Abstract
Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy.
Highlights
Separating foliage and woody components within the forest canopy is a key step toward better simulating various eco-hydrological processes including canopy storage [1,2], stemflow [3], and throughfall [4]
We paid more attention to quantitatively evaluate and objectively select three hyper-parameters used in the foliage and woody components (FWCNN) model, which greatly affect the model performance
We set the batch size as 10% of the number of training samples selected from each terrestrial laser scanning (TLS) dataset, instead of using a fixed number of samples in each epoch during the model training process, since this is more suitable for processing TLS data with variable point density
Summary
Separating foliage and woody components within the forest canopy is a key step toward better simulating various eco-hydrological processes including canopy storage [1,2], stemflow [3], and throughfall [4]. Existing researches have used TLS data to classify foliage and woody components at plot level [8,9,10,11]. Two commonly used approaches for separating foliage and woody points from TLS data are the local geometrical feature- [8,9,12] and LRI-based methods [11,13]. The linear (e.g., branches and twigs), surface (e.g., broadleaves), and random (e.g., needle clusters) distribution of local points in TLS data can be used to distinguish foliage and woody points [9,14,15].
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