A small watershed water and soil conservation monitoring method and a storage medium thereof

Abstract

The application discloses a small watershed water and soil conservation monitoring method, comprising the following steps: based on a plurality of remote sensing images of a small watershed in a period, calculating and obtaining vegetation indexes, slope indexes, slope length indexes and humidity indexes; using a principal component analysis method to analyze the vegetation indexes, the slope indexes, the slope length indexes and the humidity indexes, obtaining a first principal component RSSWCI r ; processing the first principal component RSSWCI r , and obtaining a small watershed water and soil conservation remote sensing index RSSWCI; using the soil flowing out of a camera station in a period and the sum of the small watershed water and soil conservation remote sensing index RSSWCI to calculate and obtain a soil erosion modulus. The application has the advantages of less required measured data, low monitoring cost, fast realization of small watershed soil erosion monitoring and evaluation, time and space distribution of soil erosion results, convenience, high economic benefits, and overcoming of the defects of high cost, complicated operation, slow response and inflexibility of traditional small watershed water and soil loss monitoring methods.

Description

Technical Field

[0001] This invention relates to a method for monitoring soil and water conservation in small watersheds and its storage medium, belonging to the field of small watershed soil and water monitoring technology. Background Technology

[0002] According to the water resources industry standard SL 653-2013 "Small Watershed Division and Coding Specification", the area of ​​a small watershed is generally less than 50 square kilometers. Due to the limited distribution of rainfall stations, rainfall station data is generally lacking within small watersheds. Furthermore, the lack of meteorological data such as rainfall and measured data such as soil samples makes rapid and accurate monitoring of soil erosion in small watersheds difficult.

[0003] Existing soil erosion models, such as the China Soil Loss Equation, the General Soil and Water Loss Equation, and the Improved Soil Loss Equation, all require monitoring data on rainfall and soil conditions, resulting in a large workload and high costs. While existing technologies deploy soil and water loss monitoring equipment at the outlet of small watersheds, they cannot obtain the spatial distribution changes of soil erosion within the small watershed itself. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a small watershed soil and water conservation monitoring method and its storage medium. It requires less measured data, has low monitoring cost, can quickly realize the monitoring and assessment of soil erosion in small watersheds, and obtain the spatiotemporal distribution of soil erosion results. It is convenient, economical and overcomes the defects of traditional small watershed soil and water loss monitoring methods, such as high cost, cumbersome operation, slow response speed and inflexibility.

[0005] In a first aspect, the present invention provides a method for monitoring soil and water conservation in small watersheds, comprising:

[0006] Based on pre-acquired remote sensing images of small watersheds, vegetation indices, slope indices, slope length indices, and humidity indices are calculated.

[0007] Principal component analysis was used to analyze vegetation, slope, slope length, and humidity indices to obtain the first principal component RSSWCI over a period of time. r ;

[0008] For the first principal component RSSWCI r The data was processed to obtain the Remote Sensing Index (RSSWCI) of soil and water conservation in a small watershed over a period of time.

[0009] The unit-based soil erosion modulus is calculated by using the sum of the soil outflow from the checkpoint over a period of time and the soil and water conservation remote sensing index (RSSWCI) of the small watershed.

[0010] In conjunction with the first aspect, based on pre-acquired remote sensing imagery, vegetation indices, slope indices, and slope length indices are calculated, including:

[0011] Based on pre-acquired remote sensing imagery, the surface reflectance (SR) in the near-infrared (NIR) band of the remote sensing imagery is obtained. NIR Surface reflectance SR in the visible light red band (Red) Red ;

[0012] Surface reflectance (SR) using near-infrared (NIR) band remote sensing imagery NIR Surface reflectance SR in the visible red band (Red) Red Calculate the vegetation index NDVI:

[0013]

[0014] Obtain digital elevation model data for a small watershed to determine the slope θ;

[0015] Using the slope θ, calculate the slope index S and the slope length index L:

[0016] S = 10.8sinθ + 0.03 θ < 5°

[0017] S=16.8sinθ-0.5 5°≤θ<10°

[0018] S=21.9sinθ-0.96 θ≥10°,

[0019]

[0020] In conjunction with the first aspect, the remote sensing imagery was acquired using one of the following: Landsat 5TM, Landsat 8 / 9 OLI Land Imager, Sentinel-2MSI, or the GF series of high-resolution satellites.

