Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

Area of Science:

• Remote Sensing
• Forestry Science
• Agricultural Technology

Background:

• Normalized Difference Vegetation Index (NDVI) is crucial for plant health assessment in agriculture and forestry.
• Traditional sensors lack vertical data; dual-wavelength LiDAR offers this but faces cost and efficiency challenges.
• Existing dual-wavelength LiDAR systems are expensive and slow due to multiple detectors.

Purpose of the Study:

• To develop a cost-effective and efficient dual-wavelength LiDAR system for enhanced NDVI data collection.
• To overcome the limitations of existing LiDAR systems in detecting dual-wavelength echoes.
• To enable improved precision agriculture and forest management through detailed vegetation structure analysis.

Main Methods:

• Introduced a time-stretched method to separate two laser wavelengths (600 nm and 800 nm) in the time dimension.
• Utilized a supercontinuum laser and a single-pixel detector with specialized time-stretched transmission optics.
• Validated ranging accuracy (approx. 3 mm) and evaluated system performance on various vegetation types and conditions.

Main Results:

• Achieved high ranging accuracy (approx. 3 mm) with a high sampling rate oscilloscope.
• Demonstrated the system's ability to detect NDVI variations linked to chlorophyll and water content in plants.
• Analyzed the impact of incidence angle on reflectance and NDVI using the radar equation.

Conclusions:

• The proposed time-stretched dual-wavelength LiDAR system is effective for agricultural and forestry applications.
• This technology offers a cost-effective and efficient solution for precision agriculture and forest management.
• The system provides a new technological approach for detailed vegetation structure and health monitoring.