Method for inverting overground biomass of songarus apetalus by using multi-source remote sensing data

Abstract

The invention discloses a songarus apetalus aboveground biomass inversion method using multi-source remote sensing data. The method is realized through the multi-source remote sensing data. The method comprises the following steps: firstly, performing quadrat survey to obtain biomass measured data, and scanning by using an unmanned aerial vehicle laser radar to obtain tree height data; and then, combining a quadrat survey result, biomass measured data and tree height data to construct a tree height-biomass inversion model, and estimating biomass distribution of songarus apetalus in a laser radar scanning area. Then, collecting sentinel No.2 and Landsat series images, extracting spectral features and tree age data, performing regression analysis, and constructing a songarus apetalus biomass inversion model based on satellite remote sensing data; and finally, estimating the biomass of the songarus apetalus in a satellite remote sensing image range by utilizing the model and satellite remote sensing data. According to the method, high-efficiency and high-precision overground biomass inversion of the songarus apetala is realized.

Description

Technical Field

[0001] The present invention relates to the field of vegetation biomass estimation, and in particular to a method for inverting the aboveground biomass of Sonneratia apetala using multi-source remote sensing data. Background Art

[0002] Mangroves, a vegetation community encompassing trees and shrubs that grow at the land-sea interface in tropical and subtropical regions, provide important ecological services such as carbon sequestration, wind and wave protection, and siltation and beach protection. Their carbon sequestration capacity per unit area is significantly higher than that of terrestrial forests. Sonneratia apetala is an excellent arbor mangrove. Due to its waterlogging tolerance, rapid growth rate, and strong adaptability, it has been widely introduced in South my country over the past few decades and is a key species for mangrove restoration and tidal flat afforestation. Furthermore, large-scale afforestation of Sonneratia apetala has accumulated significant biomass. Accurately estimating the aboveground biomass of Sonneratia apetala is crucial for evaluating the effectiveness of past mangrove conservation efforts and calculating future carbon stocks. However, the restoration of Sonneratia apetala is spatially fragmented, with significant spatial heterogeneity in its growth. Furthermore, its presence is primarily located on intermittently flooded muddy beaches. Traditional field surveys require manual entry into forested areas to measure tree structural parameters, which is difficult and inefficient, making large-scale field surveys impractical. Therefore, it is necessary to make full use of drone and satellite remote sensing technology to assist ground surveys and quickly obtain the structural information and growth status of surface trees through LiDAR and multispectral data.

[0003] Chinese invention application number 202311577475.2, "Mangrove Ecosystem Carbon Storage Assessment Method Based on Remote Sensing and DeepLab V3+," uses remote sensing images and the DeepLab model to classify mangrove populations, and combines field survey results to obtain carbon density data for each population. The area of each population is calculated and multiplied by the corresponding population's carbon density;

[0004] The method proposed in the Chinese invention application with application number 202311156020.3, "A method for estimating aboveground carbon storage in mangrove plants in the marine-terrestrial ecotone," first classifies coastal landforms, uses drone photography to assist in selecting samples for the classification model, and then constructs a biomass inversion model using field sample survey data.

[0005] The Chinese invention application with application number 202010622479.8, "A method for inverting mangrove biomass using aerial images and laser data," uses drone images and LiDAR data to calculate spectral data, texture data, and structural parameter data, and constructs a biomass model based on biomass data from field sample surveys.

[0006] The aforementioned technical solution primarily relies on directly constructing a biomass inversion model by correlating quadrat-scale field survey data with spectral, texture, and point cloud information from satellite and drone imagery. Ground surveys based on manual measurements are time-consuming and labor-intensive, and compared to drone surveys, the survey area is smaller in the same amount of time. Furthermore, mangrove biomass accumulation is closely related to age, a factor currently not considered by existing technologies. Summary of the Invention

[0007] In order to solve the technical problem of low estimation efficiency in the existing mangrove biomass estimation, the present invention provides a method for inverting the aboveground biomass of Sonneratia apetala using multi-source remote sensing data. The technical solution adopted by the present invention is:

[0008] The present invention provides a method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data, the method comprising:

[0009] Conduct a sample survey on the preset sample area, calculate the biomass based on the sample survey results, and use the calculated results as the measured biomass data for fitting;

[0010] Detecting a preset UAV laser radar scanning area, and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results, wherein the laser radar scanning area covers the sample area;

[0011] A tree height-biomass inversion model is constructed based on the sample survey results, the biomass measured data for fitting, and the tree height data inverted based on the three-dimensional point cloud data, and the biomass distribution of all Sonneratia apetalas within the laser radar scanning area is obtained through the tree height-biomass inversion model;

[0012] Collecting Sentinel-2 remote sensing images and Landsat series images covering the lidar scanning area, performing feature extraction on the Sentinel-2 remote sensing images and the Landsat series images, respectively, to obtain spectral characteristics and tree age data corresponding to the lidar scanning area;

[0013] Regression analysis was performed on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to the LiDAR scanning area to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data.

