Vegetation recovery monitoring method based on image analysis

Abstract

The invention relates to the technical field of vegetation monitoring, in particular to a vegetation recovery monitoring method based on image analysis. The method comprises the following steps: selecting shot images for vegetation recovery in a monitoring area range, processing the shot images, performing area division on the monitoring area, determining processed shot images corresponding to each division area, sequentially determining vegetation indexes of the corresponding division areas, and determining vegetation recovery variations of the corresponding division areas according to the vegetation indexes. And judging whether the vegetation recovery meets the standard or not based on the vegetation recovery variation, judging the reason that the vegetation recovery does not meet the standard based on the regional dispersity, judging the reason that the vegetation recovery does not meet the standard based on the standard deviation rate, and adjusting or sending out a corresponding notification according to corresponding parameters. According to the invention, timely analysis and intelligent adjustment of the image are effectively realized according to the vegetation restoration condition, and the monitoring efficiency of vegetation restoration is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of vegetation monitoring technology, and in particular to a method for monitoring vegetation restoration based on image analysis. Background Technology

[0002] Vegetation restoration refers to the process by which damaged vegetation ecosystems gradually regain their function and structure through natural recovery or human intervention. Image-based vegetation restoration monitoring utilizes remote sensing images, drone aerial photography, or ground-based images to assess the restoration status of regional vegetation. Vegetation restoration is crucial to the ecological environment and human life, preventing soil erosion and desertification, regulating climate, purifying the environment, and maintaining biodiversity. Vegetation restoration monitoring is essential for determining whether expected goals have been achieved, quantifying vegetation changes, and providing data support for management decisions. Image-based vegetation restoration monitoring addresses the shortcomings of traditional monitoring methods, such as reliance on manual labor, limited coverage areas, subjective judgments, and high costs. Research on image-based vegetation restoration monitoring has significant practical implications in the field of vegetation restoration monitoring.

[0003] Chinese Patent Publication No. CN117523387A discloses a vegetation cover monitoring system and method. The vegetation cover monitoring system includes: an image acquisition module that uses an image sensor mounted on a drone to capture images of a target area and obtain image data; an image processing module that stitches the image data together to obtain a panoramic image; an image recognition module that uses a vegetation recognition model to identify the vegetated and non-vegetated areas in the panoramic image, and renders the vegetated areas to generate a rendered panoramic image; and an image analysis module that obtains vegetation cover information of the target area based on the rendered panoramic image.

[0004] Therefore, the above scheme acquires image data of the target area through an image acquisition module, and calculates the vegetation cover of the target area through image processing, image recognition, and image analysis modules, thus obtaining vegetation cover information and providing a reliable basis for the protection and restoration of natural vegetation. However, the above scheme cannot perform timely analysis and intelligent adjustment of images based on vegetation restoration status, thereby failing to guarantee the monitoring efficiency of vegetation restoration. Summary of the Invention

[0005] Therefore, this invention provides a vegetation restoration monitoring method based on image analysis to overcome the problem that the existing technology cannot achieve timely analysis and intelligent adjustment of images according to the vegetation restoration status, resulting in low monitoring efficiency of vegetation restoration.

[0006] To achieve the above objectives, the present invention provides a vegetation restoration monitoring method based on image analysis, comprising: Select images of vegetation restoration within the monitoring area; The captured images are processed to improve monitoring accuracy; The monitoring area is divided into regions, and the processed captured images corresponding to each region are determined sequentially. Based on the processed captured images, the vegetation index of the corresponding divided regions is determined sequentially. The vegetation restoration change in the corresponding divided area is determined based on the vegetation index. The determination of whether vegetation restoration meets the standards is based on the changes in vegetation restoration. When determining that vegetation restoration does not meet the standards, the reasons for non-compliance are based on the regional dispersion. The reason for determining the regional dispersion is to adjust the cloud probability threshold based on cloud coverage or to adjust the image resolution based on boundary pixel density; after the cloud probability threshold is increased, the cloud shadow buffer distance is increased based on the solar altitude angle. After the cloud shadow buffer distance or the image resolution adjustment is completed, a determination is made on whether the vegetation restoration meets the standard based on the amount of vegetation restoration change; When vegetation restoration is deemed to be non-compliant with standards, the aerosol optical thickness is adjusted based on the change in vegetation restoration. After the aerosol optical thickness adjustment is completed, a determination is made as to whether the vegetation restoration meets the standard based on the change in vegetation restoration. When vegetation restoration is deemed not to meet the standards, the reason for non-compliance is determined based on the standard deviation rate; and a corresponding notification is issued based on the standard deviation rate determination result.

