A method and system for analyzing light inhibition effects of tree canopies

Abstract

The present invention relates to a method and system for analyzing the light inhibition effect of tree canopies. The method comprises: reconstructing the tree canopy's three-dimensional structure and introducing a time dimension to obtain a canopy structural model; simulating the light distribution of the tree canopy based on the canopy structural model to obtain light distribution characteristics; establishing a photosynthetic productivity assessment model for the tree canopy based on the light distribution characteristics; and analyzing the light inhibition effect of the tree canopy to be analyzed based on the photosynthetic productivity assessment model to obtain analysis results. Through accurate three-dimensional canopy reconstruction and efficient light distribution simulation, the present invention significantly improves the prediction accuracy of coniferous tree photosynthetic productivity.

Description

Technical Field

[0001] The present invention relates to the technical field of photosynthetic productivity analysis, and in particular to a method and system for analyzing light inhibition effects of tree canopies. Background Art

[0002] Light distribution patterns in tree canopies play a decisive role in photosynthetic productivity and ecosystem carbon sequestration, and are one of the core elements of precision forestry and management.

[0003] Existing light distribution analysis mainly relies on two-dimensional images or rough three-dimensional reconstruction, resulting in a lack of accurate canopy structure reconstruction. Since traditional methods cannot accurately reconstruct complex canopy structures, errors in light energy distribution occur, affecting the accurate assessment of plant photosynthetic productivity; secondly, photoinhibition is a key factor in the regulation of plant photosynthesis. Existing methods lack high-resolution three-dimensional models to quantitatively analyze the long-term impact of photoinhibition on plant canopy carbon gain; in addition, for coniferous species, seasonal dynamic processes such as new leaf development, needle angle changes, and old leaf shedding directly affect light interception and photosynthetic productivity. Traditional static canopy models cannot capture the morphological and physiological changes of needles at different growth stages, resulting in systematic deviations in the estimation of light energy utilization efficiency throughout the year.

[0004] Therefore, this field urgently needs a high-precision, time-considered, and universally adaptable three-dimensional tree canopy structure analysis method. Summary of the Invention

[0005] The present invention provides a method and system for analyzing the light inhibition effect of tree canopies, so as to solve the defects of the prior art.

[0006] The present invention provides a method for analyzing the light inhibition effect of a tree canopy, comprising:

[0007] S1: Reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the canopy structure model;

[0008] S2: Based on the canopy structure model, light distribution simulation is performed on the tree canopy to obtain light distribution characteristics;

[0009] S3: establishing a photosynthetic productivity evaluation model for tree canopies based on the light distribution characteristics;

[0010] S4: performing a light inhibition effect analysis on the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain an analysis result.

[0011] According to a method for analyzing light inhibition effects of tree canopies provided by the present invention, step S1 further comprises:

[0012] S11: Collect multi-angle images of tree canopies;

[0013] S12: Inputting the multi-angle image into a first convolutional network to obtain three-dimensional point cloud data;

[0014] S13: performing dynamic mesh optimization on the three-dimensional point cloud data to obtain a canopy reconstruction model;

[0015] S14: Constructing a seasonal variation model of tree canopies, and fusing the seasonal variation model with the canopy reconstruction model to obtain a canopy structure model.

[0016] According to the light inhibition effect analysis method for tree canopies provided by the present invention, step S14 further includes:

[0017] S141: Periodically collect growth parameters of tree canopies;

[0018] S142: Extracting change characteristics through the growth parameters, and constructing a seasonal change model according to the change characteristics;

[0019] S143: Constructing the mapping relationship between the canopy space coordinate system and the time dimension;

[0020] S144: Perform multi-temporal fusion of the seasonal variation model and the canopy reconstruction model according to the mapping relationship to obtain a canopy structure model.

[0021] According to the light inhibition effect analysis method for tree canopies provided by the present invention, step S2 further includes:

[0022] S21: Based on the canopy spatial structure of the canopy structure model, a light tracing algorithm with adaptively adjusted tracing accuracy is used to calculate the light density of the canopy;

[0023] S22: Introduce light attenuation factors to different canopies and calculate the light transmittance of multiple canopies;

[0024] S23: Adjusting the light density according to the light transmittance to obtain light distribution characteristics described by a four-dimensional light field.