[0014] The Sonneratia apetala biomass inversion model based on satellite remote sensing data is used to estimate the Sonneratia apetala biomass within the range of satellite remote sensing images using satellite remote sensing data.

[0015] As a preferred solution, a method of conducting a quadrat survey on a preset quadrat area, calculating biomass based on the quadrat survey results, and using the calculated results as measured biomass data for fitting includes:

[0016] Conducting a quadrat survey on the preset sample area to obtain the measured tree height data, measured diameter at breast height data, and latitude and longitude coordinates of each Sonneratia apetala tree in the preset sample area;

[0017] The measured data of tree height and diameter at breast height of each Sonneratia apetala tree in the sample area were substituted into the preset allometric growth equation of Sonneratia apetala to calculate the biomass of each Sonneratia apetala tree in the sample area. The calculated results were used as the measured biomass data for fitting. The specific calculation formula is as follows:

[0018] W=a(D 2 H) b

[0019] Where W is biomass, D is diameter at breast height, H is tree height, and a and b are constants.

[0020] As a preferred solution, a method for detecting a preset drone lidar scanning area and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results includes:

[0021] The drones equipped with LiDAR sensors detect the preset drone lidar scanning areas and obtain three-dimensional point cloud data;

[0022] Obtaining a canopy height model corresponding to the laser radar scanning area based on the three-dimensional point cloud data;

[0023] The canopy height model is segmented into individual trees using a preset watershed segmentation algorithm, and the highest point within the canopy of each Sonneratia apetala tree is used as the tree height to obtain tree height data based on inversion of three-dimensional point cloud data.

[0024] As a preferred solution, the method for obtaining a canopy height model corresponding to the laser radar scanning area based on the three-dimensional point cloud data includes:

[0025] The three-dimensional point cloud data is input into the LiDAR360 software for point cloud denoising and ground point classification, and the classified ground point set and vegetation point cloud set are obtained.

[0026] Generate digital elevation model and digital surface model based on the classified ground point set and vegetation point cloud set respectively;

[0027] The digital elevation model is subtracted from the digital surface model to obtain the canopy height model corresponding to the lidar scanning area.

[0028] As a preferred solution, the method of generating a digital elevation model and a digital surface model based on the classified ground point set and vegetation point cloud set respectively includes:

[0029] Based on the classified ground point set, the digital elevation model is generated using the irregular triangulated network interpolation method;

[0030] Based on the classified vegetation point cloud collection, the digital surface model is generated using the inverse distance weighted interpolation method.

[0031] As a preferred solution, a method for constructing a tree height-biomass inversion model using the sample survey results, the measured biomass data for fitting, and the tree height data inverted based on three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area using the tree height-biomass inversion model includes:

[0032] According to the latitude and longitude coordinates of each Sonneratia apetala tree in the sample survey results, the measured biomass data for fitting is matched one by one with the tree height data inverted based on the three-dimensional point cloud data;

[0033] The least squares method is used to construct a biomass inversion model based on the tree height inversion of lidar data for the matched biomass measured data and tree height data;

[0034] The biomass inversion model for inverting tree height based on lidar data is verified and calibrated using the tree height measured data, the diameter at breast height measured data, and the latitude and longitude coordinates, and finally a tree height-biomass inversion model is obtained;

[0035] The tree height data inverted based on the three-dimensional point cloud data is input into the tree height-biomass inversion model to obtain the biomass distribution of all Sonneratia apetala in the laser radar scanning area.

[0036] As a preferred solution, a method for constructing a biomass inversion model for inverting tree height based on lidar data using the least squares method for matched biomass measured data and tree height data includes the following:

[0037] According to the linear relationship between DBH and tree height of Sonneratia apetala during a certain growth period, and considering the form of the allometric growth equation of Sonneratia apetala, the correlation equation between the biomass of Sonneratia apetala and tree height was constructed, and the nonlinear polynomial fitting was performed in MATLAB:

[0038] W=(aH 3 +bH 2 +cH) d

[0039] Where W is the biomass, H is the tree height, and a, b, c, and d are constants.

