Method and device for monitoring and evaluating the vegetation restoration degree in arid or desert areas by radar remote sensing

Abstract

The application relates to a radar remote sensing monitoring and evaluation method and device for vegetation recovery degree in arid or desert areas, which comprises a data acquisition module, a feature calculation module, an index calculation module and an index representation module; by acquiring target vector data and various related biophysical index grid data of a target area, the biophysical index change trajectory features of different vegetation types in the target area at the same time are determined; a new radar vegetation monitoring index is calculated, and a grid value for representing the management effect and vegetation monitoring of the target area is obtained, and the grid value is used as an evaluation index of the vegetation recovery degree of the target area. The application can solve the problem that there is no reasonable radar vegetation index application in the vegetation monitoring and management effect evaluation of synthetic aperture radar in arid areas, so as to facilitate relevant personnel to perform rapid all-weather regional vegetation resource monitoring, environmental protection and management condition evaluation in arid or desert areas, and save manpower and resources.

Description

Technical Field

[0001] This application relates to the field of surface resource monitoring technology, specifically to a radar remote sensing monitoring and assessment method and device for assessing the degree of vegetation restoration in arid or desert regions. Background Technology

[0002] Natural resources refer to substances in nature that humans can directly obtain for production and daily life. Biological resources, water resources, and land resources, which can be reproduced or recycled in a relatively short period, are considered renewable resources. However, the renewal and restoration of renewable resources require a certain amount of time, and the destruction of vegetation resources in ecologically fragile areas will require a significant amount of human and material resources to curb this trend. Therefore, timely monitoring of land use and vegetation cover in arid or desert areas is an important guarantee for effectively protecting the environment in arid regions and achieving sustainable development of natural resources.

[0003] As a typical example of arid regions, desert vegetation plays a crucial role in windbreak and sand fixation, controlling sandstorms, and preventing farmland on the desert's edge from being eroded by sand. The Mu Us Desert is characterized by its long history of formation and arid climate with little rainfall. Since the 1960s, human activities have accelerated vegetation degradation, leading to increased desertification, dust storms, and a series of other ecological and environmental problems. In response, the state implemented a series of environmental protection policies and adopted ecological restoration techniques in the Mu Us Desert after 1990 to carry out regional ecological governance and vegetation restoration. With the implementation of these measures and the investment of significant human and material resources, the treated area has continuously expanded, and the effects of the restoration have become increasingly apparent. However, currently, assessing the effectiveness of regional governance still requires on-site verification of vegetation types in addition to checking the vegetation coverage area. Therefore, accurately understanding the degree of vegetation coverage and vegetation types, and thus the current state of vegetation and the effectiveness of governance, is a crucial foundation for further identifying gaps and taking subsequent measures.

[0004] The main remote sensing methods for monitoring vegetation restoration in traditional arid or desert regions are as follows:

[0005] The first type is the thematic index inversion method, which mainly uses optical remote sensing data sources. Its research and development path is mainly divided into two types: (1) monitoring by using the sensitivity of vegetation chlorophyll (blue and red light absorption, green and near-infrared reflection) in the visible light and infrared bands. This is essentially monitoring the vegetation canopy, mainly based on ratios such as the Standardized Differential Vegetation Index (NDVI), Soil Corrected Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Simple Ratio Vegetation Index (SR), and Normalized Differential Vegetation Index (GDVI); (2) linear transformation of images according to the distribution characteristics of different land types, such as KT transformation, i.e., cap transformation. However, the wavelength of optical remote sensing is relatively short and it is difficult to penetrate cloud and rain areas, making it impossible to conduct all-weather regional monitoring.

[0006] The second method is classification, which involves manual on-site measurement and surveying of the soil and vegetation restoration in the monitoring area to understand the remediation status, and then using data sources such as optical remote sensing to classify land use types. However, this method requires a large amount of human and material resources, making it unsuitable for large-scale monitoring and evaluation, and it is also limited by the shortcomings of optical remote sensing.

[0007] In summary, the shortcomings of traditional remote sensing monitoring and assessment methods for vegetation restoration in arid or desert regions hinder relevant personnel from conducting environmental protection and regional governance assessments related to resource development. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of optical remote sensing, which is difficult to penetrate cloud and rain areas and cannot achieve all-weather regional monitoring. It aims to develop a radar remote sensing monitoring and assessment method and device for vegetation and restoration in arid or desert areas, so as to enable relevant personnel to conduct rapid all-weather regional vegetation resource monitoring, environmental protection and governance assessment in arid or desert areas, saving manpower and material resources.

