A model is developed to understand the relationship between satellite-derived NDVI and rainfall data in a large tropical catchment. Two Fourier-based modeling techniques with a seasonal component, viz. a seasonal model (SM) and a linear perturbation model (LPM) are tested, and their performance in reproducing the observed NDVI was evaluated. The methodology makes use of 15 years of 10-day composite time series data of rainfall and NDVI, which is estimated from NOAA-AVHRR data, both of which constitute concurrent data from 1982-96.

Forested landscape is not meant for the animals only birds also inhabit the forest. Many components of the environment, including vegetation structure, plant species composition and vegetation stratification affect the distribution of bird species.

Climate models and satellite observations both indicate that the total amount of water in the atmosphere will increase at a rate of 7% per kelvin of surface warming. However, the climate models predict that global precipitation will increase at a much slower rate of 1 to 3% per kelvin. A recent analysis of satellite observations does not support this prediction of a muted response of precipitation to global warming. Rather, the observations suggest that precipitation and total atmospheric water have increased at about the same rate over the past two decades.

Freshwater basins are vulnerable to human-induced change for a number of reasons. The change detection study of Ansupa lake, Orissa was carried out by using multi-temporal satellite data clearly indicates the decrease in wetland area. Due to high anthropogenic pressure, the wetland is shrinking day-by-day.

Hydrogeomorphological mapping is one of the best-suited approaches to explore the possibility of groundwater resources especially in those areas where availability of surface water in insufficient. The present paper is an attempt to study hydrogeomorphological features and their prospects for groundwater exploration in Chandraprabha basin, Chandauli District of Vindhayan Upland, U.P. using remotely sensed data.