Secondary poplar-birch forest site quality evaluation system and method integrated with single-tree stand growth model

Abstract

The application discloses a secondary poplar-birch forest site quality evaluation system and method integrated with a single-tree stand growth model. The system comprises a data acquisition module for acquiring current data and historical data of site parameters and stand structure of a target forest site; a data processing module integrated with a single-tree stand growth model taking tree size, species dominance and site parameters as independent variables to express the growth process of single trees in time succession with site parameters of the target forest site; at the same time, the current data and the historical data are input into the single-tree stand growth model, and the growth at the stand level is simulated in combination with stand density change and tree mortality probability to output a prediction sequence of stand volume of each tree species changing with time; and an evaluation module for performing quantification of site quality of the secondary poplar-birch forest according to an evaluation value based on an effective dominant interval of the tree species in the prediction sequence and according to the site parameters. The application realizes multi-scene adaptive evaluation of site quality of the secondary poplar-birch forest.

Description

Technical Field

[0001] This invention relates to the field of forest data evaluation and management technology, specifically to a site quality evaluation system and method for secondary poplar and birch forests that integrates individual tree stand growth models. Background Technology

[0002] Secondary poplar and birch forests are an important forest type in Northeast my country, playing a crucial role in timber supply, carbon sequestration maintenance, and biodiversity conservation. Scientific evaluation of their site quality is of great significance for accurately predicting forest productivity, optimizing stand structure, and developing multi-objective management plans.

[0003] Existing site quality assessment methods are mainly divided into two categories: direct assessment methods and indirect modeling methods. Direct assessment methods, such as the position index method and site type classification method, are based on empirical regression of site factors (elevation, slope, soil nutrients, etc.) and stand volume. While simple and easy to implement, they do not fully consider the structural dynamics within the stand and the interactions between tree species, making it difficult to reflect the differentiated impact of different tree species compositions and spatial structures on productivity. Indirect modeling methods, although incorporating stand growth models, often remain at the overall stand level, lacking a detailed depiction of individual tree growth processes and the dynamic changes in species dominance. In particular, they fail to quantify the promoting effect of specific tree species on target functions (such as productivity or carbon storage) during stand succession and their effective expression range. Furthermore, existing assessment systems often use a single site quality indicator, failing to consider the assessment needs under different management objectives, such as long-term functional stability and short-term peak contribution. The assessment model is rigid and difficult to adapt to diverse application scenarios such as ecological public welfare forests, fast-growing and high-yield forests, and multifunctional near-natural management.

[0004] In summary, there is an urgent need for a site quality evaluation system and method for secondary poplar and birch forests that can integrate individual tree growth dynamics, quantify the promoting effect of tree species dominance, and support flexible evaluation in multiple scenarios. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a site quality evaluation system and method for secondary poplar and birch forests that integrates individual tree stand growth models.

[0006] A first aspect of the present invention provides a site quality evaluation system for secondary poplar and birch forests that integrates individual tree stand growth models, comprising: The data acquisition module is configured to acquire current and historical data on the site parameters and stand structure of the target forest land; The data processing module is configured to integrate a single-tree stand growth model that uses tree size, tree species dominance, and site parameters as independent variables to express the growth process of a single tree over time as the site parameters of the target forest land change. Simultaneously, it is configured to input the current data and the historical data into the single-tree stand growth model, combine the stand density change and tree mortality probability to simulate the growth at the stand level, and output a predicted sequence of stand volume change over time for each tree species. The evaluation module is configured to quantify the site quality of secondary poplar and birch forests based on site parameters, using the evaluation value in the predicted sequence as a benchmark based on the effective dominance expression interval of the tree species.

[0007] As a preferred embodiment, the site parameters include topographic structure factors and soil physicochemical factors. The topographic structure factors include elevation, slope, aspect, and position. The soil physicochemical factors include soil layer thickness, soil organic carbon content, total nitrogen content, total phosphorus content, available nitrogen content, and available phosphorus content; The forest stand structure includes tree species composition, diameter at breast height per tree, tree height, crown width, and number of trees per unit area; The current data refers to the measured data during the current survey period, while the historical data includes remeasurement data from previous fixed sample plots and dominant tree analysis data.

[0008] As a preferred embodiment, the individual tree stand growth model is a periodic growth model of individual tree diameter at breast height (DBH), and its expression is: in, For the periodic growth of breast diameter, Diameter at breast height, Characterizes the potential growth function determined by tree size; To use the competition index The tree species dominance correction function is represented by the competition index, which is calculated based on the size ratio and spatial relationship between the target tree and its neighboring trees, and reflects the dominance of the tree species in the forest stand. The site correction function is obtained by fitting the historical data using the site parameters as independent variables and a nonlinear mixed-effects method.

[0009] As a preferred embodiment, the single-tree stand growth model further includes a tree mortality probability sub-model, which uses the competition index or the relative diameter at breast height as independent variables and outputs the probability of single-tree death. When simulating stand growth by combining stand density changes, the data processing module removes dead trees in the iteration step according to the probability of individual tree death, and updates the stand density and diameter distribution to simulate the stand self-thinning process and density changes.

[0010] As a preferred embodiment, the evaluation module is configured to perform the following steps: Based on the contribution type of tree species to the predetermined target function of secondary poplar and birch forests, effective dominance expression intervals are set for different tree species. The effective dominance expression interval is a sub-interval within the range of tree species dominance values ​​that promotes the target function. The dominance time series values ​​of each tree species at different forest ages are extracted from the predicted sequence. When the dominance time series value falls into the corresponding effective dominance expression interval, it is determined that the tree species is in a promoting state during that period. The evaluation value is generated by combining the cumulative duration and / or weighted contribution of each tree species in the promoting state.

[0011] As a preferred approach, when the evaluation module performs quantification, it performs site type clustering based on whether the dominance evaluation value of the main tree species at the baseline age reaches the effective dominance expression interval threshold, and constructs a site quality scoring function with the effective dominance expression conformity as the independent variable, outputting the site quality level and potential productivity.

[0012] As a preferred approach, when the evaluation module performs quantification, it performs site type clustering based on whether the dominance evaluation value of the main tree species at the baseline age reaches the effective dominance expression interval threshold, and constructs a site quality scoring function with the effective dominance expression conformity as the independent variable, outputting the site quality level and potential productivity.

