<p>Rockfall along mountainous roadways poses a hazard to transportation infrastructure, commercial traffic, and the public. Lidar scanning and photogrammetry are powerful tools to create high-resolution point cloud models of rock slopes and quantify change, facilitating rockfall volume estimation. The empirical magnitude–cumulative frequency (MCF) distribution of rockfall defines the number of rockfalls of various sizes that occurred for a certain monitored time and area. Quantitative rockfall hazard assessment can be based on the MCF curve as an empirical estimate of future activity. Four remote-sensing-based rockfall inventories of Colorado rock slopes were studied to determine the typical length of monitoring necessary to produce an MCF power law that accurately reflects long-term rockfall activity. Uncertainty of the power law was evaluated for each rockfall inventory and related to practical hazard metrics. The time required for the MCF power law fit parameters to stabilize varies for different slopes depending on source area size and rockfall frequency. Bootstrapped confidence intervals on the fit parameters were used to quantify MCF power law variability over time and with the addition of new rockfalls to the database. The results of this research include guidelines for minimum rock slope monitoring time and database size to adequately constrain the rockfall magnitude–frequency relationship. Implications of uncertainty propagation into estimates of occurrence probability for given rockfall volumes over specific periods are also addressed.</p>