Sediment transport evaluation method, system, medium and device based on vegetation dynamics

Abstract

The present application relates to the technical field of soil erosion evaluation, and provides a sediment transport evaluation method, system, medium and equipment based on vegetation dynamics, which comprises: for each pixel in the to-be-evaluated region, a comprehensive roughness factor or a normalized vegetation index is dynamically selected based on a drought index to calculate a slope weighting factor of different pixels, and a connectivity index is calculated in combination with the slope of each pixel and the flow path length of the water flow from the steepest slope direction to the downstream river channel; for each grid in the to-be-evaluated region, a sediment transport rate is calculated based on the connectivity index, a transport threshold factor is determined based on the daily precipitation, and the sediment transport rate is dynamically regulated by using the transport threshold factor; and the product of the regulated sediment transport rate and the soil erosion amount is taken as the sediment yield. The connectivity of the extremely sparse vegetation area can be avoided from being overestimated, and thus the precision of the sediment yield evaluation is improved.

Description

Technical Field

[0001] This invention belongs to the field of soil erosion assessment technology, and particularly relates to methods, systems, media and equipment for assessing sediment transport based on vegetation dynamics. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In the field of soil erosion and sediment transport research, vegetation plays a crucial and complex role in regulating the erosion process. On the one hand, vegetation can reduce the generation of erosion sources by increasing surface cover and stabilizing soil structure; on the other hand, vegetation can affect the efficiency of sediment transport on slopes and the proportion of sediment entering river channels by altering surface roughness and constructing physical barriers. However, many current soil erosion and sediment transport models struggle to dynamically integrate this dual regulatory effect of vegetation cover changes, and most simplify this complex process or treat it statically.

[0004] This approach makes it difficult for the model to accurately depict the impact of vegetation on soil erosion and sediment connectivity when facing real-world scenarios with dynamic changes in vegetation cover. As a result, it is difficult to accurately reflect the real situation under the interaction of natural elements when assessing soil erosion and sediment transport. Especially in areas with diverse and frequently changing vegetation cover, the model simulation results deviate significantly from the actual situation and cannot meet the needs of accurately assessing soil erosion and sediment transport processes. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a method, system, medium, and equipment for assessing sediment transport based on vegetation dynamics. The connectivity index calculation integrates multiple factors such as topography, vegetation, and hydrology, and dynamically selects the vegetation index weighting factor based on the drought index. This allows it to adapt to different vegetation cover and climate / hydrological conditions, enhancing the flexibility and accuracy of its application in diverse regions (such as arid areas and areas with different vegetation cover). It can more accurately reflect the connectivity and erosion control mechanisms under the interaction of natural elements, and avoids overestimating the connectivity of areas with extremely sparse vegetation, thereby improving the accuracy of sediment yield assessment.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a method for assessing sediment transport based on vegetation dynamics, comprising:

[0008] For each pixel in the area to be evaluated, based on the drought index, the comprehensive roughness factor or normalized vegetation index is dynamically selected to calculate the slope weighting factor of different pixels. Combined with the slope of each pixel and the length of the water flow path from the steepest slope to the downstream river channel, the connectivity index is calculated.

[0009] For each grid cell in the area to be evaluated, the sediment transport rate is calculated based on the connectivity index. The transport threshold factor is determined based on the daily precipitation. The sediment transport rate is dynamically regulated using the transport threshold factor. The product of the regulated sediment transport rate and the soil erosion is taken as the sediment yield.

[0010] Furthermore, the slope weighting factor for the i-th pixel is:

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] ;

[0016] ;

[0017] in, It is the residual terrain value at the j-th cell within the moving window. It is the average residual terrain value of the pixels within the moving window, and RI is the roughness index. It is the maximum value of RI. It is the minimum value of RI. The normalized roughness index is CRI, the comprehensive roughness factor is n, the Manning roughness coefficient is n, α and β are set coefficients, AI is the drought index, NDVI is the normalized vegetation index, and W is W. C W is the vegetation association weight factor. CRI This is the roughness correlation weighting factor.

[0018] Furthermore, the drought index is the ratio of annual average rainfall to annual average reference evaporation.

[0019] Furthermore, the transfer threshold factor for the k-th grid is:

[0020] ;

[0021] in, This refers to daily precipitation. This is for adjusting the coefficient.

[0022] Furthermore, the soil erosion amount is the product of rainfall erosion factor, soil erodibility factor, slope length and slope factor, cover management factor and support factor.

