A method for evaluating forest land utilization

Abstract

This invention discloses a method for evaluating forest land utilization rate. The method includes: acquiring forest land area and utilization rate data for the area to be evaluated, and classifying forest land area into grades; obtaining a guide curve model based on scatter data fitting; calculating the standard deviation of forest land utilization rate within each grade, and fitting a standard deviation equation between the standard deviation of utilization rate and the median of forest land area grade; using a benchmark area and its corresponding utilization rate as a reference, and combining the guide curve model and the standard deviation equation, calculating the evaluation interval of forest land utilization rate; and classifying forest land utilization rate into fully utilized, moderately utilized, and underutilized types according to the evaluation interval. This invention improves the refinement and scientific level of forest land management by establishing zoning and classification evaluation standards, achieving differentiated management. By fully utilizing existing forest resource survey and monitoring system data, it eliminates the need for additional investment in utilization rate calculation, achieving cost reduction and efficiency improvement, and ensuring the efficiency of monitoring data utilization.

Description

Technical Field

[0001] This invention relates to the field of forest land management technology, specifically a method for evaluating forest land utilization. Background Technology

[0002] Forest land utilization rate is a core indicator for measuring the efficiency of forest land resource development. It is defined as the percentage of forested land area to the total area of ​​suitable forest land. Forest land utilization rate reflects the degree of forest land resource allocation and utilization and is an important basis for evaluating the effectiveness of forest land protection and utilization. Improving forest land utilization rate is of great significance for ensuring timber security and ecological security. Scientific and reasonable forest land utilization rate standards can guide forestry operators to adopt intensive and sustainable management methods to achieve sustainable utilization of forest land resources. Improving forest land utilization rate is an important way to improve the level of land conservation and intensive use and optimize the spatial layout of forest land. Clarifying forest land utilization rate can guide the scientific allocation of forest land resources, optimize the direction of utilization, and improve output efficiency. Currently, the FAO and UNFCCC frameworks have land use change statistical systems, and similar indicators such as the land use index are used in forest land use planning. However, there is no direct standard for forest land utilization rate, and even less evaluation methods. Although relevant standards for forest land classification and surveys have been established, there is a lack of evaluation methods for the basic indicator of forest land utilization rate. At present, forest land management is shifting from expansion to quality improvement, and there is a lack of utilization rate evaluation methods that are adapted to the needs of forest land management and policies in the new stage. This is incompatible with the requirements of promoting high-quality development and high-level protection in a coordinated manner. At present, due to the large differences in natural geography and statistical standards in various regions, cross-regional data comparison and the scientific nature of decision-making are affected. In order to promote the standardization and scientification of forest land management and improve the level of forest land protection and utilization, a scientific evaluation method for forest land utilization rate is particularly important. Summary of the Invention

[0003] This invention provides a method for evaluating forest land utilization, which can effectively solve the problem of evaluating forest land utilization using the currently ineffective evaluation methods mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating forest land utilization rate, which evaluates forest land utilization rate based on zoning calculations and classifies it into three types: full utilization, moderate utilization, and low utilization, including the following evaluation process: Step 1: Classification of forest land area; Step 2: Guide curve fitting; Step 3: Fitting the standard deviation equation; Step 4: Calculate the evaluation interval.

[0005] According to the above technical solution, step 1 aims to establish a sample grouping system in a statistical sense, based on the stratified sampling theory in statistical analysis, by grouping areas with similar forest land resource endowments into the same level. In the specific implementation of the division, it is necessary to collect forest area data of all statistical units in the area to be evaluated to form a complete sample set. Then, according to the grading principle commonly used in statistics, the sample is divided into several continuous intervals according to the area size. The determination of the number of gradings and the width of the intervals needs to take into account other factors.

[0006] According to the above technical solution, in step 1, after the division is completed, it is necessary to calculate the standard deviation of the forest land utilization rate data in each level. The standard deviation is used as the core indicator to measure the degree of data dispersion, reflecting the degree of difference in forest land utilization rate of each statistical unit within the area level. Based on the status of forest land resources in the region, forest land area is classified into grades for calculating standard deviation. Four intervals are defined: 500,000-990,000 hectares, 1,000,000-1,490,000 hectares, 1,500,000-1,990,000 hectares, and over 2,000,000 hectares. The standard deviation of forest land utilization rate is calculated for each period.

