A multi-source data-driven ecological benefit assessment method for silt dams in small watersheds

Abstract

The present application relates to a multi-source data-driven method for evaluating the ecological benefits of a silt dam based on a small watershed, and relates to the field of soil and water conservation. The method includes: obtaining a digital elevation model of the area to be evaluated, and dividing the area to be evaluated into small watersheds according to the digital elevation model; selecting indicators for evaluating the ecological benefits of silt dams, wherein the indicators include soil erosion modulus, soil conservation, vegetation coverage, new forest rate, cultivated land area reduction rate, soil moisture content and habitat quality; obtaining multi-source data, constructing an automatic preparation algorithm for each indicator, and calculating the value of each indicator year by year and small watershed by small watershed based on the multi-source data and the automatic preparation algorithm; calculating the comprehensive weight of each indicator; and obtaining the ecological benefits of silt dams in each small watershed every year based on the values ​​of each indicator and the comprehensive weight of each indicator. The present application is used to solve the problem that it is currently impossible to conduct a refined evaluation of the ecological benefits of silt dams in a large area for many consecutive years.

Description

Technical Field

[0001] The present application relates to the field of soil and water conservation, and in particular to a multi-source data-driven ecological benefit assessment method for a silt dam based on a small watershed. Background Art

[0002] The problem of soil erosion in my country is quite serious. According to the 2021 China Soil and Water Conservation Bulletin, the total area of ​​soil erosion in the country is 2.6742 million square kilometers, which has had a serious impact on the sustainable development of my country's economy. Warping dams are one of the key measures for the comprehensive management of soil erosion on the Loess Plateau. They have played an important role in retaining mud and conserving soil, preventing floods and reducing disasters, reclaiming land for farmland, ensuring ecological security, and promoting food production. According to the 2021 Yellow River Basin Soil and Water Conservation Bulletin, as of 2021, there are 6,265 large-scale silt dams, 10,500 medium-sized silt dams, and 40,200 small silt dams in the Loess Plateau region of the Yellow River Basin.

[0003] Small watersheds are the basic units for soil erosion and water source protection. In the construction of silt dams, a complete ditch dam group protection system is built with small watersheds as units and ditch control backbone projects as the main body, combining backbone dams, medium-sized dams, and small dams, and combining mud interception, silt discharge, and water storage. Some small watersheds are distributed with dozens or even hundreds of different types of silt dams, while some small watersheds are sparsely distributed. The number and distribution of silt dams have an important impact on the local ecological environment. The ecological benefit evaluation of silt dams can provide a theoretical basis for the sustainable development of silt dams in northern Shaanxi and even in larger areas.

[0004] At present, the benefit analysis of silt dams mainly focuses on the analysis of sand reduction benefits based on observation data, the analysis of soil improvement benefits based on survey data, or the preliminary analysis of economic benefits and ecological benefits based on statistical data. Due to the lack of refined data support, the current ecological benefit analysis of silt dams in the Loess Plateau is concentrated on the analysis of several typical small watersheds in certain years, without a large-scale continuous year benefit evaluation, which cannot reflect the overall ecological benefits of silt dams in the Loess Plateau.

[0005] Sediment reduction benefit analysis and soil improvement benefit analysis based on observation data and survey data can better reflect the benefits of small watersheds in terms of sediment reduction and soil improvement, but cannot comprehensively evaluate the ecological benefits of silt dams. At present, there are only observation data of typical small watersheds and survey data at points, which cannot be used for large-scale evaluation. Observation and survey data for each year or consecutive years are needed to conduct ecological benefit evaluation of silt dams for many years.

[0006] Ecological benefit evaluation based on statistical data is generally at the regional, municipal or county level. Small watershed-level statistical data used in ecological benefit evaluation based on small watersheds is difficult to obtain. The relevant indicator data such as soil and water conservation benefits, improved ecological benefits, improved soil benefits, biodiversity, etc. required for ecological benefit evaluation of silt dams are also difficult to obtain completely based on statistical data.