[0021] In conjunction with the first aspect, based on pre-acquired remote sensing imagery, humidity indices are calculated, including:

[0022] Remote sensing images were acquired using Landsat 5 TM or Landsat 8 / 9 OLI Land Imager to obtain the surface reflectance (SR) of blue light from the remote sensing images. Blue Surface reflectance of green light (SR) Green Surface reflectance of red light (SR) Red Surface reflectance (SR) in the near-infrared band NIR Surface reflectance SR of the first shortwave infrared band SWIR1 SWIR1 Surface reflectance SR in the second shortwave infrared band SWIR2 SWIR2 ;

[0023] Using the surface reflectivity SR of blue light B1ue Surface reflectance of green light (SR) GreenSurface reflectance of red light (SR) Red Surface reflectance (SR) in the near-infrared band NIR Surface reflectance SR of the first shortwave infrared band SWIR1 SWIR1 Surface reflectance SR in the second shortwave infrared band SWIR2 SWIR2 Calculate humidity index;

[0024] The humidity indexes to be calculated include:

[0025] If the remote sensing image was acquired using the Landsat 5TM satellite, the humidity index WI-TM is calculated using the following formula:

[0026] WI-TM = 0.0315SR Blue +0.2021SR Green +0.3102SR Red +0.1594SR NIR -0.6806SR SWIR1 -0.6109SR SWIR2 ;

[0027] If the remote sensing image was acquired using the Landsat 8 / 9 OLI land imager, the humidity index WI-OLI is calculated using the following formula:

[0028] WI-OLI = 0.1511SR Blue +0.1973SR Green +0.3283SR Red +0.34071SR NIR -0.7117SR SWIR1 -0.4559SR SWIR2 .

[0029] In conjunction with the first aspect, based on pre-acquired remote sensing imagery, humidity indices are calculated, including:

[0030] Remote sensing images were acquired using the Sentinel1-2MSI satellite, and the surface reflectance (SR) of the first band of the remote sensing images was obtained. B1 Surface reflectance SR in the second band B2 The third band of surface reflectance SR B3 Surface reflectance SR in the fourth band B4 Surface reflectance SR in the fifth band B5 Surface reflectance SR in the sixth band B6 Surface reflectance SR in the seventh band B7 Surface reflectance SR in the eighth band B8 Surface reflectance SR in the ninth band B9Surface reflectance SR in the tenth band B10 Surface reflectance SR in the eleventh band B11 Surface reflectance SR in the twelfth band B12 and the surface reflectance SR of the B8A band B8A The humidity index WI-MSI is calculated using the following formula:

[0031] WI-MSI = 0.0649SR B1 +0.1363SR B2 +0.2802SR B3 +0.3072SR B4 +0.5288SR B5 +0.1379SR B6 +-0.0001SR B7 +-0.0807SR B8 +-0.0302SR B9 +0.0003SR B10 +-0.4064SR B11 +-0.5602SR B12 +-0.1389SR B8A ;

[0032] Remote sensing images were acquired using the WFV sensor of the Gaofen-1 or Gaofen-6 satellite, and the surface reflectance (SR) of blue light from these images was obtained. Blue Surface reflectance of green light (SR) Green Surface reflectance of red light (SR) Red Surface reflectance SR in the near-infrared band NIR The humidity index WI-WFV is calculated using the following formula:

[0033] WI-WFV = -3.3274SR from March to May Blue +2.9079SR Green -0.1218SR Red -0.6975SR NIR +0.0389;

[0034] WI-WFV = 0.4148SR from June to August. Blue -1.0599SR Green +0.1507SR Red +0.0122SR NIR +0.0617;

[0035] WI-WFV = -1.4472SR from September to November. Blue +1.4179SR Green -1.4519SR Red+0.1505SR NIR -0.0160;

[0036] WI-WFV = -2.7081SR in December, January, and February. Blue +4.0653SR Green -1.8452SR Red -0.5275SR NIR +0.0180.

[0037] Combining the first aspect, principal component analysis was used to analyze vegetation indicators, slope indicators, slope length indicators, and humidity indicators to obtain the first principal component RSSWCI over a period of time. r ,include:

[0038] The dimensions of vegetation index, slope index, slope length index and humidity index over a period of time are unified to the range [0, 1] to obtain the normalized vegetation index, humidity index, slope index and slope length index over a period of time.