[0023] Furthermore, the coverage management factor is:

[0024] ;

[0025] VI= ;

[0026] Wherein, SAVI represents the Soil Adjusted Vegetation Index; NDVI is the Normalized Difference Vegetation Index; α and β are set coefficients; AI is the drought index; and VI is a conditionally selected vegetation index.

[0027] Furthermore, the regulated sediment transport rate is the product of the transport threshold factor and the sediment transport rate.

[0028] A second aspect of the present invention provides a sediment transport assessment system based on vegetation dynamics, comprising:

[0029] The connectivity assessment module is configured to: for each cell in the area to be assessed, dynamically select the comprehensive roughness factor or normalized vegetation index based on the drought index to calculate the slope weighting factor of different cells, and calculate the connectivity index by combining the slope of each cell and the length of the water flow path from the steepest slope to the downstream river channel.

[0030] The sediment yield assessment module is configured to: calculate the sediment transport rate for each grid cell in the area to be assessed based on the connectivity index, determine the transport threshold factor based on the daily precipitation, dynamically regulate the sediment transport rate using the transport threshold factor, and use the product of the regulated sediment transport rate and soil erosion as the sediment yield.

[0031] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method for assessing sediment transport based on vegetation dynamics.

[0032] A fourth aspect of the present invention provides a computer device including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein the processor executes the program to implement the steps in the above-described method for assessing sediment transport based on vegetation dynamics.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] The connectivity index calculation of this invention integrates multiple factors such as topography, vegetation, and hydrology, and dynamically selects the vegetation index weighting factor based on the drought index. It can adapt to different vegetation cover and climate and hydrological conditions, enhance the flexibility and accuracy of application in diverse regions (such as arid areas and areas with different vegetation cover), and can more accurately reflect the connectivity and erosion control mechanisms under the interaction of natural elements. Moreover, it can avoid overestimating the connectivity of areas with extremely sparse vegetation, thereby improving the accuracy of sediment yield assessment.

[0035] This invention combines the static sediment transport rate with the transport threshold factor that reflects the dynamic influence of precipitation to achieve dynamic correction of the sediment transport ratio. This makes the process of sediment transport from erosion to river channel more realistic, avoiding the problem that a single static parameter is difficult to adapt to variable precipitation conditions. This makes the sediment transport simulation more accurate and provides more reliable support for the analysis of watershed sediment yield-transport relationship.

[0036] This invention aims to minimize the impact of soil brightness on vegetation estimation in arid regions. It uses the drought index to identify affected arid regions and introduces the SAVI index to calculate vegetation protection factors in extremely arid areas, thereby improving the accuracy of vegetation protection factor calculation in different regions (such as arid areas). Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a flowchart of the sediment transport assessment method based on vegetation dynamics according to Embodiment 1 of the present invention;

[0039] Figure 2 These are experimental comparison diagrams of Embodiment 1 of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0042] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] Example 1

[0044] This embodiment provides a method for assessing sediment transport based on vegetation dynamics.

[0045] The sediment transport assessment method based on vegetation dynamics provided in this embodiment enables a systematic assessment of watershed sediment yield under the conditions of considering vegetation dynamics and structural connectivity changes.

[0046] The sediment transport assessment method based on vegetation dynamics provided in this embodiment, such as... Figure 1 As shown, it includes the following steps:

[0047] Step 1: Soil erosion assessment.

[0048] This embodiment uses the Universal Soil Loss Equation (USLE) model to estimate soil erosion. USLE is a widely used empirical model that considers multiple factors such as rainfall, topography, soil erodibility, soil and water conservation measures, and vegetation cover. This model is widely used because of its simple parameters, readily available data, abundant literature, and strong adaptability.

[0049] The amount of soil erosion caused by sheet erosion and grooving erosion is calculated using the following formula:

[0050] ;

[0051] in, Represents soil erosion rate (t·ha) -1 ·yr -1 R is the rainfall erosion factor (Mg·ha·h·ha) -1 MJ -1 ·mm -1 K is the soil erodibility factor (Mg·ha·h·ha). -1 MJ -1 ·mm -1 ), LS is the slope length and slope factor (dimensionless), C is the coverage management factor (dimensionless), and P is the support factor (dimensionless).

[0052] Rainfall is a key factor influencing soil erosion, and the rainfall erosivity factor (R factor) is a dynamic indicator reflecting the state of soil erosion.

[0053] ;

[0054] Among them, P l It represents the erosive rainfall in the first month of the bi-monthly period, with P being the rainfall reference value.

