CN117934584A - Optimal catchment area threshold determining method based on DEM resolution - Google Patents
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Abstract
The application relates to the technical field of soil erosion evaluation, discloses a method for determining an optimal catchment area threshold based on DEM resolution, and aims to solve the problems of complex process and poor accuracy of the existing optimal catchment area threshold determination method, wherein the scheme mainly comprises the following steps: generating multiple resolution DEM data based on ANUDEM algorithm; setting a plurality of grid numbers for DEM data of each resolution respectively to determine a corresponding catchment area threshold; determining the corresponding slope length and slope length index of each catchment area threshold value, and calculating the corresponding slope length factor and the average value thereof; aiming at DEM data of each resolution, determining a corresponding optimal catchment area threshold value based on a mean value variable point method; and obtaining a relation between the DEM resolution and the optimal catchment area threshold value according to the optimal catchment area threshold value fitting corresponding to the DEM data of each resolution, and determining the optimal catchment area threshold value based on the relation. The application improves the efficiency and accuracy of determining the optimal catchment area threshold value, and is particularly suitable for soil erosion calculation.
Description
Technical Field
The application relates to the technical field of soil erosion evaluation, in particular to a method for determining an optimal catchment area threshold based on DEM resolution.
Background
Soil erosion not only can cause site hazards such as soil degradation, fertility reduction and soil desertification, but also can cause off-site hazards such as river sediment accumulation, river and lake water pollution and the like. Therefore, the regular investigation and evaluation of the soil erosion condition is helpful for objectively reflecting the current situation of water and soil loss and the effect of water and soil conservation treatment, and the work is also the foundation for compiling comprehensive water and soil conservation treatment plans, river basin or regional ecological protection and restoration schemes, water and soil conservation schemes for production and construction projects and the like. The Chinese soil erosion equation (CSLE) comprehensively considers the soil erosion environment, the topography and topography characteristics and the water and soil conservation measure characteristics of China in structure, and is more suitable for investigation and evaluation of soil erosion of China as a whole.
The Chinese soil erosion equation reflects the influence of topography on soil erosion mainly through gradient factors and gradient factors, wherein the gradient factors are calculated based on gradient lengths, the gradient length extraction is based on a digital elevation model (Digital Flevation Model, DEM), when the gradient length is extracted by taking the DEM as a data source, a catchment area threshold value is used for determining the endpoint of the gradient length and the distribution of a river network, and when the catchment area is larger than the catchment area threshold value, the grid is defined as a channel, namely the gradient length is terminated. Where the catchment area refers to the area occupied by all of the water flowing into the cells of the grid. Therefore, a reasonable catchment area threshold can accurately define a channel, directly influence the calculation of a slope length factor, and further influence the soil erosion investigation precision.
In the prior art, the determination of the optimal catchment area threshold value in the soil erosion calculation is less studied, and the determination of the catchment area threshold value has great randomness and subjectivity when the slope length factor calculation is carried out, so that the final soil erosion calculation precision is lower.
Kong Fanzhe et al propose a determination of a catchment area threshold when extracting a river network by using a DEM, which mainly uses the relationship between the river source density (or river network density) and the catchment area threshold to determine an ideal catchment area threshold, and the area threshold when the river source density (or river network density) tends to be stable is required. However, the method only judges the point where the river source density (or river network density) tends to be stable by visual observation, the calculation process is complex and complicated, and certain error still exists compared with the true value.
Disclosure of Invention
The application aims to solve the problems of complicated process and poor accuracy of the existing optimal catchment area threshold value determining method, and provides an optimal catchment area threshold value determining method based on DEM resolution.
The technical scheme adopted by the application for solving the technical problems is as follows:
An optimal catchment area threshold determination method based on DEM resolution, the method comprising:
generating multiple resolution DEM data based on ANUDEM algorithm;
Setting a plurality of grid numbers for the DEM data with each resolution, and setting corresponding catchment area thresholds for the DEM data with different resolutions and different grid numbers;
determining the corresponding slope length and slope length index of each catchment area threshold value, and calculating the corresponding slope length factor and the average value thereof according to the slope length and slope length index;
aiming at DEM data of each resolution, determining a corresponding optimal catchment area threshold value according to a corresponding slope length factor mean value and based on a mean value variable point method;
and obtaining a relation between the DEM resolution and the optimal catchment area threshold value according to the optimal catchment area threshold value fitting corresponding to the DEM data of each resolution, and determining the optimal catchment area threshold value based on the relation.
