Guozhen Lai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Guozhen Lai has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Chengchuan Zhou
Jiangxi Academy of Forestry, Nanchang, P.R. China
1 shared publication - Liting Liu
Jiangxi Academy of Forestry, Nanchang, P.R. China
1 shared publication - Meng Cao
Jiangxi Forestry Resources Monitoring Center, Nanchang 330045, China
1 shared publication - Xun Zhong
Jiangxi Academy of Forestry, Nanchang 330032, China
1 shared publication - Xunzhi Ouyang
College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China
1 shared publication - Zhiwen Guo
Jiangxi Academy of Forestry, Nanchang 330032, China
1 shared publication
Latest Publications
- Open Access
Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method
The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.
Open AccessExtracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method
The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.