Vegetation growth state monitoring method and system based on spatial variability of vegetation coverage

Abstract

The application provides a vegetation growth state monitoring method and system based on vegetation coverage spatial variability, comprising: acquiring multi-temporal normalized vegetation index data and pixel position information of an observation area; calculating multi-temporal vegetation coverage of the observation area by using the normalized vegetation index; calculating vegetation coverage spatial variability by using the vegetation coverage data; performing pixel matching and overlapping area extraction on a multi-temporal vegetation coverage spatial variability distribution image of the observation area; acquiring a multi-temporal vegetation coverage spatial variability statistical distribution curve of the overlapping observation area, and then determining vegetation growth state changes of the observation area. The application can be used in the field of satellite remote sensing monitoring of surface vegetation growth state, and is an effective and important technical means for monitoring regional ecosystem environmental changes.

Description

Technical Field

[0001] This invention relates to the technical field of satellite remote sensing monitoring of the growth status of surface vegetation, specifically, to a method and system for monitoring vegetation growth status based on the spatial variability of vegetation cover. Background Technology

[0002] Vegetation is defined as the vegetation community covering a specific area of ​​the Earth's surface. It is a crucial component of terrestrial ecosystems and plays a vital role as an indicator of climate change within the context of global climate change. Vegetation cover refers to the percentage of the total area covered by vegetation in a given region, primarily reflecting the distribution of vegetation on the ground surface. Influenced by factors such as regional climate and human activities, it is a key indicator for measuring changes in the regional ecological environment. Therefore, studying the spatiotemporal trends of vegetation cover is of great significance for regional ecological environment monitoring, assessment, protection, restoration, and maintaining the relationship between ecological protection and socio-economic development. Satellite remote sensing observations offer advantages such as wide observation range, high reliability, and abundant data products, providing suitable data sources for analyzing vegetation growth status and facilitating the monitoring of regional vegetation cover distribution characteristics and changes.

[0003] Current methods for monitoring vegetation growth status mainly utilize long-term satellite remote sensing data to analyze the spatial distribution characteristics and trends of vegetation cover in a certain observation area. The research objects are mostly focused on the spatiotemporal variation trends of statistical features on long-term pixels, but little attention is paid to the differences in vegetation growth status between the current observation pixel and neighboring pixels, making it impossible to accurately obtain the changing trends of different vegetation cover boundary areas within the observation area.

[0004] Therefore, it is necessary to propose a method and system for monitoring vegetation growth status based on the spatial variability of vegetation cover, so as to effectively monitor the vegetation growth status, especially for monitoring the status changes of different vegetation cover boundary areas. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and system for monitoring vegetation growth status based on the spatial variability of vegetation cover.

[0006] According to the present invention, a method for monitoring vegetation growth status based on spatial variability of vegetation cover is provided, the method comprising the following steps:

[0007] Step S1: Obtain multi-temporal normalized vegetation index data and pixel location information for the observation area;

[0008] Step S2: Calculate the multi-temporal vegetation coverage of the observation area using the normalized vegetation index data;

[0009] Step S3: Calculate the spatial variability of vegetation cover using the vegetation cover data;

[0010] Step S4: Perform pixel matching and overlapping region extraction on the multi-temporal spatial variability distribution image of vegetation cover in the observation area;

[0011] Step S5: Using the spatial variability of vegetation cover in the multi-temporal phases of the overlapping observation area, obtain the statistical distribution curve of the spatial variability of vegetation cover, compare the characteristics of the statistical distribution curve, and then determine the changes in vegetation growth status.

[0012] Preferably, the multi-temporal normalized vegetation index data in step S1 is remote sensing data at the current time and before that time, and the observation areas of the remote sensing data have overlapping parts, specifically including normalized vegetation index data and pixel location information.

[0013] Preferably, in step S2, the vegetation cover is calculated using the multi-temporal normalized vegetation index of the observation area based on a pixel-based binary model, and the calculation formula is as follows:

[0014]

[0015] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers were selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

[0016] Preferably, in step S3, the multi-temporal vegetation cover data is used in conjunction with the vegetation growth status of neighboring pixels to define the spatial variability of vegetation cover as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of the eight neighboring pixels to the vegetation cover of the current pixel, thereby obtaining the spatial variability of vegetation cover in the observation area.

[0017] Preferably, step S4 includes the following steps:

[0018] Step S4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching.

