A method for calculating high-resolution surface wind erosion index

Abstract

The present invention discloses a method for calculating a high-resolution surface wind erosion index, which comprises the following steps: step 1, selecting high-resolution remote sensing data; step 2, preprocessing data; step 3, removing snow; step 4, calculating the normalized vegetation index; step 5, calculating the surface wind erosion intensity; step 6, post-processing data. The present invention can perform refined display and seasonal update of the surface wind erosion index in river valleys and other areas, and has certain positive significance in the numerical simulation and forecasting of regional high-resolution sandstorm weather.

Description

Technical Field

[0001] The invention belongs to the field of environmental remote sensing and relates to a method for calculating a high-resolution surface wind erosion index. Background Art

[0002] Dust in the atmosphere can cause serious environmental pollution, directly affect human health and living environment, and form serious environmental problems. Wind and sand activities are one of the main factors affecting the ecological environment and social sustainable development in arid and semi-arid areas. Numerical models are powerful tools for simulating and predicting dust processes, studying dust cycles and their impacts. Since the late 1980s, many scholars have been committed to researching and developing models that can be used to simulate global, regional and local dust processes, and have developed a series of model systems including global dust transport models and regional dust process models. In general, most of these models can simulate the basic characteristics of global or regional dust cycle processes, but there is still a large uncertainty in the model simulation results. One of the key reasons is the simulation of the sand-raising process. In the numerical model, the surface wind erosion index used to characterize the sand source has problems such as too coarse resolution leading to missing values ​​in local areas such as river valleys, which poses a huge challenge to the numerical simulation and operational forecasting of local sandstorm weather. Summary of the invention

[0003] The purpose of the present invention is to propose a calculation method for a high-resolution surface wind erosion index (EROD) to solve the problem that the regional wind erosion index is missing and does not change with the seasons in a high-resolution sand and dust forecast numerical model.

[0004] The specific technical solution to achieve the purpose of the invention is:

[0005] A method for calculating a high-resolution wind erosion index (EROD) is provided, wherein the specific steps of the method are as follows:

[0006] Step 1, data selection: the regional surface wind erosion conditions are divided into four seasons, and the Sentinel-2 remote sensing images are downloaded from the European Space Agency Copernicus Data Center by season;

[0007] Step 2: data preprocessing, specifically: use SNAP software to stitch images; use SNAP software to export band by band to .img format; use ENVI software to synthesize the image Band 2 (BLUE), Band 3 (GREEN), Band 4 (RED), Band 8 (NIR), and Band 11 (SWIR); use ENVI software to reproject the synthesized image to UTM Zone 46North, WGS-84 coordinate system; use ENVI software to crop the target area image; based on the bare land type in ESRI 10m Land Cover 2020, mask the target area image to extract bare land.

[0008] Step 3: Remove snow: Apply the Normalized Difference Snow Index (NDSI) to the remote sensing image to remove the residual snow in the area. The calculation formula is as follows:

[0009]

[0010] Where GREEN is the green band and SWIR is the shortwave infrared band.

[0011] Step 4: Calculate the Normalized Difference Vegetation Index (NDVI) of the target area. The formula is as follows:

[0012]

[0013] Where NIR is the near infrared band and RED is the red band.

[0014] Step 5: Calculate the surface wind erosion intensity: Use the EROD formula to evaluate the wind erosion degree. The formula is as follows:

[0015]

[0016] In the formula, R f Specify the pixel reflectance of the target area, R max The target area specifies the maximum reflectance value of the pixels in the band.

[0017] Furthermore, in the step three, snow is removed according to NDSI, and the selected GREEN parameter is Band 3 of Sentinel-2 data, and the SWIR parameter is Band 11 of Sentinel-2 data; NDSI>0 is defined as a snowy area and is removed by mask, and NDSI≤0 is defined as a snow-free area and is retained by mask.

[0018] Furthermore, the NIR parameter in step 4 is the Band 8 band of the Sentinel 2 data, and the RED parameter is the Band 4 band of the Sentinel 2 data.

[0019] Furthermore, in step 5, R f The parameter is the reflectance pixel value of the target area in Band 2 of the Sentinel-2 data, R max The parameter is the maximum value of the reflectance pixel in the target area of ​​Band 2 of the Sentinel-2 data. The valid data range is set to remove noise; NDVI only retains the non-negative part and the negative value is zeroed.

[0020] Beneficial Effects

[0021] The present invention proposes a method for calculating a high-resolution surface wind erosion index (EROD), which can be used to solve the problem of regional wind erosion index missing and not changing with the seasons in numerical models. In the mesoscale numerical model, the invention can provide a detailed description and seasonal update of the regional surface wind erosion index (EROD), which has a certain positive significance in the regional high-resolution sandstorm weather numerical simulation and forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the spatial range map of the target area of ​​the present invention;

[0023] Figure 2 This is a true color composite image of the target area of ​​the present invention from Sentinel-2;

[0024] Figure 3 It is the spring EROD binary map of the target area of ​​the present invention;

[0025] Figure 4 It is the EROD binary map of the target area in summer of the present invention;

[0026] Figure 5 The EROD binary map of the target area in autumn of the present invention;

[0027] Figure 6 It is the EROD binary map of the target area in winter of the present invention;

