A stem water content fdr calibration method based on wood density parameterization and application thereof

Abstract

The application provides a stem water content FDR calibration method based on wood density parameterization and application thereof, and aims at the problems of strong species specificity and lack of a general framework in the existing stem water content calibration using a frequency domain reflectometry (FDR) dielectric constant. The wood density of a target tree species is obtained, the stem dielectric constant is measured by using an FDR sensor and the logarithmic value thereof is calculated, the temperature influence is eliminated by using a temperature correction formula independent of the density, and finally the density parameterization general model: volume water content=(α×wood density+β)×lg(ε)+γ is substituted, wherein α, β and γ are experimental calibration constants. Based on experiments of 16 subtropical tree species, the calibration slope is extremely significantly positively correlated with the wood density, the intercept is irrelevant to the density, the prediction performance R 2 is improved from 0.69 to 0.75 after the introduction of the density parameter. The application significantly reduces the calibration cost of multi-species stem water monitoring, realizes long-term in-situ monitoring of cross-species universality and temperature correction, and is suitable for tree species with secondary xylem.

Description

Technical Field

[0001] This application relates to the field of forestry measurement technology, and more specifically, to a stem moisture content FDR calibration method based on wood density parameterization and its application. Background Technology

[0002] Stem water release accounts for 10% to 50% of a tree's daily transpiration, making it a crucial water buffering mechanism. By dynamically regulating xylem water potential, stem water release effectively mitigates momentary imbalances between leaf transpiration demand and root water supply, thereby maintaining xylem hydraulic integrity and enhancing the tree's adaptability to drought stress. With global climate change exacerbating the frequency and severity of extreme drought events, stem water content dynamics have become a core indicator for predicting tree mortality risk. Therefore, accurate and continuous monitoring of stem water content is of great significance for deepening research on tree water relationships, quantitatively assessing forest drought vulnerability, and developing scientific forest management strategies.

[0003] However, achieving long-term, in-situ, continuous monitoring of tree trunk moisture using existing technologies faces significant technical challenges. While traditional drying and weighing methods offer high accuracy, they are destructive measurements and cannot provide continuous time-series data. High-precision alternatives such as nuclear magnetic resonance (NMR), computed tomography (CT), or resistivity tomography can acquire high-resolution moisture distribution information, but these methods are expensive, complex to operate, and some pose radiation risks, making them unsuitable for large-scale, long-term field monitoring.

[0004] Frequency domain reflectometry (FDR) in electromagnetic sensors is a research hotspot for monitoring tree trunk moisture. Its principle relies on the difference in dielectric constants between water, wood, and air, determining volumetric water content by measuring the apparent dielectric constant. Compared to time domain reflectometry (TDR), FDR technology exhibits significant advantages, specifically lower probe manufacturing costs, greater adaptability in shape design, more flexible hardware system setup, and significantly reduced requirements for probe length.

[0005] To ensure measurement accuracy, TDR technology typically requires long probes of at least 10 cm. However, the sapwood of most tree species is only a few centimeters thick. Such long probes can easily penetrate the sapwood layer completely, causing interference from non-target areas of the heartwood, thus affecting the accuracy and reliability of the monitoring data. In contrast, FDR technology has no strict requirements on probe length and can be designed with short probes of 3-5 cm. These probes can be precisely inserted into the sapwood layer without penetrating the heartwood, effectively avoiding signal interference from non-target areas. This makes it more suitable for in-situ, long-term, continuous monitoring of trunk moisture, providing technical support for the accurate capture of dynamic changes in trunk moisture.

[0006] While FDR is a relatively mature technology for soil moisture monitoring, its application in tree trunk measurement presents a challenge in terms of species-specific calibration curves. Tree trunks are highly heterogeneous and porous media, and their dielectric properties are affected by multiple factors such as water content, wood density, and xylem anatomy. Different tree species require separate calibration curves, which severely restricts the widespread application of this technology in multi-species forest ecosystems.

[0007] There are significant disagreements in academic research regarding the construction of a universal calibration model for FDR (Fiber to the Tree Species) incorporating wood density. He et al. (2021) argued that due to species-specific differences in xylem anatomy and water storage mechanisms, it is difficult to establish a universal calibration equation, requiring modeling that couples wood density with anatomical characteristics. However, Martius et al. (2024), in their study of palm trees in tropical forests, showed that wood density has no significant impact on FDR calibration, and the density factor can be ignored, allowing for the establishment of a single-slope universal model. This controversy indicates that the effect of wood density on FDR calibration is xylem type-dependent: in dicotyledons and gymnosperms with typical secondary xylem and distinct heartwood and sapwood differentiation, wood density is a key influencing factor; while in palm trees, which are predominantly parenchyma and lack typical secondary xylem, the regulatory role of wood density is significantly weakened.