[0039] Principal component analysis was performed using the normalized vegetation index NDVI, humidity index WI, slope index S, and slope length index L over a period of time to obtain the first principal component RSSWCI over that period of time. r :

[0040] RSSWCI r =PCA1(NDVI, WI, S, L),

[0041] In the formula, PCA1 is the first component function obtained after principal component analysis;

[0042] For the first principal component RSSWCI over a period of time r The data were processed to obtain the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a period of time.

[0043] Combining the first aspect, the first principal component RSSWCI over a period of time... r The data was processed to obtain the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a certain period of time, including:

[0044] Using the first principal component RSSWCI over a period of time r Calculate the remote sensing index RSSWCI0 for soil and water conservation in a small watershed over an initial period:

[0045] RSSWCI0=1-RSSWCI r ;

[0046] Normalize RSSWCI0 to obtain the remote sensing index RSSWCI for soil and water conservation in a small watershed over a period of time:

[0047]

[0048] In the formula, RSSWCI min The minimum RSSWCI value within the small watershed, RSSWCI max This represents the maximum RSSWCI value within the small watershed;

[0049] Calculate the average value of the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a period of time to obtain the RSSWCI. 均 .

[0050] Combining the first aspect, the soil erosion modulus with units is calculated by using the sum of the soil outflow from the checkpoints and the small watershed soil and water conservation remote sensing index RSSWCI over a period of time, including:

[0051] If there is a checkpoint station at the outlet of a micro-watershed within a small watershed, and the amount of soil flowing out of the checkpoint station within a certain period of time is a tons, then the sum of the remote sensing index (RSSWCI) of soil and water conservation in that micro-watershed can be obtained as b through overlay analysis.

[0052] The conversion coefficient c of the small watershed soil and water conservation remote sensing index RSSWCI was converted into the soil erosion modulus through regression analysis, and c = a / b.

[0053] The soil erosion modulus with units can be calculated using the following formula:

[0054] Soil erosion modulus with units = conversion factor c × RSSWCI 均 .

[0055] In a second aspect, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in any of the first aspects.

[0056] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects.

[0057] The beneficial effects achieved by this invention are as follows:

[0058] This invention provides a method for monitoring soil and water conservation in small watersheds and its storage medium. Based on pre-acquired remote sensing images of the small watershed, vegetation, slope, slope length, and humidity indices are calculated to monitor soil and water conservation in the small watershed. This invention requires less measured data and has low monitoring costs. Principal component analysis is used to analyze the vegetation, slope, slope length, and humidity indices to obtain the first principal component RSSWCI. r For the first principal component RSSWCI rThe data were processed to obtain the Remote Sensing Index for Soil and Water Conservation in Small Watersheds (RSSWCI).

[0059] Since both the Ecological Environment Quality Index (RSEI) and the Remote Sensing Index for Soil and Water Conservation (RSSWCI) are quantifiable, this invention utilizes the sum of the soil outflow from checkpoints and the RSSWCI of small watersheds over a period of time to calculate a unit-based soil erosion modulus. This allows the RSSWCI to be calibrated to the soil erosion modulus, with the unit of the soil erosion modulus being t / (km²). 2 ·y), which is tons per square kilometer per year, realizes the dimensionless RSSWCI to have a dimension, and provides a reference for the assessment of soil erosion in small watersheds;

[0060] This invention can quickly monitor and assess soil erosion in small watersheds, and obtain the spatiotemporal distribution of soil erosion results. It is convenient, economical, and overcomes the shortcomings of traditional small watershed soil and water loss monitoring methods, such as high cost, cumbersome operation, slow response speed, and inflexibility.

[0061] Due to the influence of weather and satellite revisit cycles, it is difficult to obtain monthly remote sensing data from a single satellite. Therefore, the method of this invention combines multi-source satellite data such as OLI, MSI and WFV to calculate the monthly RSSWCI over a period of time based on remote sensing data over a period of time, providing a reference for the assessment of soil erosion in small watersheds. Attached Figure Description

[0062] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a schematic diagram of a small watershed in some embodiments of this application. Detailed Implementation

[0064] To facilitate the explanation of the technical solution of this application, some concepts involved in this application will be explained first below.

