A method for monitoring alfalfa canopy height based on UAV laser radar

Abstract

The present invention relates to the field of laser radar monitoring of vegetation canopy height, and specifically to a method for monitoring alfalfa canopy height based on unmanned aerial vehicle laser radar. Aiming at the problem that the canopy height of herbaceous plants is relatively low and the monitoring error based on unmanned aerial vehicle laser radar is large, a new correction canopy height model is established using the measured canopy height of alfalfa, so that the vegetation canopy height information can be obtained more accurately using unmanned aerial vehicle laser radar.

Description

Technical Field

[0001] The present invention relates to the field of laser radar monitoring of vegetation canopy height, and in particular to a method for monitoring alfalfa canopy height based on unmanned aerial vehicle laser radar. Background Art

[0002] Alfalfa, as an important legume forage, is widely planted due to its high protein content, strong adaptability and wide range of uses. Traditionally, the canopy height of alfalfa is generally measured by field measurement, which is time-consuming, labor-intensive, costly and suitable for small-scale monitoring. UAVs can be equipped with a variety of sensors, such as multispectral, hyperspectral, thermal infrared, and lidar, to provide multi-source data for UAV remote sensing monitoring. As one of the means to obtain vegetation height information, UAV lidar is widely used in forest canopy height monitoring. The vegetation canopy height is mainly obtained through the canopy height model (CHM), that is, the digital surface model (DSM) minus the digital elevation model (DEM). However, due to the low height of herbaceous plants, the vegetation canopy height monitoring error based on this method is large, which limits the application of vegetation canopy height monitoring based on UAV lidar. Summary of the invention

[0003] Based on the above purpose, the purpose of the present invention is to overcome the shortcomings of the prior art, propose a new alfalfa canopy height model based on UAV laser radar, and reduce the error of UAV laser radar monitoring of herbaceous plant canopy height.

[0004] The technical solution of the present application is: a method for monitoring alfalfa canopy height based on unmanned aerial vehicle laser radar, comprising the following steps:

[0005] S1. Obtaining cloud data of alfalfa samples based on UAV-mounted lidar sensors;

[0006] S2. Obtain the digital surface model (DSM) and digital elevation model of the target area through UAV lidar data

[0007] (DEM);

[0008] S3. Obtain the measured height (CH) data of alfalfa canopy through ground measurement;

[0009] S4. The canopy height model (CHM) was calibrated based on the measured ground height data of alfalfa to obtain the alfalfa canopy height.

[0010] Furthermore, the S2 is specifically:

[0011] Preprocessing of UAV lidar data, including point cloud denoising and point cloud ground point classification;

[0012] Generate DSM and DEM of the target area based on the dense point cloud data obtained by UAV lidar.

[0013] Furthermore, S3 specifically includes: using a nine-point sampling method to manually measure the canopy height of alfalfa when it is relatively low and relatively high, respectively, to obtain the measured alfalfa canopy height (CH) data.

[0014] Furthermore, a 0.5 m×0.5 m sample plot is set during the sampling, and the latitude and longitude of the sample points are recorded using RTK.

[0015] Furthermore, the S4 is specifically:

[0016] Through DSM and measured canopy height (CH) data, a DEM (DEM2) corrected based on the measured canopy height of alfalfa was obtained;

[0017] Establish the regression relationship between DEM2 and DEM to obtain a new corrected DEM (DEM3);

[0018] The alfalfa canopy height was obtained based on the calibrated canopy height model (DSM-DEM3).

[0019] Furthermore, the formula for establishing the new correction canopy height model is: DSM-CH=DEM2;

[0020] DEM3=0.9974*DEM+0.3867; Corrected CHM=DSM-DEM3.

[0021] Furthermore, the monitoring method also includes evaluating the accuracy of the canopy height model of the case, specifically:

[0022] The samples are divided into training samples and validation samples in a ratio of 7:3;

[0023] By calculating the root mean square error (RMSE), relative root mean square error (RMSE r )Evaluate the model effect.

[0024] The beneficial effects of the present application are as follows: the present invention utilizes the measured height data of alfalfa canopy to establish a new corrected canopy height model, thereby achieving more accurate acquisition of alfalfa canopy height information. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a method for monitoring alfalfa canopy height based on UAV laser radar of the present invention;

[0026] Figure 2 This is the regression relationship diagram between the DEM (DEM2) corrected based on the measured alfalfa height data and the original DEM. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with a specific implementation case. This implementation case is implemented based on the technology of the present invention, and a detailed implementation method and specific operation process are now given to illustrate that the present invention is creative, but the protection scope of the present invention is not limited to the following examples.

[0028] Taking a certain alfalfa planting experimental area as an example, the first embodiment of the present application is a method for monitoring the alfalfa canopy height based on a UAV laser radar, such as Figure 1 As shown, the following steps are included:

[0029] S1. Obtaining alfalfa sample point cloud data based on a UAV-mounted lidar sensor.

