Unmanned aerial vehicle remote sensing estimation method and estimation device for aboveground biomass of desert steppe shrubs

Abstract

The invention discloses a desert steppe shrub above-ground biomass unmanned aerial vehicle remote sensing estimation method and estimation device, the method is applied to desert steppe shrub above-ground biomass estimation, and the method comprises the following steps: processing a spectrum image of desert steppe shrubs in a survey sample plot to obtain a crown breadth area of each shrub in the survey sample plot; performing inversion on the laser radar data of the desert steppe shrubs in the survey sample plot to obtain the height of each shrub in the survey sample plot; and inputting the crown breadth area and height of each shrub in the survey sample plot into the overground biomass estimation composite model to obtain an overground biomass estimation value of each shrub in the survey sample plot range. By means of the method, efficient and non-destructive aboveground biomass estimation of desert steppe shrubs such as a relict endangered species, namely, the tetrasyntha mongolica can be achieved, and therefore a scientific basis and technical support are provided for protection of the tetrasyntha mongolica.

Description

Technical Field

[0001] The present application relates to the field of ecological remote sensing, and in particular to a method and device for remotely estimating the above-ground biomass of shrubs in the western desert steppe area by using an unmanned aerial vehicle (UAV). Background Art

[0002] At present, with the increasing attention paid to the global ecosystem, relic endangered species such as Tetraena mongolica play an irreplaceable role in studying the historical evolution of the earth's ecology and maintaining ecological balance as an important part of biodiversity. However, affected by various factors, the population of relic endangered species is facing a continuous decline in the endangered situation. Therefore, the monitoring and protection of relic endangered species are extremely urgent. In the management of ecological resources in nature reserves, above-ground biomass is one of the key indicators for measuring the health status of the ecosystem and for rational resource planning and protection. For general vegetation types, through long-term research and practice, a variety of methods for estimating above-ground biomass have been established. For example, methods based on field quadrat measurements combined with statistical analysis, methods using remote sensing data combined with vegetation indices and empirical models, etc. However, it is extremely difficult to estimate the above-ground biomass of nature reserves where relic endangered species such as Tetraena mongolica are located. Therefore, there is an urgent need to develop a method for remotely estimating the above-ground biomass of desert steppe shrubs by using an unmanned aerial vehicle (UAV) to achieve efficient and non-destructive estimation of the above-ground biomass of desert steppe shrubs such as relic endangered species like Tetraena mongolica. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method and device for remotely estimating the above-ground biomass of desert steppe shrubs by using an unmanned aerial vehicle (UAV). The method can achieve efficient and non-destructive estimation of the above-ground biomass of desert steppe shrubs such as relic endangered species like Tetraena mongolica, thereby providing a scientific basis and technical support for the protection of Tetraena mongolica.

[0004] To achieve the above purpose, the embodiments of the present application provide a method for remotely estimating the above-ground biomass of desert steppe shrubs by using an unmanned aerial vehicle (UAV), which is applied to the estimation of the above-ground biomass of desert steppe shrubs and includes:

[0005] Processing the spectral images of desert steppe shrubs in the surveyed plot to obtain the crown area of each shrub in the surveyed plot;

[0006] Inverting the lidar data of desert steppe shrubs in the surveyed plot to obtain the height of each shrub in the surveyed plot;

[0007] Input the crown area and height of each shrub in the surveyed plot into the composite model for estimating above-ground biomass to obtain the estimated above-ground biomass of each shrub within the surveyed plot. Among them, the composite model for estimating above-ground biomass is fitted with the phenotypic structural characteristics of the shrub as the independent variable and the above-ground biomass of the shrub as the dependent variable. The phenotypic structural characteristics of the shrub include the pseudo-measured crown area and the measured height of the shrub, and the above-ground biomass of the shrub includes the measured above-ground biomass and the pseudo-measured above-ground biomass of the shrub.

[0008] Optionally, the spectral image of the desert steppe shrub is obtained by a multi-spectral sensor carried by a drone in a preset drone surveyed plot. The process of processing the spectral image of the desert steppe shrub in the surveyed plot is as follows:

[0009] Stitch the spectral images collected by the multi-spectral sensor to obtain the stitched spectral image.

[0010] Input the stitched spectral image into an image segmentation model to identify the boundaries and feature information of the shrub and the background objects.

[0011] Mark the segmentation regions corresponding to each shrub according to the boundaries and feature information of the shrub and the background objects.

[0012] Calculate the crown area of each shrub according to the segmentation region corresponding to each shrub and the spatial resolution of the image.

[0013] Optionally, the lidar data is obtained by a lidar carried by a drone in a preset drone surveyed plot. The process of inverting the lidar data of the desert steppe shrub in the surveyed plot is as follows:

[0014] Preprocess the lidar data to obtain a subset of shrub point clouds, including:

[0015] Unify the lidar data collected from different perspectives and at different times into the same coordinate system, and use a registration algorithm to stitch together the lidar data to form a complete point cloud dataset of the plot.

[0016] Denoise the point cloud dataset of the plot.

