Basin sediment connectivity assessment method based on MATLAB platform and considering relative surface roughness

Abstract

The invention discloses a watershed sediment connectivity evaluation method based on an MATLAB platform and considering relative surface roughness. The method comprises the following steps: acquiring digital elevation model (DEM) data of a target watershed; obtaining a digital elevation model DEMF after depression filling based on the DEM; calculating a flow direction matrix FD; calculating a catchment area A of a catchment area above each DEM cell, and extracting water system position vector data S; calculating a surface roughness factor RRF and a surface undulation factor SLF based on the DEMF, and generating a surface relative roughness factor RRSF; calculating a weighted confluence path length DISTW based on the FD and the RRSF; calculating a sediment'source 'component Dup and a sediment'sink' component Ddown based on A, S, DISTW and FD; and calculating a connectivity index RIC of the target drainage basin based on the Dup and the Ddown. According to the method, the influence of micro-topography evolution of the erosion-driven drainage basin on the sediment connectivity can be more comprehensively represented.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and water conservation, and specifically to a method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness. Background Art

[0002] Soil and water loss is one of the main ecological environment problems faced by China. Severe soil and water loss intensifies land degradation, non-point source pollution, and sedimentation in rivers, lakes, and reservoirs, affecting water quality safety, flood control safety, and ecological safety, and restricting the sustainable development of the regional economy. Through the real-time monitoring of the sediment transport flux at the check stations in the watershed, the characteristics of soil and water loss in the watershed can be grasped macroscopically; however, this monitoring method belongs to the black box method, which can only evaluate the sediment output flux of the watershed from the outlet of the watershed, and a large amount of manpower and material resources are required to build positioning monitoring facilities, making it difficult to serve the monitoring of soil and water loss in remote mountainous watersheds. In addition, considering the complex migration mechanism and diverse paths of eroded sediment in the watershed, the sediment volume at the check station is difficult to truly reflect the soil erosion status in the watershed, and it is even more impossible to accurately identify the source of sediment and the key sediment transport paths, which restricts the precise control of soil and water loss based on source treatment and process interception. Therefore, a quantitative index that characterizes the potential risk area of eroded sediment and the contribution of the risk area to sediment transport in the river is extremely urgent. Sediment connectivity can characterize the sediment transfer process of a complex heterogeneous system, can quantify the sediment cascade relationship between different geomorphic or landscape units in the watershed, highlight the ease or difficulty of eroded sediment being transported out of the watershed, and focus on the impact of watershed geomorphic characteristics or landscape patterns on sediment transport. It is an effective means to identify hot spots of eroded sediment and key transport nodes, and plays an important role in carrying out precise control of soil and water loss and optimizing the zoning layout of soil and water conservation measures.

[0003] However, the traditional methods for evaluating sediment connectivity mainly rely on the ArcGIS platform. When processing data, they are limited by the software's computing resources, with complex calculations and many iterations. Each step of the output data must go through the embarrassing situation of being saved, called, and saved again, occupying a large amount of computer storage space and being unable to batch process a large number of target watersheds. In addition, the ArcGIS platform is a mature packaged software, and it is difficult for users to carry out personalized assignment of the weights of the analysis data at the raster cell level, which limits the dynamic adjustment of the analysis data based on the prior knowledge of the watershed by users. Moreover, the existing calculations of sediment connectivity indices generally consider the slope factor and the surface roughness factor. However, the analysis scales of the slope factor and the surface roughness factor do not match during the calculation process, affecting the accuracy of sediment connectivity evaluation. In addition, a large number of existing studies have shown that the relative surface roughness has a greater impact on the transport of eroded sediment in the watershed than the surface roughness, and there are differences in how the surface roughness is incorporated into the calculation of the sediment connectivity index. Summary of the Invention

[0004] The object of the present invention is to provide a method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness. Based on the MATLAB platform, it can calculate the sediment connectivity index of the watershed batch, quickly and with high precision. During the calculation of the connectivity index, there is no need to save intermediate data, thus saving hard disk storage space. When calculating the connectivity index, it fully considers the relative surface roughness rather than the surface roughness's influence on sediment connectivity, can more comprehensively characterize the influence of erosion-driven microtopography evolution in the watershed on sediment connectivity, and enhances the description of the mutual feedback process between the erosion process and microtopography evolution.