[0007] According to the above technical solution, step 2 is a key step in establishing the intrinsic quantitative relationship between forest area and utilization rate. Its core purpose is to use a mathematical model to depict the objective law that the larger the area, the closer the utilization rate is to a certain value. Its technical principle is based on regression analysis method, which fits the scatter plot of forest area-utilization rate by selecting an appropriate curve form. The guide curve is nonlinear, and there is a clear nonlinear relationship between forest land utilization rate and area: that is, when the area is small, the forest land utilization rate is often low due to natural factors. As the area increases, the available land resources increase, the scale effect gradually appears, and the utilization rate rises and tends to stabilize.

[0008] According to the above technical solution, in step 2, the selection of the guide curve directly affects the scientificity and accuracy of the forest land utilization rate zoning evaluation. Specifically, logarithmic curve model, hyperbola model and exponential model are used to fit the forest land area and utilization rate, and the fitting result of the guide curve model is determined.

[0009] According to the above technical solution, in step 2, the model parameters are solved using the least squares method, which minimizes the sum of squared residuals between all observed values ​​and fitted values. After the fitting is completed, the overall goodness of fit of the model is evaluated by the coefficient of determination. The closer the coefficient of determination is to 1, the stronger the model's ability to interpret the data.

[0010] According to the above technical solution, step 3 establishes a mathematical relationship between the median of forest area grade and the standard deviation of utilization rate. Its core purpose is to realize the spatial interpolation and extrapolation prediction of standard deviation. Based on the function approximation theory, through the known standard deviation data of each grade, a continuous function of standard deviation changing with area is fitted, and the corresponding theoretical value of standard deviation is calculated for any area value. In actual evaluation work, due to the limitations of sample size and distribution, not all area grades have enough data to support standard deviation calculation. By fitting the standard deviation equation, a quantitative relationship between area and dispersion can be established.

[0011] According to the above technical solution, in step 3, a logarithmic linear equation is used for fitting, that is, the standard deviation is linearly related to the logarithm of the area; The model fitting uses the same least squares method as the guide curve to solve for the two parameters, intercept and slope. Specifically, it fits the standard deviation equation of the utilization rate of each forest area grade.

[0012] According to the above technical solution, step 4, as the final output link, has the core purpose of establishing a complete and operable forest land utilization rate classification evaluation standard system. By comprehensively applying the calculation results of steps 1-3 and through a systematic calculation process, it provides utilization rate evaluation intervals for areas of different sizes. The calculation of the evaluation interval involves the systematic determination of three key parameters, including the determination of the benchmark area, the determination of the utilization rate interval, and the derivation of the forest land utilization rate evaluation interval.

[0013] According to the above technical solution, the benchmark area is determined as follows: the benchmark area is the reference system of the evaluation system, and the benchmark area has a very significant impact on the calculation of the forest land utilization rate evaluation interval. The utilization rate interval is determined as follows: the interval refers to the difference in utilization rate between adjacent evaluation levels, and its determination directly affects the precision and discriminativeness of the evaluation. The evaluation interval for forest land utilization rate is: the theoretical standard deviation calculated based on the benchmark utilization rate of each area grade determined by the guide curve and combined with the standard deviation equation. Using the utilization rate of the benchmark area and benchmark level as a reference, the evaluation interval is adjusted twice based on the area difference and standard deviation difference between the area to be evaluated and the benchmark area.

[0014] Compared with the prior art, the beneficial effects of the present invention are: the present invention has a scientific and reasonable structure and is safe and convenient to use. 1. This invention establishes a quantitative mathematical relationship between forest area and utilization rate and standard deviation by fitting a guide curve and standard deviation equation. It overcomes the defects of indicator setting in traditional methods and makes dual adjustments based on the differences in natural endowment of different areas, thus ensuring the scientific nature of the evaluation interval and cross-regional comparability.

[0015] Based on zoning calculations, forest land utilization rates are evaluated and categorized into three types: full utilization, moderate utilization, and low utilization. The zoning calculations are conducted according to natural geographical zoning, evaluating reasonable ranges for forest land utilization rates in mountainous and plain counties respectively. The code and technical methods are aligned with existing standards to ensure compatibility and interoperability.

