• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.121-131
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• 2018

Climate change has caused changes in environmental factors that have a direct impact on agriculture such as temperature and precipitation. The meteorological disaster that has the greatest impact on agriculture is drought, and its forecasts are closely related to agricultural production and water supply. In the case of terrestrial data, the accuracy of the spatial map obtained by interpolating the each point data is lowered because it is based on the point observation. Therefore, acquisition of various meteorological data through satellite imagery can complement this terrestrial based drought monitoring. In this study, Evaporative Stress Index (ESI) was used as satellite data for drought determination. The ESI was developed by NASA and USDA, and is calculated through thermal observations of GOES satellites, MODIS, Landsat 5, 7 and 8. We will identify the difference between ESI and other satellite-based drought assessment indices (Vegetation Health Index, VHI, Leaf Area Index, LAI, Enhanced Vegetation Index, EVI), and use it to analyze the drought in South Korea, and examines the applicability of ESI as a new indicator of agricultural drought monitoring.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.365-365
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• 2019

가뭄은 전 세계적으로 농업을 비롯한 사회, 경제적으로 큰 피해를 주는 자연 재해이며, 향후 피해 저감을 위해 가뭄의 경향을 파악하고 지역별 가뭄 특성을 파악할 필요가 있다. 위성영상을 활용한 가뭄 판단은 광역적 범위를 대상으로 다양한 밴드를 활용한 데이터를 주기적이고 일정한 수준으로 취득 가능하다는 장점이 있다. 농업 가뭄 분야의 위성영상 활용은 미계측 지역에 대한 정확한 데이터 취득이 어려운 지점데이터의 단점을 보완할 수 있다. 위성영상을 활용한 가뭄 지수로는 Leaf Area Index (LAI), Vegetation Health Index (VHI), Enhanced Vegetation Index (EVI) 등 다양한 지수들이 있으며, 본 연구에서는 단기 가뭄 판단에 활용되고 있는 Evaporative Stress Index (ESI)를 활용하였다. 국내 행정구역 기반의 가뭄 판단을 위해 Moderate Resolution Imaging Spectramadiometer (MODIS)위성의 MOD16A2 영상을 사용하였다. MOD16A2는 land surface temperature (LST)과 LAI의 계산을 통한 실제 증발산량과 FAO-56 Penman-Monteith 공식을 사용한 잠재증발산량을 포함한 다양한 데이터를 8일 주기의 500m 해상도로 제공하고 있다. 2001년부터 2018년까지 500m 해상도의 ESI를 산정하였으며, 국내의 과거 가뭄 경향 분석과 지역별 특성 파악을 위한 표준화를 수행하였다. 그 결과 과거 극심한 가뭄이 있었던 해 (2000-2001년, 2015-2017년 등)에 대한 농업 가뭄 경향 분석이 가능하였으며, 지역별 특성을 파악한 결과 상습가뭄 지역에서 가뭄 경향을 확인하였다. 농업 가뭄 분야에서 ESI의 활용은 가뭄 조기 경보 시스템 개발 및 위성영상 기반 가뭄 모니터링 기술 개발 등에 활용 가능할 것으로 기대된다.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.7-10
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• 2005

Plant water deficiency during drought season causes physiological stress and can be a critical indicator of forest fire vulnerability. In this study, we attempt to analyze the spectral characteristics of water stressed vegetation by using the laboratory measurement on leaf samples and the canopy reflectance spectra extracted from satellite hyperspectral image data. Leaf-level reflectance spectra were measured by varying moisture content using a portable spectro-radiometer. Canopy reflectance spectra of sample forest stands of two primary species (pine and oak) located in central part of the Korean peninsula were extracted from EO-l Hyperion imaging spectrometer data obtained during the drought season in 2001 and the normal precipitation year in 2002. The preliminary analysis on the reflectance spectra shows that the spectral characteristics of leaf samples are not compatible with the ones obtained from canopy level. Although moisture content of vegetation can be influential to the radiant flux reflected from leaf-level, it may not be very straightforward to obtain the spectral characteristics that are directly related to the level of canopy moisture content. Canopy spectra form forest stands can be varied by structural variables (such as LAt, percent coverage, and biomass) other than canopy moisture content.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Spatial Information Research
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• v.17 no.1
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• pp.117-129
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• 2009

