Artificial precipitation enhancement data acquisition system and evaluation method based on remote sensing image

Abstract

The invention discloses an artificial precipitation data acquisition system and evaluation method based on a remote sensing image, and relates to the technical field of ecological environment restoration and meteorological science. Comprising the following steps: acquiring remote sensing image data and rainfall data of meteorological stations in a region, performing geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain corrected remote sensing data, and calculating the rainfall data of the meteorological stations on the basis of the corrected remote sensing data and regional historical rainfall data. The rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are respectively obtained through a regression analysis method and a pixel dichotomy method, the rainfall change data and the vegetation change data in different time periods before and after artificial rainfall enhancement are compared, the influence of rainfall enhancement on vegetation recovery is quantitatively analyzed, and the rainfall enhancement effect is analyzed by utilizing spatial-temporal dynamic change. According to the method, the influence of rainfall enhancement on the ecological environment can be evaluated more comprehensively and accurately, and a new scientific basis is provided for ecological restoration of arid regions.

Description

Technical Field

[0001] The present invention relates to the fields of ecological environment restoration and meteorological science and technology, and in particular to an artificial rainfall enhancement data acquisition system and an evaluation method based on remote sensing images. Background Art

[0002] The Shiyang River Basin is an important inland river basin in the arid and semi-arid regions of Northwest China. Its ecosystem is of great significance in global climate change and hydrological cycle. However, with the influence of climate change and human activities, the basin ecosystem has suffered serious degradation, especially the reduction of vegetation cover, the decline of runoff and severe desertification. In recent years, artificial rainfall technology has been widely used in ecological restoration in this region. However, most of the existing artificial rainfall effect evaluation methods rely on ground observation data and lack the support of large-scale long-term data series, which makes it impossible to comprehensively and objectively evaluate the rainfall effect. Therefore, a method for artificial rainfall effect evaluation based on remote sensing data is proposed, which can make up for the shortcomings of traditional methods and realize large-scale and continuous rainfall effect evaluation.

[0003] Therefore, how to provide an artificial rainfall data acquisition system and evaluation method based on remote sensing images to solve the difficulties existing in the prior art is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present invention provides an artificial rainfall data acquisition system and evaluation method based on remote sensing images, which can more comprehensively and accurately evaluate the impact of rainfall enhancement on the ecological environment, and provide a new scientific basis for ecological restoration in arid areas.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] An artificial rainfall data acquisition system based on remote sensing images, comprising an image data acquisition module, a rainfall data acquisition module, a cloud platform, a data processing module and a display module;

[0007] The image data acquisition module, the cloud platform, the first input end of the data processing module and the display module are connected in sequence, and the rainfall data acquisition module is connected to the second input end of the data processing module;

[0008] Image data acquisition module, used to obtain a variety of satellite data;

[0009] Cloud platform for integrating various satellite data to generate remote sensing data;

[0010] A rainfall data acquisition module is used to acquire precipitation data;

[0011] A data processing module is used to correct the remote sensing data and generate precipitation change data and vegetation change data in combination with precipitation data;

[0012] The display module is used to display precipitation change data and vegetation change data.

[0013] Optionally, the image data acquisition modules include TM, ETM+ and OLI, the cloud platform uses the GEE platform, the rainfall data acquisition module uses the meteorological station, and the data processing module uses the ENVI software.

[0014] A method for evaluating artificial rainfall data based on remote sensing images, applied to any one of the above-mentioned artificial rainfall data acquisition systems based on remote sensing images, comprises the following steps:

[0015] Data acquisition steps: obtain remote sensing image data and rainfall data from meteorological stations in the region;

[0016] Data processing steps: Perform geometric correction and radiation correction on remote sensing image data based on ENVI software to obtain corrected remote sensing data;

[0017] Image recognition step: Based on the corrected remote sensing data and regional historical precipitation data, the precipitation change data and vegetation change data in different time periods before and after artificial rainfall are obtained through regression analysis and pixel dichotomy;

[0018] Data evaluation steps: Compare the precipitation change data and vegetation change data in different time periods before and after artificial rainfall, quantitatively analyze the impact of rainfall enhancement on vegetation recovery, and use spatiotemporal dynamic changes to analyze the rainfall enhancement effect.

