Method for automatically monitoring dynamic change of algal bloom based on remote sensing image

Abstract

A method for automatically monitoring dynamic changes of water blooms based on remote sensing images comprises the following steps: step 1, acquiring remote sensing reflectivity data of a multi-source remote sensing satellite in a screened time sequence range, calculating a spectral index, automatically determining an extraction threshold through pixel gradient statistics and an Otsu algorithm, and accurately identifying a water bloom area; and 2, calculating the water bloom area based on the multi-temporal data, researching the seasonal change, the long-term trend and the spatial distribution rule of the water bloom area in combination with a time sequence analysis method, and evaluating the accuracy of water bloom inversion by comparing the actually measured chlorophyll-a concentration with a water bloom inversion result. The application can monitor the change of the algal bloom area in real time.

Description

Technical Field

[0001] The invention belongs to the technical field of water quality remote sensing, and in particular relates to a method for automatically monitoring dynamic changes of water blooms based on remote sensing images. Background Art

[0002] Cyanobacterial blooms are one of the most serious environmental problems in inland waters, posing a severe threat to public health and aquatic ecosystems worldwide. Blooms not only impact lake landscapes but also produce cyanobacterial toxins that directly impact human and animal health. Therefore, accurate and effective monitoring of their spatial and temporal distribution is crucial for prevention and management. Satellite remote sensing technology can quickly and accurately acquire ground-based information, regardless of geographic location or environmental constraints, making it an efficient and effective method for detecting cyanobacterial blooms.

[0003] Existing algal bloom inversion methods typically analyze algal blooms based on single-time or static remote sensing imagery, ignoring the dynamic changes in algal blooms across time and space. Traditional inversion methods rely on simple thresholding methods or regression models, which are difficult to adapt to the complex variations of algal blooms across different seasons and weather conditions. Furthermore, because the distribution of algal blooms is highly spatiotemporal, existing methods are unable to monitor changes in algal bloom areas in real time, resulting in delayed and inaccurate monitoring results.

[0004] Secondly, existing methods usually rely on remote sensing images from a single data source. Although these data can provide relatively comprehensive water body monitoring, they are still limited in terms of temporal resolution, spatial resolution and the comprehensive utilization of multi-dimensional information. Summary of the Invention

[0005] The present invention provides a method for automatically monitoring the dynamic changes of algal blooms based on remote sensing images, so as to solve the problem that existing methods cannot monitor changes in algal bloom areas in real time.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for automatically monitoring the dynamic changes of algal blooms based on remote sensing images, comprising the following steps: Step 1: Obtain remote sensing reflectance data from multi-source remote sensing satellites within the selected time series range and calculate the spectral index; then automatically determine the extraction threshold through pixel slope statistics and the Otsu algorithm to accurately identify the algal bloom area; Step 2: Calculate the algal bloom area based on multi-temporal data and combine it with time series analysis methods to study the seasonal changes, long-term trends, and spatial distribution patterns of the algal bloom area. The accuracy of the algal bloom inversion is evaluated by comparing the measured chlorophyll-a concentration with the algal bloom inversion results.

[0007] Furthermore, in step 1, the information of cyanobacteria bloom is extracted by calculating the normalized vegetation index. The calculation formula of the normalized vegetation index is:

[0008] Among them, NDVI is the normalized vegetation index, is the reflectance value in the near-infrared band, is the red band reflectivity.

[0009] Furthermore, in step one, the algal bloom has a low absorption capacity for short-wave infrared light, while clear or turbid water bodies usually show high absorption in the short-wave infrared band. The phytoplankton index effectively distinguishes cyanobacterial blooms from background water bodies by capturing this spectral difference.

[0010] Furthermore, the calculation formula of the phytoplankton index is:

[0011] Among them, FAI is the phytoplankton index, ρ red , ρ nir , ρ swir are the remote sensing reflectances of red light, near infrared, and short-wave infrared bands, respectively, red ,λ nir ,λ swir They are the center wavelengths of the red light, near infrared, and short-wave infrared bands, respectively.

