Societal value of the benefits forests provide has grown significantly, given their pivotal role in mitigating climate change and fostering the shift toward a carbon-neutral society. Due to the economic and public value of forests, which extends far beyond landowners and foresters, the forestry sector mutually interacts with society as a whole. Thus, understanding public perceptions and preferences concerning forests and forest policies from the societal viewpoint is vital for shaping future forest policy decisions. This research delved into evolving perceptions over the past 32 years, using a time-series analysis of data gathered from the 'Public awareness survey on forests'. This survey, conducted seven times between 1991 and 2023 by opinion poll agents, provides insights into changing sentiments. The findings reveal a notable increase in public satisfaction with overall forest policies. Specifically, positive sentiments were observed regarding forest rehabilitation, forest trails, education initiatives, and the establishment and functioning of forest recreation facilities. Conversely, the study highlights areas where public satisfaction remained relatively low, notably in matters concerning the use and conversion of mountainous regions, forest disaster prevention, and international forest cooperation. Additionally, the respondents emphasized the need for heightened attention to forest management, the development of forest roads, and increased efforts in overseas afforestation compared to current initiatives.
Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.
Lee, Seungmin;Wang, Wonjoon;Kim, Donghyun;Han, Heechan;Kim, Soojun;Kim, Hung Soo
Journal of Korea Water Resources Association
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v.56
no.10
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pp.619-629
/
2023
Recent intensification of climate change has led to an increase in damages caused by droughts. Currently, in Korea, the Standardized Precipitation Index (SPI) is used as a criterion to classify the intensity of droughts. Based on the accumulated precipitation over the past six months (SPI-6), meteorological drought intensities are classified into four categories: concern, caution, alert, and severe. However, there is a limitation in classifying drought intensity solely based on precipitation. To overcome the limitations of the meteorological drought warning criteria based on SPI, this study collected emergency water supply damage data from the National Drought Information Portal (NDIP) to classify drought intensity. Factors of SPI, such as precipitation, and factors used to calculate evapotranspiration, such as temperature and humidity, were indexed using min-max normalization. Coefficients for each factor were determined based on the Genetic Algorithm (GA). The drought intensity based on emergency water supply was used as the dependent variable, and the coefficients of each meteorological factor determined by GA were used as coefficients to derive a new Drought Severity Classification Index (DSCI). After deriving the DSCI, cumulative distribution functions were used to present intensity stage classification boundaries. It is anticipated that using the proposed DSCI in this study will allow for more accurate drought intensity classification than the traditional SPI, supporting decision-making for disaster management personnel.
Jo, Bugeon;Lee, Sangung;Kim, Young Do;Lee, Joo-Heon
Journal of Korea Water Resources Association
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v.56
no.11
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pp.823-835
/
2023
Recently, due to the increase in abnormal climate, rainfall intensity is increasing and drought periods are continuing. These environmental changes lead to prolonged drought conditions and difficulties in real-time recognition. In general, drought can be judged by the amount of precipitation and the number of days without rainfall. In determining the impact of drought, it is divided into meteorological drought, agricultural drought, and hydrological drought and evaluation is made using the drought index, but environmental drought evaluation is insufficient. The river water quality managed through the total water pollution cap system is vulnerable to the effects of such drought. In this study, we aim to determine the drought impact on river water quality and quantify the impact of prolonged drought on water quality. The impact of rain-free days and accumulated precipitation on river water quality was quantitatively evaluated. The Load Duration Curve (LDC), which is used to evaluate the water quality of rivers, was used to evaluate water pollution occurring at specific times. It has been observed that when the number of consecutive rainless days exceeds 14 days, the target water quality in the mid-basin is exceeded in over 60% of cases. The cumulative rainfall is set at 28 days as the criteria, with an annual average rainfall of 3%, which is 32.1 mm or less. It has been noted that changes in water quality in rivers occur when there are 14 or more rainless days and the cumulative rainfall over 28 days is 32.1 mm or less in the Gamcheon Mid-basin. Based on the results of this study, it aims to quantify the drought impact and contribute to the development of a drought water quality index for future environmental droughts.
