The rapid increase in the availability of highresolution lidar and other point cloud datasets has given rise to the use of this data for detecting change in the environment over time. This trend can be attributed in large part to the recent proliferation of Unmanned Aerial Vehicles (UAVs), which are rapidly changing the GIS landscape and are opening the broad field of remote data collection and processing to a wide audience. Continual improvements in airborne technology and the miniaturization of the requisite sensors has cultivated a nimble new branch of the industry that provides costeffective data collection services virtually on demand. This ability to employ temporal analysis of the geospatial landscape has applications in forestry, agriculture, mining, urban planning, civil engineering, and many other fields. In this presentation, we will use Global Mapper to explore two scenarios in which change has occurred at different scales, and subsequently, we will consider two different procedures for identifying this change. In the first scenario, we will look at change at a micro level by comparing two point clouds and highlighting individual points that show a 3D shift over a specified distance threshold. In the second scenario, we will look at a wider area in which the difference between two generated surfaces is calculated and modeled. Temporal consideration has emerged as an extremely important branch of geospatial analysis and this presentation will effectively demonstrate the accessibility of this technology.