The extreme rainfall events associated with climate change trigger landslides. Approximately 60% of South Korea comprises mountainous terrain, with steep slopes rendering it particularly prone to landslides. Despite the implementation of early warning systems by the Korea Forest Service (KFS), landslide damage remains substantial, with approximately 2345 hectares affected over the past 5 years, resulting in severe human and economic costs. The current 24-h early warning system, based on Tier 3 administrative division (Town), faces challenges in accurately identifying high-susceptibility landslide areas. Thus, a daily landslide susceptibility model that integrates landslide-associated conditioning factors with meteorological, topographic, and environmental data was designed to assess landslide susceptibility with a spatial resolution of 100 m. Using AutoML, we identified Random Forest as the optimal model for predicting landslide susceptibility. Training the model with landslide data from 2016 to 2022 resulted in an accuracy of 0.93, AUC of 0.98, and F- 1 score of 0.98. A kappa value of 0.85 indicated the effective classification of past landslides using testing data. Location-based validation using 2023 occurrences revealed highly susceptible classifications for 88% of 43 landslides, while spatial scale-based hazard assessment using observed data indicated high hazard for 96% of 607 landslides in Tiers 3 and 4 (Township). Weather forecasting was also found to affect accuracy, with 76% accuracy for forecasts made at 5:00 PM and 41% for forecasts made at 8:00 AM. It was confirmed that further calibration of forecasting data can enhance the performance of the susceptibility model. The designed process thus enhances landslide prevention and preparedness on both local and regional scales, offering a crucial tool for mitigating the impact of landslides in South Korea.

The integration of remote sensing and state-of-the-art deep learning models has enabled the generation of highly accurate semantic segmentation maps to serve the agricultural sector, for which continuous land monitoring is required. However, despite their wide presence in the research field, only a few such products are used in on-site decision-making processes. This is due to their incompatibility with existing datasets that are at the core of current operating processes. In this study, paddy rice mapping in South Korea was examined to determine whether it produces qualified products that can complement on-site surveys and simultaneously be compatible with existing domestic datasets. Cases of early predictions for timely rice supply control were examined using a recurrent U-Net architecture with diverse applications: chronological batch training (CBT), time-inversed padding (TIP), and super-resolution (SR). In addition, the paddy area was confirmed using diverse datasets by standardizing its spatial extent in the definition of each data manual and calibrating the levee error, which was considered a major source of incompatibility. The robustness of the recurrent U-Net in early predictions dramatically increased upon CBT and TIP, recording an F1 score of over 0.75 on July 10, when the on-site survey was performed; meanwhile, the best performance score was 0.81 at the end of the growing period. SR enhanced the spatial details of rice mapping near the levee area, which had an estimated width of 60 cm; however, the area was more similar to that in existing datasets when it was calibrated with the predicted probability of the levee ratio rather than SR. The calibration was scalable from the patch to city level, with the paddy area at both levels recording high R² for the farm map and statistics (0.99 for both the farm map and statistics at the city level, and 0.93 and 0.95, respectively, at the patch level). This study shows that remote-sensing-based paddy rice mapping can produce not only accurate but also timely and compatible predictions by integrating deep learning applications. The results show that the predictions are compatible with domestic datasets as much as they are with each other; therefore, remote-sensing approaches are expected to be more actively and practically integrated into agricultural decision-making processes.

Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R²] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R² = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage.

Forest fires have devastating effects on extensive forest areas, compromising vital ecological services such as air purification, water conservation, and recreational opportunities, thus posing a significant socioeconomic threat. Furthermore, the risk of forest fires is steadily increasing due to climate change. The most effective method for mitigating forest fire risk is proactive prevention before forest fires can occur by identifying high-risk areas based on land surface conditions. This study aimed to develop a machine learning-based forest fire diagnostic model designed for Republic of Korea, considering both satellite-derived land surface data and anthropogenic factors. For the remote sensing data, VTCI (Vegetation Temperature Condition Index) was used to reflect the land surface dryness. In addition, fire activity maps for buildings, roads and cropland were used to consider the influence of human activities. The forest fire diagnostic model yielded an accuracy of 0.89, demonstrating its effectiveness in predicting forest fire risk. To validate the effectiveness of the model, 92 short-term forest fire risk forecast maps were generated from March to May 2023 with real-time data on forest fire occurrences collected for verification. The results showed that 73% of forest fires were accurately classified within high-risk zones, confirming the operational accuracy of the model. Through the forest fire diagnostic model, we have presented the impact relationships of meteorological, topographical, and environmental data, as well as the dryness index based on satellite images and anthropogenic factors, on forest fire occurrence. Additionally, we have demonstrated the potential uses of surface condition data.

Central Asian countries, which are included the Mid-Latitude Region (MLR), need to develop regional adaptive strategies for reducing Sand and Dust Storm (SDS)-induced negative damages based on adequate information and data. To overcome current limitation about data and assessment approaches in this region, the macroscale verified methodologies were required. Therefore, this study analyzed environmental conditions based on the SDS impacts and regional differences of SDS sources and receptors to support regional SDS adaptation plans. This study aims to identify environmental conditions based on the phased SDS impact and regional differences of SDS source and receptor to support regional adaptation plans in MLR. The Normalized Difference Vegetation Index (NDVI), Aridity Index (AI), and SDS frequency were calculated based on satellite images and observed meteorological data. The relationship among SDS frequency, vegetation, and dryness was determined by performing statistical analysis. In order to reflect phased SDS impact and regional differences, SDS frequency was classified into five classes, and representative study areas were selected by dividing source and receptor in Central Asia and East Asia. The spatial analysis was performed to characterize the effect of phased SDS impact and regional distribution differences pattern of NDVI and AI. The result revealed that vegetation condition was negatively correlated with the SDS frequency, while dryness and the SDS frequency were positively correlated. In particular, the range of dryness and vegetation was related to the SDS frequency class and regional difference based on spatial analysis. Overall, the Aral Sea and the Caspian Sea can be considered as an active source of SDS in Central Asia, and the regions were likely to expand into potential SDS risk areas compared to East Asia. This study presents the possibility of potential SDS risk area using continuously monitored vegetation and dryness index, and aids in decision-making which prioritizes vegetation restoration to prevent SDS damages with the macrolevel approach in the MLR perspective.