[0028] Figure 7 This is a classification diagram of EROD values ​​in spring in the target area of ​​the present invention;

[0029] Figure 8 This is a classification diagram of EROD values ​​in the target area in summer of the present invention;

[0030] Fig. 9 This is a classification diagram of EROD values ​​in autumn in the target area of ​​the present invention;

[0031] Fig.10 This is a classification diagram of EROD values ​​in winter in the target area of ​​the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, content and advantages of the present invention clearer, the specific implementation mode of the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0033] Example

[0034] A method for calculating a high-resolution wind erosion index (EROD) comprises the following steps:

[0035] Step 1, data selection: the regional surface wind erosion conditions are divided into four seasons, and the Sentinel-2 remote sensing images are downloaded from the European Space Agency Copernicus Data Center by season;

[0036] Figure 1 This is the spatial range map of the target area selected by the present invention, and the main wind erosion area is the bare land within the Yarlung Zangbo River.

[0037] Step 2: data preprocessing, specifically: use SNAP software to stitch images; use SNAP software to export band by band to .img format; use ENVI software to synthesize the image Band2 (BLUE), Band3 (GREEN), Band4 (RED), Band8 (NIR), and Band11 (SWIF); use ENVI software to reproject the synthesized image to UTM Zone 46 North, WGS-84 coordinate system; use ENVI software to crop the target area image; based on the bare land type in the "ESRI 10m Land Cover 2020" product, mask the target area image to extract bare land.

[0038] Figure 2 This is a true-color composite image of the target area of ​​the present invention from Sentinel-2, in which the bare land within the Yarlung Zangbo River appears in bright tones.

[0039] Step 3: Remove snow: Apply the Normalized Difference Snow Index (NDSI) to the remote sensing image to remove the residual snow in the area with the rule of NDSI>0. The calculation formula is as follows:

[0040]

[0041] Where GREEN is the green band and SWIR is the shortwave infrared band.

[0042] Step 4: Calculate the Normalized Difference Vegetation Index (NDVI) of the target area. The formula is as follows:

[0043]

[0044] Where NIR is the near infrared band and RED is the red band.

[0045] Step 5: Calculate the surface wind erosion intensity: Use the EROD formula to evaluate the wind erosion degree. The formula is as follows:

[0046]

[0047] In the formula, R f Specify the pixel reflectance of the target area, R max The maximum value of pixel reflectance in the specified band of the target area. is a valid value, and NDVI ≥ 0 is a valid value.

[0048] Figure 3-Figure 6 They are the EROD binary maps of the target area of ​​the present invention in spring, summer, autumn and winter respectively. It can be seen that the EROD binary map shows significant seasonal changes. The EROD index used to describe the sand source information is mainly distributed in the bare land and mountainous areas of the Yarlung Zangbo River, which is consistent with the facts.

[0049] Step 6, data post-processing: Use ENVI software to resample the spatial resolution of the obtained results to 250m resolution and reproject to Geographic Lat / Lon, WGS84 coordinate system.

[0050] Figure 7-10 These are the EROD value classification diagrams for the target area of ​​the present invention in spring, summer, autumn and winter, respectively. It can be seen that the seasonal changes and classification characteristics are obvious, and the larger EROD values ​​are concentrated in the middle area of ​​the diagram.

Claims

1. A high-resolution surface wind erosion index calculation method, characterized in that: The steps include: Step 1, data selection: divide the local surface wind erosion degree into four seasons, and download the Sentinel-2 remote sensing images from the European Space Agency Copernicus Data Center by season; Step 2: Data preprocessing, specifically: Use SNAP software to stitch images; Use SNAP software to export band by band into image format; Use ENVI software to synthesize the image blue band Band 2, green band Band 3, red band Band 4, near infrared band Band 8, and short-wave infrared Band 11; Use ENVI software to reproject the synthesized image to UTM Zone 46North, WGS-84 coordinate system; Use ENVI software to crop the target area image; Based on the bare land type in ESRI 10m Land Cover 2020, mask the target area image to extract bare land; Step 3: Remove snow: Apply the Normalized Difference Snow Index (NDSI) to the remote sensing image to remove the residual snow in the area. The calculation formula is as follows: Where GREEN is the green band and SWIR is the shortwave infrared band. The selected GREEN parameters are the Band 3 band of the Sentinel-2 data, and the SWIR parameters are the Band 11 band of the Sentinel-2 data. The areas with NDSI>0 are defined as snowy areas and are removed by mask, and the areas with NDSI≤0 are defined as snow-free areas and are retained by mask. Step 4: Calculate the Normalized Difference Vegetation Index (NDVI) of the target area. The formula is as follows: Wherein, NIR is the near infrared band, RED is the red band; the NIR parameter is the Band 8 band of the Sentinel-2 data, and the RED parameter is the Band 4 band of the Sentinel-2 data; Step 5: Calculate the surface wind erosion intensity: Establish the EROD formula to evaluate the wind erosion degree. The formula is as follows: In the formula, R f is the reflectance pixel value of the target area in Band 2 of Sentinel-2 data, R max is the maximum value of the reflectance pixel in the target area of ​​Band 2 of the Sentinel-2 data; Defined as valid data range to remove noise; NDVI only retains the non-negative part and the negative values ​​are reset to zero.

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