The Kali sub-watershed is situated in the semi-arid region of Gujarat, India and forms a part of the Mahi River Watershed. This watershed receives an average annual rainfall of 900 mm mainly between July and September. Due to high runoff potential, evapo-transpiration and poor infiltration, drought like situation prevails in this area from December to June almost every year. In this paper, augmentation of water resource is proposed by construction of runoff harvesting structures like check dam, percolation pond, farm pond, well and subsurface dyke. The site suitability for different water harvesting structures is determined by considering spatially varying parameters like runoff potential, slope, fracture pattern and micro-watershed area. GIS is utilised as a tool to store, analyse and integrate spatial and attribute information pertaining to runoff, slope, drainage and fracture. The runoff derived by SCS-CN method is a function of runoff potential which can be expressed in terms of runoff coefficient (ratio between the runoff and rainfall) which can be classified into three classes, viz., high (> 40%), moderate (20-40%) and low (< 20%). In addition to IMSD, FAO specifications for water harvesting/recharging structures, parameters such as effective storage, rock mass permeability are herein considered to augment effective storage. Using the overlay and decision tree concepts in GIS, potential water harvesting sites are identified. The derived sites are field investigated for suitability and implementation. In all, the accuracy of the site selection at implementation level varies from 80-100%.

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Reference spectra of terrestrial targets are usually collected using field spectroradiometers for mineral abundance mapping and target detection. These spectra often have noise that masks characteristic absorption and reflection features and affects the efficiency of material mapping. This work aims at obtaining an empirical technique for reduction of high-frequency noise from field spectra. The proposed noise correction technique uses a 'normalized' measure R n , where R n ¼ (L n 7 F n )/L n for each band (n) calculated from field and laboratory spectra of test material, with F n and L n being the depth of the absorption feature in field and laboratory spectra, respectively. On the basis of the assumption of the constancy of this ratio in neighbouring bands, an empirical algorithm that approximates the ratio R n of a noisy band to the corrected ratio of an adjacent band is used to obtain the noise-corrected field spectra. The classification accuracy increases significantly when noise reduced field spectra are used as reference spectra.

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