[0019] Optionally, in the data processing step, the corrected remote sensing data includes normalized difference vegetation index NDVI and surface vegetation coverage fraction FVC data.

[0020] Optionally, the regression analysis method in the image recognition step includes: estimating the natural precipitation by in-depth study of the correlation between the historical precipitation in the target area and the comparison area, comparing the estimated natural precipitation with the actually observed precipitation, and the difference between the two is the actual precipitation change caused by the artificial weather modification operation.

[0021] Optionally, the pixel binary model in the image recognition step includes: performing pixel classification and inversion based on multispectral data to obtain the area percentage of vegetation coverage using FVC, which is expressed as:

[0022]

[0023] Among them, S is a single pixel observed by the sensor, S soil is the remote sensing information of pure soil pixels, S veg It is the remote sensing information of pure vegetation pixels.

[0024] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides an artificial rainfall data acquisition system and evaluation method based on remote sensing images, which has the following beneficial effects: 1) The present invention can quickly and accurately evaluate the impact of artificial rainfall on vegetation coverage and hydrological cycle in the study area; 2) The present invention can provide continuous, long-term data support, which makes up for the shortcomings of traditional ground observation methods, and is particularly suitable for long-term monitoring and evaluation of large-scale ecosystems; 3) It helps to optimize artificial rainfall operation plans, improve the scientificity and effectiveness of rainfall operations, and provide important data support for ecological environmental restoration in arid and semi-arid areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0026] Figure 1 A block diagram of an artificial rainfall data acquisition system based on remote sensing images disclosed in the present invention;

[0027] Figure 2 A flow chart of an artificial rainfall enhancement data evaluation method based on remote sensing images disclosed in the present invention;

[0028] Figure 3 It is a principle diagram of an artificial rainfall enhancement data evaluation method based on remote sensing images disclosed in the present invention;

[0029] Figure 4a The vegetation coverage change map before and after artificial rainfall enhancement from 2000 to 2009 disclosed by the present invention;

[0030] Figure 4b This is a diagram of vegetation coverage changes before and after artificial rainfall from 2011 to 2020 disclosed in the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Reference Figure 1As shown, the present invention discloses an artificial rainfall data acquisition system based on remote sensing images, including an image data acquisition module, a rainfall data acquisition module, a cloud platform, a data processing module and a display module;

[0033] The image data acquisition module, the cloud platform, the first input end of the data processing module and the display module are connected in sequence, and the rainfall data acquisition module is connected to the second input end of the data processing module;

[0034] Image data acquisition module, used to obtain a variety of satellite data;

[0035] Cloud platform for integrating various satellite data to generate remote sensing data;

[0036] A rainfall data acquisition module is used to acquire precipitation data;

[0037] A data processing module is used to correct the remote sensing data and generate precipitation change data and vegetation change data in combination with precipitation data;

[0038] The display module is used to display precipitation change data and vegetation change data.

[0039] Furthermore, the image data acquisition modules include TM, ETM+ and OLI, the cloud platform uses the GEE platform, the rainfall data acquisition module uses meteorological stations, and the data processing module uses ENVI software.

[0040] A method for evaluating artificial rainfall data based on remote sensing images, applied to any of the above-mentioned artificial rainfall data acquisition systems based on remote sensing images, referring to Figure 2 and Figure 3 As shown, the following steps are included:

[0041] Data acquisition steps: obtain remote sensing image data and rainfall data from meteorological stations in the region;

[0042] Data processing steps: Perform geometric correction and radiation correction on remote sensing image data based on ENVI software to obtain corrected remote sensing data;

[0043] Image recognition step: Based on the corrected remote sensing data and regional historical precipitation data, the precipitation change data and vegetation change data in different time periods before and after artificial rainfall are obtained through regression analysis and pixel dichotomy;

[0044] Data evaluation steps: Compare the precipitation change data and vegetation change data in different time periods before and after artificial rainfall, quantitatively analyze the impact of rainfall enhancement on vegetation recovery, and use spatiotemporal dynamic changes to analyze the rainfall enhancement effect.

[0045] Furthermore, in the data processing step, the corrected remote sensing data includes the Normalized Difference Vegetation Index NDVI and the Surface Vegetation Coverage Fraction (FVC) data.

