• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.13-19
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• 2016

This study presented the build-up of three-dimensional soil pollution map for precise analysis. To do this, survey on the existing pollutant region on Dongnae-gu, Busan that is the study subject, showed that it tended to produce 0.72 clusters. So, this study suggested to investigate center of $1km{\times}1km $ grid and, as the results of comparing the pollution map that input pollution figure values based on the actually investigation point showed precise results. And, it divided the standard of pollution into 5 levels in surface and underground space and the map was built up using IDW interpolation against the amount of polluted substance. The pollution of ground surface, flow of polluted substance, coefficient of permeability and ground water level that are 504 geotechnical informations were selected as the influential parameters in pollution analysis of underground space, and it calculated that to 0~20 points by dividing the characteristics. It enables the build-up of pollution map of ground surface-underground with depth that considers the characteristics of soil layers and it is considered that it is possible to analyze the general infiltration. And, it was considered that it enables more accurate forecast about influential analysis per depth and pollution of underground water.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.39-45
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• 2016

In this study, with arbitrary reference points on the water, photogrammetry accuracy analysis was conducted using unmanned aerial vehicle(UAV). A small reservoir is a research area, and twenty buoys were used as arbitrary reference points. Errors of location coordinate were identified with control of amounts of used reference points. cases are categorized by index scores per photos. Accuracy of X is 0.141m~0.166m and accuracy of Y is 0.136m~0.241m. Considering that allowable error for the maritime boundary survey is ${\pm}2m$, it is possible to get the accuracy data available for the photogrammetry of UAV using an reference point. In addition, the coefficient of correlation between the number of reference points per unit and number of buoys used as reference point and the ratio of the reference point per square measure, and percentage of buoys used as reference point and the coefficient of x and y were performed. Each element, x, and y showed a strong correlation and the coefficient of number of buoys used as reference point was irrelevant. The results of this correlation analysis can be analyzed that the number of reference points used in each picture is greater than the actual number of reference points used in location accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.2
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• pp.62-72
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• 2017

Spatio-temporal projection of evapotranspiration over croplands would be useful for assessment of climate change impact and development of adaptation strategies in agriculture. Potential evapotranspiration (PET) and dryness index (DI) during rice growing seasons were calculated using climate change scenario data provided by the National Institute of Meteorological Research (NIMR). A data processing tool for gridded climate data files, readGrADSWrapper, was used to calculate PET and DI during the current (1986-2005) and future (2006-2100) periods. Scripts were written to implement the formulas of PET and DI in R, which is an open source statistical data analysis tool. Evapotranspiration in rice fields ($PET_{Rice}$) was also determined using R scripts. The Spatio-temporal patterns of PET differed by regions in Korean Peninsula under current and future climate conditions. Overall, PET and $PET_{Rice}$ tended to increase throughout the $21^{st}$ century. Those results suggested that region-specific water resource managements would be needed to minimize the risk of water loss in the regions where considerable increases in PET would occur under the future climate conditions. For example, a number of provinces classified as a humid region were projected to become a sub-humid region in the future. The Spatio-temporal assessment of water resources based on PET and DI would help the development of climate change adaptation strategies for rice production in the 21st century. In addition, the studies on climate change impact would be facilitated using specialized data tools, e.g., readGrADSWrapper, for geospatial analysis of climate data.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.17-24
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• 2016

At present, summer cloudburst and local torrential rainfalls have increased in this country, because of climatic change. Therefore, studies on prevention of soil loss have been actively proceeded, and Korea Forest Service has offered landslide hazard map. Landslide hazard map divides risks into 5 classes, by giving weight with 9 kinds of elements. In August 25 2014, soil loss occurred in the whole Oryun Tunnel, Geumjeong-gu, Busan, because of local torrential heavy rain. As a result of comparing with landslide hazard map, the area where soil loss occurred in reality is a safety zone on hazard map. Rainfall, soil map, geological map, forest type map, gradient, drainage network, watershed, basin shape, and efflux of the whole Oryun Tunnel where soil loss occurred were analyzed. As a result of an analysis, it is judged that soil, forest type, much efflux and peak discharge, degree of water network and basin shape of a place where landslide occurred are causes of soil loss. It is judged that efflux, peak discharge, and basin shape by the localized rainfall that is not considered in landslide hazard map of them are the biggest causes of soil loss. It is judged that efflux, peak discharge, degree of water network and basin shape by the rainfall are important through a study on a causual analysis on soil loss in the whole Oryun Tunnel where is one of occurrence area where a lot of propertywere lost by the record local torrential rainfalls. A localized torrential downpour should be prepared by considering these elements on judgement of a landslide hazard area.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.69-77
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• 2005

