• Journal of the Korean Geographical Society
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• v.39 no.5 s.104
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• pp.786-803
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• 2004

These days, interests on land cover classification using not only multi-sensor data but also thematic GIS information, are increasing. Often, although we have useful GIS information for the classification, the traditional classification method like maximum likelihood estimation technique (MLE) does not allow us to use the information due to the fact that the MLE and the existing computer programs cannot handle GIS data properly. We proposed a new method for updating the image classification using multi-spectral and multi-temporal images. In this study, we have simultaneously extended the MLE to accommodate both multi-spectral images data and land cover data for land cover classification. In addition to the extended MLE method, we also have extended the empirical likelihood ratio estimation technique (LRE), which is one of non-parametric techniques, to handle simultaneously both multi-spectral images data and land cover data. The proposed procedures were evaluated using land cover map based on Landsat ETM+ images in the Anmyeon-do area in South Korea. As a result, the proposed methods showed considerable improvements in classification accuracy when compared with other single-spectral data. Improved classification images showed that the overall accuracy indicated an improvement in classification accuracy of $6.2\%$ when using MLE, and $9.2\%$ for the LRE, respectively. The case study also showed that the proposed methods enable the extraction of the area with land cover change. In conclusion, land cover classification produced through the combination of various GIS spatial data and multi-spectral images will be useful to involve complementary data to make more accurate decisions.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.537-543
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• 2003

도시의 물리적 성장을 분석하기Hl 원격탐사 자료는 매우 유용한 도구를 제공한다 할 수 있다. 도시의 물리적 성장은 도시의 토지이용과 밀접하게 관련되어 있으며 지속 가능한 도시성장을 위하여서는 토지이용을 중심으로 한 성장관리가 필수적이다. 그러나 위성영상을 이용한 도시 토지이용의 분류는 우선 그 기준이 사용자의 관점에 따라 다르고 영상의 해상도 등에 따라 달리 그 기준이 정해질 수 있다. 도시의 성장 분석을 위해서는 다중시기의 위성영상 및 항공사진을 이용하여 토지이용 분류를 수행하고 시기별 토지이용 변화와 양상을 분석함으로써 성장요인을 추출하고 이를 기반으로 향후의 도시 성장을 예측할 수 있는 성장모델 개발이 가능해 진다. 따라서 본 연구에서는 도시성장 예측모델 개발의 전 단계로써 도시의 성장관리를 위해 사용되는 다양한 공간 해상도를 지닌 원격탐사 자료의 국내외 다양한 분류기준의 검토를 통해 토지이용 분류 기준을 도시 성장관리의 측면에서 설정하고자 한다.

• Spatial Information Research
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• v.11 no.1
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• pp.13-22
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• 2003

The study of geomorphology of tidal flat using remote sensing image has been considered useful because of it's ability to acquire data periodically. Especially, the Near Infrared band of satellite image has been used to divide between land and sea area. This study extracted a borderline of the tidal flat using Landsat-5 images and generated DEM(Digital elevation model) using tide level data as elevation value. DEM is a useful tool for three-dimensional survey of geomorphology and can be used for survey of tidal flat. This study divided 8 images of 1990's into two parts - before 1994 and after 1994 - and generated DEM respectively. In this work, the areas of tidal flats are calculated and it was revealed the area of tidal flat was decreased after 1994.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.119-124
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• 2008

2000년대 들어 주기적으로 발생하고 있는 봄 가뭄에 대한 적절한 대책 마련을 위해서는 가뭄을 모니터링 할 수 있는 감시체계가 필요하며 가뭄의 심도를 정량적으로 나타내기 위한 지표가 요구된다. 또한, 가뭄의 거동 및 지역적인 심도 분석을 위해서는 면 단위의 공간적인 분석이 요구된다. 위성영상은 공간정보를 신속하고 주기적으로 제공할 수 있는 도구로 위성영상의 밴드 조합을 통해 제작된 식생지수는 1990년대 중반 이후 건조지역을 중심으로 가뭄 모니터링을 위한 도구로 활용 중이다. 본 연구에서는 MODIS 영상으로부터 제작된 정규식생지수(NDVI)를 이용하여 식생상태지수(VCI)와 정규화된 식생지수(SVI)를 제작하였으며 2000년$\sim$2007년을 대상으로 가뭄발생연도, 각 가뭄사상에 대한 심도, 가뭄다발 시기 및 다발지역을 분석하였다.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.553-563
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• 2006

