• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.148-160
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• 2012

The main purpose of this paper is to deliver a climate analysis and evaluation method based on GIS by using airborne LiDAR data and Biotop type map and to provide spatial information of climate analysis and evaluation based on Biotop type Map. At first stage, the area, slope, slope length, surface, wind corridor function and width, and obstacle factors were analyzed to obtain cold/fresh air production and wind corridor evaluation. In addition, climate evaluation was derived from those two results in the second stage. Airborne LiDAR data are useful in wind corridor analysis during the study. Correlation analysis results show that ColdAir_GRD grade was highly correlated with Surface_GRD (-0.967461139) and WindCorridor_ GRD was highly correlated with Function_GRD (-0.883883476) and Obstacle_GRD (-0.834057656). Climate Evaluation GRID was highly correlated with WindCorridor_GRD (0.927554516) than ColdAir_GRD (0.855051646). Visual validations of climate analysis and evaluation results were performed by using aerial ortho-photo image, which shows that the climate evaluation results were well related with in-situ condition. At the end, we applied climate analysis and evaluation by using Biotop map and airborne LiDAR data in Gwangmyung-Shiheung City, candidate for the Bogeumjari Housing District. The results show that the aerial percentile of the 1st Grade is 18.5%, 2nd Grade is 18.2%, 3rd Grade is 30.7%, 4th Grade is 25.2%, and 5th Grade is 7.4%. This study process provided both the spatial analysis and evaluation of climate information and statistics on behalf of each Biotop type.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Tuberculosis and Respiratory Diseases
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• v.44 no.4
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• pp.871-888
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• 1997

Background : It is now thought that the earliest manifestation of idiopathic pulmonary fibrosis is alveolitis, that is, an accumulation of inflammatory and immune effector cells within alveolar walls and spaces. Inflammatory cells including alveolar macrophages and resident normal pulmonary tissue cells participate through the release of many variable mediators such as inflammatory growth factors and cytokines, which contribute to tissue damage and finally cause chronic pulmonary inflammation and fibrosis. This study was performed to investigate the source and distribution pattern of transforming growth factor-${\beta}_1$(TGF-${\beta}_1$), platelet derived growth factor(PDGF), basic fibroblast growth factor(bFGF), interleukin 1(IL-1), interleukin 6(IL-6), tumor necrosis factor-$\alpha$ (TNF-$\alpha$) and the role of these mediators on bleomycin(BLM)-induced pulmonary injury and fibrosis in rats. Method : Wistar rats were divided into three groups(control group, BLM treated group, BLM and vitamine E treated group). Animals were sacrificed periodically at 1, 2, 3, 4, 5, 7, 14, 21, 28 days after saline or BLM administration. The effects were compared to the results of bronchoalveolar lavage fluid analysis, light microscopic findings, immunohistochemical stains for six different mediators(TGF-${\beta}_1$, PDGF, bFGF, IL-1, IL-6 and TNF-$\alpha$) and mRNA in situ hybridization for TGF-${\beta}_1$. Results : IL-1 and IL-6 are maximally expressed at postbleomycin 1~7th day which are mainly produced by neutrophils and bronchiolar epithelium. It is thought that they induce recruitment of inflammatory cells at the injury site. The expression of IL-1 and IL-6 at the bronchiolar epithelium within 7th day is an indirect evidence of contribution of bronchiolar epithelial cells to promote and maintain the inflammatory and immune responses adjacent to the airways. TNF-$\alpha$ is mainly produced by neutrophils and bronchiolar epithelial cells during 1~5th day, alveolar macrophages during 7~28th day. At the earlier period, TNF-$\alpha$ causes recruitment of inflammatory cells at the injury site and later stimulates pulmonary fibrosis. The main secreting cells of TGF-${\beta}_1$ are alveolar macrophages and bronchiolar epithelium and the target is pulmonary fibroblasts and extracellular matrix. TGF-${\beta}_1$ and PDGF stimulate proliferation of pulmonary fibroblasts and TGF-${\beta}_1$ and bFGF incite the fibroblasts to produce extracellular matrix. The vitamine E and BLM treated group shows few positive cells(p<0.05). Conclusion : After endothelial and epithelial injury, the neutrophils and bronchiolar epithelium secrete IL-1, IL-6, TNF-$\alpha$ which induce infiltration of many neutrophils. It is thought that variable enzymes and $O_2$ radicals released by these neutrophils cause destruction of normal lung architecture and progression of pulmonary fibrosis. At the 7~28th day, TGF-${\beta}_1$, PDGF, bFGF, TNF-$\alpha$ secreted by alveolar macrophages sting pulmonary fibroblasts into proliferating with increased production of extracellular matrix and finally, they make progression of pulmonary fibrosis. TNF-$\alpha$ compares quite important with TGF-${\beta}_1$ to cause pulmonary fibrosis. Vitamine E seems to decrease the extent of BLM induced pulmonary fibrosis.