[0007] Conventional ecological benefit assessments are driven by decentralized demand issues. Each assessment process for a certain area requires processes such as indicator selection, model building, and data collection. There is no systematic method and system for evaluating the ecological benefits of silt dams, and it is impossible to conduct detailed ecological benefit assessments of large areas for many consecutive years.

[0008] In summary, the current ecological benefit assessment of silt dams has the problem of being unable to conduct detailed ecological benefit assessments over large areas for many consecutive years. Summary of the invention

[0009] The present application provides a multi-source data-driven ecological benefit assessment method for silt dams based on small watersheds, which is used to solve the problem that it is currently impossible to conduct a refined assessment of the ecological benefits of silt dams in a large area for many consecutive years.

[0010] The present application embodiment provides a multi-source data-driven method for evaluating the ecological benefits of a silt dam based on a small watershed, including:

[0011] Acquire a digital elevation model of the area to be evaluated, and divide the area to be evaluated into various small watersheds according to the digital elevation model;

[0012] Select indicators for evaluating the ecological benefits of silt dams, including soil erosion modulus, soil conservation, vegetation coverage, rate of new forest land, rate of reduction of cultivated land area, soil moisture content and habitat quality;

[0013] Acquire multi-source data, and calculate the value of each of the indicators year by year and small watershed by small watershed based on the multi-source data;

[0014] Calculate the comprehensive weight of each of the indicators;

[0015] According to the numerical values ​​of each of the indicators and the comprehensive weights of each of the indicators, the ecological benefits of the silt dams in each of the small watersheds are obtained each year.

[0016] Compared with the prior art, the above technical solution provided in the embodiment of the present application has the following advantages: In the embodiment of the present application, the area to be evaluated is divided into small watersheds, and the small watershed is used as the basic unit for the ecological benefit evaluation of the silt dam. It not only takes into account the role of the comprehensive management of small watersheds in soil and water conservation, but also takes into account the data accessibility and operability of the evaluation. Through the refined, dynamic and information-based ecological benefit evaluation of small watersheds, it provides important data support, precise targets and governance directions for the subsequent comprehensive management of small watersheds. Key environmental factors that are easy to obtain over a long time series and a large range are selected as evaluation indicators. The indicators represent four aspects: soil and water conservation benefits, improved ecological benefits, improved soil benefits, and biodiversity, including seven indicators: soil erosion modulus, soil conservation, vegetation coverage, new forest land rate, cultivated land area reduction rate, soil water content and habitat quality. Using multi-source data, the values ​​of various indicators are calculated year by year and in small watersheds. Driven by multi-source data, the key steps in the ecological benefit assessment process of silt dams are modularized. Through process-based configuration, the ecological benefit of silt dams is automatically assessed every year, and large-scale silt dam ecological benefit assessments can be carried out regularly and quantitatively. The comprehensive weight of each indicator is calculated, and the ecological benefit of silt dams in each small watershed is obtained according to the values ​​of each indicator and the comprehensive weight of each indicator. The impact of silt dam construction on the regional ecological environment is comprehensively reflected, providing a decision-making basis for soil erosion control and ecological protection. It solves the problem that it is currently impossible to conduct a refined assessment of the ecological benefits of silt dams in large areas for many consecutive years. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] 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 or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] Figure 1 This is a flow chart of a multi-source data-driven method for evaluating the ecological benefits of a silt dam based on a small watershed in an embodiment of the present application;

[0020] Figure 2 This is a flow chart of a method for calculating the comprehensive weights of various indicators in a specific embodiment of the present application;

[0021] Figure 3 This is a structural diagram of a hierarchical model in a specific embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0023] In the embodiment of the present application, a multi-source data-driven ecological benefit evaluation method for a silt dam based on a small watershed is provided. Figure 1 As shown in the figure, the multi-source data-driven ecological benefit assessment method for silt dams based on small watersheds mainly includes:

[0024] Step 101: obtain a digital elevation model of the area to be evaluated, and divide the area to be evaluated into small watersheds according to the digital elevation model.