[0040] As a preferred solution, a method for collecting Sentinel-2 remote sensing images covering the lidar scanning area, performing feature extraction on the Sentinel-2 remote sensing images, and obtaining spectral features corresponding to the lidar scanning area includes:

[0041] Sentinel-2 remote sensing images covering the LiDAR scanning area were collected and preprocessed as follows:

[0042] Radiometric calibration, spatial registration, and atmospheric correction;

[0043] Resample the preprocessed Sentinel-2 remote sensing image and obtain surface reflectance data based on the resampling results;

[0044] Spectral characteristics within the range of the Sentinel-2 remote sensing image are obtained by performing remote sensing feature calculation on the surface reflectance data;

[0045] The spectral characteristics corresponding to the laser radar scanning area are obtained based on the spectral characteristics within the range of the Sentinel-2 remote sensing image.

[0046] As a preferred solution, a method for collecting Landsat series images covering the laser radar scanning area, performing feature extraction on the Landsat series images, and obtaining tree age data corresponding to the laser radar scanning area includes:

[0047] Collect Landsat long-time series images covering the LiDAR scan area and perform the following preprocessing:

[0048] Radiation correction and cloud removal processing;

[0049] NDVI time series data is constructed by calculating the NDVI value of each pixel in the preprocessed Landsat long-term image set;

[0050] The long-term dynamic changes of pixels are obtained based on the NDVI time series data, and the tree age of each pixel is calculated based on the changes to obtain the tree age data corresponding to the Landsat series image range, specifically:

[0051] Perform sliding average processing on the NDVI time series data, and use the NDVI value after sliding average processing that is higher than a preset threshold as the starting year of tree age. The distance from the current time is the tree age. If there is a continuous sharp drop in NDVI, the starting time point of tree age is recalculated;

[0052] The tree age data corresponding to the laser radar scanning area is obtained based on the tree age data corresponding to the Landsat series image range.

[0053] As a preferred solution, a method for performing regression analysis on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to the laser radar scanning area to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data includes:

[0054] First, the biomass distribution of all Sonneratia apetalas corresponding to the laser radar scanning area was statistically analyzed based on the spatial resolution of the Sentinel-2 remote sensing image.

[0055] Then, all the Sonneratia apetala biomass distribution, spectral characteristics and tree age data corresponding to the lidar scanning area were input into the preset random forest model for regression analysis to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention uses a laser radar mounted on a drone to quickly survey the biomass of Sonneratia apetala in a local area of the site, ignoring terrain obstacles. Compared with traditional survey methods, this method greatly improves efficiency and obtains greater and more survey details.

[0058] The present invention uses multi-source remote sensing data (drone-mounted laser radar, multi-source satellite remote sensing data) to fuse multi-dimensional spatiotemporal remote sensing information to complete biomass inversion and improve inversion accuracy. Each data has its own advantages. LiDAR data can obtain high-precision three-dimensional structural information of land objects. Sentinel-2 satellite images have high spatial resolution and rich spectral information. Landsat series satellite images have the longest time series of continuous data, recording the dynamic changes of land objects over a long period of time. Compared with constructing a biomass inversion model using only spectral information, adding tree age information can characterize the cumulative effect of tree height and biomass, further improving the effect of regional scale biomass inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data provided in this embodiment;

[0060] Figure 2 The canopy height model result diagram provided for this embodiment;

[0061] Figure 3 The local single tree segmentation result diagram provided in this embodiment;

[0062] Figure 4 The aboveground biomass distribution of Sonneratia apetala estimated based on UAV lidar data provided in this embodiment;

[0063] Figure 5 Some spectral index features provided for this embodiment;

[0064] Figure 6 The tree age inversion result diagram provided in this embodiment;

[0065] Figure 7 This is the regional Sonneratia apetala biomass inversion result diagram provided in this example. DETAILED DESCRIPTION

[0066] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting the present invention;

[0067] It should be clear that the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the embodiments of the present application.

[0068] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present application. The singular forms "a," "the," and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0069] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0070] In addition, in the description of this application, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship. The present invention is further described below with reference to the accompanying drawings and examples.

[0071] The present invention is further described below with reference to the accompanying drawings and embodiments.

[0072] Example 1

[0073] Please refer to Figure 1 This embodiment provides a method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data, the method comprising:

[0074] S1: Conduct a sample survey on the preset sample area, calculate the biomass based on the sample survey results, and use the calculated results as the measured biomass data for fitting;

[0075] In a specific embodiment, a method of conducting a quadrat survey on a preset quadrat area, calculating biomass based on the quadrat survey results, and using the calculated results as biomass measured data for fitting includes:

[0076] Conducting a quadrat survey on the preset sample area to obtain the measured tree height data, measured diameter at breast height data, and latitude and longitude coordinates of each Sonneratia apetala tree in the preset sample area;

[0077] The measured data of tree height and diameter at breast height of each Sonneratia apetala tree in the sample area were substituted into the preset allometric growth equation of Sonneratia apetala to calculate the biomass of each Sonneratia apetala tree in the sample area. The calculated results were used as the measured biomass data for fitting. The specific calculation formula is as follows:

[0078] W=a(D 2 H) b

[0079] Where W is biomass, D is diameter at breast height, H is tree height, and a and b are constants.

