Yiketai launches a lightweight, integrated, multi-sensor unmanned aerial vehicle remote sensing crop phenotype research and monitoring technology solution - Ecodrone^®UAS-4 Pro lightweight integrated multispectral LiDAR remote sensing system:

• Based on the independently patented UAS-4 remote sensing platform technology, it combines lightweight and multifunctional features

• Simultaneously equipped with multispectral imaging, LiDAR, and RGB imaging, with a working time of over 20 minutes

• One flight can simultaneously obtain 5/10 spectral bands, high-density point cloud data, and RGB, resulting in twice the result with half the effort in operational efficiency

• Centimeter level multispectral ground resolution, with a ground resolution of 3.4cm at 50m height and 2cm at 30m height (for high-throughput crop phenotype analysis in the field)

• LiDAR-RGBStandard precision of 2.5cm, echo frequency of 3, FOV of 70.4 degrees, optional with other specifications; RGB imaging using Sony APS-C Exmor CMOS sensor, 20MP pixel, FOV 83 degrees

• Applied in precision agriculture research, crop phenotype remote sensing, pest and disease monitoring, crop yield evaluation, forest remote sensing monitoring, carbon source sink monitoring and evaluation, ecological environment investigation and monitoring, biodiversity monitoring, and research on biological carbon sequestration

Ecodrone^®UAS-4 ProLightweight integrated multispectral LiDAR remote sensing system

Main technical indicators:

Analyze and measure parameters:

ü Canopy structure parameters: NDVI, NDRE, DVI, VOG, NDWI, GCI, LCI, etc

ü R/G/BIndex, such as greenness index, etc

ü Measurable light utilization efficiency, shallow water environment (aerosols, aerosols, etc.), chlorophyll efficiency or red edge slope, etc. (10 channels)

ü Lidar parameters: high-density true color point cloud, 3D measurement data, classification point cloud, DOM, DSM, DTM, DHM, etc

Application Case 1: Phenotypic Analysis of Rice under Different Stress Conditions

Yiketai Spectral Imaging and Unmanned Aerial Vehicle Remote Sensing Technology Research Center uses Ecodrone^®The unmanned aerial vehicle remote sensing system conducts phenotype analysis on a certain rice field. Based on the NDVI and NDRE results, it can be seen that the overall index value is relatively high except for the edge of the rice field, indicating that the chlorophyll content and green biomass of crops are high, almost saturating the NDVI value. And from the NDRE graph, the differences in physiological characteristics of rice under different treatment conditions can be more clearly seen. Generally, the higher the NDRE value, the healthier the plant.

Figure 1: Flight operation diagram in sequence; Marking diagram of different treatment methods (variety, planting density, fertilizer concentration) in rice fields; NDVI diagram; NDRE diagram

Based on the multispectral data from unmanned aerial vehicles, further research and verification have been conducted to select the optimal combination of planting varieties, planting density, and fertilizer application, which can effectively reduce resource waste and alleviate environmental problems caused by nitrogen fertilizer loss. A fitting model can be established by combining LiDAR structural information and actual measured physical and chemical data to invert crop biochemical and biomass indicators, achieving precision agricultural production research.

Application Case 2: Monitoring the Growth of Artificial Pine Forests

Yiketai Spectral Imaging and Unmanned Aerial Vehicle Remote Sensing Research Center utilizes its independently developed Ecodrone^®The LiDAR unmanned aerial vehicle remote sensing system conducted LiDAR remote sensing operations on a certain farmland artificial forest area.

Figure 2-1: LiDAR point cloud in the workspace based on height rendering

Figure 2-2: DOM and DHM models based on LiDAR point cloud

By measuring the height of the LiDAR point cloud profile and combining it with the DHM model, 15 points of artificial pine forest in plot A were randomly selected, and their height values were extracted. The average value was calculated to be 161cm. However, most of the measured heights from ground manual sampling fell within the range of 1.6-1.7m, indicating a high degree of agreement.

Figure 2-3: Height profile and measurement values of artificial pine forest based on LiDAR point cloud

Experiments have shown that based on Ecodrone^®Lidar unmanned aerial vehicle remote sensing technology is of great significance for precise vegetation classification, monitoring the characteristics of trees/crops at different growth stages, evaluating biomass, and guiding fertilization by measuring the obtained LiDAR three-dimensional information and combining it with ground sampling results.

Application Case Three: Different Growth StagesMonitoring of changes in winter wheat canopy structure

Monitoring canopy density using indicators such as leaf area index (LAI) calculated based on reflectance spectroscopy plays an important role in understanding and predicting cycling processes in the soil plant atmosphere system, as well as indicating crop health and yield estimation in farm management. German and Belgian scholars used unmanned aerial vehicle Lidar and multispectral remote sensing imaging system to collect data from the ICOS winter wheat field area in Selhausen, Germany seven times, with a time span from April 1 to July 21, 2020, to evaluate the potential application of Lidar multispectral technology in precision agriculture canopy structure estimation.

Figure 3-1: Schematic diagram of estimating plant area index (PAI) based on canopy density using airborne LiDAR measurement

Figure 3-2: Left: RGB images at different time periods and PAI and GAI obtained using Lidar and multispectral methods respectively

Right: Temporal and Spatial Changes in the Average Height of ICOS Daitian Winter Wheat

The research results indicate that during the growth stage of winter wheat before maturity, the Plant Area Index (PAI) derived from Lidar data is highly consistent with the Green Area Index (GAI) values collected through ground equipment, and is closely related to the estimated GAI values obtained from multispectral imaging, which can accurately reflect the spatial structural changes during the growth process of winter wheat. The height of winter wheat can also be effectively estimated by subtracting the digital terrain model DTM (01/04, at the beginning of the growing season) from the digital surface model DSM created by collecting point cloud data at each time period (12/05, 26/05, 09/06, 23/06). At the same time, using multispectral data to compensate Lidar PAI can distinguish between green vegetation area index and non green vegetation area index, complement each other throughout the entire crop growth cycle, and conduct crop modeling to achieve precision fertilization, crop management, and carbon storage estimation.

Yiketai Ecological Technology Company is committed to the research, development, and innovative application of ecology agriculture healthTo provide comprehensive technical solutions for precision agriculture research, crop phenotype remote sensing, pest and disease monitoring, crop yield evaluation, forest remote sensing monitoring, carbon source and sink monitoring and evaluation, ecological environment investigation and monitoring, biodiversity monitoring, and biological carbon sequestration research.

reference:

[1] Bates J S , Montzka C , Schmidt M , et al. Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR[J]. Remote Sensing, 2021, 13(4):710.