• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1805-1815
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• 2022

In 2022, wildfires broke out in Uljin-gun and Samcheok-si, which set the record for the longest forest fire in Korea, but there were no casualties. To protect local residents from wildfires, they must evacuate. Predicting the demand for evacuation in the event of wildfires is essential for the efficiency of disaster management. The purpose of this study is to analyze the human mobility patterns according to the occurrence of Uljin-gun and Samcheok-si wildfires. SKT floating population data was used in this study to analyze the human mobility patterns in Uljin-gun and Samcheok-si. The main findings are as follows. First, while the movement of the resident and visiting population decreased, the movement of the worker population was found to be similar to normal. Second, the resident population of Buk-myeon, Uljin-gun moved to the surrounding area to avoid the wildfires. Third, the region is an area judged to be safe from wildfires, and this mobility patterns are related to emergency disaster text messages. This study confirmed human mobility patterns of the population in the area where the wildfires through the floating population data, which is quantitative data. This suggests that it is important to guide residents to shelters through emergency text messages to minimize damage in the event of wildfires.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.609-620
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• 2023

In this study, we used Sentinel-1 C-band synthetic aperture radar to calculate the surface state factor (SSF) for distinguishing the frozen-thawed state of soil. The accuracy of SSF classification was analyzed through comparison with air temperature (AT), grass temperature (GT), and underground temperature (UT). For the analysis, 116 Sentinel-1B Descending nodes observed over a period of 4 years from 2017 to 2020 were established for the central region of South Korea. AT, GT, and UT data were obtained from 23 soil moisture observation points of the Rural Development Administration during the same period, and analyzed using the 06:00 am data adjacent to the shooting time of the Sentinel-1B images. The average accuracy and F1-score for all stations were 0.63 and 0.47 for AT, 0.63 and 0.48 for GT, and 0.57 and 0.21 for UT, respectively. For winter (December-February) data, the average accuracy and F1-score were 0.66 and 0.76 for AT, 0.67 and 0.76 for GT, and 0.47 and 0.44 for UT, respectively. The increase in accuracy during winter data may be attributed to the fact that errors occurring in other seasons are not included.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.228-240
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• 2023

Growing complexity in ecosystem structure and functions, under impacts of climate and land-use changes, requires interdisciplinary understandings of processes and the whole-system, and accurate estimates of the changing functions. In the last three decades, observation networks for biodiversity, ecosystems, and ecosystem functions under climate change, have been developed by interested scientists, research institutions and universities. In this paper we will review (1) the development and on-going activities of those observation networks, (2) some outcomes from forest carbon cycle studies at our super-site "Takayama site" in Japan, and (3) a few ideas how we connect in-situ and satellite observations as well as fill observation gaps in the Asia-Oceania region. There have been many intensive research and networking efforts to promote investigations for ecosystem change and functions (e.g., Long-Term Ecological Research Network), measurements of greenhouse gas, heat, and water fluxes (flux network), and biodiversity from genetic to ecosystem level (Biodiversity Observation Network). Combining those in-situ field research data with modeling analysis and satellite remote sensing allows the research communities to up-scale spatially from local to global, and temporally from the past to future. These observation networks oftern use different methodologies and target different scientific disciplines. However growing needs for comprehensive observations to understand the response of biodiversity and ecosystem functions to climate and societal changes at local, national, regional, and global scales are providing opportunities and expectations to network these networks. Among the challenges to produce and share integrated knowledge on climate, ecosystem functions and biodiversity, filling scale-gaps in space and time among the phenomena is crucial. To showcase such efforts, interdisciplinary research at 'Takayama super-site' was reviewed by focusing on studies on forest carbon cycle and phenology. A key approach to respond to multidisciplinary questions is to integrate in-situ field research, ecosystem modeling, and satellite remote sensing by developing cross-scale methodologies at long-term observation field sites called "super-sites". The research approach at 'Takayama site' in Japan showcases this response to the needs of multidisciplinary questions and further development of terrestrial ecosystem research to address environmental change issues from local to national, regional and global scales.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.2
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• pp.82-94
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• 2014