[0007] Furthermore, the process of determining whether vegetation restoration meets the standards based on the changes in vegetation restoration includes, When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, the vegetation restoration in the monitoring area is determined to meet the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the reason why the vegetation restoration does not meet the standard is determined based on the regional dispersion.

[0008] Furthermore, the process of determining the reasons why vegetation restoration does not meet the standards based on the aforementioned regional dispersion includes, When the regional dispersion is less than the preset regional dispersion, it is determined that the cloud probability threshold of the captured image does not meet the standard, and the cloud probability threshold is adjusted based on the cloud coverage. When the regional dispersion is greater than or equal to the preset regional dispersion, it is determined that the image resolution of the captured image does not meet the standard, and the image resolution is adjusted based on the boundary pixel density.

[0009] Furthermore, the process of increasing the cloud probability threshold based on the cloud coverage rate includes, The cloud probability threshold is increased based on the cloud coverage rate, and the increase in the cloud probability threshold is proportional to the cloud coverage rate.

[0010] Furthermore, the process of increasing the cloud shadow buffer distance based on the solar altitude angle after the cloud probability threshold has been increased includes: The cloud shadow buffer distance is increased based on the solar altitude angle, and the increase in the cloud shadow buffer distance is inversely proportional to the solar altitude angle.

[0011] Furthermore, the process of increasing the image resolution based on the boundary pixel density includes, The image resolution is increased based on the boundary pixel density, and the increase in image resolution is inversely proportional to the boundary pixel density.

[0012] Furthermore, the process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the cloud shadow buffer distance or the image resolution adjustment is completed includes: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the aerosol optical thickness is adjusted based on the vegetation restoration change.

[0013] Furthermore, the process of reducing the aerosol optical thickness based on the difference in vegetation restoration changes includes, The aerosol optical thickness is reduced based on the difference in vegetation restoration change, and the reduction in aerosol optical thickness is proportional to the difference in vegetation restoration change.

[0014] Furthermore, the process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the aerosol optical thickness adjustment is completed includes: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the reason why the vegetation restoration does not meet the standard is determined based on the standard deviation rate.

[0015] Furthermore, the process of determining the reasons why vegetation restoration does not meet the standard based on the aforementioned standard deviation rate includes, When the standard deviation rate is less than the preset standard deviation rate, a notification is issued that the vegetation restoration in the monitoring area does not meet the standard. When the standard deviation rate is greater than or equal to the preset standard deviation rate, a notification is issued that the image needs to be re-acquired and captured.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention determines whether vegetation restoration meets the standards based on the change in vegetation restoration, and completes the determination of whether vegetation restoration meets the standards in a timely and accurate manner. It effectively realizes timely analysis of images based on vegetation restoration status, determines the reasons for vegetation restoration not meeting the standards based on regional dispersion and standard deviation rate, and further realizes timely analysis of images based on vegetation restoration status, adjusts corresponding parameters or issues corresponding notifications. While effectively realizing intelligent adjustment of images based on vegetation restoration status, it effectively improves the monitoring efficiency of vegetation restoration.

[0017] Furthermore, this invention determines whether vegetation restoration meets the standards based on changes in vegetation restoration, effectively identifying the reasons for non-compliance based on regional dispersion. This facilitates subsequent targeted adjustments and avoids misjudgments. In addition to enabling timely analysis of images based on vegetation restoration status, it further improves the monitoring efficiency of vegetation restoration.

[0018] Furthermore, this invention determines the reasons why vegetation restoration does not meet the standards based on regional dispersion, accurately determines whether the cloud probability threshold needs to be adjusted based on cloud coverage or the image resolution needs to be adjusted based on boundary pixel density, avoids misjudgment, and promptly adjusts the cloud probability threshold or image resolution to ensure the accuracy of the determination of whether vegetation restoration meets the standards. While further realizing timely analysis of images based on vegetation restoration status, it further improves the monitoring efficiency of vegetation restoration.

[0019] Furthermore, this invention increases the cloud probability threshold based on cloud coverage, effectively avoiding misjudgments of whether vegetation restoration meets the standards due to the cloud probability threshold not meeting the standards. While further realizing intelligent adjustment of images according to the vegetation restoration status, it further improves the monitoring efficiency of vegetation restoration.

