• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.426-429
• /
• 2006

Flood caused by Typhoon and severe rain during summer is the most destructive natural disasters in Korea. Almost every year flood has resulted in a big lost of national infrastructure and loss of civilian lives. It usually takes time and great efforts to estimate the flood-related damages. Government also has pursued proper standard and tool for using state-of-art technologies. High resolution satellite imagery is one of the most promising sources of ground truth information since it provides detailed and current ground information such as building, road, and bare ground. Once high resolution imagery is utilized, it can greatly reduce the amount of field work and cost for flood related damage assessment. The classification of high resolution image is pre-required step to be utilized for the damage assessment. The classified image combined with additional data such as DEM and DSM can help to estimate the flooded areas per each classified land use. This paper applied object-oriented classification scheme to interpret an image not based in a single pixel but in meaningful image objects and their mutual relations. When comparing it with other classification algorithms, object-oriented classification was very effective and accurate. In this paper, IKONOS image is used, but similar level of high resolution Korean KOMPSAT series can be investigated once they are available.

• Korean Journal of Remote Sensing
• /
• v.23 no.2
• /
• pp.89-101
• /
• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.

• Korean Journal of Remote Sensing
• /
• v.20 no.6
• /
• pp.383-396
• /
• 2004

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.36 no.6
• /
• pp.433-442
• /
• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.51-63
• /
• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• Journal of the Korean Geographical Society
• /
• v.40 no.3 s.108
• /
• pp.296-309
• /
• 2005

In this study, we investigated the change of future land-surface and relationships of land-surface change with geo-spatial information, using a Bayesian prediction model based on a likelihood ratio function, for analysing the land-surface change of the Gongju area. We classified the land-surface satellite images, and then extracted the changing area using a way of post classification comparison. land-surface information related to the land-surface change is constructed in a GIS environment, and the map of land-surface change prediction is made using the likelihood ratio function. As the results of this study, the thematic maps which definitely influence land-surface change of rural or urban areas are elevation, water system, population density, roads, population moving, the number of establishments, land price, etc. Also, thematic maps which definitely influence the land-surface change of forests areas are elevation, slope, population density, population moving, land price, etc. As a result of land-surface change analysis, center proliferation of old and new downtown is composed near Gum-river, and the downtown area will spread around the local roads and interchange areas in the urban area. In case of agricultural areas, a small tributary of Gum-river or an area of local roads which are attached with adjacent areas showed the high probability of change. Most of the forest areas are located in southeast and from this result we can guess why the wide chestnut-tree cultivation complex is located in these areas and the capability of forest damage is very high. As a result of validation using a prediction rate curve, a capability of prediction of urban area is $80\%$, agriculture area is $55\%$, forest area is $40\%$ in higher $10\%$ of possibility which the land-surface change would occur. This integration model is unsatisfactory to Predict the forest area in the study area and thus as a future work, it is necessary to apply new thematic maps or prediction models In conclusion, we can expect that this way can be one of the most essential land-surface change studies in a few years.

• Spatial Information Research
• /
• v.23 no.3
• /
• pp.67-77
• /
• 2015

This Study aimed to construction the plan of the statistics for national greenhouse gas inventories of international standards. Currently, the statistics of the greenhouse gas inventories of South Korea, has a problem that is not able to build the changed information. In previous studies, it has been limited to the construction of the information within each category. In order to solve these problems, targeting Gyeonggi province, we analyzed the land use change by utilizing the various information such as satellite images, KLIS, UPIS. As a result, we suggested the following implementation, classification system of LULUCF category, improvement of accuracy by utilizing satellite images of high resolution, additional research for methodology. Based on these contents, we suggested the construction plan of the statistics for national greenhouse gas inventories(LULUCF sector). Frist, it is necessary to construct of land use change informations for the past 20 years, Then, it need to create the matrix of land use change by utilizing satellite images and various land information systems.

• Land and Housing Review
• /
• v.7 no.4
• /
• pp.251-259
• /
• 2016

As the spatial structure of a region is evolving into a decentralized multi-nucli model, networked connection among cities in a region is emerging as an important issue to strengthen regional competitiveness. This paper focused on the limitation of current new town planning criteria which just suggest a uniform standard for economic self-sufficiency by new-town size, without representing the network characteristics of new town. If a new town is planned as a economic strongpoint within a region, it needs to secure appropriate industrial functions. This study classified the characteristics of new towns by network analysis and reviewed the economic self-sufficiency criteria by new town types. Using various network connectedness indices, the 1st and 2nd round new towns in the capital region were analyzed, and land-use distribution of new-towns in other countries were also examined comparatively. The network characteristics of new towns are classified as three types: mono-nucleus, distributed center, and dependent. Based on this classification, planning criteria for self-sufficiency were compared among 6 new towns. This study provides implications for the amendment of "the sustainable new town planning criteria" or the revision of development plans.

• Korean Journal of Remote Sensing
• /
• v.6 no.2
• /
• pp.89-113
• /
• 1990

The purpose of this study is to develop means to apply GIS and remote sensing technology to the analysis of Korean urban open spaces. To achieve this objective, a framework of analysis of urban open spaces was developed, and then the framework was applied for the evaluation of the potential and suitability of open spaces of Ansan City, which is a new town developed to accomodate industries relocation from Seoul, Korea, mainly due to their pollution problems. The software used in this study are IDRISI, a grid-based GIS, and KMIPS, a remote sensing analysis system. Both packages are based on IBM PC/AT computers with Microsoft DOS. Landsat MSS and TM data were used for the land use classification, land use change detection, and analysis of transformed vegetation indices. The size of the geographic data base is 110 rows and 150 columns with the spatial resolution of 100m$\times$100m. The framework of analysis includes both quanititative and qualitative analysis of open spaces. The quantitative analysis includes size and distribution of open spaces, urban develpment of open spaces, and the degree of vegree of vegetation removal of the study area. The qualitative analysis includes evaluative criteria for primary productivity of land, park use potential, major visual resources, and urban environmental control. The findings of this study can be summarized as follows. First, the size of builtup areas increased 18.73km$^2$, while the size of forest land decreased 10.86km$^2$ during last ten years. Agricultural lands maintained its size, but shifted toward outside of the city into forest. Second, the potential of open spaces for park use is limited mainly due to their lack of accessibility and connectivity among open spaces, in spite of ample acreage and good site conditions. Third, major landscape elements and historic sites should be connected to the open space system of the city by new accesses and buffers.

• Korean Journal of Remote Sensing
• /
• v.33 no.6_2
• /
• pp.1061-1073
• /
• 2017

In this study, images were classified using convolutional neural network (CNN) - a deep learning technique - to investigate the feasibility of information production through a combination of artificial intelligence and spatial data. CNN determines kernel attributes based on a classification criterion and extracts information from feature maps to classify each pixel. In this study, a CNN network was constructed to classify materials with similar spectral characteristics and attribute information; this is difficult to achieve by conventional image processing techniques. A Compact Airborne Spectrographic Imager(CASI) and an Airborne Imaging Spectrometer for Application (AISA) were used on the following three study sites to test this method: Site 1, Site 2, and Site 3. Site 1 and Site 2 were agricultural lands covered in various crops,such as potato, onion, and rice. Site 3 included different buildings,such as single and joint residential facilities. Results indicated that the classification of crop species at Site 1 and Site 2 using this method yielded accuracies of 96% and 99%, respectively. At Site 3, the designation of buildings according to their purpose yielded an accuracy of 96%. Using a combination of existing land cover maps and spatial data, we propose a thematic environmental map that provides seasonal crop types and facilitates the creation of a land cover map.