Land suitability analysis for cereal production in Himachal Pradesh (India) using Geographical Information System

Land suitability analysis is prerequisite for sustainable agriculture and it plays a pivotal role in the niche based agricultural planning in mountain regions. In this paper different parameters viz. climatic (precipitation and temperature), topographic (elevation), soil type and land cover/land use have been used in order to perform land suitability evaluation for cereals food-grain crops in Himachal Pradesh using Geographic Information System (GIS). The suitability analysis was performed by digital processing of geo-referenced data (elevation, climate, soil and landcover) and calculating potential production areas by combining different types of geographical data through decision rules framed for each crop in ArcView spatial analyst. Suitable areas have been delineated for cereal crops in the form of land suitability maps. In comparison to the actual area under cereal crops, the possibility of further expansion under each cereal crop was determined. These discriminated areas appear suitable for growing these crops and can be harnessed efficiently for achieving long term sustainability and food security.     

Utilization of remote sensing data and GIS tools for and use sustainability analysis: case study in El-Hammam area, Egypt

The North-Western Coast of Egypt (NWCE) represents one of the high priority regions for future development in the country. El-Hammam area is located in the NWCE with an area of 94752 acres and is one of the main challenging regions for sustaianble development. In this study, we have used remote sensing and soil data in combination with GIS tools, for land use sustainable analysis (SLU) in El-Hammam area. The SLU was established based on various factors such as: land capability and suitability, water resources availability, economic return from water and financial return from land and water. A physiographic soil map for the study area was prepared using remote sensing and GIS. Multiple field surveys were carried out for collecting information on various soil map units (SMUs) and their profiles. Laboratory analysis for the collected samples was performed, and then the soil properties were stored as attributes in a geographical soil database linked with the SMUs. Furthermore, land capability assessment was done to define the suitable areas for agricultural production using a capability model built in ALES software. Results indicate that the area currently lacks high capability and moderate capability classes. By improving the soil properties, the soil can attain potential capability; and 55630 acres will become marginally capable. The assessment of soil physical suitability for different land use types (LUTs) were analysed in ALES software, in order to generate the most suitable areas. The results from the land suitability analysis indicated that, 17114 acres are moderately suitable for wheat and sorghum; whereas 15823 acres are moderately suitable for barley and 12752 acres are moderately suitable for maize, olive and figs. Finally, the SLU was investigated based on two scenarios; (1) the most SLU under the conditions of shortage of irrigation water: clover, barley and sorghum against figs, as the irrigation requirements for barley and sorghum are low; (2) the most sustainable land use in the conditions of irrigation availability will be wheat and maize against figs and guava. From the results it is quite evident that GIS combined with modeling approaches are powerful tools for decision making in the study area.     

Response of west Indian coastal regions and Kavaratti lagoon to the November-2009 tropical cyclone Phyan

Response of the coastal regions of eastern Arabian Sea (AS) and Kavaratti Island lagoon in the AS to the tropical cyclonic storm `Phyan??, which developed in winter in the south-eastern AS and swept northward along the eastern AS during 9?C12 November 2009 until its landfall at the northwest coast of India, is examined based on in situ and satellite-derived measurements. Wind was predominantly south/south-westerly and the maximum wind speed (U<sub>10</sub>) of ~16 m/s occurred at Kavaratti Island region followed by ~8 m/s at Dwarka (Gujarat) and ~7 m/s at Diu (located south of Dwarka) as well as two southwest Indian coastal locations (Mangalore and Malpe). All other west Indian coastal sites recorded maximum wind speed of ~5?C6 m/s. Gust factor (i.e., gust-to-speed ratio) during peak storm event was highly variable with respect to topography, with steep hilly stations (Karwar and Ratnagiri) and proximate thick and tall vegetation-rich site (Kochi) exhibiting large values (~6), whereas Island station (Kavaratti) exhibiting ~1 (indicating consistently steady wind). Rainfall in association with Phyan was temporally scattered, with the highest 24-h accumulated precipitation (~60 mm) at Karwar and ~45 mm at several other west Indian coastal sites. Impact of Phyan on the west Indian coastal regions was manifested in terms of intensified significant waves (~2.2 m at Karwar and Panaji), sea surface cooling (~5°C at Calicut), and moderate surge (~50 cm at Verem, Goa). The surface waves were south-westerly and the peak wave period (T <sub>p</sub>) shortened from ~10?C17 s to ~5?C10 s during Phyan, indicating their transition from the long-period `swell?? to the short-period `sea??. Reduction in the spread of the mean wave period (T _z) from ~5?C10 s to a steady period of ~6 s was another manifestation of the influence of the cyclone on the surface wave field. Several factors such as (1) water piling-up at the coast supported by south/south-westerly wind and seaward flow of the excess water in the rivers due to heavy rains, (2) reduction of piling-up at the coast, supported by the upstream penetration of seawater into the rivers, and (3) possible interaction of upstream flow with river run-off, together resulted in the observed moderate surge at the west Indian coast. Despite the intense wind forcing, Kavaratti Island lagoon experienced insignificantly weak surge (~7 cm) because of lack of river influx and absence of a sufficiently large land boundary required for the generation and sustenance of wave/wind-driven water mass piling-up at the land?Csea interface.     

