CN112363134A - Method for extracting forest stand density of complex forest region in mountainous region based on airborne low-density LiDAR - Google Patents
Method for extracting forest stand density of complex forest region in mountainous region based on airborne low-density LiDAR Download PDFInfo
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- CN112363134A CN112363134A CN202011106314.1A CN202011106314A CN112363134A CN 112363134 A CN112363134 A CN 112363134A CN 202011106314 A CN202011106314 A CN 202011106314A CN 112363134 A CN112363134 A CN 112363134A
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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
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The invention aims to solve the problem of improving the estimation precision of forest stand density in complex mountain forest zones. The research of estimating forest vegetation structure parameters by using airborne LiDAR point clouds, such as aboveground biomass, storage amount, forest stand average height and the like, is increasing. But the estimation of forest stand density is still not accurate, especially in mountainous areas with complex terrain. The method explores a plurality of variables, finally finds that the point cloud density variable is a variable with high correlation of forest stand density, and establishes a nonlinear forest stand density regression model by using the variable to realize quick and high-precision estimation of forest stand density.
Description
Technical Field
The invention relates to the field of vegetation remote sensing, in particular to a method for extracting forest stand density based on airborne low-density LiDAR mountainous complex forest zones.
Background
Forest stand density can be estimated using LiDAR data from two aspects, one based on high density point clouds (typically greater than 4 points/m)2) Carrying out single-tree scale segmentation on the data, and extracting single-tree crowns so as to count the forest stand density; on the other hand, the method is extracted according to the forest stand scale, and the forest stand parameters are estimated through the statistical relationship between the sample point cloud metrics parameters and the forest stand attributes. The second method metrics relates to crown height variables, such as mean height and crown density variables (describing frequency of echoes by a ratio of number of total echoes above a certain height). But only a few of these variables remain in the final estimation model. A large number of models for different types of forests and tree species have emerged in the existing methods. However, in this respect, most structural variables can obtain medium or high correlation, and the forest stand density estimation accuracy is relatively low, which is worth further exploration.
Disclosure of Invention
Aiming at the problems, the invention provides a mountain complex forest stand density extraction method based on airborne low-density LiDAR
The method comprises the steps of firstly acquiring a synchronous data set and a corresponding sample plot data set by airborne low-density point cloud and synchronously acquiring aerial image data, and estimating the forest stand density of a forest in a research area by using a part of ground sample plot data and the synchronous data set, so that necessary preprocessing including spatial position matching between the data sets is required to be completed, and the extraction precision is ensured. And exploring a plurality of variables, finally finding out that the point cloud density variable is a variable with higher forest stand density correlation, and establishing a univariate nonlinear forest stand density empirical model by using the variable.
Detailed Description
Firstly, acquiring airborne LiDAR data and collecting a ground sample plot data set in a research area, the method needs to use a part of ground sample plot data and a synchronous data set to construct a regression model to complete the estimation of the forest stand density of the forest in the research area, and therefore, accurate preprocessing is a necessary step. And the geometric registration of the ground sample plot data and the airborne LiDAR data is completed, and the extraction precision is ensured.
Then, metrics variables including percentile heights (10 percentile, 20 percentile, …, 100 percentile), different percentile height densities (10 percentile height density, 20 percentile height density, …, 100 percentile height density), skewness, kurtosis, standard error, meanHt, stdHt, etc. in the LiDAR data are extracted. Analyzing the relation between each variable and stand density, finding that the relation between the point cloud number Count variable and stand density is relatively close, especially
D = Count2/Count0, wherein Count2 is the number of point clouds with ground points more than 2m, and Count0 is the number of all ground points.
And establishing a nonlinear regression model of the D and the actual forest stand density, and estimating the forest stand density parameters of the research area through the established model.
Claims (1)
1. A method for extracting forest stand density based on airborne low-density LiDAR mountainous complex forest zones is characterized by comprising the following steps:
step 1, firstly, collecting airborne LiDAR data (low density, point cloud density is about 2 points per square meter) covering a research area, and completing preprocessing of a data set;
step 2, collecting necessary quantity of ground sample plot survey data, counting forest stand density, taking national forest resource type clearing data as an example, taking all forest trees in a sample plot as the actual forest stand density (including bamboo forest), and ensuring the accuracy of the positions between data sets (airborne LiDAR data and ground sample plot data);
step 3, point cloud data preprocessing, including noise point removal, ground point classification, DEM generation and normalized point cloud production, namely subtracting the elevation of each point cloud from the elevation of the corresponding ground point (as the point cloud data preprocessing is not in the range claimed by the patent of the invention, no key statement is made); collecting LiDAR metrics information such as Mean, Standard deviation, Skewness, Kurtosis, Quardmatic Mean, Count, etc. in units of sample plot;
step 4, exploring a plurality of variables to find that the correlation between the Count variable and the related variable and the forest stand density is higher than that between the Count variable and the related variable and other variables, wherein the main variables to be collected mainly comprise: count0 and Count2 respectively represent points with point cloud height thresholds set to be greater than 0m and greater than 2m respectively, and a new variable D is introduceddensity= Count2/Count0 as an argument of the stand density estimation model;
step 5, adding DdensityAnd as independent variables of the model, the actual forest stand density is used as a dependent variable, a nonlinear univariate regression model is established, and the established regression model is used for forest stand density estimation in a research area, so that high-precision drawing of the forest stand density is realized.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011106314.1A CN112363134A (en) | 2020-10-16 | 2020-10-16 | Method for extracting forest stand density of complex forest region in mountainous region based on airborne low-density LiDAR |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011106314.1A CN112363134A (en) | 2020-10-16 | 2020-10-16 | Method for extracting forest stand density of complex forest region in mountainous region based on airborne low-density LiDAR |
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| CN112363134A true CN112363134A (en) | 2021-02-12 |
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| CN202011106314.1A Pending CN112363134A (en) | 2020-10-16 | 2020-10-16 | Method for extracting forest stand density of complex forest region in mountainous region based on airborne low-density LiDAR |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113156394A (en) * | 2021-03-31 | 2021-07-23 | 国家林业和草原局华东调查规划设计院 | Forest resource monitoring method and device based on laser radar and storage medium |
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2020
- 2020-10-16 CN CN202011106314.1A patent/CN112363134A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113156394A (en) * | 2021-03-31 | 2021-07-23 | 国家林业和草原局华东调查规划设计院 | Forest resource monitoring method and device based on laser radar and storage medium |
| CN113156394B (en) * | 2021-03-31 | 2024-04-12 | 国家林业和草原局华东调查规划设计院 | Forest resource monitoring method and device based on laser radar and storage medium |
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