Calibration Method for Calculating Topographic Factors from 30m Resolution Global Geographical Elevation Data

Abstract

The present invention provides a calibration method for calculating terrain factors from 30m-resolution global geographical elevation data, comprising the steps of: S1, creating a terrain factor calibration model for 30m-resolution ASTER GDEM data; S2, obtaining the publicly available ASTER GDEM data of the target area to be calibrated; S3, processing the obtained publicly available data to obtain the relevant parameters of the mean slope length factor, mean slope factor and mean slope of the target area required by the model; S4, calibrating the original slope factor and original slope length factor in the publicly available data pixel by pixel according to the calibration model and the relevant parameters to obtain terrain factors with improved accuracy. By establishing a terrain factor calibration model and calibrating the terrain factors pixel by pixel with the watershed as the unit, the present invention makes the terrain factors calculated from low-resolution DEM data and high-resolution DEM data approximate at the watershed scale, realizes the calculation of terrain factors with higher accuracy from low-resolution data DEM of global geographical elevation data, and improves the accuracy of soil erosion model estimation.

Description

Technical Field

[0001] The present invention relates to the technical field of soil erosion, specifically to the technical field of obtaining terrain factors with high precision using publicly available data at low resolution in a certain area, and particularly to a calibration method for calculating terrain factors from 30m resolution global digital elevation data based on watershed division. Background Art

[0002] The terrain factor LS includes the slope length factor L and the slope factor S, which are factors reflecting the influence of terrain characteristics on erosion in the USLE, RUSLE or CSLE models. When applied at medium and small scales, the 1:10,000 or 1:50,000 national standard topographic maps are mostly used for calculation. However, when applied at large scales such as globally or in large regions, generally only the terrain data of global digital elevation data can be selected for calculation, such as ASTER GDEM (30m resolution). ASTER GDEM, full name Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model, that is, Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model, like SRTM, is a digital elevation DEM with a global spatial resolution of 30 meters. However, the resolution reliability of the publicly available data is poor, and the local terrain details are insufficient. The terrain factor LS calculated from it will have systematic deviations, which is not conducive to the accurate estimation of the soil erosion model. Therefore, it is necessary to establish a calibration / calibration model for the LS terrain factor.

[0003] In existing research, some scholars have established model relationships for terrain factors at different scales, but most studies focus on aspects such as slope, unit catchment area, and comparison of terrain factors at different scales. There are few downscaling models for the LS factor involved in soil erosion, and there are few studies on downscaling processing or improving the accuracy of calculating the LS factor from low-resolution DEMs. To fill this gap, it is necessary to further explore the downscaling processing or calibration method for calculating the LS terrain factor from low-resolution data. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention calibrates the publicly available ASTER GDEM data with a resolution of 30m by establishing a calibration model for the LS terrain factor, and performs downscaling of the LS factor pixel by pixel in each watershed on the basis of watershed division, so as to improve the accuracy of the LS terrain factor calculated from low-resolution DEM data and improve the accuracy of the soil erosion prediction model.

[0005] The present invention discloses a calibration method for calculating terrain factors from 30m resolution global digital elevation data, which includes the following steps:

[0006] S1. Create a terrain factor correction model for ASTER GDEM data with a resolution of 30m:

[0007] The terrain factor correction model includes a pixel-based slope factor correction model and a pixel-based slope length factor correction model:

[0008] The pixel-based slope factor correction model is:

[0009] S′ 30,i = f s30 S 30,i (1)

[0010]

[0011] Where S′ 30,i is the corrected slope factor of the i-th pixel in a certain basin, i ∈ {1, 2,..., n}, and n is the total number of pixels in the basin; f s30 is the correction coefficient of the slope factor, a dimensionless number, and S 30,i is the original slope factor of the i-th pixel in the basin; is the mean value of the slope factor S 30 in the basin;

[0012] When S′ 30,i is greater than 9.995, then set S′ 30,i = 9.995;

[0013] The pixel-based slope length factor correction model is:

[0014] L′ 30,i = f L30 L 30,i (3)