[0016] 2. By utilizing the existing forest resource survey and monitoring system, we can fully utilize existing monitoring data without increasing investment in utilization rate calculations, thus achieving cost reduction and efficiency improvement. We have clarified the connotation and definition of forest land utilization rate, unified the calculation formula, data collection and processing methods, established a standardized evaluation system, standardized data quality control and processes, and achieved high public awareness, which is conducive to promotion. Furthermore, through zoning evaluation, we can scientifically guide the rational use of forest land resources and avoid one-size-fits-all management evaluation and inappropriate development indicator settings. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] In the attached diagram: Figure 1 This is a flowchart of the evaluation method of the present invention; Figure 2 This is a schematic diagram of the fitting result of the logarithmic curve of the present invention; Figure 3 This is a schematic diagram of the fitting result of the hyperbola of the present invention; Figure 4 This is a schematic diagram of the fitting results of the exponential model of the present invention; Figure 5 This is a schematic diagram of the model fitting results of this invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. It should be particularly noted that the statistical units (such as City A, City B, etc.), the specifically collected forest area data, the specific area grade intervals (such as 500,000-990,000 hectares, etc.) listed in this embodiment are merely illustrative application examples to illustrate the calculation process of the present invention in detail, and should not be construed as any limitation on the scope of application and protection of the method of the present invention.

[0020] Example: Figure 1-5As shown, this invention provides a technical solution: a method for evaluating forest land utilization. This method is applied to forest land management evaluation, forest land spatial layout optimization, afforestation space potential assessment, and the calculation of forest land area and forest coverage indicators. Based on zonal calculations, it evaluates forest land utilization, categorizing it into three types: full utilization, moderate utilization, and low utilization. The evaluation process includes the following: Step 1: Classification of forest land area; Step 2: Guide curve fitting; Step 3: Fitting the standard deviation equation; Step 4: Calculate the evaluation interval.

[0021] Based on the above technical solution, step 1 aims to establish a statistically significant sample grouping system so as to calculate the dispersion of forest land utilization rate within each level, i.e., standard deviation. Specifically, based on the stratified sampling theory in statistical analysis, by grouping areas with similar forest land resource endowments into the same level, the systematic bias caused by excessive differences in area is eliminated. In the specific implementation of the division, it is necessary to collect forest area data of all statistical units in the area to be evaluated to form a complete sample set. The statistical units include administrative units such as cities, prefectures, and counties. Then, according to the grading principle commonly used in statistics, the sample is divided into several continuous intervals according to the area size. The determination of the number of gradings and the width of the intervals needs to take into account other factors. Other factors include: first, the total sample size, ensuring that each level contains a sufficient number of samples to guarantee statistical significance; second, the distribution characteristics of regional forest land resources, avoiding situations where some levels have too few samples, thus affecting representativeness; and third, the ease of calculation and the interpretability of the results.

[0022] Based on the above technical solution, in step 1, after the division is completed, it is necessary to calculate the standard deviation of the forest land utilization rate data in each level. The standard deviation is used as the core indicator to measure the dispersion of data, reflecting the degree of difference in forest land utilization rate of each statistical unit within the area level. The larger the standard deviation, the more obvious the difference in forest land utilization rate in the same level area and the higher the degree of management imbalance. The smaller the standard deviation, the more concentrated the distribution of forest land utilization rate in the level area, which is convenient for establishing a unified evaluation standard. Based on the regional forest land resource status, forest land area is classified into grades for calculating the standard deviation, divided into four intervals, as shown in the table below:

[0023] In the table above, the four forest land area levels are: 500,000-990,000 hectares, 1,000,000-1,490,000 hectares, 1,500,000-1,990,000 hectares, and over 2,000,000 hectares. The standard deviation of forest land utilization rate for each period is as follows: 500,000-990,000 hectares: including City C and State M, with a standard deviation of 0.07; 1,000,000-1,490,000 hectares: including cities A, D, E, G, K, and N, with a standard deviation of 0.13; 1.5-1.99 million hectares: including City B, City F, State I, State L, and State O, with a standard deviation of 0.03; Grade 2 million hectares or more: includes H state and J state, with a standard deviation of 0.14.

[0024] Based on the above technical solution, step 2 is a key step in establishing the intrinsic quantitative relationship between forest area and utilization rate. Its core purpose is to use a mathematical model to depict the objective law that the larger the area, the closer the utilization rate is to a certain value. Its technical principle is based on regression analysis. By selecting an appropriate curve form, the forest area-utilization rate scatter plot is fitted, thereby revealing the functional relationship between the two. The guide curve is chosen to be nonlinear, and there is a clear nonlinear relationship between forest land utilization rate and area: that is, when the area is small, the forest land utilization rate is often low due to natural factors, including topography, geomorphology, and hydrothermal conditions. As the area increases, the available land resources increase, the scale effect gradually appears, and the utilization rate rises and tends to stabilize. Therefore, the curve model needs to have the characteristics of rapid growth first and then gradual slowdown.