Following the signing of the Paris Agreement on Climate Change (UNFCCC, 2015), the world is expanding greenhouse gas reduction activities through comprehensive participation that includes not only developed countries but also developing countries. Major countries around the world are placing high expectations on the effectiveness of total carbon emissions regulation through the carbon emissions market. However, in order to obtain carbon credits, third-party verification is required based on quantitative carbon reduction data. Accordingly, in this paper, we developed an AIoT high-efficiency street light for carbon emissions and conducted a performance analysis study to measure the luminous efficiency of the lighting fixture. To obtain carbon emissions rights, we used high-efficiency LED PKG, developed our own high-voltage PFC, and developed high-efficiency lighting fixtures capable of communication. For communication, the 2.4GHz LoRa method was adopted between the lighting fixture and the gateway. Lens design was conducted through simulation of Korea Expressway Corporation's standard streetlight types A, B, and C. The performance of the streetlight was verified as being more efficient than other existing products through the measurement of luminous efficiency by an accredited rating agency, and it is expected that carbon emissions rights will be obtained by reducing electrical energy through this.
Dongjun Kim;Gihong Min;Jihun Shin;Youngtae Choe;Kilyoong Choi;Sang Hyo Sim;Wonho Yang
Journal of Environmental Health Sciences
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v.50
no.1
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pp.16-24
/
2024
Background: Indoor PM2.5 concentrations in residential houses can be affected by various factors depending on the season. This is because not only do the climate characteristics depend on the season, but the activity patterns of occupants are also different. Objectives: The purpose of this study is to compare factors affecting indoor PM2.5 concentrations in apartments and detached houses in Daegu according to seasonal changes. Methods: This study included 20 households in Daegu, South Korea. The study was conducted during the summer (from July 10 to August 10, 2023) and the autumn (from September 11 to October 9, 2023). A sensor-based instrument for PM2.5 levels was installed in the living room of each residence, and measurements were taken continuously for 24 hours at intervals of one minute during the measurement period. Based on the air quality monitoring system data in Daegu, outdoor PM2.5 concentrations were estimated using ordinary kriging (OK) in Python. In addition, the indoor activities of the occupants were investigated using a time-activity pattern diary. The affecting factors of indoor PM2.5 concentration were analyzed using multiple regression analysis. Results: Indoor and outdoor PM2.5 concentrations of the residences during summer were 15.27±11.09 ㎍/m3 and 11.52±7.56 ㎍/m3, respectively. Indoor and outdoor PM2.5 concentrations during autumn were 13.82±9.61 ㎍/m3 and 9.57±5.50 ㎍/m3, respectively. The PM2.5 concentrations were higher in summer compared to autumn both indoors and outdoors. The primary factor affecting indoor PM2.5 concentration in summer was occupant activity. On the other hand, during the autumn season, the primary affecting factor was outdoor PM2.5 concentration. Conclusions: Indoor PM2.5 concentration in residential houses is affected by occupant activity such as the inflow of outdoor PM2.5 concentration, cooking, and cleaning, as found in previous studies. However, it was revealed that there were differences depending on the season.
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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v.15
no.2
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pp.86-96
/
2010
The oxygen utilization rate (OUR) is one of the crucial parameters for ocean carbon cycling and climate models. However, parameterization of OUR in the East Sea (Sea of Japan) is yet to be established. We estimated the basin-specific OURs in the East Sea and fitted them with exponential functions with depth by using pCFC- 12 age and apparent oxygen utilization (AOU) measured in summer 1999. The estimated OURs are higher in the upper water column and decrease with depth, in general. The vertical distributions of the estimated OURs in the Western and Eastern Japan Basins (WJB & EJB) are very similar. The OURs in the Ulleung Basin (UB) varied greatly depending on whether the surface layer (0~200 m) data are included in the OUR estimate or not. Apparently, weaker oxygen consumption occurs in the deep layer of Yamato Basin (YB). The ranges of the OURs between 200 m and 2000 m at WJB, EJB, UB, and YB are 8.15~0.83, 8.11~0.68, 5.29~0.73, and 7.31~0.06 ${\mu}mol$$kg^{-1}$$yr^{-1}$, respectively. Consideration of the wintertime surface water oxygen disequilibrium condition in estimating the OUR will be necessary in the future study.
