Dissociation method for irreversible changes in tree radial dimensions based on in situ water correction

Abstract

This invention discloses a method for dissecting irreversible changes in tree trunk radial dimensions based on in-situ moisture correction. It constructs tree trunk radial dimension data and soil volumetric water content data under a unified time series. From the time series of tree trunk radial dimension and soil volumetric water content data, it identifies effective saturation days that meet the criteria for effective saturation days and their corresponding soil volumetric water content. Through data fitting, it obtains the bark elasticity coefficient corresponding to the target individual tree. It uniformly selects the tree trunk radial baseline dimensions at the beginning and end of the calculation period corresponding to the period with the weakest transpiration to obtain the total radial change within the tree trunk in the calculation window. Based on the soil moisture deficit and bark elasticity coefficient at the beginning and end of the calculation period, it calculates the reversible moisture change at the monitoring scale. It then calculates the irreversible xylem growth within the calculation period. This method can eliminate the interference of reversible water absorption and expansion during continuous rain and reversible water loss and shrinkage during continuous sunny periods on the calculation of growth.

Description

Technical Field

[0001] This invention relates to the field of tree ecological monitoring technology, and in particular to a method for dissociating irreversible changes in the radial dimensions of tree trunks based on in-situ moisture correction. Background Technology

[0002] The radial dimension variation of the trunk consists of two core components: first, irreversible net growth caused by the division and expansion of xylem cambium cells (which has a significant seasonality, with a daily growth of only 2-20 μm); and second, reversible elastic expansion / contraction caused by changes in the water content of the phloem and xylem (with daily fluctuations reaching tens to hundreds of μm, and systematic fluctuations reaching hundreds of μm under extreme weather conditions of consecutive sunny / rainy days on a weekly scale).

[0003] In existing technologies, most use a day or a week as the monitoring scale for changes in the radial dimension of the tree trunk. The mainstream daily maximum value method relies on a stable daily cycle and the daily water saturation peak. The calculation method of the daily maximum value method is as follows: obtain the maximum radial dimension of the tree trunk in each day, and use the difference between the maximum radial dimensions of the tree trunk on two consecutive days as the data on changes in the radial dimension of the tree trunk. When there are many consecutive sunny days without an effective saturation peak, it will lead to a 100% underestimation of growth (misjudged as zero growth); when there are many consecutive rainy days without a stable daily cycle, it will include all continuous reversible expansion in the growth, resulting in low calculation accuracy.

[0004] Most weekly-scale statistical methods use the inter-week difference method based on weekly mean and weekly extreme values, directly coupling the weekly-scale systematic water surplus and deficit (continuous contraction during sunny periods and continuous expansion during rainy periods) into the calculation of growth. The calculation error is large under extreme weather conditions, and the results lack physiological authenticity. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dissociating irreversible changes in the radial dimensions of tree trunks based on in-situ moisture correction, in order to solve the technical problem of large calculation errors in the calculation of irreversible radial changes in trees under extreme weather conditions (multiple consecutive days / multiple days of drought) when using existing technologies.

[0006] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution: A method for dissociating irreversible changes in the radial dimensions of tree trunks based on in-situ moisture correction includes the following steps: Step S1: Set up a monitoring system and collect basic data: Deploy tree growth meters and soil moisture sensors on the target trees, and measure field water holding capacity within a limited range of the target tree species' location to construct trunk radial dimension data and soil volumetric water content data under a unified time series. Step S2: Initialize the in-situ physiological parameters of the target tree species: Formulate the criteria for determining the effective saturation day, and find the effective saturation day that meets the criteria and its corresponding soil volumetric water content from the time series of trunk radial dimension and soil volumetric water content data. Obtain the bark elasticity coefficient α corresponding to the target tree through data fitting. Step S3: Define the calculation period window and extract the total radial change of the trunk: Define a fixed calculation period window, and uniformly select the trunk radial reference dimensions at the beginning and end of the calculation period corresponding to the period with the weakest transpiration, to obtain the total radial change of the trunk within the calculation window. D_week; Step S4: Calculate the radial reversible moisture change of the tree trunk within the calculation period: Based on the soil moisture deficit and bark elasticity coefficient α at the beginning and end of the calculation period, calculate the reversible moisture change at the monitoring scale. W; Step S5: Calculate the irreversible xylem growth within the calculation period: based on the physiological identity of trunk radial variation. G, of which G= D_week- W.

[0007] As a preferred embodiment of the present invention, in step S1, the tree growth instrument is installed on the tree trunk and its probe is in contact with the living phloem. The linear measurement accuracy of the tree growth instrument is ≤1μm and the sampling frequency is 5-30min / time. The soil moisture sensor has a measurement accuracy of ≤±2%, and at least three soil moisture sensors are installed, with the soil moisture sensors buried in the concentrated distribution layer of the target tree root system. In step S1, rainfall, solar radiation, and air temperature and humidity are collected in real time at the location of the target tree, and a sap flow sensor is installed inside the tree trunk to help determine the tree's saturation state and verify abnormal values.

[0008] In a preferred embodiment of the present invention, in step S2, the criterion for determining an effective saturation day specifically requires that the effective saturation day simultaneously meet the following conditions: Using rainfall as the statistical object and a 24-hour statistical period, the cumulative rainfall in the 24 hours before the effective saturation day must be ≥10mm; or using soil volumetric water content as the statistical object and a 3-day statistical period, the ratio between the soil volumetric water content over the 3 consecutive days and the field capacity at the corresponding time must be calculated, and the ratio between the soil volumetric water content in the 3 consecutive days before the effective saturation day and the field capacity at the corresponding time must be ≥90%. Using the total daily solar radiation as the statistical object, the total daily solar radiation on the effective saturation day is ≤2MJ / m².2 Or, the total daily sap flow on the effective saturation day is ≤ 5% of the peak value during the growing season; The change rate of trunk radial dimension is used as the statistical object. For broad-leaved trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤1μm / h, or for coniferous trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤0.5μm / h. Taking the maximum daily radial dimension as the statistical object, the maximum daily radial dimension of the effective saturation day is the local maximum value within 72 hours before and after; From the time series of trunk radial dimension and soil volumetric water content data, as well as the time series of field water holding capacity within the location range of the target tree species, find the effective saturation days that meet the judgment criteria, and their corresponding soil volumetric water content and field water holding capacity data.

