Abstract

Forests are depleted drastically at higher rates to cope up the needs of increasing population. The present study was carried out to assess the impact of anthropogenic and natural disturbances (tsunami) on the vegetation of North Andaman islands using three different satellite images of different time period by visual image interpretation technique. A higher proportion of land cover and vegetation converted into agriculture, settlement, sand and water. Assessment of overall forest change from 1976 to 2005 is 117 Km 2 , with forest loss at the rate of 3.8 Km 2 / year. Simulation study for future forest scenario predicted an increase in agriculture / settlement area to be 196, 296, 392 and 492 Km 2 for the next 25, 50, 75 and 100 years, coupled with the conversion of forest areas of 131, 227, 320 and 427 Km 2 . Finally by the end of 100 years the estimated forest area of 1271 Km 2 (1999 data) reduces to 846 Km 2 if proper conservative actions are not taken.

Abstract

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual

Abstract

A Special Economic Zone (SEZ) is a geographical region that has economic laws more liberal than a country's typical economic laws. In the recent years, India has witnessed a confrontation between farmers and the state, the reason being use of highly productive land for setting up SEZs with some of the cases occupying a centre stage in the World fora (Nandigram violence). In this paper, we propose an integrated solution for identifying nonagricultural lands for setting up SEZs using MODIS NDVI product and remote sensing techniques. The study also identifies multi- cropping practices and reinforces the potential of time-series MODIS data for mapping and land cover classification at regional scales.