[0008] Meanwhile, temperature is a crucial factor limiting the accuracy of FDR measurements. The polarization properties of dielectric molecules change with temperature, resulting in a significant temperature dependence of the trunk dielectric constant. However, the variation of temperature effects among different wood species and its interaction mechanism with wood density have not yet been systematically elucidated, nor has a mature and reliable temperature correction method been developed.

[0009] Therefore, there is an urgent need to develop a cross-species universal FDR calibration method that can comprehensively consider the influence of wood density, has temperature correction capability, and is applicable to tree species with secondary xylem, so as to achieve long-term, accurate, and low-cost monitoring of stem moisture content. Summary of the Invention

[0010] To address the shortcomings of existing technologies, such as the strong species specificity of calibration models, the unclear role of wood density in FDR calibration (especially for tree species with typical secondary xylem), and the lack of a systematic correction scheme for temperature effects, this invention provides a stem moisture content FDR calibration method based on wood density parameterization and its application. First, the wood density of the target tree species is obtained, and the dielectric constant of the stem is measured and its common logarithm is calculated using a frequency domain reflectance sensor. Then, the common logarithm of the dielectric constant is linearly corrected using a pre-calibrated temperature correction coefficient to eliminate the influence of temperature on the measurement signal. Finally, the wood density and the corrected common logarithm of the dielectric constant are substituted into the following general calibration model: Volumetric moisture content = (α × wood density + β) × lg(ε) + γ, where α, β, and γ are model constants calibrated experimentally.

[0011] This invention is based on 16 subtropical tree species with typical secondary xylem, with a coverage of 0.31–0.62 g·cm³. -3 Experimental data across a range of wood densities confirmed a strong positive correlation between the FDR calibration slope and wood density (r=0.81), while the intercept was independent of wood density (r=-0.07). Based on this, the constructed general parameterized wood density model improved cross-species prediction performance from R... 2 =Increased from 0.69 to 0.75.

[0012] Unlike existing wood density-independent models applicable to tropical forests (including palm trees), this invention is explicitly applicable to tree species with secondary xylem and heartwood-sapwood differentiation, such as diffuse-porous, ring-porous, and non-porous woods. It transforms the mechanistic regulation of wood density on bound water ratio and dielectric sensitivity into a simple and easy-to-use linear parameterization, and provides a temperature correction coefficient independent of wood density (average slope 0.002706°C). -1 ).

[0013] This invention also provides a monitoring device comprising an FDR probe, a temperature sensor, an input module, a memory, and a processor, and its application in monitoring stem water content of forest trees, assessing drought vulnerability, and monitoring the health of urban trees. This invention significantly reduces the calibration cost of multi-species forest moisture monitoring and provides a universal, robust, and easily deployable technical tool for long-term in-situ monitoring across species.

[0014] In a first aspect, the present invention provides a stem moisture content FDR calibration method based on wood density parameterization, comprising the following steps: Obtain the wood density of the target tree species; The dielectric constant ε of the stem of the target tree species is measured using a frequency domain reflectance sensor, and the common logarithmic value of the dielectric constant ε is calculated and corrected. The volumetric moisture content of the stem is calculated by substituting the common logarithmic values ​​of the wood density and the corrected dielectric constant ε into the density parameterization general calibration model. The density parameterized universal calibration model has the following characteristics as shown in equation (1): (1) In the formula, This refers to the volumetric water content. , , These are the model constants calibrated experimentally. The commonly used logarithmic value of the corrected dielectric constant ε This refers to the density of the wood.

[0015] Preferably, the =0.3211, the =0.1088, the =-0.0186.

[0016] As a preferred option, the commonly used logarithmic value of the corrected dielectric constant ε The steps include: The temperature T of the stem is measured, and the common logarithmic value of the original dielectric constant ε is corrected according to equation (2): (2) in, This is the temperature correction factor. For reference temperature, This is the commonly used logarithmic value of the original dielectric constant ε.

