• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.2
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• pp.80-91
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• 2003

In this research, re-using band ratio data was proposed and examined as a method of raising the accuracy in landcover classification which is using satellite data.In order to determine the band which is used to calculation in the classified item, the six bands except the band 6 were combined with the band in which combination is possible and the landcover classification by MLC of supervised classification was carried out. In the result of landcover classification which is combined with forty nine combination, Two bands which were mostly used by band combination in the accuracy belonged inside the 10th place of a higher rank were selected and also calculated. landcover classification were performed again after the calculation result had been recombinated from the research. In addition, the new landcover classification result was compared and examined with the landcover classification using the old data. From the result of which was compared and examined the new landcover classification data recombinated calculation result with landcover classification using the original data, The classification accuracy of the new landcover classification data recombinated calculation result became low in ground but became improved in the all class. Specially The accuracy to urban area is very improved. therefore, it determined that reusing band ratio data is very useful when we need to analyze landcover classification and land information to urban area after that.

• Ecology and Resilient Infrastructure
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• v.7 no.3
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• pp.199-207
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• 2020

Against the backdrop of frequent weather disasters such as floods, droughts, and heat waves worldwide, urban parks should provide functions for the safety of urban residents as well as rest, culture, and ecological functions. In this study, a classification system for urban disaster prevention parks is proposed for the safety of the urbanites with the aim of securing a complex function in a green space in response to climate changes in the city. Analytical indicators were extracted through literature research, and the classification system was verified through on-site surveys of the target sites and interviews with those involved. The large class for evaluation was divided into three types: location, spatial composition, and disaster prevention complex facilities of urban parks; the direction of improvement was proposed for problems identified through empirical analysis.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.598-601
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• 2007

Urban growth management is essential for sustainable urban growth. Monitoring physical urban built-up area is a task of great significance to manage urban growth. Detecting urbanized area is essential for monitoring urbanized area. Although image classifications using satellite imagery are among the conventional methods for detecting urbanized area, they requires very tedious and hard work, especially if time-series remote sensing data have to be processed. In this paper, we propose an effective urbanized area detecting method based on normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). To verify the proposed method, we extract urbanized area using two methods; one is conventional supervised classification method and the other is the proposed method. Experiments shows that two methods are consistent with 98% in 1998, 99.3% in 2000, namely the consistency of two methods is very high. Because the proposed method requires no more process without band operations, it can reduce time and effort. Compared with the supervised classification method, the proposed method using vegetation indices can serve as quick and efficient alternatives for detecting urbanized area.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.1
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• pp.73-83
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• 1999

The land use/cover information, which is periodically obtained from satellite imagery, can be effectively applied to change detection in rapidly changing urban areas. Also it can be used not only as base maps for spatial database in urban information system but as decision-making data for desired urban planning and development direction. In this study, we carried out both unsupervised and supervised classification on land use from Landsat TM and JERS-1 OPS data, which were collected respectively in 1991 and 1997, covering Kumi City and then detected land use changes.

• Journal of Urban Science
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• v.9 no.2
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• pp.21-30
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• 2020

The purpose of this study is to present a plan to distinguish 229 local governments nationwide by taking into account various characteristics such as population, employment, housing, and industry of the region for customized urban policies in the era of metropolitan areas. The National Statistical Portal (KOSIS) collected and standardized data related to population, housing, industry, and finance by region from 2000 to 2015 for the classification of regional types necessary for customized urban policies, and this was used to classify them into regional types that considered population, employment, housing and industry. The summary of the analysis results is as follows. First, as a result of the regional type classification, 10 key employment sites (4.4%), 5 employment centers (2.2%), 38 residential centers (16.6%), 20 growth areas (8.7%), 26 industrial cities (11.4%), 35 low-fertile farming and fishing villages (15.3%) and 95 stagnant areas (41.5%). Second, the Seoul metropolitan area is the most diverse type of metropolitan area in the country, with most of its core employment sites inside Seoul, residential centers inside and outside Seoul, and growth areas in the southeastern part of the country (Busan, Ulsan, and Gyeongsangnam-do) are mixed with industrial and growth areas centered around Busan, Ulsan and surrounding areas, while the rest of the local governments are found to be low-fertile farming villages or stagnant areas. Daegu (Daegu, Gyeongbuk) is an industrial city in Daegu, and the rest of the local governments are either low-density farming and fishing villages or stagnant areas. The Honam region (Gwangju and Jeolla) was found to be a low-mill farming and fishing village or stagnant area except for Gwangju, while the Chungcheong region (Daejeon, Sejong, and Chungcheong) was seen as a growth area with areas adjacent to Daejeon, Sejong, and the Seoul metropolitan area, and some industrial cities were included. Finally, the Gangwon area was mostly classified as low-density farming and fishing villages and stagnant areas.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.588-595
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• 2003