[0065] See Figure 1By quantitatively inverting remote sensing images of vegetation growth periods over several years before and after small watershed soil and water conservation projects, the vegetation index, slope index, slope length index, and humidity index for each year are obtained. Principal component analysis is then used to analyze these four indicators to obtain the first principal component. After calculation using the first principal component, the Remote Sensing Soil and Water Conservation Index in Small Watersheds (RSSWCI) is obtained. The remote sensing images can be data from the US Landsat 5TM (Thematic Mapper) or the Landsat 8 / 9OLI Operational Land Imager, or data from the European Space Agency's Sentinel-2 MSI (Multispectral Imager), or data from China's Gaofen series satellites, such as Gaofen-1 or Gaofen-6 (GF).

[0066] This invention provides a method for monitoring soil and water conservation in small watersheds. Based on pre-acquired remote sensing images of a small watershed from January to December of a given year, vegetation, slope, slope length, and humidity indices for January to December are calculated. This invention requires less measured data and has low monitoring costs. Principal component analysis is used to analyze the vegetation, slope, slope length, and humidity indices to obtain the first principal component RSSWCI for January to December. r The first principal component RSSWCI for January to December r The soil erosion modulus with units is calculated by processing the soil outflow from the checkpoints during January to December to obtain the Remote Sensing Index of Soil and Water Conservation (RSSWCI) for the small watershed. The sum of the RSSWCI and the soil outflow from the checkpoints during January to December is then used to calculate the soil erosion modulus with units. This invention enables rapid monitoring and assessment of soil erosion in small watersheds, providing the spatiotemporal distribution of soil erosion results. It is convenient, economical, and overcomes the shortcomings of traditional small watershed soil erosion monitoring methods, such as high cost, cumbersome operation, slow response time, and inflexibility.

[0067] In this embodiment of the application, vegetation indices, slope indices, and slope length indices are calculated based on pre-acquired remote sensing images of a small watershed from January to December. This includes: obtaining the surface reflectance (SR) of the near-infrared (NIR) band of the remote sensing images based on the pre-acquired remote sensing images of the small watershed from January to December. NIR Surface reflectance SR in the visible light red band (Red) Red ; Surface reflectance (SR) of near-infrared (NIR) band remote sensing imagery NIR Surface reflectance SR in the visible red band (Red)Red Calculate the vegetation index NDVI:

[0068]

[0069] Obtain digital elevation model data for a small watershed to determine the slope θ. Many vegetation indices are commonly used; the Normalized Difference Vegetation Index (NDVI) integrates visible and near-infrared reflectance spectral information sensitive to vegetation. It is frequently used in regional vegetation status studies, serving as the best indicator of plant growth status and spatial distribution density, and is also an important index of regional surface vegetation cover and plant growth. NDVI is the ratio of the difference between the near-infrared (NIR) and visible red (Red) surface reflectance (SR) values ​​to the sum of these two values.

[0070] Digital elevation model data for small watersheds can be downloaded from the Geospatial Data Cloud Platform of the Computer Network Information Center, Chinese Academy of Sciences (http: / / www.gscloud.cn). After mosaicking and filling depressions, the slope θ (in degrees) is first obtained. Using the slope θ, the slope index S (slope factor) and slope length index L are calculated.

[0071] S = 10.8sinθ + 0.03 θ < 5°

[0072] S=16.8sinθ-0.5 5°≤θ<10°

[0073] S = 21.9sinθ - 0.96 θ ≥ 10°

[0074]

[0075] In the embodiments of this application, the remote sensing imagery is acquired using one of the following: Landsat 5 TM, Landsat 8 / 9 OLI Land Imager, Sentinel-2 MSI satellite, and Gaofen series satellite GF.