[0017] Separate the ground points and shrub points from the denoised point cloud dataset of the plot to obtain a subset of shrub point clouds.

[0018] Perform clustering analysis on the subset of shrub point clouds to obtain the range of each shrub in the subset of shrub point clouds, including:

[0019] According to the spatial aggregation characteristics of the point cloud, cluster the point clouds of the same shrub into the same category, so that each clustering result corresponds to a shrub, in order to divide the range of each shrub in the subset of shrub point clouds.

[0020] Perform height inversion on the point cloud data of each shrub to obtain the remote sensing height of each shrub, including:

[0021] Use the spatial coordinate query operation to obtain the coordinates of the maximum point and the minimum point of the point cloud of each shrub in the vertical direction. Among them, the coordinates of the maximum point represent the top position of the shrub, and the coordinates of the minimum point represent the bottom position of the shrub;

[0022] Subtract the coordinates of the minimum point from the coordinates of the maximum point to obtain the remote sensing height of each shrub in the surveyed plot.

[0023] Optionally, the process of obtaining the pseudo-measured crown area of the shrub is as follows:

[0024] Conduct a sampling survey on the shrubs in the UAV surveyed plot, and collect the attribute characteristics and phenotypic structure characteristics of N surveyed shrubs. Among them, the attribute characteristics of the shrubs include shrub name, shrub category and geographical location, and the phenotypic structure characteristics of the shrubs include shrub height, north-south width of the crown and east-west width of the crown;

[0025] According to the similarity between the attribute characteristics and phenotypic structure characteristics of each surveyed shrub and the attribute characteristics and phenotypic structure characteristics of n dominant species shrubs that have completed above-ground biomass sampling, divide the N surveyed shrubs into n groups;

[0026] Input the north-south width and east-west width of the crowns of the n groups of shrubs into the relationship model between the crown area of the shrub and the north-south width and east-west width of the crown constructed for the n dominant species shrubs, and calculate the pseudo-measured crown area of each group of shrubs.

[0027] Optionally, the construction process of the relationship model between the crown area of the shrub and the north-south width and east-west width of the crown is as follows:

[0028] Taking the crown area of the measured shrubs as the dependent variable and the north-south width and east-west width of the crown as the independent variables, construct the relationship model between the crown area of the shrub and the north-south width and east-west width of the crown for each of the n dominant species shrubs respectively; among them, the relationship model is:

[0029] X k = G(s, w),

[0030] In the formula, X k represents the crown area of the shrub, G() represents the fitting function, s represents the north-south width of the crown of the shrub, and w represents the east-west width of the crown of the shrub.

[0031] Optionally, the process of obtaining the pseudo-measured above-ground biomass is as follows:

[0032] The phenotypic structural characteristics of n groups of shrubs are respectively input into the above-ground biomass estimation models for n dominant shrub species to calculate the pseudo-measured above-ground biomass of each group of shrubs.

[0033] Among them, the above-ground biomass estimation models for n dominant shrub species are obtained by fitting with the measured above-ground biomass of the corresponding dominant shrub species as the dependent variable and the phenotypic structural characteristics of the corresponding dominant shrub species as the independent variables.

[0034] Optionally, the phenotypic structural characteristics of the corresponding dominant shrub species include the crown area of the dominant shrub species and the height of the dominant shrub species. Among them, the crown area of the dominant shrub species is calculated by inputting the north-south width and east-west width of the crown of the dominant shrub species into the relationship model.

[0035] Optionally, the above-ground biomass estimation models for n dominant shrub species are as follows:

[0036] AGB t ={f 1 (X t ),f 2 (X t ),...,f i (X t ),...,f n (X t )},

[0037] In the formula, AGBt represents the above-ground biomass of shrubs, and f i (X t ) represents the fitting function of the i-th dominant shrub species, i = 1, 2,..., n, where n represents the number of dominant shrub species, and X t represents the phenotypic structural characteristics of the dominant shrub species.

[0038] The embodiment of the present application also provides an unmanned aerial vehicle remote sensing estimation device for the above-ground biomass of desert steppe shrubs, which is applied to the estimation of the above-ground biomass of desert steppe shrubs and includes:

[0039] An image processing module configured to process the spectral images of desert steppe shrubs in the surveyed plot to obtain the crown area of each shrub in the surveyed plot;

[0040] An inversion module configured to invert the lidar data of desert steppe shrubs in the surveyed plot to obtain the height of each shrub in the surveyed plot;

[0041] An estimation module configured to input the crown area and height of each shrub in the surveyed plot into a composite model for estimating aboveground biomass to obtain an estimated value of the aboveground biomass of each shrub within the surveyed plot; wherein, the composite model for estimating aboveground biomass is obtained by fitting the phenotypic structural characteristics of the shrub and the aboveground biomass of the shrub, the phenotypic structural characteristics of the shrub include the pseudo-measured crown area and the measured height of the shrub, and the aboveground biomass of the shrub includes the measured aboveground biomass and the pseudo-measured aboveground biomass of the shrub.

