Mangrove forest carbon reserve calculation method and device based on airborne laser radar point cloud

Abstract

The invention relates to a mangrove forest carbon reserve calculation method and device based on airborne laser radar point clouds, and the method comprises the steps: extracting the height and crown breadth parameters of a single tree through the laser radar point cloud data of an unmanned plane, and calculating the carbon reserve of the single tree through a different-speed growth equation of a corresponding tree species, establishing a function relationship between the single-tree carbon density and the single-tree height of the mangrove forest; and filtering the laser radar point cloud data to obtain a canopy height model, and substituting the canopy height model into a mangrove forest carbon density equation between the single-tree carbon density and the single-tree height to obtain the mangrove forest regional carbon reserve. Therefore, the problems that the current red forest carbon reserve estimation precision is difficult to evaluate and the result lacks theoretical interpretation are solved.

Description

Technical Field

[0001] The present application relates to the interdisciplinary technical field of forest ecology and surveying and remote sensing, and in particular to a method and device for calculating mangrove carbon reserves based on airborne laser radar point cloud. Background Art

[0002] "Blue carbon" refers to the carbon absorbed and stored by marine and coastal ecosystems. Coastal wetlands such as salt marshes and mangroves are the main components of blue carbon ecosystems. The area of ​​coastal wetlands accounts for less than 0.5% of the seabed, and the plant biomass accounts for 0.05% of terrestrial plants, but their carbon storage contributes 15% of the global terrestrial carbon storage and more than 50% of the marine carbon storage. As a typical representative, mangroves have a particularly significant impact on the global carbon cycle.

[0003] However, the reliability of existing mangrove carbon storage estimates is poor, making it difficult to achieve high-resolution and timely monitoring of mangrove carbon sinks over a large area, which needs to be urgently addressed. Summary of the invention

[0004] The present application provides a method and device for calculating mangrove carbon reserves based on airborne laser radar point cloud, so as to solve the problems that the current mangrove forest carbon reserve estimation accuracy is difficult to evaluate and the results lack theoretical explanation.

[0005] The first aspect of the present application provides a method for calculating mangrove carbon reserves based on airborne lidar point clouds, comprising the following steps: obtaining lidar point cloud data of a target mangrove forest area, and collecting multiple target single tree point clouds through the lidar point cloud data, and extracting the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, the crown width parameters and a preset tree species allometric growth equation; establishing a functional relationship between the single tree height and the single tree carbon density, and determining the functional relationship corresponding to the single tree point cloud. A plurality of target functions to be fitted are obtained, and model fitting is performed on the plurality of target functions to be fitted to obtain correlation coefficients corresponding to each of the plurality of target functions to be fitted, and a mangrove carbon density equation corresponding to the target mangrove forest area is established according to the correlation coefficients; ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

[0006] Optionally, in one embodiment of the present application, the extracting of the tree height and crown width parameters corresponding to each target tree point cloud in the multiple target tree point clouds, and calculating the tree carbon density corresponding to each target tree point cloud based on the tree height, the crown width parameters and a preset tree species allometric growth equation, includes: counting multiple characteristic values ​​of the target tree corresponding to each target tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the tree, the highest point of the tree, the longest crown width of the tree and the shortest crown width of the tree; The tree height of the single tree is calculated by using the longest crown width of the single tree and the shortest crown width of the single tree, and the crown width parameters are calculated by substituting the tree height and the crown width parameters into the tree species allometric growth equation to calculate the single tree biomass, and the single tree carbon stock corresponding to the target single tree is calculated according to the single tree biomass and the preset red forest carbon content coefficient; based on the crown width parameters, the single tree crown area corresponding to the target single tree is calculated, and the single tree carbon density is calculated according to the single tree crown area and the single tree carbon stock.