[0015]

[0016] In the formula, L′ 30,i is the corrected slope length factor of the i-th pixel in a certain basin, i ∈ {1, 2,..., n}, and n is the total number of pixels in the basin; f L30 is the correction coefficient of the slope length factor, a dimensionless number; L 30,i is the original slope length factor of the i-th pixel in the basin, is the mean value of the slope length factor L 30 in the basin, is the mean value of the slope θ 30 in the basin;

[0017] When L′ 30,i is greater than 5.511, then set L′ 30,i = 5.511;

[0018] S2. Obtain the 30m resolution ASTER GDEM public data of the target area to be corrected;

[0019] S3. Process the obtained public data to obtain the relevant parameters required by the model;

[0020] The relevant parameters include the mean value of the slope factor of the target area the mean value of the slope length factor and the mean value of the slope

[0021] S4. According to the terrain factor correction model in step S1 and the relevant parameters obtained in step S3, correct the original slope factor S based on pixels 30,i and the original slope length factor L based on pixels 30,i in the public data to obtain the corrected terrain factor.

[0022] Further, the specific steps of step S3 are as follows:

[0023] S31. Process the collected 30m resolution ASTER GDEM public data to obtain the raster file for small watershed division of the target area;

[0024] S32. Convert the raster file for small watershed division into a vector file for small watersheds;

[0025] S33. Further process the obtained vector file for small watersheds, and merge the small watersheds with an area smaller than the threshold into the surrounding larger watersheds;

[0026] S34. Respectively count the slope length factor L 30 , slope factor S 30 and slope θ 30 ;

[0027] S35. Calculate the average values of the slope length factor L 30 , slope factor S 30 and slope θ 30 of all small watersheds to obtain the mean value of the slope factor of the target area the mean value of the slope length factor and the mean value of the slope

[0028] Further, the specific steps of step S32 are as follows: Load the raster file for small watershed division into the geographic information system platform ArcGIS, and use the from raster to polygon tool to convert the raster file for small watershed division into a vector file for small watersheds.

[0029] Further, in step S31, the LS calculation tool is used to process the public data.

[0030] Further, the threshold value in step S33 is 10 km 2 .

[0031] Further, the statistics in step S34 are performed based on the ArcGIS platform.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The present invention creates a correction model for terrain factors of 30m resolution ASTER GDEM data. This model is easy to use and has high accuracy, which helps to quickly and efficiently obtain terrain factors on a large scale, can improve the accuracy of LS terrain factors calculated from low-resolution DEM data, and provides a new method for optimizing terrain factors.

[0034] 2. The present invention fully considers the spatial variability in the watershed and terrain, and performs the downscaling of the terrain factor LS according to the watershed division, which is applicable at both the regional and watershed scales, and thus can improve the accuracy of the soil erosion prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of the correction method for calculating terrain factors of 30m resolution global geographic elevation data according to the present invention;

[0036] Figure 2 is a comparison of terrain factors calculated from ASTER GDEM and 1:50,000 DEM in a typical area of an embodiment of the present invention;

[0037] Figure 3 is a comparison chart of the corrected result of the slope factor calculated from ASTER GDEM in a typical area of an embodiment of the present invention and the result calculated from 1:50,000 DEM;

[0038] Figure 4 is a comparison chart of the corrected result of the slope length factor calculated from ASTER GDEM in a typical area of an embodiment of the present invention and the result calculated from 1:50,000 DEM;

[0039] Figure 5 is a comparison chart of the slope factor calculated from ASTER GDEM in the verification area of an embodiment of the present invention before correction (left figure), after correction (right figure) and the result calculated from 1:50,000 DEM;

[0040] Figure 6 is a comparison chart of the slope length factor calculated from ASTER GDEM in the verification area of an embodiment of the present invention before correction (left figure), after correction (right figure) and the result calculated from 1:50,000 DEM;

[0041] Figure 7ASTER GDEM data map of a certain area in the verification area for an embodiment of the present invention;