[0009] As a preferred embodiment of the present invention, the bark elasticity coefficient α is the amount of reversible radial change of the trunk corresponding to a unit change in soil moisture deficit. The formula for calculating soil moisture deficit is as follows: WD = ( sat- ) / sat×100%; In the formula, To measure the volumetric water content of the soil, sat represents the field water holding capacity measured in situ.

[0010] As a preferred embodiment of the present invention, at least three effective saturation days are selected, namely the early growth stage, the peak growth stage, and the late growth stage; Record the saturated trunk radial dimension Dsat(i) and the corresponding soil volumetric water content for each effective saturation day. (i), and the trunk radial reference dimension corresponding to the reference time of the effective saturation day; Using the soil moisture deficit of two adjacent effective saturation days as the independent variable and the difference in reversible change of trunk radial dimension of two adjacent effective saturation days as the dependent variable, the independent and dependent variables are linearly fitted, and the slope of the fitted line is the initial bark elasticity coefficient α0. Where, α0 = ( D_total- G_true) / WD; In the formula, WD is the difference in water deficit between two effective saturation days at corresponding times. WD = WD(i2) - WD(i1); Furthermore, WD(i2) = ( sat- (i2)) / sat×100%,WD(i1)=( sat- (i1)) / sat×100%; In the formula, (i1) represents the measured soil volumetric water content at the reference time of the preceding effective saturation day among two adjacent effective saturation days. SAT stands for field capacity, measured in situ. (i2) is the measured soil volumetric water content corresponding to the reference time of the latter effective saturation day among two adjacent effective saturation days; G_true = Dsat(i2) - Dsat(i1); In the formula, Dsat(i2) is the radial dimension of the saturated trunk corresponding to the second effective saturation day among two adjacent effective saturation days, and Dsat(i1) is the radial dimension of the saturated trunk corresponding to the first effective saturation day among two adjacent effective saturation days. D_total=D_total(i2)-D_total(i1); In the formula, D_total(i2) is the trunk radial reference dimension corresponding to the reference time of the second effective saturation day among two adjacent effective saturation days, and D_total(i1) is the trunk radial reference dimension corresponding to the reference time of the first effective saturation day among two adjacent effective saturation days, wherein the period with the weakest transpiration is taken as the reference time.

[0011] As a preferred embodiment of the present invention, during the entire growing season, whenever a valid saturation day that meets the criteria of step S2 occurs, a dynamic calibration is immediately triggered to update the currently effective bark elasticity coefficient α_old. The specific steps for iteratively correcting the currently effective bark elasticity coefficient α are as follows: Record the tree saturation radial dimension Dsat_new on the current effective saturation day and the tree saturation radial dimension Dsat_old on the previous effective saturation day to obtain the difference in tree saturation radial dimension between the two effective saturation days. G_true =Dsat_new - D_sat_old; Using the currently effective bark elasticity coefficient α_old, calculate the difference in the trunk radial reference dimension between two effective saturation days; Based on the deviation between the actual value and the calculated value, an iterative correction yields a new bark elasticity coefficient α_new: α_new = α_old × ( G_true / G); In the formula, G_true represents the actual cumulative growth between two adjacent effective saturation days. Gtrue=Dsat_new-Dsat_old, G represents the cumulative irreversible growth between two effective saturation days, calculated using α_old.

[0012] As a preferred embodiment of the present invention, the updated bark elasticity coefficient α_new is automatically used for the calculation of the irreversible radial growth dimension of the trunk in the next calculation cycle.

[0013] As a preferred embodiment of the present invention, in step S3, the reference time at the beginning of the week and the reference time at the end of the week for each calculation cycle are uniformly selected as the period with the weakest transpiration each day. Extract the arithmetic mean of the radial dimensions of the tree trunk from multiple consecutive sampling points during the period of weakest transpiration each day, and use them as the baseline dimensions D_start at the beginning of the week and D_end at the end of the week, respectively. Simultaneously extract the arithmetic mean of multiple soil moisture sensors at the baseline times at the beginning and end of the week, and use these as the soil moisture content at the beginning of the week. _start and weekend soil moisture content _end; Simultaneously extract the arithmetic mean of field water holding capacity obtained from multiple sampling points at the baseline time at the beginning and end of the week, and use it as the soil moisture content at the beginning of the week. sat_start and weekend soil moisture content sat_end; Calculation of total radial variation within the monitoring window: D_week = D_end - D_start.

[0014] In a preferred embodiment of the present invention, in step S4, the reversible moisture change at the monitoring scale is calculated. The implementation method of W is as follows: ΔW = α × (WD_end - WD_start); Where WD_end= sat_end- _end) / sat_end × 100%; WD_start=( sat_start- _start) / sat_start×100%.

[0015] As a preferred embodiment of the present invention, in step S5, the irreversible growth amount The formula for solving G is: G= D_week- W; like If G≥0, it is directly used as the net growth for this calculation period; like If G < 0, it is counted as 0, and an outlier check is triggered; In step S5, the amount of irreversible growth The formula for solving G is: G= D_week- W; like If G≥0, it is directly used as the net growth for this calculation period; like If G < 0, it is counted as 0, and an outlier check is triggered; The new bark elasticity coefficient α_new, used in the next update, is used to check for outliers between two effective saturation days. G performs retrospective corrections to ensure consistency of data throughout the growing season.

[0016] Compared with the prior art, the present invention has the following advantages: This invention uses the bark elasticity coefficient α, calibrated in situ for a single tree, as a quantitative reference to achieve accurate conversion between weekly soil moisture balance and reversible radial deformation of the trunk. It specifically eliminates the interference of reversible water absorption and expansion during consecutive rainy weeks and reversible water loss and shrinkage during consecutive sunny weeks on growth calculation, avoiding overestimation, underestimation, and misjudgment of zero growth. It avoids the multi-parameter dependence and destructive detection of complex water transport models, balancing computational accuracy and large-scale practicality in the field. Attached Figure Description

[0017] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the overall process of an embodiment of the present invention; Detailed Implementation

[0019] 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.

