Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and flooded area caused by the collapse of the Xe-Pian Xe-Namnoy Dam in Laos on 23 July 2018, using Sentinel-1 ground range detected (GRD) images. The collapse of this dam gained worldwide attention and led to a large number of casualties at least 98 people, as well as enormous economic losses. Thus, it is worth noting that this study quantitatively analyzed changes in both the Hinlat area, which was flooded, and the Xe-Namnoy reservoir. This study aims to suggest a practical method of change detection which is to simply compute flood extent and water volume in rapidly analysis. At first, a $\mathsf{\alpha }$ -stable distribution was fitted to intensity histogram for removing the non-water-affected pixels. This fitting differs from other typical histogram fitting methods, which is applicable to histograms with two peaks, as it can be applied to histograms with not only two peaks but also one peak. Next, another type of threshold based on digital elevation model (DEM) data was used to correct for residual noise, such as speckle noise. The results revealed that about 2.2 $\times$ 10⁸ m³ water overflowed from the Xe-Namnoy reservoir, and a flooded area of about 28.1 km³ was detected in the Hinlat area shortly after the dam collapse. Furthermore, the water quantity and flooded area decreased in both study areas over time. Because only SAR GRD images were used in this study for rapid change detection, it is possible that more accurate results could be obtained using other available data, such as optical images with high spatial resolution like KOMPSAT-3, and in-situ data collected at the same time.

Yunjee Kim, Jinsoo Kim, Ki-mook Kang. Korean J. Remote Sens. 2024;40:1141-8. https://doi.org/10.7780/kjrs.2024.40.6.1.21

Aquaculture facilities are not only an important component of fisheries and local economies, but they also have significant economic and environmental impacts that require efficient and sustainable management. Additionally, knowing the exact locations of aquaculture facilities is crucial for operating ships in nearshore areas, as their presence significantly affects the safe navigation of vessels. However, current aquaculture facility data suffer from slow update cycles due to reliance on field surveys and data processing, and their low spatial resolution does not meet the accuracy requirements in the field. To address this issue, this study takes a first step toward updating aquaculture facility data in quasi-real time using satellite imagery. Specifically, we evaluated the spatial agreement between detected aquaculture facility data and existing data based on Sentinel-1 satellite imagery. While many previous studies on aquaculture facility detection have utilized optical satellites, this study aims to verify the detectability of aquaculture facilities using SAR (Synthetic Aperture Radar) imagery, which can be acquired regardless of weather conditions or time of day. The aquaculture facility data provided by the Korea Hydrographic and Oceanographic Agency is available in both polygon and point formats, with the last update date being December 19, 2024. Accordingly, we analyzed Sentinel-1 data acquired around the same time (December 20, 2024) and compared it with the polygon data. Our analysis revealed significant discrepancies between the two datasets. These findings highlight the need to update current aquaculture facility data and suggest that satellite imagery, with its ability to regularly cover broad areas, could be employed to improve the accuracy and timeliness of aquaculture data updates. This confirms the potential value and utility of satellite imagery as an effective tool for managing aquaculture facilities.

Sanghyeon Park, Mingi Jo, Yunjee Kim, Pyo-Woong Son. Korean J. Remote Sens. 2025;41:581-92. https://doi.org/10.7780/kjrs.2025.41.3.7

Yunjee Kim, Joon Hyo Rhee, Deuk Jae Cho, Pyo-Woong Son. Korean J. Remote Sens. 2025;41:65-72. https://doi.org/10.7780/kjrs.2025.41.1.6