• KIEAE Journal
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• v.11 no.6
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• pp.81-86
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• 2011

By strengthening the insulation performance of a building, a great deal of energy can be saved and a comfortable indoor environment can be offered to people. On the other hand, the climate, which has a great influence on the indoor environment, is changed by global warming. Therefore, in planning building envelope structure and design, climate change should be considered. In this paper, the optimal insulation thickness of exterior walls was calculated by an economic assessment method using heating degree-days. Additionally, how much influence climate change has on planning building insulation was investigated. The examination showed that heating degree-days have decreased by about 10% due to climate change in the past few decades. It was also shown that the optimal insulation thickness of exterior walls was thin, at about 6%, in three representative Korean cities (Seoul, Daejeon, Jeju).

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.273-273
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• 2019

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.189-189
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• 2017

Climate changes have impacted to many sectors including water resources in Vietnam. Vietnam is agricultural development country having more than 6,000 earth dam reservoirs. These reservoirs play a very important role in flow regulation for water supply to economic sectors. In the context of undesirable impacts of climate change such as increasing temparature, evaporation, and changing rainfall and rainfall pattern, water demands and inflow to reservoirs also are being influenced. This leads to changes of resevoir exploitation effects that needs to be assessed for adaptation solutions. This article summarizes evaluations on climate change impacts to 16 reservoirs in 4 regions of North-West, North-East, Central Part, and Central Highland of Vietnam. Research results showed that in the context of climate change, safety of these reservoirs will be decreased from 8% to 20% in both water supply and flood control capacity.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.405-414
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• 2021

This study predicted waxy corn harvest date in South Korea using 30-year (1991-2020) hindcasts (1-6 month lead) produced by the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. To estimate corn harvest date, the cumulative temperature is used, which accumulated the daily observed and predicted temperatures from the seeding date (5 April) to the reference temperature (1,650~2,200℃) for harvest. In terms of the mean air temperature, the hindcasts with a bias correction (20.2℃) tends to have a cold bias of about 0.1℃ for the 6 months (April to September) compared to the observation (20.3℃). The harvest date derived from bias-corrected hindcasts (DOY 187~210) well simulates one from observation (DOY 188~211), despite a slight margin of 1.1~1.3 days. The study shows the possibility of obtaining the gridded (5 km) daily temperature and corn harvest date information based on the cumulative temperature in advance for all regions of South Korea.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.368-375
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• 2023

In this study, we examined the error characteristic and bias correction method for one-month temperature forecast data produced through joint development between the Rural Development Administration and the H ong Kong University of Science and Technology. For this purpose, hindcast data from 2013 to 2021, weather observation data, and various environmental information were collected and error characteristics under various environmental conditions were analyzed. In the case of maximum and minimum temperatures, the higher the elevation and latitude, the larger the forecast error. On average, the RMSE of the forecast data corrected by the linear regression model and the XGBoost decreased by 0.203, 0.438 (maximum temperature) and 0.069, 0.390 (minimum temperature), respectively, compared to the uncorrected forecast data. Overall, XGBoost showed better error improvement than the linear regression model. Through this study, it was found that errors in prediction data are affected by topographical conditions, and that machine learning methods such as XGBoost can effectively improve errors by considering various environmental factors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.367-374
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• 2014

The monthly mean air temperature datasets of 61 stations in South Korea from 1973 to 2012 were collected to calculate trends in the warmth index (WI) and to analyze the potential enlargement of safety cultivation limit for sweet persimmon. The WI averaged over the last 40 years was 104.1 (℃·Month) at 61 stations, with the highest at Seogwipo station (WI=137.9) and the lowest at Daegwallyeong station (WI=60.9). It has increased by 1.8 (℃·Month) per 10 years over the last 40 years, with the highest in the year 1994 (WI=112.0) and the lowest in the year 1976 (WI=94.7). When the possible stations for sweet persimmon cultivation were classified by the basis on WI≥100, 38 out of the 61 weather stations were included in the safety cultivation zone for sweet persimmon for the last 40 years. On the other hand, the number of weather stations within the safety cultivation zones for sweet persimmon for the last 10 years (from 2003 to 2012) were 47 by adding additional 9 stations (Socho, Wonju, Chungju, Seosan, Uljin, Yangpyeong, Icheon, Cheonan, and Geochang stations). A further study of the climate conditions and soil characteristics is required for a better assessment of the safety cultivation zones for sweet persimmon.