Most of the evaluations of environmental effect of construction of road do not run parallel with the evaluation of the propriety of the business, but it just reviews how to reduce contamination before the beginning of the construction work. This reveals the limitation of the induction of environment friendly development from the fundamental. In this study, a function of GIS spatial analysis for 9 elements of pre-evaluation of environmental effect has been introduced, and a GIS decision making system for environmental effect of a road has been established for an integrated evaluation by using individual evaluation result. By using GIS decision making system for pre-evaluation of environmental effect that has been developed in this study and the GIS data that has been collected, it would be possible to decrease time and money for evaluation as well as fulfill the requirement of objectivity. Therefore, it would be possible to apply for pre-evaluation of environmental effect when deciding on the route of the road at the beginning stage of the construction work. Also, through the system developed in this study, the construction body could easily induce a mutual agreement with the residents by the visual evaluation material, which might result in an evaluation of environmental effect while decreasing time and money at the beginning stage of the construction.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.29-36
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• 2017

In this paper, we implement a spatial big data analysis prototype based on Spark which is an in-memory system and compares the performance by the spatial split algorithm on this basis. In cluster computing environments, big data is divided into blocks of a certain size order to balance the computing load of big data. Existing research showed that in the case of the Hadoop based spatial big data system, the split method by spatial is more effective than the general sequential split method. Hadoop based spatial data system stores raw data as it is in spatial-divided blocks. However, in the proposed Spark-based spatial analysis system, there is a difference that spatial data is converted into a memory data structure and stored in a spatial block for search efficiency. Therefore, in this paper, we propose an in-memory spatial big data prototype and a spatial split block storage method. Also, we compare the performance of existing spatial split algorithms in the proposed prototype. We presented an appropriate spatial split strategy with the Spark based big data system. In the experiment, we compared the query execution time of the spatial split algorithm, and confirmed that the BSP algorithm shows the best performance.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.69-79
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• 1998

It is one of the most crutial thing to secure efficient infrastructure of social infrastructure Including rna drinking water, sewer, gas, and electricity, etc. in modern society. Among them, the sourer system need to be properly maintained so as to sustain water quality over the large watershed thereby to provide reasonable level of living environment A few municipalities and private firms have so in been using sewer management system for assessing existing sewer network and auxiliary facilities. Such existing system can only provide functions to manage the sewer pipe itself and they can not fully estimate the amount of sewage water over the pipe through 4he network analysis due to the deficiency of the system Such a limited sewer network analysis function can only analyze the whole network under the assumption of uniformity. The results from such a process can not be fully implemented in the field. Therefore, this study emphasized the development of a sewer management system which can provide practical values from network analysts considering areal peculiarities using a zoning map utilizing a GIS. The system can support analyzing scenarios due to the changes of sewer amounts from the changes of population densities and rainfall amounts not to mention of calculating sewer amount for individual sewer pipes. furthermore, the system can support the decision making for better designing sewer facilities from the expansion of metropolitan areas and constructing satellite cities. Eventually, it will contribute to enhance the effectiveness of sewer-related works and services for residents as well as supporting a decision making for minor and major trouble-shootings.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.93-98
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• 2013

Multi-temporal high resolution satellite images are essential data for efficient city analysis and monitoring. Yet even when acquired from the same location, identical sensors as well as different sensors, these multi-temporal images have a geometric inconsistency. Matching points between images, therefore, must be extracted to match the images. With images of an urban area, however, it is difficult to extract matching points accurately because buildings, trees, bridges, and other artificial objects cause shadows over a wide area, which have different intensities and directions in multi-temporal images. In this study, we analyze a shadow effect on image matching of high resolution satellite images in urban area using Scale-Invariant Feature Transform(SIFT), the representative matching points extraction method, and automatic shadow extraction method. The shadow segments are extracted using spatial and spectral attributes derived from the image segmentation. Also, we consider information of shadow adjacency with the building edge buffer. SIFT matching points extracted from shadow segments are eliminated from matching point pairs and then image matching is performed. Finally, we evaluate the quality of matching points and image matching results, visually and quantitatively, for the analysis of shadow effect on image matching of high resolution satellite image.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.1 s.11
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• pp.35-51
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• 1998

This article aims at presenting a new framework for traffic accident analysis by proposing a new methodology for the management of the accident data and by establishing the relationship between accidents and roadway characteristics within it For the first issue, authors introduced geographic information system (GIS) into the analysis framework of the accident data since it is believed that analysis based on GIS seems to provide more effective information in reducing accidents. Point-based, line-based, and polygon (grid)-based approaches were set of along with concrete examples. Especially, the location-based scores such as localization, specialization coefficients, and Tress score have been added to identify the intensity of certain accident types within study area or grids. The second issue addressed the equation formulation of accident and fatality numbers with roadway characteristics like number of intersections and road length in a grid with a sense that (1) accidents on roadways are the function of the roadway physical characteristics rather than the socio-economical secondary data (2) the equation can be applied to the any 'suggested' area, not just region or nation, and (3) the accident forecasting model should emphasize the accident location itself more than any other factors. Some equations based on those assumption have been derived along with some future research agenda.