[0042] The method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application can achieve efficient and non-destructive estimation of the aboveground biomass of shrubs in desert steppe, such as Tetraena mongolica and other similar relic endangered species, through rapid and accurate estimation from small sample measurement points of shrubs to aerial measurement by drones. It has important value for measuring the "true value" of the aboveground biomass of shrubs in desert steppe and can provide technical references for desert steppe resource surveys, ecological environment assessments, and grassland carbon estimations. Description of the Drawings

[0043] Figure 1 It is a flowchart of the method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application;

[0044] Figure 2 It is a flowchart of step S100 in the method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application;

[0045] Figure 3 It is a flowchart of step S200 in the method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application;

[0046] Figure 4 It is a flowchart of the process for obtaining the pseudo-measured crown area in step S300 in the method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application;

[0047] Figure 5 It is a schematic diagram of the result of obtaining the aboveground biomass of each shrub in the drone surveyed plot by using the method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application;

[0048] Figure 6 It is a schematic structural diagram of the device for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by the embodiments of the present application. Detailed Embodiments

[0049] Various schemes and features of the present application are described herein with reference to the accompanying drawings. It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0050] At present, in the field of ecological resource monitoring and protection, remote sensing technology is one of the commonly used large-scale and high-efficiency monitoring methods, and is widely used in vegetation monitoring. However, for shrubs similar to Tetraena mume in desert areas, due to the particularity of their phenotypic structure, such as short plants, dense branches and small leaf area, it is difficult to directly and accurately identify the species using existing optical remote sensing data, which limits the application effect of traditional remote sensing technology in monitoring such species. Therefore, it is urgent to develop a new method that integrates advanced sensing technology and data analysis methods to efficiently and non-destructively estimate the aboveground biomass of endangered species such as Tetraena mume and the ecological environment.

[0051] The method for estimating the aboveground biomass of desert steppe shrubs by drone remote sensing provided in this application is used for estimating the aboveground biomass of desert steppe shrubs by drone remote sensing. Among them, desert steppe shrubs refer to shrub plants that grow in desert steppe areas. The desert steppe area is a specific ecological region with dual characteristics of desert and steppe. The vegetation is mainly shrubs, and also contains a very small amount of herbaceous plants, etc. The ecological environment is relatively fragile and unique. The aboveground biomass of shrubs refers to the total amount of organic matter in the aboveground parts of all shrubs in a certain area. It is an important indicator for measuring the growth status of shrubs, ecosystem productivity, and carbon storage.

[0052] The method for estimating the aboveground biomass of shrubs in desert grasslands provided by this application uses drone remote sensing technology combined with ground-measured data (e.g., aboveground biomass of shrubs and phenotypic structural parameters of shrubs, etc.) to accurately monitor the aboveground biomass of shrubs within the scope of the protected area survey plot. The method of this application can more quickly and conveniently realize the accurate monitoring of aboveground biomass of shrubs in desert grasslands, and provide technical references for desert grassland resource surveys, ecological environment estimations, and grassland carbon estimations.

[0053] The following is a detailed description of the method for estimating the biomass of shrubs on desert grasslands by drone remote sensing provided in an embodiment of the present application in conjunction with the accompanying drawings. Figure 1 The flowchart of the method for estimating the biomass of shrubs on the ground by drone remote sensing in desert grassland provided in the embodiment of the present application is as follows: Figure 1 As shown, the method comprises the following steps:

[0054] S100, processing the spectral image of the desert steppe shrubs in the survey sample plot to obtain the remote sensing crown area of ​​each shrub in the survey sample plot.

[0055] According to factors such as the research purpose and accuracy requirements, representative survey plots are selected. These survey plots can cover desert steppe shrub communities within a certain range, and their areas can range from several hundred square meters to several hectares.

[0056] When obtaining the spectral images of shrubs in the survey plots, multiple parameters need to be concerned. For example, the shooting time should be selected during a sunny day with uniform illumination to avoid the influence of shadows on the spectral information of shrubs. At the same time, determine the appropriate shooting angle and the flight altitude of the drone, etc., to ensure that the obtained spectral images of shrubs can comprehensively and accurately reflect the true situation of the shrubs in the survey plots. For example, when the flight altitude of the drone is relatively low, the image resolution is high, but the coverage range is small; when the flight altitude of the drone increases, the coverage range expands, but the resolution will decrease, and a trade-off needs to be made according to actual needs.

[0057] S200. Invert the vegetation height of the desert steppe shrubs in the survey plots using lidar data to obtain the height of each shrub in the survey plots.

[0058] Lidar (LiDAR) is an active optical remote sensing technology. It obtains information by emitting laser pulses to the target area and receiving the laser signals reflected from the target surface. In the survey plots of desert steppe shrubs, the lidar equipment can be installed on the drone so that it can cover the entire selected plot range for data collection. When the laser pulses encounter different parts of the branches, leaves, etc. of the shrubs, reflection occurs. The reflected light carries relevant information such as the spatial position and distance of the shrubs. The lidar receiving device captures the reflected light and then records a large amount of point cloud data. These point cloud data contain the relevant parameters of the laser reflected by different objects at various positions in the plot.