[0017] Preferably, the temperature correction coefficient =0.002706 °C -1 .

[0018] Preferably, the target tree species is a tree species with secondary xylem and differentiation between heartwood and sapwood, selected from diffuse-porous, ring-porous, and non-porous tree species.

[0019] Preferably, the wood density is the basic density of the wood, which is the ratio of oven-dry weight to green volume, expressed in grams per cubic centimeter.

[0020] Secondly, this invention provides a frequency domain reflectance (FDR) stem moisture monitoring device based on wood density parameterization. The device employs the stem moisture content FDR calibration method based on the general wood density parameterization model as described in claims 1-6, specifically including: Frequency domain reflectance sensor probe, used to insert into stem sapwood and measure dielectric constant. ; Temperature sensor used to measure stem temperature ; The input module is used to obtain the wood density of the target tree species; The memory stores the model constants of the density-parameterized universal calibration model. , , and temperature correction factor ; The processor, connected to the frequency domain reflectance sensor probe, temperature sensor, input module, and memory respectively, is used to calculate the original logarithm based on the dielectric constant ε, and to perform temperature correction on the original logarithm based on the measured temperature T and temperature correction coefficient k. Based on the obtained wood density and the corrected The volumetric moisture content is calculated according to the formula: volumetric moisture content = (α × wood density + β) × lg(ε) + γ.

[0021] Thirdly, the present invention provides a density-parameterized frequency domain reflectance stem moisture monitoring device, which is used in trunk moisture content monitoring and drought vulnerability assessment.

[0022] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention establishes a universal calibration model for wood density parameterization: Volumetric moisture content = (α × wood density β) × lg(ε) + γ, transforming species-specific calibration into a universal calibration using wood density as a single parameter. For new tree species, only the commonly used logarithm of the dielectric constant measured by the WD and FDR sensors, lg(ε), is needed to directly calculate the volumetric moisture content, eliminating the need for destructive drying calibration experiments on the tree species. This significantly reduces the manpower, material resources, and time costs of monitoring forest moisture for multiple tree species, breaking through the technical bottleneck of traditional FDR calibration, which requires only one calibration per tree species.

[0023] 2. This invention is based on 16 subtropical tree species (wood density range 0.31~0.62 g·cm³). -3 Based on experimental data, this invention is the first to demonstrate a strong positive correlation between the FDR calibration slope and wood density (r=0.81, p<0.001), while the calibration intercept is independent of wood density (r=-0.07, p=0.45). The general parameterized wood density model constructed based on this demonstrates its cross-species predictive performance (R0.07). 2 The accuracy of the model improved from 0.69 in the traditional simple linear regression to 0.75 (an improvement of approximately 7.75%), while the root mean square error decreased by 6.65%. This model achieves a moisture content estimation with clear physical meaning, high statistical fit, and true cross-species transferability by transforming the mechanistic regulation of wood density on the bound water ratio and dielectric sensitivity into a linear parameterized form.

[0024] 3. This invention is the first to systematically quantify the influence of temperature on the FDR measurement signal, finding that the average influence coefficient of temperature on lg(ε) is 0.002706°C. -1 Furthermore, this coefficient is not significantly correlated with wood density (r=-0.07, p=0.45). The independent linear temperature correction formula provided here eliminates measurement bias caused by environmental temperature fluctuations, enabling the method of this invention to maintain high accuracy in variable temperature environments. Simultaneously, this invention limits the model to tree species with typical secondary xylem and heartwood-sapwood differentiation, complementing existing wood density-independent models for tropical forests. This fills the technical gap in universal FDR calibration for secondary xylem tree species, providing a reliable tool for long-term in-situ monitoring of temperate, subtropical, and cold-climate forests. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 The linear relationship between lg(ε) and VWC for 16 tree species provided in the embodiments of this application is shown in the graph (solid line represents the best-fit linear regression line, m represents the slope). Figure 2 The linear relationship between lg(ε) and trunk VWC in 16 subtropical tree species provided in the embodiments of this application is shown (each colored curve represents the best linear regression fit curve for that species). Figure 3 The graph showing the relationship between WD and calibration parameters provided in the embodiments of this application is illustrated (the solid black line represents the linear regression fitting line, and the gray shaded area represents the 95% confidence interval); wherein Figure 3 (a) is a graph showing the relationship between the calibration slope and WD; Figure 3 (b) is a graph showing the relationship between calibration intercept and WD; Figure 4 The temperature response graph of the FDR signal provided in this application embodiment is shown (the black solid line represents the linear regression fitting line, and the gray shaded area represents the 95% confidence interval); wherein Figure 4 (a) is a graph showing the relationship between lg(ε) and temperature for different tree species; Figure 4 (b) shows the relationship between WD and the slope of the temperature response. Figure 5 This paper presents a comparison chart of the stem water content predicted by different calibration equations provided in the embodiments of this application with the observed values; wherein... Figure 5 (a) is a simple linear regression plot excluding WD; Figure 5 (b) A diagram of the unified calibration equations incorporated into WD; Figure 5 (c) is a diagram of a linear mixed-effects model including the interaction term between lg(ε) and WD; Figure 6 The diagram illustrates the relationship between WD and ε-based calibration slope for five temperate tree species reported by Matheny et al. (2017) in an embodiment of this application. Detailed Implementation