[0042] Figure 8a Spatial distribution map before correction of the slope factor calculated from ASTER GDEM of a certain area in the verification area for an embodiment of the present invention;

[0043] Figure 8b Spatial distribution map after correction of the slope factor calculated from ASTER GDEM of a certain area in the verification area for an embodiment of the present invention;

[0044] Figure 9a Spatial distribution map before correction of the slope length factor calculated from ASTER GDEM of a certain area in the verification area for an embodiment of the present invention;

[0045] Figure 9b Spatial distribution map after correction of the slope length factor calculated from ASTER GDEM of a certain area in the verification area for an embodiment of the present invention. Detailed implementation manners

[0046] To make the objectives, technical solutions and advantages of the present invention more clear, the following further explains in conjunction with Figures 1 - 9b and embodiments.

[0047] The objective of the present invention is to create a correction model for terrain factors of 30m resolution ASTER GDEM data of global geographic elevation data, and perform data correction based on this model. This model helps to obtain more accurate slope length factors and slope factors from DEM data with lower spatial resolution, thereby improving the accuracy of soil erosion model estimation.

[0048] First, create a terrain factor correction model for 30m resolution ASTER GDEM data of global geographic elevation data. Among them, the terrain factor correction model includes a pixel-based slope factor correction model and a pixel-based slope length factor correction model.

[0049] During the establishment of the terrain factor correction model, the present invention uses publicly available 30m low-resolution DEM data and high-resolution DEM data to conduct a comparative estimation of the LS factor, analyzes the calculation results of DEM data with different resolutions, and finds that the accuracy of the LS factor, that is, the numerical difference is relatively large, and the LS factor needs to be corrected to make its value more accurate.

[0050] The training dataset used for model establishment includes 30m-resolution DEM data of 7 typical districts (counties) in different topographies and landforms. Divided by small watersheds, relevant characteristic indicators are used to construct a non-linear model. Further, model verification is carried out to explore the applicability of the present invention. The verification dataset is 2 typical districts (counties) in different topographies and landforms. By correcting the LS factor obtained from low-resolution DEM data, it is found that the corrected LS factor is consistent with the calculation result of high-resolution DEM data. Finally, slope factor S maps and slope length factor L maps with relatively high accuracy in different topographies and landforms are obtained. Through this method, relatively accurate LS factor values can be obtained at the small watershed scale in different topographies and medium-scale regions, achieving the purpose of improving the estimation accuracy of soil erosion models.

[0051] To achieve the above object, the solution adopted in the model establishment of the present invention includes the following steps:

[0052] Step 1: Create a topographic factor correction model for 30m-resolution ASTER GDEM data.

[0053] Step 11: Correction model for slope factor S based on pixels.

[0054] Correct the slope factor S calculated from 30m-resolution DEM data by watershed, that is, add an adjustment constant to the slope factor calculated for each watershed and then multiply by a specific correction coefficient:

[0055] S′ 30,i =f s30 S 30,i (1)

[0056] In the formula, S′ 30,i is the correction result of the slope factor of the i-th pixel of a certain watershed calculated from ASTER GDEM 30m data, f s30 is the correction coefficient of the slope factor calculated from 30m-resolution ASTER GDEM 30m data of a certain watershed, a dimensionless number, and S 30,i is the slope factor of the i-th pixel of a certain watershed calculated from ASTER GDEM 30m data. Note that if the revised result S is greater than 9.995, then let S = 9.995.

[0057] The modeling formula of the correction coefficient f s30 is as follows:

[0058]

[0059] In the formula, is the mean value of the watershed slope factor S calculated from the publicly available ASTER GDEM 30m data, a dimensionless number. 30

[0060] Step 12: Correction model of pixel-based slope length factor L.

