Phyllostachys pubescens forest land biomass remote sensing estimation method and system considering biennial and biennial biomass remote sensing estimation system

Abstract

The invention discloses a moso bamboo forest land biomass remote sensing estimation method and system considering biennial and biennial moso bamboo forest land biomass remote sensing estimation, and the method comprises the steps: selecting a research region, and obtaining biennial and biennial moso bamboo forest region, bamboo age and region related information; obtaining the total aboveground biomass of a sample plot of the research area based on the moso bamboo forest areas of different years and the bamboo age; on the basis of the total above-ground biomass of the sample plot and the regional related information, establishing an on-ground biomass fitting model of the moso bamboo forest in different years; obtaining multi-source remote sensing data of the to-be-detected area, and obtaining moso bamboo feature information based on the multi-source remote sensing data; on the basis of the moso bamboo feature information and a biennial moso bamboo forest land biomass fitting model, obtaining the above-ground biomass of the to-be-measured area; and generating a spatial distribution diagram of the above-ground biomass of the moso bamboo in the to-be-measured area based on the above-ground biomass of the to-be-measured Different biomass estimation models are established by distinguishing a big year from a small year, so that the characteristic change of the moso bamboo forest in different growth stages can be more accurately captured, and the estimation precision of the biomass is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomass remote sensing estimation, and more specifically, to a method and system for remotely sensing and estimating the aboveground biomass of a moso bamboo forest considering biennial bearing characteristics. Background Art

[0002] Moso bamboo is the bamboo species with the largest distribution area and the highest economic value in China, with an area of 5.2776 million hm2. The moso bamboo forest grows rapidly and has a strong carbon storage capacity and carbon sequestration potential. Research shows that the annual carbon sequestration of the moso bamboo forest reaches 5.1 Mg / hm 2 , and the carbon storage of the moso bamboo forest in China has shown a gradually increasing trend in the past 50 years. Accurately estimating the aboveground biomass of the moso bamboo forest is of great scientific significance and practical significance for evaluating the carbon sink potential of the moso bamboo forest and realizing precise carbon sink trading. Large-scale estimation of aboveground biomass mainly relies on models that combine optical remote sensing technology with field plot data. However, optical remote sensing data is often affected by clouds and atmospheric scattering, resulting in unstable data quality, which in turn affects the accuracy of the estimation results. Secondly, optical remote sensing cannot penetrate dense tree canopies and can only obtain information on surface vegetation. For densely distributed moso bamboo forests, it is difficult to accurately reflect their true biomass. When existing methods are used for remote sensing estimation of the aboveground biomass of moso bamboo forests, the accuracy is often not ideal, and the determined coefficient (R 2 ) is relatively low.

[0003] In recent years, the progress of remote sensing technology has provided new possibilities for vegetation biomass estimation. Airborne LiDAR technology has become an important tool for forest resource monitoring due to its advantage of being able to penetrate tree canopies and obtain high-precision three-dimensional spatial information. The rapid development of unmanned aerial vehicle (UAV) technology has provided a flexible, low-cost, and high-resolution data acquisition platform for LiDAR. The LiDAR system carried by UAVs can quickly cover specific areas and obtain high-precision bamboo forest structure information, breaking through the limitations of traditional optical remote sensing technology and enabling large-scale and high-precision monitoring of the remote sensing estimation of moso bamboo aboveground biomass. However, there are biennial bearing changes in the growth cycle of moso bamboo forests, and traditional models often fail to effectively distinguish between big years and small years, resulting in instability of the estimation results. Moso bamboo grows vigorously in big years, and its biomass is significantly higher than that in small years. If this important periodic characteristic is ignored, the applicability and accuracy of the estimation model will be severely affected.

[0004] Therefore, how to accurately capture the characteristic changes of moso bamboo forests at different growth stages, so as to significantly improve the estimation accuracy of biomass, is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for remotely sensing and estimating the above-ground biomass of a moso bamboo forest considering the biennial bearing phenomenon. By establishing different biomass estimation models for the high-yield year and the low-yield year, it can more accurately capture the characteristic changes of the moso bamboo forest at different growth stages, thus significantly improving the estimation accuracy of biomass.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for remotely sensing and estimating the above-ground biomass of a moso bamboo forest considering the biennial bearing phenomenon, comprising:

[0008] Select a study area and obtain the areas of high-yield and low-yield moso bamboo forests, bamboo ages, and area-related information;

[0009] Based on the areas of high-yield and low-yield moso bamboo forests and the bamboo ages, obtain the total above-ground biomass of the sample plots in the study area;

[0010] Based on the total above-ground biomass of the sample plots and the area-related information, establish a fitting model for the above-ground biomass of high-yield and low-yield moso bamboo forests;

[0011] Obtain multi-source remote sensing data of the area to be measured, and based on the multi-source remote sensing data, obtain moso bamboo characteristic information;

[0012] Based on the moso bamboo characteristic information and the fitting model for the above-ground biomass of high-yield and low-yield moso bamboo forests, obtain the above-ground biomass of the area to be measured;

[0013] Based on the above-ground biomass of the area to be measured, generate a spatial distribution map of the above-ground biomass of moso bamboo in the area to be measured.