[0005] The technical solution provided to achieve the above object is: A method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness, comprising the following steps:

[0006] S10. Use the airborne LiDAR technology to obtain the digital elevation model (DEM) data of the target watershed;

[0007] S20. Based on the digital elevation model (DEM) of the target watershed, use the MATLAB platform to perform depression filling processing to obtain the digital elevation model DEM_F of the target watershed after depression filling;

[0008] S30. Based on the digital elevation model DEM_F of the target watershed after depression filling, use the D8 algorithm to calculate the flow direction matrix FD of the target watershed;

[0009] S40. Based on the flow direction matrix FD of the target watershed, calculate the catchment area A of the catchment area above each DEM cell of the target watershed, and extract the water system position vector data S of the target watershed in combination with the preset catchment area threshold of the catchment area above;

[0010] S50. Based on the digital elevation model DEM_F of the target watershed after depression filling, use the moving window analysis method to calculate the surface roughness factor RR_F and the surface relief factor SL_F of the target watershed respectively, and further generate the relative surface roughness factor RR_SF of the target watershed;

[0011] S60. Based on the flow direction matrix FD of the target watershed and the relative surface roughness factor RR_SF, calculate the weighted flow path length DIST_W between DEM cells of the target watershed;

[0012] S70. Based on the catchment area A of the catchment area above each DEM cell of the target watershed and the relative surface roughness factor RR_SF of the target watershed, use the regional statistical analysis method to calculate the sediment "source" component Dup of the target watershed;

[0013] S80. Calculate the cumulative confluence path length of water flow for each cell downstream based on the water system position vector data S of the target watershed, the weighted confluence path length DIST_W of the target watershed, and the flow direction matrix FD of the target watershed, and generate the sediment "sink" component Ddown of the target watershed.

[0014] S90. Calculate the connectivity index RIC of the target watershed based on the sediment "source" component Dup and the sediment "sink" component Ddown of the target watershed.

[0015] Further, in step S50, the moving window analysis method is used to calculate the surface relative roughness factor RR_SF of the target watershed. The specific process includes:

[0016] S51. Use the MATLAB platform, call the filter function, and perform smoothing processing on the filled digital elevation model DEM_F of the target watershed through a 3-cell × 3-cell moving window to obtain the smoothed terrain data DEM_FF of the target watershed.

[0017] S52. Subtract the smoothed terrain data DEM_FF of the target watershed from the filled digital elevation model DEM_F of the target watershed to obtain the random roughness terrain data DEM_EX of the target watershed.

[0018] S53. Process the random roughness terrain data DEM_EX of the target watershed through a moving window with a radius of 100 m, call the localtopography function, and calculate the variance value of the elevation data within the moving window, which is the surface roughness RR of the target watershed.

[0019] S54. Calculate the maximum value RR_MAX of the surface roughness RR of the target watershed, and divide the surface roughness RR of the target watershed by the maximum value RR_MAX to obtain the surface roughness factor RR_F of the target watershed.

[0020] Further, in step S50, the moving window analysis method is used to calculate the surface undulation factor SL_F of the target watershed. The specific process includes:

[0021] S55. Call the localtopography function, process the smoothed terrain data DEM_FF of the target watershed through a moving window with a radius of 100 m, obtain the maximum elevation value and the minimum elevation value within the moving window, and subtract the minimum elevation value from the maximum elevation value to obtain the undulation SL.

[0022] S56. Calculate the maximum value SL_MAX of the undulation SL.