[0012] Furthermore, in step one, pixel slope statistics are used to automatically determine the extraction threshold, including: there is a significant difference in the value range distribution of the spectral index between water bodies and cyanobacteria blooms. By calculating the rate of change of the spectral index value, the distribution inflection point or mutation point of the value range is found, and these points are used as the threshold basis for segmenting blooms and non-blooms.

[0013] Furthermore, in step 1, automatically determining the extraction threshold using the Otsu algorithm includes: the Otsu algorithm automatically determines an appropriate threshold according to the grayscale characteristics of the image to achieve accurate segmentation of the background and target area of ​​the image; For the spectral index image of each phase, the Otsu algorithm is used to automatically select the optimal threshold to separate the water area from the non-water area in the image.

[0014] Furthermore, in step 2, the basic steps for calculating the multi-temporal bloom area and spatiotemporal analysis are as follows: S21, counting the number of pixels with a median value of 1 in the binarized image of the algal bloom area, representing the total number of pixels in the algal bloom area; S22. Calculate the actual area of ​​the algal bloom area by counting the number of algal bloom areas and combining the spatial resolution of the image; S23. Analyze the changing trends of algal blooms using multi-time series algal bloom area data from the past five years, including seasonal and cyclical fluctuations in algal bloom occurrence and annual changes in algal bloom area. S24. Combine the algal bloom area data and spatial distribution information to further analyze the spatial distribution characteristics of algal blooms in different water bodies and assess the frequency, duration, and impact range of algal blooms in different regions. S25. Evaluate the accuracy of the inversion by comparing the measured chlorophyll-a concentration with the bloom inversion results.

[0015] Furthermore, in step S25, evaluating the accuracy of the inversion includes the following steps: S25-1. Extracting the algal bloom index value of the corresponding location on the remote sensing image based on the latitude and longitude coordinates of the measured data; eliminating outliers in the measured data and inversion results, and retaining the measured data closest to the image acquisition time; S25-2. Calculate the correlation coefficient and regression relationship between the inverted algal bloom spectral index and the measured chlorophyll-a concentration to verify the sensitivity of the spectral index to the algal bloom degree and the fitting effect; S25-3. Based on the threshold classification standard of chlorophyll-a concentration, a confusion matrix was constructed, and the classification accuracy, recall rate and Kappa coefficient were calculated to quantify the classification performance of water bloom detection; S25-4. Overlay the measured high-concentration areas with the water bloom distribution range in the remote sensing image and calculate the boundary matching degree. The higher the boundary consistency, the more reliable the inversion result.

[0016] Furthermore, in step S25-1, if the sampling point is smaller than the image pixel size, the mean value of a certain radius around the sampling point can be extracted.

[0017] Furthermore, the step S25 further includes: Step S25-5: If there are measured data from multiple time phases, compare the trends of the bloom area or concentration over time and perform time series analysis with the inversion results.

[0018] The present invention can achieve the following beneficial effects: 1. Based on the traditional spectral index-based extraction of algal bloom areas, the present invention introduces the pixel slope statistical analysis and the Otsu algorithm combined with the spectral index method, realizing a fully automated process for extracting algal bloom areas from remote sensing images, further improving the monitoring accuracy.

[0019] 2. This technology effectively filters out interference from non-water areas by calculating the slope information of each pixel in the image and comprehensively analyzing spectral characteristics and terrain features. This technology significantly improves the accuracy of algal bloom extraction, particularly in complex terrain, mountainous areas, and lakes, providing reliable support for algal bloom monitoring in diverse terrains.

[0020] 3. By using the Otsu algorithm to automatically calculate the optimal threshold and dynamically adjust it based on the image's grayscale histogram, it eliminates the need for human intervention and offers strong adaptability. This automated approach not only improves the efficiency of algal bloom monitoring but also reduces reliance on the expertise of technicians, making large-scale, continuous algal bloom monitoring possible and providing real-time data support for environmental management.

[0021] 4. This study uses multi-temporal Sentinel-2 imagery from 2017 to 2022 to comprehensively analyze the spatiotemporal variations of algal blooms. Unlike existing techniques that typically rely on static monitoring based on single-temporal imagery, this study combines long-term remote sensing data to capture the dynamic patterns of algal blooms.