[0076] In this embodiment of the application, the humidity index is calculated based on pre-acquired remote sensing images, including: acquiring remote sensing images using the Landsat 5 TM or Landsat 8 / 9 OLI land imager, and obtaining the surface reflectance (SR) of blue light from the remote sensing images. Blue Surface reflectance of green light (SR) Green Surface reflectance of red light (SR) Red Surface reflectance (SR) in the near-infrared band NIR Surface reflectance SR of the first shortwave infrared band SWIR1 SWIR1 Surface reflectance SR in the second shortwave infrared band SWIR2 SWIR2 ;Utilizing the surface reflectivity (SR) of blue lightBlue Surface reflectance of green light (SR) Green Surface reflectance of red light (SR) Red Surface reflectance (SR) in the near-infrared band NIR Surface reflectance SR of the first shortwave infrared band SWIR1 SWIR1 Surface reflectance SR in the second shortwave infrared band SWIR2 SWIR2 Calculate the humidity index.

[0077] Surface humidity can reflect the underlying surface condition to a certain extent and has an important impact on the formation of regional microclimates. This study uses the third component of the cap transformation (KT transform) as the surface humidity index. The cap transformation refers to projecting plant and soil information onto a plane in multidimensional spectral space through linear transformation and rotation, making the time trajectory of vegetation growth perpendicular to the soil brightness axis on this plane. If the remote sensing image was acquired using the Landsat 5 TM satellite, the humidity index WI-TM is calculated using the following formula:

[0078] WI-TM = 0.0315SR Blue +0.2021SR Green +0.3102SR Red +0.1594SR NIR -0.6806SR SWIR1 -0.6109SR SWIR2 ;

[0079] If the remote sensing image was acquired using the Landsat 8 / 9 OLI land imager, the humidity index WI-OLI is calculated using the following formula:

[0080] WI-OLI = 0.1511SR Blue +0.1973SR Green +0.3283SR Red +0.34071SR NIR -0.7117SR SWIR1 -0.4559SR SWIR2 .

[0081] In this embodiment of the application, a humidity index is calculated based on pre-acquired remote sensing images of a small watershed from January to December, including: obtaining the surface reflectance SR of the first band of the remote sensing images using Sentinel1-2 MSI satellites. B1 Surface reflectance SR in the second band B2 The third band of surface reflectance SR B3 Surface reflectance SR in the fourth band B4Surface reflectance SR in the fifth band B5 Surface reflectance SR in the sixth band B6 Surface reflectance SR in the seventh band B7 Surface reflectance SR in the eighth band B8 Surface reflectance SR in the ninth band B9 Surface reflectance SR in the tenth band B10 Surface reflectance SR in the eleventh band B11 Surface reflectance SR in the twelfth band B12 and the surface reflectance SR of the B8A band B8A The humidity index WI-MSI is calculated using the following formula:

[0082] WI-MSI = SR B1 +SR B2 +SR B3 +SR B4 +SR B5 +SR B6 +SR B7 +SR B8 +SR B9 +SR B10 +SR B11 +SR B12 +SR B8A ,

[0083] In the formula, SR B1 The value is 0.0649, SR B2 The value is 0.1363, SR B3 The value is 0.2802, SR B4 The value is 0.3072, SR B5 The value is 0.5288, SR B6 The value is 0.1379, SR B7 The value is -0.0001, SR B8 The value is -0.0807, SR B9 The value is -0.0302, SR B10 The value is 0.0003, SR B11 The value is -0.4064, SR B12 The value is -0.5602, SR B8A The value is -0.1389.

[0084] Using Gaofen series satellites (GF) to acquire remote sensing images, the surface reflectance (SR) of blue light from the remote sensing images was obtained. Blue Surface reflectance of green light (SR) Green Surface reflectance of red light (SR) Red Surface reflectance SR in the near-infrared bandNIR For the lack of Gaofen-1 data in the shortwave infrared band, SR SWIR1 and Gaofen-2 satellite data SR SWIR2 Since WFV imagery lacks a shortwave infrared band, the humidity index cannot be directly calculated using the cap transformation. Therefore, the following transformation relationship is proposed to reconstruct the humidity index WI-GF:

[0085] WI-WFV = -3.3274SR from March to May Blue +2.9079SR Green -0.1218SR Red -0.6975SR NIR +0.0389;

[0086] WI-WFV = 0.4148SR from June to August. Blue -1.0599SR Green +0.1507SR Red +0.0122SR NIR +0.0617;

[0087] WI-WFV = -1.4472SR from September to November. Blue +1.4179SR Gree n-1.4519SR Red +0.1505SR NIR -0.0160;

[0088] WI-WFV = -2.7081SR in December, January, and February. Blue +4.0653SR Green -1.8452SR Red -0.5275SR NIR +0.0180.