[0055] Among them, the soil erodibility factor (K factor) reflects the soil's sensitivity to erosion and transport, and is influenced by the soil's physical and chemical properties (such as texture and organic matter content):

[0056] ;

[0057] Among them, K(t·ha·h·MJ) -1 ·mm -1 ·ha -1 ) represents soil erodibility; San (%) represents sand content (0.05-2mm); Sil (%) represents silt content (0.002-0.05mm); Cla (%) represents clay content (<0.002mm); TOC (%) represents soil organic carbon content; intermediate parameter SN1 = 1-0.01San; finally, by multiplying by 0.1317, the unit of K value is converted to SI.

[0058] The LS factor represents the influence of slope length and slope on soil erosion. In this embodiment, the method proposed by Desmet and Govers (1996) is used for calculation, which is based on digital elevation model (DEM) data.

[0059] Among them, the C factor is used to reflect the control effect of vegetation cover or land management measures on soil erosion, and is defined as the ratio of the soil erosion modulus under a specific vegetation type to the soil erosion modulus under bare land conditions:

[0060] ;

[0061] VI= ;

[0062] NDVI =(( NIR - Red ) / ( NIR + Red ));

[0063] SAVI =(( NIR - Red ) / ( NIR + Red + L )) (1+ L );

[0064] ;

[0065] in, The average annual rainfall, This is the annual average reference evaporation. SAVI This indicates the soil-adjusted vegetation index, which is typically used in arid regions with low vegetation cover. NDVI Normalized Difference Vegetation Index; VI It is a vegetation index selected based on specific conditions; L Soil regulators; NIR Reflectivity in the near-infrared band; Red Indicates the reflectivity of the red band; C The value ranges from 0 to 1, with C=1 indicating no vegetation cover. The smaller the C value, the stronger the protective effect of vegetation. α and β The values ​​are 2 and 1 respectively.

[0066] To minimize the impact of soil brightness on vegetation estimation in arid regions, this embodiment uses the drought index AI to identify affected arid areas and introduces the SAVI index to calculate the vegetation protection factor C in extremely arid regions, thereby improving the accuracy of vegetation protection factor C calculation in different regions (such as arid areas).

[0067] The P-factor represents the reduction effect of soil and water conservation measures on soil loss. Its value is equal to the ratio of soil loss after implementing specific protection measures to soil loss under conditions without protection measures (such as downslope cultivation). Its calculation depends on land use data. In areas where no management measures are implemented, the P-factor is set to 1.

[0068] Step 2, sediment transport assessment.

[0069] In the connectivity index-based sediment transport assessment, digital elevation model (DEM) data and vegetation data are used as inputs to calculate the IC (connectivity index) and SDR (sediment transport rate) of each grid cell, thereby calculating the sediment yield.

[0070] In this embodiment, each grid is composed of several pixels.

[0071] (1) The IC describes the hydrological connectivity between the sediment source area and the sink area. The higher the IC value, the more likely the sediment is to be transported from the source area to the sink area. This embodiment proposes a hybrid IC calculation method to solve the problem of overestimation of arid areas when using NDVI data to calculate IC. At the same time, the catchment area and flow direction are estimated by the multi-flow direction D-infinity (DINF) algorithm instead of the D8 algorithm. The D-infinity method can more accurately reflect the flow distribution in complex terrain and improve the simulation accuracy of sediment transport paths dominated by divergent flow in mountainous watersheds.

[0072] The formula for calculating the connectivity index IC is as follows:

[0073] ;

[0074] Among them, D up With D dn These represent the components of uphill and downhill connectivity, respectively; the IC value ranges from [-∞, +∞], with a larger value indicating higher connectivity.

[0075] ;

[0076] ;

[0077] in, The average weighting factor for the uphill catchment area, Let A be the average slope of the area (m / m) and A be the catchment area (m²). 2 ), d i W represents the length (m) of the water flow path from the i-th pixel in a grid along the steepest slope to the downstream river channel. i and S i Let be the weighting factor and slope of the i-th pixel in a certain grid.

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] Wherein, RI is the roughness index, which is the standard deviation of the residual terrain calculated over a 5×5 pixel moving window, with the i-th pixel in a certain grid as the center. It is the residual terrain value at the j-th pixel within the window. It is the average of the residual terrain values ​​of the pixels within the moving window. It is the maximum value of RI. It is the minimum value of RI. CRI is the normalized roughness index, n is the Manning roughness coefficient, and the values ​​of coefficients α and β are 2 and 1, respectively. The average annual rainfall, W is the annual average reference evaporation. C W is the vegetation association weight factor. CRI This is the roughness correlation weighting factor.

