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.

Accurate estimation of river discharge (Q) globally is essential for water resources management, including flood control and drought management. However, the number of stream gauges globally used to calculate Q is decreasing. Additionally, estimating Q of transboundary rivers or rivers with unique hydrological characteristics, such as the Mekong River, is challenging using traditional hydrological methods. The most representative methods for estimating Q have been the empirical power function using at-a-station hydraulic geometry (AHG) proposed by Leopold and Maddock (1953) and the method using Manning's equation proposed by Manning (1889). Recently, Kim et al. (2019a; 2019b; 2021) improved the accuracy of Q estimation in the Congo and Mekong River Basins using ensemble learning regression for estimating Q (ELQ). However, despite ELQ's superior performance, its mathematical and hydrological framework has not been studied in detail. This review study analyzed relevant papers to understand the mathematical and hydrological significance of ELQ, which differentiates it from existing Q prediction techniques. We also analyzed cases cited in other international papers. Through this analysis, we expect to draw the contribution of the ELQ method for estimating Q to remote sensing hydrology domestically and internationally.

The mid-wave infrared (MIR) sensor onboard KOMPSAT-3A captures thermal imagery within the 3.3–5.2 μm spectral range, enabling detailed thermal analysis under both daytime and nighttime conditions. These images are extensively utilized in various applications, including urban heat island monitoring, drought assessment, environmental analysis, and thermal anomaly detection. However, temporal discrepancies between daytime and nighttime acquisitions frequently introduce radiometric inconsistencies and relative geometric dissimilarities, which pose significant challenges for accurate image registration. To address these issues, this study proposes a two-stage image registration framework that integrates radiometric normalization and feature-based alignment. In the first stage, gamma correction and Box-Cox transformation are employed to mitigate radiometric discrepancies, thereby improving feature reliability. In the second stage, the robust invariant feature transform (RIFT) algorithm is enhanced by incorporating Gaussian weighting, which refines the phase congruency (PC) map, leading to more robust feature extraction under varying conditions. Feature correspondences are filtered using the random sample consensus (RANSAC) algorithm to remove outliers, ensuring reliable matching. An affine transformation model is estimated using inlier points to align nighttime MIR imagery with daytime reference. The proposed framework, integrating radiometric and feature contrast enhancement methods, was evaluated across four distinct geographic sites. Experimental results demonstrated significant improvements over conventional methods, reducing relative geometric dissimilarities and enhancing image registration accuracy.

Wildfires have grown in scale and intensity over recent decades, profoundly impacting global carbon cycles. For instance, the 2019–2020 Australian wildfire burned 18.6 million hectares of forest and released 715 million tonnes of carbon dioxide. Assessing vegetation recovery after wildfire at such scales is challenging due to conventional satellite products’ spatial and temporal resolution limitations. This study addresses these limitations by fusing two datasets, MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 product and Landsat 8/9 Nadir Bidirectional Reflectance Distribution (NBAR) using an enhanced Flexible Spatiotemporal Data Fusion (FSDAF) algorithm that incorporates sub-pixel class fraction change information (SFSDAF). Before the fusion process, preprocessing involved detecting cloudcontaminated pixels using the Function of Mask (Fmask) algorithm and the Bidirectional Reflectance Distribution Function (BRDF) correction of Landsat. After the fusion process, Neighborhood Similar Pixel Interpolator (NSPI) was employed for the gap-filling process. The fusion images produced high-resolution spatiotemporal vegetation indices with a 30-meter spatial resolution and daily temporal coverage. By comparing these datasets with flux tower measurements, the study examined vegetation recovery in Tumbarumba, southeastern Australia. Findings revealed that the fusion dataset captured fine-scale spatial variability and temporal dynamics, providing detailed insights into localized recovery patterns and seasonal changes. The results highlighted rapid vegetation greenness recovery, though flux tower data indicated slower photosynthesis and carbon sequestration recovery. This study emphasizes the importance of highresolution imagery for accurate recovery monitoring and highlights the necessity of integrating multiple datasets for a comprehensive understanding.

