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Dehradun, India

The Indian Institute of Remote Sensing is a premier institute for research, higher education and training in the field of Remote Sensing, Geoinformatics and GPS Technology for Natural Resources, Environmental and Disaster Management under the Indian Department of Space, which was established in the year 1966. Wikipedia.

Singh C.,Indian Institute of Remote Sensing
Climate Dynamics | Year: 2013

In a climate change scenario, the present work deals with the possibility of the changes in the rainfall pattern during the principal monsoon season (June 1-September 30) of the Indian summer monsoon. For this purpose three attributes are defined as DTMR, DHMR and DNMR representing the day when 10, 50 and 90 % of the accumulated summer monsoon rainfall is achieved respectively. Using a high resolution (1° × 1°) gridded rainfall data set for the last 50 years prepared by India Meteorological Department (Rajeevan et al. 2005, in Curr Sci 91:296-306, 2006), the analysis has been carried out over the different parts of the Indian subcontinent. Using statistically robust significance tests, it is observed that the distribution of the three variables have changed significantly at 1 % (or 5 %) significance level in the last 50-year of period. The DTMR and DNMR arrive 2 days early over central India, whereas DHMR appears to arrive 6 days early over west India in the recent decades. The results presented in this paper are supported by the statistically robust significance tests; suggest an apparent change in terms of the arrival of the rainfall attributes during the last half century. © 2013 Springer-Verlag Berlin Heidelberg. Source

Aerosols affect the earth's climate system both on a regional as well as on a global scale. Several studies have identified India (the second most populous country) as one of the regional hot spots of aerosols due its increasing anthropogenic activities. The paper presents a temporal (annual and seasonal) study of aerosol optical depth (AOD) in the country using satellite data for thirteen year period (2001-2013). The Indian region is divided into four sub regions i.e., north, west, east and south. The analysis is carried out using Level 3 data from two satellite sensors, namely, MODIS (1°×1°) and MISR (0.5°×0.5°), onboard NASA's Terra platform. Annual and seasonal mean AOD variation has been studied. It is found that annual aerosol loading remains highest in Indo-Gangetic Plains (IGP) in all the years. In winter season, the overall loading is lowest for the entire country while it reaches maximum in the monsoon season. This could be attributed to the relative humidity, wind and associated rainfall patterns in the country. Also, the aerosol tendencies have been computed using the first and last six year period change in aerosol optical depth. Further, annual and seasonal trends in AOD have been calculated using weighted least square regression approach and the results have been compared. Statistically significant trends are reported at 95% confidence level. Weights are assigned corresponding to the expected errors associated with the satellite data. There is a good agreement in the seasonal tendencies and trends computed from both the sensors for winter, monsoon and post-monsoon seasons. Significantly increasing trends are found in winter and post-monsoon seasons which could be due to increase in anthropogenic activities. All the observations are separately reported for ten most populous cities of India. Delhi and Kolkata are amongst the most polluted cities in India. © 2015 Elsevier Ltd. Source

Garg V.,Indian Institute of Remote Sensing
Natural Hazards | Year: 2014

Hydrologic processes are complex, non-linear, and distributed within a watershed both spatially and temporally. One such complex pervasive process is soil erosion. This problem is usually approached directly by considering the sediment yield. Most of the hydrologic models developed and used earlier in sediment yield modeling were lumped and had no provision for including spatial and temporal variability of the terrain and climate attributes. This study investigates the suitability of a recent evolutionary technique, genetic programming (GP), in estimating sediment yield considering various meteorological and geographic features of a basin. The Arno River basin in Italy, which is prone to frequent floods, has been chosen as case study to demonstrate the GP approach. The results of the present study show that GP can efficiently capture the trend of sediment yield, even with a small set of data. The major advantage of the GP analysis is that it generates simple parsimonious expression offering some possible interpretations to the underlying process. © 2011 Springer Science+Business Media B.V. Source

Singh C.,Indian Institute of Remote Sensing
Atmospheric Research | Year: 2013

Using 1°. ×. 1° daily rainfall data set of 50. years, prepared by the India Meteorological Department, a detailed analysis of active and break spells has been carried out along with the investigation of propagation characteristics and the temporal variation of the intraseasonal oscillations of Indian summer monsoon rainfall. Present analysis reveals that the frequency of the short break spells (3. days) and moderate active spells (4-7. days) have increased after 1977 over a statistically homogenous Central Indian region (16.5-26.5°N; 74.5-86.5°E). It appears that most of the break spells are spatially localized over a smaller region of central India and the frequency of breaks over this region has increased in the recent decades. The area that is prone to breaklike conditions is found to be increasing after 1977 as compared with before 1977. It is also illustrated that the behavior of the intraseasonal oscillations differs in terms of propagation during the two study periods. A new method is also proposed to identify breaks over central Indian region, which shows a good match with previous studies. The results presented here are statistical in nature. © 2013 Elsevier B.V. Source

Maithani S.,Indian Institute of Remote Sensing
Geocarto International | Year: 2015

Land cover transformation is one of the foremost aspects of human-induced environmental change, having an extensive history dating back to antiquity. The present study aims to simulate the process of land cover change based on different policy-based scenarios so as to provide a basis for sustainable development in Doon valley, India. For this purpose, an artificial neural network-based spatial predictive model was developed for the Doon valley. The predictive model generated future land cover patterns under three policy scenarios, i.e. baseline scenario, compact growth scenario and hierarchical growth scenario (HGS). The simulated land cover patterns mirror where land cover patterns are headed in the valley by year 2021. The result suggests that unabated continuation of the present pattern of land cover transformation will result in a regional imbalance. However, this skewed development can be corrected by altering the current growth trend as revealed in the compact growth and HGSs. © 2014, © 2014 Taylor & Francis. Source

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