Method for measuring and calculating vertical gradient change of photosynthesis quantity of forest canopy and related equipment

Abstract

The invention discloses a method and related equipment for measuring and calculating the vertical gradient change of the photosynthesis quantity of a forest canopy. The method comprises the following steps: acquiring actual measurement data of a target forest region; simulating first data distribution of a forest canopy leaf area index profile by using a Weibull function, optimizing the first data distribution based on a forest canopy leaf area index peak value, and further determining a leaf area index of each position of the target forest region; simulating second data distribution of a forest canopy meteorological profile based on the leaf area index, and optimizing the second data distribution based on a preset top-bottom difference value; simulating third data distribution of a forest canopy physiological profile based on preset proportional distribution of photosynthesis rates, and optimizing the third data distribution based on a preset numerical value distribution rule; finally, the forest canopy photosynthesis vertical gradient is obtained through construction, and then the vertical gradient variable quantity is obtained through quantification. The method can realize accurate quantification of the photosynthesis vertical gradient, and can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for measuring the vertical gradient change of photosynthetic capacity in forest canopies. Background Technology

[0002] In the field of estimating the vertical gradient of forest vegetation photosynthesis, the following main problems exist: Most existing forest photosynthesis models only assume that the vegetation canopy has large leaves, leading to an underestimation of the photosynthetic capacity of the understory vegetation and failing to guarantee the accuracy of the estimated total photosynthetic capacity; some models that consider vertical gradient differences only assume differences in light gradient during forest photosynthesis, ignoring the physiological differences in vertical vegetation within the forest canopy. Under these limitations, the estimation mechanism of forest vegetation photosynthesis cannot be characterized, and there is significant uncertainty in the assessment of total forest photosynthetic capacity. Summary of the Invention

[0003] The main objective of this invention is to provide a method, apparatus, electronic device, storage medium, and program product for measuring the vertical gradient change of forest canopy photosynthetic capacity, aiming to solve at least one problem in the prior art.

[0004] To achieve the above objectives, one aspect of this invention proposes a method for measuring the vertical gradient change in forest canopy photosynthetic capacity, the method comprising: Obtain measured data for the target forest area; the measured data includes the peak leaf area index. The first data distribution of the forest canopy leaf area index profile is simulated using the Weibull function. The first data distribution is optimized based on the peak value of the forest canopy leaf area index. The leaf area index at each location in the target forest area is determined according to the forest canopy leaf area index profile. The second data distribution is based on the simulated forest canopy meteorological profile using leaf area index, and optimized based on the preset top-to-bottom difference. The second data distribution includes light profile, temperature profile, and moisture profile. The third data distribution simulates the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate, and optimizes the third data distribution based on a preset numerical distribution rule; wherein, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy. The vertical gradient of forest canopy photosynthesis was constructed based on the forest canopy leaf area index profile, forest canopy meteorological profile, and forest canopy physiological profile, and then the change in vertical gradient was quantified.

[0005] In some embodiments, optimizing the first data distribution based on the peak leaf area index of the forest canopy includes the following steps: When the peak of the leaf area index of the forest canopy is concentrated in the upper part of the forest, the value of the shape coefficient in the probability density of the Weibull function is set in the first value range. When the peak of the leaf area index of the forest canopy is concentrated in the understory, the value of the shape coefficient in the probability density of the Weibull function is set in the second value range. When the peak value of the leaf area index in the first data distribution falls into the preset understory level of the forest canopy, the first data distribution is regenerated using the Weibull function.

[0006] In some embodiments, the measured data also include a first temperature and a first atmospheric water vapor pressure difference at the top of the forest canopy, a second temperature and a second atmospheric water vapor pressure difference at the bottom of the canopy, and canopy height. A second data distribution simulating the forest canopy meteorological profile based on leaf area index includes the following steps: The illumination profile is solved using the radiative transfer equation based on the leaf area index. The illumination profile includes incident light at different heights, direct incident light, and diffused incident light. The temperature profile was simulated based on the difference between the first and second temperatures and the cumulative value of the leaf area index at different heights in the canopy. The moisture profile is simulated based on the difference between the first and second atmospheric water vapor pressure differences, combined with the cumulative value of the leaf area index at different heights in the canopy.

[0007] In some embodiments, optimizing the second data distribution based on a preset maximum top-bottom difference includes the following steps: When the difference between the simulated top temperature and bottom temperature in the temperature profile exceeds the first preset top-bottom difference value, the bottom temperature is adjusted using the top temperature as a reference and the first preset top-bottom difference value. When the difference between the simulated top atmospheric water vapor pressure difference and the bottom atmospheric water vapor pressure difference in the temperature profile exceeds the second preset top-bottom difference value, the bottom atmospheric water vapor pressure difference is adjusted using the top atmospheric water vapor pressure difference as a benchmark and the second preset top-bottom difference value.

[0008] In some embodiments, the measured data also include the chlorophyll content at the top of the forest canopy and the canopy height, and a third data distribution simulating the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rates, including the following steps: Based on the cumulative values ​​of chlorophyll content and leaf area index at different heights within the canopy, and combined with a preset proportional distribution, a univariate linear relationship is used to simulate and obtain the physiological profile of the forest canopy.

[0009] In some embodiments, optimizing the third data distribution based on a preset numerical distribution rule includes the following steps: Set the values ​​of photosynthetic rate less than 0 in the third data distribution to 0.

[0010] In some embodiments, the vertical gradient of forest canopy photosynthesis is constructed based on the forest canopy leaf area index profile, the forest canopy meteorological profile, and the forest canopy physiological profile, including the following steps: Based on the physiological profile of the forest canopy, the first type of photosynthesis was quantified by combining the Michaelis constant of carboxylation rate, photosynthetic compensation point, and intercellular carbon dioxide and oxygen concentrations. Among them, the carbon dioxide concentration is obtained based on the degree of stomatal opening and closing of the leaves, and the degree of stomatal opening and closing of the leaves is calculated based on the temperature profile, the moisture profile and the Michaelis constant; Based on the illumination profile, the second type of photosynthesis is quantified by combining carbon dioxide concentration, photosynthetic compensation point and preset maximum carboxylation rate. Substituting the first and second types of photosynthesis into the vegetation photosynthesis model framework, we can construct the vertical gradient of forest canopy photosynthesis. Specifically, if there is a value less than 0 in the vertical gradient of forest canopy photosynthesis, the corresponding value will be corrected to 0.

