• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.275-278
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• 2006

Google Earth enables people to easily find information linked to geographical locations. Google Earth consists of a collection of zoomable satellite images laid over a 3-D Earth model and any geographically referenced information can be uploaded to the Web and then downloaded directly into Google Earth. This can be achieved by encoding in Google's open file format, KML (Keyhole Markup Language), where it is visible as a new layer superimposed on the satellite images. We used KML to create and share fine resolution gridded temperature data projected to 3 climatological normal years between 2011-2100 to visualize the site-specific warming and the resultant earlier blooming of spring flowers over the Korean Peninsula. Gridded temperature and phonology data were initially prepared in ArcGIS GRID format and converted to image files (.png), which can be loaded as new layers on Google Earth. We used a high resolution LCD monitor with a 2,560 by 1,600 resolution driven by a dual link DVI card to facilitate visual effects during the demonstration.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.311-323
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• 2023

Oil spill accidents can cause various environmental issues, so it is important to quickly assess the extent and changes in the area and location of the spilled oil. In the case of oil spill detection using satellite imagery, it is possible to detect a wide range of oil spill areas by utilizing the information collected from various sensors equipped on the satellite. Previous studies have analyzed the reflectance of oil at specific wavelengths and have developed an oil spill index using bands within the specific wavelength ranges. When analyzing multiple images before and after an oil spill for monitoring purposes, a significant amount of time and computing resources are consumed due to the large volume of data. By utilizing Google Earth Engine, which allows for the analysis of large volumes of satellite imagery through a web browser, it is possible to efficiently detect oil spills. In this study, we evaluated the applicability of four types of oil spill indices in the area of various land cover using Sentinel-2 MultiSpectral Instrument data and the cloud-based Google Earth Engine platform. We assessed the separability of oil spill areas by comparing the index values for different land covers. The results of this study demonstrated the efficient utilization of Google Earth Engine in oil spill detection research and indicated that the use of oil spill index B ((B3+B4)/B2) and oil spill index C (R: B3/B2, G: (B3+B4)/B2, B: (B6+B7)/B5) can contribute to effective oil spill monitoring in other regions with complex land covers.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.314-317
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• 2010

본 연구에서는 도시계획결정 이후 예산등의 이유로 장기간 집행되지 않은 도시계획 도로시설의 가시적인 관리 및 활용에 있어 참고자료의 하나로서 Google Earth 영상을 이용하였다. 연구 대상지의 Google Earth 영상을 취득하여 미집행 시설을 추적한 결과, 실제의 토지이용현황을 구체적으로 반영함과 동시에 시간과 경제적 비용을 절약할 수 있는 추적방법으로 평가 되었다. 향후 미집행 시설의 관리나 집행 시 기초적인 참고자료로 활용할 수 있을 것으로 판단된다.

• Spatial Information Research
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• v.18 no.4
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• pp.141-149
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• 2010

One of recent trends in Web-based GIS is the system development using FOSS (Free and Open Source Software). Open Source software is independent from the technologies of commercial software and can increase the reusability and extensibility of existing systems. In this study, we developed a Web-based GIS for interactive visualization of geographic information using Google Earth and spatial DBMS(database management system). Google Earth Plug-in and Google Earth API(application programming interface) were used to embed a geo-browser in the Web browser. In order to integrate the Google Earth with a spatial DBMS, we implemented a KML(Keyhole Markup Language) generator for transmitting server-side data according to user's query and converting the data to a variety of KML for geovisualization on the Web. Our prototype system was tested using time-series of LAI(leaf area index), forest map, and crop yield statistics. The demonstration included the geovisualization of raster and vector data in the form of an animated map and a 3-D choropleth map. We anticipate our KML generator and system framework will be extended to a more comprehensive geospatial analysis system on the Web.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5903-5906
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• 2015

Background: Cholangiocarcinoma (CCA) is a serious public health problem in the Northeast of Thailand. CCA is considered to be an incurable and rapidly lethal disease. Knowledge of the distribution of CCA patients is necessary for management strategies. Objectives: This study aimed to utilize the Geographic Information System and Google $Earth^{TM}$ for distribution mapping of cholangiocarcinoma in Satuek District, Buriram, Thailand, during a 5-year period (2008-2012). Materials and Methods: In this retrospective study data were collected and reviewed from the OPD cards, definitive cases of CCA were patients who were treated in Satuek hospital and were diagnosed with CCA or ICD-10 code C22.1. CCA cases were used to analyze and calculate with ArcGIS 9.2, all of data were imported into Google Earth using the online web page www.earthpoint.us. Data were displayed at village points. Results: A total of 53 cases were diagnosed and identified as CCA. The incidence was 53.57 per 100,000 population (65.5 for males and 30.8 for females) and the majority of CCA cases were in stages IV and IIA. The average age was 67 years old. The highest attack rate was observed in Thung Wang sub-district (161.4 per 100,000 population). The map display at village points for CCA patients based on Google Earth gave a clear visual deistribution. Conclusions: CCA is still a major problem in Satuek district, Buriram province of Thailand. The Google Earth production process is very simple and easy to learn. It is suitable for the use in further development of CCA management strategies.

