In this study, an analysis were conducted to utilize the thermal infrared image using drone to present the temperature correction method of thermal infrared image and the thermal environment by the type of land cladding. The analysis was applied to the temperature correction of the thermal infrared image and total eight thermal infrared images were produced based on the land surface temperature. The thermal infrared image compared accuracy through RMSE calculation. Based on the result of RMSE, the thermal infrared image corrected by the land surface temperature was relatively accurate and contained at 2.26 to 3.58. According to the results, it is expected that the aggregation and waters will perform the functions of the green park sufficiently to improve the thermal comfort and improve the microclimate stability using the thermal infrared image and the reclassified land cover map. The results of this study obtained by Drone and the usability of the drone thermal infrared image in the detection of the thermal environment. Finally, it is expected to contribute to the improvement and management of the thermal environment in the city by being used as a basic data for the improvement and management policy of the thermal environment. Moreover, the macro view is expected to contribute to the mitigation of urban temperature reduction and heat island.

This study was undertaken to find out national level changes in area, production and yield of two major staple crops wheat and potato in Bangladesh. The time series of secondary data was collected from yearbook of agricultural statistics under Bangladesh Bureau of Statistics (BBS) and used for the statistical analysis during the thirty-year period of 1989/90-2018/19. Moreover, selected data were divided into two groups and regarded as segment (1989/90-2003/04, 2005/06-2018/19) to examine the significant level in each crop. The results of different statistical techniques showed that wheat cultivated area and production were satisfactory level but yield was not too much standard in context of country demand. In the case of potato, yield as well as cultivated area and production were crossed the significant level and fulfilled the demand of population. In recent few years, the ratio of potato production rapidly increased, compared with the cultivation area. Based on segment (period) analysis, at the first half wheat production was always below, compared with the area but second half nine years saw slightly improved. On the other hand, in both segment potato growth rate in area, production and yield were increased throughout the study period. The highest instability was also shown in area, production and yield of potato during whole as well as segmented period. There was always a positive relationship between country’s demand and supply. Both wheat and potato are considered as staple crops and based on the productive capability over cultivated area, potato showed the higher productivity for the country of Bangladesh. In consequences, potato consuming demand also rapidly increased all over the country, compared with past respectively.

This study investigated the relationship analysis between wheat cultivated area and the climate data in Sindh province of Pakistan. The extraction of wheat cultivation area is detected using the remote sensing technique. The analysis of the study area reveals the annual mean maximum and the mean minimum temperature tends to risen with a large range of changes. The trend of average annual precipitation showed a large change, thus it was confirmed that the increase and decrease yield of wheat were depend on the various growth periods of wheat crop. The most influential factors are the annual mean precipitation and the annual mean minimum temperature at the seedling stage of wheat crop. The annual precipitation, annual mean maximum, and the annual mean minimum temperature are significant at the growth period. The annual mean maximum and the annual mean minimum temperatures are significant during the ripening stage of wheat crop in the study area. The results of the study showed that wheat production varies with climate change in the Sindh province. In addition, this study will be used as an important basis for solving crop cultivation areas and production problems caused by climate change in the region.

This study examined the efficiency of satellite images in terms of detecting wheat cultivation areas, and then analyzed the possibility of climate change through an correlation analysis of time series climate data from the western regions of Gyeongnam province, Korea. Furthermore, we analyzed the effect of climate change on wheat production through a multiple regression analysis with the time series wheat production and climate data. A relatively accurate distribution was achieved on the wheat cultivation area extracted through satellite image classification with an error rate of less than 10% in comparison to the statistical data. Upon correlation analysis with time series climate data, significant results were displayed in the following changes: the monthly mean temperature of the seedling stage, the monthly mean duration of sunshine, the monthly mean temperature of the growing period, the monthly mean humidity, the monthly mean temperature of the ripening stage, and the monthly mean ground temperature. Accordingly, in the study area, the monthly mean temperature, precipitation, and ground temperature generally increased whereas the monthly mean duration of sunshine and humidity decreased. The monthly mean wind speed did not display a particular change. In the multiple regression analysis results, the greatest effect on the production and productivity of wheat as climate factors included the annual mean humidity of the seedling stage, the annual mean temperature of the wintering period, and the annual mean ground temperature of the ripening stage. These results demonstrate that there is a change in wheat production depending on the climate change in the study area. in addition, it is determined that this study will be used as important basic data in the resolution of food security problems based on climate change.

A Quantile-based Matching (QM) method has been widely used to correct the biases in global and regional climate model outputs. The basic idea of QM is to adjust the Cumulative Distribution Function (CDF) of model for the projection period on the basis of the difference between the model and observation CDFs for the training period. Therefore, the CDF of observation on training period plays an important role in quantile-based matching. Also, ensembles are highly correlated because ensemble forecasts generated from a combination of randomly perturbed initial conditions and different convective schemes in numerical weather model. We discuss the dependence of the bias correction results obtained from Qunatile-based Matching when there is correlation between ensembles and the variance of observation is larger than that of model. A simulation study is employed to understand the relation and distributional characteristics of observation and model when applying Quantile-based Matching method.

