A method for correcting grassland vegetation height in radar observations based on wind speed data

Abstract

The present invention provides a method for correcting grassland vegetation height using radar observations based on wind speed data. The method includes a three-dimensional laser scanning radar positioned above the vegetation canopy, a wind speed sensor positioned above the vegetation canopy height, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor. The method uses real-time wind speed data to determine whether the grass height retrieved by the laser radar is subject to wind disturbances, and automatically corrects the grass height value retrieved by the laser radar based on the wind speed. Compared to methods that do not correct grass height retrieved by the laser radar based on wind speed data, this method can quickly and accurately correct abnormal grass height data that is subject to wind disturbances, reduce grass height deviations caused by wind disturbances, and improve the data quality and reliability of the final grass height retrieval. This method provides strong support for further precise detection and research on the application of laser radar for grass height retrieval in the ecological field.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing data processing methods, and in particular relates to a radar-observed grassland vegetation height correction method based on wind speed data. Background Art

[0002] Currently, LiDAR-derived vegetation height is widely used in ecosystems such as forests, farmlands, and grasslands. Whether using satellites, drones, or ground-based remote sensing platforms, LiDAR vegetation height monitoring offers greater accuracy in forest ecosystems, as forest vegetation height is less susceptible to external influences (such as wind disturbances). For grasslands and some crops, to accurately determine vegetation height and improve data quality, researchers have conducted a series of quality control experiments on LiDAR-derived vegetation height, including direct elimination and manual observation-assisted correction. During the growing season, LiDAR-derived farmland or pasture vegetation is regularly measured manually. By comparing this with the LiDAR-derived pasture height, the manually observed data is used as a benchmark to eliminate data with excessive height deviations due to wind disturbances. Statistical filtering quality control is also used to eliminate anomalous height values.

[0003] Existing quality control methods have certain limitations. Due to restrictions on operating techniques and some usage scenarios, they cannot be widely applied. They have the following shortcomings:

[0004] 1) The calibration of manually measured data is time-consuming and laborious, requiring regular, long-term observations during the growing season to meet the requirements for comparative calibration of radar data, and the measurement frequency requirement is high. At the same time, manual measurement cannot meet the requirements of large-scale vegetation observation. Only some sample plots can be measured to compare with the height measured by lidar, which has certain deficiencies in representativeness. Finally, the measurement accuracy of manual measurement will be greatly reduced in windy weather, and there will be certain human errors and operational errors.

[0005] 2) Filtering quality control is a statistical method and has no ecological significance. The accuracy of grass height based on filtering still has great uncertainty. In persistent strong winds, filtering cannot accurately distinguish between normal and abnormal values ​​of vegetation height and can only be used as a compensation correction method.

[0006] Based on this, a radar-observed grassland vegetation height correction method based on wind speed data was proposed. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a radar observation grassland vegetation height correction method based on wind speed data in order to solve the problems raised in the above-mentioned background technology in view of the shortcomings of the above-mentioned prior art.

[0008] To solve the above technical problems, the present invention adopts a technical solution: a radar-observed grassland vegetation height correction method based on wind speed data, comprising a three-dimensional laser scanning radar set above the vegetation canopy, a wind speed sensor set above the height of the vegetation canopy, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor.

[0009] The measurement starts, the 3D laser scanning radar is activated, and the original point cloud data information of the vegetation is obtained. The data collector collects the original point cloud data and records the time t;

[0010] Then the wind speed sensor is operated around the clock, and the data collector continuously collects wind speed data and records the corresponding time;

[0011] The data collector obtains the wind speed data at time t, and corrects the grass height data at time t according to the relationship between grass height information and wind speed function.

[0012] Furthermore, the wind speed data at time t is obtained by automatically denoising, filtering and separating the original point cloud data through the data collector, automatically inverting the grass height information and saving it.

[0013] Furthermore, 3D laser scanning radar is used to observe the vegetation canopy vertically downward to obtain information on the grass canopy structure;

[0014] The wind speed sensor is used to obtain the real-time wind speed at the height of the grass canopy;

[0015] The data collector collects and records the real-time observation data measured by the sensor through the data collector.