[0086] This embodiment integrates multiple factors such as terrain (slope, roughness, etc.), vegetation (different indices adapt to different coverage scenarios), and hydrology (rainfall, evaporation) into the IC calculation, and dynamically selects the vegetation index weighting factor W based on the drought index AI.i It can adapt to different vegetation cover, climate and hydrological conditions, enhance the flexibility and accuracy of application in diverse regions (such as arid areas and areas with different vegetation cover), reflect the connectivity and erosion control mechanisms under the interaction of natural elements in more detail, and avoid overestimating the connectivity of areas with extremely sparse vegetation.

[0087] (2) Based on the connectivity index IC, the sediment transport rate SDR is calculated using the following formula:

[0088] ;

[0089] Among them, SDR k SDR represents the sediment transport rate of the k-th grid cell, taking into account the effects of vegetation cover and soil and water conservation measures. max The maximum theoretical value is set to 0.8; parameter IC0 represents the ratio of sediment entering the valley to slope erosion; k b Indicates the spatial connectivity of the grid and runoff; both are calibration parameters; IC k This represents the probability that the sediment in the k-th grid cell reaches the river channel; k b Set it to 2, and set IC0 to 0.5.

[0090] (3) Calculate sediment yield based on SDR. .

[0091] ;

[0092] ;

[0093] .

[0094] in, This represents the soil erosion amount of the k-th grid, which is the average soil erosion amount A of all cells in the k-th grid. It is the transfer threshold factor. This refers to the daily rainfall. For adjustment coefficients; For dynamic sediment transport rate, SY k Indicates the first Sediment yield per grid (t·ha) -1 ·yr -1 This refers to the portion of the eroded soil within the grid that actually reaches the river channel.

[0095] This embodiment introduces a transfer threshold factor. Integrating rainfall data into the calculation of sediment transport rate, making it subject to the influence of vegetation, topography and precipitation, provides support for calculating daily sediment yield.

[0096] This embodiment is illustrated by... (Transport threshold factor) based on daily precipitation By defining different functional relationships in segments, the dynamic regulation effect of precipitation intensity on sediment transport can be accurately characterized. Transport is weak during light rain, stabilizes during moderate rain, and increases during heavy rain and above according to a power function. This closely matches the actual precipitation-transport response law and improves the simulation accuracy of hydrological processes.

[0097] This embodiment will use static SDR k Compared with ER, which reflects the dynamic impact of precipitation k By combining these methods, dynamic correction of the sediment transport ratio can be achieved, making the process of sediment transport from erosion to the river channel more realistic. This avoids the problem that a single static parameter is difficult to adapt to varying precipitation conditions, making sediment transport simulation more accurate and providing more reliable support for the analysis of sediment yield-transport relationship in watersheds.

[0098] like Figure 2 As shown, this embodiment compares river sediment flux data observed at 17 monitoring stations with model simulation data. The validation area is located in the high-altitude headwaters of rivers on the southeastern edge of the Qinghai-Tibet Plateau, including the upper reaches of major rivers such as the Yarlung Tsangpo River, Nu River, Mekong River, Yangtze River, and Yellow River. Each point in the scatter plot represents the average value. Validation shows that the simulated sediment flux has good consistency with the measured data from the 17 monitoring stations (R0). 2 The value reached 0.77.

[0099] This embodiment supports simulations at different time scales, from days to years, and can make predictions based on future climate and land use change scenarios.

[0100] The main input data required for this embodiment is relatively easy to obtain on a global or regional scale, and the key parameters in the model have clear physical meaning or literature references, which facilitates the promotion and application of the model and localized calibration.

[0101] This embodiment helps guide the restoration of degraded landscapes, optimize the layout of soil and water conservation measures, and mitigate reservoir siltation by clarifying sediment transport pathways and quantifying the impact of vegetation on connectivity, thereby supporting more resilient and sustainable watershed management and climate change response strategies.

[0102] Example 2

[0103] This embodiment provides a sediment transport assessment system based on vegetation dynamics, which specifically includes:

[0104] The connectivity assessment module is configured to: for each cell in the area to be assessed, dynamically select the comprehensive roughness factor or normalized vegetation index based on the drought index to calculate the slope weighting factor of different cells, and calculate the connectivity index by combining the slope of each cell and the length of the water flow path from the steepest slope to the downstream river channel.

