Lake blue-green algae horizontal and vertical movement rate calculation method based on stationary satellite

Abstract

This invention relates to a method for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellite data. The method includes: acquiring geostationary satellite remote sensing reflectance data and calculating algal bloom coverage, i.e., the percentage of algal bloom area in each pixel relative to the total pixel area; extracting a geostationary satellite remote sensing image; extracting algal bloom pixels based on an algal bloom coverage greater than a preset threshold; and defining spatial continuity. N An area with more than one algal bloom pixel is defined as an algal cluster. The horizontal movement rate of the algal cluster is calculated based on its movement distance and time, and this horizontal movement rate characterizes the horizontal movement rate of the cyanobacteria. The algal bloom coverage of a specific pixel is obtained, and the vertical movement rate of the algal particles in that pixel is obtained based on the pixel's water depth and changes in algal bloom coverage at different times, and this vertical movement rate characterizes the vertical movement rate of the cyanobacteria. The speed calculated by the method of this invention can accurately reflect the horizontal and vertical movement of algal blooms in a given area.

Description

Technical Field

[0001] This invention relates to the field of remote sensing technology, specifically to a method for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellites. Background Technology

[0002] Human activities combined with climate change have exacerbated eutrophication in global lakes, leading to persistent cyanobacterial blooms that impact water quality, biodiversity, and human health. Remote sensing technology has been widely applied to monitor the area, duration, and severity of different types of cyanobacterial blooms in lakes and oceans worldwide. The formation mechanism of cyanobacterial blooms is mainly as follows: under eutrophic conditions, with the accumulation of biomass, algal particles aggregate and, under suitable temperature and light conditions, rise to the water surface, forming an algal bloom. The location, intensity, and duration of algal blooms are influenced by both the physiological characteristics of the cyanobacteria themselves and external physical conditions.

[0003] Algal blooms involve both vertical and horizontal movement. Current data on the vertical movement rate of algal particles are primarily obtained through laboratory and in-situ field experiments, making large-scale measurements difficult. Horizontal movement under natural water conditions is affected by wind speed and water flow; existing research is mainly based on high-frequency field observations, which are difficult to generalize. Geostationary satellites offer high temporal resolution, allowing for remote sensing estimation of both horizontal and vertical movement rates by monitoring the drift path of algal blooms. However, existing remote sensing methods only consider the maximum and minimum bloom intensities within a single day, lacking pixel-by-pixel analysis; therefore, the calculated rates cannot accurately reflect the horizontal and vertical movement of algal blooms in a specific area. Summary of the Invention

[0004] The purpose of this invention is to provide a method for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellites.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] The method for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellites includes the following steps:

[0007] Acquire geostationary satellite remote sensing reflectance data and calculate algal bloom coverage, which is the percentage of algal bloom area in each pixel to the total pixel area.

[0008] a. Calculate the horizontal movement rate of cyanobacteria;

[0009] Extract a scene of a stationary satellite remote sensing image, extract algal bloom pixels based on the condition that the algal bloom coverage is greater than a preset threshold, and define an area with more than N consecutive pixels in space as an algal bloom pixel as an algal cluster.

[0010] The horizontal movement rate of algae is calculated based on the distance and time of algal movement, and the horizontal movement rate of algae is used to characterize the horizontal movement rate of cyanobacteria.

[0011] b. Calculate the vertical movement rate of cyanobacteria;

[0012] The algal bloom coverage of a certain pixel is obtained. Based on the pixel water depth and the change of algal bloom coverage at different times, the vertical movement rate of algal particles in that pixel is obtained. The vertical movement rate of algal particles in the pixel is used to characterize the vertical movement rate of cyanobacteria.

[0013] As a preferred implementation method, the algal bloom coverage is calculated using a nonlinear decomposition model, the function of which is as follows:

[0014]

[0015] Where FAC represents algal bloom coverage, F refers to the algal bloom identification index, and m and n are fitting parameters.

[0016] As a preferred implementation method, geostationary satellite data with large-area algal blooms and cloudless conditions are selected for calculation.

[0017] In a preferred embodiment, the geostationary satellite remote sensing reflectance data is remote sensing reflectance data that has been Rayleigh corrected.

[0018] In a preferred embodiment, the algal bloom identification index is the FAI index or the AFAI index.

