• 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.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.107-107
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• 2011

최근 기후변화로 야기되는 식생의 변화는 수문기상인자인 증발산과 토양수분에 많은 영향을 끼친다. 본 연구의 목적은 식생의 변화가 수문기상인자인 토양수분에 어떠한 영향을 미치는지 분석하고자 하는데 있다. 식생인자와 수문기상 인자와의 상관관계를 알아보기 위해 Moderate Resolution Imaging Spectroradiometer(MODIS) 위성 이미지 데이터를 연구에 적용하였으며, 식생인자는 MODIS 13 Vegetation Indices Product에서 추출한 정규식생지수 Normalized Difference Vegetation Index(NDVI)를 이용하였다. 식생인자와 토양수분의 상관관계를 분석하기 위해 농업기상정보시스템(Rural Development Administration, RDA)에서 측정한 군위, 논산, 옥천, 예산 지역의 토양수분 관측값 및 Aqua 위성에 탑재된 Advanced Microwave Scanning Radiometer E(AMSR-E)를 이용하여 측정한 토양수분 관측값을 MODIS-NDVI와 비교 분석하였다. 식생인자와 수문기상인자의 시계열 자료를 이용하여 변화하는 양상을 알아내고자 하였고 상관성을 분석하여 식생인자가 수문인자에 어떠한 영향을 주는지 파악하였다. 그 결과 RDA 토양수분 관측값은 MODIS-NDVI와 거의 비슷한 경향을 나타남을 확인 할 수 있었으며, 이는 RDA와 AMSR-E의 토양수분의 관측 깊이에 따른 차이로 이 같은 현상이 나타난다고 사료된다, 또한 MODIS-NDVI, AMSR-E, RDA가 가지고 있는 각기 다른 공간 해상도(1km, 25km, point scale)가 반영된 결과라 할 수 있겠다, 추후 이를 보완한다면 보다 식생변화가 토양수분에 미치는 영향분석을 명확히 할 수 있을 것이다.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.111-119
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• 2001

This study analyzes time variability of the normalized difference vegetation index (NDVI), the leaf area index (LAI) and surface temperature (Ts) estimated from AVHRR data collected from across the Korean peninsula from 1981 to 1994. In the present study, LAI defined as vegetation density, as a function of NDVI applied for the vegetation types and Ts defined by the split-window formulation of Becker and Li (1990) with emissivity of a function of NDVI, are used. Results of the inter-annual, intra-annual and intra-seasonal variabilities in Korea show: (1) Inter-annual variability of NDVI is generally larger in the southem and eastern parts of the peninsula than in the western part. This large variability results from the significant mean variation. (2) Inter-annual variability of Ts is larger in the areas of smaller NDVI. This result shows that the NDVI play a small role in emissivity. (3) Inter-annual variability of LAI is larger in the regions of higher elevation and urban areas. Changes in LAI are unlikely to be associated with NDVI changes. (4) Changes in NDVI and Ts are likely dominant in July and are relatively small in spring and fall. (5) Urban effect would be obvious on the time-varying properties of NDVI and Ts in Seoul and the northern part of Taejon, where NDVI decreases and Ts increases with a significant magnitude.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.405-409
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• 2018

최근 기후변화로 인해 기온, 강수량 등 농업에 직접적인 영향을 주는 환경요인의 변화가 급격하게 진행되고 있으며, 식량농업기구 (Food and Agriculture Organization of the United Nations, FAO)는 기후변화로 인해 전 세계적인 식량위기가 발생할 가능성이 크다고 경고하고 있다. 농업 시스템의 생산 능력을 확보하기 위해 수자원의 효율적인 공급 및 분배, 수확량 예측, 토지 특성 파악 등 농업 생산 제한요소에 대한 빠른 정보수집이 요구되고 있다. 재해관리 분야에서 원격탐사 기술은 재해 발생을 인지하고 발생지역의 재해 진행과 피해 정도를 신속하게 제공할 수 있다는 점에서 효용성이 높다. 또한 위성 영상을 이용할 경우 접근이 용이하지 못한 지역의 조사가 수월하며, 장기적인 변화관측이나 환경감시 등 광역적 접근이 가능하다. 최근 위성영상을 통한 다양한 신호의 데이터 취득 및 가공이 가능하게 됨에 따라 주기적이고 동일한 정확도로 지상자료의 획득이 가능하다는 측면에서 인공위성을 활용한 농업 분야에서의 가뭄 분석 연구의 필요성이 대두되었다. 위성영상 신호를 통해 농업 가뭄에 활용되고 있는 지표로는 정규식생지수 (Normalized Difference Vegetation Index, NDVI) 및 식생상태지수 (Vegetation Condition Index, VCI), 식생가뭄반응지수(Vegetation Drought Response Index, VegDRI) 등이 있다. 잠재 증발산과 실제 증발산의 비를 이용한 위성영상기반의 가뭄지수인 Evaporative Stress Index (ESI)는 일반적으로 사용되는 가뭄지수인 표준강수지수(Standardized Precipitation Index, SPI), 파머가뭄심도지수 (Palmer Drought Severity Index, PDSI) 등과 비교하였을 때, 가뭄에 더 민감하고 빠른 반응을 보인다는 연구 결과로부터 짧은 기간의 급속하게 발생하는(rapid-onset) Flash drought의 가뭄판단지표로 활용되고 있다. 본 연구에서는 과거 우리나라에 발생했던 극심한 가뭄 사상을 대상으로 ESI의 가뭄분석을 통해 타 지표와의 차별성을 확인하고 농업 가뭄 모니터링의 새로운 지표로써 적용성을 검토하고자 한다.

