Tree Species Classification from Complex Laser Scanning Data in Mediterranean Forests Using Deep Learning

Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three-dimensional vegetation structure at high resolution, but the scalability of using these data for large-scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer-level graphics processing units, provide new opportunities. Here, we apply a deep learning-based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre-processing such as leaf-wood separation or quantitative reconstructions. Using modern network architectures and data augmentation techniques, and without extensive pre-processing, we are able to achieve high overall and per-species accuracy that is comparable or higher than in existing work while using data from a water-limited ecosystem complicated by structural convergence and multi-stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle-individual species identification-and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.