[0025] Based on the above technical solution, in step 2, the selection of the guide curve directly affects the scientificity and accuracy of the forest land utilization rate zoning evaluation. Specifically, logarithmic curve models, hyperbolic models, and exponential models are used to fit the forest land area and utilization rate, and the coefficient of determination R is used as the basis for the evaluation. 2 The fitting result of the guide curve model is determined by the curve form. The three candidate models are as follows: Lr=a+b×lg(A) (1; Lr=a+b / A (2; Lr=a+b×exp(-c×A) (3; In the above formula: Lr is the forest land utilization rate, A is the forest land area, and a, b, and c are the parameters to be solved. Among them, the logarithmic curve model of formula 1 assumes that the utilization rate is linearly related to the logarithm of the area. As the area increases, the increment of the utilization rate decreases. The hyperbolic model of formula 2 is based on the reciprocal transformation, reflecting the law that the utilization rate approaches a certain upper limit as the area increases. The exponential model of formula 3 adopts the form of exponential decay and has a clear asymptotic feature.

[0026] Based on the above technical solution, in step 2, the model parameters are solved using the least squares method, which minimizes the sum of squared residuals between all observed and fitted values. After fitting, the coefficient of determination R is used to... 2 To evaluate the overall goodness of fit of the model, R2 The closer a value is to 1, the stronger the model's ability to interpret the data. like Figure 2 As shown, the fitting results of the logarithmic curve are as follows: Equation: User-Defined, Logarithmic Curve: f = a + b * log(x); R Rsqr Adj Rsqr Standard Error of Estimate 0.3618 0.1309 0.0640 0.1137 Coefficient Std. Error t P VIF a 1.5801 0.5356 2.9503 0.0113 332.6123< b -0.3486 0.2491 -1.3993 0.1851 332.6123< Analysis of Variance: Uncorrected for the mean of the observations: DF SS MS Regression 2 10.4035 5.2017 Residual 13 0.1682 0.0129 Total 15 10.5717 0.7048 Corrected for the mean of the observations: DF SS MS FP Regression 1 0.0253 0.0253 1.9575 0.1852 Residual 13 0.1682 0.0129 Total 14 0.1935 0.0138; The fitting results show that the coefficient of determination R0 2 The adjusted R is 0.3618. 2 The value is 0.1309, and the standard error of the estimate is 0.1137. In the analysis of variance, the regression term has 1 degree of freedom, the residual term has 13 degrees of freedom, the p-value corresponding to the ratio of the regression mean square to the residual mean square is 0.1852, the estimated value of parameter a is 1.5801, and the estimated value of b is -0.3486. The p-value corresponding to b is 0.1851, which does not pass the significance test. Overall, the goodness of fit of the logarithmic curve is low, the explanatory power of the independent variable on the dependent variable is limited, and the overall significance of the model is insufficient. like Figure 3 As shown, the fitting results of the hyperbola are as follows: Equation: User-Defined, Hyperbolic: f = a + b / x; R Rsqr Adj Rsqr Standard Error of Estimate 0.3628 0.1317 0.0649 0.1137 Coefficient Std. Error t P VIF a 0.6857 0.1081 6.3432 <0.0001 13.5650< b 19.7081 14.0395 1.4038 0.1838 13.5650< Analysis of Variance: Uncorrected for the mean of the observations: DF SS MS Regression 2 10.4036 5.2018 Residual 13 0.1680 0.0129 Total 15 10.5717 0.7048 Corrected for the mean of the observations: DF SS MS FP Regression 1 0.0255 0.0255 1.9709 0.1838 Residual 13 0.1680 0.0129 Total 14 0.1935 0.0138; The fitting results show that the coefficient of determination R0 2 The adjusted R is 0.3628. 2The value is 0.0649, and the standard error of the estimate is 0.1137. In the analysis of variance, the regression term has 1 degree of freedom, the residual term has 13 degrees of freedom, and the corresponding P value is 0.1838. The estimated value of parameter a is 0.6857, the estimated value of b is 19.7081, and the P value of parameter b is 0.1838, which does not pass the significance test. Although the statistical significance is not high, considering the fit between the model form and the actual data trend, as well as the need for subsequent evaluation interval construction, this method ultimately selects the hyperbolic fitting result as the guide curve. like Figure 4 As shown, the fitting results of the exponential model are as follows: Equation: Exponential Decay, Single, 3 Parameter: f = a + b * exp(-c * x); R Rsqr Adj Rsqr Standard Error of Estimate 0.3607 0.1301 0.0000 0.1184 Coefficient Std. Error t P VIF a 0.7336 0.3471 2.1136 0.0562 128.8444< b 0.4706 0.9502 0.4953 0.6294 49.3472< c 0.0114 0.0410 0.2785 0.7854 298.8310< Analysis of Variance: Uncorrected for the mean of the observations: DF SS MS Regression 3 10.4034 3.4678 Residual 12 0.1683 0.0140 Total 15 10.5717 0.7048 Corrected for the mean of the observations: DF SS MS FP Regression 2 0.0252 0.0126 0.8976 0.4332 Residual 12 0.1683 0.0140 Total 14 0.1935 0.0138 The fitting results show that the coefficient of determination R0 2 The adjusted R is 0.3607. 2 The value is 0.0000, and the standard error of the estimate is 0.1184. In the analysis of variance, the regression term has 2 degrees of freedom, and the residual term has 12 degrees of freedom, with a corresponding p-value of 0.4332. The estimated values ​​of parameters a, b, and c are 0.7336, 0.4706, and 0.0114, respectively, with corresponding p-values ​​of 0.0562, 0.6294, and 0.7854, all of which failed the significance test. Furthermore, the variance inflation factors of each parameter are relatively large, indicating that the model has a serious multicollinearity problem. Therefore, the fitting effect of the exponential model is not suitable for this method.