[0025] Digital Elevation Model (DEM) is a digital simulation of ground terrain (i.e., a digital expression of terrain surface morphology) achieved through limited terrain elevation data. It is a physical ground model that represents ground elevation in the form of a set of ordered numerical arrays.

[0026] With reference to the "Small Watershed Division and Coding Specifications", the ArcGIS platform was used to first correct the DEM, then perform DEM depression filling, flow direction analysis, flow calculation, threshold screening, river channel and watershed extraction. The area to be evaluated was divided into small watersheds with an area of ​​30-70 square kilometers.

[0027] Step 102, selecting indicators for evaluating the ecological benefits of the silt dam, wherein the indicators include soil erosion modulus, soil conservation capacity, vegetation coverage, rate of newly added forest land, rate of reduction of cultivated land area, soil moisture content and habitat quality.

[0028] Step 103, obtain multi-source data, build an automatic preparation algorithm for each indicator, and calculate the value of each indicator year by year and small watershed by small watershed based on the multi-source data and the automatic preparation algorithm.

[0029] In a specific embodiment, the multi-source data includes 30m spatial resolution images from Landsat-8;

[0030] The calculation formula for the value of vegetation coverage is as follows:

[0031]

[0032] Among them, FVC is the vegetation coverage, NDVI represents the vegetation index information of the current pixel, and NDVI soilVegetation index information representing pure soil cover pixels, NDVI veg Represents the vegetation index information of pure vegetation coverage pixels, NDVI, NDVI soil and NDVI veg It is obtained based on the 30m spatial resolution image of Landsat-8.

[0033] The 30m spatial resolution images of Landsat-8 from May to October each year can be selected. Based on NDVI (Normalized Differential Vegetation Index), the pixel binary model is used to calculate the vegetation coverage. The upper and lower thresholds of NDVI can be intercepted with a confidence level of 0.5%.

[0034] In a specific embodiment, the multi-source data also includes annual rainfall, soil property data, digital elevation model and soil conservation measures factors; the soil property data includes sand content, silt content, clay content and organic carbon content.

[0035] The soil conservation measure factor is defined as the ratio of soil loss on land with specific measures to soil loss from planting along the slope, which is between 0 and 1. The value of the soil conservation measure factor without soil and water conservation measures is 1. Specifically refer to the "Soil Erosion Forecast Model" by Liu Baoyuan et al. and modify the soil conservation measure factor according to the land use type and slope information, determine the modified soil conservation measure factor value, and obtain the soil conservation measure factor layer. The forest land is assigned a value of 0.8, and the bare land, water body, building land, grassland, etc. are assigned a value of 1; since the role of soil and water conservation measures for cultivated land is proportional to the slope, the soil conservation measure factor is assigned according to the slope range.

[0036] The calculation formula of the soil erosion modulus is as follows:

[0037] USLE n =R n ×K n ×LS n ×C n ×P n

[0038] Among them, USLE n is the actual soil erosion, i.e. soil erosion modulus, in units of t·(hm 2 a) -1 , R n is the rainfall erosivity factor, unit (MJ·mm·(hm 2 ·h·a) -1 ), K n is the soil erodibility factor, unit (ton·ha·h(MJ·ha·mm)-1 ), LS n is the slope length factor (dimensionless), C n is the vegetation cover and management factor (dimensionless), P n is the soil conservation measure factor (dimensionless), and n represents the nth year.

[0039] The soil erosion modulus is actually the amount of soil erosion per unit area. The calculation formula for the value of the soil erosion modulus is the universal soil and water loss equation.

[0040] Rainfall erosivity factor R n The calculation formula is as follows:

[0041]

[0042] Among them, RF n is the rainfall in the nth year (mm), R n is the rainfall erosivity factor, i.e., the rainfall erosivity in the nth year, and c and d are model parameters.