[0007] Optionally, in one embodiment of the present application, the determining of multiple categories of target functions to be fitted corresponding to the functional relationship, and performing model fitting on the multiple categories of target functions to be fitted to obtain the correlation coefficient corresponding to each category of the multiple categories of target functions to be fitted, and establishing a mangrove carbon density equation corresponding to the target mangrove forest area based on the correlation coefficient, includes: obtaining a scatter plot between the single tree carbon density and the single tree height, and determining the multiple categories of target functions to be fitted corresponding to the functional relationship based on the scatter plot, wherein the multiple categories of target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions; performing model fitting on the multiple categories of target functions to be fitted to obtain the correlation coefficient corresponding to each category of the target functions to be fitted, and comparing the correlation coefficients of the multiple categories of target functions to be fitted to obtain the maximum correlation coefficient; obtaining the target function to be fitted corresponding to the maximum correlation coefficient, and determining the mangrove carbon density equation based on the target function to be fitted corresponding to the maximum correlation coefficient.

[0008] Optionally, in one embodiment of the present application, the ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon stock of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation, including: filtering the lidar point cloud data to obtain the ground point data and the non-ground point data, and constructing the digital elevation model through the ground point data, and establishing the digital surface model using the non-ground point data; determining a canopy height model according to the digital elevation model and the digital surface model, and substituting the canopy height model into the mangrove carbon density equation to calculate the carbon stock of the target mangrove forest area.

[0009] The second aspect of the present application provides a mangrove carbon storage calculation device based on airborne lidar point cloud, including: an extraction module, used to obtain lidar point cloud data of a target mangrove forest area, and collect multiple target single tree point clouds through the lidar point cloud data, and extract the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, the crown width parameters and a preset tree species allometric growth equation; a fitting module, used to establish a functional relationship between the single tree height and the single tree carbon density, and determine the functional relationship. A plurality of target functions to be fitted corresponding to the laser radar point cloud data are obtained, and model fitting is performed on the plurality of target functions to be fitted to obtain correlation coefficients corresponding to each of the plurality of target functions to be fitted, and a mangrove carbon density equation corresponding to the target mangrove forest area is established according to the correlation coefficients; a construction module is used to obtain ground point data and non-ground point data corresponding to the laser radar point cloud data, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

[0010] Optionally, in one embodiment of the present application, the extraction module includes: a statistical unit, used to count multiple characteristic values ​​of the target tree corresponding to each target tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the tree, the highest point of the tree, the longest crown width of the tree and the shortest crown width of the tree; a first calculation unit, used to calculate the tree height of the tree through the lowest point of the tree and the highest point of the tree, and calculate the crown width parameters using the longest crown width of the tree and the shortest crown width of the tree; a substitution unit, used to substitute the tree height and the crown width parameters into the tree species allometric growth equation to calculate the tree biomass, and calculate the tree carbon stock corresponding to the target tree based on the tree biomass and a preset red forest carbon content coefficient; a second calculation unit, used to calculate the tree crown area corresponding to the target tree based on the crown width parameters, and calculate the tree carbon density based on the tree crown area and the tree carbon stock.

[0011] Optionally, in one embodiment of the present application, the fitting module includes: an acquisition unit, used to acquire a scatter plot between the single tree carbon density and the single tree height, and determine a plurality of target functions to be fitted corresponding to the functional relationship based on the scatter plot, wherein the plurality of target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions; a comparison unit, used to perform model fitting on the plurality of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted, and compare the correlation coefficients of the plurality of target functions to be fitted to obtain the maximum correlation coefficient; a determination unit, used to acquire the target function to be fitted corresponding to the maximum correlation coefficient, and determine the mangrove carbon density equation based on the target function to be fitted corresponding to the maximum correlation coefficient.

[0012] Optionally, in one embodiment of the present application, the construction module includes: a filtering unit, used to filter the lidar point cloud data to obtain the ground point data and the non-ground point data, and construct the digital elevation model through the ground point data, and use the non-ground point data to establish the digital surface model; a third calculation unit, used to determine a canopy height model based on the digital elevation model and the digital surface model, and substitute the canopy height model into the mangrove carbon density equation to calculate the carbon storage of the target mangrove forest area.

[0013] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for calculating mangrove carbon reserves based on airborne lidar point cloud as described in the above embodiment.

[0014] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned mangrove carbon stock calculation method based on airborne lidar point cloud.

[0015] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned mangrove carbon stock calculation method based on airborne lidar point cloud.

