• Korean Journal of Soil Science and Fertilizer
• /
• v.34 no.5
• /
• pp.342-349
• /
• 2001

This study was carried out to classify and evaluate the land cover map using Landsat TM data in Pyeongtaeg City. DGPS data, aerial photography, topographical map were used for selection the training sets and accuracy assessment. The overall accuracy and Kappa coefficient of the land cover classification map(using supervised classification with 13 classes) with Landsat TM data(16 June. 1997) were respectively, 86.8%, 85.4%, but the user's accuracy of urban/village and vinyl-house was below 60%, and the producer's accuracy of read and vinyl-house below 70%. Maybe it was caused the spectral reflectance characteristics, heterogeneity and small distribution area on the artificial things such as urban/village, vinyl_house and road, etc. And then, the agricultural land cover classification system using remote sensing data in Korea was to classify level I and II. Level I consisted of 5 classes such as agricultural land, forest land, water, barren land, urban and built-up land.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.9 no.7
• /
• pp.811-818
• /
• 2014

This study was aimed to detect Submarine Groundwater Discharge (SGD) distribution image of Sea Surface Temperature (SST) using infrared band of Landsat-7 ETM+ around Jeju island. It is used to analyze SST distribution that DN value of satellite images converted into temperature. The estimation of SGD location is that extracting range of $15{\sim}17^{\circ}C$ from SST. The summer season images(July 28. 2006, Aug. 29. 2006 and Sep. 19. 2008) were used to analyze big difference between SST and temperature of SGD. The results, estimated SGD locations were occurred part of coastal area in northeastern of Jeju island.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.8 no.1
• /
• pp.13-22
• /
• 2005

This paper classifies the topography of the Saemangeum Tidal flats based on Landsat TM satellite images by unsupervised ISODATA method, and analysis of the spatiotemporal changes of the classified shapes. The sedimental topography represents various properties according to the Saemangeum Tidal Embankment progress. We well proceed this study of the sedimental changes and distributions. By specifying the topographic characteristics of inner sea areas respectively, the investigation on the case study area according to the changes of the tidal will be useful in the establishment of land reclamation plan and the land use of the reclaimed area. In addition, the estuary image can be divided into tidal flats and sea surfaces using the band 4, also the detailed topography using the band 5, respectively among Landsat TM 7 bands. This paper contributes to the efficient image processing of the spatiotemporal sedimental changes.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.400-400
• /
• 2023

급격한 기후변화로 인해 일사량, 지표면 온도 및 이산화탄소 농도가 꾸준히 상승함에 따라 수문 순환의 불균형을 초래함과 하천 및 호소 내 수질 또한 악화되고 있는 추세이다. 특히, 국내의 경우, 기후변화 및 인위적 요인에 의해 하천 및 호소에서의 수위 감소 및 수온 증가로 인해 부영양화가 증가되고 있고, 이로 인한 유해 녹조의 발생빈도를 높이는 결과를 초래한다. 현재 국내에서는 유인 수질 관측 및 자동 수질관측 시스템을 통해 주요 수질인자를 모니터링 하고 있으나 시·공간적인 변동성을 파악하는데 제한점이 있다. 이러한 한계점을 극복하기 위해 국·내외에서 광학위성을 이용한 수질인자 추정 알고리즘 개발과 관련된 연구들이 진행되고 있다. 이에 따라, 본 연구에서는 NASA에서 제공하는 Landsat-8 위성과 ESA에서 제공하는 Sentinel-2자료가 동화된 Harmonized Landsat Sentinel-2 위성자료를 활용한 클로로필-a (Chl-a)를 추정하고자 한다. 이를 위해, 본 연구에서는 1) 단순 회귀 분석, 2) Akaike information criteria (AIC) 기반 최적화 회귀 분석 및 3) Random forest (RF)를 활용하였다. 또한, HLS 위성 자료의 적용성을 평가하기 위해 미국 오하이오 주에 위치하고 있는 130여개의 중규모 및 대규모 호소에서 2000년부터 2021년까지 수집된 클로로필-a 관측치를 활용하였다. 두 가지 수질 추정 모형에 대한 정확도 검증에 앞서 오하이오 주 내에서의 클로로필-a의 시계열적 변동성에 대하여 분석하였다. 전반적으로, 2000년부터 2016년까지는 Chl-a가 꾸준히 증가하는 경향성을 나타내었으나, 그 이후로는 감소하는 추세를 나타내었다. 이를 기반으로, 각 방법론을 통해서 나온 Chl-a 추정치에 대해서 통계적 검증을 수행하였다. 결과, 단순 회귀 분석을 통해 추청된 Chl-a값의 결정계수는 0.34였지만, AIC 기반 모델과 RF모형을 사용한 결과 결정계수가 각각 0.82와 0.92로 향상된 것을 확인할 수 있었다. 이와 더불어, spatial 및 temporal window와 더불어 호소의 크기에 따른 정확도 분석 또한 수행하였다. 그 결과, temporal window 가 정확도에 가장 큰 영향을 미치는 것으로 나타났으며, 호소의 크기가 작을수록 정확도가 낮아지는 것을 확인 할 수 있었다. 본 연구의 결과를 토대로 추후 국내 호소에 대해 상기 모형들의 적용성 평가를 수행하여 효율적인 수질 모니터링 시스템 구축으로 이어질 수 있을 것으로 기대된다.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.3 no.1
• /
• pp.5-15
• /
• 2001