[0030] Taking a certain alfalfa planting experimental area as an example, three sample plots were selected when the alfalfa content was low and high, with an area of ​​0.07-0.26 km 2 The point cloud data is obtained by using a UAV equipped with a laser radar sensor. The operation time is 10:00-14:00, with a heading overlap of 50%, a lateral overlap of 70%, and a flight altitude of 50 meters.

[0031] S2. Obtain DSM and DEM of the target area through UAV lidar data.

[0032] S2.1 preprocesses the UAV lidar data, including point cloud denoising and point cloud ground point classification;

[0033] S2.2 Generate DSM and DEM of the target area based on the dense point cloud data obtained by UAV lidar.

[0034] S3. Obtain the actual height of alfalfa canopy through ground measurement;

[0035] The nine-point sampling method was used to manually measure the alfalfa canopy height when it was low and high, and the actual measured alfalfa canopy height data was obtained.

[0036] Alfalfa sample plots were selected in the monitoring area, and field data were acquired when the alfalfa height was low and high respectively. A 0.5 m × 0.5 m sample plot was set up, and the latitude and longitude of the sample points were recorded by RTK. The alfalfa canopy height was manually measured by the nine-point sampling method, and the average value was taken.

[0037] S4. Correct CHM based on the measured ground height data of alfalfa to obtain the alfalfa canopy height.

[0038] The CH and DSM of alfalfa obtained from S3 are used to establish DEM2 through DSM-CH, and then the regression relationship between DEM2 and DEM is established, such as Figure 2 As shown, a new corrected DEM (DEM3) is obtained;

[0039] Through the difference between DSM and CH, the DEM (DEM2) corrected based on the measured canopy height of alfalfa was obtained;

[0040] Establish the regression relationship between DEM2 and DEM to obtain a new corrected DEM (DEM3);

[0041] A new calibrated canopy height model was established based on the new calibrated DEM (DEM3).

[0042] Furthermore, the formula for establishing the canopy height model is: DSM-CH=DEM2;

[0043] DEM3=0.9974*DEM+0.3867; Corrected CHM=DSM-DEM3.

[0044] Furthermore, the monitoring method also includes evaluating the accuracy of the canopy height model of the case, specifically:

[0045] The samples are divided into training samples and validation samples in a ratio of 7:3;

[0046] By calculating the root mean square error (RMSE), relative root mean square error (RMSE r )Evaluate the model effect.

[0047] The calculation of root mean square error (RMSE), relative root mean square error (RMSE r )The formula is:

[0048]

[0049] Among them, Y i is the model prediction value, X i is the measured value, is the average of the measured values, and n is the number of samples.

[0050] This paper conducts experiments in alfalfa plots to obtain the measured alfalfa canopy height and uses a drone equipped with a lidar to obtain point cloud data. The new correction canopy height model can more accurately monitor the alfalfa canopy height, with an RMSE of 2-5 cm. r The invention provides a method for monitoring the height of alfalfa canopy based on unmanned aerial vehicle laser radar, and provides technical support for the inversion of vegetation canopy height by unmanned aerial vehicle laser radar.

Claims

1. A method for monitoring alfalfa canopy height based on unmanned aerial vehicle laser radar, characterized in that: The steps include: S1. Obtaining cloud data of alfalfa samples based on UAV-mounted lidar sensors; S2. Obtain the digital surface model DSM and digital elevation model DEM of the target area through the UAV lidar sensor data; S3. Obtain the measured height CH data of alfalfa canopy through ground measurement; S4. Correcting the canopy height model CHM based on the CH data of ground measured height of alfalfa, and then obtaining the alfalfa canopy height; The S3 specifically comprises: using a nine-point sampling method to manually measure the canopy height of alfalfa when it is relatively low and relatively high, respectively, to obtain the measured alfalfa canopy height CH data; The S4 is specifically: Through the digital surface model DSM and the measured canopy height CH data, the DEM2 corrected based on the measured canopy height CH of alfalfa was obtained, where DEM2 = DSM-CH; Establish the regression relationship between DEM2 and digital elevation model DEM to obtain the new corrected DEM3; The alfalfa canopy height was obtained based on the corrected canopy height model CHM=DSM-DEM3.

2. The monitoring method according to claim 1, characterized in that: The S2 is specifically: Preprocessing of UAV lidar data, including point cloud denoising and point cloud ground point classification; Generate DSM and DEM of the target area based on the dense point cloud data obtained by UAV lidar.

3. The monitoring method according to claim 2, characterized in that: During the sampling, a 0.5 m×0.5 m sample plot was set, and the latitude and longitude of the sample points were recorded using RTK.

4. The monitoring method according to claim 3, characterized in that: The formula for establishing the corrected canopy height model CHM is: DSM-CH=DEM2; DEM3=0.9974*DEM+0.3867; corrected canopy height model CHM=DSM-DEM3.

5. The monitoring method according to claim 1, characterized in that: The monitoring method further includes evaluating the accuracy of the corrected canopy height model, specifically: The samples are divided into training samples and validation samples in a ratio of 7:3; By calculating the root mean square error RMSE, relative root mean square error RMSE r Evaluate the model performance.

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