Method for estimating yield and quality of under-forest grassland

Abstract

The invention discloses a method for estimating the yield and quality of an under-forest grassland, particularly relates to the field of grassland resource evaluation, and aims to solve the problem of inaccurate nutrient content prediction caused by non-uniform distribution of sampling points in a complex terrain. An abrupt slope area with data missing caused by terrain inaccessibility is accurately recognized; on the basis, the sampling points are divided into two independent data sets of a smooth region and a steep slope region by utilizing a slope threshold value, so that the pertinence and independence of the data are ensured; for the abrupt slope area, the scheme dynamically adjusts the interpolation direction according to the topographic features and the aggregation characteristics of the slope direction, and the gravity scouring law is combined, so that the nutrient content prediction is more in line with the ecological physical law; in addition, through residual analysis and compensation field correction technologies, a prediction result is optimized, and it is ensured that the prediction result fits actual ecological characteristics. Therefore, the nutrient content prediction is more scientific, accurate and ecologically reasonable, and reliable technical support is provided for under-forest grassland resource evaluation.

Description

Technical Field

[0001] The present invention relates to the field of grassland resource assessment, and more specifically, to a method for estimating the yield and quality of underforest grassland. Background Art

[0002] In the assessment of underforest grassland resources, field data collection is often restricted by complex terrain, resulting in a significant non-uniformity in the spatial distribution of sampling points. In steep areas, continuous data gaps are formed due to the difficulty of equipment transportation, while in flat areas, the high accessibility leads to an over-dense sampling points. This polarization phenomenon of density makes the spatial coverage and statistical representativeness of the original data set seriously unbalanced. Traditional interpolation methods, such as the inverse distance weighted method, default the data missing pattern as a random distribution and rely on geometric proximity to fill the blank areas, but ignore the strong causal constraint of terrain on sampling feasibility, resulting in the wrong extrapolation of the characteristics of the gentle area to the terrain-sensitive area during the interpolation process.

[0003] When existing interpolation algorithms fill non-uniformly missing data, systematic spatial misguidance is caused by ignoring the causal relationship between terrain and the missing pattern. For example, the soil nutrients in steep slope areas actually show a vertical gradient decrease under the action of gravity scouring, but traditional methods only interpolate based on the data of the adjacent gentle areas in the horizontal direction, resulting in a false homogenization of the nutrient distribution map in the steep slope areas. The essence is that the interpolation model does not embed the terrain factors (slope, aspect, elevation) as the driving variables of the missing mechanism into the calculation framework, making the interpolation result violate the physical laws of the surface process and ultimately misleading the spatial assessment conclusions of grassland productivity and quality.

[0004] In order to solve the above problems, a technical solution is provided now. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for estimating the yield and quality of underforest grassland. By analyzing the relationship between terrain factors and the missing rate of sampling points, steep slope areas where data are missing due to terrain inaccessibility are accurately identified; on this basis, the sampling points are divided into two independent data sets, namely the gentle area and the steep slope area, by using a slope threshold to ensure the pertinence and independence of the data; for the steep slope area, the scheme dynamically adjusts the interpolation direction according to the terrain characteristics and the aggregation characteristics of the slope direction, and combines the law of gravity scouring to make the prediction of nutrient content more in line with the ecological physical laws; in addition, through residual analysis and compensation field correction technology, the prediction result is optimized to ensure its conformity to the actual ecological characteristics; effectively overcoming the problem of uneven distribution of sampling points under complex terrain, making the prediction of nutrient content more scientific, accurate and ecologically reasonable, and providing reliable technical support for resource assessment to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1. Based on the correlation analysis between topographic factors and the missing rate of sampling points, identify the steep slope areas where data is missing due to topographic inaccessibility;

[0008] S2. Using the set slope threshold, divide the sampling points into two independent data sets: the gentle slope area and the steep slope area;

[0009] S3. In the interpolation calculation of the steep slope area, according to the complex changes of topographic features and the aggregation characteristics of slope directions, dynamically adjust the interpolation direction so that the interpolation prediction value decreases along the vertical direction of the slope according to the law of gravity erosion;

[0010] S4. By analyzing the relationship between the residuals of the interpolation results and the measured points and the slope, generate a compensation field and superimpose it on the interpolation results to correct the interpolation prediction values in the steep slope area so that they conform to the ecological physical laws.

[0011] In a preferred embodiment, step S1 includes the following:

[0012] Extract slope and aspect data from the digital elevation model as basic topographic parameters; divide the study area into regular grid cells, calculate the missing rate of sampling points in each cell, defined as the result of (expected number of sampling points minus actual number of sampling points) divided by the expected number of sampling points; construct a topographic complexity index, which is obtained by multiplying the sine value of the slope by the degree of deviation of the aspect from the reference direction and then dividing by the normalization factor; then perform a non-linear regression analysis on the missing rate and the topographic complexity index to fit the power relationship model between the two; finally, set the missing rate and slope threshold according to the regression results, and screen out the grid cells with high missing rate and large slope, and mark them as steep slope areas.