[0025] According to the light inhibition effect analysis method for tree canopies provided by the present invention, step S21 further includes:

[0026] S211: Construct a bidirectional scattering distribution function model for the tree canopy surface;

[0027] S212: Based on the bidirectional scattering distribution function model, simulating the scattering characteristics of the tree canopy surface, and performing ray tracing through a parallel computing framework to obtain multi-band light propagation data;

[0028] S213: Fusing multi-band light propagation data to obtain light density.

[0029] According to the light inhibition effect analysis method for tree canopies provided by the present invention, step S3 further includes:

[0030] S31: inputting the light distribution characteristics and the canopy spatial structure corresponding to the canopy structure model as input data, and performing preprocessing to obtain multidimensional data;

[0031] S32: Inputting the multidimensional data into a deep learning model, and adaptively adjusting the parameters of the deep learning model;

[0032] S33: classify the input multidimensional data based on random forest to obtain classified data;

[0033] S34: Based on the classified data, deep learning of the light energy transfer path is performed through a second convolutional network to obtain a photosynthetic productivity evaluation model.

[0034] According to the light inhibition effect analysis method for tree canopies provided by the present invention, step S31 further includes:

[0035] S311: Input the light distribution characteristics and the canopy spatial structure image corresponding to the canopy structure model to obtain input data;

[0036] S312: Analyze the canopy image in the input data by performing a gray level co-occurrence matrix to obtain texture features;

[0037] S313: Dimensionality reduction is performed on the texture features by using the PCA method and the LDA method to obtain multidimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimensionality reduction.

[0038] According to the light inhibition effect analysis method for tree canopy provided by the present invention, step S4 further includes:

[0039] S41: inputting the collected data of the tree canopy to be analyzed into the photosynthetic productivity assessment model to obtain carbon gain impact data calculated in sections;

[0040] S42: Performing a light inhibition effect analysis on the tree canopy to be analyzed based on the carbon gain impact data to obtain an analysis result.

[0041] According to a method for analyzing light inhibition effects of tree canopies provided by the present invention, the analysis results in step S42 include:

[0042] Altitude light response curves, seasonal photoinhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves at different canopy levels.

[0043] The present invention further provides a light inhibition effect analysis system for tree canopies, which is used to perform the light inhibition effect analysis method for tree canopies as described in any one of the above items, comprising:

[0044] Reconstruction module: used to reconstruct the three-dimensional structure of tree canopies and introduce the time dimension to obtain the canopy structure model;

[0045] Simulation module: used for simulating light distribution of tree canopies based on the canopy structure model to obtain light distribution characteristics;

[0046] Modeling module: used for establishing a photosynthetic productivity evaluation model of tree canopy based on the light distribution characteristics;

[0047] Analysis module: used for performing light inhibition effect analysis on the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

[0048] The present invention provides a method and system for analyzing the light inhibition effect of tree canopies. Through three-dimensional structure reconstruction and light distribution simulation, the present invention can more accurately reflect the actual lighting conditions of the tree canopy. The present invention takes into account the complex structure of the canopy and the propagation path of light therein, thereby improving the accuracy of the light inhibition effect analysis. In addition, the present invention also introduces a time dimension. By constructing a seasonal change model of needle growth and combining it with a canopy structure model, a dynamic canopy structure model with a time dimension is formed, thereby realizing continuous monitoring and analysis of the light environment and light inhibition effect of the tree canopy throughout the year. Secondly, the present invention realizes the automated and intelligent evaluation of the photosynthetic productivity of the tree canopy, which not only reduces the complexity of manual operation, but also improves the analysis efficiency and accuracy. When establishing the photosynthetic productivity evaluation model, the present invention not only takes into account the light distribution characteristics, but also combines multidimensional data such as the canopy spatial structure and texture characteristics, making the analysis results more comprehensive and reliable.

[0049] The method of this invention can be applied to tree canopies of different species and growth conditions. By adaptively adjusting the tracking accuracy of the light tracing algorithm and the parameters of the deep learning model, the method can adapt to changes in different canopy structures and lighting conditions. By analyzing the photoinhibition effects of tree canopies, the method can provide scientific guidance for fields such as forestry management, forest ecology research, and climate change response. For example, it can guide foresters in implementing management measures such as canopy pruning and adjusting planting density to optimize the photosynthesis and growth performance of coniferous trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A schematic flow chart of a method for analyzing light inhibition effects of tree canopies provided by an embodiment of the present invention;

[0052] Figure 2 This is a schematic structural diagram of a system for analyzing light inhibition effects on tree canopies provided by an embodiment of the present invention.

