• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.172-183
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• 2012

In 2011, floods were at the worst stage of devastation in Chao Phraya river basin of Thailand. The purpose of this study is to trace the flood inundation area around Chao Phraya river basin by using Terra MODIS image because it has the ability of spatiotemporal dynamics. The MODIS indices, which included the enhanced vegetation index(EVI), land surface water index(LSWI), and the difference in the values of EVI and LSWI(DVEL), were extracted from MODIS product MOD09 8-day composite datasets with a spatial resolution of 500m from Jul. 29, 2011 to Jan. 09, 2012. We found that combined application of EVI, LSWI, and DVEL was suitable for monitoring flood inundation. For the extracted flood inundation area and water-related area. The result can be used to acquire the flood inundation data scattered and demonstrate the potential for the use of MODIS data for temporal and spatial detection of flood effects.

• 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.34 no.2_1
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• pp.267-282
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• 2018

Despite the continuous development of phenology detection studies using satellite imagery, verification through comparison with the field observed data is insufficient. Especially, in the case of Korean forests patching in various forms, it is difficult to estimate the start of season (SOS) by using only satellite images due to resolution difference. To improve the accuracy of vegetation phenology estimation, this study reconstructed the large scaled forest type map (1:5,000) with MODIS pixel resolution and produced time series vegetation phenology curves from Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from MODIS images. Based on the field observed data, extraction methods for the vegetation indices and SOS for Korean forests were compared and evaluated. We also analyzed the correlation between the composition ratio of forest types in each pixel and phenology extraction from the vegetation indices. When we compared NDVI and EVI with the field observed SOS data from the Korea National Arboretum, EVI was more accurate for Korean forests, and the first derivative was most suitable for extracting SOS in the phenology curve from the vegetation index. When the eight pixels neighboring the pixels of 7 broadleaved trees with field SOS data (center pixel) were compared to field SOS, the forest types of the best pixels with the highest correlation with the field data were deciduous forest by 67.9%, coniferous forest by 14.3%, and mixed forest by 7.7%, and the mean coefficient of determination ($R^2$) was 0.64. The average national SOS extracted from MODIS EVI were DOY 112.9 in 2014 at the earliest and DOY 129.1 in 2010 at the latest, which is about 0.16 days faster since 2003. In future research, it is necessary to expand the analysis of deciduous and mixed forests' SOS into the extraction of coniferous forest's SOS in order to understand the various climate and geomorphic factors. As such, comprehensive study should be carried out considering the diversity of forest ecosystems in Korea.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.679-681
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• 2003

The objectives of this research are as follows. First, to investigate methods for a national-scale land cover map based on multi-temporal classification of MODIS data and multi-spectral classification of Landsat TM data. Second, to investigate methods to p roduce ecological zone maps of Korea based on vegetation, climate, and topographic characteristics. The results of this research can be summarized as follows. First, NDVI and EVI of MODIS can be used to ecological mapping of the country by using monthly phenological characteris tics. Second, it was found that EVI is better than NDVI in terms of atmospheric correction and vegetation mapping of dense forests of the country. Third, several ecological zones of the country can be identified from the VI maps, but exact labeling requires much field works, and sufficient field data and macro-environmental data of the country. Finally, relationship between land cover types and natural environmental factors such as temperature, precipitation, elevation, and slope could be identified.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.305-314
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• 2016

In this study, a quantitative assessment was carried out in order to identify the agricultural drought in time and space using the Terra MODIS remote sensing data for the agricultural drought. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were selected by MOD13A3 image which shows the changes in vegetation conditions. The land cover classification was made to show only vegetation excluding water and urbanized areas in order to collect the land information efficiently by Type1 of MCD12Q1 images. NDVI and EVI index calculated using land cover classification indicates the strong seasonal tendency. Therefore, standardized Vegetation Stress Index Anomaly (VSIA) of EVI were used to estimated the medium-scale regions in Korea during the extreme drought year 2001. In addition, the agricultural drought damages were investigated in the country's past, and it was calculated based on the Standardized Precipitation Index (SPI) using the data of the ground stations. The VSIA were compared with SPI based on historical drought in Korea and application for drought assessment was made by temporal and spatial correlation analysis to diagnose the properties of agricultural droughts in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.256-256
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• 2012

2011년 태국 차오프라야 강 유역($160,813km^2$)에서 발생한 홍수에 의해 많은 피해가 발생했다. 태국 홍수는 2011년 7월 말부터 3개월간 내린 집중호우로 중부지방에 50년 만에 최악의 자연재해를 맞이하였다. 태국 북쪽 지역에서 난 강과 핑 강의 범람을 시작으로 태국 중앙 지역을 흐르는 차오프라야 강의 수위는 상류의 홍수가 하류로 내려옴에 따라 범람하여 수도 방콕까지 침수되었다. 본 연구에서는 홍수범람시 시공간적 침수상황이 파악 가능한 Terra MODIS (Moderate Resolution Imaging Spectroradiometer) 영상을 이용하여 태국 차오프라야 강 유역의 홍수에 의한 침수지역을 추정하고자 하였다. 2011년 7월 29일에서 2012년 1월 9일까지의 500 m 해상도인 MODIS product MOD09 (Surface Reflectance) 8일 합성 영상을 수집하고 식생지수 (EVI; Enhanced Vegetation Index), 지표수분지수 (LSWI; Land Surface Water Index))와 DVEL지수 (Difference Value between EVI and LSWI)를 이용하여 홍수범람 지역과 수역관련지역을 정보화 기법을 제시하였다. 본 연구의 결과는 홍수 범람지역의 자료를 정보화하고 그 결과를 정량적으로 제시하는 방법으로 활용될 수 있으며, MODIS 자료의 이용은 시공간적 하천 홍수범람지역 탐지의 가능성을 알 수 있었다.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.5
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• pp.29-45
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• 2016

