Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ≥ λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.

The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds.

Mechanically Stabilized Earth (MSE) walls retain soil on steep, unstable slopes with crest loads. Over the last decade, they are becoming quite popular due to their high cost-to-benefit ratio, design flexibility, and ease of construction. Like any civil infrastructure, MSE walls need to be continuously monitored according to transportation asset management criteria during and after the construction stage to ensure that their expected serviceability measures are met and to detect design and/or construction issues, which could lead to structural failure. Current approaches for monitoring MSE walls are mostly qualitative (e.g., visual inspection or examination). Besides being time consuming, visual inspection might have inconsistencies due to human subjectivity. This research focuses on a comprehensive strategy using a mobile LiDAR mapping System (MLS) for the acquisition and processing of point clouds covering the MSE wall. The processing strategy delivers a set of global and local performance measure for MSE walls. Moreover, it is also capable of handling MSE walls with smooth or textured panels with the latter being the focus of this research due to its more challenging nature. For this study, an ultra-high-accuracy wheel-based MLS has been developed to efficiently acquire reliable data conducive to the development of the serviceability measures. To illustrate the feasibility of the proposed acquisition/processing strategy, two case studies in this research have been conducted with the first one focusing on the comparative performance of static and mobile LiDAR in terms of the agreement of the derived serviceability measures. The second case study aims at illustrating the feasibility of the proposed strategy in handling large textured MSE walls. Results from both case studies confirm the potential of using MLS for efficient, economic, and reliable monitoring of MSE walls.

(1) Background: The Gulf of Aqaba (GoA) supports unique and diverse marine ecosystems. It is one of the highest anthropogenically impacted coasts in the Middle East region, where rapid human activities are likely to degrade these naturally diverse but stressed ecosystems. (2) Methods: Various water quality parameters were measured to assess the current status and conditions of GoA seawater including pH, total dissolved solids (TDS), total alkalinity (TA), Cl⁻, NO₃⁻, SO₄²⁻, PO₄³⁻, NH₄⁺, Ca²⁺, Mg²⁺, Na⁺, K⁺, Sr, Cd, Co, Cr, Cu, Fe, Mn, Pb, and Zn. (3) Results: The pH values indicated basic coastal waters. The elevated levels of TDS with an average of about 42 g/L indicated highly saline conditions. Relatively low levels of inorganic nutrients were observed consistent with the prevalence of oligotrophic conditions in GoA seawater. The concentrations of Ca²⁺, Mg²⁺, Na⁺, K⁺, Sr, Cl⁻, and SO₄²⁻ in surface layer varied spatially from about 423–487, 2246–2356, 9542–12,647, 513–713, 9.2–10.4, 22,173–25,992, and 317–407 mg/L, respectively. The average levels of Cd, Co, Cr, Cu, Fe, Mn, Pb and Zn ranged from 0.51, 0.38, 1.44, 1.29, 0.88, 0.38, and 6.05 µg/L, respectively. (4) Conclusions: The prevailing saline conditions of high temperatures, high evaporation rates, the water stratification and intense dust storms are major contributing factors to the observed seawater chemistry. The surface distribution of water quality variables showed spatial variations with no specific patterns, except for metal contents which exhibited southward increasing trends, closed to the industrial complex. The vast majority of these quality parameters showed relatively higher values compared to those of other regions.