[0019] Step S4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, obtain overlapping images of the same observation area, compare the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, select the overlapping part of the image, remove the rest, and obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

[0020] Preferably, step S5 includes the following steps:

[0021] Step S5.1: Using the spatial variability of vegetation cover in the overlapping observation area, statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area to obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area.

[0022] Step S5.2: Using the multi-temporal vegetation cover spatial variability statistical distribution curve, compare the differences between the statistical distribution curves to determine the changes in vegetation growth status.

[0023] The present invention also provides a vegetation growth status monitoring system based on the spatial variability of vegetation cover, the system comprising the following modules:

[0024] Module M1: Acquires multi-temporal normalized vegetation index data and pixel location information of the observation area;

[0025] Module M2: Calculates the multi-temporal vegetation coverage of the observation area using the normalized vegetation index data;

[0026] Module M3: Calculates the spatial variability of vegetation cover using the vegetation cover data;

[0027] Module M4: Performs pixel matching and overlapping region extraction on multi-temporal spatial variability distribution images of vegetation cover in the observation area;

[0028] Module M5: Utilizes the spatial variability of vegetation cover in the overlapping observation area to obtain the statistical distribution curve of the spatial variability of vegetation cover, compares the characteristics of the statistical distribution curve, and then determines the changes in vegetation growth status.

[0029] Preferably, the multi-temporal normalized vegetation index data in module M1 consists of remote sensing data from the current time and previous times, and the observation areas of the remote sensing data have overlapping parts, specifically including normalized vegetation index data and pixel location information;

[0030] In module M2, the vegetation cover is calculated using the multi-temporal normalized vegetation index of the observation area and based on a pixel-based binary model. The calculation formula is as follows:

[0031]

[0032] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers were selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation;

[0033] In module M3, the multi-temporal vegetation cover data is used in conjunction with the vegetation growth status of neighboring pixels. The spatial variability of vegetation cover is defined as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of its eight neighboring pixels to the vegetation cover of the current pixel, thus obtaining the spatial variability of vegetation cover in the observation area.

[0034] Preferably, module M4 includes the following modules:

[0035] Module M4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching.

[0036] Module M4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, obtain overlapping images of the same observation area, compare the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, select the overlapping part of the image, remove the rest, and obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

[0037] Preferably, module M5 includes the following modules:

[0038] Module M5.1: Utilize the spatial variability of vegetation cover in the overlapping observation area to statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area, and obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area.

[0039] Module M5.2: By using the multi-temporal vegetation cover spatial variability statistical distribution curve, the differences between the statistical distribution curves are compared, thereby determining the changes in vegetation growth status.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. This invention monitors vegetation growth status based on the spatial variability of vegetation cover. Existing methods for monitoring vegetation growth status mostly analyze the spatial distribution and changing trend of the average state characteristics of vegetation cover in the observation area. However, the average state characteristics often mask the differences in vegetation growth status between pixels in the observation area and cannot obtain the changing trend of different vegetation cover boundary areas in the observation area. This invention fills the gap in the existing technology.

[0042] 2. This invention can be used in the technical field of satellite remote sensing monitoring of the growth status of surface vegetation, and is an effective and important technical means for monitoring changes in the regional ecosystem environment;

[0043] 3. The vegetation growth status monitoring method based on the spatial variability of vegetation cover in this invention can effectively monitor changes in vegetation growth status by comparing the characteristics of the statistical distribution curve of the spatial variability of vegetation cover within the same observation range, especially for changes in vegetation growth status in different vegetation cover boundary areas. At the same time, pixel matching and overlapping region extraction are performed on multi-temporal remote sensing data, making the comparison results of vegetation growth status based on pixels more accurate, and the removal of non-overlapping regions also improves the efficiency of algorithm implementation. Attached Figure Description

[0044] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is a schematic diagram of the working method of the present invention;

[0046] Figure 2 These are two normalized vegetation index distribution maps observed by satellite in this invention.

[0047] Figure 3 These are two vegetation cover distribution maps observed by satellite in this invention;

[0048] Figure 4 These are two spatial variability distribution maps of vegetation cover observed by satellite in this invention.