Geostationary Ocean Color Imager-II (GOCI-II), an ocean color satellite sensor that continuously observes the seas and coasts around the Korean Peninsula, is widely used to analyze and study not only short-term changes in the ocean but also mid- to long-term changes in the marine environment. However, optical satellite observation has limitations in that missing values exist due to weather conditions such as clouds. These missing values are the biggest obstacle to continuously monitoring changes in the marine environment and predicting future trends. In this study, a robust missing value restoration model was developed to restore the missing values of GOCI-II chlorophyll-a concentration data. To achieve this, missing values were simulated by generating random clouds based on actual cloud shapes. During the restoration process, missing values were estimated based on the characteristics of pixels spatially adjacent to those where the missing data occurred. In addition, to effectively utilize the spatio-temporal features, which are a key advantage of GOCI-II images, a model combining a Convolutional Neural Network and a Bidirectional Long Short-Term Memory structure was proposed. The proposed model showed a coefficient of determination greater than 0.90 and a mean square error of approximately 0.25 mg/m³. These results were obtained by evaluating the stability and accuracy of the restored data based on cloud shapes and the ratio of missing values. This study demonstrates the potential for not only monitoring the marine environment but also predicting future changes by securing continuous spatio-temporal data through missing value restoration.

This study proposes a method to detect Global Navigation Satellite System (GNSS) interference signals by analyzing the noise levels in the GNSS L1 frequency band observed by low Earth orbit satellites, utilizing data from the Cyclone GNSS (CYGNSS) Level 1 Science Data Record Version 3.2. Using Delay Doppler Map (DDM) noise floor data from the Pacific Ocean, where GNSS interference signals are theoretically absent, the baseline noise distribution was defined. This baseline was then compared against ddm_noise_floor data from regions such as the Middle East and areas near the Korean Peninsula, where Global Positioning System (GPS) disruptions were reported in early November. The results confirmed that areas experiencing GNSS interference showed significantly higher noise levels than those observed near the Pacific. This study demonstrates the feasibility of developing technologies for GNSS interference detection using low Earth orbit satellite data, bypassing reliance on traditional GNSS ground infrastructure. Such methods are particularly promising for effectively monitoring vast areas in maritime environments where ground-based infrastructure is impractical.

Early and accurate monitoring of crop growth is crucial for precision agriculture. This study developed and evaluated a novel framework for precision monitoring of early-stage cabbage (Brassica oleracea var. capitata) using Unmanned Aerial Vehicle (UAV) multispectral imagery and a modified Faster Region-based Convolutional Neural Network (Faster R-CNN). A DJI Matrice 300 RTK UAV equipped with RGB and RedEdge-MX multispectral sensors acquired high-resolution imagery of a cabbage testbed in Goesan-gun, South Korea. A Faster R-CNN model, incorporating a ResNet-50 backbone and Feature Pyramid Network (FPN), was trained to detect individual cabbage plants. A two-stage data augmentation approach was employed: initial training with bounding box annotations, followed by refinement using 15cm buffer zones around predicted plant centroids. The model achieved a mean Average Precision (mAP) of 0.900 on an independent test set, outperforming YOLOv5s and SSD models. Two object delineation methods were compared: the 15cm buffer zones and an Excess Green (ExG)-based dissolve operation. The ExG-based dissolve method demonstrated superior performance in delineating healthy cabbage vegetation, yielding a significantly higher mean Normalized Difference Vegetation Index (NDVI) (0.470) compared to the buffer method (0.300) and a lower proportion of low NDVI values (12.54% vs. 49.38%). These results highlight the potential of integrating UAV-based multispectral imaging with a modified Faster R-CNN and an ExG-based dissolve approach for accurate and efficient early-stage cabbage monitoring, facilitating data-driven decision-making in precision agriculture.