[0043] Rainfall erosivity factor R n It is one of the potential forces causing soil erosion. Annual rainfall data is used to determine the rainfall erosivity factor. The method of Zhang Wenbo and Fu Jinsheng using daily rainfall to estimate the multi-year average erosivity is used. According to the experiment, c = 0.0534, d = 1.648. The calculated station rainfall erosivity value is first interpolated to a 1km grid by the universal kriging (UK) method, and then a 0.01°×0.01° grid data is generated by bilinear interpolation.

[0044] Soil erodibility factor K n The calculation formula is as follows:

[0045]

[0046] Among them, SAN is the content of sand particles (0.05-2mm) (%); SIL is the content of silt particles (0.002-0.05mm) (%); CLA is the content of clay particles (<0.002mm) (%); C is the organic carbon content (%); SN=1-SAN / 100.

[0047] The formula in the EPIC model proposed by Wischmeier was used to calculate and obtain the soil erodibility K value raster layer.

[0048] Slope length factor LS n The calculation formula is as follows:

[0049] LS n =L n ×S n

[0050]

[0051] m=f / (1+f)

[0052]

[0053]

[0054] Among them, L n is the slope length factor (dimensionless), m is the slope length index, λ is the slope length (m), S n is the slope factor (dimensionless), θ is the slope (°), and the slope length and slope are extracted after filling the digital elevation model.

[0055] Based on the 30-meter DEM data of the Loess Plateau, the slope gradient and length can be extracted after filling the depression.

[0056] Vegetation Cover and Management Factor C n The calculation formula is as follows:

[0057]

[0058] Among them, FVC is the vegetation cover.

[0059] Vegetation Cover and Management Factor C n The vegetation coverage of the area to be evaluated is estimated. The data source of vegetation coverage is Landsat-8 image, NDVI is used, and the method is pixel binary model. Vegetation is the main inhibitory factor of soil erosion. Vegetation coverage and management factor C n The calculation adopts the research results of Cai Chongfa.

[0060] In one specific embodiment, the multi-source data also includes the amount of silt and sediment intercepted upstream;

[0061] The soil conservation value is calculated as follows:

[0062] R n =R n ×K n ×LS n

[0063] SETRED n =RKLS n -USLE n +sed_e n

[0064] Among them, RKLS n is the potential soil erosion of the small watershed, R n is the rainfall erosivity factor, K n is the soil erodibility factor, LSn is the slope length factor, SETRED n is the soil retention amount, USLE n is the actual soil erosion, i.e. soil erosion modulus, sed_e n It is the amount of silt and sediment intercepted upstream.

[0065] In a specific embodiment, the multi-source data further includes land use data year by year; the land use data includes forest area;

[0066] The calculation formula for the value of the newly added forest land ratio is as follows:

[0067] ΔF=(F b -F a ) / B

[0068] Among them, F a is the forest area in the previous year, F b is the forest area this year, and B is the area of ​​the small watershed.

[0069] The rate of newly added forest land refers to the increase in forest land data in the land use data of two years. The land use dataset uses the Annual China Land Cover Dataset (CLCD), which is based on Landsat data, combined with stable samples extracted from the China Land Use / Cover Dataset (CLUD) and training samples collected by visual interpretation, combined with a random forest classifier to obtain the land use classification results of China from 1985 to 2021.

[0070] In a specific embodiment, the land use data also includes cultivated land area;

[0071] The calculation formula for the value of cultivated land reduction rate is as follows:

[0072] ΔP=(P b -P a ) / B

[0073] Among them, P a is the cultivated land area in the previous year, P b is the cultivated land area this year, and B is the area of ​​the small watershed.

[0074] The cultivated land area reduction rate refers to the reduction of cultivated land area data in the land use data of two years. The land use data set adopts CLCD.