[0013] A second aspect of the present invention provides a method for evaluating the site quality of secondary poplar and birch forests based on integrated individual tree stand growth models, comprising the following steps: Obtain current and historical data on site parameters and stand structure of the target forest land; A single-tree stand growth model was constructed and integrated, with tree size, species dominance and site parameters as independent variables, to express the growth process of a single tree over time as site parameters change. The current and historical data are input into the single-tree stand growth model, and stand growth is simulated by combining stand density changes and tree mortality probability. The predicted sequence of stand volume changes over time for each tree species is output. Based on the contribution of tree species to the target function, an effective advantage expression interval is preset. The tree species advantage degree time series value is extracted from the prediction sequence. When the advantage degree falls into the effective advantage expression interval, a promotion state is given, and an evaluation value based on the effective advantage expression interval is generated. The site quality of the secondary poplar and birch forest was quantified based on the evaluation values ​​and site parameters.

[0014] Compared with the prior art, the present invention has the following advantages: This invention integrates a product-type growth model with periodic growth of individual tree diameter at breast height as the core, coupled with potential growth, competitive correction and site correction. Combined with a tree mortality probability sub-model and stand density self-thinning rule, it can accurately simulate the entire process of individual growth, death and density regulation of forest stands under site conditions and time succession, and output highly reliable volume prediction sequences for each tree species, providing process dynamic data support for site quality assessment.

[0015] This invention uses an effective advantage expression interval to quantify the promotion sub-interval of tree species dominance on target functions, and constructs two evaluation values: the time proportion of promotion state and the weighted cumulative contribution of dominance. This can reflect the long-term sustainability of the tree species' promotion effect and capture the subtle differences in promotion intensity, overcoming the shortcomings of traditional indicators that cannot simultaneously evaluate functional stability and intensity contribution.

[0016] This invention sets up multiple evaluation modes, including time proportion mode, weighted contribution mode, weighted combination method mode, and condition triggering method mode, which are adapted to different application scenarios such as long-term stability evaluation of ecological public welfare forests, refined evaluation of fast-growing and high-yield forests, comprehensive evaluation of multifunctional near-natural management, and initial screening and potential classification of site quality. The modes can be flexibly selected according to management objectives, which greatly improves the applicability and decision support capabilities of the evaluation methods.

[0017] This invention utilizes a Bayesian parameter update method to perform closed-loop iterative optimization of the parameters of the individual tree growth model and the mortality probability sub-model using newly added continuous observation data. This enables the model to continuously adapt to changes in forest stand structure and environmental variations, maintaining the long-term stability of evaluation accuracy.

[0018] This invention is based on whether the effective advantage expression evaluation value of the baseline age meets the standard. It combines site parameters to cluster and classify site types, and constructs a site quality scoring function with the degree of conformity of effective advantage expression as the independent variable. It organically integrates qualitative classification and quantitative scoring, outputs site quality level and potential productivity, and provides a directly usable decision-making tool for the scientific and precise management of secondary poplar and birch forests. Attached Figure Description

[0019] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the system provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In one specific embodiment of the present invention, a method for evaluating the site quality of secondary poplar and birch forests based on an integrated single-tree stand growth model is provided. This method, based on the individual tree growth process, quantifies site quality through the interaction between species dominance and site conditions, achieving an objective evaluation of the dynamic promoting effect of multiple tree species. Specifically, the method includes the following steps: S1. Obtain current and historical data on site parameters and stand structure of the target forest land.

[0023] When acquiring data, site parameters include two categories: topographic structure factors and soil physicochemical factors. The topographic structure factors include elevation, slope, aspect, and slope position, which can be directly extracted through digital elevation models or obtained through field measurements. Slope position uses a graded coding system: 1 for uphill, 2 for middle slope, and 3 for downhill, to serve as a continuous variable in subsequent model calculations. Slope aspect is represented by angles increasing clockwise from 0° north; its sine and cosine values ​​are used when substituted into the model to eliminate the 360° cycle effect. The soil physicochemical factors include soil layer thickness, soil organic carbon content, total nitrogen content, total phosphorus content, available nitrogen content, and available phosphorus content, which can be obtained through soil drilling and laboratory chemical analysis. Soil layer thickness is the sum of the thicknesses of soil layers A and B, in centimeters (cm). Available nitrogen is determined using the alkaline hydrolysis diffusion method, in milligrams per kilogram (mg / kg); available phosphorus is determined using the molybdenum-antimony colorimetric method, in milligrams per kilogram (mg / kg).

[0024] Forest stand structure includes tree species composition, diameter at breast height (DBH) per tree, tree height, crown width, and number of trees per unit area. This is obtained through tree-by-tree measurement and location-based measurements in standard fixed plots within the target forest area. The current data refers to the measured data within the current survey period. The historical data includes remeasurement data from previous fixed plots and analysis data of dominant trees. Analysis data of dominant trees is obtained by analyzing the trunks of standard trees to obtain the DBH, tree height, and volume growth sequence. The time interval for remeasurement data from previous fixed plots is 5–10 years to capture a sufficiently long growth response cycle. The acquired data, after quality checks, is stored in a database for model building and subsequent prediction.

[0025] S2. Construct and integrate a single-tree stand growth model. This model uses tree size, species dominance, and site parameters as independent variables to express the growth process of a single tree over time as site parameters change.

[0026] In this embodiment, the constructed single-tree stand growth model is a periodic growth model of single-tree diameter at breast height, and its expression is: in, The periodic growth of diameter at breast height is expressed in centimeters per year. In this embodiment, the periodic duration is taken as 1 year, that is, it is iterated based on the annual growth. This represents the current chest diameter. The details of each part are as follows: Potential growth function Choose the Richards equation form: In the formula, This is a growth rate scale parameter, with units of... ; The shape parameter of the growth curve determines the rate at which the growth rate changes with increasing diameter at breast height; it is dimensionless. The growth rate at maturity is a dimensionless parameter that controls the rate of decline of the growth rate as it approaches its maximum diameter at breast height. This represents the progressive maximum diameter at breast height (DBH) of this tree species, expressed in centimeters (cm). The diameter at breast height (DBH) of a tree species is determined by analyzing the extreme values ​​of its growth process under optimal site conditions. Typically, the 95th percentile of the maximum DBH across all sample plots of that species is used to avoid the influence of extreme values. The rationale for using the 95th percentile instead of the maximum value is that the existence of individual supertrees in natural forests can cause the maximum value to deviate from the normal asymptotic boundary; the 95th percentile can exclude outlier interference while preserving statistical representativeness.