[0011] To achieve the above objectives, another aspect of the present invention provides an apparatus for measuring the vertical gradient change in forest canopy photosynthetic capacity, the apparatus comprising: The first module is used to acquire measured data of the target forest area; the measured data includes the peak value of the leaf area index. The second module is used to simulate the first data distribution of the forest canopy leaf area index profile using the Weibull function, optimize the first data distribution based on the peak value of the forest canopy leaf area index, and determine the leaf area index at each location in the target forest area according to the forest canopy leaf area index profile. The third module is used to simulate the second data distribution of forest canopy meteorological profiles based on leaf area index, and to optimize the second data distribution based on preset top-bottom difference; wherein, the second data distribution includes light profile, temperature profile and moisture profile; The fourth module is used to simulate the third data distribution of the physiological profile of the forest canopy based on a preset ratio distribution of photosynthetic rate, and to optimize the third data distribution based on a preset numerical distribution rule; wherein, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; The fifth module is used to construct the vertical gradient of forest canopy photosynthesis based on the forest canopy leaf area index profile, forest canopy meteorological profile, and forest canopy physiological profile, and then quantify the change in the vertical gradient.

[0012] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0013] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0014] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0015] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a method, apparatus, electronic device, storage medium, and program product for measuring the vertical gradient change of forest canopy photosynthetic capacity. This solution obtains measured data of a target forest area; wherein the measured data includes peak leaf area index (LAI); a first data distribution of the LAI profile is simulated using the Weibull function; the first data distribution is optimized based on the peak LAI; the LAI at various locations in the target forest area is determined according to the LAI profile; and a forest canopy meteorological profile is simulated based on the LAI. The second data distribution is optimized based on a preset top-to-bottom difference; the second data distribution includes light profile, temperature profile, and water profile; the third data distribution simulates the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate, and is optimized based on a preset numerical distribution rule; in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; the vertical gradient of forest canopy photosynthesis is constructed based on the forest canopy leaf area index profile, forest canopy meteorological profile, and forest canopy physiological profile, and then the change in vertical gradient is quantified. This invention, by utilizing the Weibull function to simulate and optimize the leaf area index (LAI) profile, can finely characterize the vertical distribution of leaf area from the top to the bottom of the forest canopy. This effectively distinguishes and quantifies the photosynthetic contributions of the upper canopy and understory vegetation, overcoming the fundamental flaw of traditional "large-leaf" models that underestimate understory photosynthesis, and significantly improving the accuracy of total forest photosynthetic estimation. Furthermore, this invention not only considers the light profile but also simultaneously simulates and optimizes the temperature and moisture profiles, collectively forming the forest canopy meteorological profile. This comprehensive consideration of multiple environmental factors makes the simulation of the photosynthetic environment more realistic, providing a reliable environmental input for accurately calculating the photosynthetic rate at different vertical levels and reducing the need for... To address the uncertainties caused by environmental factor estimation biases, this invention innovatively introduces physiological differences, quantifying the vertical heterogeneity of vegetation physiological characteristics. Specifically, it explicitly stipulates that the photosynthetic rate above the canopy is lower than below, thus systematically reflecting the variation of forest vegetation's physiological attributes (such as photosynthetic capacity) along the vertical gradient. This allows the characterization of photosynthetic mechanisms to deepen from a purely environmentally driven approach to a dual-driven approach of "environment and physiology," resulting in a more complete model mechanism. Finally, this invention achieves precise quantification of the vertical gradient of photosynthesis and effectively reduces uncertainty through multi-profile system integration, greatly improving the accuracy and reliability of forest total photosynthetic capacity assessment and providing strong technical support for related research and applications. Attached Figure Description

[0016] Figure 1This is a schematic diagram of an implementation environment for a method for measuring the vertical gradient change of forest canopy photosynthetic capacity provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for measuring the vertical gradient change of forest canopy photosynthetic capacity according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the principle architecture of the method for measuring the vertical gradient change of forest canopy photosynthetic capacity provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of a device for measuring the vertical gradient change of forest canopy photosynthetic capacity, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0019] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0021] To facilitate understanding of the technical solution of this invention, the technical terms that may be involved in the technical solution of this invention will first be explained: Photosynthesis in vegetation: Photosynthesis is a biochemical process in which green plants, algae, and certain bacteria use chlorophyll to absorb solar energy, convert carbon dioxide and water in the atmosphere into energy-rich organic matter (such as glucose), and release oxygen. It is the foundation of Earth's life energy and carbon-oxygen cycle.

[0022] Gross primary productivity (GPP) of vegetation canopy: Gross primary productivity (GPP) refers to the total amount of organic carbon fixed by green plants through photosynthesis per unit time and per unit area. It is the starting point of carbon cycle and the basis of energy flow in ecosystem.

[0023] Vertical gradient of forest structure: The vertical gradient of forest structure refers to the phenomenon of vertical stratification where resources such as light, temperature, and humidity decrease or change drastically with height from the canopy to the forest floor, resulting in the differentiation of trees, shrubs, herbs, and epiphytes into lattices and each occupying a suitable microhabitat.

[0024] Forest microclimate vertical gradient: Forest microclimate vertical gradient refers to the microclimate environment with significant differences between layers, where meteorological elements such as solar radiation, temperature, humidity, and wind speed decrease or change drastically with altitude from the canopy to the forest floor.

[0025] The Weibull function is a general data distribution description model that uses two parameters, "shape" and "scale," to characterize the risk rate of random events such as the vertical gradient of the forest canopy, wind speed, or lifespan, progressing from low to high and then back to low. In this patent, it is used to describe the vertical distribution of the forest canopy leaf area index.

