There is a high demand for accurate and up-to-date territorial digital twins for human activities, but their production and updating costs remain prohibitive for many applications. Their generation relies on acquiring LiDAR and/or image data over the territory of interest. Each modality has its advantages: LiDAR is more accurate but more costly, while images are noisier but less costly and more easily accessible. Combining these two technologies to produce and update digital twins is thus a promising avenue. In this paper, we propose a pipeline based on 3D change detection to update a LiDAR point cloud using newer aerial imagery. First, triangle meshes are generated from LiDAR data and image-based dense matching. Then, 3D ray tracing is used to detect changes. After removing the changed parts, the point clouds are fused to update the scene. The proposed method is demonstrated on two datasets in France. The code will be open source on Github: <code>https://github.com/whuwuteng/ChangeUpdateJN</code>.