Analyzing Animal Movement Characteristics From Location Data

When individuals of a species utilize an environment, they generate movement patterns at a variety of spatial and temporal scales. Field observations coupled with location technologies (e.g. GPS tags) enable the capture of detailed spatio‐temporal data regarding these movement patterns. These patterns contain information about species‐specific preferences regarding individual decision‐making, locational choices and the characteristics of the habitat in which the animal resides. Spatial Data Mining approaches can be used to extract repeated spatio‐temporal patterns and additional habitat preferences hidden within large spatially explicit movement datasets. We describe a method to determine the periodicity and directionality in movement exhibited by a migratory bird species. Results using a High Arctic‐nesting Svalbard Barnacle Goose movement data yielded undetected patterns that were secondarily corroborated with expert field knowledge. Individual revisits by the geese to specific locations in the breeding and wintering grounds of Svalbard, Norway and Solway, Scotland, occurred with a periodicity of 334 days . Further, the orientation of this movement was detected to be mostly north‐south. During long‐range migration the geese use the north‐south oriented Norwegian islands as “stepping stones”, Short‐range movement between mudbank roosts to feeding fields in Solway also retained a north‐south orientation.     

Forecasting prices of single family homes using GIS-defined neighborhoods

We estimate spatiotemporal models of average neighborhood single family home prices to use in predicting individual property prices. Average home-price variations are explained in terms of changes in average neighborhood house attributes, spatial attributes, and temporal economic variables. Models adopting three different definitions of neighborhoods are estimated with quarterly cross-sectional data over the period 2000–2004 from four cities in Southern California. Heteroscedasticity and autocorrelation problems are detected and adjusted for via a sequential routine. Results of these models suggest that forecasts obtained using city neighborhood average price equations may have advantage over forecasts obtained using city aggregated price equations.      

Soil resource appraisal towards land use planning using satellite remote sensing and gis a case study in patloinala micro-watershed, district puruliya, west bengal

Visual interpretation of IRS ID LISS-III fused with PAN data (1:12,500 scale) ofPatloinala micro-watershed of Puruliya district, West Bengal was carried out for delineating the physiographic units based on the variations in image characteristics. The major physiographic units identified were upland(Tanr), medium land(Baid), and low land(Bahal andKanali). The satellite remote sensing data coupled with ground truth were translated in terms of soils using composite interpretation map as base. The abstraction level attained was phases of soil series based on Soil Taxonomy. On the basis of physiographic variation and soil or soil site characteristics such as texture, depth, slope, erosion etc. the problem areas were identified and land use plan has been suggested for the overall development of the micro-watershed.     

Terrain Characterization for Soil Resource Mapping Using IRS-P6 Data and GIS - A Case Study From Basaltic Terrain of Central India

In the present study, landforms and soils have been characterized in Borgaon Manju watershed of basaltic terrain located in Akola district, Maharashtra, Central India. Terrain characterization using Shuttle Radar Topography Mission (SRTM) elevation data (90 m) and IRS-P6 LISS IV data in conjunction with adequate field surveys shows nine distinct landforms. Soil resource inventory shows fourteen soil series in the study area. Soils formed on gently sloping (3–8 %) subdued plateau are very shallow (23 cm), moderately well drained, moderate (15–40 %) surface stoniness, severely eroded, clayey and slightly alkaline in reaction, whereas, the soils formed on level to nearly level (0–1 %) slope in the main valley are very deep (>150 cm), well drained, very slight (<3 %) surface stoniness, moderately eroded with clayey surface and moderately alkaline in reaction. Soils in the watershed are grouped into Lithic Ustorthents, Vertic Haplustepts, Calcic Haplustepts, Typic Haplustepts, Typic Haplusterts and Sodic Calciusterts. The study demonstrates that the analysis of SRTM elevation data and IRS P6–IV data in Geographic Information System (GIS) with adequate field surveys helps in characterization of landforms and soils in analysis of landscape-soil relationship.     

Combining neural network with fuzzy,certainty factor and likelihood ratio concepts for spatial prediction of landslides

The landslide studies can be categorized as pre- and postdisaster studies. The predisaster studies include spatial prediction of potential landslide zones known as landslide susceptibility zonation (LSZ) mapping to identify the areas/locales susceptible to landslide hazard. The LSZ maps provide an assessment of the safety of existing habitations and infrastructural/functional elements and help plan further developmental activities in the hilly regions. Landslides are one of the natural geohazards that affect at least 15% of land area of India. Different types of landslides occur frequently in geodynamical active domains of the Himalayas. In India, various techniques have been developed and adopted for LSZ mapping of different regions. However, the technique for LSZ mapping is not yet standardized. The present research is an attempt in this direction only. In our earlier work (Kanungo et al. 2006), a detailed study on conventional, artificial neural network (ANN)- black box-, fuzzy set-based and combined neural and fuzzy weighting techniques for LSZ mapping in Darjeeling Himalayas has been documented. In this paper, other techniques such as combined neural and certainty factor concept along with combined neural and likelihood ratio techniques have been assessed in comparison with combined neural and fuzzy technique for the preparation of LSZ maps of the same study area in parts of Darjeeling Himalayas. It is observed from the present study that the LSZ map produced using combined neural and fuzzy approach appears to be the most accurate one as in this case only 2.3% of the total area is found to be categorized as very high susceptibility zone and contains 30.1% of the existing landslide area. This approach can serve as one of the key objective approaches for spatial prediction of landslide hazards in hilly terrain.     

An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Textural identification of basaltic rock mass using image processing and neural network

A new approach to identify the texture based on image processing of thin sections of different basalt rock samples is proposed here. This methodology uses RGB or grayscale image of thin section of rock sample as an input and extracts 27 numerical parameters. A multilayer perceptron neural network takes as input these parameters and provides, as output, the estimated class of texture of rock. For this purpose, we have use 300 different thin sections and extract 27 parameters from each one to train the neural network, which identifies the texture of input image according to previously defined classification. To test the methodology, 90 images (30 in each section) from different thin sections of different areas are used. This methodology has shown 92.22% accuracy to automatically identify the textures of basaltic rock using digitized image of thin sections of 140 rock samples. Therefore, present technique is further promising in geosciences and can be used to identify the texture of rock fast and accurate.     

Estimation of hypocentral parameters of local earthquakes when crustal layers have constantP-velocities and dipping interfaces

The paper describes an algorithm for estimating the hypocentral coordinates and origin time of local earthquakes when the wave speed model to be employed is a layered one with dipping interfaces. A constrained least-squared error problem has been solved using the penalty function approach, in conjunction with the sequential unconstrained optimization technique of Fiacco and McCormick. Joint confidence intervals for the computed parameters are estimated using the approach of Bard for nonlinear problems. These results show that when a hypocentre lies outside the array of recording stations and head waves from a dipping interface are involved, then its inclination must be taken into account for dip angles exceeding 5°.     

Alteration of volcanic glass to well-crystallized ferrosaponite in the vesicles of the Deccan trap basalts at Bhuleshwar Ghat section,Pune district,Maharashtra

Clay minerals formed mainly by the alteration of volcanic glass are reported by many workers. The commonly formed minerals belonging to the smectite group especially nontronite and ferrosaponite are widely reported by many in basalts. However, most of the reports suggest the formation of ferrosaponite in marine conditions (Zhou et al., 1992; Chen et al., 1996) or by hydrothermal solutions (Shayan et. al., (1988)). Origin of ferrosaponite in continental flood basalts by a mechanism of alteration of volcanic glass to ferrosaponite is reported in the paper. The different stages involved in the alteration process are also highlighted.