India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

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.

The study aims to simulate the peri-urban growth dynamics in a growing region of India using Weights of Evidence (WOE) based cellular automata model. The growth process was expressed as a function of four causative variables corresponding to which seven data layers were generated in a Geographic Information Systems environment. The model was calibrated for the period 2000–2005 using Kappa indices and fuzzy set theory based two way comparison method. The Kappa value was 0.7, while the value of K_location and K_histo were 0.81 and 0.93, respectively. The fuzzy similarity values increased for small to large neighbourhood sizes which showed that the model was able to simulate the contiguous and dense growth. However, for dispersed and isolated growth the model showed less accuracy. The model was validated for period 2005–2010 and revealed a Kappa value of 0.88, while value of K_location and K_histo were 0.91 and 0.96, respectively.