• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• Spatial Information Research
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• v.22 no.5
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• pp.9-18
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• 2014

According to the recent development of the ubiquitous computing environment, the use of spatio-temporal data from sensors with GPS is increasing, and studies on the Semantic Sensor Web using spatio-temporal data for providing different kinds of services are being actively conducted. Especially, the W3C developed the SSNO(Semantic Sensor Network Ontology) which uses sensor-related standards such as the SWE(Sensor Web Enablement) of OGC and defines classes and properties for expressing sensor data. Since these studies are available for the query processing about non-spatio-temporal sensor data, it is hard to apply them to spatio-temporal sensor data processing which uses spatio-temporal data types and operators. Therefore, in this paper, we developed the SWE based on SSNO which supports the spatio-temporal sensor data types and operators expanding spatial data types and operators in "OpenGIS Simple Feature Specification for SQL" by OGC. The system receives SensorML(Sensor Model Language) and O&M (Observations and Measurements) Schema and converts the data into SSNO. It also performs the efficient query processing which supports spatio-temporal operators and reasoning rules. In addition, we have proved that this system can be utilized for the web service by applying it to a virtual scenario.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.119-132
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• 2012

Recently, forest soil sediment disasters resulting from locally concentrated heavy rainfall have been occurring frequently in steep slope areas. The importance of landslide hazard map is emerging to analyze landslide vulnerable areas. This study was carried out to develop HyGIS-Landslide based on Hydro Geographic Information System in order to analyze forest soil sediment disaster in the mountainous river basin. HyGIS-Landslide is one of HyGIS components designed by considering the landslide hazard criteria of Korea Forest Service. It could show the distribution of landslide hazard areas after calculating the spatial data. In this system, the user could reset the weight of hazard criteria to reflect the regional characteristics of the landslide area. This component provided user interface that could make the latest spatial data available in the area of interest. HyGIS-Landslide could be applied to the surveyor's compensation score and it was possible to reflect the landslide risk exactly through it. Also, it could be used in topographic analysis techniques providing spatial analysis and making topographical parameters in HyGIS. Finally the accuracy could be acquired by calculating the landslide hazard grade map and landslide mapping data. This study applied HyGIS-Landslide at the Gangwon-do province sample site. As a result, HyGIS-Landslide could be applied to a decision support system searching for mountainous disaster risk region; it could be classified more effectively by re-weighting the landslide hazard criteria.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.1-13
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• 2015

Korea now boasts a vastly forested landscape resulting from a successful forest restoration projects carried out in the past several decades. However, Korea's forest now face new challenges, such as the rapidly increasing mature forests, climate change, and various novel forest disturbances with both natural and anthropogenic causes. Considering the extensive spatial and temporal scale of the forests and the challenges it face, it is necessary to utilize a tool that can properly tackle the issues with such nature. This brings our attention to Forest Landscape Models, which have been actively developed and used to improve our understanding of how forests respond to a variety of changes and to satisfy the society's demand on forests and its ecosystem services. A large variety of Forest Landscape Models exist, with a wide spectrum of algorithms, various selections of ecological processes they simulate, and the spatial and temporal scale they utilize, so that any researcher may find a model that fits one's use. However, it is important to properly understand the properties of such models so that the right model is used and the results are aptly interpreted. In this study, we describe and characterize the various Forest Landscape Models based on their historical roots, lineages, and development, ecological characteristics, and computational aspects, and discuss how they can be classified and what limits should be recognized to assist in model selection and utilization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.111-118
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• 2019

In this age where the social environment is changing rapidly and unpredictably, interest in safety from crime is increasing in Korean society. As the desire to live a life free from the fear of crime increases, interest in the construction of safe cities is also rising nationwide. For this, it is important to develop precision-positioning technology and support-service and intelligent security-service technology based on spatial information. Therefore, this study analyzes cases of multiple CCTV collaboration technology from among the intelligent-security technologies, and evaluates the technology's guarantee system through the evaluation system of the Technology Guarantee Fund, and evaluates continuity based on innovation, spreadability, usability, and proposed commercialization in order to enable utilization and commercialization. As a result of analyzing multiple CCTV collaborative technologies through the evaluation system of the Technology Guarantee Fund, the technology with the highest outlook was given five points, and the others were rated as excellent in terms of spreadability, usability, and differentiation. For innovation, the score was three points lower than the other evaluation items, but we expect to overcome that by introducing the latest technology and converging it with other technologies, such as the Internet of Things.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1191-1205
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• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• Korean Journal of Optics and Photonics
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• v.33 no.1
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• pp.1-10
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• 2022