[0009] The first technical solution adopted in this invention is: a radar remote sensing monitoring and assessment method for the degree of vegetation restoration in arid or desert areas, comprising the following steps:

[0010] S1: Acquire target vector data and various relevant biophysical index raster data generated from optical and radar images in arid or desert areas; wherein, the target vector data is the target area range vectorized data, and the biophysical index raster data includes optical vegetation index and radar non-vegetation index generated from images.

[0011] S2: Based on the target vector data and biophysical index raster data, the biophysical index raster data of different land surface types are projected onto the time axis based on the target vector data to determine the trajectory characteristics of the change of biophysical indexes of different vegetation types in arid areas within the same time period.

[0012] S3: Obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on radar non-vegetation indices;

[0013] S4: Based on the new radar vegetation monitoring index, obtain grid values ​​to characterize the effectiveness of governance and vegetation monitoring in arid or desert areas; use the grid values ​​as an evaluation index for the degree of vegetation restoration in arid or desert areas.

[0014] Furthermore, the method for obtaining target vector data of arid or desert areas in step S1 is to establish a distribution map of arid or desert areas based on optical remote sensing images of arid or desert areas, and to perform vectorization processing on the distribution image to obtain the target vector data.

[0015] The method for acquiring various relevant biophysical index raster data generated from optical and radar images involves performing band operations on the various relevant biophysical indicators to obtain the relevant biophysical index raster data; the optical images include Sentinel-2 optical band data, and the radar images include Sentinel-1 radar C-band data and its derived non-vegetation index data.

[0016] Furthermore, the optical vegetation indices include the Standardized Differential Vegetation Index (NDVI), the Normalized Difference Vegetation Index (GDVI), and Mining and Restoration Assessment Indices (MRAIs). The radar non-vegetation indices include scattering entropy H, scattering angle θ, and cross-polarization band echo intensity value VH. The specific formula for obtaining new radar vegetation monitoring indices for different vegetation cover types in arid regions based on radar non-vegetation indices in step S3 is as follows:

[0017]

[0018] Wherein, DRVI is the new radar vegetation monitoring index, and k is a proportional parameter applicable to different arid regions.

[0019] Furthermore, the specific method for obtaining the raster value for characterizing the governance effectiveness and vegetation monitoring of arid or desert areas based on the new radar vegetation monitoring index in step S4 is as follows: according to the threshold method, the vector boundary of the distribution range of each type of vegetation in the arid area is clipped and rasterized, the vegetation types in the distribution range of each type of vegetation in the arid area are identified, the new radar vegetation monitoring index of each raster is calculated, and the raster value of each raster is obtained.

[0020] Furthermore, in step S4, the raster value is used as an evaluation criterion for the degree of vegetation restoration in arid or desert regions as follows:

[0021] If 0 ≤ raster value < 0.2, it indicates that the governance effect of the arid area corresponding to the raster is very poor;

[0022] If 0.2 ≤ raster value < 1, it indicates that the treatment effect of the arid area corresponding to the raster is relatively good;

[0023] If the grid value is ≥1, it indicates that the governance effect of the arid area corresponding to the grid is very good.

[0024] The second technical solution adopted by the present invention is: a radar remote sensing monitoring and evaluation device for the degree of vegetation restoration in arid or desert areas, used to implement the radar remote sensing monitoring and evaluation method described in the first technical solution above, including a data acquisition module, a feature calculation module, an index calculation module and an index characterization module;

[0025] The data acquisition module is used to acquire target vector data in arid or desert areas, as well as various relevant biophysical index raster data generated from optical and radar images.

[0026] The feature calculation module is used to project different land surface types in the raster data onto the time axis based on the target vector data and biophysical index raster data, thereby determining the trajectory characteristics of the change of biophysical indicators of different vegetation types in the same time period in arid areas.

[0027] The index calculation module is used to obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on radar non-vegetation indices.

[0028] The index characterization module is used to obtain raster values ​​based on the new radar vegetation monitoring index to characterize the effectiveness of governance and vegetation monitoring in arid or desert areas.