[0059] S300. Input the crown area and height of each shrub in the survey plots into the composite model for estimating aboveground biomass to obtain the estimated values of the aboveground biomass of each shrub within the survey plot range; among them, the composite model for estimating aboveground biomass is fitted with the phenotypic structural characteristics of the shrubs as independent variables and the aboveground biomass of the shrubs as the dependent variable. The phenotypic structural characteristics of the shrubs include the pseudo-measured crown area and the measured height of the shrubs, and the aboveground biomass of the shrubs includes the measured aboveground biomass and the pseudo-measured aboveground biomass of the shrubs.

[0060] In the embodiments of the present application, through data analysis, mathematical methods such as linear regression or non-linear regression can be used to determine the correlation between the phenotypic structural characteristics of the shrubs and the aboveground biomass of the shrubs, and then the composite model for estimating aboveground biomass is fitted.

[0061] In the above step S100, in the preset drone survey plot, the spectral images of the shrubs are collected using the multi-spectral sensor carried by the drone. Figure 2As shown in the figure, the specific process of processing the spectral images of desert steppe shrubs in the surveyed plots is as follows:

[0062] S110. Stitch the spectral images collected by the multispectral sensor to obtain the stitched spectral image.

[0063] S120. Input the stitched spectral image into the image segmentation model to identify the boundaries and feature information of shrubs and background objects.

[0064] Among them, the image segmentation model can adopt the SAM model (Segment Anything Model). According to factors such as the resolution, size of the UAV multispectral image, and the required segmentation accuracy, parameters such as the segmentation threshold and the number of iterations of the SAM model can be appropriately adjusted to optimize the segmentation effect of shrubs.

[0065] Input the spectral images for segmentation and recognition into the SAM model one by one. Based on its powerful visual feature learning ability, the SAM model automatically identifies the boundaries and feature information of different objects (including shrubs and background objects, etc.) in the image and generates corresponding segmentation results. The segmentation results can be presented in the form of labeled images or binary images, etc.

[0066] To improve the accuracy of the segmentation of each shrub, optimization methods based on morphological processing or optimization methods based on filtering techniques, etc., can be used to further optimize the segmentation results.

[0067] S130. Mark the segmentation areas corresponding to each shrub according to the boundary and feature information of the shrub and the background object.

[0068] The segmentation areas of each shrub can be marked and distinguished by different numbers, colors, etc.

[0069] S140. Calculate the crown area of each shrub according to the segmentation area corresponding to each shrub and the spatial resolution of the image.

[0070] For the segmentation area of each shrub that has been marked, according to the spatial resolution of the multispectral image, use the pixel statistics method to calculate the number of pixels contained in this area, and then multiply by the actual area corresponding to each pixel to obtain the crown area of each shrub. For example, if the spatial resolution of the image is 0.1 m / pixel and the number of pixels in the segmentation area of a certain shrub is 1000, then the crown area of this shrub is 0.1×0.1×1000 = 10 m 2 .

[0071] It should be noted that in order to evaluate the accuracy of the segmentation results, a certain number of representative shrubs can be selected within the sample plot, and their true crown area can be obtained through on-site measurement. This crown area can be used as a reference standard. The crown area calculated by segmenting based on the SAM model is compared with the on-site measurement value, and error indicators such as root mean square error and mean absolute error are calculated to evaluate the accuracy of the segmentation results.

[0072] In the above step S200, in the preset UAV survey sample plot, lidar data is collected by the lidar carried by the UAV. As Figure 3 shown, the specific process of inverting the lidar data of desert steppe shrubs in the survey sample plot is as follows:

[0073] S210. Preprocess the lidar data to obtain a subset of shrub point clouds, including:

[0074] Unify the lidar data collected from different perspectives and at different times into the same coordinate system, and use a registration algorithm to splice the lidar data together to form a complete sample point cloud dataset.

[0075] Denoise the sample point cloud dataset to remove isolated abnormal points (i.e., noise points) in the point cloud caused by factors such as the instrument's own errors. Specifically, a statistical filtering method can be used. According to the spatial distribution characteristics of the point cloud, reasonable parameters such as distance thresholds are set, and points with a distance greater than the preset distance threshold from the surrounding point cloud and points that do not conform to the overall distribution law are determined as noise points and removed.

[0076] Separate the ground points and shrub points from the denoised sample point cloud dataset to obtain a subset of shrub point clouds. Specifically, a morphological-based filtering algorithm can be used to distinguish the ground and shrub points by analyzing the morphological characteristics of the terrain. Through filtering operations, a point cloud subset containing only shrubs and other shrubs is obtained to facilitate the subsequent inversion of the height of the shrubs.

[0077] S220. Perform clustering analysis on the subset of shrub point clouds to obtain the range of each shrub in the subset of shrub point clouds, including:

[0078] According to the spatial aggregation characteristics of the point cloud, the point clouds of the same shrub are clustered into the same category, so that each clustering result corresponds to one shrub, in order to divide the range of each shrub in the subset of shrub point clouds.