[0014] Preferably, the method for determining the areas of high-yield and low-yield moso bamboo forests is as follows:

[0015] Based on the bamboo shoot emergence amount in the moso bamboo forest within the study area, judge and divide the areas of high-yield and low-yield moso bamboo forests;

[0016] If the bamboo shoot emergence amount in the area is greater than or equal to the set threshold and the leaves of the moso bamboo forest are yellow, it is determined and divided into the high-yield moso bamboo forest area;

[0017] If the bamboo shoot emergence amount in the area is less than the set threshold or zero and the leaves of the moso bamboo forest are dark green, it is determined and divided into the low-yield moso bamboo forest area.

[0018] Preferably, the method for determining the bamboo age is as follows:

[0019] Based on the growth characteristics of moso bamboo, determine the bamboo age:

[0020] If there are bamboo shoot sheaths at the bottom and the bamboo poles are bluish green, it is determined as first-degree bamboo;

[0021] If the bamboo poles are green, there are hairs on the sheath rings, and there is waxy white powder, it is determined as second-degree bamboo;

[0022] If the bamboo pole is yellowish green, the lignification of the bamboo pole is heavy, and the waxy powder turns grayish white or black, it is determined as the third-degree bamboo;

[0023] If the bamboo pole is yellowish brown, it is determined as the fourth-degree bamboo.

[0024] Preferably, the method for obtaining the total aboveground biomass of the sample plot is as follows:

[0025] Dividing the moso bamboo forest area with alternate years into multiple sample plots with the same area to obtain a sample plot set;

[0026] Randomly sampling based on the sample plot set to obtain a preset number of sample plots as representative sample plots;

[0027] Measuring the breast diameters of all moso bamboos in the area based on the representative sample plots;

[0028] Obtaining the aboveground biomass of a single moso bamboo based on the breast diameter of the moso bamboo and the corresponding bamboo age;

[0029] Summarizing based on the aboveground biomass of a single moso bamboo to obtain the total aboveground biomass of the sample plot.

[0030] Preferably, the total aboveground biomass AGB of the sample plot 样地 Specifically:

[0031]

[0032] Among them, Area represents the area of the representative sample plot, a represents the biomass unit conversion coefficient, n represents the number of moso bamboos in the representative sample plot, and AGB 单株 represents the aboveground biomass of a single moso bamboo.

[0033] Preferably, the regional relevant information includes: the density of moso bamboos in the big year, the density of moso bamboos in the small year, the altitude, and the average tree height of the sample plot.

[0034] Preferably, obtaining the fitting model of the aboveground biomass of the moso bamboo forest with alternate years specifically includes:

[0035] Establishing the fitting model of the aboveground biomass of the moso bamboo forest with alternate years based on the total aboveground biomass of the sample plot, the density of moso bamboos in the big year, the density of moso bamboos in the small year, the altitude, and the average tree height of the sample plot:

[0036]

[0037] Among them, AGB 大 represents the aboveground biomass of the moso bamboo forest in the big year, P1 represents the density of moso bamboos in the big year, E represents the altitude, and AGB 小 represents the aboveground biomass of the moso bamboo forest in the small year, P2 represents the density of moso bamboos in the small year, and H represents the average tree height of the sample plot.

[0038] Preferably, the method for obtaining the Phyllostachys edulis characteristic information is as follows:

[0039] The multi-source remote sensing data includes: lidar point cloud data and multispectral data;

[0040] Based on the multispectral data, the Phyllostachys edulis areas of large and small years in the area to be measured are distinguished;

[0041] Based on the lidar point cloud data, preprocessing is carried out, and the preprocessed lidar point cloud data is segmented into individual trees to obtain the density of Phyllostachys edulis of large and small years in the area to be measured and the height of Phyllostachys edulis in the area to be measured;

[0042] The Phyllostachys edulis areas of large and small years in the area to be measured, the density of Phyllostachys edulis of large and small years in the area to be measured, and the height of Phyllostachys edulis in the area to be measured together constitute the Phyllostachys edulis characteristic information.