[0027] Experimental materials: From August to December 2025, 16 common subtropical tree species were selected as research subjects in urban green spaces in Chengdu, Sichuan Province (a subtropical humid region), including 13 broad-leaved angiosperms and 3 gymnosperms. All sampled trees were healthy, mature individuals without obvious signs of pests, diseases, mechanical damage, or growth stress. For each tree species, stem segments were collected from 4–5 different healthy individuals, with 3 stem segments approximately 30 cm in length taken from each individual. Immediately after sampling, the ends and surface of the stem segments were tightly wrapped with plastic film to minimize moisture evaporation. All stem segments were then transported back to the laboratory and stored in a 4°C refrigerator. A calibration experiment was initiated within 24 hours of sampling to maintain the original wood moisture characteristics of the samples.

[0028] The wood density (basic density, i.e., the ratio of oven-dry mass to saturated volume) and pore type of each tree species are listed in Table 1.

[0029] Table 1. Sampling tree species and their wood characteristics

[0030] 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. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, any product that is the same as or similar to the present invention, derived by any person under the guidance of the present invention or by combining the features of the present invention with other prior art, falls within the protection scope of the present invention. Furthermore, all other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of the present invention.

[0031] Specific experimental steps or conditions are not specified in the embodiments; they can be performed according to the conventional experimental steps or conditions described in the prior art. Reagents and other instruments used, unless otherwise specified, are all commercially available conventional reagent products. Furthermore, the accompanying drawings are merely illustrative diagrams of the embodiments of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore, repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0032] Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of this specification.

[0033] In the description of this invention, it should be understood that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0034] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] To enable those skilled in the art to better understand this application, the following embodiments are provided to illustrate in detail a stem moisture content FDR calibration method based on wood density parameterization and its application.

[0036] Example Example 1: Verification of the linear relationship of FDR calibration for different tree species Based on stem segment samples of 16 tree species obtained in the specific implementation method, this embodiment measures the dielectric constant ε and volumetric water content VWC of each tree species during the continuous drying process according to the aforementioned FDR calibration experimental method, and establishes a linear regression relationship.

[0037] For each tree species, a scatter plot was drawn with the common logarithm of the dielectric constant lg(ε) on the x-axis and the volumetric water content VWC on the y-axis, and a linear regression curve was fitted. The results are as follows: Figure 1As shown, lg(ε) and VWC exhibited a good linear relationship for all 16 tree species. The goodness-of-fit R² of each regression equation ranged from 0.65 to 0.87, and the regression slopes ranged from 0.18 to 0.31. All regressions passed the significance test (p < 0.01). These results indicate that within a single tree species, the linear equation VWC = slope × lg(ε) + intercept can accurately describe the relationship between FDR sensor output and stem moisture content. However, the regression parameters (slope and intercept) differ significantly among different tree species, demonstrating clear species specificity.

[0038] Figure 2 The linear fitting lines for each tree species are further displayed, visually presenting the different wood densities (WD range 0.31–0.62 g·cm³). -3 The distribution of the corresponding regression lines.

[0039] Example 2 Correlation analysis between calibration slope and wood density To further reveal Figure 1 To investigate the differences in calibration slopes among different tree species, this embodiment uses linear regression analysis to examine the calibration slope of each tree species and its wood density (WD). The results are as follows: Figure 3 As shown in Figure a, a highly significant positive correlation was found between the calibration slope and WD (r = 0.81, p < 0.001), with the linear regression equation being: slope = 0.3211 × WD + 0.1088, meaning that for every 0.1 g·cm³ increase in wood density... -3 The calibration slope increased by an average of approximately 0.032.

