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.
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.
Mehta M.,Indian Institute of Remote Sensing
Atmospheric Environment | Year: 2015
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.
Kushwaha S.P.S.,Indian Institute of Remote Sensing |
Nandy S.,Indian Institute of Remote Sensing
Biodiversity and Conservation | Year: 2012
Knowledge on the structure and composition of the plant communities has enormous significance in conservation and management of forests. The present study aimed to assess the community attributes, viz., structure, composition and diversity in the moist and dry sal (Shorea robusta) forests in the West Bengal province of India and compare them with the other sal forests of India. The phytosociological data from these forests were quantitatively analysed to work out the species richness, diversity, evenness, dominance, importance value, stand density and the basal area. The analysis showed that plant richness and diversity in moist sal forests of northern West Bengal are higher than the dry sal forests of south-west Bengal; a total of 134 tree (cbh ≥30 cm), 113 shrub and 230 herb species were recorded in the moist sal forest compared to 35 tree, 41 shrub and 96 herb species in dry sal forest. Papilionaceae was observed to be the dominant family. Dry sal forests had higher tree dominance (0.81) and stand density (1,006 stems ha -1) but lower basal area (19.62 m 2ha -1) while moist sal forest had lower tree dominance (0.18) and stand density (438 stems ha -1) but higher basal area (56.52 m 2ha -1). Tree species richness and stem density across girth classes in both the types decreased from the smallest to largest trees, while the occurrence rate of species increased with increase in girth class. A t-test showed significant differences in species richness, basal area and the stand density at 95% confidence level (p = <0.05) in the two forest types. The CCA indicated very low overall match (canonical correlation value = 0.40) between the two sets of variables from moist and dry sal types. The differences in these forests could be attributed to the distinct variations in climatic conditions- mainly the rainfall, disturbance regimes and the management practices. © 2012 Springer Science+Business Media B.V.
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.
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.
Singh A.,Indian Institute of Remote Sensing |
Kushwaha S.P.S.,Indian Institute of Remote Sensing
Ecological Modelling | Year: 2011
High quality habitat suitability maps are indispensable for the management and planning of wildlife reserves. This is particularly important for megadiverse developing countries where shortages in skilled manpower and funding may preclude the use of mathematically complex modeling techniques and resource-intensive field surveys. In this study, we propose a simulation based k-fold partitioning and re-substitution approach to refine and update logistic regression models that are widely used for habitat suitability assessment and modeling. We test the modeling strategy using data from a rapid field survey conducted for habitat suitability assessment for muntjak (Muntiacus muntjak) and goral (Naemorrhaedus goral) in the central Himalayas, India. Results obtained from simulations match expectations in terms of model behavior and in terms of published habitat associations of the investigated species. Qualitative comparisons with predictions from the GARP, MaxEnt and Bioclimatic Envelopes modeling systems also show broad agreement with predictions obtained from the proposed technique. The proposed technique is suggested as a rapid-assessment precursor to detailed habitat studies such as patch occupancy modeling in situations where funds or trained manpower are not available. © 2011 Elsevier B.V.
Nayak R.K.,Indian National Remote Sensing Centre |
Patel N.R.,Indian Institute of Remote Sensing |
Dadhwal V.K.,Indian National Remote Sensing Centre
International Journal of Climatology | Year: 2013
Using satellite observations of Normalized Difference Vegetation Index together with climate data from other sources in a terrestrial biosphere model, inter-annual variability of Net Primary Productivity (NPP) over India during 1981-2006 was studied. It is revealed that the variability is large over mixed shrub and grassland (MGL), moderate over cropland and small over the forest regions. Inter-annual variability of NPP exhibits strong positive coherence with the variability of precipitation, and weak coherence with the variability of temperature and solar radiation. Estimated linear growth rate of annual NPP is 0.005 Pg C Yr-2 which is equivalent to 8.5% over the country during past 25 years. This increase is primarily due to the enhancement of productivity over agricultural lands in the country. NPP has increased over most parts of the country during the early 15-year period (1981-1995) resulting in a 10% growth rate of national NPP budget. On the other hand, the NPP growth rate has been reduced to 2.5% during later 15 years period (1991-2005) owing to large decline of NPP over the Indo-Gangetic plains. Climate had a strong control on NPP growth rate during both the periods. © 2012 Royal Meteorological Society.
Garg V.,Indian Institute of Remote Sensing |
Jothiprakash V.,Indian Institute of Technology Bombay
Applied Soft Computing Journal | Year: 2013
The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets. © 2013 Elsevier B.V. All rights reserved.
Maithani S.,Indian Institute of Remote Sensing
Journal of the Indian Society of Remote Sensing | Year: 2010
In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model © 2011 Indian Society of Remote Sensing.