• 농촌계획
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• 제30권1호
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• 한국토양비료학회지
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• 제46권4호
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• pp.245-252
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• 2013

Leaf area index (LAI) is important in explaining the ability of crops to intercept solar energy for biomass production, amount of plant transpiration, and in understanding the impact of crop management practices on crop growth. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of RapidEye imagery obtained from 2010 to 2012 using empirical models in a rice plain in Seosan, Chungcheongnam-do. Rice plants were sampled every two weeks to investigate LAI, fresh and dry biomass from late May to early October. RapidEye images were taken from June to September every year and corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). Linear, exponential, and expolinear models were developed to relate temporal satellite NDVIs to measured LAI. The expolinear model provided more accurate results to predict LAI than linear or exponential models based on root mean square error. The LAI distribution was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when RapidEye imagery was applied to expolinear model. The spatial trend of LAI corresponded with the variation in the vegetation growth condition.

• 대기
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• 제15권1호
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• pp.1-16
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• 2005

Land-surface and soil processes significantly affect mesoscale local weather systems as well as global/regional climate. In this study, characteristics of land-surface models (LSMs) and soil models (SMs) that are frequently coupled into mesoscale meteorological models are investigated. In addition, detailed analyses on three LSMs, employed by the PSU/NCAR MM5, are provided. Some impacts of LSMs on heavy rainfall prediction are also discussed.

• 한국조경학회지
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• 제31권6호
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• pp.53-63
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• 2004

Planting models for restoration forest on the seaside have been demanded because coastal reclaimed land has increased for habitation sites, industrial complexes and new towns on the west seaside of Korea. The planting models have to consider endurance for bad environmental conditions in order to make a role to protect the urban space against the extreme seaside environment. The dominant species, relative impotance value, individuals and species number were analysed in natural forests that were exposed to extreme seaside conditions in Deokjeok island and Younghung island, Incheon. The native species such as Pinus thunbergii and Pinus densiflora, which survive on the seaside, were mainly recommended because the coastal reclaimed land had extreme environmental conditions. Stable vegetation structures could be made by multi-layer planing by using these species. A diverse vegetation community could be made according to these planting models. The maritime forests made by these planting models might be more effective for environmental adaptation and a windbreak forest than alone tree, and the young trees below 3m height could easily adapt to these conditions.

• 한국측량학회지
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• 제32권1호
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• pp.9-17
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• 2014

대기의 에너지를 측정하거나 지표면유출을 예측하는 기상 및 수문모델에서 지표면특성(식생피복)을 파악하는 것은 매우 중요한 요소이다. 1978년 Deardorff가 식생피복을 정량적으로 파악하기 위하여 식생피복지수(Vegetation Cover Fraction)를 제안한 후 식생피복지수에 관한 연구가 활발해졌다. 그러나 선행연구에서는 AVHRR, MODIS 그리고 KOMPSAT-2영상과 같은 고 저해상도 위성영상을 이용한 많은 연구가 있었으나, 중해상도 영상인 Landsat에 대한 연구는 미비한 실정이다. 따라서 본 연구는 Landsat OLI영상을 이용하여 식생피복지수 산정방법을 연구하였다. 정확하고 효율적인 식생피복지수 산정방법을 연구하기 위하여, 본 연구에서 제안된 방법과 선행연구방법을 비교평가 하였다. 실험결과 NDVI와 식생피복지수는 많은 연관성을 지니는 것으로 분석되었으며, 본 연구에서 제안된 방법을 이용한 식생피복지수가 특이점을 제외한 RMSE 7.3%로 전체 방법 중에서 가장 높은 정확도를 보였다.

• 한국환경생태학회:학술대회논문집
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• 한국환경생태학회 2000년도 정기총회 및 학술논문 발표회
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• pp.47-50
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• 2000

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.848-851
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• 2006

Green vegetation is one of the most critical factors for environment conditions thorough modulating evapotranspiration and absorption of solar radiation. Thus, fractional green vegetation cover (FVC) plays an important role in observing and managing environment. Remote sensing provides a seemingly obvious data source for quantifying FVC over large area. Therefore we compared a set of methods for estimating FVC using hyperspectral remote sensing data. For our study, we used Hyperion imagery acquired in April, 2002. In order to achieve our efforts, we analyzed simple NDVI-based method and spectral mixture analysis (SMA) models that were applied a variety of combinations of possible endmembers.