[0026] Vcmax: Vcmax is the maximum carboxylation rate that plant Rubisco enzymes can achieve per unit leaf area under saturated CO2 and light conditions, directly quantifying the photosynthetic potential of leaves.

[0027] Jmax: Jmax is the maximum electron transport rate that the electron transport chain on the thylakoid membrane of chloroplasts can reach under saturated light, marking the upper limit of the power of the photosynthetic "energy engine" of leaves.

[0028] VPD: Atmospheric vapor pressure difference (VPD) is the difference between saturated vapor pressure and actual vapor pressure. It is a measure of atmospheric dryness and an important climate regulator of photosynthesis in ecosystems.

[0029] In related technologies, most existing forest photosynthesis models only assume that the vegetation canopy has large leaves, leading to an underestimation of the photosynthetic capacity of the understory vegetation and an inability to guarantee the accuracy of the estimated total photosynthetic capacity. Models that consider vertical gradient differences only assume differences in light gradients during forest photosynthesis, ignoring the physiological differences in vertical vegetation within the forest canopy. These limitations prevent the depiction of the estimation mechanism of forest vegetation photosynthesis and result in significant uncertainty in the assessment of total forest photosynthetic capacity.

[0030] In view of this, this invention provides a method and related equipment for measuring the vertical gradient change of forest canopy photosynthesis. This method involves acquiring measured data from a target forest area, including peak leaf area index (LAI). A first data distribution is simulated using the Weibull function to model the LAI profile of the forest canopy. The first data distribution is optimized based on the peak LAI, and the LAI at various locations within the target forest area is determined according to the LAI profile. A second data distribution is simulated based on the LAI to model the meteorological profile of the forest canopy, and the second data distribution is optimized based on a preset top-to-bottom difference. The second data distribution includes light profiles, temperature profiles, and moisture profiles. A third data distribution is simulated based on a preset proportional distribution of photosynthetic rates to model the physiological profile of the forest canopy, and the third data distribution is optimized based on a preset numerical distribution rule. In the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy. A vertical gradient of forest canopy photosynthesis is constructed based on the LAI profile, meteorological profile, and physiological profile, and then the change in vertical gradient is quantified. This invention, by utilizing the Weibull function to simulate and optimize the leaf area index (LAI) profile, can finely characterize the vertical distribution of leaf area from the top to the bottom of the forest canopy. This effectively distinguishes and quantifies the photosynthetic contributions of the upper canopy and understory vegetation, overcoming the fundamental flaw of traditional "large-leaf" models that underestimate understory photosynthesis, and significantly improving the accuracy of total forest photosynthetic estimation. Furthermore, this invention not only considers the light profile but also simultaneously simulates and optimizes the temperature and moisture profiles, collectively forming the forest canopy meteorological profile. This comprehensive consideration of multiple environmental factors makes the simulation of the photosynthetic environment more realistic, providing a reliable environmental input for accurately calculating the photosynthetic rate at different vertical levels and reducing the need for... To address the uncertainties caused by environmental factor estimation biases, this invention innovatively introduces physiological differences, quantifying the vertical heterogeneity of vegetation physiological characteristics. Specifically, it explicitly stipulates that the photosynthetic rate above the canopy is lower than below, thus systematically reflecting the variation of forest vegetation's physiological attributes (such as photosynthetic capacity) along the vertical gradient. This allows the characterization of photosynthetic mechanisms to deepen from a purely environmentally driven approach to a dual-driven approach of "environment and physiology," resulting in a more complete model mechanism. Finally, this invention achieves precise quantification of the vertical gradient of photosynthesis and effectively reduces uncertainty through multi-profile system integration, greatly improving the accuracy and reliability of forest total photosynthetic capacity assessment and providing strong technical support for related research and applications.

[0031] It is understood that the method for measuring the vertical gradient change in forest canopy photosynthetic output provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiments is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0032] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0033] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0034] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0035] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0036] For example, based on Figure 1The implementation environment shown in this embodiment of the invention provides a method for measuring the vertical gradient change of forest canopy photosynthesis. The following description uses the application of this method for measuring the vertical gradient change of forest canopy photosynthesis in server 101 as an example. It can be understood that this method for measuring the vertical gradient change of forest canopy photosynthesis can also be applied to terminal 102.

[0037] Reference Figure 2 , Figure 2 This is an optional flowchart of a method for measuring the vertical gradient change of forest canopy photosynthesis provided in an embodiment of the present invention. The execution subject of this method for measuring the vertical gradient change of forest canopy photosynthesis can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S400.

[0038] Step S100: Obtain measured data of the target forest area; The measured data includes the peak value of the leaf area index; For example, in some specific embodiments, the measured data may also include a first temperature and a first atmospheric water vapor pressure difference at the top of the forest canopy, a second temperature and a second atmospheric water vapor pressure difference at the bottom of the canopy, the canopy height of the forest canopy, and the chlorophyll content at the top of the forest canopy.

[0039] Step S200: Use the Weibull function to simulate the first data distribution of the forest canopy leaf area index profile, optimize the first data distribution based on the peak value of the forest canopy leaf area index, and determine the leaf area index at each location in the target forest area according to the forest canopy leaf area index profile. It should be noted that, in some embodiments, optimizing the first data distribution based on the peak value of the leaf area index of the forest canopy may include the following steps: when the peak value of the leaf area index of the forest canopy is concentrated in the upper part of the forest, the value of the shape coefficient in the probability density of the Weibull function is set in a first value range; when the peak value of the leaf area index of the forest canopy is concentrated in the understory part of the forest, the value of the shape coefficient in the probability density of the Weibull function is set in a second value range; when the peak value of the leaf area index in the first data distribution falls into a preset understory level of the forest canopy, the first data distribution is regenerated using the Weibull function.