Further, generating the DEM data with multiple resolutions based on ANUDEM algorithm specifically includes:
DEM data with target resolution are generated through interpolation methods, data smoothing, terrain enhancement and local adaptive processing.
Further, the calculation formula of the slope length factor is as follows:
L＝(λ/22.13)m；
Wherein L represents a slope length factor, λ represents a slope length, and m represents a slope length index.
Further, the slope length index is determined according to the slope, and specifically comprises the following steps:
When θ is less than or equal to 1%, m=0.2; when 1% < θ+.ltoreq.3%, m=0.3; when 3% < θ+.ltoreq.5%, m=0.4; when θ > 5%, m=0.5, where θ represents the gradient.
Further, determining a corresponding optimal catchment area threshold according to the corresponding slope length factor mean value and based on a mean change point method, specifically comprising the following steps:
determining a slope length factor mean value corresponding to each grid number under the current resolution, and generating a slope length factor mean value data set corresponding to the grid number;
taking the slope length factor mean value data set as an original sample, calculating the average value of the original sample, and calculating the statistic of the original sample according to the average value of the original sample;
dividing the slope length factor mean value data set into two sections of samples according to the segmentation parameters, respectively calculating the mean value of each section of samples, and calculating segmented statistics according to the mean value of each section of samples;
and calculating an expected value according to the statistic of the original sample and the statistic after segmentation, and determining an optimal catchment area threshold according to the expected value.
Further, the calculation formula of the average value of the original sample is as follows:
The statistic of the original sample is calculated as follows:
wherein, Represents the average value of the original sample, N represents the number of the slope length factor averages, X t represents the t-th slope length factor average, and S represents the statistic of the original sample.
Further, the calculation formula of the segmented statistic is as follows:
Wherein S i represents the segmented statistics, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, X t1 represents the t1 st slope length factor mean, t1 = {1,2, … …, t-1}, X t2 represents the t2 nd slope length factor mean, t2 = { i, i+1, …, N }, i represents the segmentation parameter, i-1 is equal to the number of slope length factor mean in the first segment samples, i = {2,3, … …, N }, N represents the number of all slope length factor mean at the current resolution.
Further, the calculation formula of the expected value is as follows:
Where E (S-S i) represents the expected value, S i represents the segmented statistic, S represents the statistic of the original sample, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, i represents the segmentation parameter, i-1 is equal to the number of slope length factor averages in the first segment samples, i= {2,3, … …, N }, N representing the number of all slope length factor averages at the current resolution.
Further, determining an optimal catchment area threshold according to the expected value specifically includes:
And respectively calculating expected values corresponding to different values of the segmentation parameter i, determining a maximum expected value and a corresponding slope length factor mean value X i, and taking a water collection area threshold value corresponding to the slope length factor mean value X i as an optimal water collection area threshold value.
Further, the relationship between the DEM resolution and the optimal catchment area threshold is as follows:
y＝50x2；
where y represents the optimal catchment area threshold and x represents the DEM resolution.
The beneficial effects of the application are as follows: according to the method for determining the optimal catchment area threshold based on the DEM resolution, provided by the application, the DEM original data with different resolutions are generated through ANUDEM algorithm, and the method is high in precision and easy to operate; the change rule of the slope length factor mean value along with the catchment area threshold value under different DEM resolutions is counted through space, the catchment area threshold value corresponding to the slope length factor mean value when the slope length factor mean value tends to be stable is determined by adopting a mean value change point method, and the catchment area threshold value is the optimal catchment area threshold value; after the optimal catchment area threshold value of the slope length factor mean value under different DEM resolutions is obtained, the relation between the DEM resolutions and the optimal catchment area threshold value is formulated, so that the efficiency and the accuracy of determining the optimal catchment area threshold value are improved, and the efficiency and the accuracy of soil erosion calculation are further improved.
Drawings
Fig. 1 is a schematic flow chart of a method for determining an optimal catchment area threshold based on DEM resolution according to an embodiment of the application;
FIG. 2 is a schematic flow chart of another method for determining an optimal catchment area threshold based on DEM resolution according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a variation curve of a slope length factor mean value according to an embodiment of the present application;
fig. 4 is a schematic diagram of a change curve of a catchment area threshold according to an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.