[0075] In a specific embodiment, the multi-source data also includes a high-quality soil moisture dataset of 1 km per year in China;

[0076] The soil moisture content is obtained by processing the high-quality soil moisture dataset of 1 km per year in China.

[0077] SMCI1.0 (Soil Moisture of China byin situ data, version 1.0), a high-quality annual 1km soil moisture dataset in China. This dataset is based on 10 layers of soil moisture observed at 1648 sites provided by the China Meteorological Administration, using ERA5_Land time series data, leaf area index (LAI), land cover types (Landtypes), topography and soil properties (Soil properties) as covariates, and is obtained through machine learning. Soil data at a depth of 10cm can be used to average the daily data from July to August and process them into annual data.

[0078] In a specific embodiment, the multi-source data further includes a stress factor table and a stress sensitivity table;

[0079] The numerical value of habitat quality is calculated as follows:

[0080]

[0081] In the formula, Q xh is the habitat quality of grid x in habitat type (land cover) h, D xh is the stress level of grid x in habitat type (land cover) h, k is the half-saturation constant, and D xh Half of the maximum value, H h is habitat suitability; z is a normalized constant with a value of 2.5, D xh It is obtained based on the stress factor table and stress sensitivity table.

[0082] Habitat quality uses the InVEST habitat quality model. The Habitat Quality module in the InVEST habitat quality model assumes that the better the habitat quality, the higher the biodiversity. The habitat quality evaluation function contains four variables: land use layer, stress factor layer, stress factor table and stress sensitivity table. The land use data uses the CLCD data of the China Land Cover Dataset, and the stress factor table and stress sensitivity table are summarized from literature and experience.

[0083] The calculation results are automatically classified according to the quantile method of ArcGIS classification, and the habitat quality index is divided into five categories: extremely important habitat (0.996-1), high habitat (0.984-0.996), moderate habitat (0.475-0.984), general habitat (0-0.475), and non-habitat (0).

[0084] Step 104, calculating the comprehensive weight of each indicator.

[0085] In a specific embodiment, Figure 2 As shown, the comprehensive weight of each indicator is calculated, including:

[0086] Step 201, compare each index in pairs to obtain index x i and the indicator x j The comparison result of a ij , construct the judgment matrix A = (a ij ) 7×7 , where , =1,2,3,…,7, =1,2,3,…,7.

[0087] In a specific embodiment, before constructing the judgment matrix, a hierarchical model is first established. The factors that affect the ecological quality of the silt dam are roughly divided into 4 first-level indicators and 7 second-level indicators. Therefore, the target layer is the ecological benefit assessment of the silt dam in the small watershed, the criterion layer is 4 first-level indicators, and the indicator layer is 7 second-level indicators. The first-level indicators include soil and water conservation benefits, improved ecological benefits, improved soil benefits and biodiversity. The second-level indicators include soil erosion modulus, soil conservation amount, vegetation coverage, new forest land rate, cultivated land area reduction rate, soil moisture content and habitat quality. The soil and water conservation benefit corresponds to the soil erosion modulus and soil conservation amount, the improved ecological benefit corresponds to the vegetation coverage, the new forest land rate and the cultivated land area reduction rate, the improved soil benefit corresponds to the soil moisture content, and the biodiversity corresponds to the habitat quality. Figure 3 The figure shows a schematic diagram of the structure of the hierarchical model.

[0088] The consistent matrix method proposed by American operations researcher Saaty et al. is used to quantify the comparison results using scale values. The scale of the judgment matrix is ​​shown in Table 1:

[0089] Table 1 Judgment matrix scale table

[0090]

[0091] According to the judgment matrix scale table, the seven indicators in the indicator layer are compared pairwise to obtain the scale value a between the two indicators. ij All comparison results constitute the comparison judgment matrix A = (a ij ) 7×7 , where the scale value satisfies the following conditions, a ji =1 / a ij , a ii =1.

[0092] According to the constructed model hierarchy, combined with a large number of references, experience and standards, and consulting professionals, the 7 secondary indicators corresponding to the 4 criterion levels were compared pairwise to determine the judgment matrix.

