B. S. Daya Sagar

Particle Swarm Optimization and Voronoi diagram for Wireless Sensor Networks coverage optimization

The focus of this study is the sensor coverage problem. It is a crucial issue in wireless sensor networks (WSN), where a high coverage rate will ensure a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO). PSO is chosen to find the optimal position of the sensors that gives the best coverage and Voronoi diagram is used to evaluate the fitness of the solution.

Morphological approach to extract ridge and valley connectivity networks from Digital Elevation Models

The extraction of ridge and valley connectivity networks is essential for studying spatio-temporal organizations. Extraction of such connectivity networks from multiscale DEMs has lately received notable attention. A simple method is proposed to extract these networks, from a sample DEM as well as a simulated fractal DEM, using non-linear morphological transformations in a methodical way. Further, the proposed method can be adapted to extract these complex topological networks from DEMs generated from either remotely sensed or topographic data.

Fractal relation of a morphological skeleton

Abstract The morphological skeleton of a structure which possesses a crenellate outline resembles a stream network. The fractal relation of a morphological skeleton network is shown. The fractal dimension of the structure and its morphological skeleton network are computed using the box counting method. These are then compared with the estimated length-area measures and certain morphometric order ratios.

Morphological operators to extract channel networks from digital elevation models

The automated extraction of drainage networks includes generation and processing of digital elevation models (DEMs) obtained from the remotely sensed data having stereo viewing capability. The latter aspect generally aims to extract terrain features such as elevation contours and channel networks. In this technical note, the application of morphological operators to extract channel networks from the digital elevation model is described. The methodology is illustrated using a transcendentally generated DEM that bears the spatially distributed regions in grey levels, assumed as the regions of topographic reliefs and the V-shaped crenulations in successive elevation contours. The authors conclude that the adaptation of this approach to extract channel networks from DEM data is straightforward and is simple both algorithmically and computationally.

Applications of mathematical morphology in surface water body studies

Some possible applications of mathematical morphological transformations in computing the basic measures of surface water bodies are presented. Sixteen water bodies are extracted from SPOT PLA data, and the algorithm developed, based on mathematical morphological concepts, is tested to compute their basic measures.

FRACTAL-SKELETAL BASED CHANNEL NETWORK (F-SCN) IN A TRIANGULAR INITIATOR-BASIN

Fractal-skeletal based channel network (F-SCN) model, which describes how the boundary of the basin constrains the channel network evolution, produces a channel network pattern that obeys Hortons laws. The statistical features of this model conform well with real networks. For this F-SCN that depends on general shape of the initiator-basin, generating mechanism and rule, and the nature of skeletonization process, the estimation of fractal dimension (D) is Log RBLog RL. The estimated D, 1.76 is approximated to the observed value, 1.8.

Erratum to "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements"

Spatial interpolation is one of the demanding techniques in geographic information science (GISci) to generate interpolated maps in a continuous manner by using two discrete spatial and/or temporal data sets. Noise-free data (thematic layers) depicting a specific theme at varied spatial or temporal resolutions consist of connected components either in aggregated or in disaggregated forms. This short paper provides a simple framework: 1) to categorize the connected components of layered sets of two different time instants through their spatial relationships and the Hausdorff distances between the companion-connected components and 2) to generate sequential maps (interpolations) between the discrete thematic maps. Development of the median set, using Hausdorff erosion and dilation distances to interpolate between temporal frames, is demonstrated on lake geometries mapped at two different times and also on the bubonic plague epidemic spread data available for 11 consecutive years. We documented the significantly fair quality of the median sets generated for epidemic data between alternative years by visually comparing the interpolated maps with actual maps. They can be used to visualize (animate) the spatiotemporal behavior of a specific theme in a continuous sequence.

