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 ě 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.

Over the last several decades, there has been increased attention on the heavy metal contamination associated with highways because of the associated health hazards and risks. Here, the results are reported of an analysis of the content of metals in roadside dust samples of selected major highways in the Greater Toronto Area of Ontario, Canada. The metals analysed are lead (Pb), zinc (Zn), cadmium (Cd), nickel (Ni), chromium (Cr), copper (Cu), manganese (Mn), calcium (Ca), potassium (K), magnesium (Mg) and iron (Fe). In the samples collected, the recorded mean concentrations (in parts per million) are as follows: Cd (0.51), Cu (162), Fe (40,052), Cr (197.9), K (9647.6), Mg (577.4), Ca (102,349), Zn (200.3), Mn (1202.2), Pb (182.8) and Ni (58.8). The mean concentrations for the analysed samples in the study area are almost all higher than the average natural background values for the corresponding metals. The geo-accumulation index of these metals in the roadside dust under study indicates that they are not contaminated with Cr, Mn and Ca; moderately contaminate with Cd and K; strongly contaminated with Fe and Mg; strongly to extremely contaminated with Ni and Pb; and extremely contaminated with Cu and Zn. The pollution load index (PLI) is used to relate pollution to highway conditions, and the results show that PLI values are slightly low at different samples collected from Highways 401 and 404 and high in many samples collected from Highway 400 and the Don Valley Parkway. Highway 400 exhibits the highest PLI values.

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Amman-Zerqa Basin (AZB) is a major basin in Jordan. The concentration of economic, agricultural and social activities within the basin makes it of prime importance to Jordan. Intensive agricultural practices are widespread and located close to groundwater wells, which pose imminent threats to these resources. Groundwater contamination is of particular concern as groundwater resources are the principal source of water for irrigation, drinking and industrial activities. A DRASTIC model integrated with GIS tool has been used to evaluate the groundwater vulnerability of AZB. The Drastic index map showed that only 1.2% of the basin’s total area of 3792 km² lies in the no vulnerable zone and about 69% is classified as having low pollution potential. The results also revealed that about 30% of the catchment area is moderately susceptible to pollution potential and slightly 1% is potentially under high pollution risk. These results suggest that almost one third of the AZB is at moderate risk of pollution potential. These areas are mainly in the north-east and central parts of the basin where the physical factors (gentle slope and high water table) would allow more contaminants to easily move into the shallow groundwater aquifer. Areas with high vulnerability to pollution are largely located in the center of Amman old city.

Surface sediment samples were collected from Ziqlab dam in northwestern Jordan to investigate the spatial distribution of selected trace metals and assess their pollution levels. The results showed that the concentrations of Pb, Cd, and Zn exceeded the environmental background values. Cd, Ni, and Cr contents were higher than the threshold effect level (TEL) in 63, 83, and 60 % of the reservoir sediments, respectively; whereas Pb, Zn, and Cu were less than the TEL limit. The concentrations of trace metals in reservoir sediment varied spatially, but their variations showed similar trends. Elevated levels of metals observed in the western part (adjacent to the dam wall) were coincided with higher contents of clay-silt fraction and total organic matters. Multivariate analysis indicated that Pb, Co, and Mn may be related to the lithologic component and/or the application of agrochemicals in the upstream agricultural farms. However, Cd and Zn concentrations were probably elevated due to inputs from agricultural sources, including fertilizers. Evaluation of contamination levels by the Sediment Quality Guidelines of the US-EPA, revealed that sediments were non-polluted to moderately polluted with Pb, Cu, Zn, and Cr, but non-polluted to moderately to heavily polluted with Ni and non-polluted with Mn. The geoaccumulation index showed that Ziqlab sediments were unpolluted with Pb, Cu, Zn, Ni, Cr, Co, and Mn, but unpolluted to moderately polluted with Cd. The high enrichment values for Cd and Pb (>2) indicate their anthropogenic sources, whereas the remaining elements were of natural origins consistent with their low enrichment levels.

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.

Amman-Zerqa Basin (AZB) is the second largest groundwater basin in Jordan with the highest abstraction rate, where more than 28% of total abstractions in Jordan come from this basin. In view of the extensive reliance on this basin, contamination of AZB groundwater became an alarming issue. This paper develops a Modified DRASTIC model by combining the generic DRASTIC model with land use activities and lineament density for the study area with a new model map that evaluates pollution potential of groundwater resources in AZB to various types of pollution. It involves the comparison of modified DRASTIC model that integrates nitrate loading along with other DRASTIC parameters. In addition, parameters to account for differences in land use and lineaments density were added to the DRASTIC model to reflect their influences on groundwater pollution potential. The DRASTIC model showed only 0.08% (3 km 2 ) of the AZB is situated in the high vulnerability area and about 30% of the basin is located in the moderately vulnerable zone (mainly in central basin). After modifying the DRASTIC to account for lineament density, about 87% of the area was classified as having low pollution potential and no vulnerability class accounts for about 5.01% of the AZB area. The moderately susceptible zone covers 7.83% of the basin's total area and the high vulnerability area constitutes 0.13%. The vulnerability map based on land use revealed that about 71% of the study area has low pollution potential and no vulnerability area accounts for about 0.55%, whereas moderate pollution potential zone covers an area of 28.35% and the high vulnerability class constitutes 0.11% of AZB. The final DRASTIC model which combined all DRASTIC models shows that slightly more than 89% of the study area falls under low pollution risk and about 6% is considered areas with no vulnerability. The moderate pollution risk potential covers an area of about 4% of AZB and the high vulnerability class constitutes 0.21% of the basin. The results also showed that an area of about 1761 km 2 of bare soils is of low vulnerability, whereas about 28 km 2 is moderately vulnerable. For agriculture and the urban sector, approximately 1472 km 2 are located within the low vulnerability zone and about 144 km 2 are moderately vulnerable, which together account for about 8% of the total agriculture and urban area. These areas are contaminated with human activities, particularly from the agriculture. Management of land use must be considered when changing human or agricultural activity patterns in the study area, to reduce groundwater vulnerability in the basin. The results also showed that the wells with the highest nitrate levels (81-107 mg/l) were located in high vulnerable areas and are attributed to leakage from old sewage water.

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.