[0023] S57. Divide the SL undulation by the maximum value SL_MAX of the undulation SL to obtain the surface undulation factor SL_F of the target basin.

[0024] Further, generating the surface relative roughness factor RR_SF of the target basin in step S50 includes:

[0025] S58. Divide the surface undulation factor SL_F of the target basin by the surface roughness factor RR_F to obtain the relative surface roughness factor RR_SF of the target basin.

[0026] Further, the specific process of step S60 includes:

[0027] S61. Based on the flow direction matrix FD of the target basin, use the D8 algorithm and call the getdistance function to obtain the distance matrix DIST of adjacent cells in the water flow direction;

[0028] S62. Use the MATLAB platform to divide the distance matrix DIST by the surface relative roughness factor RR_SF of the target basin to obtain the weighted confluence path length between DEM cells of the target basin, denoted as DIST_W.

[0029] Further, the specific process of step S70 includes:

[0030] S71. Call the sqrt function to calculate the square root of the catchment area A above each DEM cell of the target basin

[0031] S72. Based on the flow direction matrix FD of the target basin and the relative surface roughness factor RR_SF of the target basin, call the upslopestats function to calculate the average value W of the surface relative roughness factor of the catchment area above each DEM cell;

[0032] S73. Use the MATLAB platform to multiply the average value W of the surface relative roughness factor by the square root To obtain the sediment "source" component Dup of the target basin, and the calculation formula for the sediment "source" component Dup of the target basin is:

[0033]

[0034] Further, the specific process of step S80 includes:

[0035] S81. Using the MATLAB platform, call the flowdistance function. Based on the flow direction FD and the river system location vector data S of the target basin, determine the positions of all cells NN on the confluence path along which the water flow in each DEM cell converges downstream to the river system location;

[0036] S82. Number all the cells NN on the confluence path along which the water flow, denoted as 1, 2, 3,... n. The weighted confluence path lengths corresponding to each cell are DIST_W1, DIST_W2, DIST_W3,... DIST_W n ;

[0037] S83. The calculation formula for the sediment "confluence" component Ddown of the target basin is:

[0038]

[0039] where n is the number of all cells NN along the downstream direction of each cell from the river system S; DIST_W i is the weighted confluence path length of cell i along the downstream direction.

[0040] Further, the specific process of step S90 includes:

[0041] S91. The calculation formula for the connectivity index RIC of the target basin is:

[0042]

[0043] S92. Call the GRIDobj2geotiff function to output the calculated RIC index in the tif format.

[0044] The advantages of the present invention are as follows: The method for evaluating the connectivity of basin sediment based on the MATLAB platform and considering the relative surface roughness is based on the MATLAB platform, which can calculate the connectivity index of basin sediment batch, quickly and with high precision; there is no need to save intermediate data during the calculation of the connectivity index, thus saving hard disk storage space; the influence of relative surface roughness rather than surface roughness on sediment connectivity is fully considered during the calculation of the connectivity index, which can more comprehensively characterize the influence of erosion-driven microtopography evolution on sediment connectivity and enhance the description of the mutual feedback process between erosion process and microtopography evolution; the surface roughness and topographic undulation are calculated on a unified analysis scale; the calculated sediment connectivity index RIC can identify potential risk areas of soil and water loss in the basin and evaluate the difficulty of transporting eroded sediment out of the basin, supporting the precise management of soil and water loss in the basin and optimizing the zoning layout of soil and water conservation measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1It is a flowchart of a method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness in an embodiment of the present invention;

[0046] Figure 2 It is a DEM of the target watershed constructed based on the unmanned aerial vehicle lidar technology in an embodiment of the present invention;

[0047] Figure 3 It is the digital elevation model DEM_F after filling depressions in an embodiment of the present invention;

[0048] Figure 4 It is the flow direction matrix FD of the target watershed in an embodiment of the present invention;

[0049] Figure 5 It is the water system position vector data S of the target watershed in an embodiment of the present invention;