[0013] In a preferred embodiment, step S2 includes the following:

[0014] First, use the slope threshold to preliminarily classify the sampling points into the gentle slope area or the steep slope area; then use the digital elevation model to extract the slope and aspect of each sampling point to determine its topographic features; then preliminarily mark the regional attribution of the sampling points through spatial overlay analysis with the steep slope area; use the concept of topographic buffer zone to generate a buffer zone with a unit width for the steep slope area, and based on the spatial position of the sampling point in the steep slope area, buffer zone or gentle slope area, combined with the slope value, perform secondary division; finally, eliminate the sampling points with abnormal topographic features in the steep slope area through spatial clustering analysis, so as to generate two independent data sets: the gentle slope area and the steep slope area, including sampling points and their attribute data.

[0015] In a preferred embodiment, step S3 includes the following:

[0016] For each interpolation point in the steep slope area data set, first calculate the second derivative of the elevation in the neighborhood and the local elevation range, and determine the topographic undulation degree through the product of the absolute value of the second derivative and the logarithmic term of the local elevation range.

[0017] In a preferred embodiment, the average modulus length and aspect direction entropy of the aspect vectors in the neighborhood are calculated, and the aspect aggregation degree is calculated by dividing the average modulus length by the sum of the direction entropy plus one.

[0018] In a preferred embodiment, the topographic undulation degree and the aspect aggregation degree are comprehensively calculated and processed to generate a comprehensive adjustment factor.

[0019] In a preferred embodiment, the process of dynamically adjusting the interpolation direction is as follows:

[0020] For each interpolation point, first calculate the spatial distance and aspect angle between the interpolation point and the sampling point; then, calculate the interpolation contribution degree of each neighboring sampling point, which is to multiply the square of the reciprocal of the spatial distance by an adjustment term, and the adjustment term is one plus the product of the comprehensive adjustment factor and the cosine value of the aspect angle; after that, multiply the interpolation contribution degrees of all neighboring sampling points by the corresponding nutrient content values respectively, add these products, and then divide by the sum of all interpolation contribution degrees to obtain the interpolation result, that is, the interpolation prediction value.

[0021] In a preferred embodiment, the implementation method of the gravity scouring law is as follows:

[0022] First, calculate the vertical height difference between the interpolation point and the neighboring sampling point. If the sampling point is higher than the interpolation point, the height difference is positive; then, add a decay term to the calculation of the interpolation contribution degree. Specifically, multiply a decay coefficient by the vertical height difference, take its negative value as the exponent, and calculate the power with the base of the natural logarithm to obtain the decay term, where the decay coefficient is determined according to the ecological law experience; after that, multiply this decay term by the previously calculated interpolation contribution degree to obtain the final interpolation contribution degree.

[0023] In a preferred embodiment, step S4 includes the following contents:

[0024] For each interpolation point in the steep slope area, first obtain the interpolation prediction value of the corresponding interpolation point and the compensation field value at the corresponding position; then, through numerical superposition operation, add the compensation field value and the interpolation prediction value; then, perform a range check on the superimposed result to ensure that the corrected interpolation prediction value does not exceed the ecologically reasonable range of the nutrient content; finally, record and integrate the corrected interpolation prediction values of all interpolation points to form a nutrient content prediction data set covering the entire research area, and generate a spatial distribution map, that is, the final prediction map.

[0025] The technical effects and advantages of the method for estimating the yield and quality of understory grassland in the present invention:

[0026] The present invention first analyzes the relationship between topographic factors and the missing rate of sampling points to accurately identify steep slope areas where data is missing due to topographic inaccessibility. On this basis, the sampling points are divided into two independent data sets, namely the gentle slope area and the steep slope area, by using a slope threshold to ensure the pertinence and independence of the data. For the steep slope area, the scheme dynamically adjusts the interpolation direction according to the aggregation characteristics of the topographic features and the slope direction, and combines the law of gravity scouring to make the prediction of nutrient content more in line with the ecological physical law. In addition, through residual analysis and compensation field correction technology, the prediction results are optimized to ensure their conformity to the actual ecological characteristics. It effectively overcomes the problem of uneven distribution of sampling points under complex terrain, making the prediction of nutrient content more scientific, accurate and ecologically reasonable, and providing reliable technical support for resource assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic flow chart of a method for estimating the yield and quality of understory grassland in the present invention.

