Jihee Seo;Sukyung Kim;Hyun Seok Kim;Junghwa Chun;Myoungsoo Won;Keunchang Jang
Korean Journal of Agricultural and Forest Meteorology
/
v.25
no.4
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pp.427-435
/
2023
Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R2. The models with applied correction coefficients achieve R2 values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.
Youngmin Seo;Youjeong Youn;Seoyeon Kim;Jonggu Kang;Yemin Jeong;Soyeon Choi;Yungyo Im;Yangwon Lee
Korean Journal of Remote Sensing
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v.39
no.6_1
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pp.1413-1425
/
2023
The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.
Kim, Jeong-hun;Song, Sae-hee;Ko, Lim-hwan;Nam, Hak-hyun;Jang, Jae-hyuck;Jung, Hoi-yun;Choi, Hyuck-jae
Journal of Venture Innovation
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v.5
no.1
/
pp.91-106
/
2022
Cryptocurrency, stood out the sharp cost rising of Bitcoin has been spotlighted by means of the solution for stagflation because it is decentralized with an existing currency differently. Especially getting into 4th industrial revolution, technologies using block chain and internet of things have been used in the many fields, and the power of influence is also widespread. Nevertheless like a remark of Elon Musk of Tesla CEO, the problems of environmental contamination for cryptocurrency have been pointed out continuously and the most representative of them is an enormous electric usage as the use of fossil fuels. Also the amount generated of carbon dioxide result in the acceleration of global warming mainly based on the climate changes of earth if the existing mining method is continued. On the other hand, review researches have been conducted restrictively as the connection with environmental contamination as the mining of cryptocurrency. In this study, it intended to review problems for environmental contamination as the diversification of ecological system of cryptocurrency concretely. Upon investigation existing prior documents on the putting recent data first, the mining of cryptocurrency has affected on the environmental contamination conflicting with carbon neutrality as increasement of the electric usage and electronic wastes. And POS method without the mining process appeared, but it had a demerit collapsing a decentralization and then we met turning point on appearing various environmental-friendly cryptocurrency. Finally the appearance of cryptocurrency using new renewable energy acted on the opportunity of the usage maximization of energy storage apparatus and the birth of national government intervention. Based on these results, we mention clearly that hereafter cryptocurrency will regress if not go abreast the value of currency as well as environmental approach.
BACKGROUND: Earthworms are essential detritus feeders that play a vital role in the process of decomposition of organic matter and soil metabolism. The complex process of partial breakdown of organic matter and mixing with mucous and gut microbial flora in the form of earthworm cast results in the reduction of the toxicity. This study focused on the change of cast amount and pollutant contents before and after the eating of the organic waste and upland soil with the two species of earthworm. METHODS AND RESULTS: The two species of earthworms were compared to the cast production. In the upland soil material, the daily amount of worm's cast was 1.42 g in E. andrei and 0.40 g in A. agrestis. In the organic waste material, the cast of E. andrei was 0.78~0.83 g and the cast of A. agrestis. have not been collected because all earthworms died after the treatment. The heavy metals treated in the upland soil were evaluated the impact of the worm excretion. With the E. andrei, the cast production was decreased 0.1~0.8 times in zinc, 0.2~0.5 times in copper, and 0.1~0.7 times in cadmium compared to the control treatment according to the levels of concentration. With A. agrestis, the cast amount was decreased 0.3~1.1 times in zinc, 0.2~0.3 times in copper, and 0.1~2.1 times in cadmium, respectively. The changes of pollutant contents before and after the eating of the organic wastes with E. andrei were studied. In the treatment of the Alcohol Fermentation Processing Sludge and the Fruit Juice Processing Sludge, heavy metal content of the cast was increased 0.7~53.3% compared to the sludge materials. PAHs contents were decreased 50.1% in the cast of the Alcohol Fermentation Processing Sludge and 36.6% in the cast of the Fruit Juice Processing Sludge, respectively. CONCLUSION: In conclusion, although the A. agrestis was bigger than E. andrei in size and weight, the cast amount of A. agrestis was small. The two species of earthworm was less excretion with high concentration of heavy metals. While the heavy metals such as zinc, copper, and cadmium were considerably accumulated in the cast, the total compounds, PAHs were fairly decomposed. There results would provide us for restoring contaminated soil and cleaning organic wastes.