[0053] Reference numerals: 100, reconstruction module; 200, simulation module; 300, modeling module; 400, analysis module. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the embodiments described are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] The following describes embodiments of the present invention with reference to the accompanying drawings.

[0056] like Figure 1 As shown, the present invention provides a method for analyzing the light inhibition effect of tree canopies, comprising:

[0057] S1: Reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the canopy structure model.

[0058] Wherein, step S1 further includes:

[0059] S11: Collect multi-angle images of tree canopies.

[0060] In step S11, multi-angle image data of the tree canopy is first obtained to provide a basis for subsequent three-dimensional reconstruction. Specifically, the present invention uses a camera to capture images of the tree canopy from different angles. The obtained images need to cover various parts of the canopy to ensure the integrity and accuracy of the reconstruction.

[0061] S12: Input the multi-angle image into a first convolutional network to obtain three-dimensional point cloud data.

[0062] In step S12, the MVCNN (Multi-View Convolutional Neural Network) network is used as the first convolutional network. The MVCNN deep learning model is used to process multi-view images. Features can be extracted and fused from images at different angles to generate three-dimensional point cloud data. Specifically, the input of step S12 is the multi-angle image obtained in step S11, and the output is three-dimensional point cloud data, that is, a set of points in three-dimensional space that represent the surface shape of the tree canopy.

[0063] Among them, the first convolutional network in step S12 is an MVCNN network.

[0064] In step S12, the multi-angle images are input into the MVCNN model, and the three-dimensional point cloud data of the canopy is generated through the deep learning algorithm. Compared with the traditional method, the MVCNN model can effectively fuse multi-view information and generate high-quality three-dimensional point cloud data. It maintains high reconstruction accuracy under different environmental lighting conditions and reduces reconstruction errors.

[0065] S13: Performing dynamic mesh optimization on the three-dimensional point cloud data to obtain a canopy structure model.

[0066] The purpose of step S13 is to convert the three-dimensional point cloud data into a more refined three-dimensional mesh model and optimize the details of the canopy structure. Specifically, in step S13, the three-dimensional point cloud data is first processed using a dynamic mesh optimization algorithm. Dynamic mesh optimization refers to adjusting the vertices and facets of the mesh through an algorithm to make the model smoother and more detailed, which can improve the accuracy and visualization of the model and make it closer to the real canopy structure. Then, a canopy structure model is generated, that is, a complete three-dimensional mesh model. The obtained canopy structure model can accurately reflect the shape and structure of the tree canopy.

[0067] S14: Constructing a seasonal variation model of tree canopies, and fusing the seasonal variation model with the canopy reconstruction model to obtain a canopy structure model.

[0068] Wherein, step S14 further includes:

[0069] S141: Periodically collect growth parameters of tree canopies.

[0070] In the specific implementation of step S141, fixed observation points are set up and high-precision cameras are used to regularly collect needle morphological parameters, including needle length, width, thickness, and surface area. Simultaneously, a near-infrared spectrometer is used to measure changes in the needle's internal biochemical composition, such as chlorophyll content, water content, and nitrogen content. Micro-meteorological stations are deployed to simultaneously collect environmental parameters such as temperature, humidity, and light, forming a comprehensive dataset of periodic growth parameters.

[0071] S142: Extracting change characteristics through the growth parameters, and constructing a seasonal change model according to the change characteristics.

[0072] In step S142, based on the parameters collected in step S141, time series decomposition technology can be used to separate the conifer growth data into trend items, seasonal items and random items to quantify seasonal fluctuation characteristics. Based on the obtained seasonal fluctuation characteristics, a piecewise function is used to describe the change characteristics of different seasons, and finally a seasonal change model of conifer growth is formed.

[0073] S143: Constructing a mapping relationship between the canopy space coordinate system and the time dimension.

