XST-LiDARNet In-situ eco-lidar
Products
About 55% of the Earth's land surface is covered by forest natural resources (30%) and grassland natural resources (25%), which play an important role in the global carbon cycle and climate regulation, and it is particularly important to carry out the changes in vegetation growth of forest and grassland natural resources. As LIDAR adopts active optical technology and instantaneously emits high-energy pulse signals with a large penetration depth, it is capable of detecting information below the surface of the vegetation canopy. Lidar can not only extract the ecological parameters of the vegetation canopy, but also reconstruct the three-dimensional scene of the vegetation from the point cloud data, and the monitoring of vegetation growth changes through Lidar technology can better characterize the supply capacity of the vegetation ecosystem in different periods.
Traditional LiDAR technology has many problems, such as high cost, inefficient data acquisition, inability to take into account high spatial and temporal resolution, inability to effectively capture short-term dynamic changes in vegetation, and multi-temporal discrete data cannot be effectively matched.
Based on the above practical needs, the XST-LiDARNet in-situ ecological LiDAR system developed by our company can perfectly solve the above problems in vegetation monitoring, which can continuously scan the target area, more accurately reflect the dynamics of the vegetation, maintain the observation continuity, with a time resolution of up to hourly, in-situ observation of the data, without having to consider the geometrical location of the point cloud alignment problems, the effective use of the time-series information, and accurately capture the changes in vegetation growth. Capture vegetation growth changes accurately.
XST-LiDARNet software system
Raw data:Binary structured data is a 3D point cloud map consisting of a series of XYZ coordinate information;
Point cloud data processing:Processes such as 3D coordinate transformation, noise point removal, intraday point cloud data synthesis, point cloud ground filtering, wind speed filtering, etc., combined with human judgment filtering (e.g., tower, bracket base, etc., can be manually cropped at once during installation);
Optimization of structural parameter extraction algorithms:Optimal rasterized canopy height extraction model; Estimating the daily change of vegetation volume based on volumetric surface differencing and obtaining vegetation temporal change characteristics.
Technical Parameters
| laser wavelength | 905nm |
| Echo Detection Mode | Single and first echo |
| human eye safety level | Class 1 (IEC 60825-1:2014) |
| Recommended scanning frequency | once a day |
| Range (@100klx) | 150m @10% reflectance; 3m minimum range |
| Random error in ranging (1σ) | <2cm@20m (80% reflectance) |
| Ranging system error | <±3cm@20m |
| field of view | 120° horizontal, 25° vertical |
| angular random error | <0.1° |
| Point cloud output | 452,000 points/second |
| Operating Temperature | -40℃～85℃ |
| Radar protection level | IP66 |
| Running Power Consumption | Rated 12W; Start 26W; Maximum low-temperature heating power 40W; Supply voltage: 9~18V |
| Data processing software | Built-in online data processing program |
| Measurable parameters | 3D point cloud data, mean canopy height, cover, leaf area body density vision plant area and plant volume |
| operating mode | Fully automatic, all-weather |
Application Cases
Grassland monitoring
Pilot area: located in the grasslands of Inner Mongolia
Typical grasslands: fescue, kochia, onion, etc.
Experimental data: 2021,05,21-2021,09,15
Forest monitoring
Experimental area: located at the Qingyuan Forest Ecosystem Observatory
Typical secondary forests: walnut, ash and color maple, etc.
Experimental data: 2021,08,01-2021,12,11
