Change detection using remote sensing has become increasingly important for characterization of natural disasters. Pre- and post-event LiDAR data can be used to identify and quantify changes. The main challenge consists of producing reliable change maps that are robust to differences in collection conditions, free of processing artifacts, and that take into account various sources of uncertainty such as different point densities, different acquisition geometries, georeferencing errors and geometric discrepancies. We present a simple and fast technique that accounts for these sources of uncertainty, and enables the creation of statistically significant change detection maps. The technique makes use of Bayesian inference to estimate uncertainty maps from LiDAR point clouds. Incorporation of uncertainties enables a change detection that is robust to noise due to ranging, position and attitude errors, as well as "roughness" in vegetation scans. Validation of the method was done by use of small-scale models scanned with a terrestrial LiDAR in a laboratory setting. The method was then applied to two airborne collects of the Monterey Peninsula, California acquired in 2011 and 2012. These data have significantly different point densities (8 vs. 40 pts/m2) and some misregistration errors. An original point cloud registration technique was developed, first to correct systematic shifts due to GPS and INS errors, and second to help measure large-scale changes in a consistent manner. Sparse changes were detected and interpreted mostly as construction and natural landscape evolution.