Thermal pollution reduces water quality through any process that leads to a change in the water’s ambient temperature. Karun is one of the most relevant sources of water supply in Iran, and its pollution, created by industrial, urban, and agricultural issues, has been one of the most critical challenges throughout the last few years. As the water temperature rises, the amount of dissolved oxygen in it decreases, thereby affecting the entire ecosystem associated with it. Drainage of urban and industrial runoff into surface water sources can increase the water temperature. Dams also constitute a significant part, modifying spatial patterns of temperature along river routes and causing thermal contamination. In this paper, the thermal pollution of the Karun River was assessed, and regions along this river with unusually raised water temperatures were identified and compared over 20 years. By analyzing the results, it can be found that the thermal pollution from dams has a significant impact on the downstream river environment and ecology that is considerably relevant during summer periods, showing average decreases of 3 degrees Celsius immediately beyond the dams’ locations (from 41 degrees Celsius upstream dams to 38 degrees Celsius beyond them) or even bigger (reductions of 13 degrees Celsius in one of the studied dams). Hence, our results showed that water temperature is colder downstream in the hot seasons of the year than upstream of the dams. The results suggest that the usage of remote sensing data effectively could complement collected data from ground-based sensors to estimate water temperature and to identify pollution areas. It provides experts with spatially extensive and highly synchronized data.

Three-dimensional (3D) reconstruction using images is one of the most significant topics in computer vision and photogrammetry, with wide-ranging applications in robotics, augmented reality, and mapping. This study investigates methods of 3D reconstruction using video (especially monocular video) data and focuses on techniques such as Structure from Motion (SfM), Multi-View Stereo (MVS), Visual Simultaneous Localization and Mapping (V-SLAM), and videogrammetry. Based on a statistical analysis of SCOPUS records, these methods collectively account for approximately 6863 journal publications up to the end of 2024. Among these, about 80 studies are analyzed in greater detail to identify trends and advancements in the field. The study also shows that the use of video data for real-time 3D reconstruction is commonly addressed through two main approaches: photogrammetry-based methods, which rely on precise geometric principles and offer high accuracy at the cost of greater computational demand; and V-SLAM methods, which emphasize real-time processing and provide higher speed. Furthermore, the application of IMU data and other indicators, such as color quality and keypoint detection, for selecting suitable frames for 3D reconstruction is investigated. Overall, this study compiles and categorizes video-based reconstruction methods, emphasizing the critical step of keyframe extraction. By summarizing and illustrating the general approaches, the study aims to clarify and facilitate the entry path for researchers interested in this area. Finally, the paper offers targeted recommendations for improving keyframe extraction methods to enhance the accuracy and efficiency of real-time video-based 3D reconstruction, while also outlining future research directions in addressing challenges like dynamic scenes, reducing computational costs, and integrating advanced learning-based techniques.

Terrestrial laser scanners (TLS) are well-suited for conducting area-based deformation analysis of infrastructures. Unlike common point-based geodetic sensors, TLS can measure millions of points across the environment without requiring pre-defined, signalized measurement locations. However, TLS point clouds are affected by both random variations and residual systematic errors. These uncertainty components are often addressed using only probabilistic approaches, which may inadequately or overly optimistically represent the remaining systematic errors. To overcome these limitations, this study introduces an alternative framework based on interval mathematics to bound uncertainties arising from systematic errors. The proposed methodology includes a sensitivity analysis of TLS observation correction models, examining the variability of key input parameters. Unlike the quadratic approach for variance propagation, the interval-based method enables linear uncertainty propagation, effectively characterizing residual systematic uncertainties and their maximum effects. This paper details the methodology and presents typical interval values validated through simulations and real-data experiments. The findings highlight the potential of interval-based methods to enhance the TLS uncertainty model.

One of the main fields of engineering geodesy is deformation monitoring of natural and man-made structures. Infrastructure objects such as bridges, dams, and tunnels are of special interest because their operational safety must be ensured at all times. Area-based deformation analysis can be effectively conducted using terrestrial laser scanners (TLS). Unlike the limited, pre-selected, and point-based measurements used in traditional approaches, TLS samples the environment with millions of points without the need to signal the points. Point clouds data from TLS are influenced by remaining systematic errors as well as random noise. These sources of uncertainty are frequently handled using probabilistic approaches, which may provide an inadequate or overoptimistic representation of the overall uncertainty in point cloud data. In this contribution, an alternative approach based on interval mathematics is proposed to bound the uncertainty due to remaining systematic errors. To this end, a sensitivity analysis of TLS observation correction models is performed, and typical variability of the input parameters, such as temperature, pressure, humidity, and temperature gradients, as well as instrument misalignment, is assessed. Subsequently, error bands for the polar measurement elements in the form of intervals are obtained. While variance propagation follows a quadratic form, interval-based techniques enable linear uncertainty propagation, which is more effective for describing residual systematic uncertainty and worst-case scenarios. The methodology will be explained in detail, and typical values for the obtained intervals will be discussed and highlighted in a simulation study.