The ability to measure vegetation structure at spatial scales that are biologically meaningful for wildlife is often limited because information about the spatial scale of habitat selection is lacking and there are logistical constraints to measuring vegetation structure at ever larger spatial scales. To address this challenge, we used LiDAR-derived measurements of vegetation canopy height to quantify habitat associations of riparian birds at the Cosumnes River Preserve in central California, USA. Our objectives were (1) to evaluate the utility of LiDAR (light detection and ranging) measurements for describing habitat associations of riparian passerine birds, and (2) to capitalize on the ease with which LiDAR measurements can be summarized at multiple spatial scales to evaluate the predictive performance of vegetation measurements across spatial scales from 0.2 to 50 ha. At each location where we conducted point-count surveys of the avian community, we summarized the mean and coefficient of variation of canopy height measured at five spatial scales (0.2, 0.8, 3.1, 12.6, and 50.2 ha). For each of these spatial scales, we used stepwise model selection to identify the best logistic-regression model describing patterns of occurrence for 16 species of passerine birds that were sufficiently abundant for analysis. We then used area-under-the-curve (AUC) values to identify models that performed well (AUC >0.75) on a temporally independent data set. Of the 16 species, 10 species had logistic-regression models with AUC values >0.75. For six of these species, AUC values were highest for the models with vegetation measurements at the 0.2–3 ha scale. For the other four species, AUC values were highest for the model with vegetation variables measured at the 50-ha scale. These results illustrate the utility of using LiDAR-derived measurements of vegetation to understand habitat associations of riparian birds and underscore the importance of using multiscale approaches to modeling wildlife habitat use.