Morphological decomposition of sandstone pore–space: fractal power-laws

Morphological decomposition procedure is applied to estimate fractal dimension of a pore–space, which is isolated from a sandstone microphotograph. The fractal dimensions that have been computed by considering various probing rules have precisely followed the universal power-law relationships proposed elsewhere. These results are derived by considering structuring elements such as octagon, square and rhombus that have been used to decompose the pore– space of sandstone image. The radii of the structuring elements are made to increase in a cyclic fashion. To perceive the decomposed pore image, a color-coding scheme is adapted, from which one can identify several sizes of these structuring elements that could be fit into this pore. This exercise facilitates testing of the relationship between the radius of the structuring elements that could be used to decompose the pore at different levels, and the number of decomposed shapes that could be fit into the pore while using the corresponding structuring element. From the number–radius relationship, the fractal dimensions of pore–space estimated, by considering these structuring elements, yield the values of 1.82, 1.76, and 1.79. These values are in conformity with the values arising from estimation of box dimension method, as well as the dimensions of the corresponding pore connectivity networks (PCNs). 2003 Elsevier Science Ltd. All rights reserved.

Spatial information retrieval, analysis, reasoning and modelling

The 15 papers that constitute the present issue aim to show the importance of recent developments in remote sensing and image processing in Geographical Information Science (GISci). In the past 10 years, new sensors have appeared, based on lasers or radar, or using interference principles, and the resolution of the more conventional optical devices has dropped from 10 m to 0.70 m, while the number of accessible bands, in the visible and the infrared range, has multiplied by 10. The availability of spatial data, for natural, anthropogenic and socioeconomic studies, from such a wide range of sources and a variety of formats opens new horizons to the GISci community. For example, urban areas, which the previous satellites used to resolve rather poorly, have become richer from year to year in significant details in shapes and contours. However, such new complexity leads to new problems. In relation to spatial information, schematically there are four aspects, which are also four challenges that GISci scientists face. They have to retrieve this information, which is assumed to segment the space in homogeneous zones according to some criteria. This often implies filtering steps. They must analyse the selected regions, and associate with them certain numbers and numerical functions, such as size distributions. They have to apply the above geometrical descriptors in some specific context, such as ‘what is the best place to locate a hospital, or to trace a road?’ And, sometimes, they have to conceive random or deterministic models for synthesizing the results of the analysis phase, in order to make forecasts. The authors provide short introductions to the techniques they use, but more extensive presentations may be helpful. Indeed, image processing, as well as GISci, has evolved considerably in the past two decades, and some difficult segmentations require up-to-date versions of wavelets or watersheds, for instance. The studies in this issue borrow their methodology from various sources, including wavelets, random sets (Matheron 1975), geostatistics, Radon transformation, and fuzzy geometry (Zadeh 1965). This list could have contained fractal geometry (Mandelbrot 1982) or rough set theory (Pawlak 1982) as well. Among the methods, mathematical morphology (Matheron 1975, Serra 1982) deserves a special mention because it stems from set descriptors, which have been extended to functions and partitions. Its origin makes it particularly convenient for handling high-resolution images, and the method is involved in the majority of the papers in this issue. In fact, many algebraic operations on maps (Tomlin 1983) involved in GISci-related analyses can be performed through mathematical morphology (e.g. Pullar 2001, Stell 2007). The reader will find a clear presentation of this theory in the book written by Soille (1999), a GISci scientist. We also recommend a recent text by Najman and Talbot (2010) that covers more topics, including connections, connective segmentation, random models and simulations. In March 2009, the annual conference on Spatial Information Retrieval, Analysis, Reasoning and Modelling (SIRARM) was organized at the Bangalore Centre of the Indian Statistical Institute, focusing on the themes that this Special Issue covers. The aim of SIRARM is to bring together remote sensing specialists, GISci experts, and

Universal scaling laws in surface water bodies and their zones of influence

[1] Topologically, water bodies are the first-level topographic regions that get flooded, and as the flood level gets higher, adjacent water bodies merge. The looplike network that forms along all these merging points represents zones of influence of each water body. These two topologically interdependent phenomena follow the universal scaling laws similar to certain other environmental and biological phenomena. Despite morphological variations, water bodies and their influence zones of varied sizes and shapes have different sets of scaling exponents, thereby determining that they belong to different universality classes.