[0040] For example, in some specific implementations, the forest canopy leaf area index profile simulation can be achieved as follows: Step 1: Data Distribution Assumptions: Within the forest canopy, there are distinct vertical gradient differences between the tree-shrub-grass layers and within the tree layer itself. Therefore, it is necessary to pre-define the vertical distribution characteristics of the vegetation structure. This invention utilizes the Weibull function to define the vertical gradient. The Weibull function is a continuous probability distribution with a probability density function of... (1) in For random variables, This is the proportionality coefficient. The shape factor controls the height of the peak value. In the measured data, the peak value of the leaf area index in most (e.g., more than half) forest canopies is located in the upper part of the forest canopy. The value is concentrated between 1 and 5. If the peak value of the leaf area index in the measured data is concentrated in the understory, then... It can be set to 6-10.

[0041] Step 2: Generate data and check: The Weibull function is used to define the vertical distribution of vegetation structure in the forest canopy. If the peak leaf area index is located in the first or second layer of the understory, the generated data is considered unreasonable and the data distribution needs to be reset.

[0042] Step S300: Simulate the second data distribution of forest canopy meteorological profile based on leaf area index, and optimize the second data distribution based on preset top-bottom difference; The second data distribution includes illumination profile, temperature profile, and moisture profile; It should be noted that, in some embodiments, the second data distribution for simulating forest canopy meteorological profiles based on leaf area index may include the following steps: solving for the illumination profile using the radiative transfer equation based on leaf area index; wherein, the illumination profile includes incident light, direct incident light, and diffused incident light at different heights; simulating the temperature profile based on the difference between a first temperature and a second temperature, combined with the cumulative value of leaf area index at different heights within the canopy; and simulating the moisture profile based on the difference between a first atmospheric water vapor pressure difference and a second atmospheric water vapor pressure difference, combined with the cumulative value of leaf area index at different heights within the canopy.

[0043] For example, in some specific implementations, forest canopy meteorological profiles (light, temperature, moisture) can be simulated as follows: Step 1: Data Distribution Assumptions: In the vertical direction, the simulation of forest canopy meteorological profiles is mainly related to the vertical distribution of leaf area index, i.e., it is related to the Weibull function.

[0044] Step 2: Scene Analysis and Generation The illumination profile is obtained by solving the radiative transfer equation, and the amount of illumination at different heights relative to the total incident light across the canopy is calculated as follows: (2) (3) (4) For height The amount of incident light on the surface; For height The amount of direct incident light on the surface; For height The amount of scattered incident light on the surface; The extinction coefficient is the average value of the entire canopy. The extinction coefficient at a certain height; The leaf area index accumulated up to height i; The amount of LAI on a single layer; The leaf aggregation index is the average value of the canopy. Temperature profile, simulated using cumulative leaf area index profile: (5) In formula 5, , These represent the temperatures at the top and bottom of the canopy, respectively. Represents height Leaf area index height, Represents the height of the canopy.

[0045] Moisture profile, displayed using VPD, and simulated using cumulative leaf area index profile: (6) , These represent the atmospheric water vapor pressure difference at the top and bottom of the canopy, respectively. Represents height Leaf area index height, Represents the height of the canopy.

[0046] It should be noted that, in some embodiments, optimizing the second data distribution based on a preset maximum top-bottom difference may include the following steps: when the difference between the simulated top temperature and bottom temperature in the temperature profile exceeds a first preset top-bottom difference, the bottom temperature is adjusted using the first preset top-bottom difference as a reference; when the difference between the simulated top atmospheric water vapor pressure difference and the bottom atmospheric water vapor pressure difference in the temperature profile exceeds a second preset top-bottom difference, the bottom atmospheric water vapor pressure difference is adjusted using the second preset top-bottom difference as a reference.

[0047] For example, in some specific implementations, if the simulated top-layer temperature differs from the bottom-layer temperature by more than 5 degrees Celsius along the vertical gradient, then the simulated bottom-layer temperature needs to be corrected. The correction process utilizes the maximum temperature difference between the top and bottom layers being 5 degrees Celsius, meaning the bottom layer temperature differs from the top of the canopy temperature by only 5 degrees Celsius. Simultaneously, if the simulated top-layer VPD is more than 4 hPa higher than the bottom-layer VPD, a correction is also required. The correction process utilizes the maximum VPD difference between the top and bottom layers being 4 hPa, meaning the bottom layer VPD differs from the top of the canopy VPD by only 4 hPa.

[0048] Step S400: The third data distribution of the forest canopy physiological profile is simulated based on a preset proportional distribution of photosynthetic rate, and the third data distribution is optimized based on a preset numerical distribution rule. Among them, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; It should be noted that, in some embodiments, the third data distribution for simulating the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate may include the following steps: based on the cumulative values ​​of chlorophyll content and leaf area index at different heights in the canopy, the physiological profile of the forest canopy is simulated using a univariate linear relationship in combination with the preset proportional distribution.

[0049] For example, in some specific implementations, forest canopy physiological profile (Vcmax) simulation can be achieved as follows: Step 1: Data Distribution Assumptions: Unlike previous models of vegetation physiology, this invention proposes that the rate of photosynthesis above the forest canopy is lower than that below. Forest canopy observation data show that chlorophyll content above the canopy is lower than below, indicating that the rate of photosynthesis above the canopy is lower than that below.

[0050] Step 2: Scene Analysis and Generation Using a univariate linear formula, we assume that the photosynthetic rate (Vcmax) above the forest canopy is 20% lower than that of the understory.

[0051] (7) In Formula 7, z represents the canopy height, and i represents the result at a certain height on the forest canopy. This refers to the chlorophyll content at the top of the canopy. The conversion factor is 0.2, which means that the photosynthetic efficiency above the canopy is 20% lower than that of the understory.

[0052] It should be noted that, in some embodiments, optimizing the third data distribution based on a preset numerical distribution rule may include the following steps: setting the values ​​in the third data distribution where the photosynthetic rate is less than 0 to 0.

[0053] For example, in some specific embodiments, if at certain heights in the vertical direction If the value is less than 0, then check the generated forest canopy physiological profile. Values ​​at heights less than 0 are set to 0.

