CN109164461B - Method for estimating single tree leaf loss rate by using single-station foundation laser radar point cloud data - Google Patents
Method for estimating single tree leaf loss rate by using single-station foundation laser radar point cloud data Download PDFInfo
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- CN109164461B CN109164461B CN201811087366.1A CN201811087366A CN109164461B CN 109164461 B CN109164461 B CN 109164461B CN 201811087366 A CN201811087366 A CN 201811087366A CN 109164461 B CN109164461 B CN 109164461B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
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Abstract
The invention discloses a method for estimating the leaf loss rate of single trees by using single-station foundation laser radar point cloud data, which is suitable for evaluating the severity of leaf-eating forest insect pests, belongs to the technical field of foundation radar application, and has the key technical points that: 1. calculating the point cloud density of the local area by using a specific method; 2. and estimating the single wood leaf loss rate by using the point cloud density by using a specific method. The key issues addressed include: 1. only the leaf loss rate of two single trees needs to be visually estimated during ground investigation, so that the ground investigation workload is reduced, and the investigation efficiency is greatly improved; 2. the estimation precision is higher, and the influence of artificial factors is avoided. The method is suitable for the artificial forest with consistent level of leaf loss rate, is a method for measuring the leaf loss rate of the single tree by using an instrument for the first time, and can provide basic data basis for forest pest control work.
Description
1. Field of the invention
The invention relates to a single tree leaf loss rate estimation method in the crossing field of remote sensing and forest pest and disease evaluation, in particular to a single tree leaf loss rate estimation method in the ground-based laser radar (TLS) technology, which is suitable for evaluating the severity of defoliating forest pest and belongs to the technical field of ground-based radar application.
2. Background of the invention
The leaf loss rate is an important index in forest pest control, and the severity of forest pests is reflected by calculating the ratio of the quantity of leaves which are gnawed by forest pests in the tree crowns of the single trees to the quantity of leaves before the single trees are not damaged, so that reasonable treatment measures are made. In the traditional method for estimating the leaf loss rate, the artificial visual measurement is mainly relied on, but the traditional method has obvious defects and shortcomings:
(1) The artificial visual angle is limited, and the condition from the upper part of the crown to the top end of the tree is difficult to observe for tall trees with luxuriant branches and leaves;
(2) The subtle difference of the leaf loss rate is difficult to distinguish through manual observation, the visual measurement precision is low, and the human error is large;
(3) The manual observation has the disadvantages of large workload, long required time and low efficiency, and has great limitation in large-scale victimized artificial forests.
Therefore, in the work of estimating the leaf loss rate of the single tree, the traditional method is low in precision and efficiency, and a method for improving the estimation precision and the estimation efficiency by using a remote sensing means is urgently needed, so that a powerful support is provided for determining a forest pest control scheme.
3. Summary of the invention
In order to solve the problems existing in the traditional artificial estimation of the leaf loss rate, the invention aims to provide a method for estimating the leaf loss rate of a single tree by using single-station foundation laser radar point cloud data. The method realizes the rapid estimation of the single-tree leaf loss rate through the rapid and efficient scanning and data processing of the foundation laser radar, improves the working efficiency and precision, and overcomes the defects of the traditional visual interpretation.
The purpose of the invention is realized as follows: scanning a measurement target area by using a single-station foundation laser radar, manually and visually judging the leaf loss rate range of the area, preprocessing single-tree crown point clouds, calculating the density mean value of the tree crown point clouds, and calculating the leaf loss rate of the single trees by using a fixed formula.
Compared with the traditional visual judgment, the invention has the following advantages:
(1) Only the single wood with the most serious and the lightest damage in the target area needs to be selected, and the leaf loss rate of the two single woods is visually estimated, so that compared with the traditional method, the workload of ground measurement is greatly reduced, and the field measurement time is shortened;
(2) The estimation precision is improved, the method is more objective and accurate, and the influence of the level of a worker on a measurement result is avoided;
(3) The circular sample plot with the diameter of 15 meters only needs 3 minutes of scanning time, data are rapidly obtained, the data processing flow is simple, automatic calculation can be realized, and the working efficiency is improved.
