• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.473-481
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• 2022

Environment-friendly harvesting is practiced to maintain ecosystem, landscape, and forest protection functions. The present study was conducted at Simgok-ri, Sinbuk-myeon, Pocheon, Gyeonngi-do, where a 41-50-year-old Japanese larch forest was harvested in an environment-friendly manner from 2017 to 2019. The dynamics of organic matter in this forest were investigated at three years after the harvest. Specifically, organic matter content was measured on the forest floor and in overstory biomass, litterfall, and soil up to 30 cm in depth from June 2020 to January 2021. Owing to the harvest, the amount of overstory biomass of the Japanese larch stands decreased from 142.22 to 44.20 t ha^-1. On the forest floor, the amount of organic matter was 32.87 t ha^-1 in the control plots and 23.34 t ha^-1 in the harvest plots. Annual litterfall was 4.43 t ha^-1 yr^-1 in the control plots and 1.16 t ha^-1 yr^-1 in the harvest plots. Soil bulk density in the B horizon was 0.97 g cm^-3 in the control plots and 1.06 g cm^-3 i n the harvest plots. Soil organic matter content was 11.5% in the control plots and 12.8% in the harvest plots. The total amount of soil organic matter did not differ significantly between the control plots (245.21 t ha^-1) and harvest plots (263.92 t ha^-1), although the amount of soil organic matter tended to be higher in the harvest plots. The total amount of organic matter in the forest was estimated to be 406.48 t ha^-1 in the control plots and 338.21 t ha^-1 in the harvest plots. In the harvest plots, the ratio of aboveground organic matter decreased to 13.1% and soil organic matter increased to 78.0%, indicating that the distribution of organic matter changed significantly in these plots. Overall, the carbon accumulated in aboveground biomass was substantially reduced by environment-friendly harvesting, whereas the soil carbon level increased, which played a role in mitigating the reduction of system carbon in the forest. These results highlight one possible resolution for forest management in terms of coping with climate change. However, given that only three years of environment-friendly harvesting data were analyzed, further research on the dynamics of organic matter and tree growth is needed.

• Journal of Practical Agriculture & Fisheries Research
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• v.20 no.1
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• pp.107-123
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• 2018

The global impact of climate change on agriculture is now increasing. The purpose of this study was to investigate the effect of climate change on livestock productivity. The variables that have the greatest influence on climate change factors were examined through previous studies and expert surveys. We also used the actual productivity data of livestock farmers to investigate the relationship with climate change. In order to evaluate the climate for changes in livestock productivity, national representative data (such as bullocks, dairy, pigs, laying hens, and broilers) were surveyed in Korea. Also, to select and classify evaluation indexes, we selected climate change factor variables as prior studies and studied the weighting factor of climate variable factors. In this study, the researchers of industry, academia, and farmers in the livestock sector conducted questionnaires on the indicators of vulnerability to climate change using experts, and then weighed the selected indicators using the hierarchical analysis process (AHP). In order to verify the validity of the evaluation index, was examined using domestic climate data (temperature, precipitation, humidity, etc.). Correlation and regression analysis were performed. The empirical relationship between climate change and livestock productivity was examined through this study. As a result, we used data with high reliability of statistical analysis and found that there are significant variables.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.43-54
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• 2023

Due to climate change, drought and flood occurrences have been increasing. Accurate projections of watershed discharges are imperative to effectively manage natural disasters caused by climate change. However, climate change and hydrological model uncertainty can lead to imprecise analysis. To address this issues, we used two lumped models, IHACRES and GR4J, to compare and analyze the changes in discharges under climate stress scenarios. The Hapcheon and Seomjingang dam basins were the study site, and the Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) were used for parameter optimizations. Twenty years of discharge, precipitation, and temperature (1995-2014) data were used and divided into training and testing data sets with a 70/30 split. The accuracies of the modeled results were relatively high during the training and testing periods (NSE>0.74, KGE>0.75), indicating that both models could reproduce the previously observed discharges. To explore the impacts of climate change on modeled discharges, we developed climate stress scenarios by changing precipitation from -50 % to +50 % by 1 % and temperature from 0 ℃ to 8 ℃ by 0.1 ℃ based on two decades of weather data, which resulted in 8,181 climate stress scenarios. We analyzed the yearly maximum, abundant, and ordinary discharges projected by the two lumped models. We found that the trends of the maximum and abundant discharges modeled by IHACRES and GR4J became pronounced as changes in precipitation and temperature increased. The opposite was true for the case of ordinary water levels. Our study demonstrated that the quantitative evaluations of the model uncertainty were important to reduce the impacts of climate change on water resources.

• Maritime Security
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• v.4 no.1
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• pp.1-56
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• 2022

Why has India become a key actor in the maritime-configured Indo-Pacific region? There are some external factors, but for India, its geo-strategic frontier encompassing its geopolitical and maritime interests is expanding rapidly beyond its territorial space across both the Indian and Pacific oceans amidst an increasingly arduous geopolitical and security environment. India must, therefore, acquire the ability to influence events within this strategic arena using all facets of national power, including maritime-military power. Lately, therefore, New Delhi has invested much intellectual capital to review its maritime-security strategy. India's new strategy is premised on the concept of holistic security involving the 'softer' aspects of maritime-security, and a rekindling of maritime consciousness in India, a nation that has traditionally been beset by 'sea-blindness'. The strategy adopts a region-wide, inclusive, and a more proactive approach than hitherto, as is evident in its title 'Ensuring Secure Seas: Indian Maritime Security Strategy'. While it deals with the growing concern of new non-traditional threats in the Indian littoral and the need for military deterrence and preparedness, it also addresses the imperatives for India to seek a favorable and rules-based benign environment in its immediate and extended maritime periphery, including through multi-vectored strategic partnerships dictated by its enduring principle of strategic autonomy. For a more profound and comprehensive understanding of India's maritime-security strategy, this paper examines the key unstated and implicit factors that underpin the strategy. These include India's historical and cultural evolution as a nation; its strategic geography; its geopolitical and security perceptions; and the political directions to its security forces. The paper deals specifically with India's response to maritime threats ranging from natural disasters, crime and state-sponsored terrorism to those posed by Pakistan and China, as well as the Indian Navy's envisaged security role East of the Malacca Straits. It also analyzes the aspects of organizational restructuring and force planning of India's maritime-security forces.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.