The purpose of this study is to investigate the cause of increasing dry zones through analyzing relationships between land cover and Vegetation Temperature Condition Index(VTCI) using Landsat 4-5 TM satellite images in Central Dry Zones of Myanmar. As a result of land cover classifications, while vegetation areas gradually decrease, residential area and cropland were increased. VTCI analysis shows that region (a) showed a gradual decrease in the area of severely arid, and increase in the area of moderate dry and wet, which sums up to a slight decrease in aridity. Region (b) also showed to increase in dry areas and severe aridity. The result of relational analysis between VTCI and land cover change showed high ratio of land cover change, from severe arid area to forest and residential farmland. The average VTCI decreased in the changed land covers, which indicates the relationship between aridity and land cover change and a gradual increase in the arid area was identified.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.663-666
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• 2005

Gas hydrates are ice-like compounds that form at the low temperature and high pressure conditions common in shallow marine sediments at water depths greater than 300-500 m when concentrations of methane and other hydrocarbon gases exceed saturation. Estimates of the total mass of methane carbon that resides in this reservoir vary widely. While there is general agreement that gas hydrate is a significant component of the global near-surface carbon budget, there is considerable controversy about whether it has the potential to be a major source of fossil fuel in the future and whether periods of global climate change in the past can be attributed to destabilization of this reservoir. Also essentially unknown is the interaction between gas hydrate and the subsurface biosphere. ODP Leg 204 was designed to address these questions by determining the distribution, amount and rate of formation of gas hydrate within an accretionary ridge and adjacent basin and the sources of gas for forming hydrate. Additional objectives included identification of geologic proxies for past gas hydrate occurrence and calibration of remote sensing techniques to quantify the in situ amount of gas hydrate that can be used to improve estimates where no boreholes exist. Leg 204 also provided an opportunity to test several new techniques for sampling, preserving and measuring gas hydrates. During ODP Leg 204, nine sites were drilled and cored on southern Hydrate Ridge, a topographic high in the accretionary complex of the Cascadia subduction zone, located approximately 80km west of Newport, Oregon. Previous studies of southern Hydrate Ridge had documented the presence of seafloor gas vents, outcrops of massive gas hydrate, and a pinnacle' of authigenic carbonate near the summit. Deep-towed sidescan data show an approximately $300\times500m$ area of relatively high acoustic backscatter that indicates the extent of seafloor venting. Elsewhere on southern Hydrate Ridge, the seafloor is covered with low reflectivity sediment, but the presence of a regional bottom-simulating seismic reflection (BSR) suggests that gas hydrate is widespread. The sites that were drilled and cored during ODP Leg 204 can be grouped into three end-member environments basedon the seismic data. Sites 1244 through 1247 characterize the flanks of southern Hydrate Ridge. Sites 1248-1250 characterize the summit in the region of active seafloor venting. Sites 1251 and 1252 characterize the slope basin east of Hydrate Ridge, which is a region of rapid sedimentation, in contrast to the erosional environment of Hydrate Ridge. Site 1252 was located on the flank of a secondary anticline and is the only site where no BSR is observed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.577-586
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• 2018

In this study, traffic safety technology was developed for rural areas by reviewingthe relevant literature and data from the Traffic Accident Analysis System for the Chungcheong region.The goal is to reduce traffic accidents in small regional cities and rural areas in Korea. A road shoulder recognition light was developed to fit the pedestrian characteristics of the people using transportation in rural areas. It also minimizes damage to crops due to light pollution from traffic lights and street lights, and it supplements problems of damage from collision with vehicles and agricultural machines. The efficiency of the technology developed in this study was verified by comparing and analyzing the number of traffic accidents and the saved cost before and after its installation. A test bedwas established based on rural areas and is being evaluated for its applicability and effectiveness. It is expected that the reliability of such facilities could be improved through continuous studies, data collection, and analysis.