[0074] Specifically, in step S143, a storage and query framework for four-dimensional data (x, y, z, t) is first established, where x, y, and z represent the three-dimensional spatial coordinates of the canopy, and t represents the time dimension. For the canopy structure data at discrete time points, the present invention also uses a spline interpolation method to generate a continuous time series in step S143, and applies the time series kriging method to realize the time domain interpolation of spatial structure parameters. Finally, a correlation matrix between the canopy structure characteristics and the time dimension is constructed to quantitatively characterize the evolution law of spatial position over time.

[0075] S144: Perform multi-temporal fusion of the seasonal variation model and the canopy reconstruction model according to the mapping relationship to obtain a canopy structure model.

[0076] Specifically, in step S144, the mapping relationship in step S143 is used to describe the continuous change of the canopy structure over time, and the canopy models of different growth stages can be integrated to finally form a dynamic canopy structure model with a time dimension, thereby achieving an accurate description of the canopy structure at any time point.

[0077] S2: Based on the canopy structure model, light distribution simulation is performed on the tree canopy to obtain light distribution characteristics.

[0078] The purpose of step S2 is to use the canopy structure model to simulate the distribution of light in the tree canopy, so as to obtain the light distribution characteristics. Specifically, in step S2, the canopy light density is first evaluated in real time through the light tracing algorithm and the tracing accuracy is adjusted to ensure that the light distribution simulation is more accurate in areas with dense leaves, while reducing the amount of calculation in sparse areas, thereby improving the efficiency of light distribution analysis. In addition, the layered light attenuation factor (LAF) is used to divide the canopy into layers with different light transmittances. The light distribution is adjusted according to the light reception and reflection characteristics of the layers to achieve a light distribution simulation that is closer to the natural environment.

[0079] Wherein, step S2 further includes:

[0080] S21: Based on the canopy spatial structure of the canopy structure model, the light density of the canopy is calculated by using a light tracing algorithm that adaptively adjusts the tracing accuracy.

[0081] Furthermore, in step S21, the present invention uses a ray tracing algorithm to simulate the propagation of light in the canopy to simulate the propagation path of light in the canopy and calculate the distribution density of light. It should be noted that the present invention dynamically adjusts the accuracy of ray tracing according to the complexity of the canopy structure. For example, the density of ray tracing is increased in dense areas of the canopy to improve the simulation accuracy; the density of ray tracing is reduced in sparse areas to improve computing efficiency. By tracing the paths of a large number of light rays, the light density at different positions in the canopy is calculated, that is, the number of light rays per unit area or unit volume.

[0082] Wherein, step S21 further includes:

[0083] S211: Construct a bidirectional scattering distribution function model for the tree canopy surface.

[0084] Furthermore, in step S211, based on the conifer surface structure data provided by the canopy structure model and the spectral data of the conifer surface collected at different incident angles, the Cook-Torrance microsurface BSDF model is fitted to obtain the surface roughness and specular reflection parameters, and by calibrating the Fresnel reflection coefficient and refractive index of the conifer surface, a full-angle scattering model is established. The establishment of the scattering model provides accurate surface optical characteristic parameters for subsequent ray tracing and is the basis for realizing ray tracing.

[0085] S212: Based on the bidirectional scattering distribution function model, the scattering characteristics of the tree canopy surface are simulated, and ray tracing is performed through a parallel computing framework to obtain light propagation data.

[0086] After obtaining the BSDF model of the conifer surface in step S211, this step further constructs the normal distribution function of the conifer surface and introduces the anisotropic parameters of the texture direction to achieve a more accurate simulation of the scattering characteristics. In addition, in order to handle the huge amount of calculation, the present invention also adopts a GPU parallel architecture to track a large number of rays at the same time, and in accordance with the adaptive precision principle in S21, intelligently allocates computing resources, that is, increases the light density in dense canopy areas to ensure accuracy, and appropriately reduces the density in sparse areas to improve efficiency. During the ray tracing process, it is necessary to calculate the incident light in multiple bands such as red, blue, green and near-infrared, and record the complete scattering history of each ray in detail, including the number of scattering times, angle changes, and energy attenuation, to eventually form a comprehensive multi-band light propagation data set.

[0087] S213: Fusing multi-band light propagation data to obtain light density.

