• Journal of Cadastre & Land InformatiX
• /
• v.44 no.1
• /
• pp.97-111
• /
• 2014

As urbanization and industrialization develops, the necessity of utilizing scarce land in three dimensions is raising. The issue of utilizing underground space is being actively discussed particularly when Geyeonggi-do announced GTX(Great Train Express) construction plan which aims to relieve traffic congestion in metropolitan areas. The current regulation on compensation of underground space is based on "Regulations on compensation standard complied by using underground space for construction of urban railway" but it is difficult for covering the whole rights to protect a three-dimensional right. In this context, the study is to propose the improvement plans of land right's problem and compensation issues to utilization of underground space for the right complementary. To do this, the study reviews the use situation of the classification surface right and using adjudication which defines the effect scope of underground space extending land ownership. As well as it analyzes issues about compensation standard for utilizing of underground space.

• Korean Journal of Remote Sensing
• /
• v.26 no.5
• /
• pp.477-487
• /
• 2010

In North Korea, forests were intensively degraded by forest land reclamation for food production and firewood collection since the mid-1970s. These degraded forests have to be certainly recovered for economic support, environmental protection and disaster prevention. In order to provide detailed land cover information of forest recovery project (A/R CDM), this study was focused to develop an improved classification method for degraded forest using 2.5m SPOT-5 pan-sharpened image. The degraded forest of North Korea shows various different types of texture. This study used GLCM texture bands of segmented image with spectral bands during forest cover classification. When scale factor 40/shape factor 0.3 was used as a parameter set to generate segment image, segment image was generated on suitable segment scale that could classify types of degraded forest. Forest land cover types were classified with an optimum band combination of Band1, Band2, band3, GLCM dissimilarity (band2), GLCM homogeneity (band2) and GLCM standard deviation (band3). Segment-based classification method using spectral bands and texture bands reached an 80.4% overall accuracy, but the method using only spectral bands yielded an 70.3% overall accuracy. As using spectral and texture bands, a classification accuracy of stocked forest and unstocked forest showed an increase of 23~25%. In this research, SPOT-5 pan-sharpened high-resolution satellite image could provide a very useful information for classifying the forest cover of North Korea in which field data collection was not available for ground truth data and verification directly. And segment-based classification method using texture information improved classification accuracy of degraded forest.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.4
• /
• pp.10-21
• /
• 2008

A forest functions classification map is an essential element for the management planning of national forests. This study was intended to make out the map at the stand level by utilizing the Forest Functions Evaluation Program(FFEP), developed by Korea Forest Research Institute. In this program, the potential of each function was evaluated in each grid cell, and then a forest functions estimation map was generated based on the optimum grid cell values in each sub-compartment unit. Finally, the program produced a forest functions classification map with consideration of the priority of the functions. The final forest functions classification map required for the national forest management planning made out overlapping those results which the rest of the forest classified referring priority functions classification map to national forest manager and classified according to the local administrative guidance and sustainable forest resources management guidance. The results indicated that the forest function classification using the FFEP program could be an efficient tool for providing the data required for national forest management planning. Also this study made a meaningful progress in the forest function classification by considering the local forest administrative guidance and sustainable forest resources management guidance.

• Journal of the Korean association of regional geographers
• /
• v.7 no.1
• /
• pp.35-50
• /
• 2001

Since remotely sensed images of coastal wetlands are very sensitive to spatial resolution, it is very important to select an optimum resolution for particular geographic phenomena needed to be represented. Scale is one of the most important factors in spatial analysis techniques, which is defined as a spatial and temporal interval for a measurement or observation and is determined by the spatial extent of study area or the measurement unit. In order to acquire the optimum scale for a particular subject (i.e., coastal wetlands), measuring and representing the characteristics of attribute information extracted from the remotely sensed images are required. This study aims to explore and analyze the scale effects of attribute information extracted from remotely sensed coastal wetlands images. Specifically, it is focused on identifying the effects of scale in response to spatial resolution changes and suggesting a methodology for exploring the optimum spatial resolution. The LANDSAT TM image of Sunchon Bay was classified by a supervised classification method, Six land cover types were classified and the Kappa index for this classification was 84.6%. In order to explore the effects of scale in the classification procedure, a set of images that have different spatial resolutions were created by a aggregation method. Coarser images were created with the original image by averaging the DN values of neighboring pixels. Sixteen images whose resolution range from 30 m to 480 m were generated and classified to obtain land cover information using the same training set applied to the initial classification. The values of Kappa index show a distinctive pattern according to the spatial resolution change. Up to 120m, the values of Kappa index changed little, but Kappa index decreased dramatically at the 150m. However, at the resolution of 240 m and 270m, the classification accuracy was increased. From this observation, the optimum resolution for the study area would be either at 240m or 270m with respect to the classification accuracy and the best quality of attribute information can be obtained from these resolutions. Procedures and methodologies developed from this study would be applied to similar kinds and be used as a methodology of identifying and defining an optimum spatial resolution for a given problem.