High resolution panchromatic satellite images collected by sensors such as IRS-1C/D and KOMPSAT-1 have a spatial resolution of approximately 6 ${\times}$ 6 ㎡, making them very attractive for urban applications. However, the spectral information present in these images is very limited. In order to overcome this limitation, an object-oriented classification approach is used to identify basic land cover types in urban areas. Before an image can be classified it is segmented at different aggregation levels using a multiresolution segmentation approach. In the course of this segmentation various statistical as well as topological information is collected for each segment. Based on this information it is possible to classify image objects and to arrive at much better results than by looking only at single pixels. Using an image recorded by KOMPSAT-1 over the City of Vienna a land cover classification was carried out for two areas. One was used to set up the rules for the different land cover types. The second subset was classified based on these rules, only adjusting some of the functions governing the classification process.

• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.62-69
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• 2001

본 연구에서는 해상도가 상이한 시기별 위성영상과 GIS를 이용하여 경남 통영시 한산면 추봉도 지역의 소나무재선충(Bursaphelenchus Xylophilus) 피해지역을 탐지하고 다양한 영상처리를 통하여 이를 효율적으로 추출 할 수 있는 기법을 선정하였다. 연구결과 피해지역의 공간적 범위를 추출하기 위해서는 감독분류의 MHC(Mahalanobis Distance Classification)가 유용하였고 벌채 후의 토지피복 분류로 인한 피해지역 추출을 위해서는 MLC(Maximum Likelihood Classification)가 최적한 기법으로 나타났다. 아울러 이에 관련된 GIS를 구축하여 공간정보를 추출함으로써 피해지역의 공간적 분포특성을 규명하였는데 고도 약 120-160m, 경사 21$^{\circ}$-40$^{\circ}$ 그리고 서쪽 방향 사면에서 소나무재선충이 가장 활발하게 활동하였음이 밝혀졌다.

• ETRI Journal
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• v.23 no.3
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• pp.129-137
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• 2001

A random sampling or systematic sampling method is commonly used to assess the accuracy of classification results. In remote sensing, with these sampling methods, much time and tedious work are required to acquire sufficient ground truth data. So, a more effective sampling method that can represent the characteristics of the population is required. In this study, fractal analysis is adopted as an index for reference sampling. The fractal dimensions of the whole study area and the sub-regions are calculated to select sub-regions that have the most similar dimensionality to that of the whole area. Then the whole area's classification accuracy is compared with those of sub-regions, and it is verified that the accuracies of selected sub-regions are similar to that of whole area. A new kind of reference sampling method using the above procedure is proposed. The results show that it is possible to reduce sampling area and sample size, while keeping the same level of accuracy as the existing methods.

• ETRI Journal
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• v.29 no.5
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• pp.641-648
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• 2007

In this study, we suggest a new segmentation algorithm for processing airborne laser point cloud data which is more memory efficient and faster than previous approaches. The main principle is the reading of data points along a scan line and their direct classification into homogeneous groups as a single process. The results of our experiments demonstrate that the algorithm runs faster and is more memory efficient than previous approaches. Moreover, the segmentation accuracy is generally acceptable.