[0049] Figure 5 This is a schematic diagram of the pixel matching and overlapping region extraction results of two remote sensing images observed by satellite in this invention;

[0050] Figure 6 These are the statistical distribution curves of spatial variability of vegetation cover in two scenes observed by satellite in this invention. Detailed Implementation

[0051] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0052] Example 1:

[0053] Reference Figure 1 According to the present invention, a method for monitoring vegetation growth status based on spatial variability of vegetation cover is provided, the method comprising the following steps:

[0054] Step S1: Obtain multi-temporal normalized vegetation index data and pixel location information of the observation area; the multi-temporal normalized vegetation index data are remote sensing data at the current time and before this time, and the observation areas of the remote sensing data have overlapping parts, specifically including normalized vegetation index data and pixel location information.

[0055] Step S2: Using the normalized vegetation index data, calculate the multi-temporal vegetation cover of the observation area; using the multi-temporal normalized vegetation index of the observation area, calculate the vegetation cover based on the pixel binary model, using the following formula:

[0056]

[0057] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers were selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

[0058] Step S3: Calculate the spatial variability of vegetation cover using the vegetation cover data; using the multi-temporal vegetation cover data and combining the vegetation growth status of neighboring pixels, define the spatial variability of vegetation cover as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of the 8 neighboring pixels to the vegetation cover of the current pixel, and obtain the spatial variability of vegetation cover in the observation area.

[0059] Step S4: Perform pixel matching and overlapping region extraction on the multi-temporal spatial variability distribution image of vegetation cover in the observation area;

[0060] Step S4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching.

[0061] Step S4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, obtain overlapping images of the same observation area, compare the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, select the overlapping part of the image, remove the rest, and obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

[0062] Step S5: Using the spatial variability of vegetation cover in the multi-temporal phases of the overlapping observation area, obtain the statistical distribution curve of the spatial variability of vegetation cover, compare the characteristics of the statistical distribution curve, and then determine the changes in vegetation growth status.

[0063] Step S5.1: Using the spatial variability of vegetation cover in the overlapping observation area, statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area to obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area.

[0064] Step S5.2: Using the multi-temporal vegetation cover spatial variability statistical distribution curve, compare the differences between the statistical distribution curves to determine the changes in vegetation growth status.

[0065] The present invention also provides a vegetation growth status monitoring system based on the spatial variability of vegetation cover. The vegetation growth status monitoring system based on the spatial variability of vegetation cover can be implemented by executing the process steps of the vegetation growth status monitoring method based on the spatial variability of vegetation cover. That is, those skilled in the art can understand the vegetation growth status monitoring method based on the spatial variability of vegetation cover as a preferred embodiment of the vegetation growth status monitoring system based on the spatial variability of vegetation cover.

[0066] Example 2:

[0067] The present invention also provides a vegetation growth status monitoring system based on the spatial variability of vegetation cover, the system comprising the following modules:

[0068] Module M1: Acquires multi-temporal normalized vegetation index data and pixel location information of the observation area; the multi-temporal normalized vegetation index data are remote sensing data at the current time and before this time, and the observation areas of the remote sensing data have overlapping parts, specifically including normalized vegetation index data and pixel location information;

[0069] Module M2: Using the normalized vegetation index data, calculate the multi-temporal vegetation cover of the observation area; using the multi-temporal normalized vegetation index of the observation area, calculate the vegetation cover based on the pixel binary model, using the following formula:

[0070]

[0071] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers were selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation;

[0072] Module M3: Calculates the spatial variability of vegetation cover using the vegetation cover data; using the multi-temporal vegetation cover data and combining the vegetation growth status of neighboring pixels, the spatial variability of vegetation cover is defined as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of the eight neighboring pixels to the vegetation cover of the current pixel, thus obtaining the spatial variability of vegetation cover in the observation area.

[0073] Module M4: Performs pixel matching and overlapping region extraction on multi-temporal spatial variability distribution images of vegetation cover in the observation area;

[0074] Module M4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching.

[0075] Module M4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, obtain overlapping images of the same observation area, compare the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, select the overlapping part of the image, remove the rest, and obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

[0076] Module M5: Utilize the spatial variability of vegetation cover in the multi-temporal region to obtain the statistical distribution curve of the spatial variability of vegetation cover, compare the characteristics of the statistical distribution curve, and then determine the changes in vegetation growth status.

[0077] Module M5.1: Utilize the spatial variability of vegetation cover in the overlapping observation area to statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area, and obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area.

[0078] Module M5.2: By using the multi-temporal vegetation cover spatial variability statistical distribution curve, the differences between the statistical distribution curves are compared, thereby determining the changes in vegetation growth status.