[0061] Taking the watershed as a unit, correct the slope length factor L calculated from 30m resolution DEM data, that is, multiply the slope length factor calculated from ASTER GDEM in each watershed by a correction coefficient of this watershed:

[0062] L′ 30,i =f L30 L 30,i (3)

[0063] In the formula, L′ 30,i is the correction result of the slope length factor of the i-th pixel in a certain watershed calculated from 30m resolution ASTER GDEM; f L30 is the correction coefficient of the slope length factor calculated from ASTER GDEM in this watershed; L 30,i is the slope length factor of the i-th pixel in this watershed calculated from ASTER GDEM. Note that if the revised result L is greater than 5.511, then let L = 5.511.

[0064] The modeling formula of the correction coefficient f L30 is as follows:

[0065]

[0066] In the formula, is the average value of the slope length factor of this watershed calculated from 30m resolution ASTER GDEM, a dimensionless number, θ is the slope, is the average value of the watershed slope calculated from 30m DEM data of this watershed, f L30 is the correction coefficient of the slope length factor calculated from ASTER GDEM in this watershed, a dimensionless number.

[0067] Step 2: Obtain the publicly available ASTER GDEM data of the target area to be corrected.

[0068] Step 21: A certain area can be divided into the Northeast Black Soil Region (Northeast Mountainous and Hilly Region), Northern Sandy Region (New Gansu-Mongolia Plateau Basin Region), Northern Rocky Mountainous Region (Northern Mountainous and Hilly Region), Northwest Loess Plateau Region, Southern Red Soil Region (Southern Mountainous and Hilly Region), Southwestern Purple Soil Region (Sichuan Basin and Surrounding Mountainous and Hilly Region), and Southwestern Karst Region (Yunnan-Guizhou Plateau Region) according to the erosion type. According to the soil erosion type zoning and regional topographic and geomorphic characteristics, select one county in each erosion type area as the typical area for soil conservation services, that is, the research area, with a total of 9 typical areas.

[0069] Step 22: The training dataset is the 30m resolution ASTER GDEM and 1:50,000 topographic factor data of 7 typical areas. In this application, the topographic factors of its DEM data are calculated and corrected at the small watershed scale, as shown in Table 1:

[0070] Table 1: Table of 9 typical counties (cities, districts)

[0071]

[0072] Step 23: The validation dataset is the 30m resolution ASTER GDEM and 1:50,000 topographic factor data of 2 typical areas. At the small watershed scale, the topographic factors of the data of these 2 typical areas are calculated and corrected.

[0073] Step 3: Process the obtained DEM data, and statistically obtain the relevant parameters of the average slope length factor, average slope factor, and average slope in the study area;

[0074] Use the LS tool to process the data in Step 2 to obtain a raster file for small watershed division, and process it in the software of ArcGIS 10.2 version to obtain a vector file for watershed division (the small watershed area threshold is 10 km 2 ), and statistically obtain the characteristic indexes of the topographic factors of each watershed according to the watershed division results, such as the average value of slope, the average value of topographic factors, etc.

[0075] Step 4: Correct the original slope factor and original slope length factor in the public data pixel by pixel according to the correction model and relevant parameters to obtain topographic factors with improved accuracy. Compare the corrected LS factor with the LS factor calculated from the 1:50,000 topographic map data in the same region to evaluate the effectiveness of the correction model.

[0076] Statistically obtain the characteristic indexes such as the average value and standard deviation of the S factor correction results according to the watershed division, and compare them with the high-resolution S factor values for verification. The comparison chart of the mean values before and after the correction of the slope factor S calculated from the 30m data of ASTER GDEM in this area and the calculation results of the 1:50,000 topographic map is specifically shown in Figure 5 .

[0077] Statistically obtain the characteristic indexes such as the average value and standard deviation of the L factor correction results according to the watershed division, and compare them with the high-resolution S factor values for verification. The comparison chart of the mean values before and after the correction of the slope length factor L calculated from the 30m data of ASTER GDEM in this area and the calculation results of the 1:50,000 topographic map is specifically shown in Figure 6 .

[0078] It is proved that the method of the present invention has a significant effect on the correction of topographic factors.

[0079] As a specific embodiment, taking a certain region as an example, the main implementation scheme of the present invention will be described below.