[0020] Furthermore, after increasing the cloud probability threshold, this invention increases the cloud shadow buffer distance based on the solar altitude angle. Adjusting the cloud shadow buffer distance can effectively avoid misjudging whether vegetation restoration meets the standard due to the cloud shadow buffer distance not meeting the standard. While further realizing intelligent adjustment of the image according to the vegetation restoration status, it further improves the monitoring efficiency of vegetation restoration.

[0021] Furthermore, this invention increases image resolution based on boundary pixel density, effectively avoiding misjudgments of whether vegetation restoration meets standards due to non-compliance with image resolution. While further realizing intelligent adjustment of images based on vegetation restoration status, it also further improves the monitoring efficiency of vegetation restoration.

[0022] Furthermore, after adjusting the cloud shadow buffer distance or image resolution, this invention determines whether vegetation restoration meets the standards based on the change in vegetation restoration. It can judge the adjustment effect of cloud probability threshold, cloud shadow buffer distance, and image resolution. If the vegetation restoration still does not meet the standards, the aerosol optical thickness is adjusted based on the change in vegetation restoration, further ensuring the accuracy of the judgment on whether vegetation restoration meets the standards. This further realizes timely analysis of images based on vegetation restoration and improves the monitoring efficiency of vegetation restoration.

[0023] Furthermore, this invention reduces aerosol optical thickness based on the difference in vegetation restoration changes, effectively avoiding misjudgments of whether vegetation restoration meets standards due to non-compliance of aerosol optical thickness. While further realizing intelligent adjustment of images according to vegetation restoration status, it also further improves the monitoring efficiency of vegetation restoration.

[0024] Furthermore, this invention determines whether vegetation restoration meets the standard based on the change in vegetation restoration after aerosol optical thickness adjustment. This effectively assesses the adjustment effect of aerosol optical thickness, determines whether vegetation restoration still does not meet the standard after aerosol optical thickness adjustment, and determines whether the reason for vegetation restoration not meeting the standard needs to be determined based on the standard deviation rate. This further enables timely analysis of images based on vegetation restoration status and improves the monitoring efficiency of vegetation restoration.

[0025] Furthermore, this invention determines the reasons why vegetation restoration does not meet the standards based on the standard deviation rate, and issues timely and accurate notifications that the vegetation restoration in the monitoring area does not meet the standards or that images need to be re-acquired and photographed, avoiding misjudgments. While further realizing timely analysis and intelligent adjustment of images based on the vegetation restoration status, it further improves the monitoring efficiency of vegetation restoration. Attached Figure Description

[0026] Figure 1 A block diagram of a system using an image analysis-based vegetation restoration monitoring method; Figure 2 This is a flowchart of the image analysis-based vegetation restoration monitoring method of the present invention; Figure 3 This is a flowchart illustrating the process for determining whether vegetation restoration meets the standards and the reasons why it does not meet the standards, as described in this invention. Figure 4 This is a flowchart illustrating the reasons why vegetation restoration does not meet the standards, as described in this invention. Detailed Implementation

[0027] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0028] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0029] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0030] Please see Figure 1 The diagram shown is a structural block diagram of a system using an image analysis-based vegetation restoration monitoring method. The structure of this embodiment includes a selection module, a processing module, a partitioning module, a calculation module, a judgment module, and an adjustment module; wherein, The selection module is used to select images of vegetation restoration within the monitoring area; The processing module is connected to the selection module and is used to process the captured image to improve monitoring accuracy; The segmentation module is connected to the processing module and is used to segment the monitoring area and sequentially determine the processed captured image corresponding to each segmented area. The calculation module is connected to the division module and is used to determine the vegetation index of the corresponding division area based on the processed captured image. The calculation module is also used to determine the vegetation restoration change of the corresponding divided area based on the vegetation index; The determination module is connected to the calculation module and is used to determine whether the vegetation restoration meets the standard based on the vegetation restoration change amount. The determination module is also used to determine the reason why vegetation restoration does not meet the standard based on the regional dispersion when determining that vegetation restoration does not meet the standard. The adjustment module is connected to the processing module and the determination module respectively, and is used to adjust the cloud probability threshold based on the cloud coverage rate or adjust the image resolution based on the boundary pixel density based on the reason for the regional dispersion determination; after the cloud probability threshold is increased, the cloud shadow buffer distance is increased based on the solar altitude angle. The determination module is also used to determine whether the vegetation restoration meets the standard based on the amount of vegetation restoration change after the cloud shadow buffer distance or the image resolution adjustment is completed; The adjustment module is also used to adjust the aerosol optical thickness based on the change in vegetation restoration when it is determined that the vegetation restoration does not meet the standard. The determination module is also used to determine whether the vegetation restoration meets the standard based on the vegetation restoration change after the aerosol optical thickness adjustment is completed; The adjustment module is also used to determine the reason why vegetation restoration does not meet the standard when it is determined that vegetation restoration does not meet the standard based on the standard deviation rate; and to issue a corresponding notification based on the standard deviation rate determination result.