[0027] Tree species dominance correction function In the competition index Calculated using the improved Hegyi index: in, For target tree The competition index is dimensionless; and The diameter at breast height (DBH) of the target tree and the neighboring tree are respectively, in centimeters (cm). The distance between the target tree and its neighboring trees is expressed in meters (m). This represents the number of competing neighboring trees within a preset radius centered on the target tree. The summation applies to all neighboring trees within the preset radius centered on the target tree. The preset radius is typically 2-3 times the average canopy width of the stand to ensure sufficient capture of neighbor competition effects; in this embodiment, the preset radius is 8m. This value is based on field measurements of approximately 3.5m average canopy width in secondary poplar and birch forests in Northeast China. 8m is approximately 2.3 times the average canopy width, which can cover more than 95% of the effective competing neighboring trees of the target tree. The correction function is: This is the competition effect attenuation coefficient, which controls the sensitivity of the competition index to growth inhibition. A larger value indicates more significant growth inhibition under the same level of competition; it is dimensionless. The stronger the competition, the more significant the inhibition. The smaller the size, the more pronounced the growth inhibition. Species dominance. Defined as: The closer the value is This indicates that the tree species is more dominant in the stand. Dominance The theoretical range of values ​​is When the competition index approaches infinity Approaching 0, Approaching 1; when there is no competition from neighboring trees , , .

[0028] Ground correction function Using the site parameters as independent variables, its form is: In the formula , , , , , , These are the standardized values ​​of site factors, including altitude, slope, aspect, slope position, soil thickness, available nitrogen, and available phosphorus. Standardization was performed using the Z-score method, which involves subtracting the mean of each factor from all historical plots and then dividing by the standard deviation to eliminate dimensional effects and ensure comparability of site factors within the model. to These are the correction coefficients for each site factor, dimensionless; A positive value indicates that the increase of the site factor promotes growth, while a negative value indicates that it inhibits growth. The absolute value reflects the relative contribution of the factor to growth.

[0029] All parameters in the above model All results were obtained by fitting the historical data using a nonlinear mixed-effects method. Specifically, the sample plots and individual trees were treated as random effects, and the restricted maximum likelihood method was used to ensure the robustness of the parameter estimates and the reliability of the extrapolation. When dealing with nonlinear models containing random effects, the restricted maximum likelihood method first eliminates the influence of fixed effects through linear combination, and then performs unbiased estimation of the parameter variance components, resulting in smaller bias under small sample conditions compared to the ordinary maximum likelihood method.

[0030] In addition, the single-tree stand growth model also includes a tree mortality probability sub-model, which uses the competition index and relative diameter at breast height (DBH) as independent variables to output the probability of single-tree mortality. This embodiment uses a logistic function form: in The probability of a single tree dying is given by a value of [value]. ; This represents the current competition index; This represents the ratio of diameter at breast height (DBH) to the average DBH of the stand, where... The average diameter at breast height (DBH) of living trees of the same species within the grid or sample plot for the current year is expressed in centimeters (cm). Dimensionless Indicates the pressed wood. Indicates the dominant tree; The intercept parameter determines the baseline mortality probability level; This is the competition index coefficient, with a positive expected sign, indicating that the stronger the competition, the higher the probability of the plant dying. The relative diameter at breast height (DBH) coefficient is expected to be negative, indicating that a smaller relative DBH results in a higher probability of death. The parameters of this sub-model were determined by fitting the data to the survival status of individual trees (0 for survival and 1 for death) recorded in historical fixed plot re-measurement data using the maximum likelihood estimation method.

[0031] S3. Input the current data and historical data into the single-tree stand growth model, combine the stand density change and tree mortality probability to simulate stand growth, and output the predicted sequence of stand volume change over time for each tree species.

[0032] During the simulation, the initial state is set using the survey data of each tree in the current forest stand, and the simulation is iterated in years.

[0033] For each living standing tree, first calculate the diameter at breast height (DBH) growth for the current year. Update chest diameter ; Then calculate the probability of the tree dying. ,generate Uniformly distributed random numbers are generated using the Mersenne Twister algorithm to ensure good statistical properties. If the random number is less than... If the tree is found to be dead, it will be removed in the next iteration.

[0034] After each iteration, the number of trees per unit area is recalculated, and the stand density and diameter distribution are dynamically updated according to the stand self-thinning rule. When the stand density exceeds the maximum density line for that tree species, overly dense trees are forcibly removed to simulate the self-thinning process and density changes. The maximum density line is determined based on the tree species characteristics using the Reineke self-thinning rule. Confirmed, among which The number of plants per unit area, expressed as plants per hectare. The square mean diameter at breast height (DBH) is the square root of the sum of the squares of the DBHs of all plants, expressed in centimeters (cm). The self-sparse intercept coefficient, It is a self-thinning slope index, applicable to most tree species. The value ranges from -1.5 to -1.8. , Fitting data using long-term observation data from fixed sample plots. When the actual stand density... Density greater than the maximum permissible density under the same average thorax diameter At that time, trees were randomly removed in excess proportion until the density dropped below the maximum density line. Based on the single-timber volume formula, the single-timber volume was calculated from the diameter at breast height (DBH) and tree height, and the volume of each tree species and the entire stand was obtained by summing them up.

[0035] The formula for the volume of a single timber adopts the general binary volume formula for secondary poplar and birch forests. ,in The volume of a single piece of timber is expressed in cubic meters (m³). 3 ), , , This is a regional parameter. Tree height can be obtained through a diameter at breast height (DBH) versus tree height curve, for example, using a power function. ,in Tree height (m). and Using the parameters, the data is fitted with analytical tree data or measured tree height data of the same tree species. It is extrapolated year by year to a preset prediction period (e.g., 100 years) to output the predicted sequence of stand volume changes over time for each tree species, and can also output the predicted value of aboveground biomass at the same time.

[0036] S4. Based on the contribution of tree species to the target function, a preset effective advantage expression interval is established. The tree species advantage degree time series value is extracted from the prediction sequence. When the advantage degree falls into the effective advantage expression interval, a promotion state is given, and an evaluation value based on the effective advantage expression interval is generated.

