• Korean Journal of Remote Sensing
• /
• v.37 no.1
• /
• pp.161-167
• /
• 2021

This paper presents the method to quantitatively evaluate the uncertainty of the semi-empirical Bidirectional Reflectance Distribution Function (BRDF) model for Himawari-8/AHI. The uncertainty of BRDF modeling was affected by various issues such as assumption of model and number of observations, thus, it is difficult that evaluating the performance of BRDF modeling using simple uncertainty equations. Therefore, in this paper, Monte-Carlo method, which is most dependable method to analyze dynamic complex systems through iterative simulation, was used. The 1,000 input datasets for analyzing the uncertainty of BRDF modeling were generated using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) Radiative Transfer Model (RTM) simulation with MODerate Resolution Imaging Spectroradiometer (MODIS) BRDF product. Then, we randomly selected data according to the number of observations from 4 to 35 in the input dataset and performed BRDF modeling using them. Finally, the uncertainty was calculated by comparing reproduced surface reflectance through the BRDF model and simulated surface reflectance using 6S RTM and expressed as bias and root-mean-square-error (RMSE). The bias was negative for all observations and channels, but was very small within 0.01. RMSE showed a tendency to decrease as the number of observations increased, and showed a stable value within 0.05 in all channels. In addition, our results show that when the viewing zenith angle is 40° or more, the RMSE tends to increase slightly. This information can be utilized in the uncertainty analysis of subsequently retrieved geophysical variables.

• Journal of KIBIM
• /
• v.5 no.3
• /
• pp.41-50
• /
• 2015

Increasing size and complexity of indoor structures have led to much more complication in the spatial cognition and situational awareness. Contrary to outdoor environments, occupants have limited information regarding the indoor space syntax in terms of architectural and semantic information as well as how they interact with their surroundings. The availability of such information could give conveniences to both users and managers in various aspects. In order to visualize the exact location of rooms and utilities in 3D, many studies and projects have utilized BIM models because of its promising value of representing building components. In fact, the application of BIM provides definitive spatial indoor data and creates services for indoor space management and navigation. Therefore, this paper aims to provide an overview of practices on BIM-based indoor spatial information implementation and location-based services. It is expected that enabling of technologies, data-rich content and accessibility of information products will accelerate the growth of the spatially-related markets in various fields.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.1
• /
• pp.64-73
• /
• 2015

Recent emergence of spatial big data attracts the attention of various research groups. This paper analyzes the research trend on spatial big data by text mining the related Scopus DB. We apply topic model and network analysis to the extracted abstracts of articles related to spatial big data. It was observed that optics, astronomy, and computer science are the major areas of spatial big data analysis. The major topics discovered from the articles are related to mobile/cloud/smart service of spatial big data in urban setting. Trends of discovered topics are provided over periods along with the results of topic network. We expect that uncovered areas of spatial big data research can be further explored.

• Journal of Korea Spatial Information System Society
• /
• v.2 no.2 s.4
• /
• pp.89-97
• /
• 2000

This paper deals with the method which can apply spatial-data warehouses into the spatial data infrastructure in the view of clearinghouse. First, we classify the type of available data warehouses in the consider with patterns and properties of spatial data infrastructure. Then we suggest strategies for building and expanding data warehouses into various organizations. After considering all the factors, we suppose how the national spatial data warehouse come into being.

• Spatial Information Research
• /
• v.22 no.4
• /
• pp.21-38
• /
• 2014

Since users who search maps conduct their searching using the name they already know or is commonly called rather than formal name of a specific place, they tend to fail to find their destination. In addition, in typical web map service in terms of spatial searching of map. Location information of unintended place can be provided because when spatial searching is conducted with the vocabulary 'nearby' and 'in the vicinity', location exceeding 2 km from the current location is searched altogether as well. In this research, spatial range that human can perceive is calculated by extracting POI date with the usage of twitter data of SNS, constructing spatial relations with existing POI, which is already constructed. As a result, various place names acquired could be utilized as different names of existing POI data and it is expected that new POI data would contribute to select places for constructing POI data by utilizing to recognize places having lots of POI variation. Besides, we also expect efficient spatial searching be conducted using diverse spatial vocabulary which can be used in spatial searching and spatial range that human can perceive.

• Journal of the Korean Geographical Society
• /
• v.39 no.2
• /
• pp.195-211
• /
• 2004

The innovation of information and communication technology has brought the emergence of the digital economy in which the growing importance of the Internet for the production and consumption of information has caused a rapid increase of commercial domains. Domains are basic form of Internet address for the delivery of information, but the location of registered commercial domains is not free from a spatial context. Utilizing a database of commercial domain registrations, spatial statistical methods and GIS, the spatial dimensions of the commercial domains are explored for the city of Seoul. Through this research, it was found that the commercial domains were unevenly distributed, namely 44% of commercial domains are located at 3 Gus in Seoul. The locations of commercial domains by themselves represented a strong spatial autocorrelation among adjacent places. In order to identify factors affecting spatial variation in the development of the commercial domains among Dongs, a conditional spatial autoregressive model which effectively eliminates a spatial autocorrelation was used. As a result of this research, it is clearly shown that the selective location of firms having commercial domains and their role in economic activities are influencing the spatial structure of urban with dynamic mix of spatial characteristic.

• Spatial Information Research
• /
• v.10 no.2
• /
• pp.233-246
• /
• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Korean Journal of Remote Sensing
• /
• v.30 no.3
• /
• pp.375-381
• /
• 2014

Soil moisture is an essential satellite-driven variable for understanding hydrologic, pedologic and geomorphic processes. The European Space Agency (ESA) has endorsed soil moisture as one of Climate Change Initiates (CCI) and had merged multi-satellites over 30 years. The $0.25^{\circ}$ coarse resolution soil moisture satellite data showed correlations with variables of a water stress index, Temperature-Vegetation Dryness Index (TVDI), from a stepwise regression analysis. The ancillary data from TVDI, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from MODIS were inputted to a multi-regression analysis for estimating the surface soil moisture. The estimated soil moisture was validated with in-situ soil moisture data from April, 2012 to March, 2013 at Andong observation sites in South Korea. The soil moisture estimated using satellite-based LST and NDVI showed a good agreement with the observed ground data that this approach is plausible to define spatial distribution of surface soil moisture.

• Spatial Information Research
• /
• v.22 no.2
• /
• pp.45-52
• /
• 2014

In recent years, The importance of the river pollution management has increased and central and local governments are making constant effort to work out river pollution problems. Accordingly, this paper is to take a closer look at applications of spatial data and river pollution information(aerial photograph, parcel information, pollution source, GPS data et al.). More exactly, the purpose of the present paper is to offer an applications of spatial information for development of the river pollution management system. In order to provide a framework for more detailed consideration of the quantitative analysis of river pollution source and management plan by apply GPS and GIS. For that reason, this paper was to build pollution management system of river by spatial analysis. It is to be hoped that this paper will contribute to establish the management policy for urban stream pollution source preservation.

• Korean Journal of Remote Sensing
• /
• v.31 no.4
• /
• pp.347-351
• /
• 2015

Sea ice is a key parameter for understanding the climate change in cryosphere. In this study, we investigated the correlation with the factors that influenced change of the sea ice extent. We used the Sea Ice Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedo provided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the same temporal and spatial resolution of the data and detected the sea ice using SIC data. We performed the relationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positive correlation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea ice products.