For estimating paddy field area with Landsat TM images, two dates, May 31, 1991 (transplanting stage) and August 19, 1991 (heading stage) were selected by the data analysis of digital numbers considering rice cropping calendar. Four different estimating methods (1) rule-based classification method, (2) supervised classification(maximum likelihood), (3) unsupervised classification (ISODATA, No. of class:15), (4) unsupervised classification (ISODATA, No. of class:20) were examined. Paddy field area was estimated to 7291.19 ha by non-classification method. In comparison with topographical map (1:25,000), accuracy far paddy field area was 92%. A new image stacked by 10 layers, Landsat TM band 3,4,5, RVI, and wetness in May 31,1991 and August 19,1991 was made to estimate paddy field area by both supervised and unsupervised classification method. Paddy field was classified to 9100.98 ha by supervised classification. Error matrix showed 97.2% overall accuracy far training samples. Accuracy compared with topographical map was 95%. Unsupervised classifications by ISODATA using principal axis. Paddy field area by two different classification number of criteria were 6663.60 ha and 5704.56 ha and accuracy compared with topographical map was 87% and 82%. Irrespective of the estimating methods, paddy fields were discriminated very well by using two-date Landsat TM images in May 31,1991 (transplanting stage) and August 19,1991 (heading stage). Among estimation methods, rule-based classification method was the easiest to analyze and fast to process.

• Korean Journal of Remote Sensing
• /
• v.32 no.6
• /
• pp.639-645
• /
• 2016

Yeongjong-do is seventh largest island in the west coast of Korea which is 4.8 km away in the direction of south-west from Incheon. The mudflat area around the Yeongjong-do has variable dimension according to tide level. In addition, Yeongjong-do is important area with high environmental value as wintering sites for migratory birds. The mudflat of Yeongjong-do is also meaningful region because it is used as place of education and tourist attraction. But, there are increasing concerns about preservation of mudflat area caused by artificial development such as land reclamation project and Incheon airport construction. In this paper, mudflat area was detected using Landsat 7 ETM+ images that United States Geological Survey (USGS) is providing the data in 16 days period. The false color composite was made from band 7, 5, and 3 that could dividing boundary between water and land for the purpose of appearance of boundary line in mudflat region. This area was calculated using results of classification based on false color composite images and was digitized through repetitive algorithm during research of period. Therefore, the change of northeastern area in Yeongjong-do was detected according to tide level during 16 years from 2000 to 2015 on the basis of providing period at tide station. This paper will expect as indicator for range of area in same tide level prior to the start of the research for preservation of mudflat. It will be also scientific grounds about change of mudflat area caused by artificial development.

