Measurements of vegetation structure have become a valuable tool for ecological research and environmental management. However, data describing the thermal 3D structure of canopies and how they vary both spatially and temporally remain sparse. Coincident RGB and thermal imagery from a UAV platform were collected of both a standalone tree and a relatively dense forest stand in the sub-alpine Eastern Swiss Alps. For the first time, SfM-MVS methods were used to develop 3D RGB and thermal point clouds of the two sites with point densities of 35,245 and 776 points per m2, respectively, compared to 78 points per m2 for an airborne LiDAR dataset of the same area. Despite the low resolution of the thermal imagery compared to RGB photosets, forest structural elements were accurately resolved in both point clouds. Improvements in the quality of the thermal 3D data were gained through the application of a distance filter based on the proximity of these data to the RGB 3D point data. Vertical temperature gradients of trees were negative with increasing height at the standalone tree, but were positive in the dense stand largely due to increased self-shading of incoming shortwave energy. Repeat surveys across a single morning during the snowmelt period revealed changes in the spatial distribution of canopy temperatures which are consistent with canopy warming from direct solar radiation. This is the first time that coincidentally acquired RGB and thermal imagery have been combined to generate separate RGB and thermal point clouds of 3D structures. These methods and findings demonstrate important implications for atmospheric, hydrological and ecological modeling, and have wide application for effective thermal measurements of remote environmental landscapes.