• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.1
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• pp.42-54
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• 2016

It is highly anticipated that warming temperature resulting from global climate change will affect the phenological pattern of kiwifruit, which has been commercially grown in Korea since the early 1980s. Here, we present the potential impacts of climate change on the variations of flowering day of a gold kiwifruit cultivar, Haegeum, in the Jeonnam Province, Korea. By running six global climate models (GCM), the results from this study emphasize the uncertainty in climate change scenarios. To predict the flowering day of kiwifruit, we obtained three parameters of the 'Chill-day' model for the simulation of Haegeum: $6.3^{\circ}C$ for the base temperature (Tb), 102.5 for chill requirement (Rc), and 575 for heat requirement (Rh). Two separate validations of the resulting 'Chill-day' model were conducted. First, direct comparisons were made between the observed flowering days collected from 25 kiwifruit orchards for two years (2014-15) and the simulated flowering days from the 'Chill-day' model using weather data from four weather stations near the 25 orchards. The estimation error between the observed and simulated flowering days was 5.2 days. Second, the model was simulated using temperature data extracted, for the 25 orchards, from a high-resolution digital temperature map, resulting in the error of 3.4 days. Using the RCP 4.5 and 8.5 climate change scenarios from six GCMs for the period of 2021-40, the future flowering days were simulated with the 'Chill-day' model. The predicted flowering days of Haegeum in Jeonnam were advanced more than 10 days compared to the present ones from multi-model ensemble, while some individual models resulted in quite different magnitudes of impacts, indicating the multi-model ensemble accounts for uncertainty better than individual climate models. In addition, the current flowering period of Haegeum in Jeonnam Province was predicted to expand northward, reaching over Jeonbuk and Chungnam Provinces. This preliminary result will provide a basis for the local impact assessment of climate change as more phenology models are developed for other fruit trees.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1029-1040
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• 2017

Wildfires release a large amount of greenhouse gases (GHGs) into the atmosphere. Fire radiative power (FRP) data obtained from geostationary satellites can play an important role for tracing the GHGs. This paper describes an estimation of the Himawari-8 FRP and fire emissions for Samcheock and Gangnueng wildfire in 6 May 2017. The FRP estimated using Himawari-8 well represented the temporal variability of the fire intensity, which cannot be captured by MODIS (Moderate Resolution Imaging Spectroradiometer) because of its limited temporal resolution. Fire emissions calculated from the Himwari-8 FRP showed a very similar time-series pattern compared with the AirKorea observations, but 1 to 3 hour's time-lag existed because of the distance between the station and the wildfire location. The estimated emissions were also compared with those of a previous study which analyzed fire damages using high-resolution images. They almost coincided with 12% difference for Samcheock and 2% difference for Gangneung, demonstrating a reliability of the estimation of fire emissions using our Himawari-8 FRP without high-resolution images. This study can be a reference for estimating fire emissions using the current and forthcoming geostationary satellites in East Asia and can contribute to improving accuracy of meteorological products such as AOD (aerosol optical depth).

• 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.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.

• Atmosphere
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• v.28 no.2
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• pp.201-209
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• 2018

Benefits of the next generation geostationary meteorological satellite observation (e.g., GEO-KOMPSAT-2A) are qualitatively and comprehensively described and discussed. Main beneficial phenomena for application can be listed as tropical cyclones (typhoon), high impact weather (heavy rainfall, lightning, and hail), ocean, air pollution (particulate matter), forest fire, fog, aircraft icing, volcanic eruption, and space weather. The next generation satellites with highly enhanced spatial and temporal resolution images, expanding channels, and basic and additional products are expected to create the new valuable benefits, including the contribution to the reduction of socioeconomic losses due to weather-related disasters. In particular, the new satellite observations are readily applicable to early warning and very-short time forecast application of hazardous weather phenomena, global climate change monitoring and adaptation, improvement of numerical weather forecast skill, and technical improvement of space weather monitoring and forecast. Several policy plans for expanding the application of the next generation satellite data are suggested.

• Wind and Structures
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• v.20 no.1
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• pp.59-74
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• 2015

A coupled model system for Wind Resource Assessment (WRA) was studied. Using a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, global-scale data were downscaled to the inner nested grid scale (typically a few kilometers), and then through the coupling Computational Fluid Dynamics (CFD) mode, FLUENT. High-resolution results (50 m in the horizontal direction; 10 m in the vertical direction below 150 m) of the wind speed distribution data and ultimately refined wind farm information, were obtained. The refined WRF/FLUENT system was then applied to assess the wind resource over complex terrain in the northern Poyang Lake region. The results showed that the approach is viable for the assessment of wind energy.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.105-116
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• 2011