• Journal of Korean Society for Geospatial Information Science
• /
• v.10 no.3 s.21
• /
• pp.61-69
• /
• 2002

The aim of this study is the process of developing the information system of individual announced land price that can efficiently manage the land price work, improve public reliability in services and establish systematization of the national land information. The system is adapted to the test area, as a result, it is concluded that can improve the efficiency of management and the accuracy and objectivity of land price. Thus users can easily use it without professional knowledge since it offer convenient user' environment. Furthermore, it can rapidly search character and classification of land, use-zoning, the present condition of land price and so on. And it can find out change of land price and user according to search of history.

• Journal of Environmental Impact Assessment
• /
• v.24 no.1
• /
• pp.35-50
• /
• 2015

The purposes of this study are to apply species distribution modeling in urban management planning for habitat conservation in non-urban area and to provide a detailed classification method for management zone. To achieve these objectives, Species Distribution Model was used to generate species richness and then to compare with the results from land suitability assessment. 59 species distribution models were developed by Maxent. This study used 15 model variables (5 topographical variables, 4 vegetation variables, and 6 distance variables) for Maxent models. Then species richness was created by sum of predicted species distributions. Land suitability assessment was conducted with criteria from type I of "Guidelines for land suitability assessment". After acquiring evaluation values from species richness and land suitability assessment, the results from these two models were compared according to the five grades of classification. The areas with the identical grade in Species richness and land suitability assessment are categorized and then compared each other. The comparison results are Grade1 10.92%, Grade2 37.10%, Grade3 34.56%, Grade4 20.89% and Grade5 1.73%. Grade1 and Grade5 showed the lowest agreement rate. Namely, development or conservation grade showed high disagreement between two assessment system. Therefore, the areas located between urban, agriculture, forest, and reserve have a tendency to change easily by development plans. Even though management areas are not the core area of reserve, it is important to provide a venue for species habitat and eco-corridor to protect and improve biodiversity in terms of landscape ecology. Consequently, adoption of species richness in three levels of management area classification such as conservation, production, planning should be considered in urban management plan.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1053-1065
• /
• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.361-364
• /
• 2008

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. High spatial resolution classification has a limitation when only using the spectral information due to the complex spatial arrangement of features and spectral heterogeneity within each class. Therefore, extracting the spatial information is one of the most important steps in high resolution satellite image classification. In this paper, we propose a new spatial feature extraction method. The extracted features are integrated with spectral bands to improve overall classification accuracy. The classification is achieved by applying a Support Vector Machines classifier. In order to evaluate the proposed feature extraction method, we applied our approach to KOMPSAT-2 data and compared the result with the other methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.5 no.2
• /
• pp.24-33
• /
• 1987

This study aims at obtaining more effective image processing techniques and more accurately classified image in the sphere which uses remotely sensed data. For this practice, the result of land use classification compounding summer scene with winter scene and the classified result of summer scene were compared, analyzed. From the upper analysed results, we found that Bitemporal Classification technique and $tan^{-1}$transformation were effective. Particularly, dividing crop class into two classes of farmland and field was more possible by appling Bitemporal Classification technique.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2011.11a
• /
• pp.177-179
• /
• 2011

The difference the guide specifications of between housing and land development has been caused many problems, since Korea Land Corporation and Korea Housing Corporation were merged into LH (Land and Housing Corporation) in 2009. Those are that specification criteria to establishing lower level specifications are disordered; duplicated investment on sites; management systems are conflicted site to site, which are based on different operation system from old land and housing corporations, etc. Thus, this paper aims to suggest a direction of integrated guide specification system of LH including improved and advanced classification hierarchy and specification.