In some of the flows described in the specification of the present application and the above figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, the sequence numbers of the operations being 101, 102, etc., merely for distinguishing between the various operations, the sequence numbers themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel.
The technical scheme of the embodiment of the application is suitable for application scenes in which soil erosion calculation is required, and can improve the accuracy of the soil erosion calculation.
Because the current catchment area threshold value determination has great randomness and subjectivity, the point where the river source density (or the river network density) tends to be stable is judged only by visual observation, the calculation process is complex and complicated, and certain error still exists compared with the true value.
Based on the above, the technical scheme of the application is provided, and in the embodiment of the application, DEM data with various resolutions is generated based on ANUDEM algorithm; setting a plurality of grid numbers for the DEM data with each resolution, and setting corresponding catchment area thresholds for the DEM data with different resolutions and different grid numbers; determining the corresponding slope length and slope length index of each catchment area threshold value, and calculating the corresponding slope length factor and the average value thereof according to the slope length and slope length index; aiming at DEM data of each resolution, determining a corresponding optimal catchment area threshold value according to a corresponding slope length factor mean value and based on a mean value variable point method; and obtaining a relation between the DEM resolution and the optimal catchment area threshold value according to the optimal catchment area threshold value fitting corresponding to the DEM data of each resolution, and determining the optimal catchment area threshold value based on the relation.
Specifically, the embodiment of the application generates DEM original data with different resolutions through ANUDEM algorithm, sets different catchment area thresholds under different DEM resolutions according to the number of grids based on the data, and calculates corresponding slope length factors. And calculating the optimal catchment area threshold value of the gradient length factor mean value under different resolutions by means of spatial statistics, and then, drawing a relation between the DEM resolution and the optimal catchment area threshold value, and finally, directly determining the optimal catchment area threshold value through the relation. Compared with the prior art, the method and the device have the advantages that the flow is simple, and the accuracy of determining the optimal catchment area threshold can be improved.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments.
Referring to fig. 1 and 2, the method for determining the optimal catchment area threshold based on the DEM resolution according to the embodiment of the application includes the following steps:
Step 101, generating multiple resolution DEM data based on ANUDEM algorithm;
in practical use, a representative study area was selected according to 1:5000 topographic map is used for collecting contour lines and elevation points of a research area, and vector data of boundaries, rivers, lakes and the like of the research area are combined to generate DEM data with different resolutions through ANUDEM algorithm.
The ANUDEM algorithm mainly comprises four aspects: interpolation methods (Interpolation Algorithm), data smoothing (Roughness Penalty Algorithms), terrain enhancement (Drainage Enforcement Algorithm), and local adaptation (Locally ADAPTIVE STRATEGIES). The interpolation method adopts a nested multi-resolution iterative calculation method, which ensures the high efficiency of local interpolation and has the continuity of global interpolation. Interpolation starts with a coarser initial resolution, the resolution is reduced by half each time, gradually decreasing to the target resolution. The purpose of data smoothing is to avoid the occurrence of unreal topography and to give an objective evaluation of interpolation effects. The topography strengthening algorithm is an algorithm capable of effectively representing the hydrographic relief features, and the algorithm can accurately and truly represent the surface topography features (particularly the topography features under the action of running water erosion) on the fitting surface by identifying topography feature points (concave parts and saddle parts) in elevation data and topography feature lines (ridges and channels) hidden in Gao Chengdian and contour lines and adding a group of ordered topography feature line constraint interpolation calculation to the algorithm through application to rivers (slope turning lines). Whereas locally adaptive processing aims to achieve a better fit between the fit and the observation point.
In an embodiment of the application, the resolution of DEM data generated by the ANUDEM algorithm includes 2.5m, 5m, 7.5m, 10m, 15m, 20m, 25m, 30m, 35m, 40m, 45m, 50m, 55m, 60m, 65m, 70m, 75m, 80m, 85m, 90m.