[0050] Figure 6 It is the surface roughness factor RR_F of the target watershed in an embodiment of the present invention;

[0051] Figure 7 It is the surface undulation factor SL_F of the target watershed in an embodiment of the present invention;

[0052] Figure 8 It is the relative surface roughness factor RR_SF of the target watershed in an embodiment of the present invention;

[0053] Figure 9 It is the weighted confluence path length DIST_W considering the relative surface roughness of the target watershed in an embodiment of the present invention;

[0054] Figure 10 It is the sediment "source" component Dup of the target watershed in an embodiment of the present invention;

[0055] Figure 11 It is the sediment "sink" component Ddown of the target watershed in an embodiment of the present invention;

[0056] Figure 12 It is the connectivity index RIC of the target watershed in an embodiment of the present invention;

[0057] Figure 13 It is a schematic diagram of the correlation between relative surface roughness and sediment yield from erosion. Detailed implementation manners

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0059] Please refer to Figure 1 , taking a certain target small watershed as an example, the embodiments of the present invention provide a method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness, including the following steps:

[0060] S10. Use the unmanned aerial vehicle (UAV) lidar technology to obtain the digital elevation model (DEM) data of the target watershed ( Figure 2 ). The data acquisition in step S10 uses the UAV lidar technology, including steps such as flight route design, point cloud data collection, trajectory solution, point cloud fusion and coordinate transformation, point cloud filtering, and construction of the digital elevation model DEM.

[0061] S20. Based on the digital elevation model DEM of the target watershed, use the MATLAB platform to perform depression filling processing to obtain the digital elevation model DEM_F of the target watershed after depression filling ( Figure 3 ). Specifically, use Matlab software and the fillsinks function to perform depression filling processing on the digital elevation model DEM to obtain the digital elevation model after depression filling, denoted as DEM_F.

[0062] S30. Based on the digital elevation model DEM_F of the target watershed after depression filling, use the D8 algorithm to calculate the flow direction matrix FD of the target watershed ( Figure 4 ). Specifically, use MATLAB software and the FLOWobj function embedded with the D8 algorithm to process the DEM_F to obtain the flow direction matrix of the target watershed, denoted as FD;

[0063] S40. Obtain the water system position vector data S of the target watershed ( Figure 5 ). Use MATLAB software and the STREAMobj function to process the flow direction matrix FD, calculate the catchment area A of the catchment above each DEM cell in the target watershed, and obtain the water system position vector data of the target watershed, denoted as S, by setting the catchment area threshold of the catchment above (300m 2 );

[0064] S50. Based on the digital elevation model DEM_F after depression filling in the target watershed, the surface roughness factor RR_F and the surface undulation factor SL_F of the target watershed are calculated respectively using the moving window analysis method, and further the surface relative roughness factor RR_SF of the target watershed is generated. The specific steps of step S50 include:

[0065] S51. Using the MATLAB platform, call the filter function to smooth the DEM_F through a 3-cell × 3-cell moving window to obtain the smooth terrain data DEM_FF of the target watershed;

[0066] S52. Subtract the DEM_FF from the DEM_F using the MATLAB platform to obtain the random roughness terrain data DEM_EX of the target watershed;

[0067] S53. Using the MATLAB platform, process the DEM_EX through a moving window with a radius of 100 m, call the localtopography function, and calculate the variance value of the elevation data within the moving window, which is the surface roughness RR of the target watershed;

[0068] S54. Using the MATLAB platform, calculate the maximum value RR_MAX of RR, and divide the surface roughness RR of the area by the RR_MAX to obtain the surface roughness factor RR_F of the target watershed ( Figure 6 );

[0069] S55. Using the MATLAB platform, process the DEM_FF through a moving window with a radius of 100 m, call the localtopography function, and obtain the maximum elevation value and the minimum elevation value within the moving window. The difference between the maximum elevation value and the minimum elevation value is the undulation SL;

[0070] S56. Using the MATLAB platform, calculate the maximum value SL_MAX of SL;

[0071] S57. Using the MATLAB platform, divide the SL by the SL_MAX to obtain the surface undulation factor SL_F of the target watershed ( Figure 7 );

[0072] S58. Using the MATLAB platform, divide the surface undulation factor SL_F of the target watershed by the surface roughness factor RR_F to obtain the surface relative roughness factor RR_SF of the target watershed ( Figure 8 ).