Two models, TANK and SWAT (Soil and Water Assessment Tool) were compared for simulating natural flows in the Paldang Dam upstream areas of the Han River basin in order to understand the limitations of TANK and to review the applicability and capability of SWAT. For comparison, simulation results from the previous research work were used. In the results for the calibrated watersheds (Chungju Dam and Soyanggang Dam), two models provided promising results for forecasting of daily flows with the Nash-Sutcliffe model efficiency of around 0.8. TANK simulated observations during some peak flood seasons better than SWAT, while it showed poor results during dry seasons, especially its simulations did not fall down under a certain value. It can be explained that TANK was calibrated for relatively larger flows than smaller ones. SWAT results showed a relatively good agreement with observed flows except some flood flows, and simulated inflows at the Paldang Dam considering discharges from upper dams coincided with observations with the model efficiency of around 0.9. This accounts for SWAT applicability with higher accuracy in predicting natural flows without dam operation or artificial water uses, and in assessing flow variations before and after dam development. Also, two model results were compared for other watersheds such as Pyeongchang-A, Dalcheon-B, Seomgang-B, Inbuk-A, Hangang-D, and Hongcheon-A to which calibrated TANK parameters were applied. The results were similar to the case of calibrated watersheds, that TANK simulated poor smaller flows except some flood flows and had same problem of keeping on over a certain value in dry seasons. This indicates that TANK application may have fatal uncertainties in estimating low flows used as an important index in water resources planning and management. Therefore, in order to reflect actually complex and complicated physical characteristics of Korean watersheds, and to manage efficiently water resources according to the land use and water use changes with urbanization or climate change in the future, it is necessary to utilize a physically based watershed model like SWAT rather than an existing conceptual lumped model like TANK.
Although Korean red pine (Pinus densiflora) forests near temples are valuable as forests of the cultural landscape, they are likely to be deteriorated because of vegetation succession and climate changes. The purpose of this study is to investigate the vegetation structure, the pine vitality, and the site environmental characteristics of the pine forests near temples to identify the correlation between pine tree growth and location environment. We selected Chuneunsa, Wonhyosa, Jeungsimsa, and Taeansa Temples since these four areas still had the healthy pine forests. In all four studied area, the pine trees dominate the canopy layers while the deciduous broadleaf trees mostly inhabited appeared in the lower layers. The growth of pine trees in Jeungsimsa and Wonhyosa areas was not as good as Chuneunsa area where the pine trees tended to be older. We found higher total nitrogen content in soil in Jeungsimsa area than other areas, maybe because of increase in total nitrogen caused by the development of low vegetation in the area. This peculiarity may have led to the pine trees in the area to fall behind the deciduous broadleaf trees in competition for nitrogen nutrient and thus to show deteriorated growth. The altitude and the twig length showed a negative correlation as did the degree of slope and the mean importance percentage of the pine tree. In other words, the growth environment such as soil became poorer when the altitude and the degree of slope increased, and thus the growth amount and dominance of the pine trees were lower. The degree of slope showed a positive correlation with the twig length of the pine tree. Within boundaries of location environment where the pine tree forests were dominant, it seemed that growth of the pine trees was more favorable as the slope was steeper because the trees could avoid competition with deciduous broadleaf trees. On the other hand, the growth of pine trees deteriorated as the electrical conductivity of soil increased; increase in soil nutrients might have accelerated vegetation development of deciduous broadleaf trees and thus aggravated the growth environment of pine trees to negatively affect maintaining the health of the pine tree forests.