[0054] Step S500: Based on the forest canopy leaf area index profile, forest canopy meteorological profile and forest canopy physiological profile, the vertical gradient of forest canopy photosynthesis is constructed, and then the change in vertical gradient is quantified. It should be noted that, in some embodiments, the construction of the forest canopy photosynthetic vertical gradient based on the forest canopy leaf area index profile, forest canopy meteorological profile, and forest canopy physiological profile may include the following steps: Based on the forest canopy physiological profile, the first type of photosynthetic capacity is quantified by combining the Michaelis constant of the carboxylation rate, the photosynthetic compensation point, and the carbon dioxide and oxygen concentrations between cells; wherein, the carbon dioxide concentration is derived from the degree of stomatal opening and closing, and the degree of stomatal opening and closing is calculated based on the temperature profile, water profile, and Michaelis constant; based on the light profile, the second type of photosynthetic capacity is quantified by combining the carbon dioxide concentration, the photosynthetic compensation point, and the preset maximum carboxylation rate; the first and second types of photosynthetic capacity are substituted into the vegetation photosynthetic model framework to construct the forest canopy photosynthetic vertical gradient; wherein, when there are values ​​less than 0 in the forest canopy photosynthetic vertical gradient, the corresponding values ​​are corrected to 0.

[0055] For example, in some specific implementations, the vertical gradient simulation of forest canopy photosynthesis can be achieved as follows: Step 1: Assumptions of the leaf photosynthesis model: In the classic vegetation photosynthesis model framework (FvCB model), the photosynthetic rate (An) at the leaf scale is: (8) Ac represents the photosynthetic rate under Rubisco enzyme restriction, which is mainly related to the maximum carboxylation rate of leaves. Related (as shown in formula (7)). The main influencing factor is Rubisco enzyme limitation, and the amount of Rubisco enzyme is mainly affected by leaf pigments, especially chlorophyll. This model will utilize empirical relationships under different total canopy chlorophyll contents (CCC). The variables were used to simulate the photosynthetic capacity of vegetation at different heights in the vertical direction under different canopy chlorophyll contents.

[0056] (9) (10) In formula 9 as well as These represent the intercellular carbon dioxide and oxygen concentrations. In this invention, a constant setting value is used (for example, set to a fixed value of 209). This is the compensation point for photosynthesis. is the Michaelis constant for the carboxylation rate. In Equation 10, Ac is the photosynthetic rate limiting factor for the maximum carboxylation rate. For the maximum carboxylation rate, The PAR data simulated in Equation 2-4. This is the empirical coefficient for extinction. Intercellular carbon dioxide concentration (i.e., in Equation 9) And in Formula 10 The calculation method is as follows: (11) (12) Coefficients in Formula 11-12 This represents the degree of stomata opening and closing on the blade. This represents atmospheric carbon dioxide concentration, derived from environmental data observations.

[0057] By inputting the PAR profile in the vertical direction into formulas (8)-(9), and inputting the temperature and moisture profiles into... This allows for the calculation of the vertical distribution of photosynthesis at the canopy scale. The temperature and VPD of this vertical distribution affect the calculation of intercellular... Concentration-related parameters The stomatal opening ratio is related to the temperature and VPD of each layer (as shown in Equation 12). Finally, by inputting the vegetation structure, environmental microclimate, and physiological profiles obtained from the simulation in Equations (1)-(6) into Equations (7)-(11), the photosynthetic profile in the vertical direction of the forest is calculated.

[0058] Step 2: Check the vertical profile data of photosynthesis If the value of vertical photosynthesis in a certain layer is less than 0, then profile correction is required. The values ​​of vertical photosynthesis that are less than 0 are set to 0, so that the photosynthesis profile values ​​in the vertical direction are all greater than 0.

[0059] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0060] First, it's important to note that estimating forest canopy vertical photosynthesis is a cutting-edge research area that has emerged from the intersection of ecology, forestry, remote sensing, and Earth system science over the past two decades. Its goal is to integrate the net photosynthetic rate-light response curve at the leaf scale layer by layer upwards to obtain the carbon assimilation rates (GPP, NPP) for the whole tree, stand, and even the entire region. Due to the vertical variations in light intensity attenuation, leaf angle distribution, leaf age gradient, temperature / humidity / CO2 differences, and interference from non-photosynthetic organs (branches, trunks, fruits), estimating forest canopy vertical photosynthesis is technically far more challenging than estimating grasslands or farmland. Studies have shown that the total GPP observed by the Global Flux Network is 10–30% higher than that of traditional remote sensing-light energy use efficiency models (MOD17, VPM, etc.). This is because these models treat the forest as a single "large leaf," neglecting the additional 20–40% assimilation by understory shrubs / herbs / mosses.

[0061] Therefore, previous remote sensing models have typically underestimated vegetation productivity, leading to significant uncertainties in global vegetation productivity estimates. This necessitates consideration of vertical differentiation within the forest canopy. Field observation data demonstrates that direct sunlight, diffused sunlight, multiple scattering, and the penumbra / total shading ratio vary with depth, solar angle, and sky heterogeneity; in a 20 m high temperate mixed forest, the midday photosynthetically active radiation at the top of the canopy is approximately 2000 μmol m². -2 s -1 At a height of 2 m, there might only be 50 μmol m. -2 s -1 The photosynthetic rate exhibits a complex distribution of negative exponent and intermittent light spots. Simultaneously, observational data indicate that the photosynthetic rate of the sun-exposed leaves at the top of the same tree can reach 80–120 μmol / m³. -2 s -1 The bottom shaded leaves contain only 20–40 μmol m -2 s -1 Simultaneous changes in leaf age, nitrogen redistribution, and specific leaf area (SLA) necessitate stratified calibration of the light response curve curvature k, maximum electron transport rate Jmax, and dark respiration Rd. Daytime canopy temperature differences of 2–5 °C, humidity differences of 10–30%, and CO2 concentration differences of 20–50 ppm directly affect the carboxylation rate and stomatal conductance in the vegetation photosynthesis model.

