We evaluated a hierarchical framework for seagrass monitoring in two estuaries in the northeastern USA: Little Pleasant Bay, Massachusetts, and Great South Bay/Moriches Bay, New York. This approach includes three tiers of monitoring that are integrated across spatial scales and sampling intensities. We identified monitoring attributes for determining attainment of conservation objectives to protect seagrass ecosystems from estuarine nutrient enrichment. Existing mapping programs provided large-scale information on seagrass distribution and bed sizes (tier 1 monitoring). We supplemented this with bay-wide, quadrat-based assessments of seagrass percent cover and canopy height at permanent sampling stations following a spatially distributed random design (tier 2 monitoring). Resampling simulations showed that four observations per station were sufficient to minimize bias in estimating mean percent cover on a bay-wide scale, and sample sizes of 55 stations in a 624-ha system and 198 stations in a 9,220-ha system were sufficient to detect absolute temporal increases in seagrass abundance from 25% to 49% cover and from 4% to 12% cover, respectively. We made high-resolution measurements of seagrass condition (percent cover, canopy height, total and reproductive shoot density, biomass, and seagrass depth limit) at a representative index site in each system (tier 3 monitoring). Tier 3 data helped explain system-wide changes. Our results suggest tiered monitoring as an efficient and feasible way to detect and predict changes in seagrass systems relative to multi-scale conservation objectives.