[0016] Compared with the prior art, the present invention has the following advantages:

[0017] This method uses real-time wind speed data to determine whether grass height inverted by LiDAR is subject to wind disturbances and automatically corrects the LiDAR-derived grass height value based on wind speed. Compared with methods that don't correct LiDAR-derived grass height based on wind speed data, this method can quickly and accurately correct abnormal grass height data affected by wind disturbances, reducing grass height deviations caused by wind disturbances and improving the data quality and reliability of the final grass height inversion. This method provides strong support for further precise detection and research of grass height inversion using LiDAR in the ecological field. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is an overall schematic diagram of the present invention; DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the present invention provides a technical solution: a radar observation grassland vegetation height correction method based on wind speed data, comprising a three-dimensional laser scanning radar arranged above the vegetation canopy, a wind speed sensor arranged above the height of the vegetation canopy, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor;

[0021] 3D laser scanning radar is used to observe the vegetation canopy vertically downward to obtain the grass canopy structure information;

[0022] The wind speed sensor is used to obtain the real-time wind speed at the height of the grass canopy;

[0023] The data collector collects and records the real-time observation data measured by the sensor through the data collector.

[0024] The measurement starts, the 3D laser scanning radar is activated, and the original point cloud data information of the vegetation is obtained. The data collector collects the original point cloud data and records the time t;

[0025] Then the wind speed sensor is operated around the clock, and the data collector continuously collects wind speed data and records the corresponding time;

[0026] The data collector obtains the wind speed data at time t. The wind speed data at time t is obtained by automatically denoising, filtering and separating the original point cloud data, automatically inverting the grass height information and saving it;

[0027] According to the functional relationship between grass height information and wind speed, the data of grass height inverted by lidar with different wind speeds at time t is corrected. The correction mode is:

[0028] ;

[0029] Where H is the grass height inverted by lidar under different wind speeds, unit is cm;

[0030] Hv is the grass height inverted by lidar, in cm;

[0031] F is wind speed, unit is m / s;

[0032] a(s) is the effect of wind speed on grass height under different grass growth conditions. The growth condition is expressed as the product of grass height and coverage.

[0033] Its physical meaning is that the change in grass height when the wind speed increases or decreases by 0.1m / s under different grass growth conditions, a(s) is an orthogonal polynomial expansion of φk(s):

[0034] Orthogonal Polynomial Regression

[0035] ;

[0036] Real-time wind speed data is used to determine whether the grass height inverted by LiDAR is subject to wind disturbance, and the LiDAR-inverted grass height value is automatically corrected based on wind speed. Compared with methods that do not correct LiDAR-inverted grass height based on wind speed data, this method can quickly and accurately correct abnormal grass height data affected by wind disturbance, reduce grass height deviations caused by wind disturbances, and improve the data quality and reliability of the final grass height inversion. This provides strong support for further accurate detection and research of grass height inversion using LiDAR in the ecological field.

[0037] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0038] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for correcting grassland vegetation height using radar observations based on wind speed data, characterized by: It includes a three-dimensional laser scanning radar set above the vegetation canopy, a wind speed sensor set above the height of the vegetation canopy, and a data collector connected to the three-dimensional laser scanning radar and the wind speed sensor. The measurement starts, the 3D laser scanning radar is activated, and the original point cloud data information of the vegetation is obtained. The data collector collects the original point cloud data and records the time t; Then the wind speed sensor is operated around the clock, and the data collector continuously collects wind speed data and records the corresponding time; The data collector obtains the wind speed data at time t, and corrects the grass height data at time t according to the relationship between grass height information and wind speed function; Specifically, according to the functional relationship between grass height information and wind speed, the data of grass height inverted by lidar is corrected due to the influence of different wind speeds at time t. The correction mode is: ； In the formula H is the grass height inverted by lidar under different wind speeds, in cm; H v is the grass height retrieved by LiDAR, in cm; F is the wind speed, in m / s; a(s) is the effect of wind speed on grass height under different grass growth conditions. The growth condition is expressed as the product of grass height and coverage. Its physical meaning is the change in grass height when the wind speed increases or decreases by 0.1 m / s under different grass growth conditions. φk(s) of a(s) is an orthogonal polynomial regression: 。 2. The method for correcting grassland vegetation height by radar observation based on wind speed data according to claim 1, characterized in that: The wind speed data at time t is obtained by automatically denoising, filtering and separating the original point cloud data through the data collector, automatically inverting the grass height information and saving it.

3. The method for correcting grassland vegetation height by radar observation based on wind speed data according to claim 2, characterized in that: 3D laser scanning radar is used to observe the vegetation canopy vertically downward to obtain the grass canopy structure information; The wind speed sensor is used to obtain the real-time wind speed at the height of the grass canopy; The data collector collects and records the real-time observation data measured by the sensor through the data collector.

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