简介：
Overview
This study presents a user-friendly protocol for utilizing drone-based LiDAR technology in forest recovery assessments. By integrating advanced sensor technology and deep learning models, the protocol enhances the efficiency of monitoring forest recovery after disturbances.
Key Study Components
Area of Science
- Remote sensing
- Forest ecology
- Artificial intelligence
Background
- Recent advancements in remotely piloted aircraft systems (RPAS) enable high-resolution monitoring.
- Integration of AI provides deeper insights from large datasets.
- Effective assessment and management of recovering forested lands is crucial.
- Existing methods often lack efficiency and user-friendliness.
Purpose of Study
- To develop a practical protocol for drone-based LiDAR in forest recovery assessment.
- To address the research gap in individual tree detection and segmentation.
- To reduce time and costs associated with traditional plot surveys.
Methods Used
- Setup of RPAS and RTK base station for accurate data collection.
- Collection of LiDAR and multispectral data over designated areas.
- Use of photogrammetry software for data preprocessing and corrections.
- Application of the TreeAIBox plugin for tree detection and segmentation.
Main Results
- A total of 2,755 individual trees were detected across three sites.
- Detection rates varied, with 100% for Site 2 and 21% for Site 3.
- The protocol successfully identified trees taller than one meter with high accuracy.
- It offers a versatile method for extracting individual tree metrics from LiDAR data.
Conclusions
- This protocol enhances forest recovery monitoring and assessment.
- It is effective for young trees in complex forest environments.
- The method is user-friendly and reduces the need for extensive field surveys.
What is the main advantage of using drone-based LiDAR?
Drone-based LiDAR provides high-resolution data that enhances monitoring efficiency and accuracy in forest recovery assessments.
How does the protocol improve tree detection?
The protocol integrates advanced AI models for precise segmentation and identification of individual trees from LiDAR data.
What types of data are collected during the assessment?
LiDAR and multispectral data are collected to analyze forest recovery and individual tree metrics.
Can this protocol be used in various forest types?
Yes, the protocol is designed to be versatile and applicable across different forest environments.
What are the limitations of the detection rates?
Detection rates can vary significantly based on tree height and density, with shorter trees being more challenging to identify.
Is prior experience with LiDAR necessary to use this protocol?
While some familiarity with LiDAR technology is beneficial, the protocol is designed to be user-friendly for researchers at various skill levels.
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