• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1013-1025
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• 2020

Unmanned Aerial Vehicle (UAV) platform is being widely used in disaster monitoring and smart city, having the advantage of being able to quickly acquire images in small areas at a low cost. Ground Control Points (GCPs) for positioning UAV images are essential to acquire cm-level accuracy when producing UAV-based orthoimages and Digital Surface Model (DSM). However, the on-site acquisition of GCPs takes considerable manpower and time. This research aims to provide an efficient and accurate way to replace the on-site GNSS surveying with three different sources of geospatial data. The three geospatial data used in this study is as follows; 1) 25 cm aerial orthoimages, and Digital Elevation Model (DEM) based on 1:1000 digital topographic map, 2) point cloud data acquired by Mobile Mapping System (MMS), and 3) hybrid point cloud data created by merging MMS data with UAV data. For each dataset a three-dimensional positional accuracy analysis of UAV-based orthoimage and DSM was performed by comparing differences in three-dimensional coordinates of independent check point obtained with those of the RTK-GNSS survey. The result shows the third case, in which MMS data and UAV data combined, to be the most accurate, showing an RMSE accuracy of 8.9 cm in horizontal and 24.5 cm in vertical, respectively. In addition, it has been shown that the distribution of geospatial GCPs has more sensitive on the vertical accuracy than on horizontal accuracy.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.181-189
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• 2012

Recently, interest in potential for estimating water quality using high resolution satellite images is increasing. However, low SNR(Signal to Noise Ratio) over inland water and radiometric errors such as non-linearity of brightness value of high resolution satellite images often lead to accuracy degradation in water quality estimation. Therefore radiometric correction should be carried out to estimate water quality for high resolution satellite images. For KOMPSAT-2 images parameters for brightness value-radiance conversion are not available and precise radiometric correction is difficult. To exploit KOMPSAT-2 images for water quality monitoring, it is necessary to investigate non-linearity of brightness value and noise over inland water. In this paper, we performed brightness value comparison between KOMPSAT-2 images and IKONOS/GeoEye-1, which are known to show the linearity. We used the images obtained over the same area and on the same date for comparison. As a result, we showed that although KOMPSAT-2 images are more noisy;the trend of brightness value and pattern of noise are almost similar to reference images. The results showed that appropriate target area to minimize the impact of noise was $5{\times}5$. Non-linearity of brightness value between KOMPSAT-2 and reference images was not observed. Therefore we could conclude that KOMPSAT-2 may be used for estimation of water quality parameters such as concentration of chlorophyll.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.339-357
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• 2009

The Saemangeum Reclamation Project was launched as a national project in 1991 to reclaim a large coastal area of 401 km$^2$ by constructing a 33-km long dyke. The final dyke enclosure in April 2006 has transformed the tidal flat into lake and land. The dyke construction has abruptly changed not only the estuarine tidal system inside the dyke, but also the coastal marine environment outside the dyke. In this study, we investigated the spatial change of SST distribution using the Landsat-5/7 and NOAA data before and after the dyke completion in the Saemangeum area. Satellite-induced SST was verified by compared with the various in situ measurements such as tower, buoy, and water sample. The correlation coefficient resulted in above 0.96 and RMSE was about 1$^{\circ}C$ in all data. 38 Landsat satellite images from 1985 to 2007 were analyzed to estimate the temporal and spatial change of SST distribution from the beginning to the completion of the Samangeum dyke's construction. The seasonal change in detailed spatial distribution of SST was measured, however, the estimation of change during the Saemangeum dyke's construction was hard to figure out owing to the various environmental conditions. Monthly averaged SST induced from NOAA data from 1998 to 2007 has been analyzed for a complement of Landsat's temporal resolution. At the inside of the dyke, the change of SST from summer to winter was large due to the relatively high temperature in summer. In this study, multi-sensor thermal remote sensing is an efficient tool for monitoring the temporal and spatial distribution of SST in coastal area.

• Journal of the Korean Geophysical Society
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• v.5 no.4
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• pp.283-292
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• 2002

The Korean peninsula is located at the edge of the East Asian active margin. The seismic activity in the Korean Peninsula is relatively low compared with the neighboring countries China and Japan. According to the available Seismic information, the Korean Peninsula is not totally safe from the Earthquake disaster. Moreover, the area is surrounded by varies tectonic forces which is resulted from the relative movements of the surrounding tectonic plates "Pacific, Philippine Sea, Eurasian and South China". Nowadays South Korea has 65 GPS stations belong to 5 governmental organizations "each organization figure out its own GPS stations for different requirements" In order to minimize the seismic hazard in the Korean Peninsula a program for monitoring the recent crustal movement has been designed considering the uses of the available GPS station "some selected stations from the previously mentioned stations" and the tectonic settings in and around the Korean Peninsula. This program is composed of two main parts, the first part to monitor the crustal deformation around the Korean Peninsula with the collaboration of the surrounding countries "China and Japan" this part is composed of two phases "East Sea Phase and Yellow Sea Phase". These phases will be helpful in determining the deformation parameters in the East Sea and the Yellow Sea respectively While the Second part of this program, is designed to determine the deformation parameters id and around the main faults in the Korean Peninsula and the relative movement between the Korean Peninsula and the Cheju Island. Through out this study the needs of crustal movement center rose up to collect the data from the previously mentioned stations and Organizations in order to use such reliable data in different geodynamical application.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.142-149
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• 2005

To provide observed oceanography data at coastal fish and shellfish farm in the northeastern sea of the Korean peninsula on real time base, we developed real time application system for fisheries oceanography information. The system has been made up a mooring buoy system, a server for oceanography data collection, a server for archiving data and a database system, and a web server for providing fisheries oceanography information using internet. Futhermore, to support letters service on a cellular phone, we developed the communication system from mooring buoy to cell phone on real time base. The oceanography data derived from the system are water temperature speed and direction of current in surface layer middle layer and bottom layer in hour. We were able to quantify short term variation of ocean conditions within several days at shellfish farm such as a scallop sea farm using our system. To reduce damages of fish and shellfish farm from abnormal phenomena of ocean conditions such as a broken stratification of water, an occurrence of abnormal coastal cold water and warm water we will be able to move vertically and horizontally the sea farm facilities to proper conditions using real time oceanography information derive from the system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.499-506
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• 2019

NDSM (Normalized Digital Surface Model) is key information for the detailed analysis of remote sensing data. Although NDSM can be simply obtained by subtracting a DTM (Digital Terrain Model) from a DSM (Digital Surface Model), in case of UAV (Unmanned Aerial Vehicle) data, it is difficult to get an accurate DTM due to high resolution characteristics of UAV data containing a large number of complex objects on the ground such as vegetation and urban structures. In this study, RGB-based UAV vegetation index, ExG (Excess Green) was used to extract initial seed points having low ExG values for region growing such that a DTM can be generated cost-effectively based on high resolution UAV data. For this process, local window analysis was applied to resolve the problem of erroneous seed point extraction from local low ExG points. Using the DSM values of seed points, region growing was applied to merge neighboring terrain pixels. Slope criteria were adopted for the region growing process and the seed points were determined as terrain points in case the size of segments is larger than 0.25 ㎡. Various slope criteria were tested to derive the optimized value for UAV data-based NDSM generation. Finally, the extracted terrain points were evaluated and interpolation was performed using the terrain points to generate an NDSM. The proposed method was applied to agricultural area in order to extract the above ground heights of crops and check feasibility of agricultural monitoring.