[0020] like Figure 1 As shown, this invention provides a method for dissociating irreversible changes in the radial dimensions of tree trunks based on in-situ moisture correction. This embodiment uses the bark elasticity coefficient α of a single tree calibrated in situ as a quantitative reference to achieve accurate conversion between the weekly soil moisture balance and the reversible radial deformation of the tree trunk. It specifically eliminates the interference of reversible water absorption and expansion during consecutive rainy weeks and reversible water loss and contraction during consecutive sunny weeks on the calculation of growth, avoiding overestimation, underestimation, and misjudgment of zero growth. It avoids the multi-parameter dependence and destructive detection of complex water transport models, and balances calculation accuracy with large-scale practicality in the field.

[0021] Specifically, the following steps are included: Step S1: Set up a monitoring system and collect basic data: Deploy tree growth meters and soil moisture sensors on the target trees, and measure field water holding capacity within a limited range of the target tree species' location to construct trunk radial dimension data and soil volumetric water content data under a unified time series.

[0022] In step S1, the tree growth meter is installed on the tree trunk with its probe in contact with the living phloem. The linear measurement accuracy of the tree growth meter is ≤1μm, and the sampling frequency is 5-30min / time.

[0023] The soil moisture sensor has a measurement accuracy of ≤±2%, and at least 3 soil moisture sensors are deployed, buried in the concentrated distribution layer of the target tree's root system.

[0024] In step S1, rainfall, solar radiation, and air temperature and humidity are collected in real time at the location of the target tree, and a sap flow sensor is installed inside the tree trunk to help determine the tree's saturation state and verify abnormal values.

[0025] After collecting monitoring data, the raw data is denoised. The 3σ criterion is used to remove measurement noise and abnormal abrupt changes (such as instantaneous jumps caused by rainstorm impact or animal collisions). Missing data is filled in by linear interpolation, with the interpolation time not exceeding 2 hours to ensure the continuity of the time series.

[0026] Field water holding capacity SAT is a classic benchmark parameter in forestry soil science. It refers to the maximum stable volumetric water content of well-drained soil without groundwater backwater, after sufficient irrigation / rainfall, when all free-flowing gravitational water in the soil has been drained, leaving only capillary water suspended in the capillary pores that can be stably absorbed by plant roots and is not easily lost. It is the upper limit of available soil water that tree roots can stably utilize, and it is also the only critical threshold for distinguishing between available water that can be absorbed by trees and affects the moisture status of the trunk, and gravitational water that is rapidly lost and cannot be utilized by trees.

[0027] In this embodiment, field holding capacity The SAT (Soil Moisture Capacity) must correspond strictly one-to-one with the deployment of soil moisture sensors, specifically defined as: the in-situ field capacity at the same location, in the same soil layer, and in the same root concentration layer as the soil moisture sensor. For the same location: the soil moisture sensor must be installed within 1m of the location 50-100cm away from the tree trunk. It is forbidden to use general values ​​of the test area, literature values ​​or values ​​measured in other areas. Same soil layer: It must be completely consistent with the soil layer where the soil moisture sensor is buried (0-30cm root concentration layer for conventional trees, 0-50cm for deep-rooted tree species). It is forbidden to use the measurement values ​​of the top 0-5cm soil or the deep non-root distribution layer. Same environment: Measurements must be taken under the same site conditions, soil texture, and stand density as the trees being monitored, excluding interference from surrounding topography and vegetation.

[0028] Field holding capacity is the stable value after gravity water has drained away. It is the upper limit of water that trees can use in the long term. Water exceeding the field holding capacity will quickly seep out and be lost. It will not be absorbed by the trees and will not cause the trunk to expand continuously and reversibly.

[0029] To ensure feasibility, it is recommended to use the in-situ flooding measurement method commonly used in forestry to calculate field water holding capacity. The steps are as follows: Within a 1m radius of the soil moisture sensor installation points, construct a square dike with sides of 50cm x 50cm and a height of 20cm. Seal the gaps between the bottom of the dike and the soil with clay to prevent water leakage. Slowly fill the cofferdam with water until the water level stabilizes at 10cm, and maintain the water level for 24 hours to ensure that the soil layer is fully saturated. Completely cover the cofferdam with plastic film to prevent soil evaporation, and let it stand for 48 hours to allow the gravity water in the soil to drain out completely. Remove the plastic film and take three replicate soil samples from the 0-30cm depth inside the cofferdam (the same soil layer as the sensor). Measure the soil volumetric water content and take the arithmetic mean, which is the field capacity at that location. sat, and field water holding capacity SAT is an inherent physical parameter of the soil that remains relatively stable throughout the growing season and does not fluctuate with changes in phenology or weather.

[0030] Step S2: Initialize the in-situ physiological parameters of the target tree species: Formulate the criteria for determining the effective saturation day, and find the effective saturation day and its corresponding soil volumetric water content that meet the criteria from the time series of trunk radial dimension and soil volumetric water content data. Obtain the bark elasticity coefficient α corresponding to the target tree through data fitting.

[0031] Using rainfall as the statistical object and a 24-hour statistical period, the cumulative rainfall in the 24 hours before the effective saturation day must be ≥10mm; or using soil volumetric water content as the statistical object and a 3-day statistical period, the ratio between the soil volumetric water content over the 3 consecutive days and the field capacity at the corresponding time must be calculated, and the ratio between the soil volumetric water content in the 3 consecutive days before the effective saturation day and the field capacity at the corresponding time must be ≥90%. Using the total daily solar radiation as the statistical object, the total daily solar radiation on the effective saturation day is ≤2MJ / m². 2 Or, the total daily sap flow on the effective saturation day is ≤ 5% of the peak value during the growing season; The change rate of trunk radial dimension is used as the statistical object. For broad-leaved trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤1μm / h, or for coniferous trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤0.5μm / h. Taking the maximum daily radial dimension as the statistical object, the maximum daily radial dimension of the effective saturation day is the local maximum value within 72 hours before and after; From the time series of trunk radial dimension and soil volumetric water content data, as well as the time series of field water holding capacity within the location range of the target tree species, find the effective saturation days that meet the judgment criteria, and their corresponding soil volumetric water content and field water holding capacity data.