[0031] Specifically, the vegetation restoration change is the difference between the current vegetation index and the historical vegetation index. The current vegetation index is derived from the current image, and the historical vegetation index is derived from historical images. In this embodiment, the comparison is made between the vegetation restoration of the monitored area in September of the current year and September of the previous year to see if it meets the standard. Therefore, the current vegetation index is derived from the image taken in September of the current year, and the historical vegetation index is derived from the image taken in the monitored area in September of the previous year. The vegetation restoration change is the difference between the current vegetation index and the historical vegetation index. The monitored area is 100 km². 2 The monitoring area is a lightly polluted, remote region. For a single segmented region, the difference between the reflectance value of a pixel in the near-infrared band and the reflectance value of a pixel in the red band is calculated. The sum of the reflectance values ​​of the pixel in the near-infrared band and the reflectance values ​​of the pixel in the red band is obtained. The ratio of the difference to the sum is calculated. The average value of each ratio in the segmented region is then calculated, and the average value is recorded as the vegetation index of the segmented region.

[0032] Please see Figure 2 The diagram shows a flowchart of the vegetation restoration monitoring method based on image analysis according to the present invention. The method described in this embodiment includes: Step S1: Select images of vegetation restoration within the monitoring area; The captured images are processed to improve monitoring accuracy; Step S2: Divide the monitoring area into regions and sequentially determine the processed captured images corresponding to each region. Step S3: Based on the processed captured images, determine the vegetation index of the corresponding divided regions in sequence; The vegetation restoration change in the corresponding divided area is determined based on the vegetation index. Step S4: Determine whether the vegetation restoration meets the standards based on the changes in vegetation restoration. When determining that vegetation restoration does not meet the standards, the reasons for non-compliance are based on the regional dispersion. Step S5: Based on the reason for the determination of the regional dispersion, adjust the cloud probability threshold based on the cloud coverage or adjust the image resolution based on the boundary pixel density; after the cloud probability threshold is increased, increase the cloud shadow buffer distance based on the solar altitude angle. Step S6: After the cloud shadow buffer distance or the image resolution adjustment is completed, determine whether the vegetation restoration meets the standard based on the vegetation restoration change amount. Step S7: When it is determined that the vegetation restoration does not meet the standard, adjust the aerosol optical thickness based on the vegetation restoration change. Step S8: After the aerosol optical thickness adjustment is completed, a determination is made on whether the vegetation restoration meets the standard based on the vegetation restoration change amount. Step S9: When it is determined that the vegetation restoration does not meet the standard, the reason for the failure to meet the standard is determined based on the standard deviation rate; and a corresponding notification is issued based on the standard deviation rate determination result.

[0033] Please see Figure 3 The diagram shows a flowchart illustrating the process of determining whether vegetation restoration meets the standards and the reasons for non-compliance according to the present invention. The process of determining whether vegetation restoration meets the standards based on the change in vegetation restoration amount in this embodiment of the invention includes: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change Z, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. In this embodiment, the preset vegetation restoration change Z = 0.05. When the vegetation restoration change is less than the preset vegetation restoration change Z, the reason why the vegetation restoration does not meet the standard is determined based on the regional dispersion. Specifically, in this embodiment, the preset vegetation restoration change amount is set to 0.05 according to a general standard. When the vegetation restoration change amount is less than the preset vegetation restoration change amount, the reasons for the distribution of the non-compliant areas are further determined.