[0037] First, it is necessary to define the effective dominance expression interval for each tree species. The effective dominance expression interval refers to the sub-interval within the dominance value range that promotes the intended target function of the tree species or stand as a whole. The target function can be stand productivity, carbon storage, or biomass, etc. The basis for setting this interval is as follows: Dominance indices for each tree species are established by analyzing historical fixed plot data or long-term fixed experimental plot data. A scatter plot or regression relationship between dominance and target function increment (e.g., periodic accumulation growth). Using piecewise linear regression or Lowes smoothing, identify positive and negative inflection points in the dominance-function increment curve. The range of dominance values ​​that makes the target function increment significantly greater than zero is defined as the promotion interval for that tree species. For example, for aspen, when dominance... In to When the periodic increase in forest stand volume is significantly higher than in other intervals, the effective dominance expression interval is set as follows: ,in This represents the lower bound of the effective advantage expression interval. The upper and lower bounds are defined as follows: The biological significance of the lower bound lies in the fact that when the tree species dominance is below this value, the species is subordinate in the community, its growth is highly inhibited, and it cannot make a positive contribution to the target function. The upper bound is set based on the fact that excessively high dominance often leads to a single dominant community, resulting in decreased biodiversity and structural complexity, and consequently, diminishing marginal returns to the overall ecosystem function. The determination of the lower and upper bounds of the interval also requires statistical significance testing, i.e., a t-test is performed on the increments of the target function within and outside the interval, with a p-value less than 0.05 as the significance standard. For associated tree species lacking sufficient data, pre-defined boundaries can be made using expert knowledge or ecological similarity reasoning.

[0038] Then, the dominance time series values ​​of each tree species are extracted from the prediction sequence output in step S3, period by period. At each point in time, determine If a tree falls within a defined range for its species, it is marked as being in a "promoted" state during that period. An evaluation value is generated by combining the cumulative duration or contribution of each tree species in a "promoted" state.

[0039] One way to calculate the evaluation value is by the percentage of time spent promoting the state: in Total number of years predicted; This is an indicator function; it takes the value 1 when the condition inside the parentheses is true, and 0 otherwise. and These represent the lower and upper limits of the effective dominance expression range for this tree species, respectively. The range of values ​​is The closer the value is to 1, the longer the tree species will be in a promoted state during the prediction period.

[0040] Another method is to calculate the cumulative contribution based on the advantage factor: The denominator is the sum of the dominance values ​​of the tree species in all years within the prediction period, and the numerator is the weighted dominance value that only includes years that fall within the effective dominance expression interval. The range of values ​​is also... When the dominance of a tree species is generally high within the promotion interval, the weighted contribution will be higher than the time proportion; conversely, if the dominance is only slightly above the lower limit before falling into the interval, the weighted contribution will be lower than the time proportion. This difference is the source of the distinction between the two evaluation values.

[0041] The time percentage directly reflects the proportion of years the tree species lasts within the effective range, while the weighted contribution degree takes into account the influence of the dominance value within the range, which better reflects the difference in the intensity of the promoting effect.

[0042] In practical applications, depending on the evaluation purpose and data characteristics, one of the evaluation values ​​can be used separately, or the two evaluation values ​​can be used in combination.

[0043] This embodiment provides the scenarios and selection criteria for separate use: Using only the proportion of time for promoting state When the core concern of the evaluation is the long-term stability and sustainability of the tree species' promoting effect, a time-based weighting is preferable. For example, when assessing a site's ability to support long-term carbon sequestration stability, it is more important to consider whether the promoting state can be maintained over most of the period. Furthermore, when historical monitoring data contains significant noise or is discontinuous, and measurement errors in dominance may interfere with the accuracy of weighted contribution calculations, the time-based weighting, due to its binary nature, is less sensitive to errors and yields more robust results. Alternatively, only the weighted cumulative contribution based on dominance can be used. When the evaluation objective is to precisely distinguish the intensity differences in the tree species promotion effects under different site conditions or management measures, a weighted contribution ratio is more appropriate. For example, when comparing plots with different tending intensities, the proportion of time that Populus tomentosa is in the promotion interval may be the same in both plots, but the distribution center of dominance within the interval may be different. The weighted contribution ratio can capture this difference and provide more discriminative evaluation results.

[0044] This embodiment also provides examples of combined use, including weighted combination and conditional triggering.

[0045] Weighted combination method: Linearly weight the two evaluation values ​​to obtain a comprehensive evaluation value. In the formula, This is a weighting coefficient used to adjust the relative importance of time proportion and weighted contribution in the overall evaluation value. If the management objective emphasizes the functional stability of the forest stand over a long period (such as ecological public welfare forests), then... Take a larger value, such as 0.7; if more emphasis is placed on the peak contribution of function within a specific period (such as the cultivation of fast-growing and high-yield forests), then... Take the smaller value, such as 0.3.

[0046] Conditional triggering method: Initial screening is based on time percentage. A stability threshold is then set. (e.g., 0.5), this threshold is set based on the principle that a site is considered to have basic long-term support capacity for a tree species only if the species is in a promoting state for at least half of the prediction period. If so, then the time percentage is used directly as the evaluation value; if Then adopt This method serves as the final evaluation value, balancing minimum stability requirements with optimal strength selection.

[0047] During system implementation, evaluation mode parameters can be preset and selected according to the actual scenario. The specific correspondence between each scenario and the evaluation mode, as well as the selection criteria, are as follows: This embodiment provides the corresponding scenarios and applicable situations for selection evaluation: When evaluating secondary poplar and birch forests within ecological public welfare forests, water conservation forests, or nature reserves, the core management objective lies in the long-term stability and sustained functioning of the stand's ecological functions, rather than maximizing short-term timber production. In this scenario, managers are more concerned with whether the main tree species can stably maintain their promoting effect for the vast majority of the time, and are less sensitive to the specific fluctuations in dominance within the effective range. In this case, only the proportion of time spent in a promoting state should be used. In addition, when the monitoring data has a long interval (such as only being retested every 5 or 10 years), resulting in the need to obtain the dominance time series value through interpolation and large measurement noise, the time proportion, due to its binary judgment characteristics, is more robust to interpolation errors and observation noise, and can avoid the distortion of evaluation results due to abnormal fluctuations in dominance in individual years.