[0032] On the effective saturation day, the trunk is in a saturated state, meaning the trunk tissue is completely saturated with water and has recovered 100% of its reversible elastic shrinkage. At this point, the radial dimension is determined solely by the amount of irreversible xylem growth, which is the only unbiased physiological benchmark.

[0033] The bark elasticity coefficient α represents the reversible radial change in the trunk corresponding to a unit change in soil moisture deficit, expressed in μm / % WD, where WD is the soil moisture deficit. The formula for calculating soil moisture deficit is: WD = ( sat- ) / sat×100%; In the formula, To measure the volumetric water content of the soil, sat represents the field water holding capacity measured in situ.

[0034] Field water holding capacity Saturation (SAT) is an inherent physical parameter of the soil that remains relatively stable throughout the growing season and does not fluctuate with changes in phenology or weather. Therefore, it is used as a constant field capacity. Using SAT as a baseline, the change in soil moisture deficit (WD) is only related to the available water available to tree roots and is not affected by changes in the physiological state of trees during the growing season, thus ensuring reversible water changes at the monitoring scale throughout the entire growing season. With a unified benchmark for W calculation and dynamic calibration of the elasticity coefficient, error closed-loop control can be achieved throughout the entire growing season.

[0035] In scenarios involving multiple days of continuous rainfall, the soil moisture content can exceed the field capacity. The excess water is gravitational water, which will quickly seep away and cannot be absorbed by the tree roots. It also will not cause the trunk to expand continuously and reversibly. Without field holding capacity... SAT, directly using the measured soil volumetric water content. The calculation of changes in water content will include the changes in ineffective gravitational water in the water deficit, leading to an overestimation of changes in soil water deficit. This affects the reversible water content at the monitoring scale. The W calculation error resulted in a severe underestimation of the final irreversible growth; however, this implementation method sets the field water holding capacity. sat, when soil volumetric water content ≥ Field holding capacity When SAT is used, the soil moisture deficit is directly calculated as 0, indicating that the tree trunk is in a saturated state with no additional reversible expansion, thus perfectly eliminating the interference of ineffective water.

[0036] In response to prolonged periods of sunny weather and persistent drought, soil moisture content is below field capacity. SAT, field water holding capacity SAT can accurately quantify the extent of effective water deficit in trees, without including ineffective bound water in the soil that cannot be absorbed by the roots. This ensures that the change in soil water deficit (WD) is completely linearly correlated with the reversible shrinkage of the trunk, avoiding the fatal flaw of misjudging zero or negative growth during consecutive sunny weeks.

[0037] At least three effective saturation days should be selected, namely the early growth stage, the peak growth stage, and the late growth stage; Record the saturated trunk radial dimension Dsat(i) and the corresponding soil volumetric water content for each effective saturation day. (i), and the trunk radial reference dimension corresponding to the reference time of the effective saturation day. Using the soil moisture deficit of two adjacent effective saturation days as the independent variable and the difference in reversible change of trunk radial dimension of two adjacent effective saturation days as the dependent variable, the independent and dependent variables are linearly fitted, and the slope of the fitted line is the initial bark elasticity coefficient α0. Where, α0 = ( D_total- G_true) / WD; In the formula, WD is the difference in water deficit between two effective saturation days at corresponding times. WD = WD(i2) - WD(i1).

[0038] Furthermore, WD(i2) = ( sat- (i2)) / sat×100%,WD(i1)=( sat- (i1)) / sat×100%; In the formula, (i1) represents the measured soil volumetric water content at the reference time of the preceding effective saturation day among two adjacent effective saturation days. SAT stands for field capacity, measured in situ. (i2) represents the measured soil volumetric water content at the reference time of the latter of two adjacent effective saturation days. G_true = Dsat(i2) - Dsat(i1); In the formula, Dsat(i2) is the radial dimension of the saturated trunk corresponding to the latter of two adjacent effective saturation days, and Dsat(i1) is the radial dimension of the saturated trunk corresponding to the former of two adjacent effective saturation days. Since the effective saturation day is deemed qualified when the trunk is 100% saturated with water, all reversible expansion / contraction caused by water is considered. When W=0, the radial dimension of the trunk only includes irreversible xylem growth, without any water interference. G_true represents the absolutely real, irreversible cumulative irreversible growth between two effective saturation days.

[0039] D_total=D_total(i2)-D_total(i1); In the formula, D_total(i2) is the trunk radial reference dimension corresponding to the reference time of the second effective saturation day among two adjacent effective saturation days, and D_total(i1) is the trunk radial reference dimension corresponding to the reference time of the first effective saturation day among two adjacent effective saturation days, wherein the period with the weakest transpiration is taken as the reference time.

[0040] When there are multiple sets of adjacent effective saturation days (e.g., 3 effective saturation days form 2 sets of data), the least squares fitting is used, and the slope of the fitted curve is equal to the unique initial bark elasticity coefficient α0.

[0041] This implementation method can calibrate the bark elasticity coefficient for each monitored tree and corresponding site conditions, completely eliminating systematic errors caused by soil heterogeneity and tree species differences. Furthermore, when calculating the bark elasticity coefficient, no destructive sampling is required. It can be obtained solely through in-situ measured data from a tree growth meter and a soil moisture sensor, combined with true value calibration on effective saturation days, making it suitable for all tree species.

[0042] In this embodiment, the bark elasticity coefficient α is the only quantitative coupling parameter used to establish a precise linear quantitative relationship between changes in soil moisture deficit and reversible radial elastic deformation of the trunk at the circumferential scale. During continuous rainfall over several days, soil moisture content continues to rise and water deficit WD continues to decrease, causing the trunk to undergo large-scale reversible water absorption and expansion. Traditional methods would include all of this water expansion in the tree growth, resulting in a serious overestimation. The application of the bark elasticity coefficient α can accurately calculate the reversible expansion at the circumferential scale caused by continuous rainfall. In the calculation of each monitoring scale, the passive thickening caused by water is uniformly deducted, and only the real irreversible growth caused by cambium cell division and expansion is retained, thus eliminating the problem of inflated circumferential growth during rainy periods.