[0073] S60. Calculate the weighted confluence path length DIST_W between DEM cells of the target watershed based on the flow direction matrix FD and the surface relative roughness factor RR_SF of the target watershed. The specific steps of step S60 include:

[0074] S61. Using the MATLAB platform, based on the flow direction matrix FD of the target watershed, adopt the D8 algorithm, call the getdistance function, and obtain the distance matrix DIST of adjacent cells in the water flow direction.

[0075] S62. Using the MATLAB platform, divide the distance matrix DIST by the surface relative roughness factor RR_SF of the target watershed to obtain the weighted confluence path length between DEM cells of the target watershed, denoted as DIST_W( Figure 9 ).

[0076] S70. Based on the catchment area A above each DEM cell of the target watershed and the surface relative roughness factor RR_SF of the target watershed, use the regional statistical analysis method to calculate the sediment "source" component Dup of the target watershed. The specific steps of step S70 include:

[0077] S71. Call the sqrt function to obtain the square root of the catchment area A above each DEM cell of the target watershed.

[0078] S72. Based on the flow direction matrix FD of the target watershed and the relative surface roughness factor RR_SF of the target watershed, call the upslopestats function to calculate the average value W of the surface relative roughness factor of the catchment area above each DEM cell.

[0079] S73. Using the MATLAB platform, multiply the average value W of the surface relative roughness factor by the square root to obtain the sediment "source" component Dup of the target watershed. The calculation formula for the sediment "source" component Dup( Figure 10 ) is:

[0080]

[0081] where W is the average value of the relative surface roughness factor of the catchment area above each cell, and A is the catchment area above each cell.

[0082] S80. Calculate the cumulative downstream confluence path length of the water flow for each cell based on the water system position vector data S of the target basin, the weighted confluence path length DIST_W of the target basin, and the flow direction matrix FD of the target basin, and generate the sediment "sink" component Ddown of the target basin. The specific steps of S80 include:

[0083] S81. Use the MATLAB platform to call the flowdistance function, and based on the flow direction FD and the water system position vector data S of the target basin, determine the positions of all cells NN on the confluence along - path of the water flow from each DEM cell downstream to the water system position.

[0084] S82. Number all the cells NN on the confluence along - path, denoted as 1, 2, 3,... n, and the weighted confluence path lengths corresponding to each cell are DIST_W1, DIST_W2, DIST_W3,... DIST_W n ;

[0085] S83. The calculation formula for the sediment "sink" component Ddown( Figure 11 ) of the target basin is:

[0086]

[0087] where n is the number of all cells NN along the downstream direction of each cell from the water system S; DIST_W i is the weighted confluence path length of the downstream - direction along - path cell i.

[0088] S90. Calculate the connectivity index RIC of the target basin based on the sediment "source" component Dup and the sediment "sink" component Ddown of the target basin. The specific steps of S90 include:

[0089] S91. The calculation formula for the connectivity index RIC( Figure 12 ) of the target basin is:

[0090]

[0091] S92. Call the GRIDobj2geotiff function to output the calculated RIC index in tif format.