Unmanned Aerial Vehicle (UAV) orthomosaics are widely used in various fields, including construction, environmental monitoring, and real estate, and their quality is influenced by the accuracy of interior orientation. In this study, the modes were divided into “All mode” and “Part mode” based on the interior orientation parameter settings, and lens distortion was compared between the two modes. Additionally, the effects of the number of Ground Control Points (GCPs) and their positional accuracy on the positional accuracy of UAV-based orthomosaics were evaluated for each mode. The Part mode, which applies only a subset of interior orientation parameters, exhibited greater LatLon and XY errors compared to the All mode, which applies all parameters. Using custom Python scripts, lens distortion was compared between the two modes, and the image coordinate deviations were found to be 0.160±1.347 pixels in u (image x) and 0.076±0.991 pixels in v (image y), both of which were below 2 pixels. As the number of GCPs decreased, both modes exhibited an increasing trend in GCP positional error. In terms of GCP and CP pixel errors, the All mode demonstrated a lower and more consistent error compared to the Part mode, as it was less sensitive to changes in the number of GCPs. The addition of a random offset to the GCP coordinates to vary the GCP positional accuracy showed that as the magnitude of the added offset increased, the GCP positional error exhibited a linear increase. These findings suggest that the setting of interior orientation parameters and GCP management are critical factors in determining the accuracy of UAV orthomosaics. This study is expected to provide valuable foundational data for analyzing error factors in the UAV-based orthomosaic creation process, which can be utilized in both practical and research settings.

Photosynthetically available radiation (PAR), used in primary productivity research, is calculated from the amount of solar radiation reaching the sea surface in the 400-700 nm wavelength band. This study developed and validated a Geostationary Ocean Color Imager II (GOCI-II)-based PAR algorithm to increase the utilization of GOCI-II and contribute to the production of accurate input data for primary productivity research in the waters around the Korean Peninsula. The algorithms used in MODIS and GOCI PAR studies were improved to suit the spatiotemporal and spectral resolution of GOCI-II, and daily PAR was calculated from GOCI-II hourly PAR. As a result of examining the hourly and daily PAR images on April 27, 2024, it was found that the daily PAR distribution in areas heavily affected by clouds was about 30% lower than in clear atmosphere areas. As a result of validation using ECO-PAR sensor field observation data installed at the Socheong-cho Ocean Research Station, GOCI-II daily PAR showed an underestimation tendency with 11.10% root-mean-square error (RMSE) and 9.89% mean bias error (MBE) compared to field observation data, but the determination coefficient (R²) value showed a high linear correlation of 0.98. As a result of comparing GOCI and GOCI-II daily PAR between sensors in the GOCI-II slot 4 areas on March 10, 11, and 14, 2021, after randomly selecting areas A, B, and C, high consistency was shown in areas A and B with little cloud cover, but a maximum RMSE of 6.24% was shown in area C, which was heavily affected by clouds. In the future, we plan to research to improve the accuracy of GOCI-II PAR through algorithm verification and correction by adding field observation data. The development and verification of the GOCI-II PAR algorithm are expected to help monitor the marine environment and research activities in the waters around the Korean Peninsula, which have been continued from GOCI and contribute to research on responding to changes through long-term observation data.

The Amazon plays a crucial role in mitigating global warming and preserving biodiversity, which are vital for the Earth's environment. However, deforestation has been ongoing for an extended period. Particularly, the vast scale of the region poses challenges in accurately identifying deforested areas, highlighting the growing importance of leveraging satellite information to prevent further damage and develop effective restoration plans. This study proposes a deep learning-based method to automatically generate high-resolution deforestation labels using low-resolution satellite imagery and existing deforestation label data. The proposed method initially performs primary training using low-resolution images and labeled data. Through this process, pseudo-label data are generated and used for iterative learning, ultimately improving the accuracy of deforestation area labeling on high-resolution satellite images. The output of this research can contribute to generating highly accurate high-resolution labeling data, even for satellite images without prior deforestation labels. This data can be utilized for detailed analysis of deforested areas and the development of precise restoration strategies.

