Soil conservation service evaluation method and system for arid and semi-arid regions

Abstract

The invention discloses a soil conservation service assessment method and system for arid and semi-arid regions, and relates to the technical field of ecological service assessment. The method comprises the following steps: calculating a soil conservation value under the influence of water erosion according to evaluation data of a target area and a RUSLE model; calculating a soil conservation value under the influence of wind erosion according to the evaluation data and an RWEQ model; and adding the soil conservation value under the water erosion influence and the soil conservation value under the wind erosion influence to obtain the soil conservation value of the arid and semi-arid area of the target area under the simultaneous influence of the water erosion and the wind erosion. The evaluation accuracy of the soil conservation service of the target area can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological service assessment, and in particular to a soil conservation service assessment method and system for arid and semi-arid areas. Background Art

[0002] Soil conservation service refers to the function of an ecosystem to protect soil through its structure and process, reduce the erosion of rainwater, reduce soil loss, and prevent sediment deposition. Generally speaking, it includes two aspects: one is the erosion control ability to prevent soil loss; the other is the storage and accumulation ability of sediment. Soil conservation is not only of great significance to supply services, such as water supply, but also has a positive impact on regulation services, such as flood regulation and water quality maintenance. The soil conservation capacity of an ecosystem is of great significance to maintaining ecological balance and plays an important role in strengthening ecosystem management and protection. The ecological environment in arid and semi-arid areas is fragile, soil and water erosion is relatively concentrated, and wind erosion and water erosion often occur simultaneously or alternately. Most soil conservation service assessments only focus on water erosion, ignoring the impact of wind erosion, resulting in inaccurate assessments of soil conservation services in arid and semi-arid areas and a lack of a comprehensive and systematic understanding of the long-term dynamics, spatial distribution, and regional differentiation of soil conservation services in specific regions. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for evaluating soil conservation services in arid and semi-arid areas, which can improve the evaluation accuracy of soil conservation services in target areas.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for assessing soil conservation services in arid and semi-arid regions, including:

[0006] Acquire assessment data of the target area; the assessment data includes meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data;

[0007] Calculate the soil conservation value under the influence of water erosion according to the assessment data and the RUSLE model; wherein the RUSLE model includes six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor and soil and water conservation measures factor;

[0008] Calculating the soil conservation value under wind erosion based on the assessment data and the RWEQ model; the RWEQ model includes four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor;

[0009] The soil conservation value under the influence of water erosion and the soil conservation value under the influence of wind erosion are added together to obtain the soil conservation value of the arid and semi-arid areas of the target region under the influence of both water erosion and wind erosion.

[0010] Optionally, the meteorological data include: rainfall data, average monthly wind speed, temperature, sunshine hours and snow cover factor data; the soil texture data include: soil coarse sand content, soil silt content, soil organic matter content, CaCO3 calcium carbonate content and soil clay content data; the surface roughness data include random roughness, ridge roughness, terrain undulation parameters and altitude difference data within a distance range.

[0011] Optionally, the calculation formula of the RUSLE model is:

[0012] SE P =R·L·S·K

[0013] SE a =R·L·S·K·C·P

[0014] SC=SE p -SE a =R·L·S·K·(1-C·P)

[0015] Among them, SE p is the potential annual soil erosion predicted for bare land, SE a is the actual annual soil erosion predicted for vegetated land, R is the rainfall erosivity factor, L is the slope length factor, S is the slope factor, K is the soil erodibility factor, C is the vegetation coverage and management factor, and P is the soil and water conservation measures factor. SC is the soil conservation service value.

[0016] Optionally, the calculation formula of the RWEQ model is:

[0017] SRQ=10×(SL p -SL r )×A

[0018]

[0019]

[0020] Q pmax =109.8×(WF×EF×SCF×K`)

[0021] sp=150.71×(WF×EF×SCF×K`) -0.3711

[0022]

[0023] Q rmax=109.8×((WF×EF×SCF×K`×C)

[0024] sr=150.71×(WF×EF×SCF×K`×C) -0.3711

[0025] Among them, SRQ is the windbreak and sand fixation capacity; A is the area of ​​the study area; SRA is the windbreak and sand fixation capacity; SL p is the potential amount of wind erosion; Q pmax is the maximum sediment transport capacity of potential wind; sr is the length of potential key plot; SL r is the actual amount of wind erosion; Q rmax is the maximum sediment transport capacity of the actual wind force; sp is the actual key plot length; Z represents the downwind distance; WF is the climate factor; EF and SCF are the soil erodibility factor and soil crust factor respectively; K` and C are the soil roughness factor and vegetation factor respectively.

