• The Journal of Engineering Geology
• /
• v.2 no.2
• /
• pp.141-146
• /
• 1992

Synthetic stereo-pair of Landsat TM(Thematic Mapper) and shaded relief image from DEM(Digital Elevation Model) were digitally produced. They are very useful in optimizing the geological interpretability of the remotely sensed data.

• Proceedings of the KSRS Conference
• /
• 2001.03a
• /
• pp.139-139
• /
• 2001

부산은 과거 10년간 급격한 산업화 및 도농통합의 발빠른 진행으로 토지이용의 많은 변화가 초래되었다. 이로 인하여 많은 환경문제가 야기되고 있는 실정이다. 그러므로, 본 연구에서는 부산지역에서 과거 10년간의 토지이용변화를 Landsat TM 영상을 이용하여 분석하고 이를 지리정보시스템을 이용하여 환경오염에 대한 정보제공, 식생변화, 도시계획결정에 유용한 자료를 제시하고자 한다.

• KCID journal
• /
• v.8 no.2
• /
• pp.45-54
• /
• 2001

A plenty of wastes by greenhouse cultivation affect soil and water pollution much more than those by rice cultivation in paddy field. The greenhouse on paddy field has been increased dramatically, however their physical information such as the location an

• Proceedings of the Korean Society of Agricultural Engineers Conference
• /
• 1999.10c
• /
• pp.473-478
• /
• 1999

The purpose of the study were to detect and evaluate the land use and changes on the Balan Watersheds, located southwest of Suwon, using the Thematic Mapper(TM) data. Three sests of TM taken in 1985 , 1993 and 1996 were used and the changes in the land use analyzed and compared. The suupervised and unsuperivised classification methods were adoppted to classify five land-cover categories ; Paddy , upland , forest , residential , and water. Future ladn use patterns were simulated using a Markow chain method, and the change ratios presented.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1691-1707
• /
• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Korean Journal of Remote Sensing
• /
• v.33 no.6_3
• /
• pp.1179-1185
• /
• 2017

The KOMPSAT-3A satellite acquires high-resolution MWIR images twice a day compared to conventional Earth observing satellites. New radiometric information of Earth's surface can be provided due to different characteristics from existing SWIR images or TIR images. In this study, the difference image of multitemporal images was generated and compared with existing infrared images to find the characteristics of KOMPSAT-3A MWIR satellite images. A co-registration was performed and the difference between pixel values was minimized by using PIFs (Pseudo Invariant Features) pixel-based relative normalization. The experiment using Sentinel-2 SWIR image, Landsat 8 TIR image, and KOMPSAT-3A MWIR image showed that the distinction between artifacts in the difference image of KOMPSAT-3A is prominent. It is believed that the utilization of KOMPSAT-3A MWIR images can be improved by using the characteristics of IR image.

• Journal of the Korean Society of Hazard Mitigation
• /
• v.8 no.5
• /
• pp.77-83
• /
• 2008

In this study, land cover classification and NDVI evaluation for hazard mitigation precaution are carried out in surrounding areas of Yeongi-gun, Chungcheongnam-do ($132\;km^2$) where a project for multi-functional administrative city is promoted by government. Image acquired from KOMPSAT 2, LANDSAT and ASTER is utilized and comparative evaluation on limitation in classification based on resolution was carried out. The area mainly consists of arable land including mountains, rice fields, ordinary fields, etc thus special attention was paid to the classification of rice fields and ordinary fields. For the classification of image acquired from KOMPSAT 2, segmentation technique for classification of high-resolution image was applied. To evaluate the accuracy of the classification, field investigation was conducted to examine the sample and it was compared with the land usage and classification of land category in land ledger of Korea. Acquired results were made into theme map in shape file format and it would be of great help in decision making of policy for the future-oriented development plan of multi-functional administrative city.

• Journal of Korea Water Resources Association
• /
• v.36 no.6
• /
• pp.999-1012
• /
• 2003

The objective of this study is to test the applicability of CN values suggested using land cover and treatment classification of satellite image. Applicability test is based on the comparison of observed effective rainfall and computed one. The 3 case study areas, where are the upstream of Gyeongan stage station, the upstream of Baekokpo stage station Pyungchang River basin, and the upstream of Koesan Dam, are selected to test the proposed CN values and the hydrologic and meteorologic data, Landsat-7 ETM of 2000, soil map of 1:50,000 are collected for the selected areas. The results show that the computed CN values for three study cases are 71, 63, 66, respectively, and the errors between observed and computed effective rainfall are within about 30%. It can be concluded that the proposed CN values from this study for land cover and treatment classification of satellite image not only provides more accurate results for the computation of effective rainfall, but also suggest the objective CN values and effective rainfall.

• Spatial Information Research
• /
• v.19 no.5
• /
• pp.37-46
• /
• 2011

In recent years, utilizations of satellite imagery have been extensively conducted in order to obtain accurate information on drought detection in spring season. This research also carried out utilization of satellite imagery through the various vegetation indices such as NDVI(Normalized Difference Vegeation Index), MSI(Moisture Stress Index), MidIR Index, II(Infrared Index) to find better methodology to detect drought phenomena, especially occurring in spring season. For this purpose, Landsat TM(Thematic Mapper) images were used and applied on the Yeong-cheon city. In this study, the characteristics of DN(Digital Number) for each vegetation index is analyzed, and the correlation analysis between indices and DN according to the number of days with no rain is performed. The results shows high correlation between NDVI and MSI and II with positive correlation on MSI, and negative correlation on II. This indicates the possibility for practical use of MSI, II indices with NDVI to obtain better credibility for detecting spring droughts.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_3
• /
• pp.1031-1042
• /
• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.