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.