[0079] Example 3:

[0080] This invention acquires multi-temporal normalized vegetation index (NVR) data and pixel location information of the observation area based on satellite observations; calculates multi-temporal vegetation cover in the observation area using the NVR; calculates the spatial variability of vegetation cover using the vegetation cover data; performs pixel matching and overlapping region extraction on the multi-temporal vegetation cover spatial variability distribution image of the observation area; obtains the statistical distribution curve of multi-temporal vegetation cover spatial variability in the overlapping observation area, and thus determines the changes in vegetation growth status in the observation area.

[0081] According to the present invention, a method for monitoring vegetation growth status based on spatial variability of vegetation cover is provided, such as... Figure 1 As shown, it includes the following steps:

[0082] Step S1: Obtain multi-temporal normalized vegetation index data and pixel location information of the observation area. The multi-temporal data consists of remote sensing data at the current time and data prior to this time. The observation areas of the remote sensing data have overlapping parts.

[0083] Step S2: Calculate the multi-temporal vegetation cover of the observation area using the normalized vegetation index (NVI) data. Specifically, the vegetation cover is calculated using the multi-temporal NVI of the observation area based on a pixel-based binary model, using the following formula:

[0084]

[0085] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI representing vegetation endmembers is generally selected from the 5% and 95% values ​​of the cumulative NDVI frequency in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

[0086] Step S3: Calculate the spatial variability of vegetation cover using the vegetation cover data. Combining the vegetation growth status of neighboring pixels, the spatial variability of vegetation cover is defined as the ratio of the average difference in vegetation cover between the current pixel and its eight neighboring pixels to the current pixel's vegetation cover. The spatial variability of vegetation cover in the observation area is then calculated. Specifically, a pixel location is marked as (i, j), where i represents the row number in the image and j represents the column number. The eight neighboring points around the current pixel, denoted as A1 to A8, have pixel locations in the remote sensing image as (i-1, j), (i+1, j), (i, j-1), (i, j+1), (i-1, j-1), (i-1, j+1), (i+1, j-1), (i+1, j+1). The spatial variability of vegetation cover for the current pixel can be calculated using the following formula:

[0087]

[0088] Among them, IF i,j FVC represents the spatial variability of pixel vegetation cover. i,j This represents the vegetation cover of a pixel, where n is 8 and m ranges from 1 to 8. It indicates the processing of the current pixel's 8 neighboring pixels, FVC. m This indicates the vegetation cover of neighboring pixels.

[0089] Step S4: Perform pixel matching and overlapping region extraction on the multi-temporal spatial variability distribution image of vegetation cover in the observation area. Step S4 includes the following sub-steps:

[0090] Step S4.1: For the multi-temporal vegetation cover spatial variability distribution image of the observation area, using the pixel position in the current time's vegetation cover spatial variability distribution image as the standard, mark a certain pixel position as (i, j), where i represents the row number in the image and j represents the column number. Use the inverse distance weighting method to match the pixel in the previous time's vegetation cover spatial variability distribution image to this position, obtaining a multi-temporal vegetation cover spatial variability distribution image with pixel matching. Specifically, for the four neighboring points distributed around the current time's pixel position in the previous time's vegetation cover spatial variability distribution image, denoted as A1, A2, A3, and A4, the distances between these points and the standard pixel are marked as l1, l2, l3, and l4. The vegetation cover spatial variability at the matched pixel in the previous time can be calculated using the following formula:

[0091]

[0092] Among them, w n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4, indicating that the values ​​of the four pixels closest to the standard pixel are selected for calculation. nThis indicates the distance from the standard pixel.

[0093]

[0094] Among them, IF i,j w represents the spatial variability of vegetation cover in a pixel in the spatial variability distribution image of vegetation cover at the previous time that matches the pixel in the spatial variability distribution image of vegetation cover at the current time. n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4. IF n This represents the spatial variability of vegetation cover in the pixel closest to the standard pixel.

[0095] Step S4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, overlapping images of the same observation area can be obtained. By comparing the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, the overlapping part of the image is selected, and the rest is removed to obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

[0096] Step S5: Using the multi-temporal spatial variability of vegetation cover in the overlapping observation area, obtain the statistical distribution curve of the spatial variability of vegetation cover, compare the characteristics of the statistical distribution curves, and then determine the changes in vegetation growth status. Step S5 includes the following sub-steps:

[0097] Step S5.1: Using the spatial variability of vegetation cover in the overlapping observation area, statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area to obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area.