[0016] Therefore, the embodiments of the present application have the following beneficial effects:

[0017] The embodiments of the present application can obtain the laser radar point cloud data of the target mangrove forest area, collect multiple target single tree point clouds through the laser radar point cloud data, and extract the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and the preset tree species allometric growth equation; establish a functional relationship between the single tree height and the single tree carbon density, and determine multiple types of target functions to be fitted corresponding to the functional relationship, and perform model fitting on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target functions to be fitted in the multiple types of target functions to be fitted, and establish the mangrove carbon density equation corresponding to the target mangrove forest area according to the correlation coefficient; obtain the ground point data and non-ground point data corresponding to the laser radar point cloud data, and construct a digital elevation model and a digital surface model according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation. This application uses drone remote sensing technology to help achieve high-resolution and high-efficiency monitoring of mangrove carbon sinks over a large area, thereby providing technical support for the national carbon neutrality goal. This solves the current problems of difficulty in evaluating the accuracy of mangrove carbon storage estimates and lack of theoretical explanation for the results.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flowchart of a method for calculating mangrove carbon storage based on airborne laser radar point cloud provided according to an embodiment of the present application;

[0021] Figure 2A schematic diagram of the execution logic of a method for calculating mangrove carbon storage based on airborne laser radar point cloud provided for one embodiment of the present application;

[0022] Figure 3 A schematic diagram of mangrove UAV laser radar data provided for one embodiment of the present application;

[0023] Figure 4 A schematic diagram of a mangrove single tree point cloud data, single tree height and crown width parameters provided for one embodiment of the present application;

[0024] Figure 5 A schematic diagram of different function fittings of single-tree carbon density and tree height parameters of a mangrove forest provided in one embodiment of the present application;

[0025] Figure 6 A schematic diagram of a mangrove area canopy height model provided for one embodiment of the present application;

[0026] Figure 7 A schematic diagram of carbon storage in a mangrove area provided for one embodiment of the present application;

[0027] Figure 8 This is an example diagram of a mangrove carbon storage calculation device based on airborne laser radar point cloud according to an embodiment of the present application;

[0028] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0029] Among them, 10-a mangrove carbon storage calculation device based on airborne lidar point cloud; 100-an extraction module, 200-a fitting module, 300-a construction module; 901-a memory, 902-a processor, and 903-a communication interface. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes the mangrove carbon stock calculation method and device based on airborne lidar point cloud according to an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a mangrove carbon stock calculation method based on airborne lidar point cloud. In this method, lidar point cloud data of the target mangrove forest area is acquired, and multiple target single tree point clouds are collected through the lidar point cloud data, and the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds are extracted, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and the preset tree species allometric growth equation; establish a functional relationship between the single tree height and the single tree carbon density, and Determine the multiple categories of target functions to be fitted corresponding to the functional relationship, and perform model fitting on the multiple categories of target functions to be fitted to obtain the correlation coefficient corresponding to each category of target functions to be fitted in the multiple categories of target functions to be fitted, and establish the mangrove carbon density equation corresponding to the target mangrove forest area based on the correlation coefficient; obtain the ground point data and non-ground point data corresponding to the lidar point cloud data, and construct a digital elevation model and a digital surface model based on the ground point data and non-ground point data, respectively, to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation. This application, through unmanned aerial vehicle remote sensing technology, helps to achieve high-resolution and high-timeliness monitoring of mangrove carbon sinks over a large area, thereby providing technical support for the national carbon neutrality goal. As a result, the current problems of difficulty in evaluating the accuracy of mangrove carbon storage estimation and lack of theoretical explanation for the results are solved.

[0032] Specifically, Figure 1 A flowchart of a method for calculating mangrove carbon storage based on airborne lidar point cloud provided in an embodiment of the present application.

[0033] like Figure 1 As shown, the mangrove carbon storage calculation method based on airborne laser radar point cloud includes the following steps:

[0034] In step S101, the laser radar point cloud data of the target red forest area is obtained, and multiple target single tree point clouds are collected through the laser radar point cloud data, and the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds are extracted, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and the preset tree species allometric growth equation.