[0105] The sediment yield assessment module is configured to: calculate the sediment transport rate for each grid cell in the area to be assessed based on the connectivity index, determine the transport threshold factor based on the daily precipitation, dynamically regulate the sediment transport rate using the transport threshold factor, and use the product of the regulated sediment transport rate and soil erosion as the sediment yield.

[0106] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0107] Example 3

[0108] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the vegetation dynamics-based sediment transport assessment method described in Embodiment 1 above.

[0109] Example 4

[0110] This embodiment provides a computer device, such as... Figure 3 As shown, the system includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001. The processor 1001, communication interface 1002, and computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data. When the processor 1001 executes the program, it implements the steps in the vegetation dynamics-based sediment transport assessment method described in Embodiment 1 above.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing sediment transport based on vegetation dynamics, characterized in that, include: For each pixel in the area to be evaluated, based on the drought index, the comprehensive roughness factor or normalized vegetation index is dynamically selected to calculate the slope weighting factor of different pixels. Combined with the slope of each pixel and the length of the water flow path from the steepest slope to the downstream river channel, the connectivity index is calculated. The slope weighting factor for the i-th pixel in the slope weighting factor is: ； ； ； ； ； ； in, It is the residual terrain value at the j-th cell within the moving window. It is the average residual terrain value of the pixels within the moving window, and RI is the roughness index. It is the maximum value of RI. It is the minimum value of RI. The normalized roughness index is CRI, the comprehensive roughness factor is n, the Manning roughness coefficient is n, α and β are set coefficients, AI is the drought index, NDVI is the normalized vegetation index, and W is W. C W is the vegetation association weight factor. CRI Roughness-related weighting factor; For each grid cell in the area to be evaluated, the sediment transport rate is calculated based on the connectivity index, the transport threshold factor is determined based on the daily precipitation, and the sediment transport rate is dynamically regulated using the transport threshold factor. The product of the regulated sediment transport rate and the soil erosion is taken as the sediment yield. The transfer threshold factor for the k-th grid in the transfer threshold factor is: ； in, Daily precipitation This is for adjusting the coefficient.

2. The sediment transport assessment method based on vegetation dynamics as described in claim 1, characterized in that, The drought index is the ratio of annual average rainfall to annual average reference evaporation.

3. The sediment transport assessment method based on vegetation dynamics as described in claim 1, characterized in that, The soil erosion amount is the product of rainfall erosion factor, soil erodibility factor, slope length and slope factor, cover management factor and support factor.

4. The sediment transport assessment method based on vegetation dynamics as described in claim 3, characterized in that, The coverage management factor is: ； WE= ; Wherein, SAVI represents the Soil Adjusted Vegetation Index; NDVI is the Normalized Difference Vegetation Index; α and β are set coefficients; AI is the drought index; and VI is a conditionally selected vegetation index.

5. The sediment transport assessment method based on vegetation dynamics as described in claim 1, characterized in that, The adjusted sediment transport rate is the product of the transport threshold factor and the sediment transport rate.

6. A sediment transport assessment system based on vegetation dynamics, characterized in that, include: The connectivity assessment module is configured to: for each cell in the area to be assessed, dynamically select the comprehensive roughness factor or normalized vegetation index based on the drought index to calculate the slope weighting factor of different cells, and calculate the connectivity index by combining the slope of each cell and the length of the water flow path from the steepest slope to the downstream river channel. The slope weighting factor for the i-th pixel is: ； ； ； ； ； ； in, It is the residual terrain value at the j-th cell within the moving window. It is the average residual terrain value of the pixels within the moving window, and RI is the roughness index. It is the maximum value of RI. It is the minimum value of RI. The normalized roughness index is CRI, the comprehensive roughness factor is n, the Manning roughness coefficient is n, α and β are set coefficients, AI is the drought index, NDVI is the normalized vegetation index, and W is W. C W is the vegetation association weight factor. CRI Roughness-related weighting factor; The sediment yield assessment module is configured to: for each grid cell in the area to be assessed, calculate the sediment transport rate based on the connectivity index, determine the transport threshold factor based on the daily precipitation, dynamically regulate the sediment transport rate using the transport threshold factor, and use the product of the regulated sediment transport rate and soil erosion as the sediment yield. The transfer threshold factor for the k-th grid is: ； in, Daily precipitation This is for adjusting the coefficient.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the vegetation dynamics-based sediment transport assessment method as described in any one of claims 1-5.

8. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the vegetation dynamics-based sediment transport assessment method as described in any one of claims 1-5.

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