• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.83-94
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• 2003

All the urban planning process require land use informations, which should be obtained after through intensive investigation and accurate analysis about the past and current situations and conditions of a city. Until now, the generation of land use informations from remotely sensed imagery has had many limitation because of its spatial resolution. It is now expected that the availability of high resolution satellite imagery whose spatial resolution less than 10m will reduce these limitations. For the purpose of urban growth monitoring we must first establish a urban land use classification nomenclature. In this study, we would like to establish a land use nomenclature for land use classification using remotely sensed data, especially using KOMPSAT EOC imagery.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.152-152
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• 2019

This study demonstrates a novel approach of remotely sensed estimates of stream flow at fifteen hydrological station in the Han River Basin, Korea. Multi-temporal data of the European Space Agency's Sentinel-1 SAR satellite from 19 January, 2015 to 25 August, 2018 is used to develop and validate the flow estimation model for each station. The flow estimation model is based on a power law relationship established between the remotely sensed surface area of water at a selected reach of the stream and the observed discharge. The satellite images were pre-processed for thermal noise, radiometric, speckle and terrain correction. The difference in SAR image brightness caused by the differences in SAR satellite look angle and atmospheric condition are corrected using the histogram matching technique. Selective area filtering is applied to identify the extent of the selected stream reach where the change in water surface area is highly sensitive to the change in stream discharge. Following this, an iterative procedure called the Optimum Threshold Classification Algorithm (OTC) is applied to the multi-temporal selective areas to extract a series of water surface areas. It is observed that the extracted water surface area and the stream discharge are related by the power law equation. A strong correlation coefficient ranging from 0.68 to 0.98 (mean=0.89) was observed for thirteen hydrological stations, while at two stations the relationship was highly affected by the hydraulic structures such as dam. It is further identified that the availability of remotely sensed data for a range of discharge conditions and the geometric properties of the selected stream reach such as the stream width and side slope influence the accuracy of the flow estimation model.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.117-120
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• 2006

Heavy rainfall events are occurred exceedingly various forms by a complex interaction between synoptic, dynamic and atmospheric stability. As the results, quantitative precipitation forecast is extraordinary difficult because it happens locally in a short time and has a strong spatial and temporal variations. GOES-9 imagery data provides continuous observations of the clouds in time and space at the right resolution. In this study, an power-law type algorithm(KAE: Korea auto estimator) for estimating rainfall based on the rainfall type was developed using geostationary meteorological satellite data. GOES-9 imagery and automatic weather station(AWS) measurements data were used for the classification of rainfall types and the development of estimation algorithm. Subjective and objective classification of rainfall types using GOES-9 imagery data and AWS measurements data showed that most of heavy rainfalls are occurred by the convective and mired type. Statistical analysis between AWS rainfall and GOES-IR data according to the rainfall types showed that estimation of rainfall amount using satellite data could be possible only for the convective and mixed type rainfall. The quality of KAE in estimating the rainfall amount and rainfall area is similar or slightly superior to the National Environmental Satellite Data and Information Service's auto-estimator(NESDIS AE), especially for the multi cell convective and mixed type heavy rainfalls. Also the high estimated level is denoted on the mature stage as well as decaying stages of rainfall system.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.66-80
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• 2012

It is necessary to normalize spectral image values derived from multi-temporal satellite data to a common scale in order to apply remote sensing methods for change detection, disaster mapping, crop monitoring and etc. There are two main approaches: absolute radiometric normalization and relative radiometric normalization. This study focuses on the multi-temporal satellite image processing by the use of relative radiometric normalization. Three scenes of KOMPSAT-2 imagery were processed using the Multivariate Alteration Detection(MAD) method, which has a particular advantage of selecting PIFs(Pseudo Invariant Features) automatically by canonical correlation analysis. The scenes were then applied to detect disaster areas over Sendai, Japan, which was hit by a tsunami on 11 March 2011. The case study showed that the automatic extraction of changed areas after the tsunami using relatively normalized satellite data via the MAD method was done within a high accuracy level. In addition, the relative normalization of multi-temporal satellite imagery produced better results to rapidly map disaster-affected areas with an increased confidence level.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.712-714
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• 2003

The modifications taking place within the alluvial plains impart a larger extent of disturbances to hydrologic systems. The objective of the present investigation is to detect the sensitivity of multi-temporal image data from Landsat TM (Thematic Mapper) for finding out the land-cover/land-use changes associated with alluvial low land. The eastern coast of Chiba Prefecture, Japan, forms a very important geographic unit owing to the existence of a unique alluvial landform. The alluvial plain occupied in the study area is widely known as 'Kujukuri Plain'. The TM images have been classified by means of maximum likelihood supervised classifier and the extent of changes has been estimated.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.3-6
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• 2006

이 연구에서는 다중 센서 융합과 시간적 문맥 정보의 결합을 통한 분류 정확도 향상을 목적으로 통계비 기반 결정수준 융합 기법을 제안하였다. 다중 센서 융합을 목적으로 개별 센서 자료로부터 얻어진 사후 확률의 결합에 기존 확률론적 자료 융합에서 널리 사용되어온 조건부 독립의 가정을 완화한 통계비 기반 결합 규칙을 적용하였다. 그리고 시간적 문맥 정보를 새로운 정보 근원으로 간주하고 이전 시기 자료의 분류결과로부터 추출 및 결합하였다. 이 제안기법은 통계비 기반의 틀 안에서 다중 센서의 분광정보 및 시간적 문맥 정보의 결합이 용이한 장점이 있다 제안기법의 적용성 평가를 위해 다중 시기/센서 융합의 사례연구를 수행하였다.

• Proceedings of the KSRS Conference
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• v.1
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• pp.294-296
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• 2006

Recently the development of GIS and spatial information technology is used to construct very detail forest information. In addition, in order to classify forest characteristic, the geographical characteristic information of forest could be very useful for the forest classification, In this study sampling points were arranged to clarify the difference between the orm area and the land forest area. Also, forest feature pattern could be discriminated by using satellite images and SML. This study result should be constructed to efficiency forest management in especially forest area in Jeju Island

• Journal of Korean Society of Rural Planning
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• v.6 no.2 s.12
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• pp.50-58
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• 2000

The purposes of the study were to detect and evaluate the historical land use and land cover changes on the Balan watersheds from three thematic mapper (TM) data, which were taken in 1985, 1993, and 1996. The supervised and unsupervised classification methods were adopted to classify five land cover categories: Paddy, upland, forest, residential, and water. The results indicated residential areas increased significantly during the past eleven years, Forest and paddy were converted to the urban areas. Future land cover patterns were forecasted using a Markov chain method, and the simulated land coiler change ratios presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.667-672
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• 2007

Land-use in Cheongju region is changing rapidly because of the increased interactions of human activities with the environment as population increases. We used multi-temporal Landsat images (1991 and 2000) and DEM data in a post-classification analysis with GIS to map land-use distribution and to analyse factors influencing the land-use changes for Cheongju City. Land-use statistics revealed that substantial land-use changes have taken place and that the built-up areas have expanded by about $17.57km^2(11.47%)$ over the study period (1991-2000). Agricultural lands and forests have decreased substantially while urban and barren lands have been on the increase. Rapid economic developments together with the increasing population were noted to be the major factors influencing rapid land use changes. Urban expansion has replaced urban and barren lands, thereby affecting habitat quality and leading to serious environmental degradation.

• Genomics & Informatics
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• v.12 no.4
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• pp.145-150
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• 2014

Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.