Tree Counting by Bridging 3D Point Clouds with Imagery
This work addresses the need for reliable tree counting to support forest management and climate change mitigation, though it appears incremental as it builds on existing fusion methods.
The paper tackles the problem of accurately counting trees in forests by fusing 3D LiDAR data with 2D imagery, and it shows that their method, FuseCountNet, yields more accurate tree counts compared to state-of-the-art algorithms on the NeonTreeCount dataset.
Accurate and consistent methods for counting trees based on remote sensing data are needed to support sustainable forest management, assess climate change mitigation strategies, and build trust in tree carbon credits. Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy and does not allow for easy separation of trees when the canopy is dense. We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees. We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery. The approach is compared with state-of-the-art algorithms, like operating on 3D point cloud and 2D imagery. We empirically evaluate the different methods on the NeonTreeCount data set, which we use to define a tree-counting benchmark. The experiments show that FuseCountNet yields more accurate tree counts.