[0026] Optionally, after obtaining the soil conservation value under the influence of water erosion and wind erosion, it also includes:

[0027] The index test method and coefficient of variation of measuring time variability were used to detect the changes in soil conservation services in different eras and obtain the results of time trend evolution.

[0028] Optionally, after obtaining the soil conservation value under the influence of water erosion and wind erosion, it also includes:

[0029] The spatial heterogeneity driving characteristics of soil conservation services are analyzed. The specific analysis process is as follows:

[0030] Environmental factors related to soil conservation services were selected from relevant data, and the correlation and collinearity of influencing factors were studied using the multivariate linear stepwise regression method to determine the optimal influencing factors and establish the best regression model; finally, the spatial heterogeneity of environmental factors on soil conservation services was explored through geographic detector zoning.

[0031] Optionally, the types of relevant data include five aspects: climate, vegetation, topography, land use and soil.

[0032] The present invention also provides a soil conservation service evaluation system for arid and semi-arid areas, comprising:

[0033] A data acquisition module is used to obtain evaluation data of the target area; the evaluation data includes meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data;

[0034] A water erosion impact calculation module, used to calculate the soil conservation value under the influence of water erosion according to the assessment data and the RUSLE model; wherein the RUSLE model includes six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation coverage factor and soil and water conservation measures factor;

[0035] A wind erosion impact calculation module, used to calculate the soil conservation value under the influence of wind erosion according to the evaluation data and the RWEQ model; the RWEQ model includes four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor;

[0036] The soil conservation value calculation module is used to add the soil conservation value under the influence of water erosion and the soil conservation value under the influence of wind erosion to obtain the soil conservation value of the arid and semi-arid areas of the target area under the influence of water erosion and wind erosion.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] The invention discloses a soil conservation service evaluation method and system for arid and semi-arid areas. The method comprises obtaining evaluation data of a target area; the evaluation data comprises meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data; calculating soil conservation values ​​under the influence of water erosion according to the evaluation data and a RUSLE model; wherein the RUSLE model comprises six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation coverage factor and soil and water conservation measures factor; calculating soil conservation values ​​under the influence of wind erosion according to the evaluation data and a RWEQ model; the RWEQ model comprises four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor; adding the soil conservation values ​​under the influence of water erosion and the soil conservation values ​​under the influence of wind erosion to obtain the soil conservation values ​​of the arid and semi-arid areas of the target area under the influence of both water erosion and wind erosion. The present invention can improve the assessment accuracy of soil conservation services in the target area, and study its spatiotemporal evolution and differentiation characteristics, which is beneficial to regional ecological environmental protection, explore the driving factors of spatial heterogeneity of soil conservation services, thereby identifying key factors affecting soil conservation services and providing theoretical guidance for soil erosion prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 The figure is a flow chart of the method for evaluating soil conservation services in arid and semi-arid areas according to the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The purpose of the present invention is to provide a method and system for evaluating soil conservation services in arid and semi-arid areas, which can improve the evaluation accuracy of soil conservation services in target areas.

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] like Figure 1 As shown, the present invention provides a method for evaluating soil conservation services in arid and semi-arid areas, comprising:

[0045] Step 100: Acquire assessment data of the target area; the assessment data includes meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data;

[0046] Step 200: Calculating the soil conservation value under the influence of water erosion according to the assessment data and the RUSLE model; wherein the RUSLE model includes six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor and soil and water conservation measures factor;

[0047] Step 300: Calculate the soil conservation value under the influence of wind erosion according to the evaluation data and the RWEQ model; the RWEQ model includes four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor;

[0048] Step 400: Add the soil conservation value under the influence of water erosion and the soil conservation value under the influence of wind erosion to obtain the soil conservation value of the arid and semi-arid areas of the target region under the influence of both water erosion and wind erosion.

[0049] Based on the above steps, the following embodiments are provided.

[0050] As a further processing of the soil conservation service calculation method in arid and semi-arid areas, first, the data required by the model are collected. Secondly, the RUSLE model is used to calculate the soil conservation value under the influence of water erosion; the RWEQ model is used to calculate the soil conservation value under the influence of wind erosion. Finally, the soil conservation values ​​calculated by the two models are added together to obtain the soil conservation value in arid and semi-arid areas under the influence of both water erosion and wind erosion.