[0027] Based on the above technical solution, step 3 establishes a mathematical relationship between the median of forest area grade and the standard deviation of utilization rate. Its core purpose is to realize the spatial interpolation and extrapolation prediction of standard deviation. Based on the function approximation theory, through the known standard deviation data of each grade, a continuous function of standard deviation changing with area is fitted, and the corresponding theoretical value of standard deviation is calculated for any area value. In practical evaluation work, due to limitations in sample size and distribution, not all area grades have sufficient data to support standard deviation calculation. By fitting the standard deviation equation, a quantitative relationship between area and dispersion can be established, achieving two core functions: First, for area grades with available data, the equation can be used to calculate the deviation between theoretical and actual values, verifying the rationality of the data; second, for area grades lacking data, the standard deviation estimate can be obtained through extrapolation of the equation, ensuring the integrity of the evaluation system.

[0028] Based on the above technical solution, in step 3, a linear equation in logarithmic form is used for fitting, that is, the standard deviation is linearly related to the logarithm of the area. This choice is based on the following considerations: the change in standard deviation usually shows the characteristic of first increasing and then decreasing as the area increases. Logarithmic transformation can effectively capture this nonlinear trend. At the same time, logarithmic form has the advantages of fewer parameters, clear physical meaning, and simple calculation, which facilitates subsequent application and result interpretation. The model fitting employs the same least squares method as the guide curve, solving for the intercept 'a' and slope 'b' parameters. Specifically, it fits the standard deviation equation for the utilization rate of each forest land area grade. Based on the standard deviation Sa of each forest land area grade and the median A of the forest land area grade, the following formula is used to fit the standard deviation equation for the utilization rate of the forest land area grade: Sa = a + blg(A) (4; In the above formula: Sa is the standard deviation of utilization rate, A is the median of forest area grade, and a and b are the parameters to be solved; like Figure 5 As shown, the model fitting results are as follows: Equation:User-Defined,SD_fitting: f = a + b * log(x); R Rsqr Adj Rsqr Standard Error of Estimate 0.2586 0.0669 0.0000 0.0599 Coefficient Std. Error t P VIF a -0.0422 0.3605 -0.1170 0.9176 144.7184< b 0.0635 0.1678 0.3787 0.7414 144.7184< Analysis of Variance: Uncorrected for the mean of the observations: DF SS MS Regression 2 0.0358 0.0179 Residual 2 0.0072 0.0036 Total 4 0.0430 0.0107 Corrected for the mean of the observations: DF SS MS FP Regression 1 0.0005 0.0005 0.1433 0.7414 Residual 2 0.0072 0.0036 Total 3 0.0077 0.0026.