[0088] In step S213, the scattered results of each computing core are first merged, and then the differentiated effects of different wavelengths of light on plant physiological processes are considered to perform scientific and reasonable weighted fusion. The fused data can calculate the actual amount of light in each unit volume or unit area in the three-dimensional space of the canopy, and finally construct a high-precision three-dimensional light density distribution map.

[0089] S22: Introduce light attenuation factors to different canopies and calculate the light transmittance of multiple canopies.

[0090] Furthermore, in step S22, the present invention considers the attenuation effect of light in the canopy to calculate light transmittance. The light attenuation factor quantifies the gradual attenuation of light intensity as it is absorbed and scattered by structures such as leaves and branches as it passes through the canopy. Based on the structural characteristics of different canopies (such as leaf density and thickness), corresponding light attenuation factors are introduced. Light transmittance is calculated by calculating the residual intensity of light after passing through each layer of the canopy.

[0091] S23: Adjusting the light density according to the light transmittance to obtain light distribution characteristics described by a four-dimensional light field.

[0092] In step S23, the light density is adjusted based on the light transmittance calculated in S22. For example, in areas with low light transmittance, the light density will be reduced accordingly. At the same time, the light propagation path is iteratively calculated, taking into account the multiple scattering and reflection effects of light between different canopies. By integrating the corrected light density, light transmittance, and time-varying parameters, a four-dimensional light field description represented as (x, y, z, t) is generated to fully characterize the spatial distribution of light in the canopy and its dynamic change characteristics over time. The obtained light distribution characteristics not only reflect the uneven distribution of light in the canopy, but also include seasonal change information, providing a comprehensive data foundation for the subsequent spatiotemporal dynamic analysis of the light inhibition effect.

[0093] S3: Establishing a photosynthetic productivity evaluation model for tree canopies based on the light distribution characteristics.

[0094] Step S3 further comprises:

[0095] S31: Input the light distribution characteristics and the canopy spatial structure corresponding to the canopy structure model as input data, and perform preprocessing to obtain multidimensional data.

[0096] Wherein, step S31 further includes:

[0097] S311: Input the light distribution characteristics and the canopy spatial structure image corresponding to the canopy structure model to obtain input data.

[0098] Specifically, the input data includes two parts: light distribution characteristics, that is, the distribution of light in the canopy obtained from S2; canopy spatial structure image, that is, the spatial structure image corresponding to the canopy structure model obtained from S1.

[0099] Wherein, step S311 also includes:

[0100] Data enhancement including rotation, translation and scaling is performed on the canopy spatial structure image.

[0101] Furthermore, in step S311, operations including rotation, translation and scaling are performed on the canopy spatial structure image. The rotation is to rotate the image by a certain angle to simulate the canopy structure at different viewing angles; the translation is to move the image within the plane to simulate different positions of the canopy structure; the scaling is to enlarge or reduce the image to simulate the canopy structure at different scales. The data enhancement performed in step S311 can increase the diversity and robustness of the data.

[0102] S312: Analyze the canopy image in the input data using a gray level co-occurrence matrix to obtain texture features.

[0103] The purpose of step S312 is to extract texture features from the canopy image to describe the details of the canopy structure. This step uses the Gray Level Co-occurrence Matrix (GLCM) to analyze the texture features of the canopy image. The GLCM is a statistical method for describing image texture. It calculates the grayscale value relationship between pairs of pixels in the image to extract texture features (such as contrast, correlation, energy, and homogeneity). These texture features can reflect detailed information about the canopy structure, such as the arrangement and density of leaves.

[0104] S313: Dimensionality reduction is performed on the texture features by using the PCA method and the LDA method to obtain multidimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimensionality reduction.

[0105] The purpose of step S313 is to reduce the dimension of texture features, reduce data redundancy, retain important information, and generate multidimensional data. In step S313, the present invention uses PCA (principal component analysis) and LDA (linear discriminant analysis) to reduce the dimension of texture features. The PCA projects high-dimensional data into a low-dimensional space through linear transformation, retains the main variance information of the data, and removes redundancy. The LDA retains category information as much as possible while reducing the dimension, and is suitable for classification tasks. The texture features after dimensionality reduction are integrated with light distribution characteristics and canopy spatial structure data to generate multidimensional data.

[0106] S32: Input the multidimensional data into a deep learning model, and adaptively adjust the parameters of the deep learning model.

