Light inhibition effect analysis method and system for tree canopies

Abstract

The invention relates to a light suppression effect analysis method and system for a tree canopy, and the method comprises the steps: carrying out the three-dimensional structure reconstruction of the tree canopy, introducing a time dimension, and obtaining a canopy structure model; based on the canopy structure model, performing light distribution simulation on the tree canopy to obtain light distribution characteristics; establishing a photosynthetic productivity evaluation model of the tree canopy according to the light distribution characteristics; and according to the photosynthetic productivity evaluation model, carrying out photoinhibition effect analysis on a to-be-analyzed tree canopy to obtain an analysis result. According to the method, through accurate canopy three-dimensional reconstruction and efficient light distribution simulation, the prediction precision of the photosynthetic productivity of the coniferous tree is remarkably improved.

Description

Technical Field

[0001] The 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 fixation, 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 are difficult to accurately reconstruct complex canopy structures, errors in light energy distribution occur, affecting the accurate assessment of plant photosynthetic productivity. Secondly, light inhibition is a key factor in the regulation of plant photosynthesis. Existing methods lack high-resolution three-dimensional models to quantitatively analyze the long-term effects of light inhibition 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 annual light energy utilization efficiency.

[0004] Therefore, there is an urgent need in this field for a high-precision, time-considered, and universally adaptable method for analyzing the three-dimensional structure of tree canopies. Summary of the invention

[0005] The present invention provides a method and system for analyzing 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 light inhibition effect of tree canopies, comprising: S1: Reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the 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 assessment model for tree canopies based on the light distribution characteristics; S4: Analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

[0007] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S1 further comprises: S11: Collect multi-angle images of tree canopies; S12: Inputting the multi-angle image into a first convolutional network to obtain three-dimensional point cloud data; S13: performing dynamic mesh optimization through the three-dimensional point cloud data to obtain a canopy reconstruction model; 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.

[0008] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S14 further comprises: 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: Construct the mapping relationship between the canopy space coordinate system and the time dimension; S144: According to the mapping relationship, the seasonal variation model and the canopy reconstruction model are multi-temporally integrated to obtain a canopy structure model.

[0009] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S2 further comprises: S21: Based on the canopy spatial structure of the canopy structure model, a light tracing algorithm that adaptively adjusts tracing accuracy is used to calculate and obtain the light density of the canopy; S22: Introduce light attenuation factors into different canopies and calculate the light transmittance of multiple canopies; S23: According to the light transmittance, the light density is adjusted to obtain light distribution characteristics described by a four-dimensional light field.

[0010] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S21 further comprises: S211: Construct a bidirectional scattering distribution function model for the tree canopy surface; 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; S213: Fuse the light propagation data of multiple bands to obtain light density.

[0011] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S3 further comprises: 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 parameters of the deep learning model; S33: classify the input multidimensional data based on random forest to obtain classified data; 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.

[0012] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S31 further comprises: S311: inputting 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: reducing the dimension of the texture features by PCA method and LDA method to obtain multi-dimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimension reduction.

[0013] According to a method for analyzing light inhibition effect of tree canopy provided by the present invention, step S4 further comprises: S41: inputting the collected data of the tree canopy to be analyzed into the photosynthetic productivity assessment model to obtain the carbon gain impact data calculated in sections; S42: Performing light inhibition effect analysis on the tree canopy to be analyzed according to the carbon gain impact data to obtain analysis results.

[0014] According to a method for analyzing light inhibition effect of tree canopies provided by the present invention, the analysis result in step S42 includes: Altitude light response curves of different canopies, seasonal photoinhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves.

[0015] The present invention also provides a light inhibition effect analysis system for tree canopies, which is used to execute a light inhibition effect analysis method for tree canopies as described in any one of the above items, comprising: Reconstruction module: used to reconstruct the three-dimensional structure of tree canopies and introduce the time dimension to obtain the canopy structure model; Simulation module: used to simulate the light distribution of the tree canopy based on the canopy structure model to obtain light distribution characteristics; Modeling module: used to establish a photosynthetic productivity evaluation model for tree canopies according to the light distribution characteristics; Analysis module: used to analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

[0016] The present invention provides a method and system for analyzing the light inhibition effect of a tree canopy. 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 conifer 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 a 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, so that the analysis results are more comprehensive and reliable.

[0017] The method of the present 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 present invention can adapt to changes in different canopy structures and lighting conditions. By analyzing the light inhibition effect of the tree canopy, the present invention can provide scientific guidance for forestry management, forest ecology research, and climate change response. For example, it can guide forestry workers to perform management measures such as crown pruning and adjusting planting density to optimize the photosynthesis and growth performance of coniferous trees. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative work.