• Korean Journal of Remote Sensing
• /
• v.38 no.3
• /
• pp.225-236
• /
• 2022

Urbanization due to population growth and regional development can cause various environmental problems, such as the urban heat island phenomenon. A planned city is considered an appropriate study site to analyze changes in urban climate caused by rapid urbanization in a short-term period. In this study, changes in land cover and surface heat island phenomenon were analyzed according to the development plan in Sejong City from 2013 to 2020 using Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery. The surface temperature was calculated in consideration of the thermal infrared band value provided by the satellite image and the emissivity, and based on this the surface heat island effect intensity and Urban Thermal Field Variance Index (UTFVI) change analysis were performed. The level-2 land cover map provided by the Ministry of Environment was used to confirm the change in land cover as the development progressed and the difference in the surface heat island intensity by each land cover. As a result of the analysis, it was confirmed that the urbanized area increased by 15% and the vegetation decreased by more than 28%. Expansion and intensification of the heat island phenomenon due to urban development were observed, and it was confirmed that the ecological level of the area where the heat island phenomenon occurred was very low. Therefore, It can suggest the need for a policy to improve the residential environment according to the quantitative change of the thermal environment due to rapid urbanization.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.26 no.2
• /
• pp.42-54
• /
• 2023

The population is concentrated in the metropolitan areas in Korea, and low-density residential areas are transforming into high density residential areas through redevelopment to meet this demand. However, large-scale redevelopment in a short period of time has a negative impact on the urban climate, such as generating a heat island effect due to the reduction of urban green areas. In this study, the change in surface temperature from 2013 to 2022 in the redevelpment areas of Godeok-dong and Dunchon-dong, Gangdong-gu, Seoul, was analyzed using Landsat 8 satellite images. In the Godeok-dong area, the difference in surface temperature was analyzed for the target redevelopment area, forest area, mixed forest and urban area, and low density residential area. In the Dunchon-dong area, the difference in surface temperature was analyzed for the target redevelopment area, forest area, and low density residential area. The difference in surface temperature was analyzed through multiple regression analysis conducted yearly over the three different stages in redevelopment period. The results from the multiple regression analysis show that in both areas, the land surface temperature of target redevelopment area was higher than that of the forest area and lower than low density residential area. It can be seen that these results occurred because the low-density residential area in Godeok-dong and Dunchon-dong had a lower green area ratio and a higher building-to-land ratio than the target redevelopment area. The results of this study suggest that even if low-density residential areas are transforming into high-density areas, adjusting the management of green areas and building-to-land ratio can contribute to lessen urban heat island effect.

• International Journal of Knowledge Content Development & Technology
• /
• v.7 no.1
• /
• pp.57-78
• /
• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.17 no.4
• /
• pp.52-68
• /
• 2014

In this study, we investigated the seasonal variation of SST(Sea Surface Temperature) and thermal effluents estimated by using Landsat-7 ETM+ around the Kori Nuclear Power Plant for 10 years(2000~2010). Also, we analyzed the direction and range of thermal effluents dispersion by the tidal current and tide. The results are as follows, First, we figured out the algorithm to estimate SST through the linear regression analysis of Landsat DN(Digital Number) and NOAA SST. And then, the SST was verified by compared with the in situ measurement and NOAA SST. The determination coefficient is 0.97 and root mean square error is $1.05{\sim}1.24^{\circ}C$. Second, the SST distribution of Landsat-7 estimated by linear regression equation showed $12{\sim}13^{\circ}C$ in winter, $13{\sim}19^{\circ}C$ in spring, and $24{\sim}29^{\circ}C$ and $16{\sim}24^{\circ}C$ in summer and fall. The difference of between SST and thermal effluents temperature is $6{\sim}8^{\circ}C$ except for the summer season. The difference of SST is up to $2^{\circ}C$ in August. There is hardly any dispersion of thermal effluents in August. When it comes to the spread range of thermal effluents, the rise range of more than $1^{\circ}C$ in the sea surface temperature showed up to 7.56km from east to west and 8.43km from north to south. The maximum spread area was $11.65km^2$. It is expected that the findings of this study will be used as the foundational data for marine environment monitoring on the area around the nuclear power plant.