• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.417-425
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• 2008

An image-based atmospheric correction software ATC is implemented using MATLAB and SML (Spatial Modeler Language in ERDAS IMAGINE), and it was tested using Landsat TM/ETM+ data. This ATC has two main functional modules, which are composed of a semiautomatic type and an automatic type. The semi-automatic functional module includes the Julian day (JD), Earth-Sun distance (ESD), solar zenith angle (SZA) and path radiance (PR), which are programmed as individual small functions. For the automatic functional module, these parameters are computed by using the header file of Landsat TM/ETM+. Three atmospheric correction algorithms are included: The apparent reflectance model (AR), one-percent dark object subtraction technique (DOS), and cosine approximation model (COST). The ACT is efficient as well as easy to use in a system with MATLAB and SML.

• Journal of the Korean earth science society
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• v.25 no.8
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• pp.764-773
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• 2004

The soil in wildfire area Sacheon-myeon, Gangneung, Gangweon Province, Korea, were investigated to clarify pattern of the soils. The soils were classified into 5 types on the basis of vegetation, types of organic matter. thickness of soil horizons, and completeness of soil profile. Each type showed different erosion pattern and Landsat ETM image. Coverage of plant leaves, litter, root, ash and other organic matter was an important component that affected soil color and reflectance of Landsat image (digital number). Although the NDVI (Normalized Distribution Vegetation Index) method in the wildfire area did not show much difference in soil types, the applied supervised classification method showed characteristic pattern of Landsat ETM image of soil types. This study showed that the applied supervised Landsat TM image classification in wildfire area is an effective way to estimate the distribution of erosion pattern of soil in wildfire area.

• Korean Journal of Remote Sensing
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• v.20 no.1
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• pp.13-21
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• 2004

The usefulness of the multi-temporal satellite image to monitoring the vegetation recovery process after forest fire was tested. Using multi-temporal Landsat TM and ETM＋data, NDVI and NBR changes over times were analyzed. Both NDVI and NBR values were rapidly decreased after the fire and gradually increased for all forest type and damage class. However, NBR curve showed much clearer tendency of vegetation recovery than NDVI. Both indices yielded the lowest values in severely damaged red pine forest. The results show the vegetation recovery process after forest fire can detect and monitor using multi-temporal Landsat image. NBR was proved to be useful to examine the recovering and development process of the vegetation after fire. In the not damaged forest, however the NDVI shows more potential capability to discriminate the forest types than NBR..

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.87-99
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• 2006

The Median Resolution Sensors (MRSs) for land observation such as Landsat-ETM+ and SPOT-HRV are more effective than Ocean Color Sensors (OCSs) for studying of detailed ecological and biogeochemical components of the coastal waters. In this study, we developed suspended sediment algorithm for Landsat TM/ETM+ by considering the spectral response curve of each band. To estimate suspended sediment concentration (SS) from satellite image data, there are two difference types of algorithms, that are derived for enhancing the accuracy of SS from Landsat imagery. Both empirical and remote sensing reflectance model (hereafter referred to as $R_{rs}$ model) are used here. This study tried to compare two algorithm, and verified using in situ SS data. It was found that the empirical SS algorithm using band 2 produced the best result. $R_{rs}$ model-based SS algorithm estimated higher values than empirical SS algorithm. In this study we used $R_{rs}$ model developed by Ahn (2000) focused on the Mediterranean coastal area. That's owing to the difference of oceanic characteristics between Mediterranean and Korean coastal area. In the future we will improve that $R_{rs}$ model for the Korean coastal area, then the result will be advanced.

• Journal of the Korean association of regional geographers
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• v.11 no.2
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• pp.278-290
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• 2005

This study deals with the geomorphic, environmental and land use changes by comparative analysis using Landsat TM images of 1988 year and ETM ones of 2002 year, partly together with the new Quick Bird images having 60cm resolution for more detail analysis, focusing on the Oncheon gun(county) in Pyeongnam Province, west coast zone of North Korea. The main analysis methodology is neural network classification, which is more advanced techniques for the classification of land cover and land use, with higher accuracy rate and lower errors. The TM images of 1988 year show, mainly, the on-construction tide embank for the reclamation at Gwangryangman bay and salt farm on the reclaimed tidal flat. But, ETM images of 2002 year present stabilized reclaimed land, salt farm and rice field, recently transformed from salt farm. Especially, new tidal land has been naturally developed on the coastal shallow out of tide embank and salt farm. The results of the study may help to database coastal environmental changes and to support for reasonable and productive land use of North Korea, and to manage and plan unified national land in the near future.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.2
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• pp.53-63
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• 2016