[0029] The third technical solution adopted by the present invention is: a computer storage medium for storing computer instructions, which, when executed by a processor, implement the radar remote sensing monitoring and evaluation method for the degree of vegetation restoration in arid or desert areas as described in the first technical solution above.

[0030] The fourth technical solution adopted by the present invention is: a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the program, it implements the radar remote sensing monitoring and evaluation method for the degree of vegetation restoration in arid or desert areas as described in the first technical solution above.

[0031] The beneficial effects of this invention are as follows:

[0032] (1) This invention uses radar non-vegetation indices, including scattering entropy H, scattering angle θ, and cross-polarized echo intensity VH, to jointly construct a new radar vegetation index. This invention found that constructing a plane using non-vegetation indices 1-H and θ can highlight vegetation greenness information, and the ratio (1-H) / θ is strongly positively correlated with the optical remote sensing vegetation index NDVI; therefore, this invention uses this ratio and the cross-polarized echo intensity VH, which is sensitive to vegetation canopy, to jointly construct a new radar vegetation index DRVI.

[0033] Compared with existing technologies, such as dual-polarization synthetic aperture radar vegetation index DPSVI, polarimetric radar vegetation index PRVI and radar vegetation index RVI, this invention has better sensitivity and can more effectively reflect the difference between land use status and vegetation restoration effectiveness in arid or desert areas.

[0034] (2) This invention utilizes C-band radar data from the Sentinel-1 satellite to develop new vegetation indices, which can overcome the shortcomings of short optical remote sensing wavelengths, difficulty in penetrating cloud and rain areas, and inability to conduct all-weather regional monitoring. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0037] Figure 2 This is a schematic diagram of the device structure according to an embodiment of the present invention. Detailed Implementation

[0038] To better understand the above-described objects, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Many specific details are set forth in the following description to provide a thorough understanding of the invention; however, the invention may be practiced in other ways different from those described herein, and therefore, the invention is not limited to the specific embodiments disclosed below.

[0039] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art described herein. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “connected” or “linked” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms “upper,” “lower,” “left,” “right,” etc., are used only to indicate relative positional relationships, which change accordingly when the absolute position of the described object changes.

[0040] Arid or desert regions include the areas or regions occupied by arid or desert areas. Specifically, arid or desert regions include areas with sandy surface cover, sandy underlying surface, or annual precipitation of 200mm-500mm. Depending on the development and management status within the region, arid or desert regions encompass several vegetation restoration stages—sand land, grassland, shrubland, woodland, and farmland. This invention uses the Mu Us Desert in an arid region as an example for illustration. Within each vegetation restoration stage, the degree of vegetation restoration in the Mu Us Desert after management is correlated with the vegetation range within the Mu Us sandy land.

[0041] like Figure 1 As shown, a radar remote sensing monitoring and assessment method for the degree of vegetation restoration in arid or desert regions includes the following steps:

[0042] S1: Based on optical remote sensing images of arid or desert areas, a distribution map of the arid or desert area is established. The distribution image is then vectorized to obtain the target vector data, which is the vectorized data of the target area. Taking the Mu Us Desert as an example, based on field surveys of the Mu Us Desert, and understanding the geological background of its formation, historical vegetation cover, stages of ecological restoration, human activities, and desert area management and restoration methods such as sand fixation and vegetation planting, the boundaries of the Mu Us Desert are determined by combining remote sensing images of the Mu Us Desert, and a polygon of the Mu Us Desert area is established, i.e., the distribution map of the Mu Us Desert. The distribution map of the Mu Us Desert is then vectorized to obtain the target vector data.

[0043] This invention employs vectorization algorithms or third-party processing software to vectorize distribution maps of arid or desert regions. For example, the distribution maps of arid or desert regions are imported into a geospatial processing platform such as ArcGIS or MapGIS. After converting the maps into layer files, a boundary database of the arid or desert region distribution maps is established, annotating the vegetation destruction time, restoration time, and development history of the arid or desert regions. The vector data coordinate datum of the database is set to WGS-84, and the projected coordinates are set to the UTM 49-degree zone to obtain the target vector data.