[0062] Therefore, when estimating the total photosynthetic output of forest canopy, it is necessary to start from the factors influencing photosynthesis at the leaf scale and consider the vertical gradient differences in the canopy, including physiological (maximum carboxylation rate, maximum electron transport rate), environmental (photosynthetically active radiation, water, temperature), and structural (leaf area index distribution, aggregation index distribution) differences in the vertical direction of the forest canopy. However, previous vegetation photosynthesis models only considered the existence of obvious vegetation structure and light gradients in the forest canopy, while ignoring the gradients of physiological factors and temperature and water differences, which significantly affected the estimation results of vegetation photosynthesis.

[0063] Therefore, the purpose of this invention is to provide a technical solution for measuring the vertical gradient change of forest canopy photosynthesis: this technique takes into account the higher photosynthetic rate below the forest canopy and the more suitable microclimate characteristics for growth, thereby reducing the underestimation of forest canopy photosynthesis. This technique considers physiological, microclimate, and structural gradient differences at the forest vertical canopy scale, taking into account the physiological mechanisms of changes in forest canopy photosynthesis.

[0064] like Figure 3 As shown, the technical solution of this invention can be implemented through the following process: Step 1: Simulation of Forest Canopy Leaf Area Index Profile: Step 1: Data Distribution Assumptions: Within the forest canopy, there are distinct vertical gradient differences between the tree-shrub-grass layers and within the tree layer itself. Therefore, it is necessary to pre-define the vertical distribution characteristics of the vegetation structure. This invention utilizes the Weibull function to define the vertical gradient. The Weibull function is a continuous probability distribution with the following probability density: (1) in For random variables, This is the proportionality coefficient. The shape factor controls the height of the peak value. In the measured data, the peak value of the leaf area index in most (e.g., more than half) forest canopies is located in the upper part of the forest canopy. The value is concentrated between 1 and 5. If the peak value of the leaf area index in the measured data is concentrated in the understory, then... It can be set to 6-10.

[0065] Step 2: Generate data and check: The Weibull function is used to define the vertical distribution of vegetation structure in the forest canopy. If the peak leaf area index is located in the first or second layer of the understory, the generated data is considered unreasonable and the data distribution needs to be reset.

[0066] Step Two: Simulation of Forest Canopy Meteorological Profile (Light, Temperature, Moisture): Step 1: Data Distribution Assumptions: In the vertical direction, the simulation of forest canopy meteorological profiles is mainly related to the vertical distribution of leaf area index, i.e., it is related to the Weibull function.

[0067] Step 2: Scene Analysis and Generation The illumination profile is obtained by solving the radiative transfer equation, and the amount of illumination at different heights relative to the total incident light across the canopy is calculated as follows: (2) (3) (4) For height The amount of incident light on the surface; For height The amount of direct incident light on the surface; For height The amount of scattered incident light on the surface; The extinction coefficient is the average value of the entire canopy. The extinction coefficient at a certain height; The leaf area index accumulated up to height i; The amount of LAI on a single layer; The leaf aggregation index is the average value of the canopy. Temperature profile, simulated using cumulative leaf area index profile: (5) In formula 5, , These represent the temperatures at the top and bottom of the canopy, respectively. Represents height Leaf area index height, Represents the height of the canopy.

[0068] Moisture profile, displayed using VPD, and simulated using cumulative leaf area index profile: (6) , These represent the atmospheric water vapor pressure difference at the top and bottom of the canopy, respectively. Represents height Leaf area index height, Represents the height of the canopy.

[0069] Step 3: Generate Data Check If the simulated temperature difference between the top and bottom layers along the vertical gradient exceeds 5 degrees Celsius, then the simulated temperature of the forest floor needs to be corrected. The correction process utilizes the maximum temperature difference between the top and bottom layers being 5 degrees Celsius, meaning the temperature at the bottom layer and the top of the canopy differ by only 5 degrees Celsius. Simultaneously, if the simulated top-layer VPD is more than 4 hPa higher than the bottom-layer VPD, some correction is also required. The correction process utilizes the maximum VPD difference between the top and bottom layers being 4 hPa, meaning the VPD at the bottom layer and the top of the canopy differ by only 4 hPa.

[0070] Step 3: Simulation of Forest Canopy Physiological Profile (Vcmax): Step 1: Data Distribution Assumptions: Unlike previous models of vegetation physiology, this invention proposes that the rate of photosynthesis above the forest canopy is lower than that below. Forest canopy observation data show that chlorophyll content above the canopy is lower than below, indicating that the rate of photosynthesis above the canopy is lower than that below.

[0071] Step 2: Scene Analysis and Generation Using a univariate linear formula, we assume that the photosynthetic rate (Vcmax) above the forest canopy is 20% lower than that of the understory.

[0072] (7) In Formula 7, z represents the canopy height, and i represents the result at a certain height on the forest canopy. This refers to the chlorophyll content at the top of the canopy. The conversion factor is 0.2, which means that the photosynthetic efficiency above the canopy is 20% lower than that of the understory.

[0073] Step 3: Generate data and check: If at certain heights in the vertical direction If the value is less than 0, then check the generated forest canopy physiological profile. Values ​​at heights less than 0 are set to 0.

[0074] Step 4: Simulation of the vertical gradient of forest canopy photosynthesis: Step 1: Assumptions of the leaf photosynthesis model: In the classic vegetation photosynthesis model framework (FvCB model), the photosynthetic rate (An) at the leaf scale is: (8) Ac represents the photosynthetic rate under Rubisco enzyme restriction, which is mainly related to the maximum carboxylation rate of leaves. Related (as shown in formula (7)). The main influencing factor is Rubisco enzyme limitation, and the amount of Rubisco enzyme is mainly affected by leaf pigments, especially chlorophyll. This model will utilize empirical relationships under different total canopy chlorophyll contents (CCC). The variables were used to simulate the photosynthetic capacity of vegetation at different heights in the vertical direction under different canopy chlorophyll contents.