Monitoring of cyanobacteria bloom in reservoir systems is important for water managers responsible of water supply system. Cyanobacteria affect the taste and smell of water and pose considerable filtration problems at water use places. Harmful cyanobacteria bloom in reservoir have significant economic impacts. We develop a new method for estimating the cyanobacteria bloom using Landsat TM and ETM+ data. Developed model was calibrated and cross-validated with existing in situ measurements from Daecheong Reservoir's Water Quality Monitoring Program and Algae Alarm System. Measurements data of three stations taken from 2004 to 2012 were matched with radiometrically converted reflectance data from the Landsat TM and ETM+ sensor. Stepwise multiple linear regression was used to select wavelengths in the Landsat TM and ETM+ bands 1, 2 and 4 that were most significant for predicting cyanobacteria cell number and bio-volume. Based on statistical analysis, the linear models were that included visible band ratios slightly outperformed single band models. The final monitoring models captured the extents of cyanobacteria blooms throughout the 2004-2012 study period. The results serve as an added broad area monitoring tool for water resource managers and present new insight into the initiation and propagation of cyanobacteria blooms in Daecheong reservoir.

• Proceedings of the KSRS Conference
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• v.2
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• pp.864-867
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• 2006

Thermal infrared images of Landsat-5 TM and Landsat-7 ETM+ sensors have been unrivalled sources of high resolution thermal remote sensing (60m for ETM+, 120m for TM) for more than two decades. Atmospheric effect that degrades the accuracy of Sea Surface Temperature (SST) measurement significantly, however, can not be corrected as the sensors have only one thermal channel. Recently, MODIS sensor onboard Terra satellite is equipped with dual-thermal channels (31 and 32) of which the difference of at-satellite brightness temperature can provide atmospheric correction with 1km resolution. In this study we corrected the atmospheric effect of Landsat SST by using MODIS data obtained almost simultaneously. As a case study, we produced the Landsat SST near the eastern and western coast of Korea. Then we have obtained Terra/MODIS image of the same area taken approximately 30 minutes later. Atmospheric correction term was calculated by the difference between the MODIS SST (Level 2) and the SST calculated from a single channel (31 of Level 1B). This term with 1km resolution was used for Landsat SST atmospheric correction. Comparison of in situ SST measurements and the corrected Landsat SSTs has shown a significant improvement in $R^2$ from 0.6229 to 0.7779. It is shown that the combination of the high resolution Landsat SST and the Terra/MODIS atmospheric correction can be a routine data production scheme for the thermal remote sensing of ocean.

• Korean Journal of Environment and Ecology
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• v.18 no.2
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• pp.231-235
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• 2004

Landsat satellite images were analyzed to study vegetation change patterns of warm-temperate forests from 1991 to 2002 in Wando. For this purpose, Landsat TM satellite image of 1991 and Landsat ETM image of 2002 were used for vegetation classification using ENVI image processing software. Four different forest types were set as a classification criteria; evergreen broadleaf, evergreen conifer, deciduous broadleaf, and others. Unsupervised classification method was applied to classily forest types. Although it was impossible to draw exact forest types in rocky areas because of differences in data detection time and rough resolution of image, 2002 data revealed that total 2,027ha of evergreen broadleaf forests were growing in Wando. Evergreen broadleaves and evergreen conifers increased in total areas compared to 11 years ago, but there was sharp decrease in deciduous broadleaves. GIS-based management system for warm-temperate forest was done using Arc/Info. Geographic and attribute database of Wando such as vegetation, soils, topography, land owners were built with Arc/Info and ArcView. Graphic user interface which manages and queries necessary data was developed using Avenue.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.159-172
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• 2006

In this study, the series of Landsat TM/ETM+ images was acquired to extract land surface temperature for wide-area and executed geometric correction and radiometric correction. And the land surface temperature was extracted using NASA Model, and achieved the first correction by performing land coverage category for study area and applied characteristic emission rate. Land surface temperature which was acquired by the first correction was analyzed in correlation with Meteorological Administration's temperature data by regression analysis, and established correction formula. And I wished to improve accuracy of land surface temperature extraction using satellite image by second correcting deviations between two data using establishing correction formula. As a result, land surface temperature acquired by 1st and 2st correction could be corrected in mean deviation of about ${\pm}3.0^{\circ}C$ with Meteorological Administration data. Also, I could acquire land surface temperature about study area by higher accuracy by applying to other Landsat images for re-verification of study results.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.189-197
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• 2001

Generally, remote sensing is useful to obtain the quantitative and qualitative information of a wide area. For monitoring earth resources and environment, land cover classification of remotely sensed data are needed over increasingly larger area. The objective this study is to propose the process for land cover classification method over a wide area using multi-scene satellite data. Land cover of Korean peninsula was extracted from a Landsat TM and ETM+ mosaic created from 23 scenes at 100-meter resolution. Well-known techniques that used to general image processing and classification are applied to this wide area classification. It is expected that these process is very useful to promptly and efficiently grasp of small scale spatial information such as national territorial information.