Relationship between grain quality-related traits and daily mean temperature/sunshine hours during grain filling stage was analyzed using eleven high quality rice varieties at 24 experimental sites through eight provinces of Korea in 2013~2014. In the data set pooled across varieties, experimental sites and years, grain quality-related traits such as percentage of head rice (PHR), head rice yield (HRY), protein in milled rice (PRO) and Toyo Mido Meter glossiness value (TGV) were higher at the temperature lower than $22.6^{\circ}C$ for 40 days after flowering (DAF), which was optimum for percentage of grain filling in this study. Optimum sunshine hours for 40 DAF were $6.0{\sim}6.1\;hr\;d^{-1}$when considered PHR, HRY and TGV. PRO was associated with daily mean temperature and sunshine hours for 40 DAF in more varieties than the other traits. PRO was closely correlated with daily mean temperature during early filling stage and sunshine hours during early to mid filling stage, compared to other stages during grain filling. It is concluded that general trend in the variation of grain quality-related traits could be explained by the changes in daily mean temperature and sunshine hours during grain filling. In addition, climate conditions during early grain filling stage played important roles to enhance grain quality.
Currently, many studies are being conducted to cope with climate changes due to global warming and abnormal weather. The objective of this study was to investigate the effects of weather on the growth, yield components, and quality of soybeans using weather data from 2017 and 2018. The average temperature in 2018 was higher than that in 2017 from R1 to R5 of the growth stage for all cultivars. On the other hand, precipitation in 2018 was reduced compared to that in 2017 for Daewon and Daepung-2ho. It was observed that the flowering date in 2018 was earlier than that in 2017 for Daewon and Daepung-2ho, but the flowering date for Pungsannamul in 2018 was similar to that in 2017. Simulating soil water content with the estimation model (AFKAE0.5) determined that there were fewer drought dates in 2017 than those in 2018, and drought lasted from R1 to early R5 of the growth stage in 2018. Soybean growth in 2017 was better than that in 2018, and seed yield and 100-seed weight of soybean were higher in 2017 than those in 2018 for all cultivars. The seed size in 2017 was larger than that in 2018 for all cultivars. Oil content in 2017 was higher than that in 2018; in particular, the difference between both years was observed for Daewon and Daepung-2ho. Protein content was higher in 2018 than that in 2017; however, there were different levels for each cultivar. Thus, these results indicate that the yield component and quality of soybeans are affected by high temperature and drought.
Kim, Gun-Yeob;Song, Beom-Heon;Roh, Kee-An;Hong, Suk-Young;Ko, Byung-Gu;Shim, Kyo-Moon;So, Kyu-ho
Korean Journal of Soil Science and Fertilizer
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v.41
no.6
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pp.399-407
/
2008
Importance of climate change and its impact on agriculture and environment has increased with a rise of greenhouse gases (GHGs) concentration in Earth's atmosphere, which had caused an increase of temperature in Earth. Greenhouse gas emissions such as methane($CH_4$) and nitrous oxide($N_2O$) in the field need to be assessed. GHGs fluxes using chamber systems in the fields(2004~2005) with pepper cultivation were monitored at the experimental plots of National Academy of Agricultural Science(NAAS), Rural Development Administration(RDA) located in Suwon city. $N_2O$ emission during pepper growing period was reduced to 74.0~82.1% in sandy loam soil compared with those in clay loam soil. Evaluating $N_2O$ emission at different levels of soil water conditions, $N_2O$ emission at -50 kPa were lowered to 13.2% in clay loam soil and 40.2% in sandy loam soil compared with those at -30 kPa. $CH_4$ emission was reduced to 45.7~61.6% in sandy loam soil compared with those in clay loam soil. Evaluating $CH_4$ at different levels of soil water conditions, $CH_4$ emission at -50 kPa was lowered to 69.6% in clay loam soil and 55.8% in sandy loam soil compared with those at -30 kPa. It implied that -50 kPa of soil water potential was effective for saving water and reducing GHG emissions. From the path analysis as to contribution factors for $N_2O$ emission, it appeared that contribution rate was in the order of mineral N(51.2%), soil temperature (25.8%), and soil moisture content(23.0%) in clay loam soil and soil moisture content(39.3%), soil temperature (36.4%), and mineral N(24.3%) in sandy loam soil.
Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.
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