[0048] When evaluating fast-growing and high-yield forest sites, carbon trading project forest sites, or secondary poplar and birch forests that require significant improvement in stand productivity through tending and management measures, the management objective focuses on the peak performance of stand function within a specific period and the differentiation of effects between different management measures. In this scenario, even if the main tree species in two sample plots are in the promotion interval for the same percentage of time, their dominance distribution within the interval may differ significantly, leading to different actual productivity contributions. In this case, only the dominance-weighted cumulative contribution should be selected. This model aims to capture the differences in the intensity of promoting effects, providing a more refined degree of differentiation and offering sensitive indicators for the selection of management measures. Furthermore, when the evaluated species are in a stage of drastic change in tree species competition (such as the transition period from pioneer species to climax species in a forest stand), the dominance level fluctuates significantly within the promoting range. Therefore, the weighted contribution level can more accurately reflect the comprehensive contribution level of tree species under different competitive conditions.

[0049] When secondary birch and poplar forests are managed in a multi-functional, near-natural manner, balancing both ecological benefits and timber production, a single evaluation value is insufficient to comprehensively reflect the site's overall supporting capacity for quality. In such cases, a weighted combination method should be chosen, by setting reasonable weighting coefficients. The need to balance stability and strength. For example, for forest stands where ecological benefits are primary and timber production secondary, the following should be considered: This makes time the dominant factor; for forest stands that are primarily for timber production but also consider ecological functions, the focus should be on... This allows the weighted contribution to play a greater role. The specific value of the weighting coefficient can be determined based on the priority of the objectives in the business plan or through expert scoring.

[0050] When it is necessary to rapidly screen forest stands with cultivation potential within a large area of ​​forest land, or to conduct preliminary site quality grading, the conditional triggering method is advisable. This method first uses time percentage as a stability baseline for initial screening, excluding inferior sites where tree species have been in a non-promoting state for a long time. For the forest stands that pass the initial screening, a weighted contribution method is used to select the best among the best. This tiered evaluation strategy balances baseline control and potential exploration, and is particularly suitable for forestry authorities to conduct macro-management work such as forest resource inventory and site quality zoning. Stability threshold It can be set according to management requirements, but is generally taken as 0.5, which means that the tree species should be in a promoting state for at least half of the prediction period.

[0051] When the target area lacks sufficient historical fixed-plot data to support complex parameter calibration, or when only data from a single survey is available, the reliability of the evaluation relies on more conservative and robust indicators. In this case, a state-time proportion model is adopted, and the threshold for the effective advantage expression interval is appropriately relaxed. To the median, to avoid overly strict evaluation due to insufficient data.

[0052] In its implementation, the system in this embodiment automatically calls the corresponding evaluation value calculation function and subsequent quantification process according to the selected mode, ensuring that the evaluation results are highly consistent with the business management objectives.

[0053] S5. Quantify the site quality of the secondary poplar and birch forest based on the evaluation value and site parameters.

[0054] The quantification process includes site type clustering and site quality scoring. First, a baseline age is selected. The baseline age is chosen based on the technological maturity or quantitative maturity of the main tree species in the secondary poplar-birch forest, typically taking [a specific age range]. In this age, the correlation between stand volume and site productivity is most stable because the average stand volume growth reaches its peak or begins to plateau at this age. Evaluation values ​​for major tree species at the baseline age are extracted from the predicted sequence. Set an effective advantage expression interval threshold. The distribution of evaluation values ​​for all historical plots at the baseline age was summarized, and the upper quartile was used as the threshold. The upper quartile represents the percentage of historical plots with evaluation values ​​below or above it. Using this value as the threshold means that only when site conditions support the tree species' evaluation value to reach a historically good level (top 25%) is the site considered to have the conditions to effectively support the tree species' promoting effect. If the management objectives are more lenient, the median can also be used as the threshold. If the site is deemed suitable, it is believed that the site can support the tree species to play a role within the effective dominance expression range.

[0055] Constructing a comprehensive and effective advantage expression conformity index The average value of the evaluation values ​​for the main tree species can be taken: in In this embodiment, the main tree species refer to aspen and birch, i.e. For stands with only a single dominant tree species, This is the evaluation value for that tree species. The theoretical range of values ​​and Consistency, for .

[0056] Subsequently, site type clustering was performed on all sample plots based on site parameters. (Number of clusters) The elbow rule is used to determine the number of clusters. Find the curve showing the relationship between the sum of squared errors within a group and the curve's bend. The value is generally taken as the optimal cluster number. The K-means algorithm was used to classify sites based on standardized site parameters. For each site type, a system was established based on conformity. A scoring function with measured or predicted accumulation at baseline age as the dependent variable and independent variable as the independent variable: in To score the site quality, The intercept is... The slope represents the expected increase in the base-age volume for each unit increase in conformity, expressed in cubic meters per hectare (m²). 3 / ha). Parameters , The data was obtained through least squares regression of in-type sample plot data. For the new forest stand to be evaluated, its... Then, substitute the scores into the corresponding scoring function to obtain the site quality score. Furthermore, based on the distribution of scores across all sample plots, The site is categorized into four levels: Excellent, Good, Average, and Poor. The grading can be achieved by using Jenks' natural break method to find natural discontinuities in the score distribution histogram, or by using the equal interval method to divide the score range into four equal parts, and outputting the potential productivity. The final output is the site quality grade and potential productivity, serving as the evaluation result.

[0057] When new continuous observation data is acquired (Including newly added periodic growth of individual tree diameter at breast height, mortality status, etc.) Then, the model parameters are corrected using a Bayesian parameter update method. Parameter vector to be updated. ,in The set of parameters related to the growth function. This is the parameter set for the mortality probability sub-model. The posterior distribution of the parameters obtained by fitting historical data is used as the prior distribution. If there is no historical posterior, a weak information prior can be used, i.e., a uniform distribution of parameters or a normal distribution with large variance, to minimize the influence of the prior on the posterior. The likelihood function for the new data is constructed as follows: [Assume...] The model follows a normal distribution, with the mean being the predicted value and the variance being... ; The mean square error of the historical model residuals can be taken as a fixed value, or it can also be used as a parameter. Updated together. It follows a Bernoulli distribution with probability . The overall likelihood function is: The posterior distribution of the parameters is: In the formula The expression "proportional to" means that the posterior distribution is proportional to the product of the prior distribution and the likelihood function, with the proportionality constant determined by the normalization of the total probability. Markov chain Monte Carlo (MCMC) sampling is used, running 3–4 Markov chains, each iterating 10,000–20,000 times. The first 2,000–5,000 iterations (warm-up period) are discarded. The posterior mean of the parameters after convergence is used as the updated parameter values. Convergence is verified using the Gelman-Rubin diagnostic statistic. The updated parameters are then written back to the model library for subsequent simulations and evaluations.