[0043] Preferably, the method for obtaining the density of Phyllostachys edulis of large and small years in the area to be measured and the height of Phyllostachys edulis in the area to be measured is as follows:

[0044] Based on the preprocessed lidar point cloud data, individual tree segmentation is carried out to obtain the number of Phyllostachys edulis and the height of Phyllostachys edulis in the area to be measured;

[0045] Based on the area to be measured, grid division is carried out to obtain a plurality of grid areas with the same area;

[0046] Based on the number of Phyllostachys edulis in the grid area divided by the corresponding grid area, the corresponding grid Phyllostachys edulis density is obtained;

[0047] Based on the Phyllostachys edulis areas of large and small years in the area to be measured and the grid area, the large-year grid area and the small-year grid area are determined;

[0048] Based on the mean value of the grid Phyllostachys edulis densities corresponding to all the large-year grid areas, the large-year Phyllostachys edulis density in the area to be measured is obtained;

[0049] Based on the mean value of the grid Phyllostachys edulis densities corresponding to all the small-year grid areas, the small-year Phyllostachys edulis density in the area to be measured is obtained;

[0050] Based on the mean value of the heights of Phyllostachys edulis corresponding to all the small-year grid areas, the height of Phyllostachys edulis in the area to be measured is obtained.

[0051] A remote sensing estimation system for aboveground biomass of Phyllostachys edulis considering large and small years includes: a first data acquisition module, a plot biomass acquisition module, a model construction module, a second data acquisition module, and a result output module;

[0052] The first data acquisition module is used to select a research area and obtain the Phyllostachys edulis area of large and small years, bamboo age, and area-related information;

[0053] The plot biomass acquisition module is used to obtain the total aboveground biomass of the plots in the study area based on the biennial moso bamboo forest area and the bamboo age;

[0054] The model construction module is used to establish a fitting model for the aboveground biomass of the biennial moso bamboo forest based on the total aboveground biomass of the plots and the regional relevant information;

[0055] The second data acquisition module is used to acquire multi-source remote sensing data of the area to be measured and obtain moso bamboo characteristic information based on the multi-source remote sensing data;

[0056] The result output module is used to obtain the aboveground biomass of the area to be measured based on the moso bamboo characteristic information and the fitting model for the aboveground biomass of the biennial moso bamboo forest; generate a spatial distribution map of the aboveground biomass of moso bamboo in the area to be measured based on the aboveground biomass of the area to be measured.

[0057] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a remote sensing estimation method and system for the aboveground biomass of moso bamboo forests considering biennial characteristics. By combining multi-source data such as UAV LiDAR and optical images, and establishing biomass estimation models respectively based on the characteristics of moso bamboo biennial, it can capture the characteristic changes of moso bamboo forests at different growth stages more accurately, thereby significantly improving the estimation accuracy of biomass, realizing rapid and accurate estimation of the aboveground biomass of moso bamboo at the regional scale, and improving the efficiency of moso bamboo resource monitoring and management; overcoming the limitations of traditional remote sensing technology, and providing an efficient and reliable technical means for the precise carbon sink assessment and resource management of moso bamboo forests. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0059] Figure 1 It is a flowchart of a remote sensing estimation method for the aboveground biomass of moso bamboo forests considering biennial characteristics provided by the present invention.

[0060] Figure 2 It is a schematic diagram of a moso bamboo forest in a high-yield year provided by the present invention.

[0061] Figure 3 It is a schematic diagram of a moso bamboo forest in a low-yield year provided by the present invention.

[0062] Figure 4 It is a schematic diagram of first-year bamboo provided by the present invention.

[0063] Figure 5Schematic diagram of the bamboo at the second degree provided by the present invention.

[0064] Figure 6 Schematic diagram of the bamboo at the third degree provided by the present invention.

[0065] Figure 7 Schematic diagram of the bamboo at the fourth degree provided by the present invention.

[0066] Figure 8 Schematic diagram of the image of the area to be measured obtained by the drone provided by the present invention.

[0067] Figure 9 Schematic diagram of the single-tree segmentation effect provided by the present invention.

[0068] Figure 10 Schematic diagram of the single-tree identification provided by the present invention.

[0069] Figure 11 Schematic diagram of the grid division of the area to be measured provided by the present invention.

[0070] Figure 12 Schematic diagram of the spatial distribution of the above-ground biomass of moso bamboo provided by the present invention.

[0071] Figure 13 Schematic diagram of the structure of a remote sensing estimation system for the above-ground biomass of moso bamboo considering the biennial bearing provided by the present invention. Detailed implementation manners

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0073] Embodiment 1

[0074] As Figure 1 shown, the embodiment of the present invention discloses a method for remote sensing estimation of the above-ground biomass of moso bamboo considering the biennial bearing, including:

[0075] Select a research area and obtain the biennial-bearing moso bamboo forest area, bamboo age and area-related information;

[0076] Based on the biennial-bearing moso bamboo forest area and bamboo age, obtain the total above-ground biomass of the sample plots in the research area;

[0077] Based on the total above-ground biomass of the sample plots and the area-related information, establish a fitting model for the above-ground biomass of the biennial-bearing moso bamboo forest;

[0078] Obtain multi-source remote sensing data of the area to be measured, and obtain moso bamboo characteristic information based on the multi-source remote sensing data;

[0079] The aboveground biomass of the area to be measured is obtained based on the moso bamboo characteristic information and the fitting model of the aboveground biomass of the moso bamboo forest with alternate good and poor years.