[0107] Furthermore, in step S32, the multidimensional data generated in S31 (including light distribution characteristics, canopy spatial structure, and texture characteristics after dimensionality reduction) are first input into the deep learning model, and then an adaptive optimization algorithm is used (dynamically adjusting the model's learning rate, weights and other parameters, and continuously optimizing the model's loss function through the backpropagation algorithm and gradient descent method, so that the model can better fit the data). Finally, the trained deep learning model is output to extract key features from the multidimensional data to provide support for subsequent tasks.

[0108] Specifically, the input of the deep learning model base in step S32 is characteristic data such as the angle and chlorophyll content of leaves in each layer of the canopy. The deep learning model then combines multidimensional data to optimize the parameters of the photosynthetic productivity model. Through dynamic adjustment, the model is ensured to be adaptable to different vegetation types (such as conifers and broad-leaved trees) and climatic conditions (such as light intensity and temperature). The output is a self-adaptively optimized deep learning model that can more accurately capture the changing patterns of canopy photosynthetic productivity.

[0109] S33: Classify the input multidimensional data based on random forest to obtain classified data.

[0110] Furthermore, the purpose of using random forests in the present invention is to increase the diversity of the model by using a randomly selected feature subset (light distribution characteristics, canopy structure characteristics, texture characteristics, etc.) in each decision tree during training. According to the characteristics of the multidimensional data, the random forest model classifies it into different categories (for example, canopy areas under different lighting conditions, leaf layers with different chlorophyll contents, etc.), and finally outputs classified data to represent the distribution of multidimensional data in different categories.

[0111] S34: Based on the classified data, deep learning of the light energy transfer path is performed through a second convolutional network to obtain a photosynthetic productivity evaluation model.

[0112] In step S34, the classified data generated in S33 is first input into the second convolutional network, which is then used to perform deep learning on the light energy transfer path to capture the propagation law of light in the canopy. The second convolutional network extracts the spatial features in the light energy transfer path (such as light distribution, leaf angle, chlorophyll content, etc.).

[0113] Then, combined with classified data (such as different lighting conditions and chlorophyll content categories), the second convolutional network established the relationship between the light energy transfer path and photosynthetic productivity. Through deep learning, an evaluation model capable of predicting photosynthetic productivity was generated, and finally a photosynthetic productivity prediction model was generated that can evaluate the photosynthetic efficiency under different vegetation types and climatic conditions.

[0114] The final output of the model is the analysis results of the photoinhibition effect, which may include: photoinhibition intensity: the percentage of carbon gain decrease when the light intensity exceeds the set threshold (for example, when the light intensity is 1500 μmol / m² / s, the carbon gain decreases by 20%); photoinhibition spatial distribution: the photoinhibition intensity in different areas of the canopy (such as the distribution of photoinhibition intensity at the top, middle and bottom of the canopy); photoinhibition temporal dynamics: the changes in photoinhibition effect in different time periods (such as the time period of the day when photoinhibition is strongest).

[0115] S4: performing a light inhibition effect analysis on the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain an analysis result.

[0116] Wherein, step S4 further includes:

[0117] S41: Inputting the collected data of the tree canopy to be analyzed into the photosynthetic productivity assessment model to obtain carbon gain impact data calculated in sections.

[0118] In step S41, the collected data of the tree canopy to be analyzed (such as canopy structure, leaf angle, chlorophyll content, lighting conditions, etc.) is input into the photosynthetic productivity evaluation model. The collected data can be obtained through field measurement, sensor collection or remote sensing technology. The subsequent photosynthetic productivity evaluation model simulates the light transmission path and photosynthesis process in the canopy based on the input data, calculates the carbon gain impact data, that is, the carbon fixation capacity of the canopy under different lighting conditions, and finally outputs the carbon gain impact data. The obtained carbon gain impact data reflects the photosynthetic efficiency of the canopy and its response to the light inhibition effect.

[0119] S42: Performing a light inhibition effect analysis on the tree canopy to be analyzed based on the carbon gain impact data to obtain an analysis result.