[0080] S2: Detecting a preset UAV laser radar scanning area, and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results, wherein the laser radar scanning area covers the sample area;

[0081] In a specific embodiment, a method for detecting a preset drone laser radar scanning area and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results includes:

[0082] The drones equipped with LiDAR sensors detect the preset drone lidar scanning areas and obtain three-dimensional point cloud data;

[0083] Obtaining a canopy height model corresponding to the laser radar scanning area based on the three-dimensional point cloud data;

[0084] The canopy height model is segmented into individual trees using a preset watershed segmentation algorithm, and the highest point within the canopy of each Sonneratia apetala tree is used as the tree height to obtain tree height data based on inversion of three-dimensional point cloud data.

[0085] In a specific embodiment, a method for obtaining a canopy height model corresponding to a laser radar scanning area based on the three-dimensional point cloud data includes:

[0086] The three-dimensional point cloud data is input into the LiDAR360 software for point cloud denoising and ground point classification, and the classified ground point set and vegetation point cloud set are obtained.

[0087] Generate digital elevation model and digital surface model based on the classified ground point set and vegetation point cloud set respectively;

[0088] The digital elevation model is subtracted from the digital surface model to obtain the canopy height model corresponding to the lidar scanning area.

[0089] In a specific embodiment, a method for generating a digital elevation model and a digital surface model based on a classified ground point set and a vegetation point cloud set, respectively, includes:

[0090] Based on the classified ground point set, the digital elevation model is generated using the irregular triangulated network interpolation method;

[0091] Based on the classified vegetation point cloud collection, the digital surface model is generated using the inverse distance weighted interpolation method.

[0092] S3: constructing a tree height-biomass inversion model based on the sample survey results, the biomass measured data for fitting, and the tree height data inverted based on the three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area through the tree height-biomass inversion model;

[0093] In a specific embodiment, a method for constructing a tree height-biomass inversion model using the quadrat survey results, the measured biomass data for fitting, and the tree height data inverted based on three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area using the tree height-biomass inversion model includes:

[0094] According to the latitude and longitude coordinates of each Sonneratia apetala tree in the sample survey results, the measured biomass data for fitting is matched one by one with the tree height data inverted based on the three-dimensional point cloud data;

[0095] The least squares method is used to construct a biomass inversion model based on the tree height inversion of lidar data for the matched biomass measured data and tree height data;

[0096] The biomass inversion model for inverting tree height based on lidar data is verified and calibrated using the tree height measured data, the diameter at breast height measured data, and the latitude and longitude coordinates, and finally a tree height-biomass inversion model is obtained;

[0097] The tree height data inverted based on the three-dimensional point cloud data is input into the tree height-biomass inversion model to obtain the biomass distribution of all Sonneratia apetala in the laser radar scanning area.

[0098] In a specific embodiment, a method for constructing a biomass inversion model for inverting tree height based on lidar data using the least squares method for matched measured biomass data and tree height data includes:

[0099] According to the linear relationship between DBH and tree height of Sonneratia apetala during a certain growth period, and considering the form of the allometric growth equation of Sonneratia apetala, the correlation equation between the biomass of Sonneratia apetala and tree height was constructed, and the nonlinear polynomial fitting was performed in MATLAB:

[0100] W=(aH 3 +bH 2 +cH) d

[0101] Where W is the biomass, H is the tree height, and a, b, c, and d are constants.

[0102] S4: collecting Sentinel-2 remote sensing images and Landsat series images covering the lidar scanning area, performing feature extraction on the Sentinel-2 remote sensing images and the Landsat series images, respectively, to obtain spectral characteristics and tree age data corresponding to the lidar scanning area;

[0103] In a specific embodiment, a method for collecting Sentinel-2 remote sensing images covering the laser radar scanning area, performing feature extraction on the Sentinel-2 remote sensing images, and obtaining spectral features corresponding to the laser radar scanning area includes:

[0104] Sentinel-2 remote sensing images covering the LiDAR scanning area were collected and preprocessed as follows:

[0105] Radiometric calibration, spatial registration, and atmospheric correction;

[0106] Resample the preprocessed Sentinel-2 remote sensing image and obtain surface reflectance data based on the resampling results;

[0107] Spectral characteristics within the range of the Sentinel-2 remote sensing image are obtained by performing remote sensing feature calculation on the surface reflectance data;

[0108] The spectral characteristics corresponding to the laser radar scanning area are obtained based on the spectral characteristics within the range of the Sentinel-2 remote sensing image.