[0028] Figure 2 It is a schematic diagram of the processing sub-steps of step S1 of a method for estimating the yield and quality of understory grassland in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] Embodiment 1: Figure 1 A method for estimating the yield and quality of understory grassland in the present invention is provided, including:

[0031] S1. Based on the correlation analysis between topographic factors and the missing rate of sampling points, identify the steep slope areas where data is missing due to topographic inaccessibility.

[0032] S2. Use the set slope threshold to divide the sampling points into two independent data sets, namely the gentle slope area and the steep slope area.

[0033] S3. In the interpolation calculation of the steep slope area, dynamically adjust the interpolation direction according to the complex changes of the topographic features and the aggregation characteristics of the slope direction, so that the interpolation prediction value decreases along the vertical direction of the slope according to the law of gravity scouring.

[0034] S4. By analyzing the relationship between the residual of the interpolation result and the measured point and the slope, generate a compensation field and superimpose it on the interpolation result to correct the interpolation prediction value of the steep slope area to make it conform to the ecological physical law.

[0035] In the evaluation of understory grassland resources, topographic factors such as slope and aspect significantly affect the accessibility of sampling points. Especially in steep slope areas, the inaccessibility of the terrain leads to the absence of sampling points, which in turn affects the accuracy of resource evaluation. To solve this problem, step S1 aims to identify steep slope areas where data is missing due to terrain inaccessibility through correlation analysis between topographic factors and the sampling point missing rate, providing an accurate basis for data partitioning in subsequent step S2. The input for this step is the topographic data and elevation model of the study area, and the output is the identified steep slope areas, ensuring that the subsequent steps can divide the data between gentle areas and steep slope areas based on this result.

[0036] As Figure 2 shown, step S1 includes the following:

[0037] Preprocessing of topographic data: First, obtain the high-resolution digital elevation model of the study area and extract two data items: slope and aspect. The slope refers to the inclination angle of the ground surface relative to the horizontal plane, expressed in degrees; the aspect refers to the direction angle of the slope surface, expressed in degrees, with the due north direction as the starting angle of zero degrees, increasing clockwise to three hundred and sixty degrees. Through the analysis of the digital elevation model, slope and aspect data for each location in the study area are generated, providing a basis for subsequent processing.

[0038] Grid cell division and calculation of sampling point missing rate: Divide the study area into regular grid cells, with each grid cell sized ten meters long and ten meters wide. On this basis, calculate the sampling point missing rate for each grid cell. The calculation method of the sampling point missing rate is as follows: First, determine the expected number of sampling points, which is obtained based on sampling design or historical data; then obtain the actual number of sampling points, that is, the number of sampling points collected in the field; then subtract the actual number of sampling points from the expected number of sampling points, and divide the result by the expected number of sampling points to obtain the missing rate. The value of the missing rate ranges from zero to one, reflecting the degree of missing sampling points due to terrain inaccessibility.

[0039] Construction of topographic complexity index: To quantify the impact of topographic factors on sampling point missing, calculate the topographic complexity index, which comprehensively considers the non-linear effects of slope and aspect. The calculation method is as follows: First, convert the angle value of the slope into the corresponding sine value to obtain a non-linear value between zero and one to highlight the effect of steep slopes; then calculate the angle between the aspect angle and the reference aspect, with the reference aspect set as the due north direction, and calculate the cosine value of this angle; then subtract the cosine value of this angle from one to obtain the degree of deviation of the aspect from the reference direction; then multiply the sine value of the slope by the result of one minus the cosine value of the angle, and divide the product by a normalization factor, which is the sum of one and the slope value. The finally obtained topographic complexity index can reflect the comprehensive impact of slope and aspect on sampling point accessibility.

[0040] Regression analysis of the missing rate and terrain complexity index: For each grid cell, calculate its terrain complexity index and the missing rate of sampling points respectively, and then establish a regression model between the two. This regression model adopts a non-linear form, and the specific calculation method is as follows: Take a power of the terrain complexity index, and the power exponent is used to describe the non-linear relationship between the two; then multiply the result of the power by a scale coefficient, which controls the overall influence of the terrain complexity index on the missing rate; finally, add a constant value on this basis, which represents the basic missing rate not affected by the terrain complexity. Fit the data of all grid cells by the least squares method to determine the specific values of the power exponent, scale coefficient and constant value, so as to construct a complete regression model.