[0080] A spatial distribution map of the aboveground biomass of moso bamboo in the area to be measured is generated based on the aboveground biomass of the area to be measured.

[0081] Embodiment 2

[0082] An embodiment of the present invention discloses a remote sensing estimation method for the aboveground biomass of a moso bamboo forest considering alternate good and poor years, including:

[0083] Select a study area and obtain the area of the moso bamboo forest with alternate good and poor years, bamboo age, and area-related information:

[0084] Preferably, in a specific embodiment, a field survey is carried out in the study area from May to August 2024, including a total of 98 sample plots and 14,086 moso bamboos. The statistical information of the sample plots is shown in Table 1:

[0085] Table 1 Summary of sample plot survey data

[0086]

[0087] s

[0088] Preferably, the method for confirming the area of the moso bamboo forest with alternate good and poor years is:

[0089] Based on the bamboo shoot emergence amount in the moso bamboo forest within the study area, the area of the moso bamboo forest with alternate good and poor years is judged and divided;

[0090] As Figure 2 shown, if the bamboo shoot emergence amount within the area is greater than or equal to the set threshold, resulting in insufficient nutrition for the grown bamboos and the leaves of the moso bamboo forest being yellow, it is determined and divided into the area of the moso bamboo forest in a good year;

[0091] As Figure 3 shown, if the bamboo shoot emergence amount within the area is less than the set threshold or zero, the nutrition of the grown bamboos accumulates, and the leaves of the moso bamboo forest are dark green, it is determined and divided into the area of the moso bamboo forest in a poor year.

[0092] Preferably, the method for determining the bamboo age is:

[0093] The moso bamboo forest is an uneven-aged forest. The bamboo shoots grow to the second year and change leaves once, and then change leaves every two years, increasing one degree. The bamboo age is determined based on the growth characteristics of the moso bamboo:

[0094] As Figure 4 shown, if there are bamboo shoot sheaths at the bottom and the bamboo poles are bluish-green, it is determined as the first-degree bamboo;

[0095] As Figure 5 shown, if the bamboo poles are green, there are hairs on the sheath rings, and there is waxy white powder, it is determined as the second-degree bamboo;

[0096] As Figure 6 shown, if the bamboo pole is yellowish green, the lignification of the bamboo pole is heavy, and the waxy powder turns grayish white or black, it is determined as the third-degree bamboo;

[0097] As Figure 7 shown, if the bamboo pole is yellowish brown, it is determined as the fourth-degree bamboo.

[0098] Preferably, the main types of bamboo in the bamboo forest in the high-yield year are the first-degree bamboo, the second-degree bamboo and the third-degree bamboo, and the main types of bamboo in the bamboo forest in the low-yield year are the second-degree bamboo, the third-degree bamboo and the fourth-degree bamboo.

[0099] Based on the bamboo forest areas in the high-yield and low-yield years and the bamboo age, the total aboveground biomass of the sample plots in the study area is obtained:

[0100] Preferably, the method for obtaining the total aboveground biomass of the sample plots is as follows:

[0101] Based on the bamboo forest areas in the high-yield and low-yield years, the bamboo forest is divided into a plurality of sample plots with the same area to obtain a sample plot set;

[0102] Based on random sampling of the sample plot set, a preset number of sample plots are obtained as representative sample plots;

[0103] Based on the representative sample plots, the breast diameters of all the moso bamboos in the area are measured;

[0104] Based on the breast diameters of the moso bamboos and the corresponding bamboo ages, the aboveground biomass of a single moso bamboo is obtained;

[0105] Based on the aboveground biomass of a single moso bamboo, a summary is made to obtain the total aboveground biomass of the sample plot.

[0106] Preferably, in this embodiment, the area of the sample plot is set to 20 m × 20 m, and the breast diameter at 1.3 m of each moso bamboo in the representative sample plot is measured as the breast diameter of the moso bamboo.

[0107] Preferably, based on the breast diameters of the moso bamboos and the corresponding bamboo ages, the aboveground biomass of a single moso bamboo is obtained:

[0108]

[0109] wherein, AGB 单株 represents the aboveground biomass of a single moso bamboo (kg), D represents the breast diameter, and Y represents the bamboo age (degree).

[0110] Preferably, the total aboveground biomass of the sample plot is specifically:

[0111]

[0112] wherein, AGB 样地 represents the total aboveground biomass of the sample plot (t / ha), Area represents the area of the representative sample plot, and a represents the biomass unit conversion coefficient (from kg / m 2Converted to t / ha, with a value of 10), and n represents the number of moso bamboos in the representative plot.