[0089] Within the same small watershed and the same time window, such as within the last 1-2 days, the surface humidity obtained using TM, OLI, and MSI is the same. However, the WFV sensor, lacking a shortwave infrared band, cannot directly obtain surface humidity. To improve the utilization rate of domestically produced data, it is necessary to find TM, OLI, and MSI data that are synchronously imaged during WFV Earth observations. Data within 1-2 days of each other are considered synchronous. Due to satellite orbital limitations, synchronous imaging between WFV and TM, OLI, and MSI is relatively rare; approximately 10 images within 2-3 years is considered a lot. Therefore, based on the humidity index obtained from TM, OLI, and MSI, and using overlay analysis and regression analysis, along with the four bands of WFV, the humidity index of WFV can be calculated.

[0090] This invention can not only improve the utilization rate of domestically produced satellites in supporting my country's soil and water conservation monitoring, but also reduce the dependence on foreign satellite data. At the same time, this method can also be applied to my country's environmental satellite series, which also lacks shortwave infrared bands.

[0091] In this embodiment, principal component analysis (PCA) is used to analyze vegetation, slope, slope length, and humidity indices from January to December to obtain the first principal component RSSWCI for January to December. r This includes: unifying the dimensions of vegetation, slope, slope length, and humidity indices from January to December to the range [0, 1], to obtain normalized vegetation, humidity, slope, and slope length indices.

[0092]

[0093] Substitute the maximum values ​​of vegetation index, humidity index, slope index, and slope length index into I respectively. max Substitute the minimum values ​​of vegetation index, humidity index, slope index, and slope length index into I respectively. min Substitute the values ​​of vegetation index, humidity index, slope index, and slope length index into I respectively. i The normalized vegetation index, humidity index, slope index, and slope length index were calculated respectively.

[0094] Principal component analysis was performed using normalized vegetation index NDVI, humidity index WI, slope index S, and slope length index L to obtain the first principal component RSSWCI for January to December. r :

[0095] RSSWCI r =PCA1(NDVI, WI, S, L)

[0096] In the formula, PCA1 is the first component function obtained after principal component analysis. To ensure that a large value of PCA1 represents good soil and water conservation, its reciprocal can be taken to obtain the initial remote sensing index for soil and water conservation in the small watershed from January to December, RSSWCI0.

[0097] In this embodiment of the application, the first principal component RSSWCI for January to December is analyzed. r The data was processed to obtain the Remote Sensing Index (RSSWCI) for soil and water conservation in small watersheds from January to December, including:

[0098] Using the first principal component RSSWCI from January to December r Calculate the initial small watershed soil and water conservation remote sensing index RSSWCI0:

[0099] RSSWCI0=1-RSSWCI r ;

[0100] To facilitate the measurement and comparison of the indicators, RSSWCI0 can also be normalized to obtain the small watershed soil and water conservation remote sensing index RSSWCI for January to December:

[0101]

[0102] In the formula, RSSWCI min The minimum RSSWCI value within the small watershed, RSSWCI max This represents the maximum RSSWCI value within the small watershed;

[0103] Calculate the average value of the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a period of time to obtain the RSSWCI. 均 .

[0104] RSSWCI stands for Remote Sensing Index for Soil and Water Conservation in Small Watersheds, with values ​​ranging from [0, 1]. The closer the RSSWCI value is to 1, the better the soil and water conservation effect. Since the built-up area has impermeable surfaces and the water bodies themselves are unlikely to experience soil erosion, these two parts need to be masked, i.e., removed.