[0035] The embodiment of the present application can first obtain the mangrove UAV lidar data (i.e., lidar point cloud data) of the target mangrove forest area, and select 50 highly evenly distributed single tree point clouds (i.e., multiple target single tree point clouds) from the lidar point cloud data, and extract the single tree height and crown width parameters from them, substitute the single tree height and crown width parameters into the allometric growth equation of the corresponding tree species, and calculate the single tree carbon storage in combination with the carbon content coefficient, such as Figure 2As shown, the single tree carbon stock is then divided by the single tree crown area to obtain the single tree carbon density.

[0036] Optionally, in one embodiment of the present application, the tree height and crown width parameters corresponding to each target tree point cloud in multiple target tree point clouds are extracted to calculate the tree carbon density corresponding to each target tree point cloud based on the tree height, crown width parameters and a preset tree species allometric growth equation, including: counting multiple characteristic values ​​of the target tree corresponding to each target tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the tree, the highest point of the tree, the longest crown width of the tree and the shortest crown width of the tree; calculating the tree height by the lowest point and the highest point of the tree, and calculating the crown width parameters using the longest crown width and the shortest crown width of the tree; substituting the tree height and crown width parameters into the tree species allometric growth equation to calculate the tree biomass, and calculating the tree carbon stock corresponding to the target tree based on the tree biomass and a preset red forest carbon content coefficient; calculating the tree crown area corresponding to the target tree based on the crown width parameters, and calculating the tree carbon density based on the tree crown area and the tree carbon stock.

[0037] As a possible implementation method, the embodiment of the present application is as follows Figure 3 50 single tree point clouds with uniform height distribution are selected from the mangrove UAV lidar data shown in the figure, and the single tree height and crown width parameters are extracted from them.

[0038] Specifically, the present embodiment calculates the lowest point H of each single tree. low , the highest point H high 、Longest crown width W long , shortest crown width W short And other eigenvalues, and by H high and H low Calculate the tree height parameter H = H high -H low , by W long With W short The average crown width parameter W = (W long +W short ) / 2, the height H and crown width parameters W of the separated single trees are as follows: Figure 4 shown.

[0039] Secondly, the embodiment of the present application can bring the tree height and crown width parameters into the allometric growth equation of the corresponding mangrove species to calculate the single tree biomass B, and multiply it by the carbon content coefficient CF of the corresponding mangrove to obtain the single tree carbon storage C = CF·B; then, the embodiment of the present application can divide the single tree carbon storage C by the single tree crown width area S = π·(W / 2) 2 Calculate the single tree carbon density C' = C / S. Among them, the common mangrove allometric growth equation and carbon content coefficient are shown in the following table:

[0040] Table 1

[0041]

[0042] Therefore, the embodiment of the present application extracts the tree height and crown width parameters of mangroves through lidar point cloud data, establishes the single-tree structural equation of mangroves, and then calculates the carbon density of mangroves at different heights. This is not only simple and easy, but also can save a lot of manpower and material resources.

[0043] In step S102, a functional relationship between single tree height and single tree carbon density is established, and multiple types of target functions to be fitted corresponding to the functional relationship are determined, and model fitting is performed on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted in the multiple types of target functions to be fitted, and a mangrove carbon density equation corresponding to the target mangrove forest area is established based on the correlation coefficient.

[0044] Furthermore, the embodiments of the present application also need to establish a functional relationship between the carbon density of individual mangrove trees and the height of individual trees, and appropriately select polynomial functions (i.e., multiple types of target functions to be fitted) according to the scatter distribution of individual tree height and individual tree carbon density, such as power functions, exponential functions, etc. for model fitting, so as to obtain the correlation coefficient corresponding to each type of target function to be fitted, and select the function with the highest correlation coefficient as the mangrove carbon density equation.