Step 102, setting a plurality of grid numbers for the DEM data with each resolution, and setting corresponding catchment area thresholds for the DEM data with different resolutions and different grid numbers;
In practical use, the DEM data for each resolution is set to 10, 15, 20, 25, 30, 50, 70, 90, 110, 130, 150, 200, 500, 1000, 2000, 5000 grids for DEM data for a resolution of 2.5m, based on DEM data for resolutions of 2.5m, 5m, 7.5m, 10m, 15m, 20m, 25m, 30m, 35m, 40m, 45m, 50m, 55m, 60m, 65m, 70m, 75m, 80m, 85m, and 90m, respectively, with water area thresholds of 62.5m2、93.75m2、125m2、156.25m2、187.5m2、312.5m2、437.5m2、562.5m2、687.5m2、812.5m2、937.5m2、1250m2、3125m2、6250m2、12500m2 and 31250m 2 for DEM data for a resolution of 2.5m when the number of grids is 10, 15, 20, 25, 30, 50, 70, 90, 110, 130, 150, 200, 500, 1000, 2000, and 5000, respectively.
The corresponding catchment area thresholds for grid numbers 10, 15, 20, 25, 30, 50, 70, 90 at other resolutions are shown in the following table:
The threshold catchment area values for grid numbers 110, 130, 150, 200, 500, 1000, 2000, 5000 for other resolutions are shown in the following table:
Step 103, determining the slope length and the slope length index corresponding to each catchment area threshold value, and calculating the corresponding slope length factor and the average value thereof according to the slope length and the slope length index;
The foregoing step is used for determining a slope length factor corresponding to each catchment area threshold, and specifically, in the embodiment of the present application, a calculation formula of the slope length factor is as follows:
L＝(λ/22.13)m；
Wherein 22.13 is the standard cell slope length, L represents the slope length factor, lambda represents the slope length, m represents the slope length index, and the slope length index is determined according to the slope, and the method specifically comprises the following steps:
When θ is less than or equal to 1%, m=0.2; when 1% < θ+.ltoreq.3%, m=0.3; when 3% < θ+.ltoreq.5%, m=0.4; when θ > 5%, m=0.5, where θ represents the gradient.
104, Determining a corresponding optimal catchment area threshold value according to a corresponding slope length factor mean value and based on a mean change point method aiming at DEM data of each resolution;
It will be appreciated that the mean-shift method is a statistical principle to judge and check for the presence of shift points and to determine one or more shift points in order to determine the optimum point at which the curve is slowed down by steepness by data analysis. The existing research results prove that the method has higher feasibility and reliability. The mean-shift point analysis method is a method for processing nonlinear data in mathematical statistics, and for DEM data of each resolution, the calculation steps are as follows:
Step 1041, determining a slope length factor mean value corresponding to each grid number under the current resolution, and generating a slope length factor mean value data set corresponding to the grid number;
In the embodiment of the application, the number of the slope length factor mean value data sets corresponding to the number of grids is { X 1,X2,……,Xt,……,XN }, assuming that the type of the number of grids is N, and the number of the slope length factor mean values under the current resolution is also N.
Step 1042, taking the slope length factor mean value dataset as an original sample, calculating the mean value of the original sample, and calculating the statistic of the original sample according to the mean value of the original sample;
in the embodiment of the present application, the calculation formula of the average value of the original sample is as follows:
The statistic of the original sample is calculated as follows:
wherein, Represents the average value of the original samples, N represents the number of slope length factor means, X t represents the t-th slope length factor means, t= {1,2, … …, N }, and S represents the statistic of the original samples.
Step 1043, dividing the slope length factor mean value dataset into two sections of samples according to the segmentation parameters, respectively calculating the mean value of each section of samples, and calculating segmented statistics according to the mean value of each section of samples;
In practical application, the slope length factor mean value data set may be segmented according to different segmentation parameters i, where the segmentation parameters i are used to determine the number of slope length factor mean values in two segments of samples, i= {2,3, … …, N }, the segmented slope length factor mean value includes two segments of samples, the first segment of samples is { X 1,X2,……,Xi-1 }, and the second segment of samples is { X i,Xi+1,……,XN }.
In the embodiment of the present application, the calculation formula of the segmented statistic is as follows:
Wherein S i represents the segmented statistics, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, X t1 represents the t1 st slope length factor mean, t1 = {1,2, … …, t-1}, X t2 represents the t2 nd slope length factor mean, t2 = { i, i+1, …, N }, i represents the segmentation parameter, i-1 is equal to the number of slope length factor mean in the first segment samples, i = {2,3, … …, N }, N represents the number of all slope length factor mean at the current resolution.