[0098] Step S5.2: Using the multi-temporal vegetation cover spatial variability statistical distribution curves, compare the differences between the statistical distribution curves. These differences include the probability changes in the distribution of vegetation cover spatial variability across different intervals. According to the definition of vegetation cover spatial variability, a negative value indicates that the vegetation cover of the pixel is worse than the surrounding area; a positive value indicates that the vegetation cover of the pixel is better than the surrounding area. The larger the absolute value, the more significant the difference in vegetation cover status between the current pixel and its neighboring area, indicating a greater difference in vegetation growth status, and vice versa. Therefore, the probability changes in vegetation cover spatial variability across different intervals can illustrate the differences in vegetation growth status within the observation area, especially the changes in vegetation growth status at different vegetation cover boundary areas, thereby determining the changes in vegetation growth status.

[0099] Furthermore, in conjunction with the appendix Figures 1 to 6 The vegetation growth status monitoring method based on the spatial variability of vegetation cover of the present invention is described in detail below:

[0100] Multi-temporal normalized vegetation index (NVR) data and pixel location information of the observation area were acquired through satellite observation. The multi-temporal data consisted of remote sensing data from the current time and previous times, with overlapping observation areas. The NVR distribution of the two satellite observation scenes is shown below. Figure 2 As shown.

[0101] Using the normalized vegetation index data, the vegetation cover of the observation area in multiple time phases was calculated. Based on the pixel-based binary model, the calculation formula is as follows:

[0102]

[0103] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI representing vegetation endmembers is generally selected from the 5% and 95% values ​​of the cumulative NDVI frequency in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula for calculation. The vegetation cover distribution of the two scenes observed by the satellite is as follows: Figure 3 As shown.

[0104] Using the vegetation cover data, the spatial variability of vegetation cover is calculated. Combining the vegetation growth status of neighboring pixels, the spatial variability of vegetation cover is defined as the ratio of the average difference in vegetation cover between the current pixel and its eight neighboring pixels to the current pixel's vegetation cover. This allows for the calculation of the spatial variability of vegetation cover in the observed area. Specifically, a pixel location is marked as (i, j), where i represents the row number and j represents the column number in the image. The eight neighboring points around the current pixel, denoted as A1 to A8, have pixel locations in the remote sensing image of (i-1, j), (i+1, j), (i, j-1), (i, j+1), (i-1, j-1), (i-1, j+1), (i+1, j-1), (i+1, j+1). The spatial variability of vegetation cover for the current pixel can then be calculated using the following formula:

[0105]

[0106] Among them, IF i,j FVC represents the spatial variability of pixel vegetation cover. i,j This represents the vegetation cover of a pixel, where n is 8 and m ranges from 1 to 8. It indicates the processing of the current pixel's 8 neighboring pixels, FVC. m This represents the vegetation cover of neighboring pixels. The spatial variability distribution of vegetation cover in two satellite-observed scenes is shown below. Figure 4 As shown.

[0107] For the multi-temporal spatial variability distribution image of vegetation cover in the observation area, using the pixel position in the current time's spatial variability distribution image of vegetation cover as the standard, a certain pixel position is marked as (i, j), where i represents the row number in the image and j represents the column number. The inverse distance weighting method is used to match the pixel in the previous time's spatial variability distribution image of vegetation cover to this position, resulting in a multi-temporal spatial variability distribution image of vegetation cover with pixel matching. Specifically, for the four neighboring points distributed around the current time's pixel position in the previous time's spatial variability distribution image of vegetation cover, denoted as A1, A2, A3, and A4, the distances between these points and the standard pixel are marked as l1, l2, l3, and l4. The spatial variability of vegetation cover at the matched pixel in the previous time can be calculated using the following formula:

[0108]

[0109] Among them, w n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4, indicating that the values ​​of the four pixels closest to the standard pixel are selected for calculation. n This indicates the distance from the standard pixel.

[0110]

[0111] Among them, IF i,j w represents the spatial variability of vegetation cover in a pixel in the spatial variability distribution image of vegetation cover at the previous time that matches the pixel in the spatial variability distribution image of vegetation cover at the current time. n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4. IF n This represents the spatial variability of vegetation cover in the pixel closest to the standard pixel.

[0112] By utilizing the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, overlapping images of the same observation area can be obtained. By comparing the edge pixel positions of the multi-temporal vegetation cover spatial variability distribution images, selecting the overlapping portion, and removing the remaining portion, a multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area is obtained. A schematic diagram of the pixel matching and overlapping area extraction results of two remote sensing images observed by satellite is shown below. Figure 5 As shown.