[0045] Optionally, in one embodiment of the present application, multiple categories of target functions to be fitted corresponding to the functional relationship are determined, and model fitting is performed on the multiple categories of target functions to be fitted to obtain the correlation coefficient corresponding to each category of target functions to be fitted in the multiple categories of target functions to be fitted, and a mangrove carbon density equation corresponding to the target mangrove forest area is established based on the correlation coefficient, including: obtaining a scatter plot between single tree carbon density and single tree height, and determining multiple categories of target functions to be fitted corresponding to the functional relationship based on the scatter plot, wherein the multiple categories of target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions; model fitting is performed on the multiple categories of target functions to be fitted to obtain the correlation coefficient corresponding to each category of target functions to be fitted, and the correlation coefficients of the multiple categories of target functions to be fitted are compared to obtain the maximum correlation coefficient; obtaining the target function to be fitted corresponding to the maximum correlation coefficient, and determining the mangrove carbon density equation based on the target function to be fitted corresponding to the maximum correlation coefficient.

[0046] It should be noted that the embodiment of the present application establishes a functional relationship C′=f(H) between the single tree carbon density C′ of mangroves and the single tree height data H, and obtains a scatter plot between the single tree carbon density and the single tree height, so as to select a suitable function (i.e., the target function to be fitted) according to the scatter plot of the single tree carbon density and the tree height of the mangroves, such as a linear function, a polynomial function, a power function, an exponential function, etc., for model fitting, so as to obtain the correlation coefficient corresponding to the target function to be fitted, wherein the fitting function of the single tree carbon density and the single tree height of the mangroves is shown in the following table:

[0047] Table 2

[0048]

[0049] It should be noted that the embodiment of the present application requires that the tree height distribution is relatively uniform, the number of individual trees is not less than 50, and the model fitting correlation coefficient is greater than 0.8;

[0050] Afterwards, the embodiment of the present application can select the function with the best fitting effect to establish the relationship between the mangrove carbon density and the height, such as Figure 5 As shown, the function with the highest fitting degree is selected for subsequent calculations.

[0051] In step S103, ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

[0052] Furthermore, the embodiment of the present application filters the point cloud data of the mangrove area (i.e., the lidar point cloud data) to obtain ground points and non-ground points, and generates a digital elevation model DEM from the ground points, generates a digital surface model DSM from the non-ground points, and calculates the canopy height model CHM=DSM-DEM, so as to substitute CHM into the mangrove carbon density equation to calculate the carbon storage in the area.

[0053] Optionally, in one embodiment of the present application, ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed based on the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation, including: filtering the lidar point cloud data to obtain ground point data and non-ground point data, and constructing a digital elevation model through the ground point data, and establishing a digital surface model using the non-ground point data; determining a canopy height model based on the digital elevation model and the digital surface model, and substituting the canopy height model into the mangrove carbon density equation to calculate the carbon storage of the target mangrove forest area.

[0054] It should be noted that the embodiment of the present application filters the laser radar point cloud data to obtain ground points G and non-ground points N, and generates a digital elevation model DEM from the ground points G, and generates a digital surface model DSM from the non-ground points N, wherein the spatial resolution of the DEM and DSM is P;

[0055] Secondly, the embodiment of the present application can calculate the canopy height model CHM through the digital elevation model DEM and the digital surface model DSM. The canopy height model CHM of the target red forest area is as follows: Figure 6Its mathematical expression is shown as follows:

[0056] CHM=DSM-DEM

[0057] Afterwards, the embodiment of the present application can substitute the tree height in CHM into the mangrove carbon density equation to calculate the mangrove carbon storage C pixel by pixel. i,j =P 2 ·f(H i,j ), mangrove carbon storage Figure 7 Its mathematical expression is shown as follows:

[0058]

[0059] Where m, n represent the number of rows and columns of CHM; i, j represent the i-th row and j-th column; C i,j represents the carbon storage of the pixel in the i-th row and j-th column.

[0060] Therefore, the embodiments of the present application, through the precise calculation of regional mangrove aboveground carbon storage based on lidar point cloud data, help to achieve high-resolution and high-efficiency monitoring of mangrove carbon sinks over a large area, and provide reliable technical support for the country's carbon neutrality goals.

