• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.38-38
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• 2021

전 세계적으로 기후변화의 영향으로 인해 수문·기상 등 다양한 분야에서 심각성이 야기되고 있으며, 가뭄, 집중호우, 태풍 등과 같은 자연재해의 발생빈도와 피해가 증가하고 있다. 우리나라의 경우 봄철 가뭄의 발생빈도가 증가하고 있으며, 발생지역이 확산되는 추세이다. 증발산량(evapotranspiration)은 기상학과 수문학에 주요한 농업기상 매개 변수로 다루어지며, 작물의 생육·성장에 필요한 물 수요 및 관개용수 산정에 필요한 인자로 가뭄 분석에 활용하는 중요 인자들 중 하나다. 증발산량 자료 구축에는 증발산계 (Lysimeter)를 이용하여 현장 데이터를 실측하는 방법과 구조화된 알고리즘을 통해 증발산량을 산출하는 방법으로 나누어진다. 우리나라의 경우 증발산계가 설치된 지역이 많지 않고 분포도 조밀하지 않으며, 기상, 식생, 토지 피복 등 다양한 요인들의 영향을 받는 증발산량의 특성상 실측 데이터를 구축하는 것은 현실적으로 어렵다. 이에 물수지 기법, 기상 변수 기반 추정 등 간접적인 방법을 통해 증발산량을 추정하는 연구가 일반적으로 진행되고 있다. 이에 본 연구에서는 미국항공우주국 (National Aeronautics and Space Administration, NASA) 제트 추진 연구소 (Jet Propulsion Laboratory, JPL)의 The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS)에서 제공하는 위성영상 중 증발산량 데이터를 구축하였다. 구축한 ECOSTRESS 증발산량 적합성 확인을 위해, 청미천·설마천에서 제공하는 증발산량과 비교 및 검증을 실시하였으며, 시공간적 변동성 분석을 위해 통계적 방법을 이용하였다. 본 연구에서 도출된 증발산량의 시공간 변동성 결과를 통해 지역별 가뭄 분석의 기초자료로 활용될 수 있을 것으로 사료된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.279-279
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• 2021

위성영상정보는 센서의 종류, 취득, 분석, 재난과 위성영상 특성 매칭 등의 제약으로 재난 상황에서 제한적으로 사용되었다. 일반적으로 인공위성의 종류는 탑재한 센서의 정보제공 능력 범위에 따라 분류 가능하며 이에 따라 대상 범위가 결정된다. 본 연구에서는 재난의 예측, 탐지, 사후처리를 위한 위성자료의 취득과 활용을 위해 다양한 위성과 탑재된 센서의 궤도, 공간 해상도, 파장대 등의 특성에 대하여 분석하고 재난유형별로 최적 위성영상을 선정하였다. 행정안전부에서는 재난과 재해의 유형을 자연재난(10종)과 사회재난(27종)으로 분류하였다. 위성영상 활용이 가능한 재난 유형은 가시적으로 확인이 가능한 자연재난에 해당하며 그 중 태풍, 홍수, 가뭄, 산불 등 총 4종의 재난유형별로 가용한 최적의 위성영상을 분석하였다. 재난관측에 사용 가능한 대표적인 탑재체의 종류는 극궤도 지구관측 위성에서 광학과 SAR로 구분할 수 있다. 각 기본 특성에 따라 제공되는 정보의 종류가 분류되며 광학 센서는 태양복사 및 지구복사에너지 파장 영역 중 가시광선-근적외선-단파적외선-열적외선 파장대 영역의 분광 정보를 제공할 수 있는 다중 밴드들로 구성된다. 지표의 특정 대상이나 물질을 탐지하고 변화를 감지·분석하는데 유용하여 홍수, 태풍, 지진 등 자연 및 사회 재난·재해 관측에 유용하게 이용된다. SAR 센서는 장파장의 전자기파를 방출한 후 돌아오는 신호를 활용하여 대상에 대한 정보를 획득한다. 대기의 효과 및 요소를 투과하는 주파수 대역별 장파장 밴드 정보를 활용하여 고해상도의 대상 표면, 위치, 형태 등의 정보를 측량 및 관측하므로 중·광역 지역에 제약 없이 영상정보를 획득할 수 있어 산사태, 홍수, 지진, 등의 재난 모니터링에 유용하다. 이러한 다종 위성별 센서들의 특징(공간 해상도, 파장대별 밴드 특성, 관측폭, 재방문 주기 등)들을 분석하여 재난유형별로 가용한 무료/상용 지구관측위성을 분류한 결과 태풍에는 광역관측, 정지궤도 위성, 홍수에는 광학 및 SAR 고해상도 위성, 가뭄은 광역관측, 다분광 광학 위성 그리고 산불에는 정지궤도, 광학, SAR 위성이 적합함을 알 수 있다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.511-517
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• 2006