[0075] (9) (10) In formula 9 as well as These represent the intercellular carbon dioxide and oxygen concentrations. In this invention, a constant setting value is used (for example, set to a fixed value of 209). This is the compensation point for photosynthesis. Here, Ac is the Michaelis constant representing the carboxylation rate. In Equation 10, Ac is the photosynthetic rate limiting factor for the maximum carboxylation rate. For the maximum carboxylation rate, The PAR data simulated in Equation 2-4. This is the empirical coefficient for extinction. Intercellular carbon dioxide concentration (i.e., in Equation 9) And in Formula 10 The calculation method is as follows: (11) (12) Coefficients in Formula 11-12 This represents the degree of stomata opening and closing on the blade. This represents atmospheric carbon dioxide concentration, derived from environmental data observations.

[0076] By inputting the PAR profile in the vertical direction into formulas (8)-(9), and inputting the temperature and moisture profiles into... This allows for the calculation of the vertical distribution of photosynthesis at the canopy scale. The temperature and VPD of this vertical distribution affect the calculation of intercellular... Concentration-related parameters The stomatal opening ratio is related to the temperature and VPD of each layer (as shown in Equation 12). Finally, by inputting the vegetation structure, environmental microclimate, and physiological profiles obtained from the simulation in Equations (1)-(6) into Equations (7)-(11), the photosynthetic profile in the vertical direction of the forest is calculated.

[0077] Step 2: Check the vertical profile data of photosynthesis If the value of vertical photosynthesis in a certain layer is less than 0, then profile correction is required. The values ​​of vertical photosynthesis that are less than 0 are set to 0, so that the photosynthesis profile values ​​in the vertical direction are all greater than 0.

[0078] In summary, the forest vertical gradient photosynthesis estimation model implemented in this embodiment of the invention takes into account the existence of vertical micro-meteorological gradients and physiological characteristic gradients (vertical differences in photosynthetic rates) in the forest canopy, and thus calculates the GPP differences in the global forest canopy more accurately.

[0079] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: High accuracy: It reduces the underestimation of the components of understory photosynthesis in previous models and reduces the uncertainty in the estimation of photosynthesis at the forest canopy scale.

[0080] High efficiency: Compared with previous vegetation physiological models that required calculating canopy-scale temperature and moisture profiles through vegetation photosynthesis-hydraulic action-energy balance methods, this invention utilizes a simplified physical mechanism model to simulate forest canopy temperature and moisture profiles more efficiently.

[0081] like Figure 4 As shown, this embodiment of the invention also provides a device 900 for measuring the vertical gradient change of forest canopy photosynthetic capacity, which can implement the above-described method. This device may include: The first module 910 is used to acquire measured data of the target forest area; the measured data includes the peak value of the leaf area index. The second module 920 is used to simulate the first data distribution of the forest canopy leaf area index profile using the Weibull function, optimize the first data distribution based on the peak value of the forest canopy leaf area index, and determine the leaf area index at each location in the target forest area according to the forest canopy leaf area index profile. The third module 930 is used to simulate the second data distribution of forest canopy meteorological profiles based on leaf area index, and to optimize the second data distribution based on preset top-bottom difference; wherein, the second data distribution includes light profile, temperature profile and moisture profile; The fourth module 940 is used to simulate the third data distribution of the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate, and to optimize the third data distribution based on a preset numerical distribution rule; wherein, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; The fifth module 950 is used to construct the vertical gradient of forest canopy photosynthesis based on the forest canopy leaf area index profile, forest canopy meteorological profile and forest canopy physiological profile, and then quantify the change in vertical gradient.

[0082] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0083] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0084] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0085] like Figure 5 As shown, Figure 5 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0086] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0087] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0088] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0089] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0090] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0091] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0092] The present invention provides a method, apparatus, electronic device, storage medium, and program product for measuring the vertical gradient change of forest canopy photosynthesis. This method acquires measured data from a target forest area, including peak leaf area index (LAI). It uses a Weibull function to simulate a first data distribution of the LAI profile, optimizes the first data distribution based on the peak LAI, and determines the LAI at various locations within the target forest area based on the LAI profile. It then simulates a second data distribution of the LAI meteorological profile of the forest canopy, and optimizes the second data distribution based on a preset top-to-bottom difference. The second data distribution includes light profiles, temperature profiles, and moisture profiles. A third data distribution simulates a third data distribution of the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rates, and optimizes the third data distribution based on a preset numerical distribution rule. In the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy. Finally, it constructs a vertical gradient of forest canopy photosynthesis based on the LAI profile, the meteorological profile, and the physiological profile, and then quantifies the change in the vertical gradient. This invention, by utilizing the Weibull function to simulate and optimize the leaf area index (LAI) profile, can finely characterize the vertical distribution of leaf area from the top to the bottom of the forest canopy. This effectively distinguishes and quantifies the photosynthetic contributions of the upper canopy and understory vegetation, overcoming the fundamental flaw of traditional "large-leaf" models that underestimate understory photosynthesis, and significantly improving the accuracy of total forest photosynthetic estimation. Furthermore, this invention not only considers the light profile but also simultaneously simulates and optimizes the temperature and moisture profiles, collectively forming the forest canopy meteorological profile. This comprehensive consideration of multiple environmental factors makes the simulation of the photosynthetic environment more realistic, providing a reliable environmental input for accurately calculating the photosynthetic rate at different vertical levels and reducing the need for... To address the uncertainties caused by environmental factor estimation biases, this invention innovatively introduces physiological differences, quantifying the vertical heterogeneity of vegetation physiological characteristics. Specifically, it explicitly stipulates that the photosynthetic rate above the canopy is lower than below, thus systematically reflecting the variation of forest vegetation's physiological attributes (such as photosynthetic capacity) along the vertical gradient. This allows the characterization of photosynthetic mechanisms to deepen from a purely environmentally driven approach to a dual-driven approach of "environment and physiology," resulting in a more complete model mechanism. Finally, this invention achieves precise quantification of the vertical gradient of photosynthesis and effectively reduces uncertainty through multi-profile system integration, greatly improving the accuracy and reliability of forest total photosynthetic capacity assessment and providing strong technical support for related research and applications.

