Chen Simin, Song Shaojing, Wang Yicheng, Pan Hao, Li Fashuai, Chen Yuwei
School of Computer and Information Engineering, Shanghai Polytechnic University, Shanhai 201209, China.
State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China.
Sensors (Basel). 2024 Sep 4;24(17):5741. doi: 10.3390/s24175741.
In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.
在农业和林业领域，归一化植被指数（NDVI）是评估植物生理状态的关键指标。传统成像传感器只能收集二维植被分布数据，而双波长激光雷达技术能够获取垂直分布信息，这对于森林结构恢复和精准农业管理至关重要。然而，现有的激光雷达系统在检测两个波长的回波时面临挑战，通常依赖多个探测器或阵列传感器，导致成本高昂、系统笨重且检测速度慢。本研究引入了一种时间拉伸方法，在时间维度上分离两个激光波长，从而实现更具成本效益和高效的双光谱（600纳米和800纳米）激光雷达系统。该系统利用超连续谱激光器和单像素探测器，并结合专门设计的时间拉伸传输光学器件，提高了NDVI数据收集的效率。我们验证了系统的测距性能，通过使用高采样率示波器收集数据，实现了约3毫米的精度。此外，通过检测处于各种健康状况的树枝、土壤和树叶，我们评估了系统的性能。双波长激光雷达能够检测由于叶绿素浓度和含水量差异导致的NDVI变化。此外，我们使用雷达方程分析实际场景，阐明入射角对反射率和NDVI的影响。扫描红肤杨，我们获得了其NDVI分布，展示了其物理特性。总之，基于时间拉伸方法提出的双波长激光雷达在农业和林业应用中已被证明是有效的，为未来精准农业和森林管理提供了一种新的技术方法。