[0109] In a specific embodiment, a method for collecting Landsat series images covering the laser radar scanning area, performing feature extraction on the Landsat series images, and obtaining tree age data corresponding to the laser radar scanning area includes:

[0110] Collect Landsat long-time series images covering the LiDAR scan area and perform the following preprocessing:

[0111] Radiation correction and cloud removal processing;

[0112] NDVI time series data is constructed by calculating the NDVI value of each pixel in the preprocessed Landsat long-term image set;

[0113] The long-term dynamic changes of pixels are obtained based on the NDVI time series data, and the tree age of each pixel is calculated based on the changes to obtain the tree age data corresponding to the Landsat series image range, specifically:

[0114] Perform sliding average processing on the NDVI time series data, and use the NDVI value after sliding average processing that is higher than a preset threshold as the starting year of tree age. The distance from the current time is the tree age. If there is a continuous sharp drop in NDVI, the starting time point of tree age is recalculated;

[0115] The tree age data corresponding to the laser radar scanning area is obtained based on the tree age data corresponding to the Landsat series image range.

[0116] S5: Perform regression analysis on the biomass distribution, spectral characteristics, and tree age data of all Sonneratia apetalas corresponding to the LiDAR scanning area, and construct a Sonneratia apetalas biomass inversion model based on satellite remote sensing data;

[0117] In a specific embodiment, a method for performing regression analysis on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to a laser radar scanning area to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data includes:

[0118] First, the biomass distribution of all Sonneratia apetalas corresponding to the laser radar scanning area was statistically analyzed based on the spatial resolution of the Sentinel-2 remote sensing image.

[0119] Then, all the Sonneratia apetala biomass distribution, spectral characteristics and tree age data corresponding to the lidar scanning area were input into the preset random forest model for regression analysis to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data.

[0120] S6: Estimating the biomass of Sonneratia apetala within the range of satellite remote sensing images by using the satellite remote sensing data and the Sonneratia apetala biomass inversion model based on satellite remote sensing data.

[0121] Example 2

[0122] Please refer to Figure 1 This embodiment provides a method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data, the method comprising:

[0123] S1: Conduct a sample survey on the preset sample area, calculate the biomass based on the sample survey results, and use the calculated results as the measured biomass data for fitting;

[0124] In a specific embodiment, a method of conducting a quadrat survey on a preset quadrat area, calculating biomass based on the quadrat survey results, and using the calculated results as biomass measured data for fitting includes:

[0125] Conducting a quadrat survey on the preset sample area to obtain the measured tree height data, measured diameter at breast height data, and latitude and longitude coordinates of each Sonneratia apetala tree in the preset sample area;

[0126] The measured data of tree height and diameter at breast height of each Sonneratia apetala tree in the sample area were substituted into the preset allometric growth equation of Sonneratia apetala to calculate the biomass of each Sonneratia apetala tree in the sample area. The calculated results were used as the measured biomass data for fitting. The specific calculation formula is as follows:

[0127] W=a(D 2 H) b

[0128] Where W is biomass, D is diameter at breast height, H is tree height, and a and b are constants.

[0129] Specifically, survey areas and locations were determined based on an existing dataset of the Sonneratia apetala's range. Further, drone-based aerial photography areas were selected using Google Earth and remote sensing imagery. Considering the density of Sonneratia apetala forests and the ease of ground surveys, areas of varying tree density were selected and several field measurement points were established. The height and diameter at breast height of Sonneratia apetala trees were surveyed using 10x10 meter plots.

[0130] S2: Detecting a preset UAV laser radar scanning area, and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results, wherein the laser radar scanning area covers the sample area;

[0131] In a specific embodiment, a method for detecting a preset drone laser radar scanning area and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results includes:

[0132] The drones equipped with LiDAR sensors detect the preset drone lidar scanning areas and obtain three-dimensional point cloud data;

[0133] Specifically, during favorable weather and wind conditions and low tide (when tidal flats are mostly exposed), drones equipped with LiDAR sensors were used to collect LiDAR data of the Sonneratia apetala forest near the sample area. The drones were flown at an altitude of approximately 100 meters, capturing images vertically downward with a heading and lateral overlap greater than 75%.