[0113] Based on the total aboveground biomass of the plot and regional relevant information, a fitting model for the aboveground biomass of the moso bamboo forest with big and small years is established:

[0114] Preferably, the regional relevant information includes: big-year moso bamboo density, small-year moso bamboo density, altitude, and average tree height of the plot.

[0115] Preferably, obtaining the fitting model for the aboveground biomass of the moso bamboo forest with big and small years specifically includes:

[0116] Based on the total aboveground biomass of the plot, big-year moso bamboo density, small-year moso bamboo density, altitude, and average tree height of the plot, a stepwise regression model is used to establish a fitting model for the aboveground biomass of the moso bamboo forest with big and small years:

[0117]

[0118] Among them, AGB 大 represents the aboveground biomass of the big-year moso bamboo forest, P1 represents the big-year moso bamboo density, E represents the altitude, and AGB 小 represents the aboveground biomass of the small-year moso bamboo forest, P2 represents the small-year moso bamboo density, and H represents the average tree height of the plot.

[0119] Preferably, it further includes: performing cross-validation on the fitting model for the aboveground biomass of the moso bamboo forest with big and small years to ensure the applicability of the big-year and small-year models in different environments:

[0120] To verify the stability and predictive ability of the model, the leave-one-out method is used to perform cross-validation on the modeling data. Each time, only one sample is left as the test set, and the other samples are used as the training set. If there are k samples, then it is necessary to train k times and test k times.

[0121] In a specific embodiment, 52 big-year moso bamboo forests are selected. 51 samples are used as training samples to establish the model, and 1 sample is used as the verification for accuracy verification. It is trained 51 times and tested 51 times. Finally, the verification accuracy of the fitting model AGB 大 for the aboveground biomass of the big-year moso bamboo forest is obtained; the same method is used to verify the fitting model AGB 小 for the aboveground biomass of the small-year moso bamboo forest.

[0122] In a specific embodiment, the verification R 2 of the fitting model for the aboveground biomass of the big-year moso bamboo forest is 0.67, and the verification R 2 of the fitting model for the aboveground biomass of the small-year moso bamboo forest is 0.861, indicating that the fitting degree of moso bamboo density (Density) and average height (Height) to the aboveground biomass of moso bamboo is relatively good and has strong explanatory ability.

[0123] Obtain multi-source remote sensing data of the area to be measured, and obtain moso bamboo feature information based on the multi-source remote sensing data:

[0124] Preferably, the method for obtaining moso bamboo feature information is as follows:

[0125] The multi-source remote sensing data includes: lidar point cloud data and multispectral data;

[0126] Based on the multispectral data, distinguish the moso bamboo areas of big and small years in the area to be measured;

[0127] Perform preprocessing on the lidar point cloud data, perform single-tree segmentation on the preprocessed lidar point cloud data, and obtain the moso bamboo density of big and small years in the area to be measured and the moso bamboo height in the area to be measured;

[0128] The moso bamboo areas of big and small years in the area to be measured, the moso bamboo density of big and small years in the area to be measured, and the moso bamboo height in the area to be measured together constitute the moso bamboo feature information.

[0129] Preferably, as Figure 8 shown, to ensure the clarity and stability of the data, data collection is carried out during periods of clear weather and low wind speed. In this embodiment, a DJI M300 drone equipped with a RIEGL miniVUX-1UAV lidar sensor is used to collect lidar point cloud data and high-resolution multispectral data for the area to be measured.

[0130] In this embodiment, the flight altitude of the drone is set to 120 meters, the longitudinal overlap of the images is not less than 70%, and the lateral overlap is not less than 60% to ensure the accuracy and consistency of image stitching. The resolution of the obtained drone images is better than 10 cm, the lidar point cloud density is higher than 200 / m3, and the coordinate system is WGS_1984_UTM_Zone_50N.

[0131] Preferably, the preprocessing of the lidar point cloud data specifically includes:

[0132] Perform denoising on the lidar point cloud data to improve the accuracy and usability of the data; use an improved progressive densification triangulation filtering algorithm for ground point classification. In this embodiment, the iteration distance is set to 1.4 meters and the iteration angle is set to 8°; on this basis, perform normalization processing on the point cloud data to remove the influence of terrain undulation on the elevation value of the point cloud data, thereby improving the comparability of the data and the stability of the algorithm. In this embodiment, for the elevation value Z of each point, subtract the elevation value of the nearest ground point found to obtain the normalized point cloud data.

[0133] Preferably, in this embodiment, the statistical filtering method is used for denoising, the minimum number of neighborhood points is set to 10, and the standard deviation multiple is set to 2.