[0051] The specific steps are as follows:

[0052] Step 1: Collect the data needed for the evaluation model, including meteorological data (including rainfall data, monthly average wind speed, temperature, sunshine hours, snow cover factor data), DEM data, soil texture data (including soil coarse sand content, soil silt content, soil organic matter content, CaCO3 calcium carbonate content and soil clay content data), NDVI, land use data and surface roughness data (including random roughness, ridge roughness, terrain undulation parameters and altitude difference data within a distance range).

[0053] Step 2: Use the RUSLE model to calculate the soil conservation value under the influence of water erosion. The RUSLE model comprehensively considers the impact of vegetation, meteorology, topography, soil, and soil and water conservation measures on soil erosion, provides explanatory information about soil conservation services, and the output results help understand the formation mechanism of soil conservation services and provide useful guidance for further decision-making and management. RUSLE includes six input factors: rainfall erosivity factor (R), soil erodibility factor (K), slope length and slope factor (L, S), vegetation cover factor (C) and soil and water conservation measures factor (P). The following is the specific calculation formula of the model:

[0054] SE P =R·L·S·K

[0055] SE a =R·L·S·K·C·P

[0056] SC=SE p -SE a =R·L·S·K·(1-C·P)

[0057] SE p The potential annual soil erosion predicted for bare land (tha -1 a -1), SEa is the actual annual soil erosion predicted for land with vegetation (tha -1 a -1 )R is the rainfall erosivity factor (MJmmha -1 h -1 a -1 ), L and S are topographic factors (dimensionless), L is the slope length factor, S is the slope factor, and K is the soil erodibility factor (thMJ -1 mm -1 ), C is the vegetation cover and management factor (dimensionless), P is the soil and water conservation measure factor (dimensionless), and SC is the soil conservation service value.

[0058] The rainfall erosivity factor (R) is the potential ability of rainfall to cause soil erosion. According to the USLE manual, the calculation of the R factor requires storm kinetic energy (E) and the maximum 30-minute rainfall intensity (I30). Large-scale studies generally use monthly or annual rainfall data to calculate the R factor. In order to better describe the national rainfall erosivity, this project uses a widely accepted daily rainfall erosivity model developed based on China's climate characteristics. The calculation formula is as follows:

[0059]

[0060]

[0061] Where R is the annual rainfall erosivity, R i P is the erosivity of half-month rainfall j is the daily erosivity rainfall on the jth day within half a month (only the days with Pj ≥ 12 mm are selected), which is the threshold of rainfall erosivity events in China. α and β are parameters, which are adjusted and optimized according to regional characteristics and cold and warm seasons.

[0062] The soil erodibility factor (K) reflects the ease with which soil particles are separated and transported by water. It is an important indicator for evaluating the sensitivity of soil to erosion. It is mainly related to soil physical and chemical properties such as soil texture, organic matter content, soil structure, and permeability. It is calculated using the following formula:

[0063] K EPIC =0.2+0.3exp[-0.0256m s (1-m silt / 100)]×[m silt / (m c +m silt )] 0.3 ×1-0.25orgC / [orgC+exp(3.72-2.95orgC)]×1-0.7(1-m s / 100) / (1-m s / 100)+exp[-5.51+22.9(1-m s / 100)]

[0064] K=(-0.01383+0.51575K EPIC )×0.1317

[0065] KEPIC is the soil erodibility factor calculated using the erosion-productivity evaluation model, t·hm 2 ·h / (hm 2 ·MJ·mm); ms is the percentage of sand (0.05-2mm); msilt is the percentage of clay (<0.002mm); orgC is the percentage of organic carbon (%). K is the soil erodibility factor, t·hm 2 ·h / (hm 2 ·MJ·mm).

[0066] The slope length and slope factors (L, S) reflect the impact of slope length and slope on soil erosion and are calculated according to the following formula:

[0067]

[0068] m=β / (1+β)

[0069] β=(sinθ / 0.089) / [3.0×(sinθ) 0.8 +0.56]S=10.8sinθ+0.03(θ<5.14°)

[0070] S=16.8sinθ-0.5(5.14°≤θ<10.20°)S=21.9sinθ-0.96(10.20°≤θ<28.81)

[0071] S=9.5988(θ>28.81°)

[0072] Where: L is the slope length factor; S is the slope factor; m is the slope length index; θ is the slope, (°); λ is the slope length, m.

