• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.72-78
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• 2001

In this study, high spatial resolution of IKONOS 1m image and Red(0.63~0.69) band, NIR(0.76~0.90) band in 4m image, which are the same wavelength range as Landsat TM band 3, 4, were used for extraction of the front areas of B. Xylophilus in Geuje island where is located in southern part of Korea. Moreover, since they have higher spatial resolutions than Landsat TM, they have been used for lots of studies in the field of forest and vegetation. In the results, it was validated by GPS field survey, spectral histogram analysis of IKONOS NIR band was significant available method for extracting the front areas of B. Xylophilus. In this study, 15 points were verified as real damaged trees of 22 sample points extracted from GPS field survey. This study was not only extracted the damaged trees by B. Xylophilus but also suggested the possibility of using IKONOS images for the study on the forest damages by any disease and insect pests.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.4-6
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• 2003

Optical signals from an object may vary at different conditions caused by differences in light source and sensor position. Knowledge of these variations is necessary to enable calibration of the satellite images and confirmation of the sun and sensor angles influences of the spectral signals from the objects. With the use high -resolution Ikonos$^{TM}$ multi-angular images, the bi- directional reflectance effects of mangrove trees were observed when three datasets were compared. The influence of bi- directional reflectance may affect the accuracy of interpreting satellite imagery and obtaining biophysical parameters mangrove and other vegetation by indirect means.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.140-143
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• 2004

The west coast of the Korean Peninsula is famous for its large tidal range (up to 9 m) and vast tidal flats. With comparison the sedimentary environments of open and close tidal flat using remote sensing, we select Kanghwa tidal flat and Hwang-Do tidal flat in Cheonsu Bay. Prior to surface sediment discrimination using remote sensing, sedimentary environments including intertidal OEM, hydraulic condition, and relationship between grain size and various tidal condition are investigated. Remote sensing has the potential to provide synoptic information of intertidal environments. The objectives of this study are: (i) to generate an intertidal digital elevation model (OEM) using the waterline method of Lansat TM/ETM+, (ii) to investigate the tidal channel distribution using texture analysis, and (iii) to analyze the relationship between surface grain size by using in-situ data and intertidal OEM and tidal channel density by using high-resolution satellite data such as IKONOS and Kompsat EOC. The results demonstrate that satellite remote sensing is an efficient and effective tool for a surface sediment discrimination and long term morphologic change estimation in tidal flats.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.7
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• pp.15-21
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• 2004

This study is to present a standardized agricultural land use classification scheme at various spatial resolution (from 1 m to 30 m) of satellite images including Landsat TM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (PAN) and multi-spectral (MS) images. The satellite images were interpreted especially for identifying agricultural land use, crop types, agricultural facilities and structures of 18 items. It was found that there is a threshold spatial resolution between 4 m and 6.6 m to identify the full items. Thus it is suggested that IKONOS fusion image (MS enhanced by PAN) is required to produce land use map for agricultural purpose.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.2 s.20
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• pp.39-47
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• 2002

For the purpose of data construction for the systematic management and sight-seeing guidance of the natural park, the Kumoh Mt. Province Park was selected as a pilot area. Digital topographic maps, thematic maps and satellite imagery covering the object area were processed and then landform analysis for elevation, slope, aspect and so on was conducted through DEM generation, and the landcover map and NDVI maP were extracted from Landsat TM data. The database was then constructed with these spatial data for GSIS. The image map was generated from IKONOS satellite data, which cover the pilot area data, with one meter resolution and also 3D visualization which was overlaid with main paths up a mountain were conducted. And the moving image files were produced along main paths up including main natural spectacular sights, cultural assets and management facilities. It is expected that the research result can be utilized as the fundamental data for re-assessing suitable land use and constructing Web-based guidance system.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.373-378
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• 2006

The western coast of South Korea is famous for its large and broad tidal lands. Nevertheless, land reclamation, which has been conducted on a large scale, such as Sihwa embankment construction project has accelerated coastal environmental changes in the embankment inland. For monitoring of environmental change, vegetation change detecting of the embankment inland were carried out and field survey data compared with Landsat TM, ETM+, IKONOS, and EOC satellite remotely sensed data. In order to utilize multi-temporal remotely sensed images effectively, all data set with pixel size were analyzed by same geometric correction method. To detect the tidal land vegetation change, the spectral characteristics and spatial resolution of Landsat TM and ETM+ images were analyzed by SMA(spectral mixture analysis). We obtained the 78.96% classification accuracy and Kappa index 0.2376 using March 2000 Landsat data. The SMA(spectral mixture analysis) results were considered with comparing of vegetation seasonal change detection method.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.245-250
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• 2004

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. The results of the study were valued necessary process in geometric information building. This study help to improve accuracy of classification as feature of class separability in the class through optimizing previous classification steps.