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Therefore, the exact relationship and the spatial variability analysis of hydrometeorological elements and characteristic factors is critical elements to reduce the uncertainty in rainfall -runoff model. In this study, radar rainfall grid resolution and grid resolution depending on the topographic factor in rainfall - runoff models were how to respond. In this study, semi-distribution of rainfall-runoff model using the model ModClark of Inje, Gangwon Naerin watershed was used as Gwangdeok RADAR data. The completed ModClark model was calibrated for use DEM of cell size of 30m, 150m, 250m, 350m was chosen for the application, and runoff simulated by the RADAR rainfall data of 500m, 1km, 2km, 5km, 10km from 14 to 17 on July, 2006. According to the resolution of each grid, in order to compare simulation results, the runoff hydrograph has been made and the runoff has also been simulated. As a result, it was highly runoff simulation if the cell size is DEM 30m~150m, RADAR rainfall 500m~2km for peak flow and runoff volume. In the statistical analysis results, if every DEM cell size are 500m and if RADAR rainfall cell size is 30m, relevance of model was higher. Result of sensitivity assessment, high index DEM give effect to result of distributed model. Recently, rainfall -runoff analysis is used lumped model to distributed model. So, this study is expected to make use of the efficiently decision criteria for configurated models.

• Asian Journal of Atmospheric Environment
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• v.9 no.1
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• pp.78-85
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• 2015

In the past several decades, biofuels have emerged as candidates to help mitigate the issues of global warming, fossil fuel depletion and, in some cases, atmospheric pollution. To date, the only biofuels that have achieved any significant penetration in the global transportation sector are ethanol and biodiesel. The global consumption of biodiesel was rapidly increased from 2005. The goal of this study was to examine the chemical composition on particulate pollutant emissions from a diesel engine operating on several different biodiesels. Tests were performed on non-road diesel engine. Experiments were performed on 5 different fuel blends at 2 different engine loading conditions (50% and 75%). 5 different fuel blends were ultra-low sulfur diesel (ULSD, 100%), soy biodiesel (Blend 20% and Blend 100%) and canola biodiesel (Blend 20% and Blend 100%). The chemical properties of particulate pollutants were characterized using an Aerodyne High Resolution Time of Flight Aerosol Mass Spectrometer (HR-ToF-AMS). Organic matter and nitrate were generally the most abundant aerosol components and exhibited maximum concentration of $1207{\mu}g/m^3$ and $30{\mu}g/m^3$, respectively. On average, the oxidized fragment families ($C_xH_yO_1{^+}$, and $C_xH_yO_z{^+}$) account for ~13% of the three family sum, while ~87% comes from the $C_xH_y{^+}$ family. The two peaks of $C_2H_3O_2$ (m/z 59.01) and $C_3H_7O$ (m/z 59.04) located at approximately m/z 59 could be used to identify atmospheric particulate matter directly to biodiesel exhaust, as distinguished from that created by petroleum diesel in the AMS data.

• Atmosphere
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• v.27 no.3
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• pp.261-276
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• 2017

The main objectives of this study are to introduce Global Seasonal forecasting system version5 (GloSea5) of KMA and to evaluate the performance of ensemble prediction of system. KMA has performed an operational seasonal forecast system which is a joint system between KMA and UK Met office since 2014. GloSea5 is a fully coupled global climate model which consists of atmosphere (UM), ocean (NEMO), land surface (JULES) and sea ice (CICE) components through the coupler OASIS. The model resolution, used in GloSea5, is N216L85 (~60 km in mid-latitudes) in the atmosphere and ORCA0.25L75 ($0.25^{\circ}$ on a tri-polar grid) in the ocean. In this research, we evaluate the performance of this system using by RMSE, Correlation and MSSS for ensemble mean values. The forecast (FCST) and hindcast (HCST) are separately verified, and the operational data of GloSea5 are used from 2014 to 2015. The performance skills are similar to the past study. For example, the RMSE of h500 is increased from 22.30 gpm of 1 week forecast to 53.82 gpm of 7 week forecast but there is a similar error about 50~53 gpm after 3 week forecast. The Nino Index of SST shows a great correlation (higher than 0.9) up to 7 week forecast in Nino 3.4 area. It can be concluded that GloSea5 has a great performance for seasonal prediction.

• Ecology and Resilient Infrastructure
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• v.5 no.2
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• pp.59-71
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• 2018

Lake Enriquillo, the largest lake in the Dominican Republic, recently has undergone unusual water area changes since 2001 thus it has been affected seriously by local community's livelihood. Earthquakes and seismic activities of Hispaniola plate tectonic coupled with human activities and climate change are addressed as factors causing the increasing. Thus, a thorough study on relationship between lake area changing, and those factors is needed urgently. To do so, this study applied MESMA on MODIS data to extract water area of Lake Enriquillo during 2001 and 2012 bimonthly, with six issues 12-year. MODIS provides high temporal resolution, and its coarse spatial resolution is compensated by MESMA fraction map. The increase in water area was $142.2km^2$, and the maximum lake area was $338.0km^2$ (in 2012). Water areas extracted by two Landsat scenes at two different times with three image classification approaches (ISODATA, MNDWI, and TCW) were used to assess accuracy of MODIS and MESMA results; it indicated that MESMA water areas are same as ISODATA's, less than 0.4%, while the highest difference is between MESMA and TCW, 2.4%. A number of previously formulated hypotheses of lake area change were investigated based on the outcomes of the present study, though none of them could fully explain the changes.