[0092] This invention serves to identify potential risk areas of soil and water loss in river basins and evaluate the ease of transporting eroded sediment out of the river basin, supporting the precise control of soil and water loss in river basins and optimizing the zoning layout of soil and water conservation measures; its method for evaluating sediment connectivity in river basins based on the MATLAB platform and considering surface relative roughness is based on the MATLAB platform, which can calculate the sediment connectivity index in batches, quickly, and with high precision. Based on the said case, if the ArcGIS platform is used, it takes about 35 minutes to calculate the connectivity index, while the time required for this invention's technology is only 7 seconds; there is no need to save intermediate data during the calculation of the connectivity index, thus saving hard disk storage space. Based on the said case, if the ArcGIS platform is used, storing process data such as river basin flow direction, catchment area, flow path length, and sediment "source" and "sink" components requires about 453M of hard disk space, while this invention's technology does not require saving process data; when calculating the connectivity index, it fully considers the surface relative roughness rather than the surface roughness's influence on sediment connectivity, which can more comprehensively characterize the influence of erosion-driven microtopography evolution in the river basin on sediment connectivity, enhancing the description of the mutual feedback process between erosion processes and microtopography evolution. As Figure 13 described, based on measured sediment data, if the surface roughness is used as an explanatory variable, there is a weak correlation between the surface roughness and sediment production rate. However, when the surface relative roughness is used as an explanatory variable, as the surface relative roughness increases, the sediment production rate decreases, and the correlation is significant, which conforms to the conventional expectation that the greater the roughness, the greater the resistance of the surface to runoff and sediment transport, and the lower the sediment production rate. Further, there is a significant positive correlation between sediment connectivity and sediment production rate, which conforms to the conventional understanding that the easier it is to export sediment from the river basin, the larger the sediment connectivity index, and the greater the sediment production rate; calculate the surface roughness and topographic undulation degree on a unified analysis scale; the calculated sediment connectivity index RIC can identify potential risk areas of soil and water loss in river basins and evaluate the ease of transporting eroded sediment out of the river basin, supporting the precise control of soil and water loss in river basins and optimizing the zoning layout of soil and water conservation measures. As Figure 12 described, the yellow area is a high-connectivity area and is the key area for preventing and controlling soil and water loss.

[0093] As mentioned above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness, characterized in that It includes the following steps: S10. Obtain the digital elevation model (DEM) data of the target watershed by using airborne LiDAR technology; S20. Based on the digital elevation model (DEM) of the target watershed, perform depression filling processing using the MATLAB platform to obtain the digital elevation model DEM_F of the target watershed after depression filling; S30. Based on the digital elevation model DEM_F of the target watershed after depression filling, calculate the flow direction matrix FD of the target watershed by using the D8 algorithm; S40. Based on the flow direction matrix FD of the target watershed, calculate the catchment area A of the upstream catchment above each DEM cell of the target watershed, and extract the water system position vector data S of the target watershed in combination with the preset catchment area threshold of the upstream catchment; S50. Based on the digital elevation model DEM_F of the target watershed after depression filling, use the moving window analysis method to calculate the surface roughness factor RR_F and the surface undulation factor SL_F of the target watershed respectively, and further generate the surface relative roughness factor RR_SF of the target watershed; S60. Calculate the weighted flow path length DIST_W between DEM cells of the target watershed based on the flow direction matrix FD and the surface relative roughness factor RR_SF of the target watershed; S70. Calculate the sediment "source" component Dup of the target watershed by using the regional statistical analysis method based on the catchment area A of the upstream catchment above each DEM cell of the target watershed and the surface relative roughness factor RR_SF of the target watershed; S80. Calculate the cumulative flow path length of the water flow downstream for each cell based on the water system position vector data S, the weighted flow path length DIST_W, and the flow direction matrix FD of the target watershed, and generate the sediment "sink" component Ddown of the target watershed; S90. Calculate the connectivity index RIC of the target watershed based on the sediment "source" component Dup and the sediment "sink" component Ddown of the target watershed.