• Korean Journal of Environmental Biology
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• v.41 no.3
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• pp.283-290
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• 2023

The Republic of Korea reclaimed land to increase its food self-sufficiency rate, but the yield was reduced due to abnormal climate. In this study, it was hypothesized that rapid and continuous monitoring technology could help improve yield. Using the vegetation index (VI) analysis, the drought stress index was calculated and the drought stress for corn grown in Hwaong, Saemangeum, and Yeongsan River reclaimed tidal land was predicted according to drying treatment. The vegetation index of corn did not decrease during the last 20 days of irrigation when soil moisture rapidly decreased, but decreased rapidly during the 20 days after irrigation. The reduction rate of the vegetation index according to the drying treatment was in the order of Saemangeum>Yeongsan River>Hwaong reclaimed tidal land, and normalized difference vegetation index(NDVI) decreased by approximately 50% in all reclaimed tidal lands, confirming that drought stress occurred due to the decrease in moisture content of the leaves. In addition, structure pigment chlorophyll index (SIPI) and photochemical reflectance index (PRI), which are calculated based on changes in light use efficiency and carotenoids, were reduced; drought stress caused a decrease in light use efficiency and an increase in carotenoid content. Therefore, vegetation index analysis was confirmed to be effective in evaluating and predicting drought stress in corn growing on reclaimed tidal land corn.

• Journal of the Korean Geographical Society
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• v.49 no.1
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• pp.45-56
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• 2014

Data composite methods have been typically applied to satellite-based vegetation index(VI) data to continuously acquire vegetation greenness over the land surface. Data composites are useful for construction of long-term archives of vegetation indices by minimizing missing data or contamination from noise. In addition, if multi-sensor vegetation indices that are acquired during the same composite periods are used interchangeably, data stability and continuity may be significantly enhanced. This study evaluated the influences of sensor geometry on MODIS vegetation indices and investigated data compatibility of two difference vegetation indices, the Normalized Difference Vegetation Index(NDVI) and the Enhanced Vegetation Index(EVI), for potential improvement of long-term data construction. Relationships between NDVI and EVI turned out statistically significant with variations among vegetation covers. Due to their curvilinear relationships, NDVI became saturated and leveled off as EVI reached high ranges. Correlation coefficients between Terra- and Aqua-based vegetation indices ranged from 0.747 to 0.963 for EVI, and from 0.641 to 0.880 for NDVI, showing better compatibility for EVI compared to NDVI. In-depth analyses of VI outliers that deviated from regression equations constructed from the two different sensors remain as a future study to improve their compatibility.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.77-83
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• 2015

Leaf water content is one of important indicators that shows states of vegetation. It is important to monitor vegetation water content using remote sensing for forest management. In this study, we investigated the degree of water stress in Korean peninsula with Normalized Difference Water Index (NDWI) to study the water content of vegetation canopy. We calculated the NDWI using SPOT/VEGETATION S10 channel data over forest from 1999 to 2013. We calculated Simple Moving Average (SMA) to remove temporal noises of NDWI in time series, and used standardized anomaly to investigate temporal changes. We classified the NDWI anomalies into three scales (low, moderate, and high) in order to monitor intuitively. We also investigated suitability of the NDWI as an evaluation criterion about water stress of vegetation canopy by comparing and verifying forest fires damaged area over 150 ha. Consequently, huge forest fire occurred 24 times during the study period. Also, negative anomalies appeared in every forest fire location and their neighboring areas. In particular, we found huge forest fires where NDWI anomalies were in 'high' scale.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• 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.