[0029] As can be seen from the above, using the logarithmic model Sa=a+blg(A) for fitting, the fitting results show that the coefficient of determination R0 is... 2 The adjusted R is 0.2586. 2 The value is 0.0000, and the standard error of the estimate is 0.0599. In the analysis of variance, the regression term has 1 degree of freedom, the residual term has 2 degrees of freedom, and the corresponding P-value is 0.7414. The estimated value of parameter a is -0.0422, and the estimated value of b is 0.0635. The P-value of b is 0.7414, which does not pass the significance test. Moreover, the variance inflation factor is large, and the model stability is insufficient. This fitting result is only for reference. In the actual calculation of the evaluation interval, it needs to be adjusted in combination with the theoretical value of the standard deviation. Based on the above technical solution, step 4, as the final output link, aims to establish a complete and operable forest land utilization rate classification evaluation standard system. By comprehensively applying the calculation results of steps 1-3 and through a systematic calculation process, it provides scientific and reasonable utilization rate evaluation intervals for areas of different sizes. The calculation of the evaluation interval involves the systematic determination of three key parameters, including the determination of the benchmark area, the determination of the utilization rate interval, and the derivation of the forest land utilization rate evaluation interval.

[0030] Based on the above technical solution, the benchmark area is determined as follows: The benchmark area is the reference system of the evaluation system. The benchmark area has a significant impact on the calculation of the forest land utilization rate evaluation interval. The technical principle for determining the benchmark area is: to use the survey and monitoring data to calculate the average area of ​​each statistical unit to determine the benchmark area for evaluation. The technical basis is that the average value can reflect the overall level of the regional forest land resource endowment. Using it as the benchmark, the evaluation interval is adjusted up and down to ensure that the evaluation results are both scientific and comparable. Determination of utilization rate intervals: The interval refers to the difference in utilization rate between adjacent evaluation levels. Its determination directly affects the precision and discriminative power of the evaluation. Based on the absolute variation range ΔL of forest land utilization rate in the benchmark area and the management level, the index interval C and the number of evaluation levels k are determined. The interval can be roughly calculated using the following formula: C = ΔL / k (5); Among them, ΔL is the absolute variation of forest land utilization rate, which reflects the overall fluctuation level of forest land utilization rate in the region, and k is the number of evaluation levels, which determines the level of detail of the evaluation. It is generally recommended that there be 5 evaluation levels, which can effectively distinguish different utilization levels without being too complicated and facilitates practical operation. The evaluation interval for forest land utilization rate is calculated based on the benchmark utilization rate of each area grade determined by the guide curve, combined with the theoretical standard deviation calculated by the standard deviation equation, and then the evaluation interval for each area and grade is calculated using the interval adjustment formula in Equation 6. (6); In the above formula: P ij P represents the forest land utilization rate after adjustment for area i and grade j. ik For the forest land utilization rate of the i-th area, P is the guide curve.0j P represents the forest land utilization rate of the j-th grade based on the baseline area. 0k The guide curve represents the forest land utilization rate at the baseline area, where S0 is the theoretical value of the standard deviation of forest land utilization rate at the baseline area. i Let be the theoretical value of the standard deviation of forest land utilization rate for the i-th area; The mathematical principle of this formula is: taking the utilization rate of the benchmark area and benchmark level as a reference, and making double adjustments to the evaluation interval based on the area difference and standard deviation difference between the area to be evaluated and the benchmark area, so as to ensure that the evaluation results take into account both area factors and dispersion factors. Using the hyperbolic fitting results as the guide curve, the specific evaluation interval table for forest land utilization rate in the proposed area is as follows:

[0031] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating forest land utilization rate, characterized in that: The utilization rate of forest land is evaluated based on zoning calculations, and is divided into three types: full utilization, moderate utilization, and low utilization. The evaluation process includes the following steps: Step 1: Classification of forest land area; Step 2: Guide curve fitting; Step 3: Fitting the standard deviation equation; Step 4: Calculate the evaluation interval.

2. The method for evaluating forest land utilization rate according to claim 1, characterized in that: Step 1 aims to establish a statistically significant sample grouping system, based on the stratified sampling theory in statistical analysis, by grouping areas with similar forest land resource endowments into the same level. In the specific implementation of the division, it is necessary to collect forest area data of all statistical units in the area to be evaluated to form a complete sample set. Then, according to the grading principle commonly used in statistics, the sample is divided into several continuous intervals according to the area size. The determination of the number of gradings and the width of the intervals needs to take into account other factors.