[0061] According to the method for calculating mangrove carbon stocks based on airborne lidar point clouds proposed in the embodiment of the present application, lidar point cloud data of a target mangrove forest area is obtained, and multiple target single tree point clouds are collected through the lidar point cloud data, and the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds are extracted, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and a preset tree species allometric growth equation; a functional relationship between single tree height and single tree carbon density is established, and multiple types of target functions to be fitted corresponding to the functional relationship are determined, and model fitting is performed on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target functions to be fitted in the multiple types of target functions to be fitted, and a mangrove carbon density equation corresponding to the target mangrove forest area is established according to the correlation coefficient; ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon stock of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation. This application will help achieve high-resolution and timely monitoring of mangrove carbon sinks over a large area through drone remote sensing technology, thereby providing technical support for the country's carbon neutrality goals.

[0062] Secondly, a mangrove carbon storage calculation device based on airborne laser radar point cloud proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0063] Figure 8It is a block diagram of a mangrove carbon storage calculation device based on airborne laser radar point cloud according to an embodiment of the present application.

[0064] like Figure 8 As shown, the mangrove carbon storage calculation device 10 based on airborne laser radar point cloud includes: an extraction module 100, a fitting module 200 and a construction module 300.

[0065] Among them, the extraction module 100 is used to obtain the laser radar point cloud data of the target red forest area, and collect multiple target single tree point clouds through the laser radar point cloud data, and extract the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and the preset tree species allometric growth equation.

[0066] The fitting module 200 is used to establish a functional relationship between the height of a single tree and the carbon density of a single tree, determine multiple types of target functions to be fitted corresponding to the functional relationship, and perform model fitting on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted in the multiple types of target functions to be fitted, and establish a mangrove carbon density equation corresponding to the target mangrove forest area based on the correlation coefficient.

[0067] Construction module 300 is used to obtain ground point data and non-ground point data corresponding to the lidar point cloud data, and to construct a digital elevation model and a digital surface model based on the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

[0068] Optionally, in one embodiment of the present application, the extraction module includes: a statistical unit, a first calculation unit, a substitution unit and a second calculation unit.

[0069] The statistical unit is used to count multiple characteristic values ​​of the target tree corresponding to each target tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the single tree, the highest point of the single tree, the longest crown width of the single tree, and the shortest crown width of the single tree.

[0070] The first calculation unit is used to calculate the height of a single tree through the lowest point and the highest point of the single tree, and calculate the crown width parameters using the longest crown width and the shortest crown width of the single tree.

[0071] The substitution unit is used to substitute the tree height and crown width parameters into the tree species allometric growth equation to calculate the tree biomass, and calculate the single tree carbon storage corresponding to the target tree based on the single tree biomass and the preset red forest carbon content coefficient.

[0072] The second calculation unit is used to calculate the single tree crown area corresponding to the target single tree based on the crown parameter, and calculate the single tree carbon density according to the single tree crown area and the single tree carbon storage.

[0073] Optionally, in one embodiment of the present application, the fitting module 200 includes: an acquisition unit, a comparison unit and a determination unit.

[0074] The acquisition unit is used to obtain a scatter plot between single tree carbon density and single tree height, and determine multiple target functions to be fitted corresponding to the functional relationship based on the scatter plot, wherein the multiple target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions.

[0075] The comparison unit is used to perform model fitting on multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted, and compare the correlation coefficients of multiple types of target functions to be fitted to obtain the maximum correlation coefficient.

[0076] The determination unit is used to obtain the target function to be fitted corresponding to the maximum correlation coefficient, and determine the mangrove carbon density equation according to the target function to be fitted corresponding to the maximum correlation coefficient.

[0077] Optionally, in one embodiment of the present application, the construction module 300 includes: a filtering unit and a third calculation unit.

[0078] Among them, the filtering unit is used to filter the lidar point cloud data to obtain ground point data and non-ground point data, and to build a digital elevation model through the ground point data, and to build a digital surface model using the non-ground point data.

[0079] The third calculation unit is used to determine a canopy height model based on the digital elevation model and the digital surface model, and substitute the canopy height model into the mangrove carbon density equation to calculate the carbon storage of the target mangrove forest area.

[0080] It should be noted that the aforementioned explanation of the embodiment of the method for calculating mangrove carbon stock based on airborne laser radar point cloud is also applicable to the mangrove carbon stock calculation device based on airborne laser radar point cloud of this embodiment, and will not be repeated here.