[0041] Identification of steep slope areas: According to the results of the regression model, set a threshold for the missing rate of sampling points, for example, the missing rate is greater than or equal to 0.5, which is used to represent the areas with serious missing sampling points. At the same time, set a slope threshold, for example, the slope is greater than or equal to 30 degrees, as the judgment standard for steep terrain. Screen out the grid cells that meet the following two conditions at the same time: one is that the missing rate calculated by the regression model is greater than or equal to 0.5, and the other is that the slope is greater than or equal to 30 degrees. Mark these grid cells as steep slope areas and generate their spatial distribution map as the output result, which is used to represent the areas where data is missing due to terrain inaccessibility.

[0042] In step S1, through terrain data preprocessing, missing rate calculation and construction of terrain complexity index, combined with regression analysis, the steep slope areas where data is missing due to terrain inaccessibility in the understory grassland resource assessment are accurately identified. The output steep slope areas provide a clear basis for the division in step S2.

[0043] The purpose of step S2 is to use the set slope threshold to divide the sampling points in the study area into two independent data sets, namely the gentle slope area data set and the steep slope area data set, to ensure the independence of the steep slope area data and provide a data basis for the interpolation calculation in step S3.

[0044] Step S2 includes the following content:

[0045] Extract the terrain parameters at the location of each sampling point. Using the digital elevation model, determine the slope and aspect of the location where each sampling point is located. The location information of the sampling points is represented by longitude and latitude or projected coordinates. By corresponding these coordinates with the grid cells of the digital elevation model, accurately extract the slope and aspect of each sampling point. These extracted slope and aspect data are used as the basis for subsequent division of the gentle slope area and the steep slope area, ensuring that each sampling point has an independent description of its terrain characteristics.

[0046] Through spatial overlay analysis, determine whether the coordinates of each sampling point fall within the steep slope area. If the coordinates of a sampling point are within the steep slope area, mark it as a potential sampling point in the steep slope area; if the coordinates are not within the steep slope area, mark it as a potential sampling point in the gentle slope area. This process preliminarily determines the regional type of the sampling points through spatial location matching.

[0047] Perform spatial buffering on the steep slope area to generate a buffer zone with a width of 5 meters, which is used to smooth the boundary transition between the steep slope area and the gentle slope area and reduce the interference of boundary effects. For each sampling point, execute the following judgment logic: if the sampling point is within the steep slope area and its slope value is greater than or equal to 30 degrees, classify it into the steep slope area dataset; if the sampling point is in the gentle slope area, that is, neither in the steep slope area nor in the buffer zone, and its slope value is less than 30 degrees, classify it into the gentle slope area dataset; if the sampling point is within the buffer zone, conduct secondary classification according to its slope value. If the slope value is greater than or equal to 30 degrees, classify it into the steep slope area dataset; if the slope value is less than 30 degrees, classify it into the gentle slope area dataset. The introduction of the buffer zone enhances the accuracy and stability of the dataset classification by dynamically adjusting the terrain transition area and avoids misclassification of sampling points at the boundary.

[0048] Conduct spatial clustering analysis on the sampling points and remove points with abnormal terrain features. Calculate the spatial distance and slope difference between any two sampling points in the steep slope area dataset. The spatial distance refers to the straight-line distance between two points, and the slope difference refers to the absolute value of the difference between the slope values of two sampling points. If the slope difference between a sampling point and its neighboring sampling point is greater than 10 degrees and the spatial distance between the two points is less than the average neighbor distance value of all sampling point pairs, it is considered that the terrain feature of this sampling point is abnormal, and it is removed from the steep slope area dataset. This process ensures that the steep slope area dataset only contains sampling points with consistent terrain features, meeting the requirements of independence and homogeneity of the steep slope area data.

[0049] After the above processing, finally generate the gentle slope area dataset and the steep slope area dataset. The gentle slope area dataset contains the sampling points classified as the gentle slope area and their attribute data, such as nutrient content and soil moisture, etc.; the steep slope area dataset contains the sampling points classified as the steep slope area and their attribute data. These datasets provide inputs for the interpolation calculation in the subsequent step S3, ensuring that the subsequent analysis can be based on accurately divided data.

[0050] In the evaluation of understory grassland resources, in steep slope areas, due to complex terrain and sparse sampling points, traditional interpolation methods are difficult to accurately capture the spatial distribution law of nutrient content. To address this challenge, in step S3, by quantifying the terrain undulation degree and slope aspect aggregation degree, the interpolation direction is dynamically adjusted to adapt to the spatial heterogeneity under complex terrain; at the same time, combined with the ecological and physical laws of gravitational scouring, a nutrient content attenuation mechanism along the vertical direction of the slope is introduced to ensure that the prediction results reflect the true characteristics of natural processes. The interpolation data generated by this method not only improves the accuracy of nutrient distribution prediction in steep slope areas but also provides a solid data foundation for subsequent residual analysis and compensation field construction, laying a key support for the overall accuracy of understory grassland resource evaluation.