This study identified vulnerable regions in the national parks of the Republic of Korea (ROK). The potential vulnerable regions were defined as areas showing a decline in forest productivity, low resilience, and high sensitivity to climate variations. Those regions were analyzed with a regression model and trend analysis using the Enhanced Vegetation Index (EVI) data obtained from long-term observed Moderate Resolution Imaging Spectroradiometer (MODIS) and gridded meteorological data. Results showed the area with the highest vulnerability was Naejangsan National Park in the southern part of ROK where 32.5% ($26.0km^2$) of the total area was vulnerable. This result will be useful information for future conservation planning of forest ecosystem in ROK under environmental changes, especially climate change.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.509-520
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• 2012

The objective of this study was to estimate rice yield in Korea using satellite and meteorological data such as sunshine hours or solar radiation, and rainfall. Terra and Aqua MODIS (The MOderate Resolution Imaging Spectroradiometer) products; MOD13 and MYD13 for NDVI and EVI, MOD15 and MYD15 for LAI, respectively from a NASA web site were used. Relations of NDVI, EVI, and LAI obtained in July and August from 2000 to 2011 with rice yield were investigated to find informative days for rice yield estimation. Weather data of rainfall and sunshine hours (climate data 1) or solar radiation (climate data 2) were selected to correlate rice yield. Aqua NDVI at DOY 233 was chosen to represent maximum vegetative growth of rice canopy. Sunshine hours and solar radiation during rice ripening stage were selected to represent climate condition. Multiple regression based on MODIS NDVI and sunshine hours or solar radiation were conducted to estimate rice yields in Korea. The results showed rice yield of $494.6kg\;10a^{-1}$ and $509.7kg\;10a^{-1}$ in 2011, respectively and the difference from statistics were $1.1kg\;10a^{-1}$ and $14.1kg\;10a^{-1}$, respectively. Rice yield distributions from 2002 to 2011 were presented to show spatial variability in the country.

• Journal of the Korean earth science society
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• v.27 no.7
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• pp.787-803
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• 2006

The drought index has been developed, based on a $8.6{\mu}m$ surface emissivity in the $8-12{\mu}m$ MODIS channels over the African Sahel region (10-20 N, 13 W-35 W) and the Seoul Metropolitan Area (SMA: 37.2-37.7 N, 126.6-127.2 E). The emissivity indicates the $SiO_2$ strength and can vary interannually by vegetation, water vapor, and soil moisture, as a potential indicator of drought conditions. In a well-vegetated region close to 10 N of the Sahel, the Normalized Difference Vegetation Index (NDVI) showed high sensitivity, while the emissivity did not. On the other hand, the NDVI experienced negligible variability in a poorly vegetated region near 20 N, while the emissivity reflected sensitively the effects of atmospheric water vapor and soil moisture conditions. Seasonal variations of the emissivity (0.94-0.97) have been examined over the SMA during the 2003-2004 period compared to NDVI (or Enhanced Vegetation Index; EVI). Here, the dryness was more severe in urban area with less vegetation than in suburban area; the two areas corresponded to the north and south of the Han river, respectively. The emissivity exhibiting a significant spatial correlation of ${\sim}0.8$ with the two indices can supplement their information.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.446-446
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• 2022

본 연구에서는 MODIS(MODerate resolution Imaging Spectroradiometer) 다중 위성영상을 기반으로 전국 시공간 토양수분 및 토양수분 기반의 가뭄지수 SWDI(Soil Water Deficit Index)를 산정하였다. 시공간 토양수분의 산정을 위해 입력자료로 MODIS 위성의 지표면온도(Land Surface Temperature, LST), 증발산 및 식생(Enhanced Vegetation Index, EVI; Fraction of Photosynthetically Active Radiation, FPAR; Leaf Area Index, LAI; Normalized Difference Vegetation Index, NDVI) 관련 산출물 자료와 지상 관측자료인 일 단위 강수량 자료를 구축하였다. MODIS 위성영상은 산출물별로 제공되는 QC(Quality Control) 영상을 활용해 보정을 수행하였고, 공간 강수량 자료는 기상청에서 제공하는 전국 92개 지점의 종관기상관측자료를 구축하여 공간보간기법인 역거리가중법을 적용해 생성하였다. 실측 토양수분은 농촌진흥청에서 제공하는 76개 지점의 토양 깊이 10 cm에 설치된 TDR(Time Domain Reflectomerty) 센서에서 측정된 토양수분 자료를 활용하였으며, 토양수분 모의 시 토양 속성을 고려하기 위해 국립농업과학원에서 제공하는 토양도를 구축하여 활용하였다. 토양수분 산정 모형은 다중선형회귀모형(Multiple Linear Regression Model, MLRM)을 활용하였으며, 계절 및 토성에 따른 회귀식을 산정하였다. 회귀식 기반의 토양수분과 토성별 포장용수량 및 영구위조점 값을 이용하여 SWDI를 산정하고, 실제 가뭄 발생 시기 및 지역과의 비교하고자 한다.