[0079] Specifically, a K-Means clustering algorithm based on spatial distance or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm can be used to perform clustering analysis on the separated shrub point cloud subset.

[0080] It should be noted that if there are adhesions due to overly dense growth of shrubs or there are some misclassified point clouds, morphological opening and closing operations and other processing methods can be further used to optimize the clustered point clouds, removing some small and unreasonable connection parts or filling holes, etc.

[0081] S230. Perform height inversion on the point cloud data of each shrub to obtain the remote sensing height of each shrub, including:

[0082] Use spatial coordinate query operations to obtain the maximum point coordinates and minimum point coordinates of the point cloud of each shrub in the vertical direction. Among them, the maximum point coordinates represent the top position of the shrub, and the minimum point coordinates represent the bottom position of the shrub;

[0083] Subtract the minimum point coordinates from the maximum point coordinates of each shrub to obtain the remote sensing height of each shrub in the surveyed plot, thereby completing the inversion of the remote sensing height of each shrub in the entire plot.

[0084] In the above step S300, as Figure 4 shown, the process of obtaining the pseudo-measured crown area of the shrub is as follows:

[0085] S310. Conduct a random sampling survey on the shrubs in the UAV surveyed plot, collect the attribute characteristics and phenotypic structure characteristics of N surveyed shrubs, and obtain the survey samples of N shrubs. Among them, the attribute characteristics of the shrub include the shrub name, shrub category (species, genus), and geographical location, etc., and the phenotypic structure characteristics of the shrub include the shrub height, north-south width of the crown, and east-west width of the crown.

[0086] S320. Divide the N surveyed shrubs into n groups according to the similarity between the attribute characteristics and phenotypic structure characteristics of each surveyed shrub and the attribute characteristics and phenotypic structure characteristics of n dominant species shrubs for which above-ground biomass sampling has been completed.

[0087] S330. Input the north-south width and east-west width of the crowns of the n groups of shrubs into the relationship model between the shrub crown area and the north-south width and east-west width of the crown constructed for the n dominant species shrubs, and calculate the pseudo-measured crown area of each group of shrubs.

[0088] Among them, the construction process of the relationship model between the shrub crown area and the north-south width and east-west width of the crown constructed for the n dominant species shrubs is as follows:

[0089] Taking the crown area of the measured shrubs as the dependent variable and the north-south width and east-west width of the crown as the independent variables, relationship models between the crown area of the shrubs and the north-south width and east-west width of the crown are constructed for n dominant species of shrubs respectively; wherein, the relationship model is:

[0090] X k = G(s, w) (1)

[0091] In formula (1), X k represents the crown area of the shrubs, G() represents the fitting function, s represents the north-south width of the crown of the shrubs, and w represents the east-west width of the crown of the shrubs.

[0092] In the above step S300, the process of obtaining the pseudo-measured aboveground biomass is as follows:

[0093] The phenotypic structure characteristics of n groups of shrubs are respectively input into the aboveground biomass estimation models for n dominant species of shrubs, and the pseudo-measured aboveground biomass of each group of shrubs is calculated.

[0094] Among them, the aboveground biomass estimation models for n dominant species of shrubs are obtained by fitting with the measured aboveground biomass of the corresponding dominant species of shrubs as the dependent variable and the phenotypic structure characteristics of the corresponding dominant species of shrubs as the independent variables.

[0095] Specifically, the phenotypic structure characteristics of the corresponding dominant species of shrubs include the crown area of the dominant species of shrubs and the height of the dominant species of shrubs. Among them, the crown area of the dominant species of shrubs is calculated by inputting the north-south width and east-west width of the crown of the dominant species of shrubs into the relationship model.

[0096] The aboveground biomass estimation models for n dominant species of shrubs are:

[0097] AGB t = {f 1 (X), f 2 (X),..., f i (X),..., f n (X)} (2)

[0098] In formula (2), AGBt represents the aboveground biomass for n dominant species of shrubs, f i (X) represents the fitting function of the i-th dominant species of shrubs, i = 1, 2,..., n, n represents the number of species of dominant species of shrubs, and X t represents the phenotypic structure characteristics of the dominant species of shrubs.

[0099] In the above step S300, the aboveground biomass estimation composite model can be:

[0100] AGB = F(X) (3)

[0101] In formula (3), AGB represents the aboveground biomass of shrubs, F() represents the fitting function, and X represents the phenotypic structural characteristics of shrubs.

[0102] It should be noted that both the aboveground biomass estimation model and the composite aboveground biomass estimation model for n dominant species of shrubs adopt R 2 and RMSE tests, and all models have passed the significance test (P < 0.05). That is to say, the above two models perform well in terms of goodness of fit (measured by R 2 ), reach a certain standard in terms of prediction accuracy (measured by RMSE), and the relationships between variables in the models are statistically significant (through the significance test of P < 0.05), indicating that the overall quality of these models is reliable and can be reasonably and effectively used for corresponding analysis, prediction and other application scenarios.