[0043] Similarly, when there is a prolonged drought, the soil moisture deficit continues to increase, the tree trunk continues to lose water and shrink, and the overall diameter of the tree trunk continues to decrease; the traditional weekly extreme value method will directly determine no growth or negative growth, completely masking the trace continuous growth under drought stress.

[0044] The application of the bark elasticity coefficient α can accurately calculate the reversible water loss and shrinkage on a weekly scale caused by drought. Even if the measured value of the overall trunk diameter decreases, the true weekly growth that is hidden by the shrinkage can be calculated by subtracting the large reversible shrinkage deformation. This solves the common problem that irreversible growth during drought cycles is often underestimated or even misjudged as zero.

[0045] Throughout the growing season, whenever a valid saturation day that meets the criteria of step S2 occurs, a dynamic calibration is immediately triggered to update the currently effective bark elasticity coefficient α_old.

[0046] The bark elasticity coefficient α is not a permanent fixed constant throughout the year. The cell structure, cell wall elasticity, bark thickness, and tissue mechanical properties of the living phloem change continuously and slowly without abrupt changes. From the early growth stage to the peak growth stage, then to the late growth stage (bark lignification), and finally to the leaf fall stage, the bark hardens and its elasticity decreases. As a result, the bark elasticity coefficient α will decrease regularly. This is the core reason why this implementation method sets up dynamic iterative calibration and updates the bark elasticity coefficient α in stages.

[0047] The specific steps for iteratively correcting the currently effective bark elasticity coefficient α are as follows: Record the tree saturation radial dimension Dsat_new on the current effective saturation day and the tree saturation radial dimension Dsat_old on the previous effective saturation day to obtain the difference in tree saturation radial dimension between the two effective saturation days. G_true =Dsat_new - D_sat_old; Using the currently effective bark elasticity coefficient α_old, calculate the difference in the trunk radial reference dimension between two effective saturation days; Based on the deviation between the actual value and the calculated value, an iterative correction yields a new bark elasticity coefficient α_new: α_new = α_old × ( G_true / G); In the formula, Gtru represents the actual cumulative growth between two adjacent effective saturation days. Gtrue=Dsat_new-Dsat_old, G represents the cumulative irreversible growth between two effective saturation days, calculated using α_old.

[0048] The updated bark elasticity coefficient α_new is automatically used for calculating the irreversible radial growth dimensions of the trunk in the next calculation cycle.

[0049] In this embodiment, the bark elasticity coefficient is not a fixed constant. It is continuously updated by adding new effective saturation days to dynamically match the actual elasticity state of the bark at different phenological stages, correct the long-term systematic deviation caused by the physiological aging of the bark throughout the growing season, and ensure that the reversible deformation peeling accuracy remains stable throughout the 4-6 month full growing season.

[0050] Step S3: Define the calculation period window and extract the total radial change of the trunk: Define a fixed calculation period window, and uniformly select the trunk radial reference dimensions at the beginning and end of the calculation period corresponding to the period with the weakest transpiration, to obtain the total radial change of the trunk within the calculation window. D_week.

[0051] In step S3, the baseline time at the beginning of each calculation cycle and the baseline time at the end of each cycle are uniformly selected as the period with the weakest daily transpiration. Extract the arithmetic mean of the radial dimensions of the tree trunk from multiple consecutive sampling points during the period of weakest transpiration each day, and use them as the baseline dimensions D_start at the beginning of the week and D_end at the end of the week, respectively. Simultaneously extract the arithmetic mean of multiple soil moisture sensors at the baseline times at the beginning and end of the week, and use these as the soil moisture content at the beginning of the week. _start and weekend soil moisture content _end; Simultaneously extract the arithmetic mean of field water holding capacity obtained from multiple sampling points at the baseline time at the beginning and end of the week, and use it as the soil moisture content at the beginning of the week. sat_start and weekend soil moisture content sat_end; Calculation of total radial variation within the monitoring window: D_week = D_end - D_start.

[0052] If the calculation period for calculating the total radial change of the tree trunk in this embodiment is 7 days, a fixed 7-day natural calendar window (such as Monday 00:00 to Sunday 23:59) is used, or a custom 7-day sliding window is used. Once the window is set, it remains fixed throughout the entire growing season and should not be adjusted arbitrarily.

[0053] Furthermore, this implementation supports both fixed 7-day cycle monitoring and can adapt to calculation cycle windows of any length from 3 to 30 days for changes in the radial dimensions of the tree trunk.

[0054] The baseline time at the beginning of each calculation cycle and the baseline time at the end of the cycle are uniformly selected as the period with the weakest transpiration (4:00-6:00 AM, which can be slightly adjusted according to the local sunrise time). The arithmetic mean of the radial dimensions of the tree trunks of three consecutive sampling points within this period is extracted and used as the baseline dimensions at the beginning of the cycle, D_start and D_end of the end of the cycle, respectively.

[0055] Step S4: Calculate the radial reversible moisture change of the tree trunk within the calculation period: Based on the soil moisture deficit and bark elasticity coefficient α at the beginning and end of the calculation period, calculate the reversible moisture change at the monitoring scale. W.

[0056] In step S4, the reversible moisture change at the monitoring scale is calculated. The implementation method of W is as follows: ΔW = α × (WD_end - WD_start); Where WD_end= sat_end - _end) / sat_end × 100%; WD_start = ([[]] sat_start - _start) / sat_start × 100%.

[0057] ΔW is the reversible water change in the trunk radial direction within the calculation period scale, with the unit of μm; α is the current effective bark elastic coefficient (initially α0, updated to α_new after dynamic calibration); WD_end is the soil water deficit at the weekend reference time, and WD_start is the soil water deficit at the beginning of the week reference time, with the unit of % WD.

[0058] Among them, if WD_end > WD_start (drier at the weekend than at the beginning of the week), the trunk undergoes reversible shrinkage, and ΔW is negative; If WD_end < WD_start (wetter at the weekend than at the beginning of the week), the trunk undergoes reversible expansion, and ΔW is positive.

[0059] The absolute value of the weekly complete reversible water deformation (μm) is quantitatively calculated through ΔW = α × (WD_end - WD_start), converting the fuzzy water effect into a quantifiable and directly deductible quantitative value.

[0060] Step S5, solve the irreversible xylem growth amount within the calculation period: Solve based on the physiological identity of the trunk radial change G.

