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Many scientific applications generate large spatio-temporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions are defined as contiguous sets of points whose attributes satisfy some user defined conditions, e.g. high temperature regions in a combustion simulation. At each time step, the regions of interest may be identified by first searching for all points that satisfy the conditions and then grouping the points into connected regions. To speed up this process, the searching step may use a tree-based indexing scheme, such as a KD-tree or an Octree. However, these indices are efficient only if the searches are limited to one or a small number of selected attributes. Scientific datasets often contain hundreds of attributes and scientists frequently study these attributes in complex combinations, e.g. finding regions of high temperature and low pressure. Bitmap indexing is an efficient method for searching on multiple criteria simultaneously. We apply a bitmap compression scheme to reduce the size of the indices. In addition, we show that the compressed bitmaps can be used efficiently to perform the region growing and the region tracking operations. Analyses show that our approach scales well and our tests on two datasets from simulation of the autoignition process show impressive performance.