[0103] Next, a specific embodiment is used to elaborate in detail on the method for estimating the aboveground biomass of desert steppe shrubs by using UAV remote sensing provided in the embodiments of the present application.

[0104] Three 10m×10m field survey plots are randomly set in a desert steppe shrub area.

[0105] The shrub information is surveyed plant by plant in the three 10m×10m field survey plots, and the name, geographical location (latitude and longitude) and phenotypic structure parameters of the shrubs are recorded. Among them, the phenotypic structure parameters can include phenotypic structural characteristics, coverage, dominant shrubs, number of plants and shrub ground diameter, etc. Finally, the aboveground biomass of the shrubs is collected plant by plant within the plot range.

[0106] When surveying the shrubs plant by plant, reference can be made to the "Technical Regulations for the Third National Land Survey" (TD / T 1055-2019) for implementation. (1) Collect the vegetation coverage of the desert steppe shrub area; (2) Record the dominant species and number of plants of the shrubs in the plot; (3) Measure the height of each shrub in centimeters, accurate to 0.1 cm; (4) Measure the north-south width and east-west width of the shrub crown in m 2 , accurate to 0.01 m 2 ; (5) The ground diameter is the diameter of the shrub near the ground surface in millimeters, accurate to 0.1 mm.

[0107] Specifically, in the three 10m×10m field survey plots, a total of 4 dominant species of shrubs (Tetraena mongolica, Reaumuria soongorica, Reaumuria trigyna and Salsola passerina) are collected, with a total of 119 samples, including the name (type), geographical location (latitude and longitude), phenotypic structure parameters and aboveground biomass of each shrub, etc.

[0108] Seventeen 2km×2km UAV survey plots are randomly set in the Tetraena mongolica Nature Reserve.

[0109] Use drones to collect spectral images and lidar data of shrubs. Specifically, use the DJI M3 multispectral drone to collect spectral data of shrubs, and a total of three-band data in the red, green, and blue bands are obtained. Use the L2 sensor carried by the DJI M350RTK to obtain lidar data. Among them, the flight altitude of the drone is 80m, the projection parameter is the 2000 geodetic coordinate system, and the spatial resolution is 5cm.

[0110] It should be noted that the spatial resolution determines the actual ground area represented by each pixel in the drone image. High resolution means that each pixel represents a smaller ground area, and low resolution is the opposite. For a 5cm spatial resolution, each pixel represents a ground area of 5cm × 5cm. That is to say, for an image with a 5cm spatial resolution, each pixel in the image actually represents a 5cm × 5cm square area.

[0111] Specifically, in the DJI Terra software, image stitching is performed on the drone images of each flight, and a total of 17 scenes of drone flight data are obtained.

[0112] According to the "Technical Regulations for the Third National Land Survey" (TD / T 1055-2019), investigate the phenotypic structure parameters of shrubs, that is, conduct a random sampling survey experiment on shrubs in the drone survey plot, and record the name, geographical location (latitude and longitude), shrub height, north-south width and east-west width of the crown, and ground diameter of each shrub.

[0113] Specifically, randomly collect the basic information of 810 shrubs, and divide these 810 shrubs into 17 species of shrubs according to the shrub category.

[0114] Based on the above-ground biomass and phenotypic structure characteristics of the 4 dominant species of shrubs collected, estimation models of shrub surface structure parameters and above-ground biomass are established for the 4 dominant species of shrub types respectively. Divide the 17 species of shrubs in the drone survey plot into 4 groups, which belong to Tetraena mongolica, Reaumuria soongorica, Reaumuria trigyna, and Salsola passerina respectively. Substitute the phenotypic structure characteristics of the grouped shrubs into the above-ground biomass estimation models corresponding to Tetraena mongolica, Reaumuria soongorica, Reaumuria trigyna, and Salsola passerina respectively to obtain the pseudo-measured above-ground biomass of each group of shrubs. Finally, integrate the measured biomass, pseudo-measured biomass, and phenotypic structure characteristics of each group of shrubs to obtain a total of 929 survey data, and establish an above-ground biomass estimation composite model based on this.

[0115] Use the method for estimating the above-ground biomass of desert steppe shrubs by drone remote sensing provided in this application to estimate the above-ground biomass of shrubs in the drone survey plot. Based on the above-ground biomass estimation composite model, using the shrub crown area obtained by the SAM image segmentation method as the input parameter, calculate the above-ground biomass of each shrub in the drone survey plot as shown in Figure 5 shown. AsFigure 5 As shown, different colors are used in the figure to represent the size of the aboveground biomass (AGB) of shrubs, with the unit of grams (g). The legend shows the correspondence between the colors and the ranges of aboveground biomass:

[0116] Yellow: 0.16 - 223.85 g

[0117] Light yellow: 223.85 - 559.55 g

[0118] Orange: 559.55 - 1278.88 g

[0119] Reddish brown: 1278.88 - 3906.76 g

[0120] Dark red: 3906.76 - 11152.93 g

[0121] As can be seen from Figure 5 it, the colors in most areas are light yellow and yellow, indicating that the aboveground biomass of shrubs in these areas is relatively low; while the dark red areas are fewer, and the aboveground biomass of shrubs in these areas is higher. The method for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe provided by this application combines unmanned aerial vehicle (UAV) remote sensing technology and image processing algorithms, and can relatively accurately estimate the distribution of the aboveground biomass of shrubs in the UAV survey plots in the Tetraena mongolica protection area, providing important data support for ecological research, vegetation management, etc.

