• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.170-181
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• 2015

A research on extracting croplands located nearby coastal areas using the spatial information data sets is the important task for managing the agricultural products in coastal areas. This research aims to extract the various croplands(croplands on mountains and croplands on plain areas) located nearby coastal areas using the KOMPSAT-2 imagery, the high-resolution satellite imagery, and the airborne topographic LiDAR(Light Detection And Ranging) data acquired in coastal areas of Uljin, Korea. Firstly, the NDVI(Normalized Difference Vegetation Index) imagery is generated from the KOMPSAT-2 imagery, and the vegetation areas are extracted from the NDVI imagery by using the appropriate threshold. Then, the DSM(Digital Surface Model) and DEM(Digital Elevation Model) are generated from the LiDAR data by using interpolation method, and the CHM(Canopy Height Model) is generated using the differences of the pixel values of the DSM and DEM. Then the plain areas are extracted from the CHM by using the appropriate threshold. The low slope areas are also extracted from the slope map generated using the pixel values of the DEM. Finally, the areas of intersection of the vegetation areas, the plain areas and the low slope areas are extracted with the areas higher than the threshold and they are defined as the croplands located nearby coastal areas. The statistical results show that 85% of the croplands on plain areas and 15% of the croplands on mountains located nearby coastal areas are extracted by using the proposed methodology.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.231-242
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• 2001

In 2-pass differential SAR interferometry(DInSAR), the topographic phase signature can be removed by using a digital elevation model(DEM) to isolate the contribution of deformation from interferometric phase. This method has an advantage of no unwrapping process, but applicability is limited by precision of the DEM used. The residual phase in 2-pass differential interferogram accounts for error of DEM used in the processing provided that no actual deformation exits. The objective of this paper is a preliminary study to improve DEM precision using low precision DEM and 2-pass DInSAR technique, and we applied the 2-pass DInSAR technique to Asan area. ERS-1/2 tandem complex images and DTED level 0 DEM were used for DInSAR, and the precision of resulting DEM was estimated by a 1:25,000 digital map. The input DEM can be improved by simply adding the DInSAR output to the original low precision DEM. The absolute altitude error of the improved DEM is 9.7m, which is about the half to that of the original DTED level 0 data. And absolute altitude error of the improved DEM is better than that from InSAR technique, 15.8m. This approach has an advantage over the InSAR technique in efficiently reducing layover effects over steep slope region. This study demonstrates that 2-pass DInSAR can also be used to improve DEM precision.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.13-24
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• 2000

SAR imagery usually reveals the influence of antenna look-direction on the delineation of geological structures. In this study, the look-direction bias in SAR image is quantitatively analyzed specifically for geological lineament study. Geologic lineaments are estimated using both Landsat TM and JERS-1 SAR images over the study area to quantitatively compare and analyze the look-direction bias in the SAR image. The standard geologic lineaments in the study area are established from lineaments estimated from TM images, field mapping, and fault lines in a published geologic map. The results show that lineaments normal to radar look-direction are extremely well enhanced while those parallel to look-direction are less visible as expected. However, certain lineaments even parallel to radar look-direction can still be detectable in a favorable topographic condition. Compared with TM image, the total number of detected lineaments in each direction in the SAR image increases or decreases ranging from 33% to 159% in length and from 28% to 187% in occurrence. The ratio of lineaments in SAR image to those in TM image with respect to direction can be fitted by a cosine function. The fitted function indicates that geological lineament is more easily detected in SAR image than in TM image within about $\pm$50$^{\circ}$ normal to radar look-direction. And lineaments with limited extension appear to be more sensitive to the look direction bias effect.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.150-165
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• 2013