[0019] 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; Figure 2 A schematic diagram of the structure of a light inhibition effect analysis system for tree canopies provided in an embodiment of the present invention.

[0020] Figure numerals: 100, reconstruction module; 200, simulation module; 300, modeling module; 400, analysis module. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments 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 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.

[0022] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0023] like Figure 1 As shown, the present invention provides a method for analyzing light inhibition effect of tree canopy, comprising: S1: Reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the canopy structure model.

[0024] Wherein, step S1 further comprises: S11: Collect multi-angle images of tree canopies.

[0025] 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 images obtained need to cover various parts of the canopy to ensure the integrity and accuracy of the reconstruction.

[0026] S12: Input the multi-angle image into a first convolutional network to obtain three-dimensional point cloud data.

[0027] 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, which can extract features from images at different angles and fuse them to generate three-dimensional point cloud data. Specifically, the input in 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, representing the surface shape of the tree canopy.

[0028] Among them, the first convolutional network in step S12 is an MVCNN network.

[0029] 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 a high reconstruction accuracy under different environmental lighting conditions and reduces reconstruction errors.

[0030] S13: Performing dynamic mesh optimization through the three-dimensional point cloud data to obtain a canopy structure model.

[0031] 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 faces of the mesh through an algorithm to make the model smoother and more detailed, thereby improving the accuracy and visualization of the model and making 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.

[0032] 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.

[0033] Wherein, step S14 further comprises: S141: Periodically collect growth parameters of tree canopies.

[0034] In the specific implementation of step S141, it is necessary to set fixed observation points and regularly collect needle morphological parameters, including needle length, width, thickness, surface area, etc., through high-precision cameras. At the same time, a near-infrared spectrometer is used to measure the changes in the internal biochemical components of the needles, such as chlorophyll content, water content, and nitrogen content, and a micro-meteorological station is deployed to synchronously collect environmental parameters such as temperature, humidity, and light, forming a comprehensive periodic growth parameter data set.

[0035] S142: Extracting variation characteristics through the growth parameters, and constructing a seasonal variation model according to the variation characteristics.

[0036] 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 terms, seasonal terms and random terms to quantify the seasonal fluctuation characteristics. Based on the obtained seasonal fluctuation characteristics, a piecewise function is used to describe the change characteristics of different seasons, ultimately forming a seasonal change model of conifer growth.

[0037] S143: Construct a mapping relationship between the canopy space coordinate system and the time dimension.

[0038] Specifically, in step S143, a storage and query framework for four-dimensional data (x, y, z, t) is first established, wherein x, y, 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 of the spatial position over time.

[0039] S144: According to the mapping relationship, the seasonal variation model and the canopy reconstruction model are multi-temporally integrated to obtain a canopy structure model.

[0040] 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.

[0041] S2: Based on the canopy structure model, light distribution simulation is performed on the tree canopy to obtain light distribution characteristics.

[0042] 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 light density of the canopy is first evaluated in real time by the light tracing algorithm and the tracing accuracy is adjusted to ensure that the light distribution simulation in the dense leaf area is more accurate and the amount of calculation in the sparse area is reduced, thereby improving the efficiency of the light distribution analysis. In addition, the layered light attenuation factor (LAF) is used to divide the canopy into layers with different light transmittances, and 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.

[0043] Wherein, step S2 further comprises: 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.

[0044] 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.

[0045] Wherein, step S21 further includes: S211: Construct a bidirectional scattering distribution function model for the tree canopy surface.

[0046] 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 a full-angle scattering model is established by calibrating the Fresnel reflection coefficient and refractive index of the conifer surface. The establishment of the scattering model provides accurate surface optical characteristic parameters for subsequent ray tracing, which is the basis for realizing light tracing.

[0047] 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.

[0048] 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 calculations, the present invention also uses a GPU parallel architecture to track a large number of light rays at the same time, and in accordance with the principle of adaptive precision 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 light ray in detail, including the number of scattering times, angle changes, and energy attenuation, to ultimately form a comprehensive multi-band light propagation data set.

[0049] S213: Fuse the light propagation data of multiple bands to obtain light density.

[0050] In step S213, the scattered results of each computing core are first merged, and then the differentiated effects of light of different wavelengths on plant physiological processes are considered, and a scientific and reasonable weighted fusion is performed. 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.

