This study compares Static Terrestrial Laser Scanning (STLS) with the conventional Total Station (TS) method for the geometric assessment of cylindrical storage tanks. With the crucial need for maintaining tank integrity in the oil and gas industry, STLS and TS methods are evaluated for their efficacy in assessing tank deformations. Using STLS and TS, the roundness and verticality of two cylindrical tanks were examined. A deformation analysis based on American Petroleum Institute (API) standards was then provided. Key objectives included comparing the two methods according to API standards, evaluating the workflow for STLS point cloud processing, and presenting the pros and cons of the STLS method for tank geometric assessment. The study found that STLS, with its detailed and high-resolution data acquisition, offers a substantial advantage in having a comprehensive structural assessment over TS. However, STLS requires more processing time and prior knowledge about the data to tune certain parameters and achieve accurate assessment. The project outcomes intend to enhance industry professionals’ understanding of applying STLS and TS to tank assessments, helping them choose the best method for their specific requirements.

High-quality precipitation data are crucial for various industries, including disaster prevention. In South Korea, long-term high-quality data are collected through numerous ground observation stations. However, data between these stations are reprocessed into a grid format using interpolation methods, which may not perfectly match actual precipitation. A prime example of real-time observational grid data globally is the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG) from National Aeronautics and Space Administration (NASA), while in South Korea, ground radar data are more commonly used. GPM and ground radar data exhibit distinct differences due to their respective processing methods. This study aims to analyze the characteristics of GPM and Constant Altitude Plan Position Indicator (CAPPI), representative real-time grid data, by comparing them with ground-observed precipitation data. The study period spans from 2021 to 2022, focusing on hourly data from Automated Synoptic Observing System (ASOS) sites in South Korea. The GPM data tend to underestimate precipitation compared to ASOS data, while CAPPI shows errors in estimating low precipitation amounts. Through this comparative analysis, the study anticipates identifying key considerations for utilizing these data in various applied fields, such as recalculating design rainfall, thereby aiding researchers in improving prediction accuracy by using appropriate data.

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications. An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40–70%, affecting reflectance in the red (–0.01 to 0.02) and near-infrared (NIR) bands (–0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown, reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10–20% with changes in aerosol optical thickness, 15–30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu’s thresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes, shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

We investigated the effect of spectral fitting wavelength interval variations and selection of absorption cross-section on the sulfur dioxide slant column density (SCD) retrievals from the scattered sunlight observation using a UV-Vis hyperspectral instrument. The sulfur dioxide slant column densities were retrieved from the combinations of multiple spectral fitting intervals and absorption cross-sections. The observation was carried out at the site 0.53 km away from a combustion site located in Gimhae from December 1, 2023, to January 23, 2024. The radiances were obtained on the line of measurement sight toward the stack of the combustion facility. The best spectral fitting intervals were found to be from 305.7 to 321.1 nm. In terms of the absorption cross-section dependency, the SO₂ (293 K), O₃ (223 K, 243 K) show the best spectral fitting for the observed radiances with both the smallest fitting residual and SCD error. The effects of the fitting interval and cross sections found in this study can be useful information for improving SO₂ retrievals based on UV hyperspectral measurements.

The Land Surface Temperature (LST) is a crucial parameter in identifying drought. It is essential to identify how LST can increase its accuracy, particularly in mountainous and hill areas. Increasing the LST accuracy can be achieved by applying early data processing in the correction phase, specifically in the context of topographic correction on the Lambertian model. Empirical evidence has demonstrated that this particular stage effectively enhances the process of identifying objects, especially within areas that lack direct illumination. Therefore, this research aims to examine the application of the Lambertian model in estimating LST using the Multi-Channel Method (MCM) across various physiographic regions. Lambertian model is a method that utilizes Lambertian reflectance and specifically addresses the radiance value obtained from Sun-Canopy-Sensor (SCS) and Cosine Correction measurements. Applying topographical adjustment to the LST outcome results in a notable augmentation in the dispersion of LST values. Nevertheless, the area physiography is also significant as the plains terrain tends to have an extreme LST value of ≥ 350 K. In mountainous and hilly terrains, the LST value often falls within the range of 310–325 K. The absence of topographic correction in LST results in varying values: 22 K for the plains area, 12–21 K for hilly and mountainous terrain, and 7–9 K for both plains and mountainous terrains. Furthermore, validation results indicate that employing the Lambertian model with SCS and Cosine Correction methods yields superior outcomes compared to processing without the Lambertian model, particularly in hilly and mountainous terrain. Conversely, in plain areas, the Lambertian model’s application proves suboptimal. Additionally, the relationship between physiography and LST derived using the Lambertian model shows a high average R² value of 0.99. The lowest errors (K) and root mean square error values, approximately ±2 K and 0.54, respectively, were achieved using the Lambertian model with the SCS method. Based on the findings, this research concluded that the Lambertian model could increase LST values. These corrected values are often higher than the LST values obtained without the Lambertian model.