[0105] Appendix Figure 1 The watershed system is divided into three levels: basin, small watershed, and micro-watershed. It can also be further divided into four levels, including micro-watersheds. A watershed is defined as a system with only one outlet, allowing for the establishment of checkpoints. For example, consider a micro-watershed within a small watershed with a checkpoint at its outlet. The soil loss from this checkpoint from January to December is 'a' tons. Through overlay analysis, the total RSSWCI (Soil Loss-to-Intake) of this micro-watershed is obtained as 'b'. Then, through regression analysis, the conversion coefficient from RSSWCI to soil erosion modulus is obtained as 'c', where c = a / b. If there are more than one checkpoint at a micro-watershed outlet with soil loss data for one year, the conversion coefficient 'c' is obtained using the same regression analysis method. The unit-based soil erosion modulus is calculated using the following formula: Unit-based soil erosion modulus = Conversion coefficient 'c' × RSSWCI 均 This invention converts the dimensionless RSSWCI into a soil erosion modulus, collects the true values ​​of soil and water loss from one or more slopes or runoff plots within a small watershed, and obtains the soil erosion modulus, with units of t / (km²). 2 ·y), where y represents one year, and 1y is one year. This invention calculates multiple sets of remote sensing data before and after soil and water conservation projects, enabling dynamic monitoring of soil and water conservation in small watersheds. In this application embodiment, the invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the methods described above.

[0106] In this embodiment of the application, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0107] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0108] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention described herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not invented herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0109] The above specific embodiments further illustrate the purpose, technical solution and beneficial effects of this application. It should be understood that the above are only specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of this application should be included within the scope of protection of this application.

Claims

1. A small watershed water and soil conservation monitoring method, characterized by, Comprise: Based on the remote sensing image of a small watershed in a period of time, the vegetation index, slope index, slope length index and humidity index are calculated; Using principal component analysis method to analyze vegetation index, slope index, slope length index and humidity index, a period of time to obtain the first principal component RSSWCI r ; The first principal component RSSWCI r is processed to obtain a small watershed soil and water conservation remote sensing index RSSWCI. Using the soil and small watershed water and soil conservation remote sensing index RSSWCI of the gully station in a period of time, the soil erosion modulus with unit is calculated, including: If there is a gully station or water and soil loss monitoring equipment at the outlet of a micro-watershed in a small watershed, the soil flowing out of the gully station in the same period is a ton, and the total water and soil conservation remote sensing index RSSWCI of the micro-watershed is obtained through overlay analysis b; Through regression analysis, the conversion coefficient c of the small watershed water and soil conservation remote sensing index RSSWCI to the soil erosion modulus is obtained, and c=a / b; The soil erosion modulus with unit is calculated by the following formula: The unit soil erosion modulus = conversion coefficient c x RSSWCI 均 , RSSWCI 均 is the average value of the remote sensing index RSSWCI of soil and water conservation of the small watershed in a period of time.

2. The small watershed water and soil conservation monitoring method according to claim 1, characterized in that, Based on the remote sensing image of a small watershed in a period of time, the vegetation index, slope index and slope length index are calculated, including: Based on the pre-acquired remote sensing image, the surface reflectance SR of the near-infrared band NIR of the remote sensing image is obtained NIR , the surface reflectance SR of the visible red band Red Red ; The surface reflectance SR in the near infrared band NIR of the remote sensing image NIR and the surface reflectance SR in the red band Red of the visible light Red The vegetation index NDVI is calculated: ； Obtain the slope θ by acquiring the digital elevation model data of the small watershed; Using the slope θ, the slope index S and the slope length index L are calculated: ， ; wherein is the slope length, is the slope length exponent.

3. The small watershed water and soil conservation monitoring method according to claim 1, characterized in that, The remote sensing image is obtained by using one of Landsat 5 TM, Landsat 8 / 9 OLI land imager, Sentinel-2 MSI satellite and GF series satellite.

