• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.79-88
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• 2017

The RapidEye satellite sensor has various spectral wavelength bands, and it can capture large areas with high temporal resolution. Therefore, it affords advantages in generating various types of thematic maps, including land cover maps. In this study, we applied a supervised classification scheme to generate high-resolution land cover maps using RapidEye images. To improve the classification accuracy, object-based classification was performed by adding brightness, yellowness, and greenness bands by Tasseled Cap Transformation (TCT) and Normalized Difference Water Index (NDWI) bands. It was experimentally confirmed that the classification results obtained by adding TCT and NDWI bands as input data showed high classification accuracy compared with the land cover map generated using the original RapidEye images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.1
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• pp.23-30
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• 2017

NDVI (Normalized Difference Vegetation Index) plays an important role in surface land cover classification and LST (Land Surface Temperature Extraction). Its characteristics do not full carry the information of the surface cover typically in urban areas even though it is widely used in analyses in urban areas as well as in vegetation. However, abnormal NDVI values are frequently found in urban areas. We, therefore, examined NDVI values on whether NDVI is appropriate for LST and whether there are considerations in NDVI analysis typically in urban areas because NDVI is strongly related to the surface emissivity calculation. For the study, we observed the influence of the surface settings (i.e., geometric shape and color) on NDVI values in urban area and transition features between three land cover types, vegetation, urban materials, and water. Interestingly, there were many abnormal NDVI values systematically derived by the surface settings and they might influence on NDVI and eventually LST. Also, there were distinguishable transitions based on the mixture of three surface materials. A transition scenario was described that there are three transition types of mixture (urban material-vegetation, urban material-water, and vegetation-water) based on the relationship of NDVI and LST even though they are widely distributed.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.4-4
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• 2020

Three wildfires severely damaged local towns and forests in Gangwon-do, South Korea in 2019 April 4-5. Local hydrological regime could be greatly altered by the wildfires, therefore it is important to assess its damage (e.g. area and severity) and also resultant changes in hydrological fluxes. We retrieved the Normalized-Burned Ratio (NBR) index using remote-sensing data (Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m 8-day surface reflectance data), and delineated the damaged-area based on the difference in the NBR (dNBR) before and after the wildfires. We then estimated changes in the annual evapotranspiration (AET) in 2019 using the MODIS evapotranspiration data (500-m 8-day). It was found that the damaged-area of the three wildfires was 29.50 km^2 in total, which take up 1.00-6.19% area of five catchments. It was estimated that the AET would be decreased as 0.05-1.56% over those five catchments, as compared to the pre-fire AET (2004-2018). The impact of the wildfires on the catchment AET was less severe than expected (i.e. up to 1.56%) mostly because two big wildfires were distributed across two catchments respectively (i.e. four catchments for the two wildfires) and the other wildfire was small and not severe. This study highlights the importance of assessing the area and severity of a wildfire when estimating its impact on the local hydrological cycle.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.705-718
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• 2021

Climate change has a significant impact on vegetation growth and terrestrial ecosystems. In this study, the possibility of ecological drought was investigated using satellite remote sensing data. First, the Vegetation Health Index was estimated from the Normalized Difference Vegetation Index and Land Surface Temperature provided by MODIS. Then, a joint probability model was constructed to estimate the possibility of vegetation-related drought in various precipitation/evaporation scenarios in forest areas around 60 major ASOS sites of the Meteorological Administration located throughout Korea. The results of this study show the risk pattern of drought related to forest vegetation under conditions of low atmospheric moisture supply or high atmospheric moisture demand. It also identifies the sensitivity of drought risks associated with forest vegetation under various meterological drought conditions. These findings provide insights for decision makers to assess drought risk and develop drought mitigation strategies related to forest vegetation in a warming era.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.462-466
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• 2006