This study aims to produce land-cover maps of Korean peninsula using multi-temporal MODIS (Moderate Resolution Imaging Spectroradiometer) imagery. To solve the low spatial resolution of MODIS data and enhance classification accuracy, Linear Spectral Mixture Analysis (LSMA) was employed. LSMA allowed to determine the fraction of each surface type in a pixel and develop vegetation, soil and water fraction images. To eliminate clouds, MVC (Maximum Value Composite) was utilized for vegetation fraction and MinVC (Minimum Value Composite) for soil fraction image respectively. With these images, using ISODATA unsupervised classifier, southern part of Korean peninsula was classified to low and mid level land-cover classes. The results showed that vegetation and soil fraction images reflected phenological characteristics of Korean peninsula. Paddy fields and forest could be easily detected in spring and summer data of the entire peninsula and arable land in North Korea. Secondly, in low level land-cover classification, overall accuracy was 79.94% and Kappa value was 0.70. Classification accuracy of forest (88.12%) and paddy field (85.45%) was higher than that of barren land (60.71%) and grassland (57.14%). In midlevel classification, forest class was sub-divided into deciduous and conifers and field class was sub-divided into paddy and field classes. In mid level, overall accuracy was 82.02% and Kappa value was 0.6986. Classification accuracy of deciduous (86.96%) and paddy (85.38%) were higher than that of conifers (62.50%) and field (77.08%).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.71-80
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• 2011

Thresholding is important step for detecting binary change/non-change information in the unsupervised change detection. This study proposes new unsupervised change detection method using Hyperion hyperspectral images, which are expected with data increased demand. A graph is drawn with applying the range average method for the result value through pixel-based similarity measurement, and thresholding value is decided at the maximum distance point from a straight line. The proposed method is assessed in comparison with expectation-maximization algorithm, coner method, Otsu's method using synthetic images and Hyperion hyperspectral images. Throughout the results, we validated that the proposed method can be applied simply and had similar or better performance than the other methods.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.193-197
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• 2006

지상측정 및 위성영상탑재 광학센서를 이용하여 벼 주요 생육시기에 대한 군락의 엽질소 함량을 추정하였다. 6월부터 10월에 걸쳐 주요 생육시기 $5{\sim}6$회에 걸쳐 Orbview 및 QuickBird와 같이 4m 이하의 고해상도 다중영상을 취득하였다. 위성영상 취득일에 가능한한 맞추어 인공광원을 사용하는 2종의 능동형 광학 (G)NDVI 센서를 이용한 벼 군락의 반사특성을 측정하였으며 동시에 식물체 샘플링을 통한 생육량, 엽면적지수, 엽질소 함량 등을 분석하였다. 시기별 영상의 분광반사특성 및 (G)NDVI와 벼 생육량 및 엽질소 함량과의 관계를 알아보기 위해 상관분석 및 회귀분석을 수행하였다. 지상센서 및 위성영상 유래 (G)NDVI의 값을 서로 비교해 보면 전체적으로 지상센서를 이용하여 측정한 (G)NDVI값이 위성영상 유래 (G)NDVI값보다 크게 나타났다. 하지만 두 센서 모두 엽면적지수 변화에 따른 (G)NDVI의 변화를 살펴보면 엽면적지수가 2 정도가 될 때까지는 함께 증가하다가 2보다 커지면서는 변화가 없이 머무르는 경향은 같게 나타났다. 엽면적지수의 변화는 군락의 엽질소함량 변화와 선형적인 관계($R^2=0.80$)로 나타났다. 분얼기부터 성숙초기까지의 자료를 이용하여 지상센서 및 위성영상 유래 (G)NDVI를 이용한 벼 군락의 엽질소 함량과의 관계를 살펴보니 지수함수적 관계($R^2=0.90$)로 나타났다. 위성영상 유래 (G)NDVI를 이용한 벼 군락의 엽질소 함량 추정식을 이용하여 신평면 최고쌀 생산단지에 대한 엽질소 함량 지도를 작성하였다.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.4 s.31
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• pp.3-12
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• 2004

It is necessary to manage water resources in rural areas in order to achieve proper development of new water resources, sustainable usage and reasonable distribution. This paper aims to analyze multi-temporal Landsat-7 ETM+data for soil moisture that is essential for crops in Ahnsung area. The ETM data was also fused with KOMPSAT-1 images in order to be used as backdrop watershed maps at first. Multi-temporal Images showed also the characteristics of soil moisture distribution. Images taken in April showed that rice paddy had as low reflectance as artificial features. Compared with April scenes, those taken in Hay and June showed wetness index increased in the rice paddies. The mountainous areas have almost constant moisture index, so the difference between the dates was very low while reservoirs and livers had dramatic changes. We can calculate total potential areas of distribution of moisture content within the basin and estimate the areas being sensitive to drought. Finally we can point out the sites of small rice paddies lack of water and visualize their distribution within the same basin. It can be said that multi-temporal Landsat-7 ETM+ and KOMPSAT data can be used to show broad drought with quick and simple analysis. Drought sensitiveness maps may enable the decision makers on rural water to evaluate the risk of drought and to measure mitigation, accompanied with proper data on the hydrological and climatic drought.

• 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.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1209-1219
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• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.