[0134] The canopy height model corresponding to the laser radar scanning area is obtained based on the three-dimensional point cloud data. The canopy height model is as follows: Figure 2 As shown;

[0135] Please refer to Figure 3 , the canopy height model is segmented into individual trees using a preset watershed segmentation algorithm, and the highest point within the canopy of each Sonneratia apetala tree is used as the tree height to obtain tree height data based on the inversion of the three-dimensional point cloud data;

[0136] Specifically, we use the watershed segmentation algorithm based on the canopy height model to further segment individual trees. Depending on the survey object, we should adjust the resolution, minimum and maximum values, and Gaussian smoothing parameters to achieve optimal segmentation. Here, we set the minimum tree height to 3 meters and the Gaussian smoothing factor to 1.5. The highest point within the canopy represents the tree height, and the height information of each tree is counted.

[0137] In a specific embodiment, a method for obtaining a canopy height model corresponding to a laser radar scanning area based on the three-dimensional point cloud data includes:

[0138] The three-dimensional point cloud data is input into the LiDAR360 software for point cloud denoising and ground point classification, and the classified ground point set and vegetation point cloud set are obtained.

[0139] Generate digital elevation model and digital surface model based on the classified ground point set and vegetation point cloud set respectively;

[0140] The digital elevation model is subtracted from the digital surface model to obtain the canopy height model corresponding to the lidar scanning area.

[0141] In a specific embodiment, a method for generating a digital elevation model and a digital surface model based on a classified ground point set and a vegetation point cloud set, respectively, includes:

[0142] Based on the classified ground point set, the digital elevation model is generated using the irregular triangulated network interpolation method;

[0143] Based on the classified vegetation point cloud collection, the digital surface model is generated using the inverse distance weighted interpolation method with a spatial resolution of 0.5m.

[0144] S3: constructing a tree height-biomass inversion model based on the sample survey results, the biomass measured data for fitting, and the tree height data inverted based on the three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area through the tree height-biomass inversion model;

[0145] In a specific embodiment, a method for constructing a tree height-biomass inversion model using the quadrat survey results, the measured biomass data for fitting, and the tree height data inverted based on three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area using the tree height-biomass inversion model includes:

[0146] According to the latitude and longitude coordinates of each Sonneratia apetala tree in the sample survey results, the measured biomass data for fitting is matched one by one with the tree height data inverted based on the three-dimensional point cloud data;

[0147] The least squares method is used to construct a biomass inversion model based on the tree height inversion of lidar data for the matched biomass measured data and tree height data;

[0148] The biomass inversion model for inverting tree height based on lidar data is verified and calibrated using the tree height measured data, the diameter at breast height measured data, and the latitude and longitude coordinates, and finally a tree height-biomass inversion model is obtained;

[0149] The tree height data inverted based on the three-dimensional point cloud data is input into the tree height-biomass inversion model to obtain the biomass distribution of all Sonneratia apetala in the laser radar scanning area.

[0150] In a specific embodiment, a method for constructing a biomass inversion model for inverting tree height based on lidar data using the least squares method for matched measured biomass data and tree height data includes:

[0151] It should be noted that according to research, the DBH value of Sonneratia apetala and its tree height have a good linear relationship within a certain growth period.

[0152] According to the linear relationship between DBH and tree height of Sonneratia apetala during a certain growth period, and considering the form of the allometric growth equation of Sonneratia apetala, the correlation equation between the biomass of Sonneratia apetala and tree height was constructed, and the nonlinear polynomial fitting was performed in MATLAB:

[0153] W=(aH 3 +bH 2 +cH) d

[0154] Where W is the biomass, H is the tree height, and a, b, c, and d are constants.

[0155] S4: collecting Sentinel-2 remote sensing images and Landsat series images covering the lidar scanning area, performing feature extraction on the Sentinel-2 remote sensing images and the Landsat series images, respectively, to obtain spectral characteristics and tree age data corresponding to the lidar scanning area;

[0156] In a specific embodiment, please refer to Figure 5 , collecting Sentinel-2 remote sensing images covering the laser radar scanning area, performing feature extraction on the Sentinel-2 remote sensing images, and obtaining spectral features corresponding to the laser radar scanning area includes:

[0157] Sentinel-2 remote sensing images covering the LiDAR scanning area were collected and preprocessed as follows:

[0158] Radiometric calibration, spatial registration, and atmospheric correction;

[0159] Resample the preprocessed Sentinel-2 remote sensing image and obtain surface reflectance data based on the resampling results;

[0160] Spectral characteristics within the range of the Sentinel-2 remote sensing image are obtained by performing remote sensing feature calculation on the surface reflectance data;

[0161] The spectral characteristics corresponding to the laser radar scanning area are obtained based on the spectral characteristics within the range of the Sentinel-2 remote sensing image.