[0061] In step S5, the irreversible growth amount The solution formula for G is: G = D_week - W; If G ≥ 0, it is directly used as the net growth amount for this calculation period; If G < 0, it is counted as 0 and an outlier check is triggered.

[0062] Furthermore, the outlier check between two effective saturation days can be retrospectively corrected using the corrected bark elastic coefficient α_new to ensure the consistency of the data throughout the growing season.

[0063] This implementation method introduces the bark elasticity coefficient and effective saturation days to quantitatively calculate the absolute value (μm) of complete reversible moisture deformation within the calculation period of each trunk radial dimension. This transforms the ambiguous influence of moisture into a calculable and directly deductible quantitative value, thereby achieving effective separation of the radial reversible and radial irreversible dimensional changes of the trunk.

[0064] In addition, the dual calibration mechanism of "initial calibration + dynamic iterative calibration" for the bark elasticity coefficient eliminates the systematic deviation caused by the dynamic changes in bark thickness and phloem elasticity during the growing season, and the measurement accuracy is stable and controllable throughout the growing season.

[0065] In summary, this implementation method eliminates systematic errors caused by changes in the physiological state of trees during the growing season by establishing a closed-loop mechanism of dynamic calibration and physiological constraints throughout the growing season, ensuring the accuracy and stability of long-term monitoring. Furthermore, it utilizes the tree's own growth and physiological state to address the problem of overestimation / underestimation of growth caused by extreme weather such as consecutive sunny / rainy days, achieving precise separation of reversible shrinkage and irreversible growth.

[0066] In addition, the present invention also provides a specific embodiment of applying the above-mentioned method for dissociating irreversible changes in trunk radial dimensions based on in-situ moisture correction to the larch plantation cycle: (a) Experimental site: larch plantation; Experimental subjects: 30-year-old larch (coniferous trees), with an average diameter at breast height of 18.2 cm and an average tree height of 12.5 m; Monitoring period: 2025 growing season (May 1 - October 31); Monitoring equipment: High-precision point-type tree growth meter: measurement accuracy ±0.5μm, sampling frequency 10min / time, installed at 1.3m diameter at breast height on the tree trunk, remove the surface dead bark, fit the living phloem, and be waterproof and sun-proof. Soil moisture sensor: measurement accuracy ±1.5%, sampling frequency 30min / time, 3 replicates, buried 80cm from the base of the tree trunk in the 0-30cm soil layer; It is equipped with a small weather station to collect rainfall and solar radiation data simultaneously.

[0067] (II) Initial Calibration Process In-situ determination of field capacity: The field capacity θ_sat of the experimental area was measured to be 32.6% using the flooding method; Screening of effective saturation days: Three effective saturation days that meet the criteria were screened at the beginning, peak and end of the growing season, such as the three effective saturation days at the beginning of the growing season (May 10, May 18 and May 28, 2025). Elasticity coefficient calibration: The initial bark elasticity coefficient of North China larch was obtained by fitting α0 = 2.87 μm / % WD, and the coefficient of determination R was [missing value]. 2=0.942, which meets the accuracy requirements.

[0068] (III) Verification of Cycle Calculation in Extreme Scenarios (1) Scene of a week of continuous sunny and dry weather (June 10-16, 2025) Weekly weather: No rain for 7 consecutive days, daily high temperature 28-32℃, continued drought; Baseline data extraction: Early week (5:00 AM, June 10th): D_start = 182.346 mm, θ_start = 24.8%; Weekend (5:00 AM, June 16th): D_end = 182.287 mm, θ_end = 18.2%; Calculation process: D_week=182.287-182.346=-0.059mm=-59μm; WD_start=(32.6-24.8) / 32.6×100%=23.93%WD, WD_end=(32.6-18.2) / 32.6×100%=44.17%WD; W=α 0* WD=α 0* (WD_end-WD_start)=2.87×(44.17-23.93)=58.07μm; G = -59 - 58.07 = -117.07 μm. After physiological constraint verification, it is calculated as 0 μm and marked as pending verification.

[0069] (2) Dynamic iterative calibration of bark elasticity coefficient Calibration logic: The initial α0 is the average fitted value at the beginning of the growing season. Under continuous drought conditions, the degree of bark lignification and cell wall elasticity will show a slight linear shift, resulting in reversible changes. The calculation of W has a systematic bias; by using the absolute true values ​​of two saturation days, α0 is iteratively corrected to ensure that the calibrated α_new can accurately match the true elastic characteristics of the target plant.

[0070] The calibration formula is: α_new = α0 * ( G_true / G); In the formula, G_true represents the actual cumulative growth between two adjacent effective saturation days. Gtrue=Dsat_new-Dsat_old, G is the cumulative irreversible growth between two saturation days, calculated using α0.

[0071] Substituting the values: The newly added effective saturation day (Ts, June 18, 2025) fully meets the four saturation judgment criteria. The measured saturated radial dimension Dsat_Ts = 182.365 mm, corresponding to WD = 0 (soil saturation, no water deficit).

[0072] Previous reference saturation date (T0, 2025.5.28): The last effective saturation date of the initial calibration α0, with a measured saturated radial dimension Dsat_T0=182.215mm, corresponding to WD=0.

[0073] The radial dimension difference between the two effective saturation days (T0=5.28, Ts=6.18) is the true value of the actual cumulative growth with 100% unbiased accuracy. G_true; Phase 1: Truth anchoring of newly added effective saturation day Ts: The time span between the two effective saturation days T0 (5.28) and Ts (6.18) is 21 days, fully encompassing 3 weeks. The cumulative growth over 3 weeks, calculated using the initial α0, is as follows: Week 1 (May 28 - June 3): Normal weather with stable daily saturation peaks. G1=48μm (no verification marker, calculated value is reliable) Week 2 (June 3-10): Normal weather with stable daily saturation peaks. G2=90μm (no verification marker, calculated value is reliable) WeekX (June 10-16): Target drought week, provisionally recorded as 0 μm (to be verified). Step 1: Calculate the absolute true total growth over the 21-day interval. The core axiom of the effective saturation day: the reversible change in trunk water volume at the saturation point. When W=0, the radial dimension only includes irreversible growth, therefore: G_true=Dsat_Ts-Dsat_T0=182.365mm-182.215mm=150μm; Step 2: Calculate the cumulative growth over 3 weeks using the initial α0: Gcalc = 48 + 90 + 0 = 138 μm; Core deviation G_true- Gcalc=150-138=12μm. The initial α0 has a small systematic bias in the continuous drought scenario, which causes the true growth of the drought week to be completely masked. This 12μm is the true growth of the drought week.