[0073] Vegetation coverage factor (C) is a factor that reflects the impact of vegetation on soil erosion based on the different ground vegetation coverage conditions. It is closely related to land use type and coverage degree. It reflects the impact of the ecosystem on soil erosion and is a positive factor in controlling soil erosion. Vegetation coverage is defined as the percentage of the vertical projection area of ​​vegetation per unit area. It is a comprehensive quantitative indicator of vegetation coverage and growth conditions. It can indicate ecosystem changes to a certain extent. The calculation formula is as follows:

[0074]

[0075] Where: F c is vegetation coverage (%); NDVI min is the Normalized Difference Vegetation Index (NDVI) for pixels without vegetation cover; NDVI max It is the NDVI value of the pixel completely covered by vegetation; by calculating the NDVI frequency cumulative value, the NDVI value with a frequency of 5% is taken as the NDVI value of the pixel without vegetation coverage, and the NDVI value with a frequency of 95% is taken as the NDVI value of the pixel completely covered by vegetation.

[0076] C=1(F c )

[0077] C=0.6508-0.3436lgF c (0 <F c ≤78.3%)

[0078] C=0(F c >78.3%)

[0079] Where: C is the vegetation coverage factor (dimensionless), Fc is the vegetation coverage (%).

[0080] The soil and water conservation measures factor is between 0 and 1. When the P value is 1, it means that the land surface in the area has taken good soil and water conservation measures; when the P value is 0, it means that no soil and water conservation measures have been taken on the land surface in the area. At present, there is no specific calculation formula for the P factor, which is summarized from previous studies and literature.

[0081] Step 3: Use the RWEQ model to calculate the soil conservation value under the influence of wind erosion. The WEQ model is a field-scale empirical model based on physics. The model is relatively simple in structure and the data of the factors involved are highly available. At the same time, it fully considers the effects of wind force, surface cover, soil physical and chemical properties, soil crust, surface roughness and other factors. The wind-resistance and sand-fixing capacity (SRQ) is the regional annual average potential soil wind erosion capacity (SL p ) and the actual soil wind erosion amount (SL r ), and the windbreak and sand fixation capacity (SRA) is the windbreak and sand fixation capacity per unit land area. The RWEQ model contains four factors: meteorological factors, soil erodibility and crust factors, vegetation factors, and surface roughness factors. The specific calculation formula of the model is as follows:

[0082] SRQ=10×(SL p -SL r )×A

[0083]

[0084]

[0085] Q pmax =109.8×(WF×EF×SCF×K`)

[0086] sp=150.71×(WF×EF×SCF×K`) -0.3711

[0087]

[0088] Q rmax =109.8×((WF×EF×SCF×K`×C)

[0089] sr=150.71×(WF×EF×SCF×K`×C) -0.3711

[0090] Where: SRQ is the windbreak and sand fixation capacity (t / a); A is the area of ​​the study area (hm2) 2 ; SRA is windbreak and sand fixation capacity (t / hm 2 );SL p is the potential wind erosion amount (kg / m 2 );Q pmax is the maximum sediment transport capacity of potential wind force (kg / m); sr is the length of potential critical plot (m); SL r is the actual wind erosion amount (kg / m 2 );Q rmax is the maximum sediment transport capacity of the actual wind force (kg / m); sp is the actual key plot length (m); Z represents the downwind distance (taken as 50m); WF is the climate factor (kg / m); EF and SCF are the soil erodibility factor and soil crust factor respectively; K` and C are the soil roughness factor and vegetation factor respectively.

[0091] Meteorological factors:

[0092] Under natural conditions, soil wind erosion is affected by meteorological factors such as wind speed, temperature, rainfall, solar radiation and snowfall. The meteorological factor (WF) is the comprehensive impact of various meteorological factors on wind erosion. The calculation formula is as follows:

[0093] WF=Wf×(ρ / g)×SW×SD

[0094] Wf=u 2 ×(u 2 -u 1 ) 2 ×Nd

[0095] Where: WF is the meteorological factor (kg / m); Wf is the wind force factor (m / s 3 ); g is the acceleration due to gravity (taken as 9.8m / s 2); air density ((1.205kg / m at 20℃ 3 ); SW and SD are soil moisture factor and snow cover factor, respectively) (number of days without snow cover / total number of days studied, with snow cover depth <25.4 mm defined as no snow cover); u1 is the wind speed for raising sand (taken as 5 m / s); u 2 is the monthly average wind speed (m / s) of the weather station; Nd is the number of days in each month with wind speed greater than 5m / s. The wind factor and soil moisture factor are obtained by interpolating the monthly average wind speed, precipitation, temperature, sunshine hours and other data. The snow cover factor is calculated using the long-term series data set of snow depth in China.