[0051] S22: Introduce light attenuation factors into different canopies and calculate the light transmittance of multiple canopies.

[0052] Furthermore, in step S22, the present invention considers the attenuation effect of light in the canopy to calculate the light transmittance. The light attenuation factor means that when light passes through the canopy, it will be absorbed and scattered by structures such as leaves and branches, resulting in a gradual weakening of the light intensity. The light attenuation factor is used to quantify this weakening effect. According to the structural characteristics of different canopies (such as leaf density, thickness, etc.), the corresponding light attenuation factor is introduced, and the light transmittance can be obtained by calculating the remaining intensity of light after passing through each layer of the canopy.

[0053] S23: According to the light transmittance, the light density is adjusted to obtain light distribution characteristics described by a four-dimensional light field.

[0054] In step S23, the light density is adjusted according to 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 considering 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 contain seasonal change information, providing a comprehensive data basis for the subsequent spatiotemporal dynamic analysis of light inhibition effects.

[0055] S3: Establishing a photosynthetic productivity evaluation model for the tree canopy based on the light distribution characteristics.

[0056] Step S3 further comprises: 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 multi-dimensional data.

[0057] Wherein, step S31 further comprises: S311: Input the light distribution characteristics and the canopy spatial structure image corresponding to the canopy structure model to obtain input data.

[0058] 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.

[0059] Wherein, step S311 also includes: The canopy spatial structure image is subjected to data enhancement including rotation, translation and scaling.

[0060] 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 in a 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.

[0061] S312: Analyze the canopy image in the input data using a gray level co-occurrence matrix to obtain texture features.

[0062] The purpose of step S312 is to extract texture features from the canopy image to describe the details of the canopy structure. In this step, the gray level co-occurrence matrix (GLCM) is used to analyze the texture features of the canopy image. GLCM is a statistical method for describing image texture. By calculating the gray value relationship between pixel pairs in the image, texture features (such as contrast, correlation, energy, homogeneity, etc.) are extracted. These texture features can reflect the details of the canopy structure, such as the arrangement and density of leaves.

[0063] S313: reducing the dimension of the texture features by PCA method and LDA method to obtain multi-dimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimension reduction.

[0064] The purpose of step S313 is to reduce the dimension of texture features, reduce data redundancy, retain important information, and generate multi-dimensional 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 a 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 multi-dimensional data.

[0065] S32: Input the multidimensional data into a deep learning model, and adaptively adjust the parameters of the deep learning model.

[0066] 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 learning rate, weight and other parameters of the model, and continuously optimizing the loss function of the model through the back propagation algorithm and the gradient descent method, so that the model can better fit the data, and finally outputting the trained deep learning model to extract key features from the multidimensional data to provide support for subsequent tasks.

[0067] Specifically, the input of the deep learning model base in step S32 is the characteristic data such as the angle of leaves in each layer of the canopy and the chlorophyll content. The deep learning model then combines the multidimensional data to optimize the parameters of the photosynthetic productivity model. Through dynamic adjustment, the adaptability of the model to different vegetation types (such as conifers and broad-leaved trees) and climatic conditions (such as light intensity and temperature) is ensured. The output is a self-adaptively optimized deep learning model that can more accurately capture the changing patterns of canopy photosynthetic productivity.

[0068] S33: Classify the input multidimensional data based on random forest to obtain classified data.

[0069] 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 the multidimensional data in different categories.

[0070] 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.

[0071] In step S34, the classified data generated in S33 is first input into the second convolutional network, and then the second convolutional network is 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.).

[0072] Then, combined with the classified data (such as different light 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.

[0073] The final output of the model is the analysis result of the light inhibition effect, which may include: light inhibition 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%); spatial distribution of light inhibition: the light inhibition intensity in different areas of the canopy (such as the distribution of light inhibition intensity at the top, middle and bottom of the canopy); temporal dynamics of light inhibition: changes in light inhibition effects in different time periods (such as the time period of the day when light inhibition is strongest).

[0074] S4: Analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

[0075] Wherein, step S4 further comprises: S41: Inputting the collected data of the tree canopy to be analyzed into the photosynthetic productivity assessment model to obtain the carbon gain impact data calculated in sections.

[0076] 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 photosynthesis efficiency of the canopy and its response to the light inhibition effect.

[0077] S42: Performing light inhibition effect analysis on the tree canopy to be analyzed according to the carbon gain impact data to obtain analysis results.

[0078] The analysis results in step S42 include: The light response curves of different canopy heights, seasonal light inhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves. In step S42, based on the carbon gain impact data obtained in S41, a systematic light inhibition effect analysis is performed. Specifically, the CO2 exchange rate at different canopy heights is 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 drawn by 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. The fluorescence measurement 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.