An industrial heat island (IHI) refers to the phenomenon in which industrial complexes exhibit higher temperatures than their surrounding areas. It is classified as a subtype of the urban heat island (UHI). Most IHI studies have focused on the local scale (0.1–10 km), limiting their integration with broader UHI research. Additionally, the factors that influence UHI within industrial complexes remain understudied. To address these gaps, this study proposes a geospatial framework to analyze IHI, strengthen its connection with UHI research, and identify key industrial factors. The primary dataset for this framework is land surface temperature (LST) obtained from Landsat-8 imagery. First, a hierarchical approach examines IHI at the mesoscale (10–100 km), using a chi-square test to determine its phenomenological independence at the city level. If IHI is present at an adequate scale, the LST profile method is applied to measure IHI extent and intensity at the local scale. Second, geographically weighted regression (GWR) quantifies the influence of industrial factors, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), digital elevation model (DEM), normalized difference built-up index (NDBI), soil-adjusted vegetation index (SAVI), automated water extraction index (AWEI), and workers. To validate the feasibility of this framework, it was applied to Incheon, South Korea, a city with diverse and aging industrial complexes. As a result, mesoscale analysis confirmed a significant association (p<0.05) between industrial complexes and UHI across all seasons. The local scale analysis indicated that IHI intensity was highest in summer but weakened in fall and winter, diverging from conventional UHI patterns. In addition, GWR results demonstrated varying impacts of industrial factors across complexes. The most influential variable was the industrial activity factor (SO₂ and NO₂). SO₂ exhibited a strong positive correlation with IHI, while NO₂ exhibited a negative correlation. For the industrial space factor, DEM indicated that lower elevations corresponded to higher IHI intensity. SAVI exhibited a moderate negative correlation, but its influence varied depending on vegetation type. However, NDBI and AWEI produced results that contradicted the trends identified in numerous UHI studies, likely due to coastal influences diminishing NDBI's effect and AWEI reflecting intensified IHI due to industrial wastewaterinduced water quality degradation. In the industrial workforce factor, workers also showed a strong positive correlation. A comparison of GWR and OLS models confirmed GWR's superior performance, with higher adjusted R² (0.9126 vs. 0.3677) and lower AICc values (111,146.4 vs. 171,605.4). In conclusion, this study establishes a scalable geospatial framework for IHI analysis, reinforcing its connection with UHI research. The findings underscore the need for tailored environmental measures addressing the unique characteristics of each industrial complex. Future studies should refine the framework by quantitatively connecting the results between the two scales and incorporating simulation models.

Synthetic aperture radar (SAR) image restoration plays a crucial role in enhancing spatial resolution and suppressing noise, enabling various applications such as terrain analysis, disaster monitoring, and military reconnaissance. However, SAR images exhibit speckle noise, strong directional characteristics, and multiple scattering effects, making them challenging to process with conventional image restoration techniques. To address these challenges, convolutional neural network (CNN)-based models such as superresolution convolutional neural network (SRCNN), very deep super-resolution (VDSR), and residual channel attention network (RCAN) have been widely employed. While these models effectively capture local features, they are limited in modeling the complex structural characteristics and long-range dependencies inherent in SAR imagery, leading to suboptimal restoration of fine details. In this study, we propose Enhanced SwinIR, an improved Transformer-based model designed to overcome these limitations. The proposed model integrates the Combined Attention Mechanism, which fuses window-based local attention with global attention and incorporates the Edge Enhance Residual Block, which employs a learnable Sobel filter to improve edge preservation. Experimental evaluations using SAR images from the ERS-1 and ERS-2 satellites demonstrate that the Enhanced SwinIR model outperforms CNN-based models (SRCNN, VDSR, RCAN), achieving a peak signal-to-noise ratio (PSNR) of 23.413 dB, an increase of 0.956 dB compared to the CNN average of 22.457 dB. Additionally, it achieves a structural similarity index (SSIM) of 0.912, surpassing the CNN average of 0.893 by 0.019, and a speckle suppression index (SSI) of 8.653, an improvement of 0.830 over the CNN average of 7.823. Furthermore, compared to the original SwinIR, Enhanced SwinIR improves SSIM by 0.003 and SSI by 0.067. These results confirm that Enhanced SwinIR significantly enhances SAR image restoration performance, particularly in terms of structural similarity and speckle noise suppression.

