• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.5-24
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• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.715-727
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• 2023

To provide detailed and appropriate meteorological information in mountainous areas, the Korea Forest Service has established an Automatic Mountain Meteorology Observation Station (AMOS) network in major mountainous regions since 2012, and 464 stations are currently operated. In this study, we proposed an optimal kriging technique with lapse rate correction to produce gridded temperature data suitable for Korean forests using AMOS point observations. First, the outliers of the AMOS temperature data were removed through statistical processing. Then, an optimized theoretical variogram, which best approximates the empirical variogram, was derived to perform the optimal kriging with lapse rate correction. A 500-meter resolution Kriging map for temperature was created to reflect the elevation variations in Korean mountainous terrain. A blind evaluation of the method using a spatially unbiased validation sample showed a correlation coefficient of 0.899 to 0.953 and an error of 0.933 to 1.230℃, indicating a slight accuracy improvement compared to regular kriging without lapse rate correction. However, the critical advantage of the proposed method is that it can appropriately represent the complex terrain of Korean forests, such as local variations in mountainous areas and coastal forests in Gangwon province and topographical differences in Jirisan and Naejangsan and their surrounding forests.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.