Vegetation Analysis in Uttara Kannada District Using GIS and Remote Sensing Techniques

Uttara Kannada is the only district in Karnataka, which has a forested area of about 80% and falls in the region of the Western Ghats. It is considered to be a very resourceful in terms of abundant natural resources and constitutes an important district in Karnataka. The forest resources of the district are under pressure as a large portion of the forested area has been converted to non-forestry activities since independence owing to the increased demands from human and animal population resulting in degradation of the forest ecosystem. This has led to poor productivity and regenerative capacity which is evident in the form of barren hill tops, etc in Coastal taluks of Uttara Kannada, entailing regular monitoring of the forest resources very essential. The classification of forest is a prerequisite for managing forest resources. Geographical Information System (GIS), allows the spatial and temporal analysis of the features of interest, and helps in solving the problem of deforestation and associated environmental and ecological problems. Spatial and temporal tools such as GIS and remotely sensed data helps the planners and decision makers in evolving the sustainable strategies for management and conservation of natural resources. Uttara Kannada district was classified on the basis of the land-use using supervised hard classifiers. The land use categories identified were urban area, water bodies, agricultural land, forest cover, and waste land. Further classification was carried out on the basis of forest type. The types of forest categorised were semi-evergreen, evergreen, moist deciduous, dry deciduous, plantations and scrub, thorny and non-forested area. The identified classes were correlated with the ground data collected during field visits. The observed results were compared with the historic data and the changes in the forest cover were analysed. From the assessment made it was clear that there has been a considerable degree of forest loss in certain areas of the district. It was also observed that plantations and social forests have increased drastically over the last fifteen years,and natural forests have declined.

Fusion of multi resolution remote sensing data for urban sprawl analysis

Urban population is growing at around 2.3 percent per annum in India. This is leading to urbanisation and often fuelling the dispersed development in the outskirts of urban and village centres with impacts such as loss of agricultural land, open space, and ecologically sensitive habitats. This type of upsurge is very much prevalent and persistent in most places, often inferred as sprawl. The direct implication of such urban sprawl is the change in land use and land cover of the region and lack of basic amenities, since planners are unable to visualise this type of growth patterns. This growth is normally left out in all government surveys (even in national population census), as this cannot be grouped under either urban or rural centre. The investigation of patterns of growth is very crucial from regional planning point of view to provide basic amenities in the region. The growth patterns of urban sprawl can be analysed and understood with the availability of temporal multi-sensor, multi-resolution spatial data. In order to optimise these spectral and spatial resolutions, image fusion techniques are required. This aids in integrating a lower spatial resolution multispectral (MSS) image (for example, IKONOS MSS bands of 4m spatial resolution) with a higher spatial resolution panchromatic (PAN) image (IKONOS PAN band of 1m spatial resolution) based on a simple spectral preservation fusion technique - the Smoothing Filter-based Intensity Modulation (SFIM). Spatial details are modulated to a co-registered lower resolution MSS image without altering its spectral properties and contrast by using a ratio between a higher resolution image and its low pass filtered (smoothing filter) image. The visual evaluation and statistical analysis confirms that SFIM is a superior fusion technique for improving spatial detail of MSS images with the preservation of spectral properties.

Multi-point sensing system for plantar pressure measurement

In this paper, the development of a novel multipoint pressure sensor system suitable for the measurement of human foot pressure distribution has been presented. It essentially consists of a matrix of cantilever sensing elements supported by beams. Foil type strain gauges have been employed for the conversion of foot pressure in to proportional electrical response. Information on the signal conditioning circuitry used is given. Also, the results obtained on the performance of the system are included.

Health Monitoring Of Composite Structures Based On Acoustic Wave Sensing Using Fiber Optic Sensors

Structural Health Monitoring has gained wide acceptance in the recent past as a means to monitor a structure and provide an early warning of an unsafe condition using real-time data. Utilization of structurally integrated, distributed sensors to monitor the health of a structure through accurate interpretation of sensor signals and real-time data processing can greatly reduce the inspection burden. The rapid improvement of the Fiber Optic Sensor technology for strain, vibration, ultrasonic and acoustic emission measurements in recent times makes it feasible alternative to the traditional strain gauges, PVDF and conventional Piezoelectric sensors used for Non Destructive Evaluation (NDE) and Structural Health Monitoring (SHM). Optical fiber-based sensors offer advantages over conventional strain gauges, and PZT devices in terms of size, ease of embedment, immunity from electromagnetic interference (EMI) and potential for multiplexing a number of sensors. The objective of this paper is to demonstrate the acoustic wave sensing using Extrinsic Fabry-Perot Interferometric (EFPI) sensor on a GFRP composite laminates. For this purpose experiments have been carried out initially for strain measurement with Fiber Optic Sensors on GFRP laminates with intentionally introduced holes of different sizes as defects. The results obtained from these experiments are presented in this paper. Numerical modeling has been carried out to obtain the relationship between the defect size and strain.

Ethanol sensing using CuO/MWNT thin film

Copper (II) oxide (CuO)/multiwall carbon nanotube (MWNT) thin film based ethanol-sensors were fabricated by dispersing CVD-prepared MWNTs in varying concentration over DC magnetron sputtered-CuO films. The responses of these sensors as a function of MWNT concentrations and temperatures were measured, and compared. The sensing response was the maximum at an operating temperature near 400 degrees C for all the samples irrespective of the MWNTs dispersed over them. At optimum operating temperature (T(opt)) of 407 +/- 1 degrees C, the response is linear for 100-700 ppm range and tends to saturate at higher concentrations. In comparison with bare CuO sample, the response of CuO/MWNT sensing films increased up to 50% in the linear range. The response improvement for 2500 ppm of ethanol was up to 90% compared to bare CuO sample. In addition, the sensing response time also reduced to around 23% for lowest ethanol concentration at T(opt). However, a decrease in the sensor response was observed on films with very high concentrations of MWNTs. (C) 2011 Elsevier B.V. All rights reserved.

Ru(4+) ion in CeO(2) (Ce(0.95)Ru(0.05)O(2-delta)): A non-deactivating, non-platinum catalyst for water gas shift reaction

Hydrogen is a clean energy carrier and highest energy density fuel. Water gas shift (WGS) reaction is an important reaction to generate hydrogen from steam reforming of CO. A new WGS catalyst, Ce(1-x)Ru(x)O(2-delta) (0 <= x <= 0.1) was prepared by hydrothermal method using melamine as a complexing agent. The Catalyst does not require any pre-treatment. Among the several compositions prepared and tested, Ce(0.95)Ru(0.05)O(2-delta) (5% Ru(4+) ion substituted in CeO(2)) showed very high WGS activity in terms of high conversion rate (20.5 mu mol.g(-1).s(-1) at 275 degrees C) and low activation energy (12.1 kcal/mol). Over 99% conversion of CO to CO(2) by H(2)O is observed with 100% H(2) selectivity at >= 275 degrees C. In presence of externally fed CO(2) and H(2) also, complete conversion of CO to CO(2) was observed with 100% H(2) selectivity in the temperature range of 305-385 degrees C. Catalyst does not deactivate in long duration on/off WGS reaction cycle due to absence of surface carbon and carbonate formation and sintering of Ru. Due to highly acidic nature of Ru(4+) ion, surface carbonate formation is also inhibited. Sintering of noble metal (Ru) is avoided in this catalyst because Ru remains in Ru(4+) ionic state in the Ce(1-x)Ru(x)O(2-delta) catalyst.