[0096] Soil erodibility and crusting factors:

[0097] Soil erodibility is affected by soil particle size and the content of organic matter, clay, calcium carbonate, etc. The hard crust on the soil surface can effectively prevent wind erosion. Therefore, the soil erodibility factor (EF) can be determined from the soil physical and chemical conditions. The soil crust factor (SCF) is the ability of soil crust to resist wind erosion under certain soil physical and chemical conditions. The calculation formula is as follows:

[0098]

[0099]

[0100] Where: sa is the soil coarse sand content (%); si is the soil silt content (%); cl is the soil clay content (%); OM is the soil organic matter content (%); CaCO 3 is the calcium carbonate content (%). Soil data are derived from soil maps and the attached soil attribute tables and spatial data. Different soil types and corresponding material contents in the region are used to estimate the erodibility factor and crusting factor.

[0101] Vegetation Factor:

[0102] Vegetation has an important impact on the soil wind erosion process, not only increasing the surface roughness but also increasing the wind speed of sand, and hindering the movement of soil particles. The vegetation cover factor (C) represents the degree of vegetation's inhibition of wind erosion, and the calculation formula is:

[0103] C=e -0.0438SC

[0104]

[0105] Where: SC is vegetation coverage (%); NDVImax and NDVImin are the maximum and minimum values ​​of NDVI respectively. NDVI data is judged whether it has been preprocessed by geometric correction, radiation correction, atmospheric correction, etc. At the same time, the data set is noise-removed, and then the MRT projection conversion tool is used for projection and format conversion batch processing, and the maximum value synthesis method is used to obtain NDVI data.

[0106] Surface roughness factor:

[0107] Topographic factors also have a significant impact on the wind erosion process. Surface roughness (K`) refers to the impact of soil blocks and ridges produced by farming on wind erosion, including random roughness (C rr ) and soil ridge roughness (K r ). Since the random roughness caused by tillage is difficult to obtain in regional scale assessment, the Smith-Carson equation is used to calculate the terrain undulation caused by soil ridges instead. The calculation formula is:

[0108]

[0109]

[0110] Where: K r is the soil ridge roughness (cm): C rr is the random roughness (taken as 0 this time); K` is the terrain roughness factor (cm); L is the terrain undulation parameter; is the altitude difference within the range of distance L.

[0111] Step 4: Add the soil conservation values ​​calculated by the RUSLE model and the RWEQ model to obtain the soil conservation value in arid and semi-arid areas under the influence of water erosion and wind erosion.

[0112] As a further processing of the soil conservation service calculation method in arid and semi-arid areas, the temporal trend evolution analysis process specifically includes: First, the Mann-Kendall (MK) test method, an indicator for measuring temporal variability, is used to detect the long-term trend of soil conservation services in arid and semi-arid areas. The MK test is a non-parametric statistical test method that has been widely used for trend testing in climate and hydrological studies. A 95% significance level is used to determine whether the trend is statistically significant. The slope of the long-term trend is quantified by the Kendall slope. Secondly, the coefficient of variation (CV) is used to measure the interannual variability of soil conservation services to determine the changes in soil conservation services in different decades.

[0113] The specific steps are as follows:

[0114] Step 1: Perform MK trend test. Assume a set of time series X = (x 1 ,x 2 ,x3 ,…,x n ), (n is the number of variables and n≥10), establish the standard normal distribution statistic Z:

[0115]

[0116] in:

[0117]

[0118]

[0119]

[0120] Where: Var(S) is the variance, S is approximately normal distribution, q is the number of groups with the same data, and t, is the number of data with the same data as the pth group. In the trend test, the null hypothesis H0 indicates that the data set samples are independent and identically distributed, and there is no trend. Given a significance level, if |Z|≥Z 1-α / 2 Then the original hypothesis H 0 If Z>0, it means that the sequence has a significant upward trend. On the contrary, if Z<0, it means that the sequence has a significant downward trend. On the contrary, the original hypothesis H 0 be accepted.