[0040] Regression analysis was performed between the calibration intercept and WD for each tree species, and the results are as follows: Figure 3 As shown in b, the calibration intercept was not significantly correlated with WD (r=-0.07, p=0.45), indicating that the calibration intercept for different tree species can be considered a constant. The intercept value obtained by fitting all data was -0.0186.

[0041] The above findings indicate that, in tree species with secondary xylem, the species specificity of FDR calibration is mainly driven by wood density, and the intercept is basically constant, providing a basis for establishing a general parameterized model of wood density.

[0042] Example 3: The Influence of Temperature on FDR Signal and Determination of Correction Coefficients This embodiment analyzes the effect of temperature on the output lg(ε) of the FDR sensor based on data from the temperature effect experimental group. Under the condition that the stem water content remained basically constant (water loss <0.5%), the lg(ε) values ​​were measured at different temperatures (0℃, 5℃, 10℃, 15℃, 20℃, 25℃, 30℃), and the lg(ε)-T relationship curves for each tree species were plotted. The results are as follows: Figure 4 As shown in a.

[0043] For all tree species, lg(ε) showed a significant positive linear relationship with temperature T (p<0.0001), and the slope of the temperature response for each tree species ranged from 0.001381 to 0.004003 °C. -1 Between these points, the average slope is 0.002706 °C. -1 This indicates that for every 1°C increase in temperature, lg(ε) increases by an average of approximately 0.0027.

[0044] Further regression analysis was performed on the temperature response slope of each tree species and its wood density (WD). The results are as follows: Figure 4 As shown in b. There was no significant correlation between the two (r=-0.07, p=0.45), indicating that the effect of temperature on the FDR signal is independent of wood density. Based on the above results, the temperature correction formula is determined as follows:

[0045] L is the value obtained directly from the sensor measurement and converted, T is the measured temperature (°C), and 25 is the reference temperature, which is the standard temperature at which the model was established in this experiment. When calculating the stem moisture content, the original lg(ε) should first be corrected to the correction value at 25°C before being substituted into the general parameterized wood density model.

[0046] Example 4: Comparison of Predictive Performance between the General Parametric Model for Wood Density and Existing Models This embodiment compares the cross-species prediction performance of the proposed general model for wood density parameterization with that of a traditional simple linear regression model. Experimental data from all 16 tree species were combined, and the volumetric moisture content (VWC) was predicted using the following three methods: Method A (existing technology): A simple linear regression VWC = a × lg(ε) + b is used, without distinguishing tree species or using timber density. The results are as follows... Figure 5 As shown in figure a, the model fit R 2 =0.69, RMSE=0.03.

[0047] Method B (this invention): The general parameterization model for wood density, VWC = (α × wood density + β) × lg(ε) + γ, is used. The results are as follows... Figure 5 As shown in b, the model fit R... 2 =0.75 (an improvement of approximately 7.75% compared to method A), RMSE=0.03 (a decrease of approximately 6.65%) Method C (linear mixed-effects model): VWC = β1·lg(ε) + β2·WD + β3·lg(ε)·WD + 0.0147 + b species The hybrid model considers the interaction term between lg(ε) and WD, with tree species as the random intercept. The results are as follows: Figure 5As shown in c, the marginal R 2 =0.75, condition R 2 =0.78, of which the random intercept of tree species contributes only 3.4% to the total variance.

[0048] The above comparison shows that the general model for parameterizing wood density proposed in this invention has a very close predictive performance across tree species to the linear mixed-effects model that requires species intercept, and does not require recalibrating the random effects for each new tree species, which significantly reduces the implementation cost of multi-tree-species monitoring.

[0049] Example 5: Model Applicability Verification Based on Independent Temperate Tree Species Data To verify whether the general model for parameterized wood density established in this invention is applicable to tree species in different climate zones, this embodiment uses independent publicly available data for cross-validation.

[0050] The ε-VWC calibration slope data for five temperate tree species were obtained from the literature reported by Matheny et al. (2017). This literature only provided individual calibration equations for each species, without revealing the relationship between the calibration slope and wood density, nor proposing any universal calibration model. In this embodiment, the density values ​​at 12% moisture content for the above five temperate tree species were obtained from The Wood Database, and converted to basic density using a conversion factor of 0.828 to ensure consistency with the wood density definition in Embodiment 1 of this invention.

