• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.55-61
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• 2011

The land use has been changed artificially and caused the result of temperature increase of city compared with the outside of city or region of park and forest. The purpose of this research is to analyze the change of the urban surface temperature with land use zoning in Jinju using Landsat TM/$ETM^+$ imagery and to provide the correlation between NDVI(Normalized Difference Vegetation Index) and urban surface temperature change. The results presented that the spatial distribution of urban surface temperature was depending on the change of NDVI values on land use zoning. Considering to the average temperature by land use zoning, industrial area was the highest temperature but green area was the lowest temperature. Also as a result of comparing the correlation between surface temperature and NDVI, the green and residential area had higher correlation values than the commercial and industrial area. These results will be played a part as one of the major factors for implementing the sustainable urban planning considering the urban heat island effect problem.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.451-457
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• 2014

Recently, the land degradation and pasture erosion in Tuv, located around Ulaanbaatar of Mongolia, have been increasing sharply due to escalating developments of mining sectors, well as the density of populations. Because of that, we have chosen the urban and mining area of Tuv for our study target. During the study, the temporal changes of land cover in Tuv, Mongolia were observed by the Landsat TM/+ETM satellite images from 2001 to 2009 that provided the fundamental dataset to apply NDVI and K-Mean algorithm of Unsupervised Classification and Maximum likelihood classification(MLC) of Supervised Classification in order to conclude in land cover change analyzation. The result of our study implies that the growth of mining area, the climate change, and the density of population led the land degradation to desertification.

• 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 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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• 2001.03a
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• pp.9-14
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• 2001

오랜 기간동안 진행되는 사회간접자본 건설의 진행 상황을 관측하는 것은 대규모 공사의 종합적인 관리를 위해 필수불가결한 요소이다. 동북아 지역의 중추 공항 기능을 담당할 영종도 국제공항의 공사진행 과정을 관측하기 위하여 인공위성 영상 자료가 활용되었다. 바다위에 건설되는 공항의 특성으로 인하여 방조제 건설과 매립공사가 수행되었다. 활주로, 유도로, 여객터미널과 복합교통센터 등이 건설되었으며, 공항의 건설로 산림이 훼손되고 양식장과 염전이 매립되는 것이 관측되었다. 이러한 공항공사의 진척상태를 분석하기 위해서 시계열 Landsat TM 영상을 사용하였으며, 타 위성영상에서는 공항의 공사현황이 어느정도 분석가능한지를 가늠하기 위해서 KOMPSAT EOC, IRS-1C PAN, RADARSAT SAR 영상이 활용되었다. 시계열 Landsat TM 영상에서는 공항 부지의 매립 진척 현황과 산림의 벌채 등을 잘 분석할 수 있었다. KOMPSAT EOC 과 IRS-1C PAN 영상은 높은 공간해상력으로 건설에 사용된 가건물과 같은 세부적인 시설물을 관측할 수 있었다. 15m PAN 영상을 제공하는 Landsat ETM은 IHS 합성 후 분석하였는데, 기존의 TM 영상에서 분류하지 못했던 방조제의 도로와 성토를 구분할 수 있었다. RADARSAT SAR 영상에서는 광학영상에서 볼 수 없었던 독특한 정부 를 얻을 수 있었다.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.231-237
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• 2007

To monitor process of vegetation rehabilitation at the damaged area after large-fire is required a lot of manpowers and budgets. However the analysis of vegetation recovery using satellite imagery can be obtaining rapid and objective result remotely in the large damaged area. Space and airbone sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Burn severity incorporates both short- and long-term post-fire effects on the local and regional environment. Burn severity is defined by the degree to which an ecosystem has changed owing to the fire. To classify fire damaged area and analyze burn severity of Samcheok fire area occurred in 2000, Cheongyang fire 2002, and Yangyang fire 2005 was utilized Landsat TM and ETM+ imagery. Therefore the objective of the present paper is to quantitatively classify fire damaged area and analyze burn severity using normalized burn index(NBR) of pre- and post-fire's Landsat satellite imagery.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.340-343
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• 2004

Since the tide embankment construction started in 1991, the coastal environment in and around the Saemangeum area has undergone changes rapidly, there is a need for monitoring the environmental change in this region. Owing to high temporal and spatial heterogeneity of the coastal ecosystem and processes as well as the expense with traditional filed sampling at discrete locations, satellite remote sensing measurements offer a unique perspective on mapping a large region simultaneously because of the synoptic and repeat coverage and that quantitative algorithms used for estimating constituents' concentration in the coastal environments. Thus, the main objectives of the present study are to analyze the retrieved Suspended Sediment (SS) pattern to predict changes after the commencement of the tide embankment construction work in 1991. This is accomplished with a series of the Landsat TM/ETM+ imagery acquired from 1985-2002 (a total of 18 imageries). Instead of a simple empirical algorithm, we implement an analytical SS algorithm, developed by Ahn et al. (2003), which is especially developed for estimating SS concentration (SSC) in Case-2 waters. The results show that there is a significant change in SS pattern, which is mainly influenced by the tide and tidal height after the construction of the embankment work. As the construction progressed, the distribution pattern of SS has greatly changed, and the rate of SS concentration in the gap area of the dyke of post-construction has significantly increased.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.337-350
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• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.441-444
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• 2005

The goal of this study is to investigate the relationship between microphytobenthic biomass and normalized vegetation index obtained from Landsat TM images. Monitoring a seasonal change of microphytobenthic biomass in the sand bar is specifically focused. Since the study area, Hampyeong Bay, was difficult to approach, we failed to obtain ground truths simultaneously on satellite image acquisition. Instead, chlorophyll-a concentration in surface top layer was measured on different dates for microphytobenthic biomass. Although data were acquired on different dates, a correlation between the field and satellite images was calculated for investigating general trends of seasonal change. NDVI and tasseled cap transformed images were also used to review the variation of microphytobenthic biomass by using Landsat TM and ETM+ images. Atmosphere effects were corrected by applying COST model. Seaweeds were also flouring in the same season of microphytobentic blooming. Songseok-ri area was minimally affected by seaweeds from February to May, and selected as a test site. NDVI value was classified into high-, moderate-, and low-grade. It was well developed over fme-grained sediments and rapidly reduced from May to November over sand bar. In this bay, correlation between grain size and microphytobenthic biomass was clearly seen. From the classified NDVI and tasseled cap transformed data, we finally constructed spatial distribution and seasonal variation maps of microphytobenthic biomass.