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process

Urban, industrial, and tourist developments are considered of high priority in Egypt. In the current research, the site suitability investigation for rating the different environmental, geological, and geotechnical conditions facing civil engineering projects were assessed using a geographic information system (GIS) multi-criteria approach. The study area is one of the most promising areas for urban and touristic as well as industrial developments in Egypt, which is located on the NW coast of the Gulf of Suez. This area may face several geo-environmental problems that will limit its suitability for civil projects. Weighted GIS model, which integrates different types of data sources, such as land use/cover, geological, geomorphological, geophysical, environmental, remote sensing, and field data, can be achieved to create a site suitability map. In this paper, an analytical hierarchy process approach has been used to develop the weighted model for different factors. As a result of this study, areas of potential geotechnical and geo-environmental hazards that could impact the design and construction of civil projects were identified. Therefore, changes can be made early in the design process before significant design efforts are being invested.     

Electrofacies in gas shale from well log data via cluster analysis: A case study of the Perth Basin,Western Australia

Identifying reservoir electrofacies has an important role in determining hydrocarbon bearing intervals. In this study, electrofacies of the Kockatea Formation in the Perth Basin were determined via cluster analysis. In this method, distance data were initially calculated and then connected spatially by using a linkage function. The dendrogram function was used to extract the cluster tree for formations over the study area. Input logs were sonic log (DT), gamma ray log (GR), resistivity log (IND), and spontaneous potential (SP). A total of 30 reservoir electrofacies were identified within this formation. Integrated geochemical and petrophysics data showed that zones with electrofacies 3, 4, 9, and 10 have potential for shale gas production. In addition, the results showed that cluster analysis is a precise, rapid, and cost-effective method for zoning reservoirs and determining electrofacies in hydrocarbon reservoirs.     

A family of exact solutions of Einstein-Maxwell field equations in isotropic coordinates: an application to optimization of quark star mass

In the present paper, we have obtained a class of charged super dense star models, starting with a static spherically symmetric metric in isotropic coordinates for perfect fluid by considering Hajj-Boutros (in J. Math. Phys. 27:1363, 1986) type metric potential and a specific choice of electrical intensity which involves a parameter K. The resulting solutions represent charged fluid spheres joining smoothly with the Reissner-Nordstrom metric at the pressure free interface. The solutions so obtained are utilized to construct the models for super-dense star like neutron stars (ρ _b =2 and 2.7×10¹⁴ g/cm³) and Quark stars (ρ _b =4.6888×10¹⁴ g/cm³). Our solution is well behaved for all values of n satisfying the inequalities \(4 < n \le4(4 + \sqrt{2} )\) and K satisfying the inequalities 0≤K≤0.24988, depending upon the value of n. Corresponding to n=4.001 and K=0.24988, we observe that the maximum mass of quark star M=2.335M _⊙ and radius R=10.04 km. Further, this maximum mass limit of quark star is in the order of maximum mass of stable Strange Quark Star established by Dong et al. (in arXiv:1207.0429v3, 2013). The robustness of our results is that the models are alike with the recent discoveries.     

Landslide susceptibility mapping at Zonouz Plain,Iran using genetic programming and comparison with frequency ratio,logistic regression,and artificial neural network models

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.     

Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.