• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.63-71
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• 2013

This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.98-106
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• 2003

Because satellite images have the advantage of high resolution, multi-spectral, revisit and wide swath characteristics, it is increased to utilize satellite image and get information little by little in nowadays. In order to utilize remote sensed images effectively, it is necessary to process satellite images through many processing steps. Among them, geometric correction is essential step for satellite image processing. In this study, we constructed geometric correction data using LANDSAT satellite images. First, we extracted GCPs from maps and constructed database over the Korean peninsular. Second, LANDSAT satellite images, 165 scenes were corrected geometrically using GCP database. Finally, we made 7 mosaic images by means of geometric correction images over Korean peninsular. We think that constructed geometric correction data will be used for many application fields as basic data.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.31-39
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• 2007

In this study, the hydrosphere change of the Daecheong dam basin was detected qualitatively and quantitatively using Landsat satellite images until recentness since the construction of Daecheong dam. The hydrosphere change of the basin was analyzed by applying supervised classification about Landsat satellite images which were classified according to the hydrosphere, vegetation, road and etc. for four distinct years which are 1981, 1987, 1993, and 2002 year. Landsat satellite images of each year were achieved overlay analysis with extracting only the hydrosphere, and though these results, the hydrosphere change of the Daecheong dam basin was monitored efficiently.

• Korean Journal of Soil Science and Fertilizer
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• v.30 no.2
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• pp.135-139
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• 1997

Vegetation indices were analyzed from the LANDSAT images of Yeoju, Kwangju and Yangpyong Gun of Kyunggi Province, obtained on September 22, 1983 and September 12, 1994, to evaluate the changes in landuses. It was found that there were notable changes in the landuses in the observed area over 11 years. Particularly, in Choweol Myun of Kwanju Gun, in Yangpyong Eup of Yangpyong Gun, and in Heungcheon Myun of Yeoju Gun, decrease in the area under vegetatin was most remarkable. These changes in the area under vegetatin are considered as the results of the construction of highways and the expansion of urban areas. The LANDSAT images overestimated the area under rice cultivation, when compared to the official statistic year book. However, it was found that the areas of the land under rice cultivation estimated by LANDSAT images for each Myun were close enough to those from the official statistics suggesting that the LANDSAT images can be used for the estimation of landuse status.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.861-871
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• 2019

Landsat satellite images have been increasingly used for disaster damage analysis and disaster monitoring because they can be used for periodic and broad observation of disaster damage area. However, periodic disaster monitoring has limitation because of areas having missing data due to clouds as a characteristic of optical satellite images. Therefore, a study needs to be conducted for restoration of missing areas. This study detected and removed clouds and cloud shadows by using the quality assessment (QA) band provided when acquiring Landsat-8 images, and performed image restoration of removed areas through a spatial and temporal adaptive reflectance fusion (STARFM) algorithm. The restored image by the proposed method is compared with the restored image by conventional image restoration method throught MLC method. As a results, the restoration method by STARFM showed an overall accuracy of 89.40%, and it is confirmed that the restoration method is more efficient than the conventional image restoration method. Therefore, the results of this study are expected to increase the utilization of disaster analysis using Landsat satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.2
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• pp.187-194
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• 1998

In this study, effective multispectral Landsat TM band combinations for a merging with the high resolution IRS-1C PAN data using the IHS method to improve landuse accuracy is discussed. From the pre-classified image using the merged images with TM all six band images(with the exception of band 6 image) and PAN image, a sample data which has ten classes was generated. An evaluation of the overall classification accuracy for the representative seven merged images which were merged using each TM three-band images and IRS-1C PAN image by IHS method for the sample area. The increase in classification accuracy is most significant with the inclusion of two of TM4, TM5 and TM7 infrared band images. Especially, the largest increase(11.8 percent) in landuse classification accuracy were investigated when Landsat TM247 bands were merged with IRS-1C PAN data. The classification accuracy when TM three band image and PAN image were used without merging is higher than result of the case of using the merged images.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.5
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• pp.39-48
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• 2006

The objective of this research was to find an indirect method to estimate land surface temperature (LST) efficiently, using Landsat images. Agricultural fields including paddy fields have long been known to have multi-functions beneficial to the environment and ecology of the urban surrounding areas. Among these functions, the ambient temperature cooling (ATC) effect is widely acknowledged. However, quantitative and regional assessment of such effect has not been performed. Thermal remote sensing has been used over urban areas to assess the ATC effect, Thermal Island Effect(TIE), and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of paddy fields and urban climates, focusing primarily on the ATC effect. Landsat satellite images were used to determine the surface temperatures of different land cover types of a $44km^{2}$ study area in Cheongiu, Korea. The results show that the ATC is a function of paddy area percentage in Landsat pixels. Landsat pixels with higher paddy area percentage have much more cooling effect. The use of satellite data may contribute to a globally consistent method for analysis of ATC effect.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.561-571
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• 2019

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.55-60
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• 2007

The purpose of this study is to evaluate the temporal land cover change by gradual urbanization of Gyeongan-cheon watershed. This study used the five land use of Landsat TM satellite images(l987, 1991, 2001, 2004) which were classified by maximum likelihood method. The five land use maps examine its accuracy by error matrix and administrative district statistics. This study analyze land use patterns in the past using time.series Landsat satellite images, and predict 2004 year land use using a CA-Markov combined CA(Cellular Automata) and Markov process, and examine its appropriateness. Finally, predict 2030, 2060 year land use maps by CA-Markov model were constructed from the classified images.