Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.Keywords Drought Á Standardized precipitation index (SPI) Á Normalized difference vegetation index (NDVI) Á Water supply vegetation index (WSVI) Á

The river Brahmputra flowing through the state of Assam (India) floods every year. The analysis of spatial extent and temporal pattern of flood-inundated areas is of prime importance for mitigation of floods. With the development of remote sensing techniques, flood mapping for large areas can be done easily. In case of flood affected area mapping of large area it will not be feasible to use high-resolution data, because the whole area will be covered in number of scenes. Therefore use of NOAA (National Oceaongraphic Atmospheric Administrative) data is quite useful in such type of studies. NOAA-AVHRR (Advanced Very High Resolution Radiometer) data have the potential for flood monitoring due to high frequency of global coverage, wide swath, high repetivity and low cost. In this study, NOAA-AVHRR data have been used for mapping of flood-affected area during the year 2003. On the basis of spectral characteristics of land and water, a methodology for water identification is presented. The maximum spatial extent of floods, generated by compiling the available cloud free maps, is informative about flood damages. Analysis of results reveals that in the months of July and August almost 25-30% of the area was flood affected. Also the result indicates that in some districts, the flood-affected area is very high.

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Snow is a dynamic natural element, the distribution of which is largely controlled by latitude and altitude. In the tropical country like India, snow distribution is mostly controlled by altitude. The present study aims to identify the relationship between snow accumulation with elevation and aspect in rugged terrain in the Himalayan region. The river basins of four tributaries of the River Indus i.e. Satluj, Chenab, Ravi and Beas located in the western Himalaya were considered for study. Snow covered area was estimated for a period of 2 years (01 Jan 2003 to 17 Dec 2004) using MODIS 8 days' maximum snow cover products. Aspect and classified relief maps were prepared using the USGS DEM. The interrelationship between aspect, elevation and snow cover area was determined for all the four river basins and comparative analysis has been made. A 2 years average shows that Satluj has the minimum snow covered area 23%, while Chenab has the highest snow covered area i.e. 42%, Ravi and Beas has 33% and 38% respectively. The minimum elevation from where snow covered area appears has been calculated and it has been observed that in case of Satluj, snow appears at a higher elevation (1,369 m) while in Chenab snow appears at an elevation of 834 m, followed by Ravi (1,058 m) and Beas (1,264 m). It was found that aspect has a major impact on snow accumulation in the lower elevations in all the basins as compared to higher elevations. Snow accumulates most in the northwest and northeast aspect. The rate of change in snow cover with elevation is determined for all the river basins and it has been concluded that Satluj has the lowest rate of change of snow cover with elevation (1.3% per 100 m), Chenab 1.8% per 100 m, followed by Ravi 2% per 100 m and Beas (2% per 100 m).

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