[0058] In a specific embodiment of the present invention, a site quality evaluation system for secondary poplar and birch forests integrating a single-tree stand growth model is provided, comprising: The data acquisition module is used to acquire current and historical data on site parameters and forest stand structure of the target forest land.

[0059] The data processing module integrates the single-tree stand growth model, inputs current and historical data into the model, simulates stand growth by combining stand density changes and tree mortality probabilities, and outputs a predicted sequence of stand volume for each tree species. This module contains a model library and a simulation engine. During iterations, the simulation engine removes dead trees according to mortality probability and updates stand density and diameter distribution.

[0060] The evaluation module quantifies site quality based on evaluation values ​​derived from the effective dominance expression intervals of tree species in the predicted sequence, combined with site parameters. Specifically, it sets effective dominance expression intervals for different tree species; extracts dominance time-series values ​​from the predicted sequence, determines the promotion status, and generates evaluation values; based on whether the baseline age evaluation value reaches a threshold, it clusters and classifies site types using site parameters, constructs a scoring function with conformity as the independent variable, and outputs the site quality level and potential productivity. The evaluation module also provides an evaluation mode selection function, allowing users to choose from time percentage mode, weighted contribution mode, weighted combination method mode, conditional trigger method mode, or default mode according to the actual scenario, and customize relevant parameters.

[0061] The model optimization module uses newly added continuous observation data to correct and optimize the growth function parameters and mortality probability sub-model parameters through the Bayesian parameter update method.

[0062] Through the collaboration of the above modules, the system automates the entire process from data acquisition, growth simulation, dominance evaluation to site quality quantification.

[0063] Example 2 This example uses a 9-hectare (300m × 300m) natural secondary poplar and birch forest within the operational area of ​​a forestry bureau in Northeast China as an example to demonstrate how a grid-based method can be used to achieve precise site quality evaluation at a fine scale. The overall terrain of this site is low hills, with an elevation ranging from 560m to 640m and varying slope aspects. The soil type is mainly dark brown soil, but there is significant spatial heterogeneity in soil layer thickness and nutrient status.

[0064] The forest was divided into 25 grid units (5 rows × 5 columns) at a grid size of 60m × 60m. A 0.06-hectare (24.5m × 24.5m) square plot was established at the center of each grid for tree measurement, location determination, and site factor analysis. Grid division ensured that the evaluation results captured micro-site changes, providing a basis for precision management. Since site conditions and stand structure often change gradually between adjacent grids, to highlight key areas and avoid redundancy, this example selects three grids (A5, C3, and D1) with significant differences in site characteristics and representativeness to illustrate the evaluation process in detail. The evaluation methods for the remaining grids are the same; only the results are summarized.

[0065] In this embodiment, the calculation process for representative grids numbered A5, C3, and D1 is provided: Grid A5: Located on the upper part of the northwest slope, at an altitude of 635m, with a slope of 18°, aspect NW320°, slope code 1 (uphill), soil thickness of 32cm, available nitrogen of 118mg / kg, and available phosphorus of 6.5mg / kg. The plot contains 58 living trees, including 26 aspen, 19 birch, and 13 associated species such as purple linden. The average diameter at breast height (DBH) is 17.2cm for aspen and 14.8cm for birch. The stand density is 967 trees / hectare.

[0066] Grid C3: Located on a gentle central terrace, at an altitude of 595m, a slope of 5°, an aspect of SE140°, a slope code of 2 (middle slope), a soil thickness of 50cm, and available nitrogen of 162mg / kg and available phosphorus of 9.8mg / kg. The plot contains 71 standing trees: 30 aspen, 24 birch, and 17 others. The average diameter at breast height (DBH) is 20.5cm for aspen and 16.3cm for birch. The stand density is 1183 trees / hectare.

[0067] Grid D1: Located at the foot of a southeast-facing valley slope, at an altitude of 570m, a slope of 12°, an aspect of SE120°, slope code 3 (downhill), a soil thickness of 65cm, and available nitrogen of 210mg / kg and available phosphorus of 12.4mg / kg. The plot contains 82 standing trees: 21 aspen, 38 birch, and 23 others. The birch population is significantly higher. The average diameter at breast height (DBH) is 22.1cm for aspen and 18.9cm for birch. The stand density is 1367 trees / hectare.

[0068] Based on data from 120 historical fixed sample plots and 30 individual tree analyses accumulated throughout the entire operation area, a periodic growth model for diameter at breast height (DBH) and a mortality probability sub-model were fitted for each tree species, yielding the following global parameters: Aspen: , , , , Site coefficient , , , , , , Mortality model parameters , , .

[0069] Birch: , , , , Site coefficient , , , , , , Mortality model parameters , , .

[0070] Taking grid A5 as an example, the above model is substituted. Initialization is done with the coordinates and diameter at breast height (DBH) of each tree within the sample plot, and a preset radius of 7m is used to calculate the competition index for each tree. A 100-year stand growth is simulated, with dominance and volume recorded annually. The effective dominance expression range for aspen is defined. Birch The baseline age is 40 years. Two evaluation values ​​are calculated and a weighted composite is used. ). Obtained aspen. Birch Historical sample plot threshold The aspen score was 0.70, and the birch score was 0.60. The birch score did not fully meet the standard, resulting in an overall compliance rate of [missing information]. The standardized site parameters of the grid were then subjected to K-means clustering (an existing clustering model). The site is classified as Type III (Medium Location). The scoring function for this type is: Substituting... It is classified as "medium" grade, with a predicted volume of approximately 145m³ in 40 years. 3 / ha.

[0071] The site conditions in grid C3 are significantly better than those in A5, with deeper soil layers and abundant nutrients, resulting in better simulation of aspen growth. Birch All exceeded the threshold. Clustered into Type II (Good Site), scoring function ,have to Grade "Good", potential productivity approximately 180m 3 / ha. Grid D1 is located in a fertile valley habitat, dominated by birch, with aspen having a lower evaluation value. Birch , It belongs to Type I (superior site), scoring function ,have to Grade "Excellent", with a potential productivity of up to 210m 3 / ha.

[0072] The same process was followed for the remaining 22 grids, and will not be elaborated here. The evaluation results show that the site quality within the 9-hectare forest land exhibits significant spatial differentiation: excellent sites are mainly distributed in southeast-facing valleys and slope toes (grids D1-D3, E1-E2), accounting for approximately 18% of the area; good sites are concentrated in the central terraces and gentle slopes (grids B3-C4), accounting for approximately 45%; and medium-grade sites are mostly located on the upper part of the northwest slope and steep slope areas (grids A1-A5), accounting for approximately 37%. By generating a site quality grade distribution map through spatial interpolation, differentiated management can be intuitively guided. Target tree cultivation can be implemented in excellent site areas, while replanting and soil improvement should be emphasized in medium-grade site areas.