[0044] Various relevant biophysical indicators are processed using image band operations to obtain raster data of these indicators. Different biophysical indicators are processed using different methods based on their classification. The biophysical indicator raster data includes optical vegetation indices and radar non-vegetation indices generated from the image. The optical vegetation indices include the Normalized Differential Vegetation Index (NDVI), the Normalized Difference Vegetation Index (GDVI), and Mining and Restoration Assessment Indices (MRAIs). The radar non-vegetation indices include scattering entropy H, scattering angle θ, and cross-polarization band echo intensity value VH.

[0045] Multi-temporal Sentinel-2 MSI images from selected years were processed using the SNAP (Sentinels Application Platform), a complete remote sensing image data processing platform provided by the European Space Agency specifically for the Sentinel series satellites. These Sentinel-2 MSI images were acquired under cloud cover conditions of less than 5%. Using the computer's command prompt (CMD), the Sen2Cor toolset provided by SNAP, specifically for generating and formatting Sentinel-2 Level 2A products, was run to perform atmospheric, topographic, and cirrus cloud corrections on the Sentinel-2 Level 1C data. This removed atmospheric effects caused by factors such as atmospheric scattering, converted the radiance (DN) of the image spectrum into surface reflectance, and then used a resampling tool to uniformly resample the lower-resolution bands of the image to 10m resolution data.

[0046] The Sentinel-2 optical band data and the Sentinel-1 radar C-band and its derived non-vegetation index data are substituted into the ENVI (The Environment or Visualizing Images) band calculator for band calculation to obtain optical vegetation indices, such as the Standardized Differential Vegetation Index (NDVI), the Normalized Difference Vegetation Index (GDVI), mining and restoration assessment indicators (MRAIs), and radar non-vegetation indices, such as scattering entropy (H), scattering angle (θ), and cross-polarized echo intensity (VH), to obtain the raster data of each index.

[0047] The formula for calculating the Standardized Vegetation Index (NDVI) is as follows:

[0048] NDVI=(ρ NIR -ρ R ) / (ρ NIR +ρ R )

[0049] Where, ρ NIR ρ is the near-infrared reflectance. R This represents the reflectivity in the infrared band.

[0050] The formula for calculating the Normalized Difference Vegetation Index (GDVI) is as follows:

[0051] GDVI=(ρ NIR n -ρ R n ) / (ρ NIR n +ρ R n )

[0052] Where n is a power, and its value is an integer greater than or equal to 1. In this embodiment of the invention, the value of n is 2.

[0053] The formula for calculating Mining and Recovery Assessment Indicators (MRAIs) is as follows:

[0054] MRAI = GDVI / (α + TCB)

[0055] Where α is the albedo, representing a measure of diffuse reflection of total solar radiation over a land surface, ranging from 0 to 1, and TCB is the Tasselled Cap Transformation Brightness. In the Sentinel-2 satellite, the specific formula for calculating TCB is:

[0056] TCB=0.2381B1+0.2569B2+0.2934B3+0.3020B4+0.3099B5+0.3740B6+0.4180B7

[0057] +0.358B8+0.3834B 8A +0.0103B9+0.002B 10 +0.0896B 11 +0.078B 12

[0058] Among them, B i Represents the reflectivity of the i-th band of Sentinel-2, i = 1, 2, 3, 4, 5, 6, 7, 8, 8A, 9, 10, 11, 12.

[0059] The albedo α in the Sentinel-2 satellite is expressed as follows:

[0060]

[0061] Scattering entropy H is a key parameter measuring the randomness of electromagnetic wave scattering by the existing Earth's surface and is considered a fundamental parameter for assessing the importance of polarization measurements in remote sensing. The high sensitivity of scattering entropy H to changes in surface features is beneficial for characterizing detailed information about different vegetation types. Increased surface vegetation increases the randomness of surface scattering to varying degrees. The formula for calculating scattering entropy H is shown below:

[0062]

[0063] Among them, P k It is a pseudo-probability, P k The formula is as follows:

[0064]

[0065] Where, λ i These are the eigenvectors of the Sentinel-1 radar band covariance matrix C2. It is the sum of k eigenvectors.

[0066] The scattering angle θ is a parameter proven to have promising applications in vegetation phenology and land surface classification. This parameter is used to reflect changes in comprehensive land cover information in arid or desert regions. The formula for calculating the scattering angle θ is as follows:

[0067]

[0068] Among them, C 11 and C 22 , representing the diagonal values ​​of the covariance matrix of the dual-polarization radar, Span represents the total scattered power, and m represents the polarizability of the dual-polarization radar. This index measures the degree to which the ground surface depolarizes electromagnetic waves, and its formula is as follows:

[0069]

[0070] Where |C2| is the value of the covariance matrix of the dual-polarization radar, and Tr(C2) is the trace of the covariance matrix of the dual-polarization radar.