The reduction of natural cover and the increase in artificial surface caused by urbanization lead to surface urban heat island. New towns are considered an appropriate study site to analyze surface urban heat island affected by land cover transformations, due to their development characteristics of rapid urbanization. This study aims to monitor surface urban heat island intensity (SUHII) resulting from land cover changes. We study Magok New Town as a representative urban development case in Seoul, South Korea. SUHII is defined as the difference in the mean land surface temperature (LST) between developed and undeveloped areas, with LST measured using Landsat satellite imagery. We build long-term data for the observation period from April 16, 2003, to October 15, 2023, considering the development period of Magok New Town. We gather 101 high-quality data points from approximately 20.5 years to enable detailed and comprehensive observation of land surface temperature changes. We compare changes in Magok’s SUHII (experimental group) with those of four surrounding areas, two undeveloped and two previously developed areas (control groups). The results show that the SUHII of Magok New Town has converged to resemble the surrounding developed areas as urban development has progressed. In particular, the two developed areas in Gangseo-gu exhibit relatively high SUHII variability throughout the observation period, whereas the SUHII variability in Magok is observed to gradually increase over time. The cause of this phenomenon is that SUHII typically shows higher values in the summer and lower values in the winter, and urban areas, the seasonal variation in SUHII is significantly larger. Therefore, as urban development progressed in Magok, the seasonal variation in SUHII widened, deteriorating Magok’s urban microclimate. The spatial distribution of SUHII is relatively high in airports, transportation facilities, and industrial, and construction areas due to the influence of surface environment and urban activity characteristics. In the residential area of Magok, SUHII is lower than in Gayang, due to the underground construction of transportation facilities and the design of parks. The green area in Magok Central Park offsets the low SUHII during the Cool and Cold seasons, but its effect is limited during the Hot and Warm seasons.

Current global climate change increases water-related disasters like floods, making the Korean Peninsula vulnerable to typhoons and rainfall damage. In North Korea, flood analysis relies on remote sensing due to poor infrastructure and observation systems. Synthetic aperture radar (SAR) imagery, unaffected by clouds, is effective for flood analysis. This study used Sentinel-1 SAR image differencing to assess 2023 flooding in North Korea. After differencing images from North and South Korea, random forest classification identified flooded areas. The accuracy of this method was confirmed by its kappa value of 0.9506, demonstrating its efficacy in the remote sensing of flood damage in areas with limited access, free from meteorological interference. The analysis results indicate substantial flood damage at the target sites compared with past cases. Specifically, Typhoon Khanun resulted in a flooded area of 7.8638 km², highlighting the susceptibility of this area to typhoon-related flooding. Additionally, the area flooded due to heavy rain exceeded 1 km², although it was less severe than the flood damage in South Korea during the same period, suggesting the need for further comparative research between North and South Korea. Future research could enhance the quality by combining the use of various satellite systems and more reliable classification maps.

The technology for classifying objects using remote sensing images has gained increasing attention recently with the advent of high-resolution satellite imagery and advancements in deep learning techniques. One of the representative methods for classifying objects is object-based image classification, which involves segmenting the image and using the attributes of the segmented regions to generate classification results at the object level. In urban areas, the presence of various buildings with different colors and shapes, along with numerous shadows, makes it difficult to distinguish objects like water bodies and vegetation from buildings. This paper proposes an object-based image classification method that incorporates the normalized difference water index (NDWI), which is effective for detecting water body characteristics, along with RGB band imagery. The experimental results using images from Compact Advanced Satellite 500-1 demonstrate that the proposed method yields improved results with an overall accuracy of 93.7% and a kappa coefficient of 90.6%, outperforming methods that utilize only the RGB bands, RGB bands combined with NIR bands, and RGB bands combined with the normalized difference vegetation index (NDVI).