[0079] 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 photoinhibition of different intensities, 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 tree species and carbon sink optimization. Figure 2 As shown, the present invention also provides a light inhibition effect analysis system for tree canopy, comprising: Reconstruction module 100: used to reconstruct the three-dimensional structure of the tree canopy and introduce the time dimension to obtain the canopy structure model; Simulation module 200: used to simulate the light distribution of the tree canopy based on the canopy structure model to obtain light distribution characteristics; Modeling module 300: used to establish a photosynthetic productivity evaluation model of a tree canopy according to the light distribution characteristics; Analysis module 400: used to analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

[0080] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0081] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0082] The present invention provides a method and system for analyzing light inhibition effects of tree canopies. By combining an adaptive light tracing mechanism with an autonomous learning algorithm, the parameters can be automatically adjusted according to differences in canopy morphology, thereby 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, thereby ensuring real-time processing performance. By adding data enhancement and self-correction mechanisms, the algorithm has strong repeatability of experimental results, thereby enabling the method to exhibit stable prediction capabilities under different environments. In addition, due to the powerful capabilities of the algorithm in light distribution simulation and light inhibition effect evaluation, the algorithm is suitable for different types of forests and agricultural ecosystems, thereby providing technical support for precision forestry and intelligent agricultural management.

[0083] The present invention provides a method for analyzing plant canopy light distribution and light inhibition effect based on high-resolution three-dimensional data, which 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.

[0084] 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 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; 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 assessment model for tree canopies based on the light distribution characteristics; S4: Analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

2. A method for analyzing light inhibition effect of tree canopy according to claim 1, characterized in that: Step S1 further comprises: S11: Collect multi-angle images of tree canopies; S12: Inputting the multi-angle image into a first convolutional network to obtain three-dimensional point cloud data; S13: performing dynamic mesh optimization through the three-dimensional point cloud data to obtain a canopy reconstruction model; 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.

3. A method for analyzing light inhibition effect of tree canopy according to claim 2, characterized in that: Step S14 further comprises: 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: Construct the mapping relationship between the canopy space coordinate system and the time dimension; S144: According to the mapping relationship, the seasonal variation model and the canopy reconstruction model are multi-temporally integrated to obtain a canopy structure model.

4. 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 that adaptively adjusts tracing accuracy is used to calculate and obtain the light density of the canopy; S22: Introduce light attenuation factors into different canopies and calculate the light transmittance of multiple canopies; S23: According to the light transmittance, the light density is adjusted to obtain light distribution characteristics described by a four-dimensional light field.

5. A method for analyzing light inhibition effect of tree canopy according to claim 4, characterized in that: Step S21 further includes: S211: Construct a bidirectional scattering distribution function model for the tree canopy surface; 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 light propagation data; S213: Fuse the light propagation data of multiple bands to obtain light density.

6. The method for analyzing light inhibition effect of tree canopy according to claim 1, characterized in that: Step S3 further comprises: 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 parameters of the deep learning model; S33: classify the input multidimensional data based on random forest to obtain classified data; 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.

7. A method for analyzing light inhibition effect of tree canopies according to claim 6, characterized in that: Step S31 further includes: S311: inputting 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: reducing the dimension of the texture features by PCA method and LDA method to obtain multi-dimensional data including the light distribution features, the canopy spatial structure corresponding to the canopy structure model, and the texture features after dimension reduction.

8. The method for analyzing light inhibition effect of tree canopy 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 the carbon gain impact data calculated in sections; S42: Performing light inhibition effect analysis on the tree canopy to be analyzed according to the carbon gain impact data to obtain analysis results.

9. A method for analyzing light inhibition effect of tree canopies according to claim 8, characterized in that: The analysis results in step S42 include: Altitude light response curves of different canopies, seasonal photoinhibition sensitivity index, and dark-adapted fluorescence values ​​of leaves.

10. A light inhibition effect analysis system for tree canopies, used to execute a light inhibition effect analysis method for tree canopies as claimed in any one of claims 1 to 9, 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; Simulation module: used to simulate the light distribution of the tree canopy based on the canopy structure model to obtain light distribution characteristics; Modeling module: used to establish a photosynthetic productivity evaluation model for tree canopies according to the light distribution characteristics; Analysis module: used to analyze the light inhibition effect of the tree canopy to be analyzed according to the photosynthetic productivity assessment model to obtain analysis results.

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