[0071] The cross-polarization band echo intensity value (VH) is the echo intensity value when the electric field vectors transmitted and received by the radar antenna are polarized differently. In the Sentinel-1 satellite, it is represented by the echo intensity value of the vertically polarized transmit band and the horizontally polarized receive band. The cross-polarization band is an important parameter for monitoring crop biological, physical, and phenological phenomena, and it is better suited for monitoring vegetation scatterers than the co-polarization band.

[0072] S2: Based on the target vector data and biophysical index raster data, the biophysical index raster data of different land surface types are projected onto the time axis based on the target vector data to determine the trajectory characteristics of the changes in biophysical indicators of different vegetation types in arid areas within the same time period (e.g., 2 years or longer).

[0073] This invention utilizes the average values ​​of biophysical indicators for different land cover types within the statistical distribution range of ENVI to reveal the performance and variation patterns of various biophysical indicators for different land cover types in arid or desert regions. Generally, optical vegetation indices increase with increasing greenness in sandy land, grassland, shrubland, forest, and farmland in the Mu Us Desert. However, existing radar vegetation indices, such as the dual-polarization synthetic aperture radar vegetation index DPSVI and the polarization radar vegetation index PRVI, exhibit different effects and do not increase in the above land cover order. In contrast, scattering entropy H shows an increasing trend with increasing vegetation greenness across different land cover types; the scattering angle θ shows a clustering effect for different land cover types; and the cross-polarization band echo intensity value VH exhibits high sensitivity to the scattering response of different vegetation types.

[0074] S3: Obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on non-vegetation indices. In this embodiment of the invention, new radar vegetation monitoring indices for different vegetation cover types in arid regions are obtained based on the ratio of a highly sensitive and dynamic scattering index to a scattering type parameter, and the product of this ratio and the scattering parameter. The highly sensitive and dynamic scattering index refers to the scattering entropy H, which highly reflects the randomness of electromagnetic wave scattering from the Earth's surface. The scattering type parameter is the scattering angle θ, derived from the radar covariance matrix, which reflects different vegetation types on the Earth's surface. The scattering parameter refers to the radar polarization mode that is sensitive to the vegetation type on the surface of arid regions, i.e., the cross-polarization band echo intensity value VH. The specific calculation formula for the new radar vegetation monitoring index is as follows:

[0075]

[0076] Wherein, DRVI is the new radar vegetation monitoring index, and k is a proportional parameter applicable to different arid regions. In this embodiment of the invention, the preferred value of k is -100. This embodiment of the invention utilizes Sentinel-1 satellite C-band radar data to develop a new vegetation index, which can overcome the shortcomings of optical remote sensing wavelengths being too short to penetrate cloud and rain areas and unable to conduct all-weather regional monitoring.

[0077] S4: Perform band calculations on the vegetation monitoring index to obtain raster values ​​used to characterize the effectiveness of vegetation monitoring and control in arid or desert areas. The specific method is as follows:

[0078] Substitute the Sentinel-2 optical band data and the Sentinel-1 radar C-band and its derived non-vegetation index data into the ENVI band calculator for band calculation to obtain the optical remote sensing vegetation index, the radar non-vegetation index, and the raster layer of the new radar vegetation monitoring index DRVI.

[0079] Based on the threshold method, the vector boundaries of the distribution range of various land cover types in arid regions are clipped and rasterized. Vegetation types within the distribution range of each land cover type in arid regions are identified. A new Radar Vegetation Monitoring Index (DRVI) is calculated for each raster and used as the raster value for each raster. This raster value is then used as an evaluation index for the degree of vegetation restoration in arid or desert regions to assess the status of sand control. The specific evaluation criteria are as follows:

[0080] If 0 ≤ raster value < 0.2, it indicates that the governance effect of the arid area corresponding to the raster is very poor;

[0081] If 0.2 ≤ raster value < 1, it indicates that the treatment effect of the arid area corresponding to the raster is relatively good;

[0082] If the grid value is ≥1, it indicates that the governance effect of the arid area corresponding to the grid is very good.