[0113] By utilizing the spatial variability of vegetation cover in the overlapping observation area across multiple time periods, the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area are statistically analyzed to obtain the statistical distribution curve of the spatial variability of vegetation cover in multiple time periods. The statistical distribution curves of the spatial variability of vegetation cover in the two satellite observation scenes are shown below. Figure 6 As shown.

[0114] By utilizing the multi-temporal vegetation cover spatial variability statistical distribution curves, the differences between the statistical distribution curves are compared. These differences include the probability changes in the distribution of vegetation cover spatial variability across different intervals. According to the definition of vegetation cover spatial variability, a negative value indicates that the vegetation cover of that pixel is worse than the surrounding area; a positive value indicates that the vegetation cover of that pixel is better than the surrounding area. The larger the absolute value, the more significant the difference in vegetation cover status between the current pixel and its neighborhood, indicating a greater difference in vegetation growth status, and vice versa. Therefore, the probability changes in vegetation cover spatial variability across different intervals can illustrate the differences in vegetation growth status within the observation area, especially the changes in vegetation growth status at the boundaries of different vegetation covers, thereby determining the changes in vegetation growth status. Figure 6 The distribution frequency of the spatial variability of vegetation cover in the observation area at the current time is higher in the positive region than before this time, indicating that the differences between different vegetation cover boundary areas in the observation area are increasing.

[0115] The present invention also provides a vegetation growth status monitoring system based on the spatial variability of vegetation cover. The vegetation growth status monitoring system based on the spatial variability of vegetation cover can be implemented by executing the process steps of the vegetation growth status monitoring method based on the spatial variability of vegetation cover. That is, those skilled in the art can understand the vegetation growth status monitoring method based on the spatial variability of vegetation cover as a preferred embodiment of the vegetation growth status monitoring system based on the spatial variability of vegetation cover.

[0116] A vegetation growth status monitoring system based on spatial variability of vegetation cover, provided by the present invention, includes:

[0117] Module M1: Acquires multi-temporal normalized vegetation index data and pixel location information of the observation area. The multi-temporal data consists of remote sensing data at the current time and data prior to that time, and the observation areas of the remote sensing data have overlapping parts.

[0118] Module M2: Using multi-temporal normalized vegetation index data, vegetation cover is calculated based on a pixel-based binary model. The calculation formula is as follows:

[0119]

[0120] Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. vegThe NDVI representing vegetation endmembers is generally selected from the 5% and 95% values ​​of the cumulative NDVI frequency in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

[0121] Module M3: Utilizes the vegetation cover data to calculate the spatial variability of vegetation cover. Module M3, combining the vegetation growth status of neighboring pixels, defines the spatial variability of vegetation cover as the ratio of the average difference between the vegetation cover of the current pixel and its eight neighboring pixels to the vegetation cover of the current pixel, thus calculating the spatial variability of vegetation cover in the observation area. Specifically, a pixel position is marked as (i, j), where i represents the row number and j represents the column number in the image. The eight neighboring points around the current pixel, denoted as A1 to A8, have pixel positions in the remote sensing image as (i-1, j), (i+1, j), (i, j-1), (i, j+1), (i-1, j-1), (i-1, j+1), (i+1, j-1), (i+1, j+1). The spatial variability of vegetation cover for the current pixel can be calculated using the following formula:

[0122]

[0123] Among them, IF i,j FVC represents the spatial variability of pixel vegetation cover. i,j This represents the vegetation cover of a pixel, where n is 8 and m ranges from 1 to 8. It indicates the processing of the current pixel's 8 neighboring pixels, FVC. m This indicates the vegetation cover of neighboring pixels.

[0124] Module M4: Performs pixel matching and overlapping region extraction on the multi-temporal vegetation cover spatial variability distribution image of the observation area. Module M4 includes the following sub-steps: Module M4.1: For the multi-temporal vegetation cover spatial variability distribution image of the observation area, using the pixel position in the current time's vegetation cover spatial variability distribution image as the standard, a certain pixel position is marked as (i, j), where i represents the row number in the image and j represents the column number. Using the inverse distance weighting method, the pixel in the previous time's vegetation cover spatial variability distribution image is matched to this position, resulting in a pixel-matched multi-temporal vegetation cover spatial variability distribution image. Specifically, for the previous time's vegetation cover spatial variability distribution image, the four neighboring points distributed around the current time's pixel position are denoted as A1, A2, A3, and A4. The distances between these points and the standard pixel are marked as l1, l2, l3, and l4. The vegetation cover spatial variability at the matched pixel in the previous time can be calculated using the following formula:

[0125]

[0126] Among them, w n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4, indicating that the values ​​of the four pixels closest to the standard pixel are selected for calculation. n This indicates the distance from the standard pixel.