[0051] Step S3 includes the following:

[0052] The calculation of the terrain undulation degree is used to measure the degree of terrain fluctuation, and the logic is as follows: First, for each interpolation point in the steep slope area dataset, obtain the elevation data within its surrounding 3×3 neighborhood. Then, calculate the second derivative of the elevation. The specific method is to subtract the elevation value of the interpolation point from the elevation values of four adjacent points (i.e., the points in the up, down, left, and right directions) within the neighborhood, then add these four differences, and subtract four times the elevation value of the interpolation point to obtain a value reflecting the change in terrain curvature. Subsequently, calculate the difference between the maximum and minimum elevations within the neighborhood to obtain the local elevation range. After that, to avoid the problem of zero values in logarithmic calculations, first add one to the local elevation range, then take its natural logarithm, and multiply this logarithmic value by the absolute value of the previously calculated second derivative to finally obtain the terrain undulation degree. The larger the value of the terrain undulation degree, the more severe the terrain undulation. For example, it can be obtained in the following way:

[0053] For each interpolation point in the steep slope area dataset, extract the elevation data within its 3×3 neighborhood (denoted as ). Calculate the second derivative of the elevation (denoted as ), representing the change in terrain curvature, in discrete form:

[0054]

[0055] where is the elevation of the interpolation point, and the neighborhood point coordinates are and . Calculate the local elevation range (denoted as ), that is, the difference between the maximum and minimum elevations within the 3×3 neighborhood:

[0056]

[0057] The calculation formula for the terrain undulation degree TR is:

[0058]

[0059] The larger the terrain undulation degree value, the more significant the terrain undulation, and stronger adjustment along the vertical direction of the slope surface is required during interpolation.

[0060] The calculation of slope aspect aggregation degree is used to evaluate the consistency of local slope aspects. The acquisition logic is as follows: First, for each interpolation point, extract the slope aspect data of all points within its 5×5 neighborhood. The slope aspect is expressed in degrees. Then, calculate the average modulus length of the slope aspect vectors. Specifically, calculate the average of the cosine values of each slope aspect to obtain an average cosine value, and calculate the average of the sine values of each slope aspect to obtain an average sine value. Then, add the square of the average cosine value to the square of the average sine value, and take the square root of this sum to obtain a value representing the slope aspect intensity. Subsequently, calculate the direction entropy of the slope aspect. Specifically, divide the slope aspect data into 8 direction intervals at every 45 degrees, count the frequency of slope aspects within each interval, and calculate the entropy value based on these frequencies. The larger the entropy value, the more dispersed the slope aspects. After that, divide the average modulus length by the sum of the direction entropy plus one to obtain the slope aspect aggregation degree. The larger the value of the slope aspect aggregation degree, the more consistent the slope aspects. For example, it can be obtained through the following method:

[0061] For each interpolation point, extract the slope aspect data within its 5x5 neighborhood (denoted as ), in degrees. Calculate the average modulus length of the slope aspect vectors (denoted as ), representing the slope aspect intensity:

[0062]

[0063] where is the number of neighborhood points. The modulus length is simplified to 1, but the vector components are retained for calculation to maintain accuracy. Calculate the slope aspect direction entropy (denoted as ), representing the degree of disorder of the slope aspect distribution:

[0064]

[0065] where is the frequency of the slope aspect in the th direction interval (divided into 8 direction intervals, each interval is 45°), . The calculation formula for the slope aspect aggregation degree AD is:

[0066]

[0067] The larger the value of the slope aspect aggregation degree, the more concentrated the slope aspects, and more consideration should be given to the consistency of the slope surface direction when adjusting the interpolation direction.

[0068] The generation of the comprehensive adjustment factor is used to dynamically adjust the interpolation direction. By calculating the non-linear relationship between the terrain undulation degree and the aspect aggregation degree, the following logic is obtained: First, multiply the terrain undulation degree by the aspect aggregation degree, and then divide this product by the value of the sum of the terrain undulation degree and the aspect aggregation degree plus one to obtain a normalized adjustment term. Next, calculate an exponential decay term. Specifically, divide the terrain undulation degree by the value of the aspect aggregation degree plus one, take its negative value as the exponent, calculate the power with the base of the natural logarithm, and then subtract this power value from one to obtain the exponential decay term. After that, multiply the normalized adjustment term by the exponential decay term to obtain the comprehensive adjustment factor. The value of the comprehensive adjustment factor ranges from 0 to 1, and the larger the value, the greater the intensity of the interpolation direction adjustment. For example, the comprehensive adjustment factor can be obtained in the following way:

[0069] Calculation formula:

[0070]

[0071] In the formula,

[0072] Normalized term, balancing the product effect of the terrain undulation degree and the aspect aggregation degree, with a value ranging from 0 to 1.