[0093] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for measuring the vertical gradient change in forest canopy photosynthetic capacity, characterized in that, The method includes the following steps: Obtain measured data of the target forest area; wherein, the measured data includes the peak value of the leaf area index; The first data distribution of the forest canopy leaf area index profile is simulated using the Weibull function. The first data distribution is optimized based on the peak value of the leaf area index of the forest canopy. The leaf area index of each location in the target forest area is determined according to the forest canopy leaf area index profile. The second data distribution is based on the simulated forest canopy meteorological profile using the leaf area index, and optimized based on a preset top-to-bottom difference; wherein, the second data distribution includes light profile, temperature profile, and moisture profile; A third data distribution simulating the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate is used, and the third data distribution is optimized based on a preset numerical distribution rule; wherein, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; Based on the forest canopy leaf area index profile, the forest canopy meteorological profile, and the forest canopy physiological profile, a vertical gradient of forest canopy photosynthesis is constructed, and then the change in vertical gradient is quantified.

2. The method according to claim 1, characterized in that, The optimization of the first data distribution based on the peak leaf area index of the forest canopy includes the following steps: When the peak value of the leaf area index of the forest canopy is concentrated in the upper part of the forest, the value of the shape coefficient in the probability density of the Weibull function is set in the first value range. When the peak value of the leaf area index of the forest canopy is concentrated in the understory, the value of the shape coefficient in the probability density of the Weibull function is set in the second numerical range. When the peak value of the leaf area index in the first data distribution falls into the preset understory level of the forest canopy, the first data distribution is regenerated using the Weibull function.

3. The method according to claim 1, characterized in that, The measured data also includes the first temperature and first atmospheric water vapor pressure difference at the top of the forest canopy, the second temperature and second atmospheric water vapor pressure difference at the bottom of the canopy, and the canopy height. The second data distribution based on the leaf area index to simulate the meteorological profile of the forest canopy includes the following steps: Based on the leaf area index, the illumination profile is solved using the radiative transfer equation; The illumination profile includes incident light at different heights, direct incident light, and diffused incident light. The temperature profile is simulated based on the difference between the first temperature and the second temperature, combined with the cumulative value of the leaf area index at different heights in the canopy height. The moisture profile is simulated based on the difference between the first atmospheric water vapor pressure difference and the second atmospheric water vapor pressure difference, combined with the cumulative value of the leaf area index at different heights in the canopy height.

4. The method according to claim 1, characterized in that, The optimization of the second data distribution based on the preset maximum top-bottom difference includes the following steps: When the difference between the simulated top temperature and bottom temperature in the temperature profile exceeds a first preset top-bottom difference value, the bottom temperature is adjusted using the top temperature as a reference and the first preset top-bottom difference value. When the difference between the simulated top atmospheric water vapor pressure difference and the bottom atmospheric water vapor pressure difference in the temperature profile exceeds the second preset top-bottom difference value, the bottom atmospheric water vapor pressure difference is adjusted using the second preset top-bottom difference value as a reference.

5. The method according to claim 1, characterized in that, The measured data also include the chlorophyll content at the top of the forest canopy and the canopy height. The third data distribution, which simulates the physiological profile of the forest canopy based on a preset proportional distribution of photosynthetic rate, includes the following steps: Based on the chlorophyll content and the cumulative values ​​of the leaf area index at different heights within the canopy, the physiological profile of the forest canopy is simulated using a univariate linear relationship in conjunction with the preset proportional distribution.

6. The method according to claim 1, characterized in that, The optimization of the third data distribution based on preset numerical distribution rules includes the following steps: Set the values ​​of photosynthetic rates less than 0 in the third data distribution to 0.

7. The method according to claim 1, characterized in that, The process of constructing the vertical gradient of forest canopy photosynthesis based on the forest canopy leaf area index profile, the forest canopy meteorological profile, and the forest canopy physiological profile includes the following steps: Based on the aforementioned forest canopy physiological profile, the first type of photosynthesis is quantified by combining the Michaelis constant of carboxylation rate, photosynthetic compensation point, and intercellular carbon dioxide and oxygen concentrations. The carbon dioxide concentration is obtained based on the degree of stomatal opening and closing of the leaves, and the degree of stomatal opening and closing of the leaves is calculated based on the temperature profile, the moisture profile, and the Michaelis constant. Based on the illumination profile, the second type of photosynthesis is quantified by combining the carbon dioxide concentration, the photosynthesis compensation point, and the preset maximum carboxylation rate. The vertical gradient of forest canopy photosynthesis is constructed by substituting the first type of photosynthesis and the second type of photosynthesis into the vegetation photosynthesis model framework. Specifically, if there is a value less than 0 in the vertical gradient of photosynthesis in the forest canopy, the corresponding value will be corrected to 0.

8. A device for measuring the vertical gradient change in forest canopy photosynthetic capacity, characterized in that, The device includes: The first module is used to acquire measured data of the target forest area; wherein, the measured data includes the peak value of the leaf area index; The second module is used to simulate the first data distribution of the forest canopy leaf area index profile using the Weibull function, optimize the first data distribution based on the peak value of the leaf area index of the forest canopy, and determine the leaf area index at each location in the target forest area according to the forest canopy leaf area index profile. The third module is used to simulate the second data distribution of the forest canopy meteorological profile based on the leaf area index, and to optimize the second data distribution based on a preset top-bottom difference; wherein, the second data distribution includes light profile, temperature profile and moisture profile; The fourth module is used to simulate the third data distribution of the physiological profile of the forest canopy based on a preset ratio distribution of photosynthetic rate, and to optimize the third data distribution based on a preset numerical distribution rule; wherein, in the third data distribution, the photosynthetic rate above the forest canopy is lower than the photosynthetic rate below the forest canopy; The fifth module is used to construct the vertical gradient of forest canopy photosynthesis based on the forest canopy leaf area index profile, the forest canopy meteorological profile, and the forest canopy physiological profile, and then quantify the change in the vertical gradient.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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