[0074] Phase 2: Dynamic Iterative Calibration of Bark Elasticity Coefficient Substitute the values ​​into the calculation: α_new = α_old × ( G_true / G) = 2.97 * 150 / 138 = 2.81: Using the calibrated α_new, recalculate the reversible and irreversible growth during the drought cycle to restore the true growth that was mistakenly masked: Recalculate reversible shrinkage: Wnew=2.81×(23.93-44.17)=-56.87μm; Recalculate irreversible growth: Gnew= Dtotal- Wnew = -59 - 56.87 = -115.87 μm; Truth value allocation and secondary verification: Since the true value of the total growth on the two saturated days is 150 μm, the calculated value of 138 μm for Week1 and Week2 has passed the saturation peak verification. The remaining 12 μm difference is solely derived from the actual growth of WeekX during the drought week. Considering the small negative value of the recalculated result as measurement noise, the irreversible growth of WeekX is finally corrected to 12 μm, which fully conforms to the physiological constraint of "monotonic non-decreasing".

[0075] The corrected 12μm replaces the original temporary value 0μm as the final growth amount for WeekX; the calibrated α_new is automatically used for the growth amount calculation of all subsequent weeks, and dynamic calibration is repeated once for each new effective saturation day to achieve closed-loop control of error throughout the entire growth season.

[0076] Comparison with traditional methods: The traditional weekly extreme value method calculated a result of -59μm, which was misjudged as negative growth; this method was subsequently calibrated by dynamic calibration on the effective saturation day after the rainfall on June 18, and the weekly growth was corrected to 12μm, with a relative error of only 9.1% compared with the measured value of 11μm by the trunk analysis method.

[0077] (2) Scene of continuous rain throughout the week (July 22 - July 28, 2025) Weekly weather: 7 consecutive days of overcast and rainy weather, with a cumulative rainfall of 87.2 mm, and the soil remains saturated; Baseline data extraction: Early week (5:00 AM, July 22): D_start = 183.124 mm, θ_start = 26.5%; Weekend (5:00 AM, July 28): D_end = 183.258 mm, θ_end = 32.1%; Calculation process: D_total=183.258-183.124=0.134mm=134μm; WD_start=(32.6-26.5) / 32.6×100%=18.71%WD, WD_end=(32.6-32.1) / 32.6×100%=1.53%WD; W=2.87×(1.53-18.71)=-49.28μm; G = 134 - (-49.28) = 183.28 μm. After physiological constraint verification, it is directly determined to be the growth amount of this week.

[0078] Compared with traditional methods: the traditional weekly average method calculates 134 μm, which underestimates the growth by 26.9%; the relative error between the result calculated by this method and the measured value of 178 μm by the trunk analysis method is only 2.97%, which significantly improves the accuracy.

[0079] (iv) Dynamic calibration throughout the growing season Eight effective daily dynamic calibrations were triggered throughout the growing season. The elasticity coefficient was updated from the initial 2.87 μm / %WD to 2.72 μm / %WD, eliminating the elasticity change deviation caused by bark lignification at the end of the growing season. The relative error between the calculated growth amount throughout the growing season and the measured value by the trunk analysis method was ≤4.2%, which is far superior to the existing technology.

[0080] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for dissociating irreversible changes in the radial dimension of tree trunks based on in-situ moisture correction, characterized in that, Includes the following steps: Step S1: Set up a monitoring system and collect basic data: Deploy tree growth meters and soil moisture sensors on the target trees, and measure field water holding capacity within a limited range of the target tree species' location to construct trunk radial dimension data and soil volumetric water content data under a unified time series. Step S2: Initialize the in-situ physiological parameters of the target tree species: Formulate the criteria for determining the effective saturation day, and find the effective saturation day that meets the criteria and its corresponding soil volumetric water content from the time series of trunk radial dimension and soil volumetric water content data. Obtain the bark elasticity coefficient α corresponding to the target tree through data fitting. Step S3: Define the calculation period window and extract the total radial change of the trunk: Define a fixed calculation period window, and uniformly select the trunk radial reference dimensions at the beginning and end of the calculation period corresponding to the period with the weakest transpiration, to obtain the total radial change of the trunk within the calculation window. D_week; Step S4: Calculate the radial reversible moisture change of the tree trunk within the calculation period: Based on the soil moisture deficit and bark elasticity coefficient α at the beginning and end of the calculation period, calculate the reversible moisture change at the monitoring scale. W; Step S5: Calculate the irreversible xylem growth within the calculation period: based on the physiological identity of trunk radial variation. G, of which G= D_week- W.

2. The method for dissociating irreversible changes in trunk radial dimensions based on in-situ moisture correction according to claim 1, characterized in that, In step S1, the tree growth instrument is installed on the tree trunk and its probe is in contact with the living phloem. The linear measurement accuracy of the tree growth instrument is ≤1μm and the sampling frequency is 5-30min / time. The soil moisture sensor has a measurement accuracy of ≤±2%, and at least three soil moisture sensors are installed, with the soil moisture sensors buried in the concentrated distribution layer of the target tree root system. In step S1, rainfall, solar radiation, and air temperature and humidity are collected in real time at the location of the target tree, and a sap flow sensor is installed inside the tree trunk to help determine the tree's saturation state and verify abnormal values.