[0073] Exponential decay term, enhancing the adjustment effect of the balanced terrain undulation degree on the aspect aggregation degree. When the balanced terrain undulation degree is much larger than the aspect aggregation degree, the comprehensive adjustment factor approaches the maximum value of the normalized term. The value of the comprehensive adjustment factor ranges from 0 to 1, controlling the intensity of the interpolation direction adjustment, and the larger the value, the stronger the direction adjustment.

[0074] The dynamic adjustment of the interpolation direction adopts an improved inverse distance weighting method in the interpolation calculation of the steep slope area and is realized by introducing the comprehensive adjustment factor. The specific steps are as follows: First, for each interpolation point, calculate its spatial distance from the neighboring sampling points and the difference between the aspect of the interpolation point and the aspect of the sampling point, which is called the aspect angle. Next, calculate the interpolation contribution degree of each neighboring sampling point. Specifically, multiply the square of the reciprocal of the spatial distance by an adjustment term, which is one plus the product of the comprehensive adjustment factor and the cosine value of the aspect angle. After that, multiply the interpolation contribution degrees of all neighboring sampling points by the corresponding nutrient content values respectively, add up these products, and then divide by the sum of all interpolation contribution degrees to obtain the interpolation result, that is, the interpolation prediction value. In this way, the interpolation direction is dynamically adjusted according to the terrain and aspect characteristics. For example, the difference result is determined in the following way:

[0075] For each interpolation point, calculate its spatial distance from the neighboring sampling points (denoted as ) and the aspect angle (denoted as ), that is, the difference between the aspect of the interpolation point and the aspect of the sampling point. The adjusted interpolation contribution degree (denoted as ) The calculation formula is:

[0076]

[0077] The final interpolation result (denoted as ) is:

[0078]

[0079] Where is the interpolation prediction value of adjacent sampling points, is the number of adjacent points.

[0080] The realization of the gravity scouring law aims to make the interpolation prediction value decrease along the vertical direction of the slope surface, which is specifically completed by introducing an attenuation function of the vertical height difference. The steps are as follows: First, calculate the vertical height difference between the interpolation point and the adjacent sampling points. If the sampling point is higher than the interpolation point, the height difference is positive. Then, add an attenuation term to the calculation of the interpolation contribution degree. Specifically, multiply an attenuation coefficient by the vertical height difference, take its negative value as the exponent, and calculate the power with the base of the natural logarithm to obtain the attenuation term, where the attenuation coefficient is determined according to the ecological law experience. After that, multiply this attenuation term by the previously calculated interpolation contribution degree to obtain the final interpolation contribution degree. Through this process, the interpolation result shows a decreasing trend along the vertical direction of the slope surface. For example, the process of obtaining the interpolation contribution degree is as follows:

[0081] Calculate the vertical height difference between the interpolation point and the adjacent sampling points (denoted as ). Add an attenuation term to the calculation of the interpolation contribution degree:

[0082]

[0083] Where is the attenuation coefficient, which controls the decreasing speed of nutrients with the increase of height.

[0084] Step S3 generates a comprehensive adjustment factor by calculating the terrain undulation degree and the slope aspect aggregation degree, realizes the dynamic adjustment of the interpolation direction, and introduces an attenuation function of the vertical height difference to ensure that the interpolation prediction value decreases along the vertical direction of the slope surface, which conforms to the ecological law. It improves the accuracy of nutrient distribution prediction in the steep slope area.

[0085] Step S4 analyzes the relationship between the interpolation result and the residuals and slopes of the measured sampling points, combines terrain features such as the terrain undulation degree and the slope aspect aggregation degree, constructs a compensation field, and superimposes it on the interpolation result to correct the interpolation prediction value in the steep slope area. This process aims to make up for the deficiency of missing sampling points in the steep slope area due to terrain inaccessibility, make the interpolation prediction value in the steep slope area more in line with the ecological law, and thus provide high-precision and ecologically reasonable nutrient content distribution data support for the evaluation of underforest grassland resources.