• 한국방재학회 논문집
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• 제5권3호
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• pp.47-55
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• 2005

도시하천의 관리와 계획에 있어 식수에 따른 수리학적 특성의 변화는 명확히 분석되어야 한다. 본 연구에서는 도시하천의 식수기준을 분석하고, 도시하천의 고수부지내 식수가 가능한 지역을 표시한 식수허가지도 제작 방법을 검토하였다. 또한 식수에 따른 수리학적 영향을 준2차원 수치모형, HEC-RAS, FESWMS 모형을 활용하여 분석하였다 중랑천의 장안교부터 군자교까지의 구간을 대상으로 선정하여 100년 빈도 홍수량에 식수허가지도를 제작한 결과, 교목의 식수시에는 하천의 우안에 $0.5{\sim}1$본/ha 정도의 식수가 가능하였으며, 관목의 경우에는 좌안 및 우안의 중요수방구간을 제외한 지역에 식재가 가능하였다. 또한 관목의 식재에 따른 수리학적 영향은 약 12cm 정도의 수위상승 결과를 나타내었다. 따라서 대상구간에 식수에 따른 수위 상승은 적은 것으로 판단되므로 식수허가지도에 따라 식수가 가능할 것으로 판단된다.

• 대한원격탐사학회지
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• 제26권5호
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• pp.537-547
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• 2010

FPAR는 다양한 육상 생태계 모텔에서 중요한 입력변수로 사용된다. 이 때문에 다양한 global product의 형태로 제공되고 있다. 하지만 한반도를 영역으로 하는 연구에 이를 바로 적용 시 오차가 발생할 수 있고, 이것은 위성자료를 이용한 지면 정보 산출에 있어서 직접적인 오차요인이 된다. 따라서 본 연구에서는 Terra/MODIS와 SPOT/VEGETATION 그리고 ECOCLIMAP 자료를 이용해 한반도에 최적화된 FPAR를 산출 하였고, 또한 기존에 사용하였던 LAI와의 관계식을 사용하지 않고, SPOT/VGT NDVI 로부터 계산된 FVC (Fraction Vegetation Cover)를 직접 이용하여 FPAR를 산출 하였다. 이를 위해 식생지수의 선형/비선형 관계를 이용하여 구하는 경험적인 방법을 적용하여 회귀분석을 수행한 결과 cropland와 forest에서 각각 결정계수 (Coefficient of Determination, $R^2$)가 0.9039. 0.7901으로 정확도가 높은 관계식을 도출해내었다. 최종적으로 Reference FPAR 자료와의 비교 분석을 통해 본 연구에서 산출된 FPAR가 전반적인 패턴을 잘 표현하면서 불규칙하게 발생하던 노이즈 또한 보정된 것을 확인 할 수 있었다. 이렇게 한반도에 최적화된 입력변수의 사용은 산출물의 정확도뿐만 아니라 연구의 질 향상에도 도움을 줄 것으로 사료된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.529-534
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• 2009

Climate models, both global and regional, have increased in sophistication and are being run at increasingly higher resolutions. The Land Surface Models (LSMs) coupled to these climate models have evolved from simple bucket models to sophisticated Soil-Vegetation-Atmosphere Transfer (SVAT) schemes needed to support complex linkages and processes. However, some underpinnings of terrestrial hydrologic parameterizations so crucial in the predictions of surface water and energy fluxes cause model errors that often manifest as non-linear drifts in the dynamic response of land surface processes. This requires the improved parameterizations of key processes for the terrestrial hydrologic scheme to improve the model predictability in surface water and energy fluxes. The Common Land Model (CLM), one of state-of-the-art LSMs, is the land component of the Community Climate System Model (CCSM). However, CLM also has energy and water biases resulting from deficiencies in some parameterizations related to hydrological processes. This research presents the implementation of a selected set of parameterizations and their effects on the runoff prediction. The modifications consist of new parameterizations for soil hydraulic conductivity, water table depth, frozen soil, soil water availability, and topographically controlled baseflow. The results from a set of offline simulations are compared with observed data to assess the performance of the new model. It is expected that the advanced terrestrial hydrologic scheme coupled to the current CLM can improve model predictability for better prediction of runoff that has a large impact on the surface water and energy balance crucial to climate variability and change studies.