[0122] Based on the same inventive concept, as Figure 6 shown, an embodiment of this application also provides a device for remotely sensing and estimating the aboveground biomass of shrubs in desert steppe, including:

[0123] An image processing module, configured to process the spectral images of desert steppe shrubs in the survey plots to obtain the crown areas of each shrub in the survey plots;

[0124] An inversion module, configured to invert the lidar data of desert steppe shrubs in the survey plots to obtain the heights of each shrub in the survey plots;

[0125] An estimation module, configured to input the remotely sensed crown areas and heights of each shrub in the survey plots into the composite model for estimating aboveground biomass to obtain the estimated values of the aboveground biomass of each shrub within the range of the survey plots; wherein, the composite model for estimating aboveground biomass is obtained by fitting based on the phenotypic structural characteristics of shrubs and the aboveground biomass of shrubs, the phenotypic structural characteristics of shrubs include the pseudo-measured crown areas and measured heights of shrubs, and the aboveground biomass of shrubs includes the measured aboveground biomass and pseudo-measured aboveground biomass of shrubs.

[0126] Optionally, the image processing module is further configured to:

[0127] Stitch the spectral images collected by the multispectral sensor to obtain the stitched spectral image;

[0128] Input the stitched spectral image into the image segmentation model to identify the boundaries and feature information of shrubs and background objects;

[0129] Mark the segmentation regions corresponding to each shrub according to the boundaries and feature information of the shrubs and background objects;

[0130] Calculate the crown area of each shrub according to the segmentation region corresponding to each shrub and the spatial resolution of the image.

[0131] Optionally, the inversion module is further configured to:

[0132] Preprocess the lidar data to obtain a subset of shrub point clouds;

[0133] Perform clustering analysis on the subset of shrub point clouds to obtain the range of each shrub in the subset of shrub point clouds;

[0134] Perform height inversion on the point cloud data of each shrub to obtain the height of each shrub.

[0135] It should also be understood that the first, second, third, fourth, and various numerical numbers involved in this article are only for the convenience of description and are not used to limit the scope of this application.

[0136] It should be understood that the term "and / or" in this article is only a correlation relationship describing associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0137] In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or instructions in the form of software. The steps of the method disclosed in combination with the embodiments of this application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by a combination of the hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0138] In various embodiments of this application, the magnitude of the serial numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0139] Those of ordinary skill in the art will recognize that the various illustrative logical blocks (ILBs) and steps described in connection with the embodiments disclosed herein can be implemented in either electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0140] In several embodiments provided in this application, it should be understood that the disclosed method and device for remotely sensing and estimating the above-ground biomass of desert steppe shrubs can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units (or modules) is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings, direct couplings, or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0143] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0144] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for estimating aboveground biomass of shrubs in desert steppes by remote sensing by unmanned aerial vehicles, characterized in that: Applications in the estimation of aboveground biomass of shrubs in desert steppes include: The spectral images of desert steppe shrubs in the surveyed sample plots were processed to obtain the crown area of ​​each shrub in the surveyed sample plots. The LiDAR data of desert steppe shrubs in the survey plots were inverted to obtain the height of each shrub in the survey plots; The crown area and height of each shrub in the survey sample plot were input into the composite model for estimating aboveground biomass to obtain the estimated value of aboveground biomass of each shrub within the survey sample plot. Among them, the composite model for estimating aboveground biomass was fitted with the phenotypic structural characteristics of the shrub as the independent variable and the aboveground biomass of the shrub as the dependent variable. The phenotypic structural characteristics of the shrub included the pseudo-measured crown area and measured height of the shrub, and the aboveground biomass of the shrub included the measured aboveground biomass of the shrub and the pseudo-measured aboveground biomass.

2. The method according to claim 1, characterized in that The spectral image of the desert steppe shrubs is collected by a multispectral sensor carried by a drone in a preset drone survey plot. The process of processing the spectral image of the desert steppe shrubs in the survey plot is as follows: Splicing the spectral images collected by the multispectral sensor to obtain a spliced ​​spectral image; The spliced ​​spectral images are input into the image segmentation model to identify the boundaries and feature information of the shrubs and background objects; According to the boundary and feature information between the shrubs and the background objects, mark the segmented area corresponding to each shrub; The crown area of ​​each shrub was calculated based on the segmented area corresponding to each shrub and the spatial resolution of the image.

