• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1623-1631
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• 2022

Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1777-1788
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• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.382-392
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• 2023

Recent reports have indicated a rapid increase in the frequency of sediment disasters due to climate change and other changes in the geological environment. Given this alarming situation and the recent increase in the frequency of land creep in Korea, systematic and efficient recovery and management of land creep areas is essential. The purpose of this study is to identify disaster vulnerability by conducting a physical exploration of land creep in San 4-1, Jayeon-ri, Gaegun-myeon, Yangpyeong-gun, Gyeonggi-do, and examine stability by identifying the overall geological structure of the affected ground. In addition, drilling surveys are conducted to verify the reliability of the measured data. The results of the study reveal that low specific resistance abnormalities are distributed in the upper part of the soil layer and weathering zone and that this section is a 50-120 m exploration line. It is also confirmed to be a low-hardness ground area where tensile cracks are observed. Therefore, there is a need for research focused on developing measures to reduce economic and social damage within the domestic context by continuously monitoring indicators of land creep and identifying land creep risks.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.5-16
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• 2007

The monitoring and investigation of underwater landslide help to understand its mechanism, increase the usefuless of design and construction and reduce the losses. This paper presents three high resolution geophysical techniques electrical resisitance, ultrasonic wave reflection imaging, and shear wave tomography conducted to determine the lab-scaled submerged landslide. Electrical resistance profiles of a soil mass obtained by an electrical resistance probe provide detailed information to assess the spatial distribution of the soil mass with milimetric resolution. An ultrasonic wave image obtained by recording the reflections from interfaces of different impedance materials permits detecting layers and landslide with submilimetric resolution. The pixel based image of immersed landslides is created by the inversion of the boundary information achieved from the traveling time of shear waves. The experimental results show that the ultrasonic wave imaging and the electrical resistance can provide complementary information; and their association with S-wave tomography image can produce a 3-D view of the underwater landslide. This study suggests that geophysical techniques may be effective tools for the detection of the underwater landslides and spatial distribution offshore.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.157-168
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• 2023

Image segmentation and supervised classification techniques are widely used to monitor the ground surface using high-resolution remote sensing images. In order to classify various objects, a process of defining a class corresponding to each object and selecting samples belonging to each class is required. Existing methods for extracting class samples should select a sufficient number of samples having similar intensity characteristics for each class. This process depends on the user's visual identification and takes a lot of time. Representative samples of the class extracted are likely to vary depending on the user, and as a result, the classification performance is greatly affected by the class sample extraction result. In this study, we propose an image classification technique that minimizes user intervention when extracting class samples by applying the histogram back-projection technique and has consistent intensity characteristics of samples belonging to classes. The proposed classification technique using histogram back-projection showed improved classification accuracy in both the experiment using hue subchannels of the hue saturation value transformed image from Compact Advanced Satellite 500-1 imagery and the experiment using the original image compared to the technique that did not use histogram back-projection.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.549-560
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• 2015

With the global scale impact of atmospheric $CO_2$ in global warming and climate system, it is necessary to monitor the $CO_2$ concentration continuously on a global scale, where satellite remote sensing has played a significant role recently. In this study, global monthly $CO_2$ concentrations obtained by satellite remote sensing were compared with ground-based measurements at Anmyeon-do and Gosan Korean Global Atmosphere Watch Center. Atmospheric $CO_2$ concentration has increased from 371.87 ppm in January 1999 to 405.50 ppm in December 2013 at Anmyeon-do station (KMA, 2013). Comparison of the continuous measurements by flask air sampling at Anmyeon-do shows the same trend and seasonal variations with those of global monthly mean dataset. Nevertheless, the trends of $CO_2$ over Northeast Asia showed the higher than those of global and the trends also changes with different slope. $CO_2$ products derived from Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS) were compared with ground-based measurement at Anmyeon-do. The monthly mean values of GOSAT and AIRS data are systemically lower than those obtained at Anmyeon-do, however, the seasonal cycle of satellite products present the similar trend with values of global and Anmyeon-do. The accuracy of $CO_2$ products from GOSAT and AIRS were evaluated statistically for two years from January 2011 to December 2012. GOSAT showed good correlation with the correlation coefficient, RMSD and bias of 0.947, 5.610 and -5.280 to ground-based measurements respectively, while AIRS showed reasonable comparison with 0.737, 8.574 and -7.316 at Anmyeon-do station, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.2
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• pp.144-159
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• 2006

Hydrological models are tools intended to realistically represent the basin's complex system in which hydrological characteristics result from a number of physical, vegetative, climatic, and anthropomorphic factors. Spatially distributed hydrological models were first developed in the 1960s, Remote sensing(RS) data and Geographical Information System(GIS) play a rapidly increasing role in the field of hydrology and water resources development. Although very few remotely sensed data can applied in hydrology, such information is of great. One of the greatest advantage of using RS data for hydrological modeling and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful model analysis, prediction and validation. In this paper, SLURP model is selected as semi-distributed hydrological model and MODIS Leaf Area Index(LAI), Normalized Difference Vegetation Index(NDVI) as Remote sensing input data to hydrological modeling of Kyung An-chen basin. The outlet of the Kyung An stage site was simulated, We evaluated two RS data, based on ability of SLURP model to simulate daily streamflows, and How the two RS data influence the sensitivity of simulated Evapotranspiration.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1479-1487
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• 2013

The river levee collapse and flood damages are dramatically increased due to the floods which caused by abnormal weather nowadays. The counterplan like TDR(Time Domain Reflectometry) river levee leaking exploration technique is needed to that levee failure causes of levee failure such as levee failure by penetration, piping, inadequate levee materials selection, poor compaction are almost 52% of the failure. This research practiced various comparing experiments of existing TDR(probe and tube types) and developing CAP type TDR to evaluate acrylic small CAP mould and low-cost TDR levee leaking monitoring system which was used probe type TDR. As the result, evaluated TDR system had 20cm critical exploration performance which was a leaking exploration performance, The functional ratio of TDR exploration sensitivity of dry density was sensitive more than 3 times than dry density, and weathered granite soil foundation water contents(w)-dielectric constant(${\epsilon}$) corelation formula was suggested to measure functional ratio on developing cap type TDR system.