[0093] Step 202, calculate the maximum eigenvalue λ of the judgment matrixmax And the normalized eigenvector V, according to the maximum eigenvalue λ max Calculate the consistency index CI. After determining that the judgment matrix passes the consistency test according to the consistency index CI, the eigenvalue v in the normalized eigenvector V is i As the i-th index x obtained by the hierarchical analysis method i The first weight of .

[0094] In a specific embodiment, the calculation formula of the normalized feature vector V is as follows:

[0095]

[0096]

[0097] V=(v1,v2,…,v7) T

[0098] Maximum eigenvalue λ max The calculation formula is as follows:

[0099]

[0100] The calculation formula of consistency index CI is as follows:

[0101]

[0102] Step 203, using the entropy method to calculate the i-th index x i The second weight w i .

[0103] In a specific embodiment, the i-th indicator x i The second weight w i The calculation formula is as follows:

[0104]

[0105]

[0106]

[0107] Among them, Y ig is the value of the i-th index xi of the g-th sample, i = 1, 2, 3, ..., 7, g = 1, 2, 3, ..., t, p ig is the i-th index x i The value of the g-th sample accounts for the i-th index x i The specific gravity, E i is the i-th index x i The entropy value, w i is the i-th index x iThe second weight of .

[0108] Step 204: according to the first weight v of the i-th indicator i and the i-th index x i The second weight w i , get the comprehensive weight Q of the i-th indicator i .

[0109] In a specific embodiment, the comprehensive weight Q of the i-th indicator is i The calculation formula is as follows:

[0110]

[0111]

[0112]

[0113] Among them, α i and β i They are used to represent the first weight v of the i-th indicator. i and the i-th index x i The second weight w i the relative importance of .

[0114] By combining the subjective weighted analytic hierarchy process with the objective weighted entropy method, an entropy-analytic hierarchy process model of the combined model is formed. Through the complementarity of subjective and objective assignments, a more reasonable ecological benefit evaluation model for silt dams is constructed.

[0115] Step 105, according to the values ​​of various indicators and the comprehensive weights of various indicators, the ecological benefits of the silt dams in each small watershed are obtained each year.

[0116] In a specific embodiment, the calculation formula for the ecological benefit of the silt dam in each small watershed each year is as follows:

[0117]

[0118] Among them, Q i is the comprehensive weight of the i-th indicator, I i is the value of the ith indicator, and EF is the ecological benefit of the silt dam in each small watershed every year.

[0119] In a specific embodiment, Suide County, Shaanxi Province, a typical concentrated area of ​​silt dams, is used as the experimental area. Suide County is located in the Wuding River Basin and belongs to the first sub-area of ​​the Loess Hilly Gully Region, with a total land area of ​​1853 km 2 There are about 700 large and medium-sized dams in the county.

[0120] 1) The 30-meter DEM data of Suide County was collected and the DEM was corrected using ArcGIS platform. Then DEM filling, flow direction analysis, flow calculation, threshold screening, river channel and watershed extraction were performed, and Suide County was divided into 24 small watersheds.

[0121] 2) Construct an ecological benefit evaluation index system for silt dams, including seven index factors: soil conservation, soil erosion modulus, vegetation coverage, new forest land rate, cultivated land reduction rate, soil moisture content and habitat quality.

[0122] 3) Combine remote sensing inversion, physical models and other methods to realize the automatic preparation algorithm of annual data for each factor. Soil conservation and soil erosion modulus data are calculated by the InVEST model SDR module using the general soil and water loss equation. Vegetation coverage is inverted from Landsat 8 data. The rate of newly added forest land and the rate of reduction of cultivated land area are calculated from annual land use data. Soil moisture content uses China's annual 1km high-quality soil moisture dataset. Habitat quality is calculated using the HabitatQuality module of the InVEST model.

