Understanding how habitat quality in heterogeneous landscapes governs the distribution and fitness of individuals is a fundamental aspect of ecology. While mean individual fitness is generally considered a key to assessing habitat quality, a comprehensive understanding of habitat quality in heterogeneous landscapes requires estimates of dispersal rates among habitat types. The increasing accessibility of genomic approaches, combined with field-based demographic methods, provides novel opportunities for incorporating dispersal estimation into assessments of habitat quality. In this study, we integrated genomic kinship approaches with field-based estimates of fitness components and Approximate Bayesian Computation (ABC) procedures to estimate habitat-specific dispersal rates and characterize habitat quality in two-toed sloths (Choloepus hoffmanni) occurring in a Costa Rican agricultural ecosystem. Field-based observations indicated that birth and survival rates were similar in a sparsely-shaded cacao farm and adjacent cattle pasture-forest mosaic. Sloth density was threefold higher in pasture compared to cacao, whereas home range size and overlap were greater in cacao compared to pasture. Dispersal rates were similar between the two habitats, as estimated using ABC procedures applied to the spatial distribution of pairs of related individuals identified using 3,431 SNP and 11 microsatellite locus genotypes. Our results indicate that crops produced under a sparse overstory can, in some cases, constitute lower quality habitat than pasture-forest mosaics for sloths, perhaps because of differences in food resources or predator communities. Finally, our study demonstrates that integrating field-based demographic approaches with genomic methods can provide a powerful means for characterizing habitat quality for animal populations occurring in heterogeneous landscapes.