, an urban heat island is occurring in these areas due to this characteristic, and in particular, the urban heat island in Cheongna International City is getting serious. In this study, the urban heat island in Cheongna International City was evaluated and analyzed by classified into the three periods after the reclamation project: farmland(2001-2008), development(2009-2013) and artificial grassland(2014-2020). The land cover map and Landsat time-series imagery were utilized for measuring the differences of the land surface temperatures between the urbanized areas and the grassland/forest areas in Cheongna International City. The statistical results showed that the differences in the land surface temperature between these areas were calculated to be at most 0℃ during the period of farmland, at most 3.60℃ during the period of development, and at most 2.51℃ during the period of grassland. This study proved that the urban heat island phenomenon increased when the urbanized areas increased, and the urban heat island phenomenon decreased when the artificial grassland areas increased in Cheongna International City where the reclamation project was carried out. The statistical results derived through this research can be used as the reference data for identifying the urban heat island problem in urban planning and establishing the reduction plan.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.151-157
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• 2014

Diverse and complex facilities have been on the increase in urban areas in accordance with rapid urbanization. Along the lines of the increase in facilities, the risk of fire has increased. In particular, fire accidents as well as traffic accidents accounted for the highest rate in artificial disasters. Therefore, the National Fire Information Systems managed by the National Emergency Management Agency (NEMA) appeared for the effective fire management. The NEMA has provided the public with the Internet services regarding information about fire outbreak since 2007. This study acquired data from both NEMA and the Jinju City Fire Department. It constructed the fire data of Jinju City and calculated the change in spatial density targeting fire, occurred in Jinju city with a view to examining the fire risk of facilities by conducting a time series analysis on the trends of fire outbreak over a span of periods between 2007 and 2013. It also conducted an analysis of Moran's I, Getis-Ord Gi. Therefore, it came to select higher hot spots in terms of fire location and fire density. In addition, it attempted to calculate the levels of fire hazard by drawing up the matrix of personal injury and property damage, depending on facilities to present the methods, which can predict the risk of fire occurrence in urban areas.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.235-244
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• 2016

In large-area crop classification with MODIS data, a mixed pixel problem caused by the low resolution of MODIS data has been one of main issues. To mitigate this problem, this paper proposes a hierarchical classification algorithm that selectively classifies the specific crop class of interest by using their spectral characteristics. This selective classification algorithm can reduce mixed pixel effects between crops and improve classification performance. The methodological developments are illustrated via a case study in Jilin city, China with MODIS Normalized Difference Vegetation Index (NDVI) and Near InfRared (NIR) reflectance datasets. First, paddy fields were extracted from unsupervised classification of NIR reflectance. Non-paddy areas were then classified into corn and bean using time-series NDVI datasets. In the case study result, the proposed classification algorithm showed the best classification performance by selectively classifying crops having similar spectral characteristics, compared with traditional direct supervised classification of time-series NDVI and NIR datasets. Thus, it is expected that the proposed selective hierarchical classification algorithm would be effectively used for producing reliable crop maps.