2. The method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the surface relative roughness according to claim 1, wherein: In step S50, when calculating the surface relative roughness factor RR_SF of the target watershed by using the moving window analysis method, the specific process includes: S51. Use the MATLAB platform to call the filter function, and smooth the digital elevation model DEM_F of the target watershed after depression filling through a 3-cell × 3-cell moving window to obtain the smooth terrain data DEM_FF of the target watershed; S52. Subtract the smooth terrain data DEM_FF of the target watershed from the digital elevation model DEM_F of the target watershed after depression filling to obtain the random roughness terrain data DEM_EX of the target watershed; S53. Process the random roughness terrain data DEM_EX of the target watershed through a moving window with a radius of 100 m, call the localtopography function, and calculate the variance value of the elevation data within the moving window, which is the surface roughness RR of the target watershed. S54. Calculate the maximum value RR_MAX of the surface roughness RR of the target basin, and divide the surface roughness RR of the target basin by the maximum value RR_MAX to obtain the surface roughness factor RR_F of the target basin.

3. The method for evaluating the sediment connectivity of a basin based on the MATLAB platform and considering the relative surface roughness as claimed in claim 1, wherein: In step S50, the moving window analysis method is used to calculate the surface undulation factor SL_F of the target basin, and the specific process includes: S55. Call the localtopography function, process the smoothed terrain data DEM_FF of the target basin through a moving window with a radius of 100 m, obtain the maximum elevation and the minimum elevation within the moving window, and subtract the minimum elevation from the maximum elevation to obtain the undulation SL. S56. Calculate the maximum value SL_MAX of the undulation SL. S57. Divide the undulation SL by the maximum value SL_MAX of the undulation SL to obtain the surface undulation factor SL_F of the target basin.

4. The method for evaluating the sediment connectivity of a basin based on the MATLAB platform and considering the relative surface roughness as claimed in claim 1, wherein: Generating the surface relative roughness factor RR_SF of the target basin in step S50 includes: S58. Divide the surface undulation factor SL_F of the target basin by the surface roughness factor RR_F to obtain the relative surface roughness factor RR_SF of the target basin.

5. The method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness as described in claim 1, wherein: The specific process of step S60 includes: S61. Based on the flow direction matrix FD of the target basin, adopt the D8 algorithm, call the getdistance function, and obtain the distance matrix DIST between adjacent cells in the water flow direction. S62. Use the MATLAB platform to divide the distance matrix DIST by the surface relative roughness factor RR_SF of the target basin to obtain the weighted confluence path length between DEM cells of the target basin, denoted as DIST_W.

6. The method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness as claimed in claim 1, wherein: The specific process of step S70 includes: S71. Call the sqrt function to obtain the square root of the catchment area A of the catchment above each DEM cell in the target basin S72. Based on the flow direction matrix FD of the target basin and the relative surface roughness factor RR_SF of the target basin, call the upslopestats function to calculate the average value W of the surface relative roughness factors of the upstream catchment area corresponding to each DEM cell. S73. Using the MATLAB platform, multiply the average value W of the surface relative roughness factor by the square root to obtain the sediment "source" component Dup of the target basin. The calculation formula for the sediment "source" component Dup of the target basin is:

7. The method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness as described in claim 1, wherein: The specific process of step S80 includes: S81. Use the MATLAB platform, call the flowdistance function, and based on the flow direction FD of the target basin and the water system position vector data S, determine the positions of all cells NN on the confluence along-path from each DEM cell flowing downstream to the water system position. S82. Number all the cells NN on the flow path of the busbar as 1, 2, 3, … n, and the weighted flow path lengths corresponding to each cell are DIST_W1, DIST_W2, DIST_W3, … DIST_W n ; The calculation formula for the sediment "sink" component Ddown of the target basin is: where n is the number of all cells NN along the downstream direction of each cell from the water system S; DIST_W i is the weighted confluence path length of cell i along the downstream direction.

8. The method for evaluating the sediment connectivity of a watershed based on the MATLAB platform and considering the relative surface roughness as described in claim 1, wherein: The specific process of step S90 includes: S91. The calculation formula for the connectivity index RIC of the target basin is: S92. Call the GRIDobj2geotiff function to output the calculated RIC index in the tif format.

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