Similarly, the average value of the first segment of samples is calculated as follows:
the average value of the second segment samples is calculated as follows:
step 1044, calculating an expected value according to the statistic of the original sample and the segmented statistic, and determining an optimal catchment area threshold according to the expected value.
In the embodiment of the present application, the calculation formula of the expected value is as follows:
Where E (S-S i) represents the expected value, S i represents the segmented statistic, S represents the statistic of the original sample, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, i represents the segmentation parameter, i-1 is equal to the number of slope length factor averages in the first segment samples, i= {2,3, … …, N }, N representing the number of all slope length factor averages at the current resolution.
According to the embodiment of the application, the expected values corresponding to the different values of the segmentation parameter i are calculated respectively, the maximum expected value and the corresponding slope length factor X i are determined, and the water collecting area threshold corresponding to the slope length factor X i is used as the optimal water collecting area threshold.
Through space statistics, a change rule curve of the slope length factor mean value of the research area along with the threshold value of the catchment area under different resolutions is shown in figure 3. In fig. 3, the resolution of the right curves from bottom to top is 2.5m, 5m, 7.5m, 10m, 15m, 20m, 25m, 30m, 35m, 40m, 45m, 50m, 55m, 60m, 65m, 70m, 75m, 80m, 85m and 90m, respectively. When calculating the slope length for a data source with DEM, the extraction of the channel is controlled by the catchment area threshold, and a grid with catchment area greater than the catchment area threshold is considered a channel. The optimal catchment area threshold is determined during channel extraction, and most of the optimal catchment area threshold is based on the relationship between the river network density and the catchment area threshold, and the core idea is to take the catchment area threshold corresponding to the stable river network density change area as an optimal value. The change rule diagram of the slope length factor mean value of the research area along with the catchment area threshold under different resolutions can be seen, the slope length factor mean value calculated by each DEM resolution shows a trend of increasing and then gradually stabilizing, and when the slope length factor mean value tends to be stable, the catchment area threshold corresponding to the slope length factor mean value is the optimal catchment area threshold according to the research results of the former. Based on the application premise of the mean value variable point method, when the expected value is maximum, the optimal point of the curve in fig. 3, which is changed from abrupt to gradual, is found, the river network density change area is stable, and the catchment area threshold corresponding to the corresponding slope length factor mean value is the optimal catchment area threshold.
And 105, fitting according to the optimal catchment area threshold corresponding to the DEM data of each resolution to obtain a relation between the DEM resolution and the optimal catchment area threshold, and determining the optimal catchment area threshold based on the relation.
Based on the premise of applying the mean value variable point method, the embodiment of the application calculates the statistics of the original sample and the segmented statistics to respectively calculate the optimal catchment area threshold corresponding to the slope length factor mean value under different DEM resolutions, and the result is shown in fig. 4. From the results, the optimal catchment area threshold value shows an increasing trend along with the reduction of the accuracy of the DEM, and based on the rule, the embodiment of the application develops the relation between the resolution of the DEM and the optimal catchment area threshold value as follows:
y＝50x2；
where y represents the optimal catchment area threshold and x represents the DEM resolution.
In practical application, if soil erosion calculation is required, a corresponding optimal catchment area threshold can be determined according to the corresponding DEM resolution and based on the relational expression, so that the efficiency and accuracy of determining the optimal catchment area threshold are improved, and the efficiency and accuracy of soil erosion calculation are further improved.
In summary, according to the method for determining the optimal catchment area threshold based on the DEM resolution provided by the embodiment of the application, DEM original data with different resolutions are generated through ANUDEM algorithm, and the method is high in accuracy and easy to operate; the change rule of the slope length factor mean value along with the catchment area threshold value under different DEM resolutions is counted through space, the catchment area threshold value corresponding to the slope length factor mean value when the slope length factor mean value tends to be stable is determined by adopting a mean value change point method, and the catchment area threshold value is the optimal catchment area threshold value; after the optimal catchment area threshold values of slope length factors under different DEM resolutions are obtained, a relation between the DEM resolutions and the optimal catchment area threshold values is formulated, so that the efficiency and the accuracy of determining the optimal catchment area threshold values are improved, and the efficiency and the accuracy of soil erosion calculation are also improved.