South Korea has been undergoing spring drought periodically and diverse researches using vegetation index have been carried out to monitor spring droughts. The strength of the vegetation index-based drought monitoring is that the monitoring method enables efficient spatio-temporal grasp of changes in drought events. According to the development of low resolution satellite images such as MODIS, which are characterized by outstanding temporal resolution, the use of the method is expected to increase. Drought analysis using vegetation index considered only meteorological factor as a cause that affects vitality of vegetation. But many indirect and direct factors affect vegetation stress, So many uncertainties are involved in such method of analysis. To secure objectivity of drought analysis that uses vegetation index it is therefore necessary to compare the method with most representative drought analysis tools that are used for drought management. In this study, PDSI and SPI which a meteorological drought index that quantifies drought and that is used as a basic index for drought monitoring and MODIS NDVI are compared to propose correlation among them and to show usefulness of drought assessment that uses vegetation index. This study shows changing patterns of NDVI and SPI 6-month are similar and correlation between NDVI and SPI was highest in inland vegetation cover.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.16-28
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• 2013

Recently the various applications and spectral indices development of airborne hyperspectral imagery(A-HSI) has been increased. Especially the vegetation indices (VIs) were used to verify stress and vigor of vegetation. The VIs needs two or more spectral bands selectively to calculate as NIR(near infrared) and red wavelength. The A-HIS has specific band characteristics as narrow, continues and many. The A-HIS has narrow, continues and many specific band characteristics. That could be make it confuse which of bands could be explained for appropriate vegetation characteristics. If the A-HIS bands is not the same the wavelength with VIs' development band setting, then it need a selection adequate for spectral characteristics of target vegetation. Therefore we set 4 substitute bands for NIR and red wavelength respectively and calculated two VIs combined with substitute bands such as NDVI(normalized difference vegetation index) and MSRI(modified simple ratio index). To consider the variation of each VIs, we adapted the image differencing method of change detection technique. Also, we used spectral derivative to identify appropriate bands for spectral characteristics of digital forest cover type map. The result of adequate bands for two VIs selected red #3 as 680.2nm and NIR #2 as 801.7nm. This wavelength was good for any forest type in low variations.

• Korean Journal of Remote Sensing
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• v.30 no.3
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• pp.375-381
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• 2014

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The $0.25^{\circ}$ coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.6
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• pp.21-33
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• 2007

Different vegetation indices from satellite images have been used for monitoring drought damages, and this study aimed to develop a drought index using NOAA/AVHRR NDVI(Normalized Difference Vegetation Index) and to analyze the temporal and spatial distribution of spring drought severity in North Korea from 1998 to 2001. A new drought index, DevNDVI(Deviation of NDVI), was defined as the difference between a monthly NDVI and average monthly NDVI at the same cover area, and the DevNDVI images at all years except for 2001 demonstrated the drought-damaged areas referred from various domestic and foreign publications. The vegetation of 2001 showed high vitality despite the least amount of rainfall among the target years, and the reason was investigated that higher temperature above normal average would shift the growing stages of plants ahead. Therefore, complementary methods like plant growth models or ground survey data should be adopted in order to evaluate drought-induced plant stress using satellite-based NDVI and to make up far the distortion induced by other environments than lack of precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.179-189
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• 2010

This paper presents a numerical investigation of vorticity generation in fully vegetated open-channel flows. The Reynolds stress model is used for the turbulence closure. Open-channel flows with rough bed-smooth sidewalls and smooth bed-rough sidewalls are simulated. The computed vectors show that in channel flows with rough bed and rough sidewalls, the free-surface secondary currents become relatively smaller and larger, respectively, compared with that of plain channel flows. Also, open-channel flows over vegetation are simulated. The computed bottom vortex occupies the entire water depth, while the free-surface vortex is reduced. The contours of turbulent anisotropy and Reynolds stress are presented with different density of vegetation. The budget analysis of vorticity equation is carried out to investigate the generation mechanism of secondary currents. The results of the budget analysis show that in plain open-channel flow, the production by anisotropy is important in the vicinity of the wall and free-surface boundaries, and the production by Reynolds stress is important in the region away from the boundaries. However, this rule is not effective in vegetated channel flows. Also, in plain channel flows, the vorticity is generated mainly in the vicinity of the free-surface and the bottom, while in vegetated channel flows, the regions of the bottom and vegetation height are important to generate the vorticity.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.