In this study, we quantify vegetation fire activity in India using the MODerate resolution Imaging Spectroradiometer (MODIS) active fire datasets. We assessed different fire regime attributes, i.e., fire frequency, seasonality, intensity and the type of vegetation burnt in diverse geographical regions. MODIS data from 2002-2010 revealed an average of 63696 fire counts per year with the highest during 2009. Fire season in India extends from October to June with the peak during March. The K-means algorithm identified hotspot regions of fire clusters in diverse regions of India. We examined fire radiative power (FRP) data in the hotspot regions to address which fires burn intensively than others based on the vegetation type. We first assessed the best statistical fit distributions for the FRP data using the probability density functions (PDFs) and ranked them based on Kolmogorov-Smirnov statistic. We then described the fire intensities using empirical cumulative distribution functions (CDFs). Results suggest diverse pdfs for the FRP data that included Burr, Dagum, Johnson as well as Pearson distribution and they varied based on the vegetation type burnt. Analysis from empirical CDFs suggested relatively high fire intensity for closed broadleaved evergreen/ semi-deciduous forests than the other vegetation types. Although, annual sum of FRP for agricultural fires was less than the closed broadleaved evergreen forests, the values were higher than the mosaic vegetation category and broadleaved deciduous forests. These results on fire hotspots and FRP will be useful to address the impact of vegetation fires on air pollution and climate in India.