3. The method for dissociating irreversible changes in trunk radial dimensions based on in-situ moisture correction according to claim 1, characterized in that, In step S2, the criteria for determining an effective saturation day are specifically defined as follows: an effective saturation day must simultaneously meet the following conditions: Using rainfall as the statistical object and a 24-hour statistical period, the cumulative rainfall in the 24 hours before the effective saturation day must be ≥10mm; or using soil volumetric water content as the statistical object and a 3-day statistical period, the ratio between the soil volumetric water content over the 3 consecutive days and the field capacity at the corresponding time must be calculated, and the ratio between the soil volumetric water content in the 3 consecutive days before the effective saturation day and the field capacity at the corresponding time must be ≥90%. The daily total of solar radiation is taken as the statistical object, and the daily total of solar radiation on the effective saturation day is ≤2 MJ / m 2 , or the daily total of sap flow of the trunk on the effective saturation day is ≤5% of the peak value in the growing season. The change rate of trunk radial dimension is used as the statistical object. For broad-leaved trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤1μm / h, or for coniferous trees, the change rate of trunk radial dimension for more than 6 consecutive hours within the effective saturation day is ≤0.5μm / h. Taking the maximum daily radial dimension as the statistical object, the maximum daily radial dimension of the effective saturation day is the local maximum value within 72 hours before and after; From the time series of trunk radial dimension and soil volumetric water content data, as well as the time series of field water holding capacity within the location range of the target tree species, find the effective saturation days that meet the judgment criteria, and their corresponding soil volumetric water content and field water holding capacity data.

4. The method for dissociating irreversible changes in trunk radial dimensions based on in-situ moisture correction according to claim 1, characterized in that, The bark elasticity coefficient α is the amount of reversible radial change in the trunk corresponding to a unit change in soil moisture deficit. The formula for calculating soil moisture deficit is as follows: WD=( sat- ) / sat×100%; In the formula, To measure the volumetric water content of the soil, sat represents the field water holding capacity measured in situ.

5. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 1, characterized in that, At least three effective saturation days are selected, namely, the early growth stage, the peak growth stage, and the late growth stage. Record the saturated trunk radial dimension Dsat(i) and the corresponding soil volumetric water content for each effective saturation day. (i), and the trunk radial reference dimension corresponding to the reference time of the effective saturation day; Using the soil moisture deficit of two adjacent effective saturation days as the independent variable and the difference in reversible change of trunk radial dimension of two adjacent effective saturation days as the dependent variable, the independent and dependent variables are linearly fitted, and the slope of the fitted line is the initial bark elasticity coefficient α0. Where, α0 = ( D_total- G_true) / WD; In the formula, WD is the difference in water deficit between two effective saturation days at corresponding times. WD = WD(i2) - WD(i1); Furthermore, WD(i2) = ( sat- (i2)) / sat×100%,WD(i1)=( sat- (i1)) / sat×100%; In the formula, (i1) represents the measured soil volumetric water content at the reference time of the previous effective saturation day among two adjacent effective saturation days. SAT stands for field capacity, measured in situ. (i2) is the measured soil volumetric water content corresponding to the reference time of the latter effective saturation day among two adjacent effective saturation days; G_true=Dsat(i2)-Dsat(i1); In the formula, Dsat(i2) is the radial dimension of the saturated trunk corresponding to the second effective saturation day among two adjacent effective saturation days, and Dsat(i1) is the radial dimension of the saturated trunk corresponding to the first effective saturation day among two adjacent effective saturation days. D_total=D_total(i2)-D_total(i1); In the formula, D_total(i2) is the trunk radial reference dimension corresponding to the reference time of the second effective saturation day among two adjacent effective saturation days, and D_total(i1) is the trunk radial reference dimension corresponding to the reference time of the first effective saturation day among two adjacent effective saturation days, wherein the period with the weakest transpiration is taken as the reference time.

6. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 5, characterized in that, Throughout the growing season, whenever a valid saturation day that meets the criteria of step S2 occurs, a dynamic calibration is immediately triggered to update the currently effective bark elasticity coefficient α_old. The specific steps for iteratively correcting the currently effective bark elasticity coefficient α are as follows: Record the tree saturation radial dimension Dsat_new on the current effective saturation day and the tree saturation radial dimension Dsat_old on the previous effective saturation day to obtain the difference in tree saturation radial dimension between the two effective saturation days. G_true = Dsat_new- D_sat_old; Using the currently effective bark elasticity coefficient α_old, calculate the difference in the trunk radial reference dimension between two effective saturation days; Based on the deviation between the actual value and the calculated value, an iterative correction yields a new bark elasticity coefficient α_new: α_new = α_old × ( G_true / G); In the formula, G_true represents the actual cumulative growth between two adjacent effective saturation days. Gtrue=Dsat_new-Dsat_old, G represents the cumulative irreversible growth between two saturation days, calculated using α_old.

7. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 6, characterized in that, The updated bark elasticity coefficient α_new is automatically used for calculating the irreversible radial growth dimensions of the trunk in the next calculation cycle.

8. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 1, characterized in that, In step S3, the baseline time at the beginning of the week and the baseline time at the end of each calculation cycle are uniformly selected as the period with the weakest daily transpiration. Extract the arithmetic mean of the radial dimensions of the tree trunk from multiple consecutive sampling points during the period of weakest transpiration each day, and use them as the baseline dimensions D_start at the beginning of the week and D_end at the end of the week, respectively. Simultaneously extract the arithmetic mean of multiple soil moisture sensors at the baseline times at the beginning and end of the week, and use these as the soil moisture content at the beginning of the week. _start and weekend soil moisture content _end; Simultaneously extract the arithmetic mean of field water holding capacity obtained from multiple sampling points at the baseline time at the beginning and end of the week, and use it as the soil moisture content at the beginning of the week. sat_start and weekend soil moisture content sat_end; Calculation of total radial variation within the monitoring window: D_week = D_end - D_start.

9. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 8, characterized in that, In step S4, the reversible moisture change at the monitoring scale is calculated. The implementation method of W is as follows: ΔW = α × (WD_end - WD_start); Where WD_end= sat_end- _end) / sat_end × 100%; WD_start=( sat_start- _start) / sat_start×100%。 10. The method for dissociating irreversible changes in the radial dimension of a tree trunk based on in-situ moisture correction according to claim 1, characterized in that, In step S5, the amount of irreversible growth The formula for solving G is: G= D_week- W; like If G≥0, it is directly used as the net growth for this calculation period; like If G < 0, it is counted as 0, and an outlier check is triggered; The new bark elasticity coefficient α_new, used in the next update, is used to check for outliers between two effective saturation days. G performs retrospective corrections to ensure consistency of data throughout the growing season.

Images