[0034] Please continue reading. Figure 3 As shown, the process of determining the reasons why vegetation restoration does not meet the standards based on the regional dispersion in this embodiment of the invention includes: When the regional dispersion is less than the preset regional dispersion F, it is determined that the cloud probability threshold of the captured image does not meet the standard, and the cloud probability threshold is adjusted based on the cloud coverage. In this embodiment, the preset regional dispersion F = 0.3. When the regional dispersion is greater than or equal to the preset regional dispersion F, it is determined that the image resolution of the captured image does not meet the standard, and the image resolution is adjusted based on the boundary pixel density; Specifically, the distribution of the non-compliant regions is determined, with non-compliant regions recorded as 1 and compliant regions recorded as 0. Moran's I index of the monitored region is calculated, and the calculated value is recorded as the regional dispersion. Based on the actual test results, the preset regional dispersion in this embodiment is set to 0.3. If the regional dispersion is less than the preset regional dispersion, it indicates that the non-standard division areas are concentrated, and the cloud probability threshold of the monitoring area needs to be adjusted. The cloud coverage rate can be used to precisely adjust the cloud probability threshold. If the regional dispersion is greater than or equal to the preset regional dispersion, the non-standard division areas are scattered and not concentrated, and the image resolution should be adjusted accordingly based on the boundary pixel density.

[0035] Please continue reading. Figure 3 As shown, the process of increasing the cloud probability threshold based on the cloud coverage rate in this embodiment of the invention includes: When the cloud coverage rate is greater than the second preset cloud coverage rate R2, the cloud probability threshold is increased to 1.98 times the initial cloud probability threshold, wherein in this embodiment, the second preset cloud coverage rate R2 = 50%; When the cloud coverage rate is less than or equal to the second preset cloud coverage rate R2 and greater than the first preset cloud coverage rate R1, the cloud probability threshold is increased to 1.50 times the initial cloud probability threshold, wherein, in this embodiment, the first preset cloud coverage rate R1 = 35%; When the cloud coverage rate is less than or equal to the first preset cloud coverage rate R1, the cloud probability threshold is increased to 1.27 times the initial cloud probability threshold. Specifically, in this embodiment, the cloud coverage rate ranges from 10% to 60%, the initial cloud probability threshold is 22%, the cloud probability threshold can be increased to a maximum of 60%, and the increase factor of the cloud probability threshold ranges from 1 to 2.72. The higher the cloud coverage rate, the higher the cloud probability threshold. The values ​​of cloud coverage rate and cloud probability threshold are both derived from actual debugging results.

[0036] Please continue reading. Figure 3 As shown, the process of increasing the cloud shadow buffer distance based on the solar altitude angle after the cloud probability threshold is increased in this embodiment of the invention includes: When the solar altitude angle is greater than the second preset solar altitude angle A2, the cloud shadow buffer distance is increased to 1.33 times the initial cloud shadow buffer distance, wherein, in this embodiment, the second preset solar altitude angle A2 = 36.9°; When the solar altitude angle is less than or equal to the second preset solar altitude angle A2 and greater than the first preset solar altitude angle A1, the cloud shadow buffer distance is increased to 1.79 times the initial cloud shadow buffer distance. In this embodiment, the first preset solar altitude angle A1 = 17.8°. When the solar altitude angle is less than or equal to the first preset solar altitude angle A1, the cloud shadow buffer distance is increased to 2.27 times the initial cloud shadow buffer distance; Specifically, the solar altitude angle ranges from 0° to 50°, the initial cloud shadow buffer distance is 118m, the maximum cloud shadow buffer distance can be increased to 300m, and the increase factor of the cloud shadow buffer distance ranges from 1 to 2.54. The lower the solar altitude angle, the greater the increase factor of the cloud shadow buffer distance. The values ​​of the solar altitude angle and the cloud shadow buffer distance are obtained from the actual debugging results.

[0037] Please see Figure 4 The diagram shows a flowchart illustrating the reasons why vegetation restoration does not meet the standards according to the present invention. The process of increasing the image resolution based on the boundary pixel density in this embodiment of the invention includes: When the boundary pixel density is greater than the second preset boundary pixel density L2, the image resolution is increased to 0.73 times the initial image resolution, wherein in this embodiment, the second preset boundary pixel density L2 = 1.56; When the boundary pixel density is less than or equal to the second preset boundary pixel density L2 and greater than the first preset boundary pixel density L1, the image resolution is increased to 0.47 times the initial image resolution, wherein, in this embodiment, the first preset boundary pixel density L1 = 0.97; When the boundary pixel density is less than or equal to the first preset boundary pixel density L1, the image resolution is increased to 0.26 times the initial image resolution; Specifically, the boundary pixel density ranges from 0.5 pixels / m to 2 pixels / m, the initial image resolution is 3m / pixel, the image resolution can be increased to a maximum of 0.5m / pixel, and the increase factor of the image resolution ranges from 0.17 to 1. The lower the boundary pixel density, the greater the increase factor of the image resolution. The values ​​of boundary pixel density and image resolution are obtained from actual debugging results.