[0073] If subsequent measurements are performed on some grid areas for five consecutive years, the new data will be input into the model optimization module, and Bayesian methods will be used to update and fine-tune the parameters. For example, the coefficient of available nitrogen in the soil of the valley area can be discovered. There is an increasing trend, and the updated version can improve the accuracy of local predictions.

[0074] This grid-based evaluation method fully reveals the impact of micro-site heterogeneity on the production potential of secondary poplar and birch forests, providing quantitative support for the refined management of large-scale forest land.

[0075] Example 3 This embodiment uses a 0.06-hectare secondary poplar and birch forest fixed plot in a nature reserve in Northeast China as the object to demonstrate how to use the promoting state time ratio model to evaluate site quality in the context of long-term stability evaluation of ecological public welfare forests.

[0076] This nature reserve prioritizes water conservation and biodiversity protection, requiring the long-term stable functioning of forest stands and disregarding short-term timber production. Therefore, based on the selection criteria for the evaluation model in this method, the proportion of time spent in the promoting state should be adopted. As an evaluation metric, the focus is on the duration of the main tree species’ effective dominance expression range, i.e., the long-term stability of the promoting effect, rather than the specific fluctuation range of dominance within the range.

[0077] The sample plot is located in the middle of the northwest slope, at an altitude of 610m, a slope of 12°, an aspect of NW310°, a slope code of 2 (middle slope), a soil thickness of 42cm, and soil available nitrogen of 132mg / kg and available phosphorus of 7.5mg / kg. Tree survey results: 25 aspen trees, average diameter at breast height (DBH) 16.8cm; 20 birch trees, average DBH 14.2cm; and 11 other associated tree species. The stand density is 933 trees / hectare. Historical data includes resurveyed data from 85 similar fixed sample plots accumulated in this reserve and analytical data from 20 individual trees.

[0078] Global parameters were obtained by fitting a nonlinear mixed-effects model based on historical data of the protected area. Aspen: , , , , Site coefficient , , , , , , Mortality model parameters , , Birch: , , , , Site coefficient , , , , , , Mortality model parameters , , .

[0079] Based on the scenario, the objective of this evaluation is long-term ecological function stability, falling under the long-term stability evaluation scenario for ecological public welfare forests. Therefore, the "Time Percentage Mode" is selected in the system evaluation mode menu. At this point, only the following calculations are performed: Weighted contribution is not calculated.

[0080] Determining effective dominance expression intervals through historical data analysis: Aspen Birch Initialize with current data per tree and simulate 100 years of stand growth. Extract the temporal values ​​of dominance for aspen and birch each year. The number of years falling within their respective valid intervals is counted.

[0081] Simulations show that aspen has an advantage in 74 out of 100 years. Time percentage ; Birch trees have a 61-year advantage. Time percentage .

[0082] Statistics on the baseline age (40 years) of all historical sample plots in the protected area show that the upper quartile of the aspen age is [missing information]. birch is Aspen native species Birch The birch trees did not fully meet the standards. However, considering that the evaluation of ecological public welfare forests focuses on the comprehensive stability of the main tree species, the overall compliance rate was calculated. .

[0083] After standardizing the site parameters of the sample plots, the existing K-means clustering model was used ( The site is classified as Site Type III (Medium Site). The scoring function for this type is... Substituting into The corresponding level is "medium". The predicted baseline age volume is approximately 152 m³. 3 The / ha rating indicates that the site's productivity is at a moderate level under the long-term stability objective. It is recommended to maintain the existing forest stand structure and avoid high-intensity management.

[0084] The rationale for choosing the time-proportion model in this scenario is as follows: Monitoring data within the protected area are collected at long intervals (once every 10 years), and the dominance time-series values ​​rely on interpolation, introducing uncertainty. The binary judgment method of time proportion is insensitive to interpolation errors, resulting in more robust evaluation results. If a weighted contribution rate were used, interpolation bias could lead to a shift in the center of gravity of the dominance distribution, thus affecting the reliability of the evaluation. Furthermore, the management objective of this protected area requires the forest stands to continuously perform their water conservation function; the time proportion directly measures the duration of the promoting state, which aligns closely with the management objective.

[0085] This embodiment takes a secondary poplar and birch forest in a fast-growing and high-yield forest site of a forestry bureau in Northeast China as an example to demonstrate how to conduct a refined site quality evaluation using the dominance-weighted cumulative contribution model in the context of pursuing maximum timber yield and carbon sequestration benefits.

[0086] The site management objective is to significantly enhance stand productivity and carbon storage through thinning and target tree cultivation. Therefore, the evaluation method must be able to sensitively distinguish the intensity differences in the promoting effects of different tree species under different site conditions or management practices. Based on the selection criteria, a dominance-weighted cumulative contribution should be used. As an evaluation value, it captures the center of gravity of the advantage distribution within the effective range, providing a sensitive indicator for the selection of business measures.

[0087] Example 4 The sample plot covers an area of ​​0.06 hectares, located on the lower part of a gentle southeast-facing slope at an altitude of 585m, a slope of 6°, an aspect of SE130°, a slope code of 3 (downhill), a soil thickness of 58cm, and available nitrogen of 175mg / kg and available phosphorus of 10.5mg / kg. Tree surveyed per species: 32 aspen trees (average diameter at breast height 21.3cm), 26 birch trees (average diameter at breast height 17.6cm), and 15 other trees. The stand density is 1217 trees / hectare. Historical data includes annual fixed-plot resurveys and analytical tree data for this site.

[0088] Parameters were obtained by fitting site data. Aspen: , , , , ; , , , , , , Death parameters , , Birch: , , , , ; , , , , , , Death parameters , , .

[0089] The evaluation objective is to finely differentiate the intensity of the promoting effect; therefore, the "weighted contribution model" is selected. The effective advantage expression interval is set as before, and the *Aspen* species are also included. Birch Simulate 100 years, extract the dominance for each year, and calculate the weighted contribution.

[0090] The number of years in which the aspen tree is within the effective period is 78, and its weighted contribution is... .

