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.

The role of forests to sequester carbon is considered an important strategy for mitigating climate change and achieving net zero emissions. However, forests in North Korea have continued to be cleared since the 1990s due to the lack of food and energy resources. Deforestation in this country has not been accurately classified nor consistently reported because of the characteristics of small patches. This study precisely determined the area of deforested land in North Korea through the vegetation phenological classification using high-resolution satellite imagery and deep learning algorithms. Effective afforestation target sites in North Korea were identified with priority grade. The U-Net deep learning algorithm and time-series Sentinel-2 satellite images were applied to phenological classification; the results reflected the small patch-like characteristics of deforestation in North Korea. Based on the phenological classification, the land cover of the country was classified with an accuracy of 84.6%; this included 2.6 million ha of unstocked forest and reclaimed forest. Sites for afforestation were prioritized into five grades based on deforested characteristics, altitude and slope. Forest area is expanded and the forest ecosystem is restored through successful afforestation, this may improve the overall ecosystem services in North Korea. In the long term, it will be possible to contribute to carbon neutrality and greenhouse gas reduction on the Korean Peninsula level through optimal afforestation by using these outcomes.

This study investigated a method for directly estimating methane emissions from rice paddy fields at the field level using drone-based time-series vegetation indices at a town scale. Drone optical and spectral images were captured approximately 15 times from April to November to acquire time-series vegetation indices and optical orthoimages. An empirical regression model validated in previous international studies was applied to calculate cumulative methane emissions throughout the rice cultivation process. Methane emissions were estimated using the vegetation index and yield data were used as input variables for each growth phase. Methane emissions from rice paddies showed maximum values of 309 kg CH₄ ha⁻¹, within a 7% range compared to similar studies, and minimum values of 138 kg CH₄ ha⁻¹, with differences ranging from 29% to 58%. The average emissions were calculated at 247 kg CH₄/ha, revealing slightly lower average values but individual field values within a similar range. The results suggest that drone-based remote sensing technology is an efficient and cost-effective alternative to traditional field measurements for greenhouse gas emission assessments. However, adjustments and validations according to rice varieties and local cultivation environments are necessary. Overcoming these limitations can help establish sustainable agricultural management practices and achieve local greenhouse gas reduction targets.

Forest fires destroy millions of forest acres globally, damaging ecosystem services, emitting carbon into the atmosphere, and causing billions of dollars in socio-economic losses, including loss of life. Bhutan, despite its small size, is grappling with forest fires compounded by climate extremes, a remote location, and a lack of preparedness. Under such conditions, it is important to develop policy, technology, and action to respond to forest fires based on scientific research. This study used the Random Forest model (RF) and Shared Socioeconomic Pathways (SSP) approach to create a model for predicting forest fires in Bhutan by considering climate data, spatial data, and forest fire occurrence factors. The RF algorithm was used to analyze the correlations between various input data and predict the change in future forest fire risk in Bhutan. Using SSP scenarios that considered changes due to future population and economic growth, it was possible to predict the risk of forest fires accurately and spatially in Bhutan. The study found that the average forest fire risk was highest during the months of October to January, which are also the dry seasons with low precipitation. However, it also showed high variability across climate change scenarios. SSP scenarios also confirmed the possibility of a significant increase in forest fire risk in the future. The results of this study can be used to support specific policy decisions for forest fire prevention and response in Bhutan and to contribute to the development of forest fire prediction technology in the light of the changing global climate.

Rice cultivation is a significant source of methane emissions, contributing substantially to the agricultural sector's greenhouse gas inventory. In this study, we applied the DeNitrification-DeComposition (DNDC) model to simulate greenhouse gas emissions from rice paddies in South Korea, analyzing spatial and temporal changes under various Shared Socioeconomic Pathways (SSP) climate scenarios. Our results indicate that soil organic carbon is the most influential factor driving emissions, with regions rich in organic carbon showing the highest levels of methane emissions. Under the SSP1-2.6 scenario, which assumes strong climate mitigation efforts, emissions increase slightly in early decades (2030s ~ 2050s) but stabilize in later decades (2050s ~ 2070s). In contrast, the SSP5-8.5 scenario, characterized by minimal climate action, projects a significant rise in emissions throughout the 21st century. Spatial analysis reveals that regions with high emissions per hectare do not necessarily have the highest total emissions, as total emissions are also influenced by the extent of cultivated land. Traditional rice-growing regions along the west coast, despite lower per-hectare emissions, show higher total emissions due to their larger cultivation areas. These findings underscore the need for targeted mitigation strategies that address both emission intensity and scale, highlighting the importance of soil organic carbon management. Our study offers valuable insights for sustainable agricultural policies aimed at reducing greenhouse gas emissions while maintaining productivity in the face of climate change.