Rapid and reliable assessment of wildfire spread is critical for minimizing ecological and socioeconomic damage. Polar-orbiting satellites have high spatial resolution but low temporal resolution, limiting their ability to capture the rapid dynamics of wildfire expansion. To address this limitation, we propose a near-real-time framework for estimating wildfire extent using high-frequency (2-minute) observations from the GEO-KOMPSAT-2A (GK2A) geostationary satellite, employing Visible Infrared Imaging Radiometer Suite (VIIRS) VNP14IMG products as reference data. A +/- 10-minute temporal averaging scheme was introduced to mitigate single-observation noise and enhance detection stability. Model performance was evaluated across six large wildfires in South Korea, with negative samples down-sampled at a ratio of 1:5 relative to positive fire pixels. In repeated random-split (7:3) and region hold-out evaluations, the Extreme Gradient Boosting (XGBoost) model achieved a mean F1 score of 0.958, slightly higher than that obtained by Random Forest (RF; 0.950). For the Uljin (2022) wildfire, XGBoost achieved an F1 score of 0.948, whereas RF achieved a score of 0.741. The superiority of XGBoost was further confirmed via independent full-pixel validation for the Uiseong (2025) and Uljin (2022) wildfires, obtaining precisions of 0.812 and 0.682, respectively, and F1 scores of 0.729 and 0.699, respectively. For both wildfires, RF yielded higher recall but generated a greater number of false positives. These differences may be attributed to the inherent characteristics of the models, with XGBoost's gradient-boosting approach emphasizing precision and overall accuracy, and RF tending to favor recall, often at the cost of increased false positives. The timeseries analysis demonstrated that, with +/- 10-minute averaging, wildfire growth can be reliably tracked from approximately 10 minutes after ignition onward at 2-minute intervals. This suggests that GK2A observations can be exploited not only for wildfire detection but also for early-stage monitoring of fire spread, thereby supporting rapid decision-making for resource allocation and initial suppression strategies.

The International Civil Aviation Organization (ICAO) emphasizes the importance of developing technologies to enhance the safety and efficiency of air traffic through trajectory-based operations (TBO). In this context, this study focuses on the NARAE-Weather system, currently under development in Korea, and its core component, the 4D-Wx Application Programming Interface (API) distribution service, to propose an approach for providing aviation weather information to support air traffic operations. The NARAE-Weather system integrates diverse meteorological data to deliver standardized weather forecasts optimized for trajectory based (4DT), regions of interest (ROI), and points of interest (POI), enabling customized aviation operation support. This paper evaluates the service scope and technical feasibility of the 4D-Wx API and outlines a direction for supporting air traffic operations through the provision of multidimensional weather information. Specifically, the study examines the effectiveness of delivering realtime weather information via the API to support trajectory-based operations.

The normalized difference snow index (NDSI) is a key indicator used to identify and map snow-covered areas by normalizing the reflectance difference between visible and shortwave infrared bands detected by satellite sensors. This study analyzed the effects of atmospheric correction on NDSI and snow cover detection characteristics according to land cover types. The study used data from the geostationary satellite (GK-2A/AMI) from November 2022 to April 2023. Comparing top-of-atmosphere (TOA) reflectance-based NDSI (NDSITOA) and top-of-canopy (TOC) reflectance-based NDSI (NDSITOC), NDSITOC generally showed higher values. Time series analysis revealed that the difference between the two NDSI values was relatively high when the snow-covered area was extensive. Comparison with S-NPP/VIIRS snow cover showed that NDSITOC-based snow detection had a higher agreement rate than NDSITOA-based snow detection (NDSITOA 72.36%, NDSITOC 75.88%). Analysis by land cover type showed the highest snow cover detection agreement rate in grasslands and croplands, while forest areas showed the lowest agreement rate. These findings emphasize the importance of atmospheric correction in NDSI-based snow cover detection and confirm the need for a customized approach considering land cover characteristics. This study provides a foundation for offering more reliable snow cover information in various fields such as climate change research, water resource management, aviation weather forecasting, and disaster management.

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization.

In support of the National ATM Reformation and Enhancement Plan (NARAE), a trajectory based aviation weather service is under development through the NARAE-Weather project. Specifically, weather data presented in a standardized digital format facilitates the seamless integration of digital weather data with air traffic information. Thus, this paper introduces an approach that entails structuring numerical model data to integrate aviation weather information and flight trajectory data. The extraction results using structurally transformed data showed superior performance compared to the results extracted from the original data in terms of performance, and this research is poised to enhance the safety and efficiency of airline operations.

Monitoring of post wildfire provides important information for vegetation restoration. In particular, remote sensing data are known to provide useful information necessary for monitoring. However, there are insufficient research results which is monitoring the vegetation recovery using remote sensing data. This study is directed to monitoring post-wildfire vegetation restoration. It proposes a method for monitoring vegetation restoration using Sentinel-2 satellite data by compositing Tasseled Cap linear regression trend in a post wildfire study sites. Although it is a simple visualization technique using satellite images, it was able to confirm the possibility of effective monitoring.

The purpose of this study was to optimize a composite method for the Geostationary Ocean Color Imager (GOCI), which is the first geostationary ocean color sensor in the world. Before interpreting the sensitivity of each composite with ground measurements, we evaluated the accuracy of bidirectional reflectance distribution function (BRDF) performance by comparing modeled surface reflectance from BRDF simulation with GOCI-measured surface reflectance according to composite period. The root mean square error values for modeled and measured surface reflectance showed reasonable accuracy for all of composite days since each BRDF composite period includes at least seven cloud-free angular sampling for all BRDF performances. Also, GOCI-BRDF-adjusted NDVIs with four different composite periods were compared with field-observation NDVI and we interpreted the sensitivity of temporal crop dynamics of GOCI-BRDF-adjusted NDVIs. The results showed that vegetation index seasonal profiles appeared similar to vegetation growth curves in both field observations from crop scans and GOCI normalized difference vegetation index (NDVI) data. Finally, we showed that a 12-day composite period was optimal in terms of BRDF simulation accuracy, surface coverage, and real-time sensitivity.