Forests are an important natural resource that play a major role in sustaining a number of vital geochemical and bioclimatic processes. Since damage to forests due to natural and anthropogenic factors can have long-lasting impacts on the health of the planet, monitoring and estimating forest cover and its losses at global, regional and local scales is of primary concern. Developing forest cover estimation techniques that utilize remote sensing datasets offers global applicability at high temporal frequencies. However, estimating forest cover using satellite observations is challenging in the presence of heterogeneous vegetation types, each having its unique data characteristics. In this paper, we explore techniques for incorporating information about the vegetation type in forest cover estimation algorithms. We show that utilizing the vegetation type improves performance regardless of the choice of input data or forest cover learning algorithm. We also provide a mechanism to automatically extract information about the vegetation type by partitioning the input data using clustering.