This study aims to investigate the original position, the writer of signboard handwriting and the period of Wibongmun and Joyangru in order to restore Wibongmun and Joyangru which have been designated as tangible cultural properties (有形文化財). They also have to be moved in the Gangwon Provincial Office. Wibongmun and Joyangru were established as government offices in chuncheon(春川官衙) and they were used as attached buildings in Chunceon (春川離宮) in 1890. Wibongmun was moved to Gangwon Provincial Office 5 times and Joyangru was moved twice. In order to move them back to the original place, by using the topographic map made by the Japanese Government-General in Korea, we find out Joyangru was located in the exit of Gangwon Provincial Office and greenhouse and we also figure out Wibongmun was located in the garden. While we study historical evidence on handwriting, we also find out the handwriting of Joyangmun was written by Songhaong (松下翁) Jo, Yun-Hyeong (曺允亨). Especially, Joyangru had played a role as a government office and it may be called 'Joyangru' after reconstructing 'Joyangmun' when attached buildings were established. Through this study, we found that the first period and reason of establishing Wibongmun and Joyangru was at least before 1788. Through this study, we can find the period of both and its historic meaning more clearly.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.461-471
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• 2018

In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the $CO_2$ potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the $CO_2$ potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of $CO_2$ potential reduction hotspot areas among the deforestation areas was 15, taking up the $CO_2$ potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a $CO_2$ potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many $CO_2$ potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the $CO_2$ potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital). Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1277-1290
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• 2020

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.741-749
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• 2022

Major space agencies are planning for the rover-based lunar exploration since water-ice was detected in permanently shadowed regions (PSR). Although sunlight does not directly reach the PSRs, it is expected that reflected sunlight sustains a certain level of low-light environment. In this research, the indoor testbed was made to simulate the PSR's lighting and topological conditions, to which low light enhancement methods (CLAHE, Dehaze, RetinexNet, GLADNet) were applied to restore image brightness and color as well as to investigate their influences on the performance of feature extraction and matching methods (SIFT, SURF, ORB, AKAZE). The experiment results show that GLADNet and Dehaze images in order significantly improve image brightness and color. However, the performance of the feature extraction and matching methods were improved by Dehaze and GLADNet images in order, especially for ORB and AKAZE. Thus, in the lunar exploration, Dehaze is appropriate for building 3D topographic map whereas GLADNet is adequate for geological investigation.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.55-70
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• 2021

In this study, in the field of remote sensing, where the scope of application is rapidly expanding to fields such as land monitoring, disaster prediction, facility safety inspection, and maintenance of cultural properties, monitoring of rural space and surrounding environment using UAV is utilized. It was carried out to verify the possibility, and the following main results were derived. First, the aerial image taken with an unmanned aerial vehicle had a much higher image size and spatial resolution than the aerial image provided by the National Geographic Information Service. It was suitable for analysis due to its high accuracy. Second, the more the number of photographed photos and the more complex the terrain features, the more the point cloud included in the aerial image taken with the UAV was extracted. As the amount of point cloud increases, accurate 3D mapping is possible, For accurate 3D mapping, it is judged that a point cloud acquisition method for difficult-to-photograph parts in the air is required. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. Fourth, the digital elevation model(DEM) produced with aerial image taken with an UAV can visually express the altitude and shape of the topography of the study site, so it can be used as data to predict the effects of topographical changes due to changes in rural space. Therefore, it is possible to utilize various results using the data included in the aerial image taken by the UAV. In this study, the superiority of images acquired by UAV was verified by comparison with existing images, and the effect of 3D mapping on rural space monitoring was visually analyzed. If various types of spatial data such as GIS analysis and topographic map production are collected and utilized using data that can be acquired by unmanned aerial vehicles, it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.293-304
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• 2022

In this study, to analyze the effect of buildings on the image co-registration performance, co-registration results are compared according to the presence or absence of matching points extracted from buildings. To remove the matching points extracted from buildings, a building mask generated by extracting building objects from the digital topographic map was used. In addition, matching points extraction performance and image co-registration accuracy were analyzed according to the magnitude of the convergence angle. Image co-registration results were compared by applying the affine and piecewise linear transformation models, respectively. According to the experimental results, the affine transformation model showed an overall improvement in accuracy after removing the matching points extracted from buildings. On the other hand, the piecewise linear transformation model improved the accuracy at the checkpoints including the surrounding buildings, but the accuracy improvement was not significant at checkpoints in the flat area without the existence of buildings. In addition, when the piecewise linear transformation model was applied, stable accuracy of less than 2 pixels was derived from images with a convergence angle of 20° or less.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.