[0038] Please continue reading. Figure 4 As shown, the process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the cloud shadow buffer distance or the image resolution adjustment is completed in this embodiment of the invention includes: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change Z, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change Z, the aerosol optical thickness is adjusted based on the vegetation restoration change. Specifically, if the vegetation restoration change still does not meet the standard after the cloud probability threshold and the image resolution are adjusted, it is determined to be a radiation and atmospheric correction problem, and the aerosol optical thickness needs to be adjusted. The vegetation restoration change can be effectively adjusted based on the vegetation restoration change.

[0039] Please continue reading. Figure 4 As shown, the process of reducing the aerosol optical thickness based on the difference in vegetation restoration changes in this embodiment of the invention includes: When the difference in vegetation restoration change is greater than the second preset difference in vegetation restoration change ΔJ2, the aerosol optical thickness is reduced to 0.62 times the initial aerosol optical thickness. In this embodiment, the second preset difference in vegetation restoration change ΔJ2 = 0.76. When the difference in vegetation restoration change is less than or equal to the second preset difference in vegetation restoration change ΔJ2 and greater than the first preset difference in vegetation restoration change ΔJ1, the aerosol optical thickness is reduced to 0.78 times the initial aerosol optical thickness. In this embodiment, the first preset difference in vegetation restoration change ΔJ1 = 0.38. When the difference in vegetation restoration change is less than or equal to the first preset difference in vegetation restoration change △J1, the aerosol optical thickness is reduced to 0.91 times the initial aerosol optical thickness. Specifically, the difference in vegetation restoration change is the difference between the preset vegetation restoration change and the vegetation restoration change; The value of the vegetation restoration change difference ranges from 0 to 1.05, the initial aerosol optical thickness is 0.15, the aerosol optical thickness can be reduced to a minimum of 0.08, and the reduction factor of the aerosol optical thickness ranges from 0.53 to 1. The larger the vegetation restoration change difference, the smaller the vegetation restoration change, and the larger the reduction factor of the aerosol optical thickness. The values ​​of the vegetation restoration change difference and the aerosol optical thickness are obtained from the actual debugging results.

[0040] Please continue reading. Figure 4 As shown, the process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the aerosol optical thickness adjustment in this embodiment of the invention includes: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change Z, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change Z, the reason why the vegetation restoration does not meet the standard is determined based on the standard deviation rate. Specifically, if the vegetation recovery change still does not meet the standard after the aerosol optical thickness adjustment is completed, it is necessary to determine whether the captured image needs to be re-acquired, and the standard deviation rate can accurately determine this.

[0041] Please continue reading. Figure 4 As shown, the process of determining the reasons why vegetation restoration does not meet the standard based on the standard deviation rate in this embodiment of the invention includes: When the standard deviation rate is less than the preset standard deviation rate X, a notification is issued that the vegetation restoration in the monitoring area does not meet the standard. In this embodiment, the preset standard deviation rate X = 0.15. When the standard deviation rate is greater than or equal to the preset standard deviation rate X, a notification is issued that the image needs to be re-acquired and captured. Specifically, the vegetation index of the monitoring area in this embodiment for September of this year, September of the previous year, and September of the year before last is calculated, and the mean and standard deviation are calculated respectively. Then, the ratio of the standard deviation to the mean is calculated, and the obtained ratio is recorded as the standard deviation rate. The preset standard deviation rate in this embodiment is set to 0.15, which is an actual empirical threshold. If the standard deviation rate is greater than or equal to the preset standard deviation rate, it indicates that the captured image needs to be re-captured to avoid misjudgment of vegetation recovery due to the image not meeting the standard.