[0080] The embodiment of the present invention provides a correction method for calculating topographic factors of 30m resolution global geographic elevation data, including:

[0081] Step 1: Create a topographic factor correction model for 30m resolution ASTER GDEM data.

[0082] Step 11: Correction model of slope factor S based on pixels.

[0083] Taking the basin as a unit, correct the slope factor S calculated from 30m resolution DEM data, that is, multiply the slope factor calculated for each basin by a specific correction coefficient after adding an adjustment constant:

[0084] S′ 30,i =f s30 S 30,i (1)

[0085] In the formula, S′ 30,i is the correction result of the slope factor of the i-th pixel in a certain basin calculated from ASTER GDEM 30m data, f s30 is the correction coefficient of the slope factor calculated from 30m resolution ASTER GDEM 30m data in a certain basin, a dimensionless number, and S 30,i is the slope factor of the i-th pixel in a certain basin calculated from ASTER GDEM 30m data. Note that if the revised result S is greater than 9.995, then let S = 9.995.

[0086] The modeling formula of the correction coefficient f s30 is as follows:

[0087]

[0088] In the formula, is the mean value of the basin slope factor S calculated from the publicly available ASTER GDEM 30m data, a dimensionless number. 30

[0089] Step 12: Correction model of slope length factor L based on pixels.

[0090] Taking the basin as a unit, correct the slope length factor L calculated from 30m resolution DEM data, that is, multiply the slope length factor calculated by ASTER GDEM in each basin by a correction coefficient of that basin:

[0091] L′ 30,i =f L30 L 30,i (3) ​

[0092] where L′ 30,i is the correction result of the slope length factor of the i-th pixel in a certain watershed calculated by the 30m resolution ASTER GDEM; f L30 is the correction coefficient of the slope length factor calculated by the ASTER GDEM of this watershed; L 30,i is the slope length factor of the i-th pixel in this watershed calculated by the ASTER GDEM. Note that if the revised result L is greater than 5.511, then let L = 5.511.

[0093] The correction coefficient f L30 has the following modeling formula:

[0094]

[0095] where is the average value of the slope length factor of this watershed calculated by the 30m resolution ASTER GDEM, a dimensionless number, θ is the slope, is the average value of the watershed slope calculated from the 30m DEM data of this watershed, f L30 is the correction coefficient of the slope length factor calculated by the ASTER GDEM of this watershed, a dimensionless number.

[0096] Step 2: Determine the study area and obtain the public DEM data of the target area to be optimized.

[0097] Select a region from many typical regions as the study area of the embodiment. The training data set uses the public DEM data of ASTER GDEM (30m resolution), such as Figure 7 . The validation data set uses the 1:50,000 topographic map.

[0098] Step 3: Process the data in Step 2 to obtain the parameters required for the model, such as the average value of the slope length factor, the average value of the slope factor, and the average value of the slope.

[0099] Use the LS tool to process the obtained 30m DEM data. In the parameter settings, the catchment area is set to 30000000, and other parameters use the default values to obtain the sub-watershed division raster file. Load it into ArcGIS 10.2 and use the fromraster to polygon tool to process the sub-watershed division raster file into a vector file. Subsequently, use the Eliminate tool to merge the small patches with an area of 10 km 2 and below into the surrounding watersheds with a large area. Use the Zonal Statistics as Table tool to statistically analyze the relevant characteristic indexes of the raster files of the slope factor and the slope length factor, such as the average value, the standard deviation, etc.

[0100] Step 4: Use the calibration model in Step 1 to calibrate the LS factor values obtained from the 30m DEM to obtain a slope factor S map and a slope length factor L map with higher precision in this area. For details, see Figures 8a - 9b .

[0101] It can be seen from the figure that the method of the present invention makes the terrain factors calculated at low resolution and high resolution approximate at the watershed scale, which has a significant effect on the calibration of terrain factors, thereby effectively improving the accuracy of soil erosion model estimation.

[0102] The terrain factor calibration model constructed by the present invention obtains high-precision L factor and S factor data from DEM data with low spatial resolution, improving the accuracy of soil erosion model estimation.