In this study we introduce a new method for high-spatial-resolution continuous wave (CW) aerosol lidar that has a high spatial resolution in the near field and a low spatial resolution at long distances. A normal lidar system uses a nanosecond-pulse laser and measures the round-trip TOF between the aerosol and laser to obtain range resolution. In this study, however, we propose a new type of spatially resolving aerosol lidar that uses laser-scattering images. Using a laser-light-scattering image, we have calculated the distance of each scattering aerosol image for a given pixel, and recovered the short-range aerosol extinction. For this purpose, we have calculated the distance image and the contribution range of the aerosol to the given one-pixel image, and finally we have calculated the extinction coefficients of the aerosol with range-resolved information. In the case of traditional aerosol lidar, we can only obtain the aerosol extinction coefficients above 400 m. Using our suggested method, it was possible to extend the range of the extinction coefficient lower then several tens of meters. Finally, we can remove the unknown short-range region of pulsed aerosol lidar using our method.

• Computers and Concrete
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• v.32 no.3
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• pp.247-261
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• 2023

Due to the non-uniform distribution of meso-structure, the diffusion of chloride ions in concrete show the characteristics of characteristics of randomness and fuzziness, which leads to the non-uniform distribution of chloride ions and the non-uniform corrosion of steel rebar in concrete. This phenomenon is supposed as the main reason causing the uncertainty of the bearing capacity deterioration of reinforced concrete structures. In order to analyze and predict the durability of reinforced concrete structures under chloride environment, the random features of chloride ions transport in concrete were studied in this research from in situ meso-structure of concrete. Based on X-ray CT technology, the spatial distribution of coarse aggregates and pores were recognized and extracted from a cylinder concrete specimen. In considering the influence of ITZ, the in situ mesostructure of concrete specimen was reconstructed to conduct a numerical simulation on the diffusion of chloride ions in concrete, which was verified through electronic microprobe technology. Then a stochastic study was performed to investigate the distribution of chloride ions concentration in space and time. The research indicates that the influence of coarse aggregate on chloride ions diffusion is the synthetic action of tortuosity and ITZ effect. The spatial distribution of coarse aggregates and pores is the main reason leading to the non-uniform distribution of chloride ions both in spatial and time scale. The chloride ions concentration under a certain time and the time under a certain concentration both satisfy the Lognormal distribution, which are accepted by Kolmogorov-Smirnov test and Chi-square test. This research provides an efficient method for obtain mass stochastic data from limited but representative samples, which lays a solid foundation for the investigation on the service properties of reinforced concrete structures.

• Journal of the Korean earth science society
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• v.44 no.1
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• pp.1-12
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• 2023

Oceanic wind waves have been recognized as one of the important indicators of global warming and climate change. It is necessary to study the spatial and temporal variability of significant wave height (SWH) and wave direction in the Yellow Sea and a part of the East China Sea, which is directly affected by the East Asian monsoon and climate change. In this study, the spatial and temporal variability including seasonal and interannual variability of SWH and wave direction in the Yellow Sea and East China Sea were analyzed using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data. Prior to analyzing the variability of SWH and wave direction using the model reanalysis, the accuracy was verified through comparison with SWH and wave direction measurements from Ieodo Ocean Science Station (I-ORS). The mean SWH ranged from 0.3 to 1.6 m, and was higher in the south than in the north and higher in the center of the Yellow Sea than in the coast. The standard deviation of the SWH also showed a pattern similar to the mean. In the Yellow Sea, SWH and wave direction showed clear seasonal variability. SWH was generally highest in winter and lowest in late spring or early summer. Due to the influence of the monsoon, the wave direction propagated mainly to the south in winter and to the north in summer. The seasonal variability of SWH showed predominant interannual variability with strong variability of annual amplitudes due to the influence of typhoons in summer.