[0134] Preferably, the method for obtaining the density of Moso bamboo in the large and small years of the area to be measured and the height of Moso bamboo in the area to be measured is as follows:

[0135] Based on the preprocessed lidar point cloud data, individual tree segmentation is performed to obtain the number of Moso bamboos and the height of Moso bamboos in the area to be measured;

[0136] Based on the area to be measured, grid division is performed to obtain multiple grid areas with the same area;

[0137] Based on the number of Moso bamboos in the grid area divided by the corresponding grid area, the corresponding grid Moso bamboo density is obtained;

[0138] Based on the large and small year Moso bamboo areas and grid areas in the area to be measured, the large year grid area and the small year grid area are determined;

[0139] Based on the average value of the grid Moso bamboo densities corresponding to all the large year grid areas, the large year Moso bamboo density in the area to be measured is obtained;

[0140] Based on the average value of the grid Moso bamboo densities corresponding to all the small year grid areas, the small year Moso bamboo density in the area to be measured is obtained;

[0141] Based on the average value of the Moso bamboo heights corresponding to all the small year grid areas, the Moso bamboo height in the area to be measured is obtained.

[0142] Preferably, in this embodiment, an individual tree segmentation algorithm based on seed points is adopted. Seed points are manually added at the breast diameter of the individual tree in the point cloud data. The algorithm will search for points within the breast diameter radius or the nearest points based on the three-dimensional coordinates of the seed points as the initial seed point clusters for subsequent segmentation, and finally identify all the Moso bamboos in the area to be measured based on the segmentation results.

[0143] Preferably, the clustering threshold affects the efficiency and accuracy of individual tree segmentation, and the minimum number of clustering points affects the segmentation effect of the individual tree canopy. In this embodiment, the clustering threshold is set to 0.2 m, and the minimum number of clustering points is set to 300. The individual tree segmentation effect is as Figure 9 shown.

[0144] Preferably, the tree height of each individual Moso bamboo is calculated simultaneously during the individual tree segmentation process:

[0145] For the point cloud of each individual Moso bamboo segmented, find the elevation value h max of its highest point and the elevation value h min of its lowest point. Through the formula H = h max - h min calculate the tree height H of this individual Moso bamboo. When finding the lowest point, according to the planar position of the Moso bamboo point cloud, search for the nearest ground point in the extracted ground point cloud, and its elevation value is used as h min .

[0146] Preferably, as Figure 10As shown in the figure, all the moso bamboos in the area to be measured are finally identified based on the individual tree segmentation results. The overall accuracy (AO), recognition accuracy (AD), tree height fitting accuracy (P), and coefficient of determination (R2) of the individual tree segmentation are calculated using the following formulas to verify the individual tree segmentation results:

[0147]

[0148] Among them, N C represents the total number of correctly identified individual trees, N F represents the total number of individual trees in the field survey, N D represents the total number of identified individual trees, and n represents the total number of identified single moso bamboos; W i represents the tree height extracted from the lidar point cloud; w i represents the true value of the measured tree height corresponding to the extracted individual tree; represents w i average value; represents W i average value.

[0149] In this embodiment, the overall accuracy of individual tree segmentation is 98.78%, the recognition accuracy is 96.42%, the tree height fitting accuracy is 84.57%, and the coefficient of determination is 0.83%, indicating that the segmentation algorithm has high accuracy.

[0150] Preferably, the average altitude of the area to be measured is calculated based on the DEM data with a resolution of 30 m in the ASTER GDEM product provided by the National Aeronautics and Space Administration (NASA) of the United States.

[0151] Preferably, as Figure 11 shown, in this embodiment, the area to be measured is divided into several grids, specifically standard grids of 10 m × 10 m, and the number of moso bamboos in each grid is counted to calculate the moso bamboo density.

[0152] The aboveground biomass of the area to be measured is obtained based on the moso bamboo characteristic information and the aboveground biomass fitting model of the moso bamboo forest in the on-year and off-year:

[0153] Preferably, based on the obtained on-year moso bamboo density, off-year moso bamboo density, moso bamboo height, and average altitude of the area to be measured, they are respectively substituted into the aboveground biomass fitting model of the moso bamboo forest in the on-year and off-year to obtain the aboveground biomass of the area to be measured.

[0154] Based on the aboveground biomass of the area to be measured, a spatial distribution map of the aboveground biomass of the moso bamboo in the area to be measured is generated.

[0155] Preferably, as Figure 12 shown, in order to visually display the aboveground biomass distribution in the area to be measured, the ArcGIS software is used to generate a spatial distribution map of the aboveground biomass of the moso bamboo. In the figure, AGB refers to the aboveground biomass of the moso bamboo per unit area.