[0162] In a specific embodiment, please refer to Figure 6 , collecting Landsat series images covering the laser radar scanning area, performing feature extraction on the Landsat series images, and obtaining tree age data corresponding to the laser radar scanning area includes:

[0163] Collect Landsat long-time series images covering the LiDAR scan area and perform the following preprocessing:

[0164] Radiation correction and cloud removal processing;

[0165] NDVI time series data is constructed by calculating the NDVI value of each pixel in the preprocessed Landsat long-term image set;

[0166] The long-term dynamic changes of pixels are obtained based on the NDVI time series data, and the tree age of each pixel is calculated based on the changes to obtain the tree age data corresponding to the Landsat series image range, specifically:

[0167] Perform sliding average processing on the NDVI time series data, and use the NDVI value after sliding average processing that is higher than a preset threshold as the starting year of tree age. The distance from the current time is the tree age. If there is a continuous sharp drop in NDVI, the starting time point of tree age is recalculated;

[0168] The tree age data corresponding to the laser radar scanning area is obtained based on the tree age data corresponding to the Landsat series image range.

[0169] S5: Perform regression analysis on the biomass distribution, spectral characteristics, and tree age data of all Sonneratia apetalas corresponding to the LiDAR scanning area, and construct a Sonneratia apetalas biomass inversion model based on satellite remote sensing data;

[0170] In a specific embodiment, a method for performing regression analysis on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to a laser radar scanning area to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data includes:

[0171] Please refer to Figure 4 First, the biomass distribution of all Sonneratia apetalas corresponding to the laser radar scanning area is statistically analyzed in grid format according to the spatial resolution of the Sentinel-2 remote sensing image;

[0172] Specifically, the biomass distribution of all Sonneratia apetalas corresponding to the laser radar scanning area is subjected to grid statistics at a spatial resolution of 10*10 meters.

[0173] Then, all the Sonneratia apetala biomass distribution, spectral characteristics and tree age data corresponding to the lidar scanning area were input into the preset random forest model for regression analysis to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data.

[0174] S6: Please refer to Figure 7 The Sonneratia apetala biomass inversion model based on satellite remote sensing data is used to estimate the Sonneratia apetala biomass within the range of satellite remote sensing images using satellite remote sensing data.

[0175] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data, characterized in that: The method comprises: Conduct a sample survey on the preset sample area, calculate the biomass based on the sample survey results, and use the calculated results as the measured biomass data for fitting; Detecting a preset UAV laser radar scanning area, and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results, wherein the laser radar scanning area covers the sample area; A tree height-biomass inversion model is constructed based on the sample survey results, the biomass measured data for fitting, and the tree height data inverted based on the three-dimensional point cloud data, and the biomass distribution of all Sonneratia apetalas within the laser radar scanning area is obtained through the tree height-biomass inversion model; Collecting Sentinel-2 remote sensing images and Landsat series images covering the lidar scanning area, performing feature extraction on the Sentinel-2 remote sensing images and the Landsat series images, respectively, to obtain spectral characteristics and tree age data corresponding to the lidar scanning area; Regression analysis was performed on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to the LiDAR scanning area to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data. The Sonneratia apetala biomass inversion model based on satellite remote sensing data is used to estimate the Sonneratia apetala biomass within the range of satellite remote sensing images using satellite remote sensing data.

2. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 1, characterized in that: Methods for conducting a sample survey on a preset sample area, calculating biomass based on the sample survey results, and using the calculated results as measured biomass data for fitting include: Conducting a quadrat survey on the preset sample area to obtain the measured tree height data, measured diameter at breast height data, and latitude and longitude coordinates of each Sonneratia apetala tree in the preset sample area; The measured data of tree height and diameter at breast height of each Sonneratia apetala tree in the sample area were substituted into the preset allometric growth equation of Sonneratia apetala to calculate the biomass of each Sonneratia apetala tree in the sample area. The calculated results were used as the measured biomass data for fitting. The specific calculation formula is as follows: W=a(D 2 H) b Where W is biomass, D is diameter at breast height, H is tree height, and a and b are constants.

3. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 1, characterized in that: The method for detecting a preset UAV lidar scanning area and obtaining tree height data based on three-dimensional point cloud data inversion according to the detection results includes: The drones equipped with LiDAR sensors detect the preset drone lidar scanning areas and obtain three-dimensional point cloud data; Obtaining a canopy height model corresponding to the laser radar scanning area based on the three-dimensional point cloud data; The canopy height model is segmented into individual trees using a preset watershed segmentation algorithm, and the highest point within the canopy of each Sonneratia apetala tree is used as the tree height to obtain tree height data based on inversion of three-dimensional point cloud data.

4. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 3, wherein: The method for obtaining a canopy height model corresponding to a laser radar scanning area according to the three-dimensional point cloud data includes: Input the three-dimensional point cloud data into the LiDAR360 software to perform point cloud denoising and ground point classification in sequence to obtain a classified ground point set and a vegetation point cloud set; Generate digital elevation model and digital surface model based on the classified ground point set and vegetation point cloud set respectively; The digital elevation model is subtracted from the digital surface model to obtain the canopy height model corresponding to the lidar scanning area.

5. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 4, characterized in that: Methods for generating a digital elevation model and a digital surface model based on a classified ground point set and a vegetation point cloud set, respectively, include: Based on the classified ground point set, the digital elevation model is generated using the irregular triangulated network interpolation method; Based on the classified vegetation point cloud collection, the digital surface model is generated using the inverse distance weighted interpolation method.

6. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 2, wherein: The method of constructing a tree height-biomass inversion model based on the sample survey results, the biomass measured data for fitting, and the tree height data inverted based on the three-dimensional point cloud data, and obtaining the biomass distribution of all Sonneratia apetala in the laser radar scanning area through the tree height-biomass inversion model includes: According to the latitude and longitude coordinates of each Sonneratia apetala tree in the sample survey results, the measured biomass data for fitting is matched one by one with the tree height data inverted based on the three-dimensional point cloud data; The least squares method is used to construct a biomass inversion model based on the tree height inversion of lidar data for the matched biomass measured data and tree height data; The biomass inversion model for inverting tree height based on lidar data is verified and calibrated using the tree height measured data, the diameter at breast height measured data, and the latitude and longitude coordinates, and finally a tree height-biomass inversion model is obtained; The tree height data inverted based on the three-dimensional point cloud data is input into the tree height-biomass inversion model to obtain the biomass distribution of all Sonneratia apetala in the laser radar scanning area.

7. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 6, characterized in that: The method of using the least squares method to construct a biomass inversion model based on LiDAR data to invert tree height using matched biomass measured data and tree height data includes: According to the linear relationship between DBH and tree height of Sonneratia apetala during a certain growth period, and considering the form of the allometric growth equation of Sonneratia apetala, the correlation equation between the biomass of Sonneratia apetala and tree height was constructed, and the nonlinear polynomial fitting was performed in MATLAB: W=(aH 3 +bH 2 +cH) d Where W is the biomass, H is the tree height, and a, b, c, and d are constants.

8. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 1, characterized in that: The method of collecting Sentinel-2 remote sensing images covering the laser radar scanning area, performing feature extraction on the Sentinel-2 remote sensing images, and obtaining spectral features corresponding to the laser radar scanning area includes: Sentinel-2 remote sensing images covering the LiDAR scanning area were collected and preprocessed as follows: Radiometric calibration, spatial registration, and atmospheric correction; Resample the preprocessed Sentinel-2 remote sensing image and obtain surface reflectance data based on the resampling results; Spectral characteristics within the range of the Sentinel-2 remote sensing image are obtained by performing remote sensing feature calculation on the surface reflectance data; The spectral characteristics corresponding to the laser radar scanning area are obtained based on the spectral characteristics within the range of the Sentinel-2 remote sensing image.

9. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 1, characterized in that: The method of collecting Landsat series images covering the laser radar scanning area, performing feature extraction on the Landsat series images, and obtaining tree age data corresponding to the laser radar scanning area includes: Collect Landsat long-time series images covering the LiDAR scan area and perform the following preprocessing: Radiation correction and cloud removal processing; NDVI time series data is constructed by calculating the NDVI value of each pixel in the preprocessed Landsat long-term image set; The long-term dynamic changes of pixels are obtained based on the NDVI time series data, and the tree age of each pixel is calculated based on the changes to obtain the tree age data corresponding to the Landsat series image range, specifically: Perform sliding average processing on the NDVI time series data, and use the NDVI value after sliding average processing that is higher than a preset threshold as the starting year of tree age. The distance from the current time is the tree age. If there is a continuous sharp drop in NDVI, the starting time point of tree age is recalculated; The tree age data corresponding to the laser radar scanning area is obtained based on the tree age data corresponding to the Landsat series image range.

10. The method for inverting aboveground biomass of Sonneratia apetala using multi-source remote sensing data according to claim 1, characterized in that: Regression analysis was performed on all Sonneratia apetala biomass distribution, spectral characteristics, and tree age data corresponding to the lidar scanning area. The method for constructing a Sonneratia apetala biomass inversion model based on satellite remote sensing data includes the following: First, the biomass distribution of all Sonneratia apetalas corresponding to the laser radar scanning area was statistically analyzed based on the spatial resolution of the Sentinel-2 remote sensing image. Then, all the Sonneratia apetala biomass distribution, spectral characteristics and tree age data corresponding to the lidar scanning area were input into the preset random forest model for regression analysis to construct a Sonneratia apetala biomass inversion model based on satellite remote sensing data.

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