[0127]

[0128] Among them, IF i,j w represents the spatial variability of vegetation cover in a pixel in the spatial variability distribution image of vegetation cover at the previous time that matches the pixel in the spatial variability distribution image of vegetation cover at the current time. n This represents the inverse distance weighting factor of the four pixels surrounding the standard pixel, where n ranges from 1 to 4. IF n This represents the spatial variability of vegetation cover in the pixel closest to the standard pixel. Module M4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, overlapping images of the same observation area can be obtained. By comparing the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, selecting the overlapping part of the image, and removing the rest, a multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area is obtained.

[0129] Module M5: Utilizing the multi-temporal spatial variability of vegetation cover in the overlapping observation area, a statistical distribution curve of the spatial variability of vegetation cover is obtained. The characteristics of the statistical distribution curves are compared to determine the changes in vegetation growth status. Module M5 includes the following sub-steps: Module M5.1: Utilizing the multi-temporal spatial variability of vegetation cover in the overlapping observation area, the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area are statistically analyzed to obtain a statistical distribution curve of the spatial variability of vegetation cover in the multi-temporal area. Module M5.2: Using the statistical distribution curve of the spatial variability of vegetation cover in the multi-temporal area, the differences between the statistical distribution curves are compared. The differences between the statistical distribution curves include the probability changes of the spatial variability of vegetation cover distribution in different intervals. According to the definition of the spatial variability of vegetation cover, if the value is negative, it indicates that the vegetation cover of the pixel is worse than that of the surrounding area; if the value is positive, it indicates that the vegetation cover of the pixel is better than that of the surrounding area. The larger the absolute value of this value, the more significant the difference between the current pixel's vegetation cover status and its neighboring area, indicating a greater difference in vegetation growth status, and vice versa. Therefore, the probability variation of the spatial variability of vegetation cover in different intervals can illustrate the differences in vegetation growth status within the observation area, especially the changes in vegetation growth status in boundary areas of different vegetation covers, thereby determining the changes in vegetation growth status.

[0130] According to the present invention, a computer-readable storage medium storing a computer program is provided, wherein when the computer program is executed by a processor, the steps of the method for monitoring vegetation growth status based on spatial variability of vegetation cover are implemented.

[0131] An electronic device according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the computer program, when executed by the processor, implements the steps of the vegetation growth status monitoring method based on the spatial variability of vegetation cover.

[0132] Those skilled in the art can understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0133] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0134] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for monitoring vegetation growth status based on spatial variability of vegetation cover, characterized in that, The method includes the following steps: Step S1: Obtain multi-temporal normalized vegetation index data and pixel location information for the observation area; Step S2: Calculate the multi-temporal vegetation coverage of the observation area using the normalized vegetation index data; Step S3: Calculate the spatial variability of vegetation cover using the vegetation cover data; Step S4: Perform pixel matching and overlapping region extraction on the multi-temporal spatial variability distribution image of vegetation cover in the observation area; Step S5: Using the spatial variability of vegetation cover in multiple time phases of the overlapping observation area, obtain the statistical distribution curve of the spatial variability of vegetation cover, compare the characteristics of the statistical distribution curve, and then determine the changes in vegetation growth status. In step S3, the multi-temporal vegetation cover data is used in conjunction with the vegetation growth status of neighboring pixels to define the spatial variability of vegetation cover as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of its eight neighboring pixels to the vegetation cover of the current pixel, thereby obtaining the spatial variability of vegetation cover in the observation area.

2. The method for monitoring vegetation growth status based on spatial variability of vegetation cover according to claim 1, characterized in that, The multi-temporal normalized vegetation index data in step S1 consists of remote sensing data from the current time and previous times. The observation areas of the remote sensing data have overlapping parts, specifically including normalized vegetation index data and pixel location information.

