• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.515-523
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• 2018

In order to obtain consistent change detection result for multi-temporal satellite images, preprocessing must be performed. In particular, the preprocessing related to the spectral values can be performed by the radiometric normalization, and relative radiometric normalization is generally utilized. However, most relative radiometric normalization methods assume a linear relationship between the two images, and nonlinear spectral characteristics such as phenological differences are not considered. Therefore, this study proposes a relative radiometric normalization which assumes nonlinear relationships that can perform compositive normalization of radiometric and phenological characteristics. The proposed method selects the subject and reference images, and then extracts the radiometric control set samples through the no-change method. In addition, spectral indexes as well as pixel values are extracted in order to consider sufficient information, and modeling of nonlinear relationships is performed through multilayer perceptron. Finally, the proposed method is compared with the conventional relative radiometric normalization methods, which shows that the proposed method is visually and quantitatively superior.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.

• Progress in Medical Physics
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• v.7 no.1
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• pp.25-35
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• 1996

Optical measurements of turbid biological tissue have provided a means to study tissue metabolism, tissue blood perfusion and blood oxygenation non-invasively. We used the red light of 660nm and infrared of 880nm to measure the blood fractional volume and oxygen saturation of biological tissue. In vivo reflectance data were obtained the physiological change from the deep tissue in human subject. The data evaluation was assessed by examining the slopes of the plotter index for the changes in oxygen saturation and blood fraction volume. The index is the natural logarithm of the ratio of reflected light intensity from measured medium to reference intensity at each wavelength. According to the experimental results, oxygen index changes significantly in the muscle of calf during exercise.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.483-497
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• 2016

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of barley and wheat growth prediction equation by using UAV derived vegetation index. UAV imagery was taken on the test plots six times from late February to late June during the barley and wheat growing season. The field spectral reflectance during growing period for the 5 variety (Keunal-bori, Huinchalssal-bori, Saechalssal-bori, Keumkang and Jopum) were measured using ground spectroradiometer and three growth parameters, including plant height, shoot dry weight and number of tiller were investigated for each ground survey. Among the 6 Vegetation Indices (VI), the RVI, NDVI, NGRDI and GLI between measured and image derived showed high relationship with the coefficient of determination respectively. Using the field investigation data, the vegetation indices regression curves were derived, and the growth parameters were tried to compare with the VIs value.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.981-996
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• 2018

In agriculture, remote sensing data using earth observation satellites have many advantages over other methods in terms of time, space, and efficiency. This study analyzed the changes of reflectance and vegetation index according to atmospheric correction of images before using satellite images in agriculture. Top OF Atmosphere (TOA) reflectance and surface reflectance through atmospheric correction were calculated to compare the reflectance of each band and Normalized Vegetation difference Index (NDVI). As a result, the NDVI observed from field measurement sensors and satellites showed a higher agreement and correlation than the TOA reflectance calculated from surface reflectance using atmospheric correction. Comparing NDVI before and after atmospheric correction for multi-temporal images, NDVI increased after atmospheric corrected in all images. garlic and onion cultivation area and forest where the vegetation health was high area NDVI increased more 0.1. Because the NIR images are included in the water vapor band, atmospheric correction is greatly affected. Therefore, atmospheric correction is a very important process for NDVI time-series analysis in applying image to agricultural field.

• 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.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.193-197
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• 2006

지상측정 및 위성영상탑재 광학센서를 이용하여 벼 주요 생육시기에 대한 군락의 엽질소 함량을 추정하였다. 6월부터 10월에 걸쳐 주요 생육시기 $5{\sim}6$회에 걸쳐 Orbview 및 QuickBird와 같이 4m 이하의 고해상도 다중영상을 취득하였다. 위성영상 취득일에 가능한한 맞추어 인공광원을 사용하는 2종의 능동형 광학 (G)NDVI 센서를 이용한 벼 군락의 반사특성을 측정하였으며 동시에 식물체 샘플링을 통한 생육량, 엽면적지수, 엽질소 함량 등을 분석하였다. 시기별 영상의 분광반사특성 및 (G)NDVI와 벼 생육량 및 엽질소 함량과의 관계를 알아보기 위해 상관분석 및 회귀분석을 수행하였다. 지상센서 및 위성영상 유래 (G)NDVI의 값을 서로 비교해 보면 전체적으로 지상센서를 이용하여 측정한 (G)NDVI값이 위성영상 유래 (G)NDVI값보다 크게 나타났다. 하지만 두 센서 모두 엽면적지수 변화에 따른 (G)NDVI의 변화를 살펴보면 엽면적지수가 2 정도가 될 때까지는 함께 증가하다가 2보다 커지면서는 변화가 없이 머무르는 경향은 같게 나타났다. 엽면적지수의 변화는 군락의 엽질소함량 변화와 선형적인 관계($R^2=0.80$)로 나타났다. 분얼기부터 성숙초기까지의 자료를 이용하여 지상센서 및 위성영상 유래 (G)NDVI를 이용한 벼 군락의 엽질소 함량과의 관계를 살펴보니 지수함수적 관계($R^2=0.90$)로 나타났다. 위성영상 유래 (G)NDVI를 이용한 벼 군락의 엽질소 함량 추정식을 이용하여 신평면 최고쌀 생산단지에 대한 엽질소 함량 지도를 작성하였다.

• Journal of Korea Water Resources Association
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• v.47 no.10
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• pp.935-943
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• 2014

Normalized Difference Vegetation Index (NDVI) used as input data for various hydrologic models plays a key role in understanding the variation of Hydrometeological parameters and Interaction between surface and atmosphere. Many studies have been conducted to estimate accurate remotely-sensed NDVI using spectral characteristics of vegetation. In this study, we conducted comparative analysis between Communication, Ocean and Meteorological Satellite and MOderate-Resolution Imaging Spectroradiometer (MODIS) NDVI. For comparison, Maximum Value Composite (MVC) was used to estimate 8-day and 16-day composite COMS NDVI. Both 8-day and 16-day COMS NDVI showed high statistical results compared with MODIS NDVI. Based on the results in this study, it can be concluded that COMS can be widely applicable for further ecological and hydrological studies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.