[0121] Step 2: Calculate the coefficient of variation (CV). The coefficient of variation (CV) refers to the ratio of the standard deviation of the data to the mean value. It is often used to measure the volatility of geographic data and to measure the interannual variability of soil conservation services. The formula is as follows:

[0122] Coefficient of variation C·V = (standard deviation SD / mean value Mean) × 100%

[0123] The smaller the value, the smaller the fluctuation, indicating that the interannual variation of soil conservation services is smaller; conversely, the greater the fluctuation, the greater the interannual variation.

[0124] As a further processing process of the soil conservation service calculation method in arid and semi-arid areas, the spatial heterogeneity driving characteristic analysis process specifically includes: first, selecting environmental factors related to soil conservation services from five aspects: climate, vegetation, topography, land use and soil, roughly including precipitation, temperature, snow cover index, wind speed, sunshine hours, NDVI, elevation, slope, land use type, soil type, lithology, landscape pattern indicators, etc. Secondly, the correlation and collinearity of the influencing factors are studied with the help of the multivariate linear stepwise regression method, the optimal influencing factors are selected, and the optimal regression model is established. Finally, the explanatory power of environmental factors on the spatial heterogeneity of soil conservation services is explored through geographic detector zoning.

[0125] Step 1: Select environmental factor indicators. Select environmental factors related to soil conservation services from five aspects: climate, vegetation, topography, land use and soil, including precipitation, temperature, snow cover index, wind speed, sunshine hours, NDVI, elevation, slope, land use type, soil type, lithology, etc., as shown in Table 1.

[0126] Table 1 Influencing factors and driving factors

[0127]

[0128]

[0129] Step 2: Select the optimal environmental factors. The multivariate linear stepwise regression method is used to study the correlation and collinearity of the influencing factors. Variables that have a significant impact on soil conservation services are selected from all available variables, and other variables that have no significant impact are eliminated to select the optimal influencing factors. In addition to eliminating less important variables, the stepwise regression method also eliminates variables that are highly correlated with other variables to reduce the degree of multicollinearity. After introducing the variables one by one, an F test should be performed on the model as a whole, and a t test should be performed on each variable one by one. The variables introduced later may make the original variables insignificant, and they need to be deleted to ensure that the original model only contains significant variables before the new variables are introduced. When performing collinearity diagnosis, when the VIF value is greater than or equal to 10, it is considered that there is serious collinearity between the variables and they should be eliminated. The multicollinearity between the explanatory variables that remain in the model is not obvious.

[0130] Step 3: Use geographic detectors to analyze the driving factors of spatial heterogeneity of soil conservation services in arid and semi-arid areas. The geographic detector is a tool for detecting spatial heterogeneity, which can objectively quantify the impact of geographical elements on the natural environment. Therefore, it can quantitatively reveal the explanatory power of influencing factors on dependent variables and detect the interaction between two influencing factors on dependent variables. It is an effective tool for identifying and analyzing the driving factors of spatial heterogeneity of geographical phenomena. The geographic detector includes two aspects: factor detection and interaction detection.

[0131] Factor detection: The core concept behind this function is to determine whether the changes in specific environmental factors and geographical phenomena are significantly consistent in space. The q value is used to quantify the degree to which factor X explains the spatial heterogeneity of attribute Y, which can be expressed as:

[0132]

[0133] where h = 1, ..., L is the stratification of variable Y or factor X, N h and N are the number of cells in the h layer and the entire region, respectively. and σ 2are the variances of the Y values ​​of the h layer and the entire region, respectively. The value range of q is [0,1]. The larger the q value, the stronger the explanatory power of the independent variable X on the attribute Y.

[0134] Interaction detector: This feature identifies the interactions between different influencing factors by evaluating whether the interaction between factors X1 and X2 strengthens or weakens the explanatory power of the dependent variable Y, or whether the effects of these factors on Y are independent. The detected interactions can be divided into several categories: nonlinear weakening, single factor nonlinear weakening, two-factor enhancement, and independent nonlinear enhancement. In the geographic detector, soil conservation service value is used as the Y attribute, and environmental factors such as vegetation cover, land use type, precipitation, slope, elevation, and lithology type are used as X factors.

[0135] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0136] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A soil conservation service assessment method for arid and semi-arid areas, characterized in that: include: Acquire assessment data of the target area; the assessment data includes meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data; Calculate the soil conservation value under the influence of water erosion according to the assessment data and the RUSLE model; wherein the RUSLE model includes six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor and soil and water conservation measures factor; Calculating the soil conservation value under wind erosion based on the assessment data and the RWEQ model; the RWEQ model includes four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor; The soil conservation value under the influence of water erosion and the soil conservation value under the influence of wind erosion are added together to obtain the soil conservation value of the arid and semi-arid areas of the target region under the influence of both water erosion and wind erosion.

