The IUCN Red List categories and criteria are the most widely used framework for assessing the relative extinction risk of species. The criteria are based on quantitative thresholds relating to the size, trends and structure of species' distributions and populations. However, data on these parameters are sparse and uncertain for many species and unavailable for others, potentially leading to their misclassification, or classification as Data Deficient. Here we propose an approach combining data on land-cover change and species-specific habitat preferences, population abundance and dispersal distance to estimate key parameters (extent of occurrence, maximum area of occupancy, population size and trend, and degree of fragmentation) and hence IUCN Red List categories. We demonstrate the applicability of our approach for non-pelagic birds and terrestrial mammals globally (∼15,000 species), generating predictions fairly consistent with published Red List assessments, but more optimistic overall. We predict 4.2% of species (467 birds and 143 mammals) to be more threatened than currently assessed, and 20.2% of Data Deficient species (10 birds and 114 mammals) to be at risk of extinction. However, incorporating the habitat fragmentation sub-criterion reduced these predictions 1.5-2.3% and 6.4-14.9% (depending on the quantitative definition of fragmentation) of threatened and Data Deficient species respectively, highlighting the need for improved guidance to Red List assessors on applying this aspect of the Red List criteria. Our approach can be used to complement traditional methods of estimating parameters for Red List assessments. Furthermore, it can readily provide an early warning system to identify species potentially warranting changes in their extinction risk category based on periodic updates of land cover information. Given that our method relies on optimistic assumptions about species distribution and abundance, all species predicted to be more at risk than currently evaluated should be prioritized for reassessment.