3. The method for evaluating forest land utilization rate according to claim 2, characterized in that: In step 1, after the division is completed, the standard deviation of the forest land utilization rate data in each level needs to be calculated. The standard deviation is used as the core indicator to measure the degree of data dispersion, reflecting the degree of difference in forest land utilization rate of each statistical unit within the area level. Based on the status of forest land resources in the region, forest land area is classified into grades for calculating standard deviation. Four intervals are defined: 500,000-990,000 hectares, 1,000,000-1,490,000 hectares, 1,500,000-1,990,000 hectares, and over 2,000,000 hectares. The standard deviation of forest land utilization rate is calculated for each period.

4. The method for evaluating forest land utilization rate according to claim 1, characterized in that: Step 2 is a key step in establishing the intrinsic quantitative relationship between forest area and utilization rate. Its core purpose is to use a mathematical model to depict the objective law that the larger the area, the closer the utilization rate is to a certain value. Its technical principle is based on regression analysis, which fits the scatter plot of forest area-utilization rate by selecting an appropriate curve form. The guide curve is nonlinear, and there is a clear nonlinear relationship between forest land utilization rate and area: that is, when the area is small, the forest land utilization rate is often low due to natural factors. As the area increases, the available land resources increase, the scale effect gradually appears, and the utilization rate rises and tends to stabilize.

5. The method for evaluating forest land utilization rate according to claim 4, characterized in that: In step 2, the selection of the guide curve directly affects the scientificity and accuracy of the forest land utilization rate zoning evaluation. Specifically, logarithmic curve model, hyperbola model and exponential model are used to fit the forest land area and utilization rate, and the fitting result of the guide curve model is determined.

6. The method for evaluating forest land utilization rate according to claim 4, characterized in that: In step 2, the model parameters are solved using the least squares method, which minimizes the sum of squared residuals between all observed values ​​and fitted values. After fitting, the overall goodness of fit of the model is evaluated by the coefficient of determination. The closer the coefficient of determination is to 1, the stronger the model's ability to interpret the data.

7. The method for evaluating forest land utilization rate according to claim 1, characterized in that: Step 3 establishes a mathematical relationship between the median of forest area grade and the standard deviation of utilization rate. Its core purpose is to realize the spatial interpolation and extrapolation prediction of standard deviation. Based on the function approximation theory, a continuous function of standard deviation as a function of area is fitted using the known standard deviation data of each grade, and the corresponding theoretical value of standard deviation is calculated for any area value. In actual evaluation work, due to the limitations of sample size and distribution, not all area grades have enough data to support standard deviation calculation. By fitting the standard deviation equation, a quantitative relationship between area and dispersion can be established.

8. The method for evaluating forest land utilization rate according to claim 7, characterized in that: In step 3, a linear equation in logarithmic form is used for fitting, that is, the standard deviation is linearly related to the logarithm of the area. The model fitting uses the same least squares method as the guide curve to solve for the two parameters, intercept and slope. Specifically, it fits the standard deviation equation of the utilization rate of each forest area grade.

9. The method for evaluating forest land utilization rate according to claim 7, characterized in that: Step 4, as the final output, aims to establish a complete and operable graded evaluation standard system for forest land utilization. By comprehensively applying the calculation results of steps 1-3 and through a systematic calculation process, it provides utilization evaluation intervals for areas of different sizes. The calculation of the evaluation interval involves the systematic determination of three key parameters, including the determination of the benchmark area, the determination of the utilization rate interval, and the derivation of the forest land utilization rate evaluation interval.

10. A method for evaluating forest land utilization rate according to claim 9, characterized in that: The benchmark area is determined as follows: the benchmark area is the reference system for the evaluation system, and the benchmark area has a very significant impact on the calculation of the forest land utilization rate evaluation interval. The utilization rate interval is determined as follows: the interval refers to the difference in utilization rate between adjacent evaluation levels, and its determination directly affects the precision and discriminativeness of the evaluation. The evaluation interval for forest land utilization rate is: the theoretical standard deviation calculated based on the benchmark utilization rate of each area grade determined by the guide curve and combined with the standard deviation equation. Using the utilization rate of the benchmark area and benchmark level as a reference, the evaluation interval is adjusted twice based on the area difference and standard deviation difference between the area to be evaluated and the benchmark area.

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