[0091] The numerator is calculated to be 42.56 and the denominator is 63.45, resulting in... The effective period for the birch tree is 69 years. The numerator is 28.74, and the denominator is 46.92. Therefore... .

[0092] In contrast, if we calculate the time percentage, the aspen is... birch is Both accounted for a relatively high percentage of time, with little difference between them. However, the weighted contribution showed that *Populus tomentosa*... with birch The difference is even more pronounced, indicating that the dominance value of Aspen is higher within the effective range, resulting in a greater contribution, while the dominance value of Birch is mostly in the lower and middle ranges, resulting in a relatively weaker contribution. This difference cannot be reflected in the time percentage.

[0093] Upper quartile of the weighted contribution distribution of site-historical sample plots in Aspen birch is Aspen native species Birch All met the standards. Overall compliance rate .

[0094] Site parameter clustering determines that the plot belongs to site type II (good site), scoring function. ,have to Grade "Good", potential productivity approximately 175m 3 / ha.

[0095] Management recommendations: Aspen contributes significantly and should be prioritized for cultivation as a target tree; birch contributes relatively little, so it is recommended to thin out some birch trees to reduce their competitive pressure on aspen, thereby increasing the dominance of aspen and moving it towards the higher end of the range, thus further improving overall productivity.

[0096] The reason for choosing the weighted contribution model in this scenario is that the management of fast-growing and high-yield forests requires precise identification of the differences in the contribution intensity of different tree species, and even different individuals of the same tree species, to productivity in order to optimize the tending plan. While time percentages can reflect stability, they cannot distinguish the position of dominance within an interval. In this example, both tree species have relatively high time percentages (0.78 and 0.69). If the time percentage model were used, the overall compliance would be lower. ,score The score was approximately 49.1, also rated "Good," but it failed to reveal the issue of Baihua's low contribution, which might lead to business decisions overlooking Baihua's competitive inhibitory effect. The weighted contribution model, by reducing the evaluation value of low-intensity contributors, provides a more sensitive quantitative basis for differentiated operations, meeting the needs of refined management in this scenario.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, function, and operation of implementations of apparatus, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description; sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A site quality evaluation system for secondary poplar and birch forests integrating single-tree stand growth models, characterized in that, include: The data acquisition module is configured to acquire current and historical data on the site parameters and stand structure of the target forest land; The data processing module is configured to integrate a single-tree stand growth model that uses tree size, tree species dominance, and site parameters as independent variables to express the growth process of a single tree over time as the site parameters of the target forest land change. Simultaneously, it is configured to input the current data and the historical data into the single-tree stand growth model, combine the stand density change and tree mortality probability to simulate the growth at the stand level, and output a predicted sequence of stand volume change over time for each tree species. The evaluation module is configured to quantify the site quality of secondary poplar and birch forests based on site parameters, using the evaluation value in the predicted sequence as a benchmark based on the effective dominance expression interval of the tree species.

2. The system according to claim 1, characterized in that, The site parameters include topographic structure factors and soil physicochemical factors. The topographic structure factors include elevation, slope, aspect, and position. The soil physicochemical factors include soil layer thickness, soil organic carbon content, total nitrogen content, total phosphorus content, available nitrogen content, and available phosphorus content; The forest stand structure includes tree species composition, diameter at breast height per tree, tree height, crown width, and number of trees per unit area; The current data refers to the measured data during the current survey period, while the historical data includes remeasurement data from previous fixed sample plots and dominant tree analysis data.

3. The system according to claim 1, characterized in that, The single-tree stand growth model is a periodic growth model of single-tree diameter at breast height, and its expression is: in, For the periodic growth of breast diameter, Diameter at breast height, Characterizes the potential growth function determined by tree size; To use the competition index The tree species dominance correction function is represented by the competition index, which is calculated based on the size ratio and spatial relationship between the target tree and its neighboring trees, and reflects the dominance of the tree species in the forest stand. The site correction function is obtained by fitting the historical data using the site parameters as independent variables and a nonlinear mixed-effects method.

4. The system according to claim 3, characterized in that, The single-tree stand growth model also includes a tree mortality probability sub-model, which uses the competition index or the relative diameter at breast height as independent variables and outputs the probability of single-tree death. When simulating stand growth by combining stand density changes, the data processing module removes dead trees in the iteration step according to the probability of individual tree death, and updates the stand density and diameter distribution to simulate the stand self-thinning process and density changes.

5. The system according to claim 4, characterized in that, The evaluation module is configured to perform the following steps: Based on the contribution type of tree species to the predetermined target function of secondary poplar and birch forests, effective dominance expression intervals are set for different tree species. The effective dominance expression interval is a sub-interval within the range of tree species dominance values ​​that promotes the target function. The dominance time series values ​​of each tree species at different forest ages are extracted from the predicted sequence. When the dominance time series value falls into the corresponding effective dominance expression interval, it is determined that the tree species is in a promoting state during that period. The evaluation value is generated by combining the cumulative duration and / or weighted contribution of each tree species in the promoting state.

6. The system according to claim 5, characterized in that, When the evaluation module performs quantification, it determines whether the dominance evaluation value of the main tree species under the baseline age reaches the effective dominance expression interval threshold, combines the site parameters to perform site type clustering, and constructs a site quality scoring function with the effective dominance expression conformity as the independent variable, outputting the site quality level and potential productivity.

7. The system according to claim 3 or 4, characterized in that, It also includes a model optimization module, which is configured to use the newly added continuous observation data from the data acquisition module to correct and optimize the growth function parameters and mortality probability sub-model parameters in the single-tree stand growth model through a Bayesian parameter update method.

8. A method for evaluating the site quality of secondary poplar and birch forests based on an integrated single-tree stand growth model, characterized in that, Includes the following steps: Obtain current and historical data on site parameters and stand structure of the target forest land; A single-tree stand growth model was constructed and integrated, with tree size, species dominance and site parameters as independent variables, to express the growth process of a single tree over time as site parameters change. The current and historical data are input into the single-tree stand growth model, and stand growth is simulated by combining stand density changes and tree mortality probability. The predicted sequence of stand volume changes over time for each tree species is output. Based on the contribution of tree species to the target function, an effective advantage expression interval is preset. The tree species advantage degree time series value is extracted from the prediction sequence. When the advantage degree falls into the effective advantage expression interval, a promotion state is given, and an evaluation value based on the effective advantage expression interval is generated. The site quality of the secondary poplar and birch forest was quantified based on the evaluation values ​​and site parameters.

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