[0156] Example 3

[0157] As Figure 13 shown, a remote sensing estimation system for aboveground biomass of Moso bamboo considering biennial bearing includes: a first data acquisition module, a plot biomass acquisition module, a model construction module, a second data acquisition module, and a result output module;

[0158] The first data acquisition module is used to select a study area and obtain the biennial bearing Moso bamboo forest area, bamboo age, and area-related information;

[0159] The plot biomass acquisition module is used to obtain the total aboveground biomass of the plots in the study area based on the biennial bearing Moso bamboo forest area and bamboo age;

[0160] The model construction module is used to establish a fitting model for aboveground biomass of biennial bearing Moso bamboo forest based on the total aboveground biomass of the plots and area-related information;

[0161] The second data acquisition module is used to obtain multi-source remote sensing data of the area to be measured and obtain Moso bamboo characteristic information based on the multi-source remote sensing data;

[0162] The result output module is used to obtain the aboveground biomass of the area to be measured based on the Moso bamboo characteristic information and the fitting model for aboveground biomass of biennial bearing Moso bamboo forest; generate a spatial distribution map of the aboveground biomass of Moso bamboo in the area to be measured based on the aboveground biomass of the area to be measured.

[0163] Preferably, the implementation processes of the various modules of the system of the present invention correspond one by one to the corresponding method content above.

[0164] Example 4

[0165] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus;

[0166] The memory is used to store a computer program;

[0167] When the processor is used to execute the program stored in the memory, it can implement a remote sensing estimation method for aboveground biomass of Moso bamboo considering biennial bearing as in Example 1 or 2.

[0168] The electronic device may include: a processor, a communications interface, a memory, and a communication bus. Among them, the processor, the communications interface, and the memory complete their mutual communication through the communication bus. The processor may call the logic instructions in the memory to execute the method for remotely estimating the above-ground biomass of a moso bamboo forest considering biennial bearing in Embodiment 1 or 2.

[0169] In addition, when the logic instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0170] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference may be made to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference may be made to the description in the method part.

[0171] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A remote sensing estimation method for aboveground biomass of moso bamboo forests considering biennial bearing characteristics, characterized in that, Including: Select a research area and obtain information related to the alternate-bearing moso bamboo forest area, bamboo age, and the area; Based on the alternate-bearing moso bamboo forest area and the bamboo age, obtain the total aboveground biomass of the sample plots in the research area; Based on the total aboveground biomass of the sample plots and the area-related information, establish a fitting model for the aboveground biomass of the alternate-bearing moso bamboo forest; Obtain multi-source remote sensing data of the area to be measured, and based on the multi-source remote sensing data, obtain moso bamboo characteristic information; Based on the moso bamboo characteristic information and the fitting model for the aboveground biomass of the alternate-bearing moso bamboo forest, obtain the aboveground biomass of the area to be measured; Based on the aboveground biomass of the area to be measured, generate a spatial distribution map of the aboveground biomass of moso bamboo in the area to be measured.

2. The remote sensing estimation method of aboveground biomass of moso bamboo forest considering biennial bearing according to claim 1, characterized in that, The method for confirming the alternate-bearing moso bamboo forest area is as follows: Based on the bamboo shoot emergence amount in the moso bamboo forest within the research area, divide and judge the alternate-bearing moso bamboo forest area; If the bamboo shoot emergence amount in the area is greater than or equal to the set threshold and the leaves of the moso bamboo forest are yellow, it is judged and divided into the big-year moso bamboo forest area; If the bamboo shoot emergence amount in the area is less than the set threshold or zero and the leaves of the moso bamboo forest are dark green, it is judged and divided into the small-year moso bamboo forest area.

3. A method for remotely sensing and estimating the aboveground biomass of a moso bamboo forest considering biennial bearing, as claimed in claim 1, wherein The method for determining the bamboo age is as follows: Determine the bamboo age based on the growth characteristics of moso bamboo: If there are bamboo shoot sheaths at the bottom and the bamboo poles are bluish green, it is judged as first-degree bamboo; If the bamboo poles are green, there are hairs on the sheath rings, and there is waxy white powder, it is judged as second-degree bamboo; If the bamboo poles are yellowish green, the bamboo poles are highly lignified, and the waxy powder turns grayish white or black, it is judged as third-degree bamboo; If the bamboo poles are yellowish brown, it is judged as fourth-degree bamboo.

4. The remote sensing estimation method of aboveground biomass of a moso bamboo forest considering biennial bearing according to claim 1, characterized in that, The method for obtaining the total aboveground biomass of the sample plots is as follows: Based on the alternate-bearing moso bamboo forest area, divide it into multiple sample plots with the same area to obtain a sample plot set; Based on the sample plot set, randomly sample to obtain a preset number of sample plots as representative sample plots; Based on the representative sample plots, measure the DBH of all moso bamboos in the area; Based on the DBH of the moso bamboo and the corresponding bamboo age, obtain the aboveground biomass of a single moso bamboo; Based on the aboveground biomass of a single moso bamboo, summarize to obtain the total aboveground biomass of the sample plots.

