Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Chenxin Sun; Chengwei Huang; Huaiqing Zhang; Bangqian Chen; Feng An; Liwen Wang; Ting Yun

doi:10.3389/fpls.2022.914974

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Front Plant Sci. 2022 Jun 14:13:914974. doi: 10.3389/fpls.2022.914974. eCollection 2022.

Authors

Chenxin Sun¹, Chengwei Huang¹, Huaiqing Zhang², Bangqian Chen³, Feng An³, Liwen Wang¹, Ting Yun^{1

4}

Affiliations

¹ School of Information Science and Technology, Nanjing Forestry University, Nanjing, China.
² Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China.
³ Danzhou Investigation and Experiment Station of Tropical Crops, Ministry of Agriculture, Rubber Research Institute, Chinese Academy of Tropical Agricultural Sciences, Danzhou, China.
⁴ College of Forestry, Nanjing Forestry University, Nanjing, China.

Abstract

Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR) data is of great significance to forest resource assessment and smart management. After proof-of-concept studies, advanced deep learning methods have been shown to have high efficiency and accuracy in remote sensing data analysis and geoscience problem solving. This study proposes a novel concept for synergetic use of the YOLO-v4 deep learning network based on heightmaps directly generated from airborne LiDAR data for ITC segmentation and a computer graphics algorithm for refinement of the segmentation results involving overlapping tree crowns. This concept overcomes the limitations experienced by existing ITC segmentation methods that use aerial photographs to obtain texture and crown appearance information and commonly encounter interference due to heterogeneous solar illumination intensities or interlacing branches and leaves. Three generative adversarial networks (WGAN, CycleGAN, and SinGAN) were employed to generate synthetic images. These images were coupled with manually labeled training samples to train the network. Three forest plots, namely, a tree nursery, forest landscape and mixed tree plantation, were used to verify the effectiveness of our approach. The results showed that the overall recall of our method for detecting ITCs in the three forest plot types reached 83.6%, with an overall precision of 81.4%. Compared with reference field measurement data, the coefficient of determination R ² was ≥ 79.93% for tree crown width estimation, and the accuracy of our deep learning method was not influenced by the values of key parameters, yielding 3.9% greater accuracy than the traditional watershed method. The results demonstrate an enhancement of tree crown segmentation in the form of a heightmap for different forest plot types using the concept of deep learning, and our method bypasses the visual complications arising from aerial images featuring diverse textures and unordered scanned points with irregular geometrical properties.

Keywords: airborne LiDAR; deep learning; forest parameter retrieval; heightmap; individual tree crown segmentation.