4. The small watershed water and soil conservation monitoring method according to claim 3, characterized in that, Based on the pre-acquired remote sensing image, the humidity index is calculated, including: a remote sensing image is acquired using a Landsat 5 TM or Landsat 8 / 9 OLI land imager, obtaining the surface reflectance SR of the remote sensing image in the blue light Blue , in the green light Green , in the red light Red , in the near infrared band NIR , in the first short wave infrared band SWIR1 SWIR1 , and in the second short wave infrared band SWIR2 SWIR2 ; Surface reflectance SR in blue light Blue Surface reflectance SR in green light Green Surface reflectance SR in red light Red Surface reflectance SR in near infrared NIR Surface reflectance SR in first short wave infrared SWIR1 SWIR1 Surface reflectance SR in second short wave infrared SWIR2 SWIR2 calculating a moisture index; Wherein, the calculation of the humidity index includes: If the remote sensing image is obtained by using Landsat 5 TM, the humidity index WI-TM is calculated by the following formula: ； If the remote sensing image is obtained by using Landsat 8 / 9 OLI land imager, the humidity index WI-OLI is calculated by the following formula: 。 5. The small watershed water and soil conservation monitoring method according to claim 3, characterized in that, Based on the pre-acquired remote sensing image, the humidity index is calculated, including: using a Sentinel-2 MSI satellite to acquire a remote sensing image, obtaining a surface reflectance SR of a first band of the remote sensing image B1 a surface reflectance SR of a second band of the remote sensing image B2 a surface reflectance SR of a third band of the remote sensing image B3 a surface reflectance SR of a fourth band of the remote sensing image B4 a surface reflectance SR of a fifth band of the remote sensing image B5 a surface reflectance SR of a sixth band of the remote sensing image B6 a surface reflectance SR of a seventh band of the remote sensing image B7 a surface reflectance SR of an eighth band of the remote sensing image B8 a surface reflectance SR of a ninth band of the remote sensing image B9 a surface reflectance SR of a tenth band of the remote sensing image B10 a surface reflectance SR of an eleventh band of the remote sensing image B11 a surface reflectance SR of a twelfth band of the remote sensing image B12 and a surface reflectance SR of a B8A band B8A a humidity index WI-MSI is calculated using the following formula: WI - MSI = 0.0649SR B1 + 0.1363SR B2 + 0.2802SR B3 + 0.3072SR B4 + 0.5288SR B5 + 0.1379SR B6 - 0.0001SR B7 - 0.0807SR B8 - 0.0302SR B9 + 0.0003SR B10 - 0.4064SR B11 - 0.5602SR B12 - 0.1389SR B8A ; SR of blue light of the remote sensing image is obtained by using the WFV sensor of the Gaofen No. 1 or No. 6 satellite to acquire a remote sensing image Blue SR of green light of the remote sensing image Green SR of red light of the remote sensing image Red SR of near-infrared band of the remote sensing image NIR The humidity index WI-WFV is calculated by using the following formula: 3-5 month WI-WFV = -3.3274 SR Blue +2.9079 SR Green -0.1218 SR Red -0.6975 SR NIR +0.0389; 6-8 months WI-WFV = 0.4148 SR Blue -1.0599 SR Green +0.1507 SR Red +0.0122 SR NIR +0.0617; 9-11 months WI-WFV = -1.4472 SR Blue +1.4179 SR Green -1.4519 SR Red +0.1505 SR NIR -0.0160; -2.7081 SR in December, January and February Blue +4.0653 SR Green -1.8452 SR Red -0.5275 SR NIR +0.0180.

6. The small watershed water and soil conservation monitoring method according to claim 1, characterized in that, Using principal component analysis method to analyze vegetation index, slope index, slope length index and humidity index, a first principal component RSSWCI is obtained in a period of time r , comprising: The dimensions of the vegetation index, slope index, slope length index and humidity index in a period of time are unified to [0, 1], and the normalized vegetation index, humidity index, slope index and slope length index in a period of time are obtained; The vegetation index NDVI, the humidity index WI, the slope index S and the slope length index L in the period after normalization are used to perform principal component analysis to obtain the first principal component RSSWCI in the period r : ， Wherein, PCA1 is the first component function obtained by principal component analysis; For the first principal component RSSWCI over a period of time r The data were processed to obtain the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a period of time.

7. The small watershed water and soil conservation monitoring method according to claim 6, characterized in that, For the first principal component RSSWCI over a period of time r The data was processed to obtain the Remote Sensing Index (RSSWCI) for soil and water conservation in a small watershed over a certain period of time, including: The first principal component RSSWCI in a period of time is used r The initial small watershed soil and water conservation remote sensing index RSSWCI0 in a period of time is calculated: RSSWCI0= 1 - RSSWCI r ; The RSSWCI0 is normalized to obtain the small watershed water and soil conservation remote sensing index RSSWCI in a period of time: ， where RSSWCI min is the minimum value of RSSWCI values within the small watershed, RSSWCI max is the maximum value of RSSWCI values within the small watershed. The average value of the remote sensing index of soil and water conservation RSSWCI of the small watershed in a period of time is calculated to obtain RSSWCI 均 .

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to realize the steps of the method of any one of claims 1 to 7.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 7.

Images