2. The soil conservation service evaluation method for arid and semi-arid areas according to claim 1, characterized in that: The meteorological data include: rainfall data, average monthly wind speed, temperature, sunshine hours and snow cover factor data; the soil texture data include: soil coarse sand content, soil silt content, soil organic matter content, CaCO3 calcium carbonate content and soil clay content data; the surface roughness data include random roughness, ridge roughness, terrain undulation parameters and altitude difference data within a distance range.

3. The soil conservation service evaluation method for arid and semi-arid areas according to claim 1, characterized in that: The calculation formula of the RUSLE model is: SEP=RL·S·K SEa=R·L·S·K·C·P SC=SEp-SEa=R·L·S·K·(1-C·P) Among them, SEp is the potential annual soil erosion predicted for bare land, SEa is the actual annual soil erosion predicted for land with vegetation, R is the rainfall erosivity factor, L is the slope length factor, S is the slope factor, K is the soil erodibility factor, C is the vegetation coverage and management factor, and P is the soil and water conservation measures factor. SC is the soil conservation service value.

4. The soil conservation service evaluation method for arid and semi-arid areas according to claim 1, characterized in that: The calculation formula of the RWEQ model is: SRQ = 10 × × (SLp - SLr) × A Qp max =109.8×(WF×EF×SCF×K sp=150.71×WF×EF×SCF×K 0.3711 Qmax=109.8×(WF×EF×SCF×K`×C) sr=150.71×(WF×EF×SCF×K`×C) 0.3711 Among them, SRQ is the wind-proof and sand-fixing capacity; A is the area of ​​the study area; SRA is the wind-proof and sand-fixing capacity; SLp is the potential wind erosion; Qpmax is the maximum sand-carrying capacity of the potential wind force; sr is the potential key plot length; SLr is the actual wind erosion; Qrmax is the maximum sand-carrying capacity of the actual wind force; sp is the actual key plot length; Z represents the downwind distance; WF is the climate factor; EF and SCF are the soil erodibility factor and soil crust factor, respectively; K` and C are the soil roughness factor and vegetation factor, respectively.

5. The soil conservation service evaluation method for arid and semi-arid areas according to claim 1, characterized in that: After obtaining the soil conservation value under the influence of water erosion and wind erosion, it also includes: The index test method and coefficient of variation of measuring time variability were used to detect the changes in soil conservation services in different eras and obtain the results of time trend evolution.

6. The soil conservation service evaluation method for arid and semi-arid areas according to claim 1, characterized in that: After obtaining the soil conservation value under the influence of water erosion and wind erosion, it also includes: The spatial heterogeneity driving characteristics of soil conservation services are analyzed. The specific analysis process is as follows: Environmental factors related to soil conservation services were selected from relevant data, and the correlation and collinearity of influencing factors were studied using the multivariate linear stepwise regression method to determine the optimal influencing factors and establish the best regression model; finally, the spatial heterogeneity of environmental factors on soil conservation services was explored through geographic detector zoning.

7. The soil conservation service evaluation method for arid and semi-arid areas according to claim 6, characterized in that: The types of relevant data include five aspects: climate, vegetation, topography, land use and soil.

8. A soil conservation service evaluation system for arid and semi-arid areas, characterized in that: include: A data acquisition module is used to obtain evaluation data of the target area; the evaluation data includes meteorological data, DEM data, soil texture data, NDVI data, land use data and surface roughness data; A water erosion impact calculation module, used to calculate the soil conservation value under the influence of water erosion according to the assessment data and the RUSLE model; wherein the RUSLE model includes six input factors, namely: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation coverage factor and soil and water conservation measures factor; A wind erosion impact calculation module, used to calculate the soil conservation value under the influence of wind erosion according to the evaluation data and the RWEQ model; the RWEQ model includes four input factors, namely: meteorological factor, soil erodibility and crust factor, vegetation factor and surface roughness factor; The soil conservation value calculation module is used to add the soil conservation value under the influence of water erosion and the soil conservation value under the influence of wind erosion to obtain the soil conservation value of the arid and semi-arid areas of the target area under the influence of water erosion and wind erosion.

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