[0046] Furthermore, the regression analysis method in the image recognition step includes: estimating the natural precipitation by in-depth study of the correlation between the historical precipitation in the target area and the comparison area, comparing the estimated natural precipitation with the actually observed precipitation, and the difference between the two is the actual precipitation change brought about by the artificial weather modification operation.

[0047] Furthermore, the pixel binary model in the image recognition step includes: pixel classification and inversion based on multispectral data, and obtaining the area proportion of vegetation coverage using FVC, which is expressed as:

[0048]

[0049] Among them, S is a single pixel observed by the sensor, S soil is the remote sensing information of pure soil pixels, S veg It is the remote sensing information of pure vegetation pixels.

[0050] In a specific embodiment, a method for evaluating artificial rainfall data based on remote sensing images comprises the following steps:

[0051] Data acquisition steps: obtain remote sensing image data and rainfall data from meteorological stations in the region;

[0052] Specifically, Landsat images from 2000 to 2020 were downloaded from the GEE platform and combined with meteorological data. The images used were 30-meter resolution surface reflectance images collected by Landsat 5ETM, 7ETM+, and Landsat 8OLI sensors.

[0053] Data processing steps: Perform geometric correction and radiation correction on the remote sensing image data based on ENVI software to obtain the corrected remote sensing data.

[0054] Specifically, the downloaded images were atmospherically corrected, and ENVI software was used for geometric and radiation correction to obtain the normalized difference vegetation index (NDVI) and fractional vegetation coverage (FVC) data, which are expressed as follows:

[0055]

[0056] Among them, NIR: reflectance of the near-infrared band of Landsat image, Red: reflectance of the infrared band of Landsat image; the single pixel observed by the sensor is S, and the remote sensing information of pure vegetation pixel is S veg , the remote sensing information of pure soil pixels is S soil .

[0057] Image recognition steps: Based on the corrected remote sensing data and regional historical precipitation data, the precipitation change data and vegetation change data in different time periods before and after artificial rainfall are obtained through regression analysis and pixel dichotomy. The results are as follows: Figure 4a and Figure 4b shown.

[0058] Specifically, it includes: establishing a pixel binary model, combining regional historical precipitation data, and calculating the changes in precipitation and vegetation in different time periods before and after artificial rainfall through regression analysis.

[0059] (1) The pixel binary model is a multispectral reflectance data analysis method based on remote sensing images. In a single pixel, the green vegetation part is Sa and the land part is Sb. Then:

[0060] S=Sa+Sb,

[0061] Assuming that the mixed pixel consists of only vegetation and bare land, the percentage of vegetation coverage is expressed by FVC, and the bare land coverage area is expressed by 1-FVC. Then:

[0062] Sa=FVC×S veg , Sb=(1-FVC)×S soil ,

[0063] We can get:

[0064] (2) Regression analysis: By deeply studying the correlation between the historical precipitation in the target area and the comparison area, the natural precipitation in the target area during a specific operation period can be accurately estimated. This estimated value is then compared with the actual observed precipitation in the target area. The difference between the two can be expressed as the actual precipitation change caused by the weather modification operation, that is, the operation effect.

[0065] (3) The FVC of the study area is divided into five levels: FVC < 5%, which is a low vegetation coverage area; 5% ≤ FVC < 25%, which is a medium-low vegetation coverage area; 25% ≤ FVC < 50%, which is a medium vegetation coverage area; 50% ≤ FVC < 75%, which is a medium-high vegetation coverage area; FVC ≥ 75%, which is a high vegetation coverage area.

[0066] Data evaluation steps: Compare the precipitation change data and vegetation change data in different time periods before and after artificial rainfall, quantitatively analyze the impact of rainfall enhancement on vegetation recovery, and use spatiotemporal dynamic changes to analyze the rainfall enhancement effect.

[0067] Specifically: (1) Since the implementation of artificial rainfall enhancement (2010-2020), the absolute rainfall increase from April to October was 42.9 mm, and the average relative rainfall increase rate was 20.8%. The results from April and September to October passed the reliability test of 0.05, and the results from May to August passed the reliability test of 0.1. The effect of artificial rainfall enhancement in spring (April-May) and autumn (September-October) was better than that in summer, and the relative rainfall increase rate reached more than 15.4%.