[0123] 4) Collect Landsat data, annual land use data, rainfall from 1980 to 2022, soil attribute data, soil and water conservation measures, stress factor table and stress sensitivity and other multi-source data of Suide County, and calculate the values ​​of 7 indicator factors for each watershed. And perform data normalization in each watershed, and process the surface data into point data by summing or averaging.

[0124] 5) The weight of each indicator is determined by using the indicator weight determination method based on the hierarchical analysis method and the entropy weight method. First, the judgment matrix is ​​constructed. The maximum eigenvalue λ is calculated. max And the normalized feature vector V, the calculated weight values ​​are: {0.2567, 0.1453, 0.2487, 0.0714, 0.12453, 0.1475, 0.0847}, which passes the consistency index test.

[0125] 6) Use the entropy method to calculate the weights. The weights obtained are {0.1457, 0.2414, 0.1597, 0.0457, 0.2169, 0.1567, 0.1023, 0.0521}.

[0126] 7) Calculate the comprehensive weights. The weights obtained are {0.1874, 0.1978, 0.2541, 0.1241, 0.1421, 0.0584}.

[0127] 8) Ecological benefit assessment of each watershed: Multiply the watershed index factor value calculated in step 4) by the comprehensive weight to obtain the ecological benefit score of the small watershed.

[0128] In summary, in the embodiment of the present application, the area to be evaluated is divided into small watersheds, and the small watershed is used as the basic unit for the ecological benefit evaluation of the silt dam. It not only takes into account the role of the comprehensive management of small watersheds in soil and water conservation, but also takes into account the accessibility of data and the operability of evaluation. Through the refined, dynamic and information-based ecological benefit evaluation of small watersheds, it provides important data support, precise targets and governance directions for the subsequent comprehensive management of small watersheds. Key environmental factors that are easy to obtain over a long time series and a large range are selected as evaluation indicators. The indicators characterize four aspects: soil and water conservation benefits, improved ecological benefits, improved soil benefits, and biodiversity, including soil erosion modulus, soil conservation, vegetation coverage, new forest land rate, cultivated land area reduction rate, soil moisture content and habitat quality. Seven indicators. Using multi-source data, the values ​​of each indicator are calculated year by year and small watershed by small watershed. Driven by multi-source data, the key steps in the ecological benefit evaluation process of the silt dam are modularized, and the automatic evaluation of the ecological benefits of the silt dam is realized every year through process-based configuration, which can regularly and quantitatively evaluate the ecological benefits of a large range of silt dams. The comprehensive weight of each indicator is calculated, and the ecological benefits of the silt dam in each small watershed are obtained according to the value of each indicator and the comprehensive weight of each indicator. The impact of silt dam construction on the regional ecological environment is comprehensively reflected, providing a decision-making basis for soil erosion control and ecological protection. This solves the problem that it is currently impossible to conduct a refined assessment of the ecological benefits of silt dams in a large area for many consecutive years.

[0129] It should be noted that, in this article, relational terms such as "first" and "second" 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 terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0130] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A multi-source data-driven ecological benefit assessment method for silt dams based on small watersheds, characterized in that: include: Acquire a digital elevation model of the area to be evaluated, and divide the area to be evaluated into various small watersheds according to the digital elevation model; Select indicators for evaluating the ecological benefits of silt dams, including soil erosion modulus, soil conservation, vegetation coverage, rate of new forest land, rate of reduction of cultivated land area, soil moisture content and habitat quality; Acquire multi-source data, construct an automatic preparation algorithm for each of the indicators, and calculate the value of each of the indicators year by year and small watershed by small watershed based on the multi-source data and the automatic preparation algorithm; Calculate the comprehensive weight of each of the above indicators; According to the values ​​of each of the indicators and the comprehensive weights of each of the indicators, the ecological benefits of the silt dams in each of the small watersheds are obtained each year; The multi-source data also includes land use data year by year; the land use data includes forest area; The calculation formula of the value of the newly added forest land rate is as follows: in, is the forest area in the previous year, is the forest area this year, B is the area of ​​the small watershed; The land use data also includes cultivated land area; The calculation formula of the numerical value of the cultivated land area reduction rate is as follows: in, is the cultivated land area in the previous year, is the cultivated land area this year, B is the area of ​​the small watershed; The multi-source data also includes China's annual 1km high-quality soil moisture dataset; The soil moisture value is obtained by processing the annual 1 km high-quality soil moisture dataset of China.

2. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 1 is characterized in that: The multi-source data includes 30m spatial resolution images from Landsat-8; The calculation formula of the numerical value of the vegetation coverage is as follows: in, is the vegetation cover, Represents the vegetation index information of the current pixel, Represents the vegetation index information of pure soil cover pixels, Represents the vegetation index information of pure vegetation covered pixels, , and It is obtained based on the 30m spatial resolution image of Landsat-8.

3. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 2 is characterized in that: The multi-source data also includes annual rainfall, soil property data, the digital elevation model and soil conservation measures factors; the soil property data includes sand content, silt content, clay content and organic carbon content; The calculation formula of the value of the soil erosion modulus is as follows: in, is the actual soil erosion amount, i.e. soil erosion modulus, in units of , is the rainfall erosivity factor, in units , is the soil erodibility factor, in units , is the slope length factor, is the vegetation cover and management factor, is the soil conservation measure factor, n Indicates n Year; Rainfall erosivity factor The calculation formula is as follows: in, For the n Annual rainfall, is the rainfall erosivity factor, i.e. n Annual rainfall erosivity, and are model parameters; Soil erodibility factor The calculation formula is as follows: Among them, SAN is the sand content; SIL is the silt content; CLA is the clay content; C is the organic carbon content; SN=1-SAN / 100; Slope length factor The calculation formula is as follows: = in, is the slope length factor, m is the slope length index, is the slope length, is the slope factor, is the slope, and the slope length and slope are extracted after the digital elevation model is filled with depressions; Vegetation cover and management factors The calculation formula is as follows: in, is the vegetation cover.

4. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 3 is characterized in that: The multi-source data also include the amount of silt and sediment intercepted upstream; The calculation formula of the soil conservation value is as follows: in, is the potential soil erosion of the small watershed, is the rainfall erosivity factor, It is the soil erodibility factor. is the slope length factor, is the soil retention capacity, is the actual soil erosion amount, i.e. the soil erosion modulus, It is the amount of silt and sediment intercepted upstream.

5. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 1 is characterized in that: The multi-source data also includes a stress factor table and a stress sensitivity table; The calculation formula of the value of the habitat quality is as follows: In the formula, Habitat type h Medium Grid x habitat quality, Habitat type h Medium Grid x The level of stress, k is the half-saturation constant, Half of the maximum value, for habitat suitability; z is a normalization constant with a value of 2.

5. It is obtained according to the stress factor table and the stress sensitivity table.

6. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 1 is characterized in that: The calculating of the comprehensive weight of each of the indicators comprises: Compare each of the indicators in pairs to obtain the indicators and indicators Comparison results , construct the judgment matrix ,in, , ; Calculate the maximum eigenvalue of the judgment matrix and the normalized eigenvector V , according to the maximum eigenvalue Calculate consistency index CI , according to the consistency index CI After determining that the judgment matrix passes the consistency test, the normalized eigenvector V The eigenvalues ​​in As the first i Indicators The first weight of Use the entropy method to calculate the i Indicators The second weight ; According to i The first weight of the indicator and i Indicators The second weight , obtain the i The comprehensive weight of the indicators .

7. The multi-source data driven ecological benefit assessment method of a silt dam based on a small watershed according to claim 1 is characterized in that: The calculation formula for the ecological benefits of the silt dams in each small watershed each year is as follows: in, It is i The comprehensive weight of the indicators, It is i The value of an indicator, It is the ecological benefit of the silt dams in each small watershed every year.

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