4. The specific implementation mode is as follows:
the invention comprises the following steps: a method of estimating a singletree leaf loss rate using single station ground based lidar point cloud data, the method comprising the steps of:
the method comprises the following steps: placing a foundation laser radar scanning instrument in the measuring area, and scanning the target area;
step two: visually selecting two single trees with the maximum and minimum damage degrees in the target area, and visually measuring the leaf loss rate of the two single trees and recording the leaf loss rate as DR max And DR min ；
Step three: performing single-tree segmentation on the laser radar point cloud data, and extracting single-tree crown area point cloud;
step four: converting the point cloud data from a space rectangular coordinate system to a coordinate value of a spherical polar coordinate system by using formulas (1) - (3), wherein (x, y, z) is the coordinate value of the point cloud under the space rectangular coordinate system, and (r, alpha, beta) is the coordinate value of the point cloud under the spherical polar coordinate system;
step five: dividing a spherical polar coordinate space at intervals of 0.5 degrees, 0.5 degrees and 0.05m, and calculating the number of point clouds in each subspace;
step six: removing the subspace with the point cloud number of 0, calculating the subspace average point cloud number in the crown range, and recording the maximum and minimum average point cloud numbers of all single trees in the research area as DS max And DS min ；
Step seven: calculating the leaf loss rate of the target single wood by using the formula (4) to ensure that DR is achieved object Target Single Wood defoliation Rate, DR min And DR max Visual inspection of the maximum and minimum leaf loss rate, DS, of the singletree in the field max And DS min Is the maximum and minimum of the mean cloud density of all single tree crown points, DS object Taking the point cloud density mean value of the target single tree crown:
in order to verify the effectiveness and the measurement precision of the method, the applicant uses the Chinese pine artificial forest in partial areas of Liaoning province as an experimental object, estimates the single-wood precision by using the method, and calculates the error by comparing with the ground measured value. The experimental subject condition is shown in table 1, and the precision condition is shown in table 2. It can be seen that the sample estimation accuracy is 94% or more when the difference between the maximum and minimum leaf loss rates within the sample plot (hereinafter referred to as the sample leaf loss rate difference) is within 10%, the sample estimation accuracy is 88% or more when the sample leaf loss rate difference is within 20%, the sample estimation accuracy is 83% or more when the sample leaf loss rate difference is within 30%, the sample estimation accuracy is 79% or more when the sample leaf loss rate difference is within 40%, and the sample estimation accuracy is 75% or more when the sample leaf loss rate difference is within 50%. The method is effective for the measurement area with the defoliation difference value less than 50%, and has high measurement precision.
TABLE 1 Overall overview of Single-wood defoliation Rate measurements
TABLE 2 measurement of Single Tree defoliation Rate error Using the method herein
Note: the horizontal and vertical table heads of the table respectively represent the maximum and minimum leaf loss rates in the measuring area, and the number in the table is the root mean square error measured by using the method in the leaf loss rate interval.
Claims (1)
1. A method for estimating the single wood leaf loss rate by using single station foundation laser radar point cloud data is characterized by comprising the following steps:
the method comprises the following steps: placing a foundation laser radar scanning instrument in the measuring area, and scanning the target area;
step two: visually selecting two single trees with the maximum and minimum damage degrees in the target area, and visually measuring the leaf loss rate of the two single trees and recording the leaf loss rate as DR max And DR min ；
Step three: performing single-tree segmentation on the laser radar point cloud data, and extracting single-tree crown region point clouds;
step four: converting the point cloud data from a space rectangular coordinate system to a coordinate value of a spherical polar coordinate system by using formulas (1) - (3), wherein (x, y, z) is the coordinate value of the point cloud under the space rectangular coordinate system, and (r, alpha, beta) is the coordinate value of the point cloud under the spherical polar coordinate system;
step five: dividing a spherical polar coordinate space at intervals of 0.5 degrees, 0.5 degrees and 0.05m, and calculating the number of point clouds in each subspace;
step six: removing the subspace with the point cloud number of 0, calculating the subspace average point cloud number in the crown range, and recording the maximum and minimum average point cloud numbers of all single trees in the research area as DS max And DS min ；
Step seven: calculating the leaf loss rate of the target single wood by using the formula (4) to ensure that DR is achieved object Target Single Wood defoliation Rate, DR min And DR max Visual inspection of the maximum and minimum leaf loss rate, DS, of the singletree in the field max And DS min Is the maximum and minimum of the mean cloud Density of all Single Tree crown points, DS object Taking the point cloud density mean value of the crown of the target single tree as follows:
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| FI117490B (en) * | 2004-03-15 | 2006-10-31 | Geodeettinen Laitos | Procedure for defining attributes for tree stocks using a laser scanner, image information and interpretation of individual trees |
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| CN106845399B (en) * | 2017-01-18 | 2020-06-09 | 北京林业大学 | Method for extracting single-tree information from LiDAR point cloud by using hierarchical clustering mode |
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