The increasing frequency of extreme weather events caused by climate change has highlighted the importance of systematic reservoir management to ensure a stable supply of agricultural water. Dredging, in particular, is one of the key management strategies to prevent reservoir functionality degradation due to sediment accumulation. However, dredging projects are often carried out relying heavily on the subjective judgment and experience of field personnel due to constraints in time and resources. This study proposes a method for objectively prioritizing dredging sites using Sentinel-1 Synthetic Aperture Radar (SAR) imagery. The gradual changes in reservoir capacity, presumably caused by sedimentation or erosion, were estimated by analyzing the slopes of the satellite-derived surface area to field-observed surface area ratio for 10 reservoirs from 2015 to 2023. The results indicated that Dongbu (–2.094e-05 day^–1), Yedang (–1.263e-05 day^–1), and Seongju (–8.051e-06 day^–1) reservoirs experienced the most significant declines in surface area ratio over time, making them the highest-priority sites for dredging. The use of satellite imagery for dredging site selection is expected to support rational decision-making and improve the efficiency of reservoir management.

Accurate and efficient tree type classification in urban forests is crucial for effective management, informed policy decisions, and enhancing urban resilience, particularly with increasing urbanization and climate change. This study developed and evaluated a practical methodology for classifying coniferous and broadleaf trees in the Chungbuk National University Arboretum, South Korea. The study utilized drone-acquired, high-resolution RGB imagery and a Support Vector Machine (SVM) classifier. The workflow encompassed drone image acquisition, concurrent ground truth data collection, image preprocessing, feature extraction (including RGB color bands and Gray-Level Co-occurrence Matrix [GLCM] texture features [TFs]), and SVM model training, optimization, and evaluation. Different SVM kernels (Linear, RBF, Polynomial, Sigmoid) and feature combinations were investigated to optimize model performance, with a specific focus on processing time for practical application. Results indicated that RGB color bands were the primary drivers of accurate classification, while most GLCM TFs provided minimal additional benefit in this specific context. The RBF kernel, with optimized hyperparameters (C=10, γ=0.01), achieved the highest overall accuracy (99%) and F1-score (0.99), while the Linear kernel provided similar accuracy but with a longer processing time. Notably, the drone-based classification significantly outperformed the outdated Korea Forest Service forest map in representing the current forest composition, highlighting the limitations of traditional mapping methods for dynamic urban environments. This research contributes a cost-effective and accurate method for urban forest assessment, demonstrating the value of drone technology and readily available RGB imagery. The entire process, from image acquisition to classification, was completed in approximately 12 hours, showcasing its efficiency. Although this study focused on only two tree types in a single season, the developed methodology shows potential for broader application in classifying a wider range of species and informing management practices across different seasons by considering the phenological stages of trees. The proposed approach provides urban planners and forest managers with a valuable tool for enhancing ecosystem services and improving the quality of life in urban areas. This study underscores the potential of drone technology to revolutionize urban forest monitoring and management practices, paving the way for more sustainable and informed decisionmaking, particularly in rapidly urbanizing regions.

In this study, we mainly used a special observation dataset from vehicle observation-based temperature sensors for a tropical night episode that occurred on August 4-5, 2022. We also use a local data assimilation and prediction system (LDAPS) to understand the influence of determinant meteorological conditions on tropical night intensity (TNI), particularly at the local scale level in Gangneung City. For the purpose of comparing characteristics of anthropogenic heat and TNI, we divided the study area into two road sections (urban and suburban). Interestingly, results demonstrated that strong (~7 m·s^-1) southwesterly winds enhanced TNI up to 4.3°C (the average temperature reaching 29.3°C) in the suburban areas during the last observation period (02:00-02:45 KST). However, we could not suggest a direct correlation between urban heat island intensity (UHII) and TNI. LDAPS showed a large amount of moisture transport (~0.05 m·s^-1) flowing into the study area, along with an increase in moisture flux convergence at 02:00 KST on August 5, 2022. In addition, the analysis of vertical cross-section indicated the formation of high air temperature area exceeding 29°C on the leeward side of the Taebaek Mountains, which is attributable to local scale adiabatic heating under the compression of air by strong downslope wind along the eastern slope of the high mountain in the west of Gangneung City, resulting in the persistence of tropical night near the coastal inland. The current results can contribute valuable insights into the monitoring and mitigation of extreme tropical night events in urban areas to support the development of strategies for public health, urban planning, and climate adaptation.

Korean Journal of Remote Sensing 2024, 40(6), 1409-1419. DOI: https://doi.org/10.7780/kjrs.2024.40.6.3.3
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