[0042] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0043] 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 vegetation restoration monitoring method based on image analysis, characterized in that, include: Select images of vegetation restoration within the monitoring area; The captured images are processed to improve monitoring accuracy; The monitoring area is divided into regions, and the processed captured images corresponding to each region are determined sequentially. Based on the processed captured images, the vegetation index of the corresponding divided regions is determined sequentially. The vegetation restoration change in the corresponding divided area is determined based on the vegetation index. The determination of whether vegetation restoration meets the standards is based on the changes in vegetation restoration. When determining that vegetation restoration does not meet the standards, the reasons for non-compliance are based on the regional dispersion. The reason for determining the regional dispersion is to adjust the cloud probability threshold based on cloud coverage or to adjust the image resolution based on boundary pixel density; after the cloud probability threshold is increased, the cloud shadow buffer distance is increased based on the solar altitude angle. After the cloud shadow buffer distance or the image resolution adjustment is completed, a determination is made on whether the vegetation restoration meets the standard based on the amount of vegetation restoration change; When vegetation restoration is deemed to be non-compliant with standards, the aerosol optical thickness is adjusted based on the change in vegetation restoration. After the aerosol optical thickness adjustment is completed, a determination is made as to whether the vegetation restoration meets the standard based on the change in vegetation restoration. When vegetation restoration is deemed not to meet the standards, the reason for non-compliance is determined based on the standard deviation rate; and a corresponding notification is issued based on the standard deviation rate determination result.

2. The vegetation restoration monitoring method based on image analysis according to claim 1, characterized in that, The process of determining whether vegetation restoration meets the standards based on the changes in vegetation restoration includes the following steps: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, the vegetation restoration in the monitoring area is determined to meet the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the reason why the vegetation restoration does not meet the standard is determined based on the regional dispersion.

3. The vegetation restoration monitoring method based on image analysis according to claim 2, characterized in that, The process of determining the reasons why vegetation restoration does not meet the standards based on the aforementioned regional dispersion includes: When the regional dispersion is less than the preset regional dispersion, it is determined that the cloud probability threshold of the captured image does not meet the standard, and the cloud probability threshold is adjusted based on the cloud coverage. When the regional dispersion is greater than or equal to the preset regional dispersion, it is determined that the image resolution of the captured image does not meet the standard, and the image resolution is adjusted based on the boundary pixel density.

4. The vegetation restoration monitoring method based on image analysis according to claim 3, characterized in that, The process of increasing the cloud probability threshold based on the cloud coverage rate includes: The cloud probability threshold is increased based on the cloud coverage rate, and the increase in the cloud probability threshold is proportional to the cloud coverage rate.

5. The vegetation restoration monitoring method based on image analysis according to claim 4, characterized in that, The process of increasing the cloud shadow buffer distance based on the solar altitude angle after the cloud probability threshold has been increased includes: The cloud shadow buffer distance is increased based on the solar altitude angle, and the increase in the cloud shadow buffer distance is inversely proportional to the solar altitude angle.

6. The vegetation restoration monitoring method based on image analysis according to claim 5, characterized in that, The process of increasing the image resolution based on the boundary pixel density includes: The image resolution is increased based on the boundary pixel density, and the increase in image resolution is inversely proportional to the boundary pixel density.

7. The vegetation restoration monitoring method based on image analysis according to claim 6, characterized in that, The process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the cloud shadow buffer distance or the image resolution adjustment is completed includes the following steps: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the aerosol optical thickness is adjusted based on the vegetation restoration change.

8. The vegetation restoration monitoring method based on image analysis according to claim 7, characterized in that, The process of reducing the aerosol optical thickness based on the difference in vegetation restoration changes includes: The aerosol optical thickness is reduced based on the difference in vegetation restoration change, and the reduction in aerosol optical thickness is proportional to the difference in vegetation restoration change.

9. The vegetation restoration monitoring method based on image analysis according to claim 8, characterized in that, The process of determining whether vegetation restoration meets the standard based on the change in vegetation restoration after the aerosol optical thickness adjustment is completed includes the following steps: When the vegetation restoration change is greater than or equal to the preset vegetation restoration change, it is determined that the vegetation restoration in the monitoring area meets the standard, the periodic determination of vegetation restoration is completed, and the vegetation restoration for the next period is determined. When the vegetation restoration change is less than the preset vegetation restoration change, the reason why the vegetation restoration does not meet the standard is determined based on the standard deviation rate.

10. The vegetation restoration monitoring method based on image analysis according to claim 9, characterized in that, The process of determining the reasons why vegetation restoration does not meet the standards based on the aforementioned standard deviation rate includes: When the standard deviation rate is less than the preset standard deviation rate, a notification is issued that the vegetation restoration in the monitoring area does not meet the standard. When the standard deviation rate is greater than or equal to the preset standard deviation rate, a notification is issued that the image needs to be re-acquired and captured.

Images