5. A method for remotely estimating the aboveground biomass of a moso bamboo forest considering biennial bearing, as described in claim 4, wherein The aboveground biomass AGB of the plot 样地 Specifically: Among them, Area represents the area of the representative plot, a represents the biomass unit conversion coefficient, n represents the number of Phyllostachys edulis in the representative plot, and AGB 单株 represents the aboveground biomass of a single Phyllostachys edulis plant.

6. The remote sensing estimation method of aboveground biomass of a moso bamboo forest considering biennial bearing according to claim 5, characterized in that, The area-related information includes: big-year moso bamboo density, small-year moso bamboo density, altitude, and average tree height of the sample plots.

7. A method for remotely sensing and estimating the aboveground biomass of a moso bamboo forest considering biennial bearing, characterized in that, Specifically obtaining the fitting model for the aboveground biomass of the alternate-bearing moso bamboo forest includes: Based on the total aboveground biomass of the sample plots, the big-year moso bamboo density, the small-year moso bamboo density, the altitude, and the average tree height of the sample plots, establish the fitting model for the aboveground biomass of the alternate-bearing moso bamboo forest: Among them, AGB 大 represents the aboveground biomass of the bamboo forest in the high-yield year, P1 represents the density of bamboo in the high-yield year, E represents the altitude, and AGB 小 represents the aboveground biomass of the bamboo forest in the low-yield year, P2 represents the density of bamboo in the low-yield year, and H represents the average tree height of the sample plot.

8. A method for remotely sensing and estimating the aboveground biomass of a moso bamboo forest considering biennial bearing, characterized in that, The method for obtaining the moso bamboo characteristic information is as follows: The multi-source remote sensing data includes: LiDAR point cloud data and multi-spectral data; Based on the multi-spectral data, distinguish and obtain the alternate-bearing moso bamboo areas in the area to be measured; Based on the LiDAR point cloud data, perform preprocessing, and perform single-tree segmentation on the preprocessed LiDAR point cloud data to obtain the alternate-bearing moso bamboo density in the area to be measured and the moso bamboo height in the area to be measured; The alternate-bearing moso bamboo areas in the area to be measured, the alternate-bearing moso bamboo density in the area to be measured, and the moso bamboo height in the area to be measured together constitute the moso bamboo characteristic information.

9. The remote sensing estimation method of aboveground biomass of moso bamboo forest considering alternate bearing according to claim 8, characterized in that, The method for obtaining the alternate-bearing moso bamboo density in the area to be measured and the moso bamboo height in the area to be measured is as follows: Based on the preprocessed LiDAR point cloud data, perform single-tree segmentation to obtain the number of moso bamboos and the moso bamboo height in the area to be measured; Based on the area to be measured, grid division is carried out to obtain multiple grid areas with the same area; Based on the number of moso bamboos in the network area divided by the corresponding grid area, the corresponding grid moso bamboo density is obtained; Based on the large and small year moso bamboo areas and the grid areas in the area to be measured, the large year grid areas and the small year grid areas are determined; Based on the grid moso bamboo densities corresponding to all the large year grid areas, the average value is taken to obtain the large year moso bamboo density of the area to be measured; Based on the grid moso bamboo densities corresponding to all the small year grid areas, the average value is taken to obtain the small year moso bamboo density of the area to be measured; Based on the average value of the moso bamboo heights corresponding to all the small year grid areas, the moso bamboo height of the area to be measured is obtained.

10. A remote sensing estimation system for the aboveground biomass of a moso bamboo forest considering biennial bearing, which is applied to a method for remote sensing estimation of the aboveground biomass of a moso bamboo forest considering biennial bearing according to any one of claims 1-9, is characterized in that, It includes: The first data acquisition module, the sample plot biomass acquisition module, the model construction module, the second data acquisition module and the result output module; The first data acquisition module is used to select the research area and obtain the large and small year moso bamboo forest areas, bamboo ages and area-related information; The sample plot biomass acquisition module is used to obtain the total aboveground biomass of the sample plot in the research area based on the large and small year moso bamboo forest areas and the bamboo ages; The model construction module is used to establish a fitting model for the aboveground biomass of the large and small year moso bamboo forests based on the total aboveground biomass of the sample plot and the area-related information; The second data acquisition module is used to acquire multi-source remote sensing data of the area to be measured and obtain moso bamboo characteristic information based on the multi-source remote sensing data; The result output module is used to obtain the aboveground biomass of the area to be measured based on the moso bamboo characteristic information and the fitting model for the aboveground biomass of the large and small year moso bamboo forests; generate a spatial distribution map of the moso bamboo aboveground biomass of the area to be measured based on the aboveground biomass of the area to be measured.

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