[0081] The mangrove carbon stock calculation device based on airborne lidar point cloud proposed in the embodiment of the present application includes an extraction module for obtaining lidar point cloud data of a target mangrove forest area, and collecting multiple target single tree point clouds through the lidar point cloud data, and extracting the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, crown width parameters and a preset tree species allometric growth equation; a fitting module for establishing a functional relationship between the single tree height and the single tree carbon density, and determining the functional relationship Corresponding multi-class target functions to be fitted, and model fitting of the multi-class target functions to be fitted to obtain the correlation coefficient corresponding to each class of target functions to be fitted in the multi-class target functions to be fitted, and establish the mangrove carbon density equation corresponding to the target mangrove forest area according to the correlation coefficient; construct a module for obtaining ground point data and non-ground point data corresponding to the lidar point cloud data, and construct a digital elevation model and a digital surface model according to the ground point data and non-ground point data, respectively, to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation. This application uses drone remote sensing technology to help achieve high-resolution and high-timeliness monitoring of mangrove carbon sinks over a large area, thereby providing technical support for the national carbon neutrality goal.

[0082] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0083] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0084] When the processor 902 executes the program, the mangrove carbon storage calculation method based on the airborne laser radar point cloud provided in the above embodiment is implemented.

[0085] Furthermore, the electronic device further comprises:

[0086] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0087] The memory 901 is used to store computer programs that can be executed on the processor 902 .

[0088] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0089] If the memory 901, the processor 902 and the communication interface 903 are implemented independently, the communication interface 903, the memory 901 and the processor 902 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0090] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0091] The processor 902 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0092] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for calculating mangrove carbon reserves based on airborne lidar point cloud.

[0093] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned mangrove carbon storage calculation method based on airborne lidar point cloud.

[0094] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0095] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0096] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0098] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0100] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0101] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for calculating mangrove carbon storage based on airborne laser radar point cloud, characterized in that: The following steps are involved: Acquire laser radar point cloud data of the target red forest area, collect multiple target single tree point clouds through the laser radar point cloud data, and extract the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, the crown width parameters and the preset tree species allometric growth equation; Establishing a functional relationship between the single tree height and the single tree carbon density, and determining multiple types of target functions to be fitted corresponding to the functional relationship, and performing model fitting on the multiple types of target functions to be fitted to obtain a correlation coefficient corresponding to each type of target function to be fitted in the multiple types of target functions to be fitted, and establishing a mangrove carbon density equation corresponding to the target mangrove forest area according to the correlation coefficient; The ground point data and non-ground point data corresponding to the lidar point cloud data are obtained, and a digital elevation model and a digital surface model are constructed according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

2. The method according to claim 1, characterized in that The extracting the tree height and crown width parameters corresponding to each target tree point cloud from the plurality of target tree point clouds, and calculating the tree carbon density corresponding to each target tree point cloud based on the tree height, the crown width parameters and a preset tree species allometric growth equation, includes: Counting multiple characteristic values ​​of the target single tree corresponding to each target single tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the single tree, the highest point of the single tree, the longest crown width of the single tree, and the shortest crown width of the single tree; The height of the single tree is calculated by the lowest point of the single tree and the highest point of the single tree, and the crown width parameter is calculated by using the longest crown width of the single tree and the shortest crown width of the single tree; Substituting the tree height and the crown width parameters into the tree species allometric growth equation to calculate the tree biomass, and calculating the tree carbon storage corresponding to the target tree according to the tree biomass and a preset red forest carbon content coefficient; Based on the crown width parameter, the single tree crown width area corresponding to the target single tree is calculated, and the single tree carbon density is calculated according to the single tree crown width area and the single tree carbon stock.

3. The method according to claim 2, characterized in that The determining of multiple types of target functions to be fitted corresponding to the functional relationship, and performing model fitting on the multiple types of target functions to be fitted to obtain correlation coefficients corresponding to each type of target functions to be fitted in the multiple types of target functions to be fitted, and establishing a mangrove carbon density equation corresponding to the target mangrove forest area according to the correlation coefficients, including: Obtaining a scatter plot between the single tree carbon density and the single tree height, and determining multiple types of target functions to be fitted corresponding to the functional relationship according to the scatter plot, wherein the multiple types of target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions; Performing model fitting on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted, and comparing the correlation coefficients of the multiple types of target functions to be fitted to obtain the maximum correlation coefficient; The target function to be fitted corresponding to the maximum correlation coefficient is obtained, and the mangrove carbon density equation is determined according to the target function to be fitted corresponding to the maximum correlation coefficient.