In this paper, we studied the techniques to analyze the flying safety area focused on the air base rules for military that has been the criteria of the altitude restrictions around the airfield for both civilian and military purposes in Korea. We wanted to present the effective method to analyze the restricted area and to help solving problems that could result recently from the altitude restrictions around the airfield at the beginning of the development projects. To do this we proposed the methods to effectively generate the model of the flying safety area in accordance with the air base rules using 3D GIS techniques and to automatically identify the obstructions caused by the natural and man-made features in those areas. To apply the proposed methods actually to the airfield chosen for the study area, we presented the approaches to generate geospatial informations based on the commercial digital maps and satellite imagery and by generating the flying safety area model, identifying the obstructions, and visualizing the integrated model for the flying safety area analysis we showed the practical usability of the proposed techniques.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.921-932
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• 2023

Land cover change due to urban population concentration and urban expansion can cause various environmental problems such as urban heat islands. In particular, New towns are considered an appropriate study site to analyze changes in urban climate due to rapid urbanization in a short period. This study used Landsat satellite imagery to compare and analyze the land cover changes before and after the development of two new towns with different plans, and the resulting changes in surface urban heat island (SUHI) phenomena. Correlation analysis was also conducted between urban structural features that may affect the SUHI intensity. The results of the analysis confirm the rapid change in land cover as new town development progresses and the direct intensification of the SUHI phenomenon. This study confirms the differences in SUHI caused by different urban plans and suggests the need for three-dimensional urban planning to improve the thermal environment.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.179-190
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• 2024

This study aims to compare supervised classification methods with phenology-based approaches, specifically pixel-based and segment-based methods, for accurate crop mapping in agricultural landscapes. We utilized Sentinel-2A imagery, which provides multispectral data for accurate crop mapping. 31 normalized difference vegetation index (NDVI) images were calculated from the Sentinel-2A data. Next, we employed phenology-based approaches to extract valuable information from the NDVI time series. A set of 10 phenology metrics was extracted from the NDVI data. For the supervised classification, we employed the maximum likelihood (MaxLike) algorithm. For the phenology-based approaches, we implemented both pixel-based and segment-based methods. The results indicate that phenology-based approaches outperformed the MaxLike algorithm in regions with frequent rainfall and cloudy conditions. The segment-based phenology approach demonstrated the highest kappa coefficient of 0.85, indicating a high level of agreement with the ground truth data. The pixel-based phenology approach also achieved a commendable kappa coefficient of 0.81, indicating its effectiveness in accurately classifying the crop types. On the other hand, the supervised classification method (MaxLike) yielded a lower kappa coefficient of 0.74. Our study suggests that segment-based phenology mapping is a suitable approach for regions like South Korea, where continuous cloud-free satellite images are scarce. However, establishing precise classification thresholds remains challenging due to the lack of adequately sampled NDVI data. Despite this limitation, the phenology-based approach demonstrates its potential in crop classification, particularly in regions with varying weather patterns.

• Korean Journal of Environmental Agriculture
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• v.27 no.2
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• pp.120-132
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• 2008

Remotely sensed images from a satellite can be applied for detecting and quantifying spatial and temporal variations in terms of landuse & landcover, crop growth, and disaster for agricultural applications. The purposes of this study were to analyze topography using DEM(digital elevation model) and classify landuse & landcover into 10 classes-paddy field, dry field, forest, bare land, grass & bush, water body, reclaimed land, salt farm, residence & building, and others-using Landsat TM images in North Korea. Elevation was greater than 1,000 meters in the eastern part of North Korea around Ranggang-do where Kaemagowon was located. Pyeongnam and Hwangnam in the western part of North Korea were low in elevation. Topography of North Korea showed typical 'east-high and west-low' landform characteristics. Landcover classification of North Korea using spectral reflectance of multi-temporal Landsat TM images was performed and the statistics of each landcover by administrative district, slope, and agroclimatic zone were calculated in terms of area. Forest areas accounted for 69.6 percent of the whole area while the areas of dry fields and paddy fields were 15.7 percent and 4.2 percent, respectively. Bare land and water body occupied 6.6 percent and 1.6 percent, respectively. Residence & building reached less than 1 percent of the country. Paddy field areas concentrated in the A slope ranged from 0 to 2 percent(greater than 80 percent). The dry field areas were shown in the A slope the most, followed by D, E, C, B, and F slopes. According to the statistics by agroclimatic zone, paddy and dry fields were mainly distributed in the North plain region(N-6) and North western coastal region(N-7). Forest areas were evenly distributed all over the agroclimatic regions. Periodic landcover analysis of North Korea based on remote sensing technique using satellite imagery can produce spatial and temporal statistics information for future landuse management and planning of North Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.1
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• pp.89-100
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• 2010