[0022] 5. By analyzing the changing trend of algal bloom area throughout the year, its seasonal characteristics can be revealed; through comparative analysis over many years, the long-term trend of algal bloom expansion or reduction can be explored, and the impact of environmental changes on algal bloom outbreaks can be identified.

[0023] 6. The method provided by this invention can be combined with multi-temporal imagery to provide more comprehensive and detailed dynamic monitoring results by analyzing the changes in algal bloom area over time. This provides a quantitative basis for water pollution assessment and management. Spatiotemporal analysis can also be used to locate high-incidence algal bloom areas, providing early warning information to decision makers and facilitating the development of targeted remediation measures. Dynamic monitoring technology elevates algal bloom monitoring from "status record" to "change prediction," enabling early detection and response to ecological issues. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention will be further described below with reference to the accompanying drawings and examples: Figure 1 The present invention is a flow chart of automatically monitoring the dynamic changes of water blooms based on remote sensing images. DETAILED DESCRIPTION

[0025] To facilitate understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The accompanying drawings provide embodiments of the present application. However, the present application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present application more thorough and comprehensive.

[0026] like Figure 1 As shown, a method for automatically monitoring the dynamic changes of algal blooms based on remote sensing images includes the following steps: Step 1: Obtain remote sensing reflectance data from multi-source remote sensing satellites within the selected time series range, calculate the spectral index, and automatically determine the extraction threshold through pixel slope statistics and the Otsu algorithm to accurately identify the algal bloom area.

[0027] Among them, the idea of ​​constructing the spectral index model is: Cyanobacterial blooms have similar spectral characteristics to green vegetation: low remote sensing reflectance in the red band and a sharp increase in reflectance in the near-infrared band. The red band ranges from 630 to 690 nm, while the near-infrared band ranges from 740 to 890 nm. Therefore, information about cyanobacterial blooms can be extracted by calculating the Normalized Difference Vegetation Index.

[0028] The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used indicators for quantifying vegetation in the field of remote sensing. The calculation formula for the NDVI is as follows:

[0029] Where, NDVI is the normalized vegetation index; is the reflectance value in the near-infrared band; is the red band reflectance. Due to differences in satellite types, the channel numbers for the near-infrared band and the red band are different.

[0030] The phytoplankton index is a water bloom extraction method based on a multispectral band design. This index significantly enhances the spectral difference between cyanobacterial blooms and background water bodies by combining data from the red, near-infrared, and short-wave infrared bands. It is particularly suitable for detecting cyanobacterial blooms. Compared with water bodies, water blooms have a lower absorption capacity for short-wave infrared light, and clear or turbid water bodies usually appear highly absorbing or "black" in the short-wave infrared band, almost completely opaque. Therefore, the phytoplankton index can effectively distinguish cyanobacterial blooms from background water bodies by capturing this spectral difference, and can show high extraction accuracy even in turbid water bodies. The specific formula is as follows:

[0031] Where, ρ red , ρ nir , ρ swir are the remote sensing reflectances of red light, near infrared, and short-wave infrared bands respectively; λ red ,λ nir ,λ swir They are the center wavelengths of the red light, near infrared, and short-wave infrared bands, respectively.

[0032] The pixel slope statistics method and Otsu algorithm are as follows: The pixel slope statistics and analysis method determines the bloom detection threshold by analyzing the distribution characteristics of the rate of change of spectral index values. The range distribution of spectral indices such as the Normalized Difference Vegetation Index, the Phytoplankton Index, and the Normalized Chlorophyll Index differ significantly between water bodies and cyanobacterial blooms. By calculating the rate of change of spectral index values, inflection points or mutation points in the range distribution can be identified. These points often serve as the threshold for distinguishing blooms from non-blooms.