4. The method according to claim 3, characterized in that The obtaining of ground point data and non-ground point data corresponding to the laser radar point cloud data, and constructing a digital elevation model and a digital surface model according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation, comprises: Filtering the laser radar point cloud data to obtain the ground point data and the non-ground point data, and constructing the digital elevation model through the ground point data, and establishing the digital surface model using the non-ground point data; A canopy height model is determined based on the digital elevation model and the digital surface model, and the canopy height model is substituted into the mangrove carbon density equation to calculate the carbon storage of the target mangrove forest area.

5. A mangrove carbon storage calculation device based on airborne laser radar point cloud, characterized in that: include: An extraction module is used to obtain the laser radar point cloud data of the target red forest area, collect multiple target single tree point clouds through the laser radar point cloud data, and extract the single tree height and crown width parameters corresponding to each target single tree point cloud in the multiple target single tree point clouds, so as to calculate the single tree carbon density corresponding to each target single tree point cloud based on the single tree height, the crown width parameters and a preset tree species allometric growth equation; A fitting module, used to establish a functional relationship between the single tree height and the single tree carbon density, and determine multiple types of target functions to be fitted corresponding to the functional relationship, and perform model fitting on the multiple types of target functions to be fitted to obtain a correlation coefficient corresponding to each type of target function to be fitted in the multiple types of target functions to be fitted, and establish a mangrove carbon density equation corresponding to the target mangrove forest area according to the correlation coefficient; A construction module is used to obtain ground point data and non-ground point data corresponding to the lidar point cloud data, and to construct a digital elevation model and a digital surface model according to the ground point data and the non-ground point data, respectively, so as to calculate the carbon storage of the target mangrove forest area through the digital elevation model, the digital surface model and the mangrove carbon density equation.

6. The device according to claim 5, characterized in that The extraction module comprises: A statistical unit, used for counting multiple characteristic values ​​of the target single tree corresponding to each target single tree point cloud, wherein the multiple characteristic values ​​include the lowest point of the single tree, the highest point of the single tree, the longest crown width of the single tree, and the shortest crown width of the single tree; A first calculation unit is used to calculate the height of the single tree by using the lowest point of the single tree and the highest point of the single tree, and calculate the crown width parameter by using the longest crown width of the single tree and the shortest crown width of the single tree; A substitution unit is used to substitute the single tree height and the crown width parameter into the tree species allometric growth equation to calculate the single tree biomass, and calculate the single tree carbon stock corresponding to the target single tree according to the single tree biomass and a preset red forest carbon content coefficient; The second calculation unit is used to calculate the single tree crown area corresponding to the target single tree based on the crown parameter, and calculate the single tree carbon density according to the single tree crown area and the single tree carbon stock.

7. The device according to claim 6, characterized in that The fitting module comprises: An acquisition unit is used to acquire a scatter plot between the single tree carbon density and the single tree height, and determine multiple types of target functions to be fitted corresponding to the functional relationship according to the scatter plot, wherein the multiple types of target functions to be fitted include linear functions, polynomial functions, power functions and exponential functions; A comparison unit is used to perform model fitting on the multiple types of target functions to be fitted to obtain the correlation coefficient corresponding to each type of target function to be fitted, and compare the correlation coefficients of the multiple types of target functions to be fitted to obtain the maximum correlation coefficient; A determination unit is used to obtain the target function to be fitted corresponding to the maximum correlation coefficient, and determine the mangrove carbon density equation according to the target function to be fitted corresponding to the maximum correlation coefficient.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for calculating mangrove carbon reserves based on airborne lidar point cloud as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the mangrove carbon storage calculation method based on airborne laser radar point cloud as described in any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for calculating mangrove carbon storage based on airborne lidar point cloud as described in any one of claims 1 to 4.

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