Agricultural reservoirs supply water for various purposes such as irrigation, maintenance, and living. Since agricultural reservoirs respond sensitively to seasonal and climate changes, it is essential to estimate supply and inflow for efficient operation, and water management should be done based on these data. However, in the case of agricultural reservoirs, the measurement of supply and inflow is relatively insufficient compared to multi-purpose dams, and inflow-supply analysis in agricultural reservoirs through water balance analysis is necessary for efficient water management. Therefore, rainfall-runoff analysis models such as ONE model and Tank model have been developed and used for reservoir water balance analysis, but the applicability analysis for ungauged watersheds is insufficient. The ONE model is designed for daily runoff calculation, and the model has one parameter, which is advantageous for calibration and ungauged watershed analysis. In this study, the water balance was analyzed through the ONE model and the Tank model for 15 watersheds upstream of dams, and R2 and NSE were used to quantitatively compare the performance of the two models. The simulation results show that the ONE model is suitable for predicting the inflow of agricultural reservoirs with the ungauged watershed
Journal of the Korean Institute of Landscape Architecture
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v.52
no.3
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pp.18-32
/
2024
This study aims to analyze the summer human thermal environment in Jeju City's outdoor parking lots by measuring microclimate data and comparing pavement and vegetation albedoes and elements through computer simulations. In measured cases, results due to albedo showed no significance, but there was a significant difference between sunny and shaded areas by trees. The sunny area had a PET (physiological equivalent temperature) in the 'very hot' level, while the shaded area exhibited a 2-step lower 'warm' level. UTCI (universal thermal climate index) also showed that the sunny area was in the 'very strong heat stress' level, whereas the shaded area was 1-step lower in the 'strong heat stress' level, confirming the role of trees in reducing incoming solar radiant energy. Simulation results, using the measured albedoes, closely resembled the measured results. Regarding vegetation, scenarios with a wide canopy, high leaf density, and narrow planting spacing were effective in mitigating the human thermal environment, and the differences due to tree height varied across scenarios. The scenario with the lowest PET value was H9W9L3D8 (tree height 9m, canopy width 9m, leaf area index 3.0, planting spacing 8m), indicating a 0.7-step decrease compared to the current landscaping scenario. Thus, it was confirmed that, among landscaping elements, trees have a significant impact on the summer human thermal environment compared to ground pavement.
Climate change has been intensifying drought frequency and severity. Such prolonged droughts reduce reservoir levels, thereby exacerbating drought impacts. While previous studies have focused on optimizing reservoir operations using historical data to mitigate these impacts, their scope is limited to analyzing past events, highlighting the need for predictive methods for future droughts. This research introduces a novel approach for predicting minimum inflow at the Seomjingang dam which has experienced significant droughts. This study utilized the Stochastic Analysis Modeling and Simulation (SAMS) 2007 to generate inflow sequences for the same period of observed inflow. Then we simulate reservoir operations to assess firm yield and predict minimum inflow through synthetic inflow analysis. Minimum inflow is defined as the inflow where firm yield is less than 95% of the synthetic inflow in many sequences during periods matching observed inflow. The results for each case indicated the firm yield for the minimum inflow is on average 9.44 m3/s, approximately 1.07 m3/s lower than the observed inflow's firm yield of 10.51 m3/s. The minimum inflow estimation can inform reservoir operation standards, facilitate multi-reservoir system reviews, and assess supplementary capabilities. Estimating minimum inflow emerges as an effective strategy for enhancing water supply reliability and mitigating shortages.
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