[0033] The core principle of the Otsu algorithm is that it can automatically determine an appropriate threshold based on the grayscale characteristics of the image to achieve accurate segmentation of the background and target areas of the image. Since variance measures the uniformity of the grayscale distribution, the greater the inter-class variance between the background and the target, the more significant the difference in their grayscale values. Therefore, when the inter-class variance between the two classes of the grayscale histogram reaches its maximum, the selected threshold can minimize misclassification and better distinguish between water bodies and land objects, thereby achieving more accurate image segmentation. For the spectral index image of each phase, the Otsu algorithm is used to automatically select the optimal threshold to separate the water areas from the non-water areas in the image.

[0034] Step 2: Calculate the algal bloom area based on multi-temporal data and combine it with time series analysis methods to study the seasonal changes, long-term trends, and spatial distribution patterns of the algal bloom area. The accuracy of the algal bloom inversion is evaluated by comparing the measured chlorophyll-a concentration with the algal bloom inversion results.

[0035] The steps for calculating multi-temporal algal bloom area and spatiotemporal analysis include: S21, counting the number of pixels with a median value of 1 in the binarized image of the algal bloom area, representing the total number of pixels in the algal bloom area; S22. Calculate the actual area of ​​the algal bloom area by counting the number of algal bloom areas and combining the spatial resolution of the image. For example, the area of ​​each pixel is , then the bloom area It can be expressed as:

[0036] in, It represents the pixel value of the algal bloom area, and N is the number of pixels in the algal bloom area.

[0037] S23. Use multi-time series algal bloom area data from 2017 to 2022 to analyze the changing trends of algal blooms, analyze the seasonal and cyclical fluctuations of algal blooms from 2017 to 2022, and the changes in algal bloom area each year; S24. Combine algal bloom area data with spatial distribution information to further analyze the spatial distribution characteristics of algal blooms in different water bodies, such as lakes, rivers, and reservoirs, and assess the frequency, duration, and impact of algal blooms in different regions. S25, chlorophyll-a concentration is one of the main characteristic parameters of algal blooms, reflecting the amount of phytoplankton in the water body. By comparing the measured chlorophyll-a concentration with the algal bloom inversion results, the accuracy of the inversion can be evaluated. The specific steps are: S25.1: Extract the bloom index value for the corresponding location on the remote sensing image based on the latitude and longitude coordinates of the measured data. If the sampling point is smaller than the image pixel size, the mean of a certain radius around the sampling point, such as 3×3 or 5×5 pixels, can be extracted to reduce the impact of spatial resolution differences. Remove outliers from the measured data and inversion results, such as chlorophyll-a concentrations or inversion index values ​​that significantly deviate from the reasonable range, and retain the measured data closest to the image acquisition time. S25.2: Calculate the correlation coefficient and regression relationship between the inverted algal bloom spectral index and the measured chlorophyll-a concentration to verify the sensitivity of the spectral index to the algal bloom degree and the fitting effect; S25.3: Based on the threshold classification criteria of chlorophyll-a concentration, a confusion matrix was constructed and the classification accuracy, recall rate, and Kappa coefficient were calculated to quantify the classification performance of algal bloom detection; S25.4: Overlay the measured high concentration areas (i.e., areas with Chl-a > 20 mg / m³) with the bloom distribution range in the remote sensing image and calculate the boundary matching degree. :

[0038] The higher the boundary consistency, the more reliable the inversion result.

[0039] S25.5: If multi-temporal measured data are available, compare the trends of bloom area or concentration over time and perform a time series analysis with the inversion results.

[0040] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for automatically monitoring the dynamic changes of algal blooms based on remote sensing images, characterized in that: The following steps are involved: Step 1: Obtain remote sensing reflectance data from multi-source remote sensing satellites within the selected time series range and calculate the spectral index; then automatically determine the extraction threshold through pixel slope statistics and the Otsu algorithm to accurately identify the algal bloom area; Step 2: Calculate the algal bloom area based on multi-temporal data and combine it with time series analysis methods to study the seasonal changes, long-term trends, and spatial distribution patterns of the algal bloom area. The accuracy of the algal bloom inversion is evaluated by comparing the measured chlorophyll-a concentration with the algal bloom inversion results.