[0103] Compared with the prior art, the present invention proposes a calibration model for obtaining high-precision terrain factors LS based on watershed division from 30m low-resolution DEM data. Since it is difficult to obtain high-precision DEM data and the LS factors calculated from low-resolution data do not fully consider the watershed distribution and terrain changes, the method model of the present invention is easy to use and has high precision, which helps to quickly and efficiently obtain relatively accurate LS factor values on a large scale. Compared with the accuracy of the LS factors obtained from low-resolution DEM data, the calibration method based on watershed division of the present invention can fully reflect the terrain changes and spatial variability, and is applicable at both the regional and watershed scales. The present invention establishes a calibration model for the LS factor according to watershed division and performs downscaling processing in order to obtain high-precision LS factors, thereby improving the accuracy of the soil erosion prediction model.

[0104] The embodiments described above are only used to describe the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A correction method for calculating terrain factors of 30m resolution global geographic elevation data, characterized in that, It includes the following steps: S1. Create a terrain factor correction model for 30m resolution ASTER GDEM data: The terrain factor correction model includes a pixel-based slope factor correction model and a pixel-based slope length factor correction model: The pixel-based slope factor correction model is: S′ 30,i = f s30 S 30,i (1) Among them, S' 30,i is the slope factor after calibration for the i-th pixel in a certain basin, i ∈ {1, 2,..., n}, where n is the total number of pixels in the basin; f s30 is the calibration coefficient of the slope factor, which is a dimensionless number, and S 30,i is the original slope factor of the i-th pixel in the basin; is the mean value of the slope factor S 30 of the basin; when S' 30,i is greater than 9.995, then let S' 30,i = 9.995; The pixel-based slope length factor correction model is: L′ 30,i = f L30 L 30,i (3) Among them, L′ 30,i is the slope length factor after correction for the i-th pixel in a certain basin, where i ∈ {1, 2,..., n}, and n is the total number of pixels in the basin; f L30 is the correction coefficient of the slope length factor, which is a dimensionless number; L 30,i is the original slope length factor of the i-th pixel in the basin, is the mean value of the slope length factor L 30 of the basin; is the mean value of the slope θ 30 of the basin; when L′ 30,i is greater than 5.511, then let L′ 30,i = 5.511; S2. Obtain the publicly available 30m resolution ASTER GDEM data of the target area to be corrected; S3. Process the publicly available data obtained in step S2 to obtain the relevant parameters required by the model; The relevant parameters include the average slope factor of each basin in the target area The average slope length factor and the average slope S4. Correct the pixel-based original slope factor S and pixel-based original slope length factor L in the public data according to the terrain factor correction model in step S1 and the relevant parameters obtained in step S3 to obtain the corrected terrain factor. 30,i and the pixel-based original slope length factor L 30,i to obtain the corrected terrain factor.

2. The calibration method for calculating topographic factors from 30m-resolution global geographic elevation data according to claim 1, wherein The specific steps of step S3 are: S31. Process the collected publicly available 30m resolution ASTER GDEM data to obtain a raster file for small watershed division, a slope raster file, a slope factor raster file, and a slope length factor raster file of the target area; S32. Convert the raster file for small watershed division into a vector file for small watersheds; S33. Further process the obtained vector file for small watersheds to merge small watersheds with an area smaller than the threshold into the surrounding large-area small watersheds; S34. Respectively count the slope length factor L within each small watershed in the small watershed vector file 30 , the slope factor S 30 and the slope θ 30 ; S35. Calculate the slope length factor L of all small watersheds respectively 30 , the slope factor S 30 and the average value of the slope θ 30 to obtain the mean value of the slope factor of the target area The mean value of the slope length factor and the mean value of the slope 3. The calibration method for calculating topographic factors from 30m resolution global geographic elevation data according to claim 2, wherein, In step S31, the LS calculation tool is used to process the publicly available data.

4. The calibration method for calculating topographic factors from 30m-resolution global geodetic elevation data according to claim 2, characterized in that, The threshold value in step S33 is 10 km 2 .

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