Monitoring of Dead Sea water surface variation using multi-temporal satellite data and GIS

Remote sensing (RS) and geographic information systems (GIS) are very useful for environmental-related studies, particularly in the field of surface water studies such as monitoring of lakes. The Dead Sea is exposed to very high evaporating process with considerable scarcity of water sources, thus leading to a remarkable shrinkage in its water surface area. The lake suffers from dry out due to the negative balance of water cycle during the previous four decades. This paper discusses the application of RS, GIS, and Global Positioning System to estimate the lowering and the shrinkage of Dead Sea water surface over the period 1810–2005. A set of multi-temporal remote sensing images were collected and processed to show the lakes aerial extend shrinkage from 1973 up to 2004. Remote sensing data were used to extract spatial information and to compute the surface areas for Dead Sea for various years. The current study aims at estimating the fluctuation of Dead Sea level over the study period with special emphasis on the environmental impact assessment that includes the degradation level of the Dead Sea. The results indicated that there is a decrease of 20 m in the level of the Dead Sea that has occurred during the study period. Further, the results showed that the water surface area of the Dead Sea has shrunk from 934.26 km² in 1973 to 640.62 km² in 2004.     

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal Himalaya

The present study was conducted along the Mugling–Narayanghat road section and its surrounding region that is most affected by landslide and related mass-movement phenomena. The main rock types in the study area are limestone, dolomite, slate, phyllite, quartzite and amphibolites of Lesser Himalaya, sandstone, mudstone and conglomerates of Siwaliks and Holocene Deposits. Due to the important role of geology and rock weathering in the instabilities, an attempt has been made to understand the relationship between these phenomena. Consequently, landslides of the road section and its surrounding region have been assessed using remote sensing, Geographical information systems and multiple field visits. A landslide inventory map was prepared and comprising 275 landslides. Nine landslides representing the whole area were selected for detailed studies. Field surveys, integrated with laboratory tests, were used as the main criteria for determining the weathering zones in the landslide area. From the overall study, it is seen that large and complex landslides are related to deep rock weathering followed by the intervention of geological structures as faults, joints and fractures. Rotational types of landslides are observed in highly weathered rocks, where the dip direction of the foliation plane together with the rock weathering plays a principle role. Shallow landslides are developed in the slope covered by residual soil or colluviums. The rock is rather fresh below these covers. Some shallow landslides (rock topples) are related to the attitude of the foliation plane and are generally observed in fresh rocks. Debris slides and debris flows occur in colluviums or residual soil-covered slopes. In few instances, they are also related to the rock fall occurring at higher slopes. The materials from the rock fall are mixed with the colluviums and other materials lying on the slope downhill and flow as debris flow. Rock falls are mainly related to the joint pattern and the slope angle. They are found in less-weathered rocks. From all these, it is concluded that the rock weathering followed by geological structures has prominent role in the rock slope instability along Mugling–Narayanghat road section and its surrounding regions.     

Variable equation of state for Bianchi type-VI<Subscript>0</Subscript> dark energy models

We present dark energy models in an anisotropic Bianchi type-VI₀ (B-VI₀) space-time with a variable equation of state (EoS). The EoS for dark energy ω is found to be time dependent and its existing range for derived models is in good agreement with the recent observations of SNe Ia data (Knop et al. in Astrophys. J. 598:102 2003), SNe Ia data with CMBR anisotropy and galaxy clustering statistics (Tegmark et al. in Astrophys. J. 606:702, 2004b) and latest a combination of cosmological datasets coming from CMB anisotropies, luminosity distances of high redshift type Ia supernovae and galaxy clustering (Hinshaw et al. in Astrophys. J. Suppl. 180:225, 2009; Komatsu et al. in Astrophys. J. Suppl. 180:330, 2009). The cosmological constant Λ is found to be a positive decreasing function of time and it approaches a small positive value at late time (i.e. the present epoch) which is corroborated by results from recent supernovae Ia observations. The physical and geometric aspects of the models are also discussed in detail.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Coupling of remote sensing data aided with field investigations for geological hazards assessment in Jazan area,Kingdom of Saudi Arabia

The city of Jazan is situated on the eastern flank of the Read Sea and considered as one of the fastest growing cities in the Kingdom of Saudi Arabia. This zone attracts a lot of investors for various development projects. Recently, many new projects have been implemented and constructed in this region including new urban areas, infrastructures, and industrial projects. However, historically this area has been challenged from different types of geological hazards. These geological hazards are catastrophic events that can cause human injury, loss of life, and economic devastation. The current study is aimed at evaluating the different types of geological hazards in Jazan city. This study is based on interpretation of satellite data such as LANDSAT and QuickBird images, existing geological maps, and physiographical characteristics with the help of field and laboratory analyses. The results of the analysis indicate that there exist various types of geological hazards in the study area mostly related to the natural factors which include (1) Sabkha soil; (2) Salt dome; (3) Loess soil; and (4) Sand dune/drift. Further, the findings of this study revealed that, most of these geological hazards have a severe impact on the ongoing development activities in Jazan area.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

Runoff modelling of a small watershed using satellite data and GIS

This study was conducted for the Nagwan watershed of the Damodar Valley Corporation (DVC), Hazaribagh, Bihar, India. Geographic Information System (GIS) was used to extract the hydrological parameters of the watershed from the remote sensing and field data. The Digital Elevation Model (DEM) was prepared using contour map (Survey of India, 1:50000 scale) of the watershed. The EASI/PACE GIS software was used to extract the topographic features and to delineate watershed and overland flow-paths from the DEM. Land use classification were generated from data of Indian Remote Sensing Satellite (IRS-1B—LISS—II) to compute runoff Curve Number (CN). Data extracted from contour map, soil map and satellite imagery, viz. drainage basin area, basin shape, average slope of the watershed, main stream channel slope, land use, hydrological soil groups and CN were used for developing an empirical model for surface runoff prediction. It was found that the model can predict runoff reasonably well and is well suited for the Nagwan watershed. Design of conservation structures can be done and their effects on direct runoff can be evaluated using the model. In broader sense it could be concluded that model can be applied for estimating runoff and evaluating its effect on structures of the Nagwan watershed.     

Rainfall–runoff modelling and water balance analysis for Al-Hindiyah barrage,Iraq using remote sensing and GIS

Investigation of rainfall–run-off modelling is an important subject to develop any available means to water supply, which maintains human life such as run-off harvesting method. This study aims to analyse and understand the rainfall–run-off relationship in a part of Babil city, Iraq. Curve number which is a function of land use, soil texture, soil moisture and land slope is used in this study. Remote sensing and GIS are used to analyse the data and to produce the run-off depth map for the study area. Then, the run-off depth is used with rainfall to investigate the relationship between them using linear correlation. This study showed a strong linear relationship between rainfall and run-off (R² = 0.992). It indicates that in the absence of rainfall data, run-off data can be used to estimate rainfall amount. Also, the study revealed through water balance analysis that there is an average monthly change in storage.     

Bulk viscous cosmological model

Robertson—Walker cosmological models with bulkviscosity are investigated explicitly with equation of statep=(-1). In particular, the physical nature of the extreme cases, i.e., degenerate vacuum bulkviscous fluid model and bulkviscous stiff fluid model are studied in detail.