The COVID-19 pandemic was caused by a highly contagious coronavirus that has triggered worldwide control actions such as social distancing and lockdowns. COVID-19 control actions have resulted in improved air quality locally and around the world in the short-term by limiting human activity. We analyzed the impacts of social distancing and transboundary pollutants on air quality changes using open data and examined the corresponding health benefits focusing on two domestic cities (Seoul and Daegu) in Korea where the spread of coronavirus was severe. During the COVID-19 pandemic, PM_2.5, PM₁₀, and NO₂ concentrations decreased significantly by 31%, 61%, and 33%, respectively, compared to the previous three years. In particular, the PM_2.5/PM₁₀ ratio fell 24.5% after the implementation of social distancing, suggesting a decrease in anthropogenic emissions. Moreover, we found that the air quality index (AQI) also improved significantly, with a focus on reducing exposure to sensitive groups. In Seoul and Daegu, improved air quality prevented 250 and 78 premature deaths, and health costs were USD 884 million and USD 278 million, respectively. On the other hand, health loss due to COVID-19 deaths was in sharp contrast to USD 7.1 million and USD 543.6 million. Our findings indicate a significant association between COVID-19 prevalence patterns and health outcomes.

The IPCC emphasizes the role of forests in the sequestration of greenhouse gases, a significant cause of climate change. Accordingly, it shows the importance of predicting changes in forests due to climate change, evaluating them to reduce vulnerability under adaptive capacity, and finding ways to find climate resilient development pathways. In this study, the KO-G-Dynamic model, a Korean growth model, was linked with the frameworks of AR5 and 6 to assess risk dynamics in the forest growth sector. At this time, the sensitivity is a variability due to the reduction in forest growth, the exposure is the forest as an object, the hazard is climate change, the adaptive capacity is forest management, and the vulnerability is a mechanism that sensitivity could not be adjusted according to adaptive capacity. The risk was assessed by ranking overall risks derived from the process of vulnerability generated by the interaction of the above factors. As a result, the current forests in Korea are age class imbalanced, and the effects of distribution are centered on fast-growing tree species. If climate change and overprotection continue, the vulnerable area expands as sensitivity increases, since the total growth reduces due to increasing over-matured forests. From the regional-based analysis, Gangwon-do and Gyeongsangnam-do mostly consist of the higher V age class, the ratio of ‘very high’ risk grade was high and the area of ‘high’ risk grade changed rapidly. However, after applying forest management scenarios of adaptive capacity such as harvesting, reforestation, and thinning based on Republic of Korea’s forest management policy, the ratio of ‘Low’ risk grades increased according to the reduction of vulnerability areas. Therefore, forest management can act as an important factor to reduce the risk of forest growth in response to climate change.

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.

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 exert a significant impact on Earth's ecological systems, resulting in consequences such as deforestation, habitat degradation, and adverse effects on environmental, economic, and social domains. The restoration of areas affected by forest fires demands substantial time and effort to return them to their original state. Proactive identification of areas prone to forest fires is crucial for minimizing the damage caused by such incidents. In this study, a forest fire diagnostic model was developed to enhance the precision of forest fire risk predictions. The model utilized remote sensing data and human activity maps. To gauge the dryness of the land surface, the Vegetation Temperature Condition Index (VTCI) was employed, and density maps of roads, buildings, and cropland were incorporated for the human activity maps. The algorithm of the model was based on the Random Forest classifier, and it was trained on forest fire occurrence data from 2016 to 2020 across South Korea. To assess the actual performance of forest fire forecasting, short-term forecasts for a 3-day period were conducted from February to May 2023. The model successfully predicted 80% of forest fires during this evaluation period.