3. The method for monitoring vegetation growth status based on spatial variability of vegetation cover according to claim 1, characterized in that, In step S2, the vegetation cover is calculated using the multi-temporal normalized vegetation index of the observation area and based on the pixel binary model. The calculation formula is as follows: Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers are selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

4. The method for monitoring vegetation growth status based on spatial variability of vegetation cover according to claim 1, characterized in that, Step S4 includes the following steps: Step S4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching. Step S4.2: Using the multi-temporal pixel-matched spatial variability distribution image of vegetation cover, obtain overlapping images of the same observation area, compare the edge pixel positions of the multi-temporal spatial variability distribution images of vegetation cover, select the overlapping part of the image, remove the rest, and obtain the multi-temporal pixel-matched spatial variability distribution image of vegetation cover in the overlapping area.

5. The method for monitoring vegetation growth status based on spatial variability of vegetation cover according to claim 1, characterized in that, Step S5 includes the following steps: Step S5.1: Using the spatial variability of vegetation cover in the overlapping observation area, statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area to obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area. Step S5.2: Using the multi-temporal vegetation cover spatial variability statistical distribution curve, compare the differences between the statistical distribution curves to determine the changes in vegetation growth status.

6. A vegetation growth status monitoring system based on spatial variability of vegetation cover, characterized in that, The system includes the following modules: Module M1: Acquires multi-temporal normalized vegetation index data and pixel location information of the observation area; Module M2: Calculates the multi-temporal vegetation coverage of the observation area using the normalized vegetation index data; Module M3: Calculates the spatial variability of vegetation cover using the vegetation cover data; Module M4: Performs pixel matching and overlapping region extraction on multi-temporal spatial variability distribution images of vegetation cover in the observation area; Module M5: Utilizes the spatial variability of vegetation cover in multiple time phases of overlapping observation areas to obtain the statistical distribution curve of the spatial variability of vegetation cover, compares the characteristics of the statistical distribution curves, and then determines the changes in vegetation growth status. In module M3, the multi-temporal vegetation cover data is used in conjunction with the vegetation growth status of neighboring pixels. The spatial variability of vegetation cover is defined as the ratio of the average difference between the vegetation cover of the current pixel and the vegetation cover of its eight neighboring pixels to the vegetation cover of the current pixel, thus obtaining the spatial variability of vegetation cover in the observation area.

7. The vegetation growth status monitoring system based on spatial variability of vegetation cover according to claim 6, characterized in that, The multi-temporal normalized vegetation index data in module M1 consists of remote sensing data from the current time and previous times. The observation areas of the remote sensing data have overlapping parts, and specifically include normalized vegetation index data and pixel location information. In module M2, the vegetation cover is calculated using the multi-temporal normalized vegetation index of the observation area and based on a pixel-based binary model. The calculation formula is as follows: Where FVC represents vegetation cover, and NDVI represents the normalized vegetation index of pixels within the observation area, NDVI soil NDVI represents the non-vegetation endmember. veg The NDVI values ​​representing vegetation endmembers are selected from the 5% and 95% cumulative NDVI frequencies in the observation area as the corresponding NDVI values. soil and NDVI veg Substitute the values ​​into the formula to perform the calculation.

8. The vegetation growth status monitoring system based on spatial variability of vegetation cover according to claim 6, characterized in that, Module M4 includes the following modules: Module M4.1: For the spatial variability distribution image of multi-temporal vegetation cover in the observation area, using the pixel position in the image at the current time as the standard, the inverse distance weighting method is used to match the pixel in the previous time image to this position, so as to obtain the multi-temporal vegetation cover spatial variability distribution image with pixel matching. Module M4.2: Utilizes multi-temporal pixel matching of vegetation cover spatial variability distribution images to obtain overlapping images of the same observation area, compares the edge pixel positions of the multi-temporal vegetation cover spatial variability distribution images, selects the overlapping part of the image, removes the rest, and obtains multi-temporal pixel matching of vegetation cover spatial variability distribution images of the overlapping area.

9. The vegetation growth status monitoring system based on spatial variability of vegetation cover according to claim 6, characterized in that, Module M5 includes the following modules: Module M5.1: Utilize the spatial variability of vegetation cover in the overlapping observation area to statistically analyze the probability density distribution characteristics of the spatial variability of vegetation cover in all pixels within the area, and obtain the statistical distribution curve of the spatial variability of vegetation cover in the overlapping observation area. Module M5.2: By using the multi-temporal vegetation cover spatial variability statistical distribution curve, the differences between the statistical distribution curves are compared, thereby determining the changes in vegetation growth status.

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