[0083] The sensitivity of the radar vegetation monitoring index relative to the optical vegetation index is calculated based on the vegetation monitoring indices for different land types, the average value of each radar vegetation index, and the difference between the values ​​of a single pixel and its neighboring pixels. This sensitivity reflects the superiority and applicability of the radar vegetation monitoring index. Higher sensitivity indicates a more sensitive radar vegetation monitoring index to changes in vegetation type, making it more suitable for monitoring vegetation dynamics and desertification control effectiveness in arid regions. Compared to other radar vegetation indices, such as the dual-polarization synthetic aperture radar vegetation index (DPSVI), polarimetric radar vegetation index (PRVI), and radar vegetation index (RVI), the new radar vegetation monitoring index DRVI obtained in this embodiment of the invention more effectively reflects the differences between land use status and vegetation restoration effectiveness in arid or desert regions after treatment.

[0084] The specific formula for calculating the sensitivity Sr of the new radar vegetation monitoring index is as follows:

[0085]

[0086] Where Sr is the sensitivity of the new radar vegetation monitoring index, d(DRVI) / d(VI) is the first derivative of the new radar vegetation monitoring index with respect to the reference vegetation index VI (such as the Standardized Differential Vegetation Index NDVI or the Normalized Differential Vegetation Index GDVI), and ΔDRVI = DRVI max -DRVI min ΔVI = VI max -VI min DRVI max and DRVI min These represent the maximum and minimum values ​​of the new radar vegetation monitoring index observed in the test area on that day, VI. max and VI min These are the maximum and minimum values ​​observed on the test area on the same day for the reference vegetation index VI, such as NDVI or GDVI. Furthermore, the sensitivity calculation formulas for DPSVI and PRVI relative to NDVI and GDVI are the same as those for the new radar vegetation monitoring indices; simply replace them with the corresponding radar vegetation indices and reference vegetation indices for calculation.

[0087] As shown in Table 1, compared with the Standardized Differential Vegetation Index (NDVI) and the Normalized Differential Vegetation Index (GDVI), the sensitivity of the new radar vegetation monitoring index DRVI described in this embodiment of the invention is 1.05-5.21 times and 1.2-7.49 times higher than that of the Dual Polarized Synthetic Aperture Radar Vegetation Index (DPSVI) and the Polarized Radar Vegetation Index (PRVI).

[0088] Table 1. Sensitivity of each radar vegetation index relative to NDVI and GDVI

[0089]

[0090] The embodiments of the present invention are as follows: Figure 2 The radar remote sensing monitoring and evaluation device shown implements the radar remote sensing monitoring and evaluation method described above. The radar remote sensing monitoring and evaluation device includes a data acquisition module, a feature calculation module, an index calculation module, and an index characterization module.

[0091] The data acquisition module is used to acquire target vector data in arid or desert areas, as well as various relevant biophysical index raster data generated from optical and radar images.

[0092] The feature calculation module is used to project different land surface types within the raster data onto the time axis based on the target vector data and biophysical index raster data, thereby determining the trajectory characteristics of biophysical index changes of different vegetation types in arid regions within the same time period.

[0093] The index calculation module is used to obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on non-vegetation indices.

[0094] The index characterization module is used to obtain raster values ​​based on the vegetation monitoring index to characterize the effectiveness of governance and vegetation monitoring in arid or desert areas.

[0095] The present invention also employs a computer storage medium for storing computer instructions, and a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer instructions are executed by the processor or the processor in the computer device executes the program, the radar remote sensing monitoring and evaluation method described above can be implemented.