The aim of this study is to produce land cover maps using satellite imagery with various degrees of high resolution and then compare the accuracy of the image types and categories. For the land cover map produced on a small-scale classification the estuary area around the Nakdong river, including an urban area, farming land and waters, was selected. The images were classified by analyzing the aerial photos taken from KOMPSAT2, Quickbird and IKONOS satellites, which all have a resolution of over 1m to the naked eye. Once all of the land cover maps with different images and land cover categories had been produced they were compared to each other. Results show that image accuracy from the aerial photos and Quickbird was relatively higher than with KOMPSAT2 and IKONOS. The agreement ratio for the large-scale classification across the classification methods ranged between 0.934 and 0.956 for most cases. The Kappa value ranged between 0.905 and 0.937; the agreement ratio for the middle-scale classification was 0.888~0.913 and the Kappa value was 0.872~0.901. The agreement ratio for the small-scale classification was 0.833~0.901 and the Kappa value was 0.813~0.888. In addition, in terms of the degree of confusion occurrence across the images, there was confusion on the urbanized arid areas and empty land in the large-scale classification. For the middle-scale classification, the confusion mainly occurred on the rice paddies, fields, house cultivating area and artificial grassland. For the small-scale classification, confusion mainly occurred on natural green fields, cultivating land with facilities, tideland and the surface of the sea. The findings of this study indicate that the classification of the high resolution images with the naked eye showed an agreement ratio of over 80%, which means that it can be used in practice. The findings also suggest that the use of higher resolution images can lead to increased accuracy in classification, indicating that the time when the images are taken is important in producing land cover maps.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1194-1202
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• 2012

Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields ($3,521km^2$) were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.1
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• pp.17-24
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• 2008

This study was performed to estimate non-$CO_2$ greenhouse gases (i.e., GHGs) emission from biomass burning at a local scale. Estimation of non-$CO_2$ GHGs emission was conducted using Landsat TM satellite imagery in order to assess the damage degree in burnt area and its effect on non-$CO_2$ GHGs emission. This approach of estimation was based on the protocol of the 2003 IPCC Guidelines. In this study, we used one of the most severe fire cases occurred Samcheock in April, 2004. Landsat TM satellite imageries of pre- and post-fire were used 1) to calculate delta normalized burn ratio (dNBR) for analyzing burnt area and burn severity of the Samcheok large-fire and 2) to quantify non-$CO_2$ GHGs emission from different size of the burnt area and the damage degree. The analysis of dNBR of the Samcheok large-fire indicated that the total burnt area was 16,200ha and the size of the burnt area differed with the burn severity: out of the total burnt area, the burn severities of Low (dNBR < 152), Moderate (dNBR = 153-190), and High (dNBR = 191-255) were 35%, 33%, and 32%, respectively. It was estimated that the burnt areas of coniferous forest, deciduous forest, and mixed forest were about 11,506ha (77%), 453ha (3%), and 2,978ha (20%), respectively. The magnitude of non-$CO_2$ GHGs emissions from the Samcheok large-fire differed significantly, showing 93% of CO (44.100Gg), 6.4% of CH4 (3.053Gg), 0.5% of $NO_x$ (0.238Gg), and 0.1% of $N_2O$ (0.038Gg). Although there were little changes in the total burnt area by the burn severity, there were differences in the emission of non-$CO_2$ GHGs with the degree of the burn severity. The maximum emission of non-$CO_2$ GHGs occurred in moderate burn severity, indicating 47% of the total emission.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.133-149
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• 2022

Existing domestic studies on estimating rice yield were mainly implemented at the level of cities and counties in the entire nation using MODIS satellite images with low spatial resolution. Unlike previous studies, this study tried to estimate rice yield at the level of eup-myon-dong in Gimje-si, Jeollabuk-do using Sentinel-2 satellite images with medium spatial resolution, rainfall and soil data, and then to evaluate its accuracy. Five vegetation indices such as NDVI, LAI, EVI2, MCARI1 and MCARI2 derived from Sentinel-2 images of August 1, 2018 for Gimje-si, Jeollabuk-do, rainfall and paddy soil-type data were aggregated by the level of eup-myon-dong and then rice yield was estimated with gamma generalized linear model, an expanded variant of multi-variate regression analysis to solve the non-normality problem of dependent variable. In the rice yield model finally developed, EVI2, rainfall days in September, and saline soils ratio were used as significant independent variables. The coefficient of determination representing the model fit was 0.68 and the RMSE for showing the model accuracy was 62.29kg/10a. This model estimated the total rice production in Gimje-si in 2018 to be 96,914.6M/T, which was very close to 94,470.3M/T the actual amount specified in the Statistical Yearbook with an error of 0.46%. Also, the rice production per unit area of Gimje-si was amounted to 552kg/10a, which was almost consistent with 550kg/10a of the statistical data. This result is similar to that of the previous studies and it demonstrated that the rice yield can be estimated using Sentinel-2 satellite images at the level of cities and counties or smaller districts in Korea.