Claims (10)
1. The method for determining the optimal catchment area threshold based on the DEM resolution is characterized by comprising the following steps of:
generating multiple resolution DEM data based on ANUDEM algorithm;
Setting a plurality of grid numbers for the DEM data with each resolution, and setting corresponding catchment area thresholds for the DEM data with different resolutions and different grid numbers;
determining the corresponding slope length and slope length index of each catchment area threshold value, and calculating the corresponding slope length factor and the average value thereof according to the slope length and slope length index;
aiming at DEM data of each resolution, determining a corresponding optimal catchment area threshold value according to a corresponding slope length factor mean value and based on a mean value variable point method;
and obtaining a relation between the DEM resolution and the optimal catchment area threshold value according to the optimal catchment area threshold value fitting corresponding to the DEM data of each resolution, and determining the optimal catchment area threshold value based on the relation.
2. The DEM resolution-based optimal catchment area threshold determination method as in claim 1, wherein generating multiple resolution DEM data based on ANUDEM algorithm specifically comprises:
DEM data with target resolution are generated through interpolation methods, data smoothing, terrain enhancement and local adaptive processing.
3. The DEM resolution-based optimal catchment area threshold determination method as claimed in claim 1, wherein the slope length factor is calculated as follows:
L＝(λ/22.13)m；
Wherein L represents a slope length factor, λ represents a slope length, and m represents a slope length index.
4. A method for determining an optimal catchment area threshold based on DEM resolution as claimed in claim 3, wherein said slope length index is determined according to the slope, and specifically comprising:
When θ is less than or equal to 1%, m=0.2; when 1% < θ+.ltoreq.3%, m=0.3; when 3% < θ+.ltoreq.5%, m=0.4; when θ > 5%, m=0.5, where θ represents the gradient.
5. The method for determining the optimal catchment area threshold based on the DEM resolution according to claim 1, wherein the method for determining the optimal catchment area threshold based on the mean value of the corresponding slope length factors and the mean value change point method specifically comprises the following steps:
determining a slope length factor mean value corresponding to each grid number under the current resolution, and generating a slope length factor mean value data set corresponding to the grid number;
taking the slope length factor mean value data set as an original sample, calculating the average value of the original sample, and calculating the statistic of the original sample according to the average value of the original sample;
dividing the slope length factor mean value data set into two sections of samples according to the segmentation parameters, respectively calculating the mean value of each section of samples, and calculating segmented statistics according to the mean value of each section of samples;
and calculating an expected value according to the statistic of the original sample and the statistic after segmentation, and determining an optimal catchment area threshold according to the expected value.
6. The DEM resolution-based optimal catchment area threshold determination method as in claim 5, wherein the average of the raw samples is calculated as follows:
The statistic of the original sample is calculated as follows:
wherein, Represents the average value of the original sample, N represents the number of the slope length factor averages, X t represents the t-th slope length factor average, and S represents the statistic of the original sample.
7. The DEM resolution-based optimal catchment area threshold determination method as in claim 5, wherein the segmented statistics are calculated as follows:
Wherein S i represents the segmented statistics, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, X t1 represents the t1 st slope length factor mean, t1 = {1,2, … …, t-1}, X t2 represents the t2 nd slope length factor mean, t2 = { i, i+1, …, N }, i represents the segmentation parameter, i-1 is equal to the number of slope length factor mean in the first segment samples, i = {2,3, … …, N }, N represents the number of all slope length factor mean at the current resolution.
8. The DEM resolution-based optimal catchment area threshold determination method as in claim 5, wherein the expected value is calculated as:
Where E (S-S i) represents the expected value, S i represents the segmented statistic, S represents the statistic of the original sample, Represents the average value of the first segment of samples,/>Representing the average value of the second segment samples, i represents the segmentation parameter, i-1 is equal to the number of slope length factor averages in the first segment samples, i= {2,3, … …, N }, N representing the number of all slope length factor averages at the current resolution.
9. The DEM resolution-based optimal catchment area threshold determination method as in claim 8, wherein determining the optimal catchment area threshold from the desired value, specifically comprises:
And respectively calculating expected values corresponding to different values of the segmentation parameter i, determining a maximum expected value and a corresponding slope length factor mean value X i, and taking a water collection area threshold value corresponding to the slope length factor mean value X i as an optimal water collection area threshold value.
10. The method of determining an optimal catchment area threshold based on DEM resolution according to claim 1, wherein the relationship between DEM resolution and optimal catchment area threshold is as follows:
y＝50x2；
where y represents the optimal catchment area threshold and x represents the DEM resolution.
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