[0120] The analysis results in step S42 include:

[0121] The light response curves, seasonal photoinhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves at different canopy heights were obtained. In step S42, a systematic photoinhibition effect analysis was performed based on the carbon gain impact data obtained in step S41. Specifically, the CO2 exchange rate at different canopy heights was first measured using a Licor 6800XT infrared gas exchange analyzer under different light intensities (usually from 0 to 2000 μmol m -2 s -1 ) to draw a light response curve, and the obtained curve can intuitively reflect the light saturation point, light compensation point and maximum photosynthetic rate at different canopy positions; at the same time, combined with the dynamic canopy structure model established in the early stage, the light response curve is plotted according to the season to construct a complete annual light response dynamic map. Secondly, the dark-adapted fluorescence value (Fv / Fm) of the leaves is measured using an AWalz MiniPam fluorometer. This indicator is an important indicator of the maximum photochemical efficiency of photosystem II and can sensitively reflect the degree of light inhibition. Fluorescence measurements will be repeated at multiple points at different heights and orientations of the canopy, and combined with the seasonal change model of conifer growth, the evolution of fluorescence parameters of the same conifer group at different growth stages will be tracked and recorded. Finally, the seasonal light inhibition sensitivity index is calculated by combining the light response curve, fluorescence parameters and model simulation results. This index quantifies the adaptability and recovery potential of conifers to strong light stress at different growth stages.

[0122] By integrating these analytical data, we can quantitatively evaluate the long-term carbon gain effects of photoinhibition, including the percentage reduction of annual carbon fixation due to different intensities of photoinhibition, the spatial distribution pattern of carbon loss induced by photoinhibition, and the dynamic characteristics of recovery at different canopy positions. This will ultimately form a comprehensive analysis of the photoinhibition effect, providing a scientific basis for the precise management of coniferous species and carbon sequestration optimization. Figure 2 As shown, the present invention also provides a light inhibition effect analysis system for tree canopies, comprising:

[0123] Reconstruction module 100: used to reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain a canopy structure model;

[0124] Simulation module 200: for simulating light distribution of tree canopies based on the canopy structure model to obtain light distribution characteristics;

[0125] Modeling module 300: for establishing a photosynthetic productivity evaluation model of a tree canopy based on the light distribution characteristics;

[0126] Analysis module 400: used to analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity evaluation model to obtain analysis results.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0128] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0129] The present invention provides a method and system for analyzing the light inhibition effect of tree canopies. By combining an adaptive light tracing mechanism with an autonomous learning algorithm, the system can automatically adjust parameters according to differences in canopy morphology, significantly improving the adaptability and applicability of the model. Secondly, while maintaining high precision, the algorithm significantly reduces the amount of calculation through multi-level light attenuation factors and adaptive dimensionality reduction optimization, ensuring real-time processing performance. By adding data enhancement and self-correction mechanisms, the algorithm has strong repeatability of experimental results, enabling the method to demonstrate stable prediction capabilities under different environments. In addition, due to the algorithm's powerful capabilities in light distribution simulation and light inhibition effect assessment, it is applicable to different types of forest and agricultural ecosystems, providing technical support for precision forestry and intelligent agricultural management.

[0130] The present invention provides a method for analyzing plant canopy light distribution and light inhibition effects based on high-resolution three-dimensional data. It overcomes the limitations of traditional two-dimensional images and simplified models, has high precision, wide adaptability and high repeatability, can provide technical support for precise forestry ecological management, and has broad application potential in carbon sink assessment, productivity optimization and other fields.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for analyzing the light inhibition effect of tree canopies, characterized in that: include: S1: Reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the canopy structure model; Wherein, step S1 further includes: S11: collecting multi-angle images of tree canopies; S12: inputting the multi-angle images into a first convolutional network to obtain three-dimensional point cloud data; S13: performing dynamic grid optimization on the three-dimensional point cloud data to obtain a canopy reconstruction model; S14: constructing a seasonal change model of the tree canopy, and fusing the seasonal change model with the canopy reconstruction model to obtain a canopy structure model; S2: Based on the canopy structure model, light distribution simulation is performed on the tree canopy to obtain light distribution characteristics; S3: establishing a photosynthetic productivity evaluation model for tree canopies based on the light distribution characteristics; Wherein, step S3 further includes: S31: inputting the light distribution characteristics and the canopy spatial structure corresponding to the canopy structure model as input data, and performing preprocessing to obtain multidimensional data; S32: inputting the multidimensional data into a deep learning model, and adaptively adjusting the parameters of the deep learning model; S33: classifying the input multidimensional data based on a random forest to obtain classified data; S34: based on the classified data, performing deep learning on the light energy transfer path through a second convolutional network to obtain a photosynthetic productivity evaluation model; S4: performing a light inhibition effect analysis on the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain an analysis result.