3. The method according to claim 1, characterized in that The laser radar data is collected by the laser radar carried by the drone in the preset drone survey plot. The process of inverting the laser radar data of the desert steppe shrubs in the survey plot is: Preprocess the LiDAR data to obtain a subset of shrub point clouds, including: The lidar data collected from different perspectives and at different times are unified into the same coordinate system, and the registration algorithm is used to stitch the lidar data together to form a complete sample point cloud data set; De-noising the sample point cloud dataset; The denoised sample point cloud dataset is separated into ground points and shrub points to obtain a shrub point cloud subset. Cluster analysis is performed on the shrub point cloud subset to obtain the range of each shrub in the shrub point cloud subset, including: According to the spatial clustering characteristics of the point cloud, the point clouds of the same shrubs are clustered into the same category, so that each clustering result corresponds to a shrub, so as to divide the range of each shrub in the shrub point cloud subset; Perform height inversion on the point cloud data of each shrub to obtain the remote sensing height of each shrub, including: The spatial coordinate query operation is used to obtain the maximum point coordinates and the minimum point coordinates of each shrub in the vertical direction of the point cloud, where the maximum point coordinates represent the top position of the shrub, and the minimum point coordinates represent the bottom position of the shrub; The remote sensing height of each shrub in the survey plot was obtained by subtracting the coordinates of the minimum point from the coordinates of the maximum point.

4. The method according to claim 1, characterized in that: The process of obtaining the pseudo measured crown area of ​​the shrub is as follows: A sampling survey of shrubs was conducted in the drone survey plot, and the attribute characteristics and phenotypic structural characteristics of N investigated shrubs were collected. The attribute characteristics of the shrubs included the shrub name, shrub category and geographical location, and the phenotypic structural characteristics of the shrubs included the shrub height, crown width from north to south and from east to west; According to the similarity between the attribute characteristics and phenotypic structural characteristics of each investigated shrub and the attribute characteristics and phenotypic structural characteristics of n dominant shrubs whose aboveground biomass sampling has been completed, the N investigated shrubs are divided into n groups; The north-south width and east-west width of the crown of n groups of shrubs are input into the relationship model between the shrub crown area and the north-south width and east-west width of the crown constructed for n dominant shrubs, and the pseudo-measured crown area of ​​each group of shrubs is calculated.

5. The method according to claim 4, characterized in that The construction process of the relationship model between the crown area of ​​the shrub and the north-south width and east-west width of the crown is as follows: With the measured crown area of ​​shrubs as the dependent variable and the north-south width and east-west width of the crown as the independent variables, the relationship model between the crown area of ​​shrubs and the north-south width and east-west width of the crown was constructed for n dominant shrubs respectively; wherein the relationship model is: X k =G(s,w), Where, X k represents the crown area of ​​the shrub, G() represents the fitting function, s represents the north-south width of the crown of the shrub, and w represents the east-west width of the crown of the shrub.

6. The method according to claim 5, characterized in that The process of obtaining the pseudo measured aboveground biomass is as follows: The phenotypic structural characteristics of n groups of shrubs are respectively input into the aboveground biomass estimation model for n dominant shrub species, and the pseudo-measured aboveground biomass of each group of shrubs is calculated; Among them, the aboveground biomass estimation model for n dominant shrub species is obtained by fitting the measured aboveground biomass of the corresponding dominant shrub species as the dependent variable and the phenotypic structural characteristics of the corresponding dominant shrub species as the independent variable.

7. The method according to claim 6, characterized in that The phenotypic structural characteristics corresponding to the dominant shrub species include the crown area of ​​the dominant shrub species and the height of the dominant shrub species, wherein the crown area of ​​the dominant shrub species is calculated by inputting the north-south width and the east-west width of the crown of the dominant shrub species into the relationship model.

8. The method according to claim 7, characterized in that The aboveground biomass estimation model for n dominant shrub species is: AGB t ={f1(X t ),f2(X t ),...,f i (X t ),...,f n (X t )}, Where AGBt represents the aboveground biomass of shrubs, f i (X t ) represents the fitting function of the i-th dominant shrub species, i = 1, 2, ..., n, n represents the number of dominant shrub species, X t Represents the phenotypic structural characteristics of dominant shrub species.

9. A UAV remote sensing estimation device for the biomass of shrubs in desert grasslands, characterized in that: Applications in the estimation of aboveground biomass of shrubs in desert steppes include: An image processing module is configured to process the spectral image of the desert steppe shrubs in the survey sample plot to obtain the crown area of ​​each shrub in the survey sample plot; An inversion module is configured to invert the laser radar data of the desert steppe shrubs in the survey sample plot to obtain the height of each shrub in the survey sample plot; The estimation module is configured to input the crown area and height of each shrub in the survey sample plot into the composite model for estimating aboveground biomass, so as to obtain the estimated value of aboveground biomass of each shrub in the survey sample plot; wherein, the composite model for estimating aboveground biomass is obtained by fitting the phenotypic structural characteristics of the shrub and the aboveground biomass of the shrub, the phenotypic structural characteristics of the shrub include the pseudo-measured crown area and measured height of the shrub, and the aboveground biomass of the shrub includes the measured aboveground biomass of the shrub and the pseudo-measured aboveground biomass.

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