Close-to-nature stream evaluation is one of the processing to make the streams over in order to keep them natural. It is integral to evaluate and make an accurate analysis of them on the purpose of maintaining streams healthy. For many instances, there are, stream organization evaluation for restoration by German government, evaluation for ecosystem protection in natural preserves by New Zealand government, and stream-view evaluation for restoration by Britain government so on. In case of the country there are analysis and evaluation of stream physical organization by Cho, Yong-hyun, Close-to-nature stream evaluation for restoration by Kim, Dong-chan, evaluation of stream properties in korea by Park, Bong-jin. Close-to-nature evaluation by Lim, Chan-uk, that is advanced version of Park, Bong-jin's, shows form of stream including waterway curve, sand bar, diversity of flow, river bed material, diversity of minor bed, minor bed bank protection works, bank protection material. It also does environment of stream including side of minor bed vegetation, width of surface of the water/width of the river etc.. By the way, this evaluation does not have free access to apply those details above in the field, it often happens that you get various outcome from the one spot. so you must need more realistic testing method to obtain more accurate data. Remote sensing method is highly recommended because this is very useful for collecting realistic data of vegetation index. what is more, it can not only scan even the minimum area within its resolving power but also do obtain data anytime. Vegetation index indicates Ratio vegetation index, Normalized difference vegetation index, Soil adjusted vegetation index, Atmospherically resistant vegetation index etc.. The research is focusing on Cheokgwa stream which is the branch of Taehwa river and shows 19 sectioned Close-to-nature stream performed according to the method by Lim, chan-uk. Besides let you know vegetation index came from image data of satellite landsat 7 with the variation of buffering area, of the day 9. may. 2003. Of all, the outcome 0.758 at 200m buffer-zone of NDVI was the best we have got so far.

• Journal of the Korean Society of Hazard Mitigation
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• v.4 no.2 s.13
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• pp.61-69
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• 2004

Drought is one of the mai or environmental disasters. Weather data, particularity rainfall, are currently the primary source of information widely used for drought monitoring. However, weather data are often from a very sparse meteorological network. Therefore, data obtained from the Advanced Very High Resolution Radiometer(AVHRR) sensor boarded on the NOAA polar-orbiting satellites have been studied as a tool for drought monitoring. The normalized difference vegetation index(NDVI) and vegetation condition index(VCI) were used in this study. Also, a simple method to detect drought Is Proposed based on climatic water balance using NOAA/AVHRR data. The results clearly show that temporal and spatial characteristics of drought in Korea can be detected and mapped by the moisture index.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.227-239
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• 2016

This study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients ($R^2$) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1615-1619
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• 2010

토양수분은 토양입자에 포함되어 있는 물을 의미하는 것으로 지표면과 대기간의 에너지 균형과 물 순환을 조절하는데 중요한 요소이다. 본 연구에서는 토양수분 산정을 위하여 2003년 1월부터 2008년 12월까지의 MODIS(Moderate Resolution Imaging Spectroradiometer) 위성관측 자료로부터 획득한 정규식생지수(NDVI: Normalized Difference Vegetation Index)자료와 지표면 온도자료, 우리나라 76개소 기상관측소 중에 자료의 보유기간이 30년 이하인 관측소와 섬 지역들을 제외한 57개 지점의 강수량, 토양온도 자료 및 우리나라 전역에 대한 토지피복, 유효토심자료를 이용하여 데이터 마이닝(Data Mining) 기법의 하나인 CART(Classification And Regression Tree) 기법을 이용하여 토양수분을 산정하였다. 먼저 신뢰성 높은 토양수분 관측 자료를 가진 용담댐 유역의 6개 지점에 대하여 토양수분을 산정하여 적용 가능성을 분석하였다. 3개 지점의 토양수분 관측치는 토양수분 산정 모형 수립에 사용하였으며 검증에 사용된 1개 지점의 토양수분의 관측치와 추정치 간의 상관계수를 확인한 결과 전체적인 토양수분의 거동을 잘 나타내고 있어 토양수분 추정 모형의 적용가능성을 확인하였다. 이를 이용하여 용담댐 유역의 토양수분 분포와 우리나라 전역에 대한 토양수분 분포도를 추정하였다. 신뢰할 수 있는 지상관측 토양수분 관측치가 다양한 지상조건에 대하여 존재하지 않는 한계가 있음에도 불구하고 제시된 토양수분산정 방법은 제한된 가용자료를 사용한 우리나라 전역의 토양수분 산정에 있어 합리적인 접근법이라 판단된다.