The purpose of this study is to identify the effectiveness of satellite images in detecting the areas of rice production in the Barisal of Bangladesh. We also investigated the effect of climate change on the crop production through comparative analysis of rice production area and production statistics with climate data at multi-temporal time scale. This analysis found that the classification of rice fields extracted through satellite image and made as the number of rice cultivation areas did not exceed 10 percent of the statistical data. Climate data analysis showed that the average temperature during the ripening stage has the greatest impact on Boro’s production. It would be more make sense if you can describe the results of how precipitation is also important for rice production in addition to temperature. Therefore, it is believed that this research could serve as a key basis for solving food security issues due to climate change.

It is difficult to measure precipitation due to spatial and temporal variability. In this study we analyzed the variability of precipitation of high- and low-rainfall regions in Bangladesh using Precipitation Concentration Index (PCI) from the data of two meteorological stations. We compared PCI values for various periods such as annual, supra-seasonal, seasonal, three and two-months. Most previous studies have analyzed the long-term precipitation in Bangladesh. We analyzed the variabilities from long-term to short-term and tried to characterize the irregular precipitation. In the result, the precipitation in Bangladesh was mostly concentrated between two and four months of the year. Future research will require more station data to understand the more detailed precipitation patterns in Bangladesh.

This study estimated sunshine duration in South Korea using cloud detection images; the 2 class and 5 class images of Communication, Ocean and Meteorological Satellite (COMS). The images were preprocessed and then compared with the observed sunshine duration from the Automated Synoptic Observing System (ASOS). Based on the result of comparing yearly and monthly sunshine duration, the results of the 5 class were better than the results of the 2 class. In the case of comparing daily sunshine duration, the results of the 5 class also showed relatively better outcome than the 2 class images. The simulation performance of the sunshine duration observed by ASOS and the sunshine duration calculated by COMS were evaluated using Kling-Gupta Efficiency (KGE) technique. The 5 class data showed relatively high efficiency. RMSEs were relatively lower in the 5 class than the 2 class image in all years (2011-2014). Therefore, the 5 class data among the COMS satellite images could provide meaningful information at the points where there is no observation of sunshine duration.

We developed the Parameter-elevation Regressions on Independent Slopes Model(PRISM)-based Dynamical downscaling Error correction(PRIDE)-Wind speed(WS) model version 3.0 to produce highresolution( 1km) grid data at a monthly time scale by using observation and Regional Climate Model(RCM) wind speed data. We consequently produced monthly wind speed grid data during the observation period(2000~2014) and the future period(2021~2100) for Representative Concentration Pathway(RCP) 2 type scenarios by using the PRIDE-WS model. The PRIDE-WS model is constructed by combining the MK(Modified Korean)-PRISM-Wind, the RCM anomaly and Cumulative Density Function(CDF) fitting, basically based on Kim et al.(2016)’s algorithm applied for daily precipitation. The upper level wind(80m altitude) was estimated by Deacon equation using surface wind speed that was produced by the PRIDE-WS model. The results show that the wind speed at the upper level generally increased during the summer season while it decreased during the spring, autumn and winter seasons.

MK-PRISM developed for wind interpolation was applied to case studies and was verified in previous studies. Thus, some tests were necessary before the model used in order to produce wind speed maps for the whole area of South Korea. In this study, the MK-PRISM was applied to producing wind speed maps of South Korea. The result showed that sharp changes occur in wind speed distribution, despite the continuous similar topographic. The primary reason for the phenomenon was that a linear regression slope between elevation and wind speed used in interpolation process was changed rapidly in some areas. This study used the landform classification data to address this problem. The improved model controlled similarly the slope of the linear regression equation in the continuous valley, slope, and ridge. Therefore, the slope of the linear regression equation does not change dramatically in the improved model. The improved model was named MK-PRISM-Wind in this study. The wind speed was similar on the ridge continuously in the wind speed distribution produced by MK-PRISM-Wind. In addition, the wind speed was more gradually changed compared to the previous model on the plains and foothills. The results mean that MK-PRISM-Wind can produce wind speed maps more reasonable than the previous model, and it can be applied to the wind speed interpolation of South Korea. High-resolution gridded wind speed map produced by MK-PRISM-Wind is expected to be utilized for various studies.

This study has calculated the change of wind speed according to the features of land surface roughness using the surface wind data provided by the Korean peninsula data of HadGEM3-RA and has analyzed the characteristics of the future upper wind over South Korea driven by several climate change scenarios. The simulation found that the more the time passes, the more the wind speed increases in the previous time period of upper wind and annual average wind speed time series analysis of three kinds of Representative Concentration Pathways (RCP). The wind speed of all three kinds of RCP increased in the summer and winter but decreased in the spring and fall in the analysis of seasonal time series and spatial distribution. The wind speed would be expected to increase in most months except April and November in the analysis of the monthly mean maximum wind speed. The histogram analysis shows the mean wind speed of upper wind over 3m/s. As the time passes, the wind speed increases more than in the past. Certain areas such as the areas under the urbanization development would be anticipated to raise the wind speed throughout all months.