[0096] This invention, through acquiring target vector data and radar imagery of arid or desert regions, generates various related biophysical index raster data. It then determines the trajectory characteristics of biophysical indicators within the distribution range of arid or desert regions, varying with different land surface types. Based on the ratio of a highly sensitive and dynamic scattering index to scattering parameters, and the product of this ratio and the scattering parameters, new radar vegetation monitoring indices for different vegetation cover types in arid regions are obtained. Based on these monitoring indices, raster values ​​are obtained to characterize the effectiveness of arid or desert region management and vegetation monitoring. By setting monitoring indices for different vegetation cover types in arid regions, the raster values ​​obtained in this invention can accurately reflect the relationship between land cover and biophysical indicators in arid or desert regions. This allows for the determination of vegetation restoration levels in different areas of arid or desert regions during the same period and the monitoring of regional environmental management effectiveness. This facilitates targeted environmental protection efforts in arid or desert regions and addresses shortcomings in regional management work.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A radar remote sensing monitoring and assessment method for the degree of vegetation restoration in arid or desert regions, characterized in that, Includes the following steps: S1: Acquire target vector data and various relevant biophysical index raster data generated from optical and radar images in arid or desert areas; wherein, the target vector data is the target area range vectorized data, and the biophysical index raster data includes optical vegetation index and radar non-vegetation index generated from images. Classify the land cover distribution map of arid or desert areas based on the characteristics of optical remote sensing images, then construct the distribution map of arid or desert areas, and vectorize the distribution image to obtain the target vector data; The method for acquiring various relevant biophysical index raster data generated from optical and radar images involves performing band operations on the various relevant biophysical indicators to obtain the relevant biophysical index raster data; the optical images include Sentinel-2 optical band data, and the radar images include Sentinel-1 C-band radar data and its derived non-vegetation index data. S2: Based on the target vector data and biophysical index raster data, the biophysical index raster data of different land surface types are projected onto the time axis based on the target vector data to determine the trajectory characteristics of the change of biophysical indexes of different vegetation types in arid areas within the same time period. The biophysical indicators for different land surface types mentioned here refer to optical vegetation indices, including the Standardized Differential Vegetation Index (NDVI), the Dryland Vegetation Index (GDVI), and Mining and Restoration Assessment Indices (MRAIs); radar non-vegetation indices include scattering entropy H, scattering angle θ, and cross-polarized band echo intensity value VH. S3: Obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on radar non-vegetation indices; here, following the steps described in S2, the specific formula for obtaining new radar vegetation monitoring indices for different vegetation cover types in arid regions based on radar non-vegetation indices is as follows: ； Wherein, DRVI is the new radar vegetation monitoring index, k is the proportional parameter applicable to different arid regions, H is the scattering entropy, θ is the scattering angle, and VH is the cross-polarization band echo intensity value; S4: Based on the new radar vegetation monitoring index, obtain grid values ​​to characterize the effectiveness of governance and vegetation monitoring in arid or desert areas; use the grid values ​​as an evaluation index for the degree of vegetation restoration in arid or desert areas. The specific method for obtaining raster values ​​for characterizing the governance effectiveness and vegetation monitoring of arid or desert areas based on the new radar vegetation monitoring index is as follows: the vector boundaries of the distribution range of various land cover types in arid areas are clipped by the threshold method or, more precisely, the density segmentation method, and then rasterized. The vegetation types within the distribution range of various land cover types in arid areas are identified, the new radar vegetation monitoring index of each raster is calculated, and the raster value of each raster is obtained. The grid value is used as an evaluation standard for the degree of vegetation restoration in arid or desert areas: If 0.0 ≤ raster value < 0.2, it indicates that the governance effect of the arid area corresponding to the raster is poor; If 0.2 ≤ raster value < 1, it indicates that the treatment effect of the arid area corresponding to the raster is relatively good; If the grid value is ≥ 1, it indicates that the governance effect of the arid area corresponding to the grid is very good.

2. A radar remote sensing monitoring and assessment device for the degree of vegetation restoration in arid or desert regions, characterized in that, The radar remote sensing monitoring and evaluation method as described in claim 1 includes a data acquisition module, a feature calculation module, an index calculation module, and an index characterization module. The data acquisition module is used to acquire target vector data in arid or desert areas, as well as various relevant biophysical index raster data generated from optical and radar images. The feature calculation module is used to project different land surface types within the raster data onto the time axis based on the target vector data and biophysical index raster data, thereby determining the trajectory characteristics of the changes in biophysical indicators of different vegetation types within the same time period in arid regions. The index calculation module is used to obtain new radar vegetation monitoring indices for different vegetation cover types in arid regions based on non-vegetation indices; The index characterization module is used to obtain raster values ​​based on the vegetation monitoring index to characterize the effectiveness of governance and vegetation monitoring in arid or desert areas.

3. A computer storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the radar remote sensing monitoring and evaluation method for the degree of vegetation restoration in arid or desert areas as described in claim 1.

4. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the radar remote sensing monitoring and evaluation method for the degree of vegetation restoration in arid or desert areas as described in claim 1.

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