• Journal of agriculture & life science
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• v.46 no.3
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• pp.47-58
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• 2012

This study was carried out to develop assessment model for the optimal site prediction of Dendropanax morbifera, Evergreen broad-leaved trees in warm temperate zone according to climate change. It was created criterion for assessment model of the optimal site prediction by quantification method to possible analysis of quantitative and qualitative data, through study relationship between growth of tree and site environmental factors. A program of the optimal site prediction was developed using program version 3.2, an Avenue and Dialog Designer tool of ESRI as GIS(geographic information system) engine. Developed program applied to test accuracy of the optimal site prediction in study area of Wando, Jeollanam-do, having a various evergreen broad-leaved trees of warm temperate zone. In the results from analysis of the optimal site prediction on Dendropanax morbifera, the characteristics of optimal site were analyzed site environmental features with 401~500m of altitude, $15^{\circ}$ of slope, hillside of local topography, alluvium of deposit type, convex of slope type and south of aspect. The mapping area per grade of the optimal site prediction in the Dendropanax morbifera showed 1,487.2ha(25.4%) of class I, 1,020.3ha(17.4%) of class II, 2,231.8ha(38.2%) of class III and 1,110.5ha(19.0%) of class IV.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.37-51
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• 2023

Spatial information is essential for interpreting urban phenomena. Methodologies for spatializing urban information, especially when it lacks location details, have been consistently developed. Typical methods include Geocoding using structured address information or place names, spatial integration with existing geospatial data, and manual tasks utilizing reference data. However, a vast number of documents produced by administrative agencies have not been deeply dealt with due to their unstructured nature, even when there's demand for spatialization. This research utilizes the natural language processing model BERT to spatialize public documents related to urban planning. It focuses on extracting sentence elements containing addresses from documents and converting them into structured data. The study used 18 years of urban planning public announcement documents as training data to train the BERT model and enhanced its performance by manually adjusting its hyperparameters. After training, the test results showed accuracy rates of 96.6% for classifying urban planning facilities, 98.5% for address recognition, and 93.1% for address cleaning. When mapping the result data on GIS, it was possible to effectively display the change history related to specific urban planning facilities. This research provides a deep understanding of the spatial context of urban planning documents, and it is hoped that through this, stakeholders can make more effective decisions.

• Journal of the Society of Disaster Information
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• v.13 no.3
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• pp.296-304
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• 2017

In order to predict about forest fire behavior we constructed a database for combustion characteristic of forest fuels in Samcheok, Gangwon-do and prepared fire risk map and fire risk rating using GIS method in this study. For the mapping autoignition temperature, ignition time, flame duration time, total heat release and total smoke release are selected as the standardized parameters and the overall risk rating was made up of the ignition risk parameters(autoignition temperature, ignition time) and the spread risk parameters(flame duration time, total heat release, total smoke release). Forest fire risk was classified into 5 grades and lower grade of fire risk rating mean to correspond to more dangerous forest fire. As a result, the overall risk rating of Samcheok was classified into three grades from 1 to 3 and Nogok-myeon and Miro-myeon were turned out the most dangerous areas for forest fire. Because of the colony of pine and oak trees and the higher fire loads, the flame propagation will be carried out quickly in these areas.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.211-216
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• 2016

Worldwide casualties caused by earthquakes, floods, fire or other disaster has been increasing. So many researchers are being actively done technical studies to ensure golden-time. In this paper if a disaster occurs, use the IoT technologies in order to secure golden-time and transmits the message after to find the user of the accident area first. When the previous job is finished, gradually finds a user of the surrounding area and transmits the message. For national emergency information, OPEN API of Korea Meteorological Administration was used. To collect detailed information on a relevant area in real time, this study established the system that connects and integrates Crowd Sensing technology with BLE (Bluetooth Low Energy) Beacon technology. Up to now, the CBS based on base station has been applied. However, this study designed and mapped DB in the integration of Beacon based user positioning and national administrative address system in order to estimate local users. In this experiment, the accuracy and speed of information dif6fusion algorithm were measured with a rise in the number of users. The experiments were conducted in a manner that increases the number of users by one thousand and was measured the accuracy and speed of the message spread transfer algorithm. Finally, became operational in less than one second in 20,000 users, it was confirmed that the notification message is sent.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.80-92
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• 2007

Forest fire is the dominant large-scale disturbance mechanism in the Korean temperate forest, and it strongly influences forest structure and function. Moreover burn severity incorporates both short- and long-term post-fire effects on the local and regional environment. Burn severity is defined by the degree to which an ecosystem has changed owing to the fire. Vegetation rehabilitation may specifically vary according to burn severity after fire. To understand burn severity and process of vegetation rehabilitation at the damaged area after large-fire is required a lot of man powers and budgets. However the analysis of burn severity in the forest area using satellite imagery can acquire rapidly information and more objective results remotely in the large-fire area. Space and airbone sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. For classifying fire damaged area and analyzing burn severity of Samcheok fire area occurred in 2000, Cheongyang fire in 2002, and Yangyang fire in 2005 we utilized Normalized Burn Ratio(NBR) technique. The NBR is temporally differenced between pre- and post-fire datasets to determine the extent and degree of change detected from burning. In this paper we use pre- and post-fire imagery from the Landsat TM and ETM+ imagery to compute the NBR and evaluate large-scale patterns of burn severity at 30m spatial resolution. 65% in the Samcheok fire area, 91% in the Cheongyang fire area and 65% in the Yangyang fire area were corresponded to burn severity class above 'High'. Therefore the use of a remotely sensed Differenced Normalized Burn Ratio(${\Delta}NBR$) by RS and GIS allows for the burn severity to be quantified spatially by mapping damaged domain and burn severity across large-fire area.