[0068] (2) The average normalized difference vegetation index (NDVI) during the growing season was between 0.132 and 0.223. After 2010, the average vegetation index during the growing season increased significantly, and the vegetation restoration effect was significant. The average vegetation index during the artificial rainfall period increased by 0.538 compared with that before artificial rainfall, and the effect of human shadow operation was significant.

[0069] (3) The vegetation coverage of the Shiyang River Basin has been increasing since 2010, and the area with low vegetation coverage has decreased significantly, from 12631.05 km before artificial rainfall enhancement to 12631.05 km after artificial rainfall enhancement. 2 After becoming rain-enhanced, it is 10984.03 km 2 , reducing 1647.02km 2 , accounting for about 4% of the basin area. The area with high vegetation coverage increased significantly, from 5670.31km before the rainfall increase to 2 After precipitation enhancement, it becomes 7061.89 km 2 , an increase of 1185.31km 2 , accounting for about 2.8% of the basin area.

[0070] (4) Before artificial rainfall in 2010, there were 629.46 km2 of sandy or bare land. 2 converted into grassland; during the artificial rainfall period from 2010 to 2020, 1017.56 km2 of sandy or bare land 2 The areas where vegetation was restored were mainly concentrated in the upper reaches of the Shiyang River Basin. The best vegetation improvement area was located in the southeast of the upper reaches of the Shiyang River Basin. Vegetation in the lower reaches and Minqin Basin was also improved to a certain extent. The effect of artificial rainfall in the Shiyang River Basin was quite significant.

[0071] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A system for acquiring artificial rainfall data based on remote sensing images, characterized in that: It includes image data acquisition module, rainfall data acquisition module, cloud platform, data processing module and display module; The image data acquisition module, the cloud platform, the first input terminal of the data processing module and the display module are connected in sequence, and the rainfall data acquisition module is connected to the second input terminal of the data processing module; Image data acquisition module, used to obtain a variety of satellite data; Cloud platform for integrating various satellite data to generate remote sensing data; A rainfall data acquisition module is used to acquire precipitation data; A data processing module is used to correct the remote sensing data and generate precipitation change data and vegetation change data in combination with precipitation data; The display module is used to display precipitation change data and vegetation change data.

2. The artificial rainfall data acquisition system based on remote sensing images according to claim 1 is characterized in that: The image data acquisition modules include TM, ETM+ and OLI, the cloud platform adopts the GEE platform, the rainfall data acquisition module adopts the meteorological station, and the data processing module adopts ENVI software.

3. A method for evaluating artificial rainfall data based on remote sensing images, applied to an artificial rainfall data acquisition system based on remote sensing images as described in any one of claims 1-2, characterized in that: The following steps are involved: Data acquisition steps: obtain remote sensing image data and rainfall data from meteorological stations in the region; Data processing steps: Perform geometric correction and radiation correction on remote sensing image data based on ENVI software to obtain corrected remote sensing data; Image recognition step: Based on the corrected remote sensing data and regional historical precipitation data, the precipitation change data and vegetation change data in different time periods before and after artificial rainfall are obtained through regression analysis and pixel dichotomy; Data evaluation steps: Compare the precipitation change data and vegetation change data in different time periods before and after artificial rainfall, quantitatively analyze the impact of rainfall enhancement on vegetation recovery, and use spatiotemporal dynamic changes to analyze the rainfall enhancement effect.

4. The artificial rainfall enhancement data evaluation method based on remote sensing images according to claim 3 is characterized in that: In the data processing step, the corrected remote sensing data include the Normalized Difference Vegetation Index (NDVI) and the Surface Vegetation Coverage (FVC) data.

5. The method for evaluating artificial rainfall data based on remote sensing images according to claim 3 is characterized in that: The regression analysis method in the image recognition step includes: estimating the natural precipitation by in-depth study of the correlation between the historical precipitation in the target area and the comparison area, comparing the estimated natural precipitation with the actually observed precipitation, and the difference between the two is the actual precipitation change brought about by the artificial weather modification operation.

6. The method for evaluating artificial rainfall data based on remote sensing images according to claim 3, characterized in that: The pixel binary model in the image recognition step includes: pixel classification and inversion based on multispectral data, and obtaining the area percentage of vegetation coverage using FVC, which is expressed as: Among them, S is a single pixel observed by the sensor, S soil is the remote sensing information of pure soil pixels, S veg It is the remote sensing information of pure vegetation pixels.

Images