[0086] Step S4 includes the following:

[0087] The purpose of residual calculation is to quantify the deviation between the interpolation result and the actual observation value. The specific process is as follows: First, for each measured sampling point in the steep slope area dataset, obtain the actual observed nutrient content value of this sampling point and the nutrient content value predicted by the interpolation method. Then, subtract the interpolation prediction value corresponding to each sampling point from its actual observed nutrient content value to calculate the difference, and this difference is the residual. The magnitude and sign of the residual reflect the degree of deviation of the interpolation prediction value from the actual observation value, serving as the quantitative basis for subsequent terrain compensation. Among them, the nutrient content refers to the quantity or concentration of specific nutrient elements in the soil or vegetation, and is usually used to evaluate the fertility of the soil and the conditions for plant growth. It mainly includes elements crucial for plant growth, such as nitrogen, phosphorus, and potassium. These nutrients directly affect the nutrient supply capacity of the soil and the growth and development of plants. Therefore, in resource assessment, analyzing the nutrient content can help understand the productivity of the grassland, the ecological health status, and the potential of resource utilization. By scientifically measuring the nutrient content, it can provide important data support and decision-making basis for grassland management and protection. Simply put, the nutrient content is a key indicator for measuring the nutritional level of the soil or vegetation.

[0088] The goal of the analysis of the relationship between the residual and the slope is to reveal the non-linear law of the change of the residual with the slope. The specific steps are as follows: First, group all the sampling points in the steep slope area dataset according to the slope value, with a stratification interval of every 5 degrees, and divide the slope into multiple slope layers. Then, within each slope layer, for all the sampling points in this layer, calculate the median of the residuals of these sampling points, and use the median to replace the average value to reduce the influence of outliers on the result. Then, take the median slope of each slope layer as the independent variable and the median of the residuals within the corresponding slope layer as the dependent variable, and use the locally weighted regression method to generate a smooth functional relationship curve. This function can accurately describe the non-linear characteristics of the change of the residual with the slope in the steep slope area.

[0089] The generation of the compensation field aims to reflect the eco-physical characteristics of the steep slope area through the non-linear combination of multi-dimensional terrain features. The specific calculation process is as follows: First, for each interpolation point in the steep slope area, calculate its slope value, slope gradient value, curvature value, terrain undulation value, and aspect concentration index value respectively. Among them, the slope gradient value is obtained by calculating the change rate of the slope in the horizontal direction, indicating the severity of the slope change; the curvature value is obtained by calculating the second-order change rate of the elevation in space, reflecting the concave and convex characteristics of the terrain; the terrain undulation value is obtained by calculating the difference between the maximum and minimum elevation values within the neighborhood of this interpolation point, characterizing the complexity of the terrain; the aspect concentration index is obtained by calculating the dispersion degree of the aspect directions within the neighborhood, and the closer its value is to 1, the more concentrated the aspect is. Then, the specific method for calculating the compensation field is as follows: First, take the function value of the residual-slope relationship function at the slope value of this interpolation point, then multiply it by an adjustment term, which is obtained by dividing the absolute value of the slope gradient by the result of adding 1 to the absolute value of the curvature, and then multiply this result by the normalized value of the terrain undulation. The normalized value is calculated by dividing the terrain undulation of this point by the maximum terrain undulation of all interpolation points in the steep slope area. Finally, multiply this result by the aspect concentration index. The compensation field value generated through the above steps can comprehensively reflect the influence of slope, terrain complexity, and aspect distribution on nutrient content.

[0090] The purpose of the compensation field superposition correction is to make the interpolation prediction value more in line with the ecological law by adjustment. For each interpolation point in the steep slope area, add the interpolation prediction value of this point to the compensation field value obtained through the calculation of the compensation field to get the corrected interpolation prediction value. Through the superposition effect of the compensation field in this addition process, the corrected interpolation prediction value shows a gradually decreasing trend along the vertical direction of the slope surface in the steep slope area, thereby reflecting the influence of eco-physical processes such as gravity scouring on nutrient distribution and ensuring that the prediction result is closer to the actual ecological characteristics.

[0091] The generation of the final prediction map aims to integrate the correction results and form complete nutrient content distribution data. The specific steps are as follows. Seamlessly splice the interpolation prediction values obtained by the interpolation method (such as the inverse distance weighting method) in the gentle area with the corrected interpolation prediction values in the steep slope area through the compensation field in space to form a nutrient content prediction data set covering the entire study area. Then, generate a spatial distribution map based on this data set, that is, the final prediction map. This prediction map can provide high-precision spatial distribution information of nutrient content and conform to the ecological law, providing reliable data support for the assessment of understory grassland resources.

[0092] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the real situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0093] It should be noted that the system of the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0094] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0095] It should be noted that in this text, if there are relational terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0096] As described above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for estimating the yield and quality of understory grassland, characterized in that, Including the steps: S1. Based on the correlation analysis between topographic factors and the missing rate of sampling points, identify the steep slope areas where data is missing due to topographic inaccessibility; S2. Use the set slope threshold to divide the sampling points into two independent data sets: the gentle slope area and the steep slope area; S3. In the interpolation calculation of the steep slope area, according to the complex changes of topographic features and the aggregation characteristics of slope directions, dynamically adjust the interpolation direction so that the interpolation prediction value decreases along the vertical direction of the slope according to the law of gravity erosion; S4. By analyzing the relationship between the residuals of the interpolation results and the measured points and the slope, generate a compensation field and superimpose it on the interpolation results to correct the interpolation prediction value in the steep slope area so that it conforms to the ecological physical law.