[0051] The data points for the above five temperate tree species were compared with the prediction curves of the general model for parameterized wood density of this invention. The results are as follows: Figure 6 As shown, the data points of the five temperate tree species are closely distributed near the model prediction curve, and the correlation coefficient between the calibration slope and the wood density is r=0.88 (p<0.05), which is highly consistent with the linear relationship established by this invention based on 16 subtropical tree species.

[0052] The above results show that the positive correlation between the calibration slope and wood density proposed in this invention is also applicable to temperate tree species with secondary xylem, and has universality across climate zones; the generalized wood density parameterization model established in this invention has good predictive ability for independent data from different climate zones.

[0053] In summary, Examples 1-5 collectively demonstrate that the present invention can effectively solve the difficulties in cross-species application caused by species specificity in the existing FDR stem moisture content calibration, significantly reduce the calibration cost of multi-species forest moisture monitoring, and take into account the influence of temperature and wood density, thus having significant technological advancement and industrial application value.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0055] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0056] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0057] The above provides a detailed description of the stem moisture content FDR calibration method based on wood density parameterization and its application. Specific examples have been used to illustrate the principle and implementation of this application. The above description of the embodiments is only for the purpose of helping to understand the method and core idea of ​​this application. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A stem moisture content FDR calibration method based on wood density parameterization, characterized in that, Includes the following steps: Obtain the wood density of the target tree species; The dielectric constant ε of the stem of the target tree species is measured using a frequency domain reflectance sensor, and the common logarithmic value of the dielectric constant ε is calculated and corrected. The volumetric moisture content of the stem is calculated by substituting the common logarithmic values ​​of the wood density and the corrected dielectric constant ε into the density parameterization general calibration model. The density parameterized universal calibration model has the following characteristics as shown in equation (1): （1） In the formula, This refers to the volumetric water content. , , These are the model constants calibrated experimentally. The commonly used logarithmic value of the corrected dielectric constant ε This refers to the density of the wood.

2. The stem moisture content FDR calibration method based on wood density parameterization according to claim 1, characterized in that, The =0.3211, the =0.1088, the =-0.0186.

3. The stem moisture content FDR calibration method based on wood density parameterization according to claim 1, characterized in that, Commonly used logarithmic values ​​for correcting the dielectric constant ε The steps include: The temperature T of the stem is measured, and the common logarithmic value of the original dielectric constant ε is corrected according to equation (2): （2） in, This is the temperature correction factor. For reference temperature, This is the commonly used logarithmic value of the original dielectric constant ε.

4. The stem moisture content FDR calibration method based on wood density parameterization according to claim 3, characterized in that, The temperature correction coefficient =0.002706 °C -1 .

5. The stem moisture content FDR calibration method based on wood density parameterization according to claim 1, characterized in that, The target tree species are those with secondary xylem and differentiation between heartwood and sapwood, selected from diffuse-porous, ring-porous, and non-porous tree species.

6. The stem moisture content FDR calibration method based on wood density parameterization according to claim 1, characterized in that, The wood density is the basic density of wood, which is the ratio of oven-dry weight to green volume, expressed in grams per cubic centimeter.

7. A frequency domain reflectance stem moisture monitoring device based on wood density parameterization, characterized in that, The device employs the stem moisture content FDR calibration method based on the general model of wood density parameterization as described in claims 1-6, specifically including: Frequency domain reflectance sensor probe, used to insert into stem sapwood and measure dielectric constant. ; Temperature sensor used to measure stem temperature ; The input module is used to obtain the wood density of the target tree species; The memory stores the model constants of the density-parameterized universal calibration model. , , and temperature correction factor ; The processor, connected to the frequency domain reflectance sensor probe, temperature sensor, input module, and memory respectively, is used to calculate the original logarithm based on the dielectric constant ε, and to perform temperature correction on the original logarithm based on the measured temperature T and temperature correction coefficient k. Based on the obtained wood density and the corrected The volumetric moisture content is calculated according to the formula: volumetric moisture content = (α × wood density + β) × lg(ε) + γ.

8. The application of the density parameterized frequency domain reflectance stem moisture monitoring device as described in claim 7 in trunk moisture content monitoring and drought vulnerability assessment.

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