• Journal of Korean Society of Rural Planning
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• v.15 no.4
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• pp.199-211
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• 2009

The aim of this paper is to investigate the applicability of high spatial resolution remote sensing images for conducting the rural amenity resources survey. There are a large number of rural amenity resources and field reconnaissance without a sufficient preliminary survey involves a big amount of cost and time even if the data quality cannot always be satisfied with the advanced study. Therefore, a new approach should be considered like the state-of-the-art remote sensing technology to support field survey of rural amenity resources as well as to identify the spatial attributes including the geographical location, pathway, area, and shape. Generally high-resolution satellite or aerial photo images are too expensive to cover a large area and not free of meteorological conditions, but recently rapidly-advanced internet-based image services, such as Google Earth, Microsoft Bing maps, Bluebirds, Daum maps, and so on, are expected to overcome the handicaps. The review of the different services shows that Google Earth would be the most feasible alternative for the survey of rural amenity resources in that it provides powerful tools to build spatial features and the attributes and the data format is completely compatible with other GIS(Geographic information system) software. Hence, this study tried to apply the Google Earth service to interpret the amenity resources and proposed the reformed work process conjugating the internet-based high-resolution images like satellite and aerial photo data.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.341-344
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• 2007

A major obstacle to classify and validate Land Cover maps is the high cost of generating reference data or multiple thematic maps for subsequent comparative analysis. In case of inaccessible area such as North Korea, the high resolution satellite imagery may be used as in situ data so as to overcome the lack of reliable reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird (0.6m) of North Korea obtained from Google Earth data provided thru internet. Monthly NDVI images of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes; coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water and built-up area. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional in situ data collection on the site where the accessibility is severely limited.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.181-188
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• 2010

This paper proposes 3D visualization systems for the real-time situation awareness and a state information of the aircraft. This system was embodied with OpenGL and the Google Earth of web base using situation data of the aircraft. The existing system has problem which speed decrease and visible restricted map because massive data of terrain and satellite photo. This system is supports the visualization tool which is economic and entire area for a real-time situation awareness with minimum flight information using Open-API of the Google Earth. Also provides a visible convenience to expansion-view using multiple location information. This research result could be used to system for the situation awareness of the aircraft from web environment.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.483-491
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• 2007

One of the major obstacles to classify and validate Land Cover maps is the high cost of acquiring reference data. In case of inaccessible areas such as North Korea, the high resolution satellite imagery may be used for reference data. The objective of this paper is to investigate the possibility of utilizing QuickBird high resolution imagery of North Korea that can be obtained from Google Earth data via internet for reference data of land cover classification. Monthly MODIS NDVI data of nine months from the summer of 2004 were classified into L=54 cluster using ISODATA algorithm, and these L clusters were assigned to 7 classes - coniferous forest, deciduous forest, mixed forest, paddy field, dry field, water, and built-up areas - by careful use of reference data obtained through visual interpretation of the high resolution imagery. The overall accuracy and Kappa index were 85.98% and 0.82, respectively, which represents about 10% point increase of classification accuracy than our previous study based on GCP point data around North Korea. Thus we can conclude that Google Earth may be used to substitute the traditional reference data collection on the site where the accessibility is severely limited.

• Journal of the Korean Geographical Society
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• v.43 no.2
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• pp.188-201
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• 2008

With recent rapid globalization, accurate information for the foreign countries is increasingly important. Errors based on inaccurate information and unequal international relationships complicate the situations. In this article, authors analyzed the Japanese place names which appear on the Google Earth images and place name database of the NGA. Google Earth already becomes a tremendous soft power in internet society; therefore, accurate information on the satellite image is more necessary than ever. This article finds that many types of errors exist in the place names in Google Earth image service. Also many place names are listed with Japanese pronunciation in GNS database as variants. The Japanese place names have not been used in topographic maps published since 1910s and 1930s. Japanese place names were widely used in US military maps published in 1946. 1:250,000 maps published in 1954, however, doesn't seem to use Japanese pronunciation any more.