2. The method of automatically monitoring the dynamic changes of algal blooms based on remote sensing images according to claim 1, characterized in that: In step 1, the information of cyanobacteria bloom is extracted by calculating the normalized vegetation index. The calculation formula of the normalized vegetation index is: Among them, NDVI is the normalized vegetation index, is the reflectance value in the near-infrared band, is the red band reflectivity.

3. The method of automatically monitoring the dynamic changes of algal blooms based on remote sensing images according to claim 1, characterized in that: In step one, the algal bloom has a low absorption capacity for short-wave infrared light, while clear or turbid water bodies usually show high absorption in the short-wave infrared band. The phytoplankton index effectively distinguishes cyanobacterial blooms from background water bodies by capturing this spectral difference.

4. The method of automatically monitoring the dynamic changes of algal blooms based on remote sensing images according to claim 3, characterized in that: The calculation formula of phytoplankton index is: Among them, FAI is the phytoplankton index, ρ red , ρ nir , ρ swir are the remote sensing reflectances of red light, near infrared, and short-wave infrared bands, respectively, red ,λ nir ,λ swir They are the center wavelengths of the red light, near infrared, and short-wave infrared bands, respectively.

5. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 1, characterized in that: In step 1, pixel slope statistics are used to automatically determine the extraction threshold, including: there are obvious differences in the value range distribution of water bodies and cyanobacteria blooms in the spectral index. By calculating the rate of change of the spectral index value, the distribution inflection points or mutation points of the value range are found. These points are used as the threshold basis for dividing algal blooms and non-algal blooms.

6. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 5, characterized in that: In step 1, the extraction threshold is automatically determined by using the Otsu algorithm, which includes: the Otsu algorithm automatically determines an appropriate threshold according to the grayscale characteristics of the image to achieve accurate segmentation of the background and target area of ​​the image; For the spectral index image of each phase, the Otsu algorithm is used to automatically select the optimal threshold to separate the water area from the non-water area in the image.

7. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 1, characterized in that: In step 2, the basic steps for calculating multi-temporal bloom area and spatiotemporal analysis are: S21, counting the number of pixels with a median value of 1 in the binarized image of the algal bloom area, representing the total number of pixels in the algal bloom area; S22. Calculate the actual area of ​​the algal bloom area by counting the number of algal bloom areas and combining the spatial resolution of the image; S23. Analyze the changing trends of algal blooms using multi-time series algal bloom area data from the past five years, including seasonal and cyclical fluctuations in algal bloom occurrence and annual changes in algal bloom area. S24. Combine the algal bloom area data and spatial distribution information to further analyze the spatial distribution characteristics of algal blooms in different water bodies and assess the frequency, duration, and impact range of algal blooms in different regions. S25. Evaluate the accuracy of the inversion by comparing the measured chlorophyll-a concentration with the bloom inversion results.

8. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 7, characterized in that: In step S25, the accuracy of the inversion is evaluated, including the following steps: S25-1. Extracting the algal bloom index value of the corresponding location on the remote sensing image based on the latitude and longitude coordinates of the measured data; eliminating outliers in the measured data and inversion results, and retaining the measured data closest to the image acquisition time; S25-2. Calculate the correlation coefficient and regression relationship between the inverted algal bloom spectral index and the measured chlorophyll-a concentration to verify the sensitivity of the spectral index to the algal bloom degree and the fitting effect; S25-3. Based on the threshold classification standard of chlorophyll-a concentration, a confusion matrix was constructed, and the classification accuracy, recall rate and Kappa coefficient were calculated to quantify the classification performance of water bloom detection; S25-4. Overlay the measured high-concentration areas with the water bloom distribution range in the remote sensing image and calculate the boundary matching degree. The higher the boundary consistency, the more reliable the inversion result.

9. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 8, characterized in that: In step S25-1, if the sampling point is smaller than the image pixel size, the mean value of a certain radius around the sampling point can be extracted.

10. The method of automatically monitoring dynamic changes of algal blooms based on remote sensing images according to claim 8, characterized in that: The step S25 further includes: Step S25-5: If there are measured data from multiple time phases, compare the trends of the bloom area or concentration over time and perform time series analysis with the inversion results.

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