This paper evaluates the applicability of a simple kriging with local means(SKLM) for highresolution spatial mapping of monthly mean temperature and rainfall in South Korea by using AWS observations in 2013 and elevation data. For an evaluation purpose, an inverse distance weighting(IDW) which has been widely applied in GIS and cokriging are also applied. From explanatory data analysis prior to spatial interpolation, negative correlations between elevation and temperature and positive correlation between elevation and rainfall were observed. Bias and root mean square errors are computed to compare prediction performance quantitatively. From the quantitative evaluation, SKLM showed the best prediction performance in all months. IDW generated abrupt changes in spatial patterns, whereas cokriging and SKLM ref lected not only the topographic effects but also the smoothing effects. In particular, local characteristics were better mapped by SKLM than by cokriging. Despite the potential of SKLM, more extensive comparative studies for data sets observed during the much longer time-period are required, since annual, seasonal, and local variations of temperature and rainfall are very severe in South Korea.

This study investigates the theoretical background of the interpolation methods that regards the topographical effect on the climate data, such as Co-kriging, Artificial Neural Network and MK-PRISM(Modified Korean Parameter-elevation Regressions on Independent Slopes Model). Prior to applying the MK-PRISM to the interpolation of wind speed, this study has improved the model to be closer to the fundamental concept of the PRISM and verified it‘s validity. Since each method has individual advantages and disadvantages, there will be a need for comparative studies in order to select an interpolation method that is suitable for the topography of Korea. This study has added a weighted value that considers the existence of clusters at the known point, and has supplemented the digital elevation models and aspects distribution of multiple scales for application. In addition, this study has allowed the consideration of sharp changes between the known point and unknown point when calculating the topographic facet weighting. The supplement model was verified through the interpolation of rainfall in Jeju Island. The coefficient of determination and KGE(Kling and Gupta Efficiency) of the model displayed the results of 0.86 and 0.87, respectively for August 2010 monthly precipitation in Jeju Island, and the model was accordingly verified. This study is able to provide the necessary information to the researchers who wish to interpolate the observation data of wind speed. Furthermore, the supplement MK-PRISM becomes available to the research on the interpolation of wind speed.

In this study, the yearly mean normalized difference vegetation index(YMNDVI) in Chungcheongnam-do was calculated using S10 NDVI data of the vegetation sensor for SPOT 4 and 5. Based on this calculation, statistical values such as mean value, standard deviation and coefficient of variation were determined. In addition, a comparative analysis was performed by calculating YMNDVI for cities and counties of Chungcheongnam-do. The YMNDVI of Chungcheongnam-do revealed a slight increase during 14 years between 1999 to 2012. However, it showed only a slight change within the range of 0.476 to 0.553, and no significant increase or decrease was noted. As a result, the highest YMNDVI was 0.553 at 2009, the lowest YMNDVI was 0.476 at 2001 and 2006. The mean value of YMNDVI in Chungcheongnam-do for 14 years turned out to be 0.502. As a result of the regional YMNDVI analysis, the highest YMNDVI region was Geumsan-gun, followed by Geryong-si, Cheongyang-gun and Gongjusi. The lowest YMNDVI region was Taean-gun, followed by Dangjin-si, Seosan-si and Seocheon-gun. An analysis of coefficient of variation in the research area showed that the mean value of Chungcheongnamdo was 4.2%, while the overall value was also not that high.

This paper generated time-series temperature maps and analyzed the characteristics of temperature distributions from monthly average temperature observations between 2010 and 2011 in Jirisan areas using topographic data and geostatistics. From variogram modeling, all months except May to August showed that the spatial variability of temperature was the greatest along the direction perpendicular to coasts. Monthly temperature has negative correlations with elevation and distances from coasts and especially the correlation between temperature and distances from coasts was very weak in summer like the variogram modeling result. For temperature distribution mapping, kriging with a trend and ordinary kriging were separately applied as a univariate kriging algorithm by considering the spatial variability structures of temperature. Simple kriging with varying local means was applied as a multivariate kriging algorithm for integrating topographic data sets. From the cross validation results, the use of topographic data in spatial prediction of temperature showed the improved predictive performance, compared with univariate kriging. This improvement in predictive performance was dependent mainly on mean and variation values of monthly temperature and the spatial auto-correlation strength of residuals, as well as the correlation between topographic data and temperature. Based on these analysis results, spatial variability analysis using variogram is effectively used to account for spatial characteristics of monthly temperature and the correlation with topographic data. Topographic data can also be a useful information source for reliable temperature mapping.