Forest biomass is an important biophysical parameter, which delivers vital and valuable information about forest health, growth, productivity, carbon cycle monitoring, forest degradation, and its ecosystem. It is an important inconstant for ecological modeling, carbon stock assessment, and climate change. Forest biomass estimation has been progressively investigated, in which the accuracy of results is good enough to estimate accuracy of biomass; therefore, more accurate estimation of biomass is important for refining the precision and its applicability of these techniques. Hyperspectral remote sensing provides more accurate information about vegetation, so with the combination of advanced hyperspectral datasets it may be a better technique to enhance the results and accuracy of spatial biomass. Airborne hyperspectral data of airborne visible infrared imaging spectrometer-next generation data were demonstrated to estimate above ground biomass (AGB) of a tropical dry deciduous forest. Atmospherically resistant vegetation index, simple ratio index (SRI), normalized difference vegetation index, and enhanced vegetation index (EVI) were used to estimate AGB, in which EVI performs better than other vegetation indices with 0.55 R square value. Plant senescence reflectance index was used to estimate the dry and senescence condition of the forest and its correlations were performed with ground biomass and other vegetation indices.