2. The method for estimating the yield and quality of understory grassland according to claim 1, characterized in that Step S1 includes the following: Extract the slope and aspect data from the digital elevation model as the topographic basic parameters; divide the study area into regular grid cells, calculate the missing rate of sampling points in each cell, which is defined as the result of subtracting the actual number of sampling points from the expected number of sampling points and then dividing by the expected number of sampling points; construct a topographic complexity index, which is obtained by multiplying the sine value of the slope by the degree of deviation of the aspect from the reference direction and then dividing by the normalization factor; then perform a non-linear regression analysis on the missing rate and the topographic complexity index to fit the power relationship model between the two; finally, set the missing rate and slope threshold according to the regression results, and screen out the grid cells with a high missing rate and a large slope, which are marked as steep slope areas.

3. The method for estimating the yield and quality of understory grassland according to claim 2, characterized in that, Step S2 includes the following: First, use the slope threshold to preliminarily classify the sampling points into the gentle slope area or the steep slope area; then use the digital elevation model to extract the slope and aspect of each sampling point to determine its topographic features; then preliminarily mark the regional attribution of the sampling points through spatial overlay analysis with the steep slope area; use the concept of topographic buffer zone to generate a buffer zone with a unit width for the steep slope area, and according to the spatial position of the sampling points in the steep slope area, buffer zone or gentle slope area, combined with the slope value, perform a secondary division; finally, eliminate the sampling points with abnormal topographic features in the steep slope area through spatial clustering analysis, so as to generate two independent data sets: the gentle slope area and the steep slope area, including the sampling points and their attribute data.

4. A method for estimating the yield and quality of understory grassland according to claim 3, characterized in that, Step S3 includes the following: For each interpolation point in the steep slope area data set, first calculate the second-order derivative of the elevation and the local elevation range in the neighborhood, and determine the topographic undulation degree through the product of the absolute value of the second-order derivative and the logarithmic term of the local elevation range.

5. The method for estimating the yield and quality of understory grassland according to claim 4, characterized in that: Calculate the average modulus length and aspect direction entropy of the aspect vectors in the neighborhood, and calculate the aspect aggregation degree by dividing the average modulus length by the sum of the direction entropy plus one.

6. The method for estimating the yield and quality of understory grassland according to claim 5, characterized in that: Perform a comprehensive calculation and processing on the topographic undulation degree and the aspect aggregation degree to generate a comprehensive adjustment factor.

7. The method for estimating the yield and quality of understory grassland according to claim 6, wherein The process of dynamically adjusting the interpolation direction is as follows: For each interpolation point, first calculate the spatial distance and slope angle between the interpolation point and the sampling points; then, calculate the interpolation contribution degree of each neighboring sampling point, which is obtained by multiplying the square of the reciprocal of the spatial distance by an adjustment term, where the adjustment term is one plus the product of a comprehensive adjustment factor and the cosine value of the slope angle; after that, multiply the interpolation contribution degrees of all neighboring sampling points by the corresponding nutrient content values respectively, sum up these products, and then divide by the sum of all interpolation contribution degrees to obtain the interpolation result, that is, the interpolation prediction value.

8. A method for estimating the yield and quality of understory grassland according to claim 7, characterized in that, The implementation method of the gravity scouring law is as follows: First, calculate the vertical height difference between the interpolation point and the neighboring sampling points. If the sampling point is higher than the interpolation point, the height difference is positive; then, add a decay term to the calculation of the interpolation contribution degree. Specifically, multiply a decay coefficient by the vertical height difference, take its negative value as the exponent, and calculate the power with the base of the natural logarithm to obtain the decay term, where the decay coefficient is determined according to the ecological law experience; after that, multiply this decay term by the previously calculated interpolation contribution degree to obtain the final interpolation contribution degree.

9. A method for estimating the yield and quality of understory grassland according to claim 7, characterized in that, Step S4 includes the following content: For each interpolation point in the steep slope area, first obtain the interpolation prediction value of the corresponding interpolation point and the compensation field value at the corresponding position; then, through a numerical superposition operation, add the compensation field value to the interpolation prediction value; next, perform a range check on the superimposed result to ensure that the corrected interpolation prediction value does not exceed the ecologically reasonable range of the nutrient content; finally, record and integrate the corrected interpolation prediction values of all interpolation points to form a nutrient content prediction data set covering the entire study area, and generate a spatial distribution map, that is, the final prediction map.

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