2. The method for analyzing light inhibition effect of tree canopies according to claim 1, characterized in that: Step S14 further includes: S141: Periodically collect growth parameters of tree canopies; S142: Extracting change characteristics through the growth parameters, and constructing a seasonal change model according to the change characteristics; S143: Constructing the mapping relationship between the canopy space coordinate system and the time dimension; S144: Perform multi-temporal fusion of the seasonal variation model and the canopy reconstruction model according to the mapping relationship to obtain a canopy structure model.

3. The method for analyzing light inhibition effect of tree canopy according to claim 1, characterized in that: Step S2 further comprises: S21: Based on the canopy spatial structure of the canopy structure model, a light tracing algorithm with adaptively adjusted tracing accuracy is used to calculate the light density of the canopy; S22: Introduce light attenuation factors to different canopies and calculate the light transmittance of multiple canopies; S23: Adjusting the light density according to the light transmittance to obtain light distribution characteristics described by a four-dimensional light field.

4. The method for analyzing light inhibition effect of tree canopy according to claim 3, characterized in that: Step S21 further includes: S211: Construct a bidirectional scattering distribution function model for the tree canopy surface; S212: simulating the scattering characteristics of the tree canopy surface based on the bidirectional scattering distribution function model, and performing ray tracing through a parallel computing framework to obtain light propagation data; S213: Fusing multi-band light propagation data to obtain light density.

5. The method for analyzing light inhibition effect of tree canopy according to claim 1, characterized in that: Step S31 further includes: S311: Input the light distribution characteristics and the canopy spatial structure image corresponding to the canopy structure model to obtain input data; S312: Analyze the canopy image in the input data by performing a gray level co-occurrence matrix to obtain texture features; S313: Dimensionality reduction is performed on the texture features by using the PCA method and the LDA method to obtain multidimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimensionality reduction.

6. The method for analyzing light inhibition effect of tree canopies according to claim 1, characterized in that: Step S4 further comprises: S41: inputting the collected data of the tree canopy to be analyzed into the photosynthetic productivity assessment model to obtain carbon gain impact data calculated in sections; S42: Performing a light inhibition effect analysis on the tree canopy to be analyzed based on the carbon gain impact data to obtain an analysis result.

7. The method for analyzing light inhibition effect of tree canopies according to claim 6, characterized in that: The analysis results in step S42 include: Altitude light response curves, seasonal photoinhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves at different canopy levels.

8. A light inhibition effect analysis system for tree canopies, used to execute the light inhibition effect analysis method for tree canopies according to any one of claims 1 to 7, characterized in that: include: Reconstruction module: used to reconstruct the three-dimensional structure of tree canopies and introduce the time dimension to obtain the canopy structure model; The reconstruction module is specifically configured to: collect multi-angle images of tree canopies; input the multi-angle images into a first convolutional network to obtain three-dimensional point cloud data; perform dynamic grid optimization on the three-dimensional point cloud data to obtain a canopy reconstruction model; construct a seasonal variation model of the tree canopy, and fuse the seasonal variation model with the canopy reconstruction model to obtain a canopy structure model; Simulation module: used for simulating light distribution of tree canopies based on the canopy structure model to obtain light distribution characteristics; Modeling module: used for establishing a photosynthetic productivity evaluation model of tree canopy based on the light distribution characteristics; The modeling module is specifically used to: input the light distribution characteristics and the canopy spatial structure corresponding to the canopy structure model as input data, and perform preprocessing to obtain multidimensional data; Inputting the multidimensional data into a deep learning model, and adaptively adjusting the parameters of the deep learning model; Classifying the input multidimensional data based on the random forest to obtain classified data; performing deep learning on the light energy transfer path through a second convolutional network based on the classified data to obtain a photosynthetic productivity evaluation model; Analysis module: used for performing light inhibition effect analysis on the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

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