[0019] As a preferred implementation method, the position information of the center of mass of the algal floc at different times is obtained, and the horizontal movement rate of the algal floc is calculated based on the change in the distance between the center of mass of the algal floc at different times.

[0020] In a preferred embodiment, the centroid of the algal cluster is determined as follows:

[0021] Calculate the mean value of the algal cluster pixel coverage and use it as the centroid of the algal cluster;

[0022] The mean coverage of the algal bloom is the ratio of the sum of the algal bloom coverage of each pixel within the algal bloom to the total number of pixels within the algal bloom.

[0023] As a preferred embodiment, the formula for calculating the vertical movement rate of the cyanobacteria is as follows:

[0024]

[0025] In the formula, Z represents the water depth of a certain pixel; FAC t1 Let FAC be the algal bloom coverage of the pixel at time t1. t2 Δt represents the algal bloom coverage of the pixel at time t2; Δt is the difference between the start and end times.

[0026] In one preferred embodiment, the water depth of the pixel is determined based on the underwater topography and water level.

[0027] As a preferred implementation, the algal bloom coverage of each pixel is divided into three types according to time variation: monotonically increasing, first increasing and then decreasing, and monotonically decreasing. For the monotonically increasing and monotonically decreasing types, t1 and t2 are the times of the first and last scenes within a day, respectively. For the first increasing and then decreasing type, it is divided into two segments: increasing and decreasing. t1 and t2 are the start and end times of the increasing segment, and the start and end times of the decreasing segment, respectively.

[0028] The method of this invention for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellites can accurately reflect the horizontal and vertical movement of algal blooms in a certain area. It is a prerequisite and foundation for improving the accuracy of satellite remote sensing monitoring of algal blooms and has significant scientific value for the operational daily monitoring and early warning of algal blooms. Attached Figure Description

[0029] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in the various figures may be denoted by the same reference numeral. For clarity, not every component is labeled in each figure. Embodiments of various aspects of the invention will now be described by way of example and with reference to the accompanying drawings, wherein:

[0030] Figure 1 The algal blooms in Taihu and Chaohu Lakes change daily;

[0031] Figure 2 This describes the patterns of algal bloom changes in different areas of Taihu and Chaohu lakes.

[0032] Figure 3 This is a flowchart of the calculation process for the horizontal and vertical velocities of algal particles;

[0033] Figure 4 It is the seasonal distribution of the vertical movement rate of algal blooms;

[0034] In the aforementioned Figures 1-4, the coordinates, symbols, or other representations expressed in English are all well-known in the field and will not be elaborated upon in this example. Detailed Implementation

[0035] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0036] Various aspects of the invention are described in this disclosure with reference to the accompanying drawings, in which numerous illustrative embodiments are shown. The embodiments of this disclosure are not necessarily defined to include all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described in more detail below, can be implemented in any of many ways, because the concepts and embodiments disclosed herein are not limited to any particular implementation. Furthermore, some aspects of the invention disclosed may be used alone or in any suitable combination with other aspects of the invention disclosed.

[0037] Example 1

[0038] This embodiment illustrates the method for calculating the horizontal and vertical motion rates of lake algal particles based on geostationary satellites, as described in this invention.

[0039] This embodiment uses a method based on geostationary satellites to calculate the horizontal and vertical motion rates of lake algal particles, and the implementation is as follows:

[0040] Using GOCI Rrc data from 2010 to 2020, the nonlinear hybrid pixel decomposition method was used to calculate the algal bloom coverage (FAC). Based on the area, size, and centroid position of the algal bloom, the horizontal movement rate of the algal bloom was calculated. When the wind speed was less than 3 m / s, assuming that the algal bloom movement was mainly vertical, the rising and sinking rates of algal particles under different distribution types were calculated. Finally, the seasonal and lake-region distribution patterns of the rising and sinking rates of algal particles in Taihu Lake and Chaohu Lake were analyzed.

[0041] As an exemplary description, the implementation of the aforementioned method will be specifically explained below with reference to the accompanying drawings.

[0042] 1) Using GOCI Rrc data from 2010 to 2020, the algal bloom coverage FAC was calculated using a nonlinear hybrid pixel decomposition method;

[0043] GOCI data from 2010 to 2020 under cloudless conditions with large-scale algal blooms were selected. The L1B data of GOCI was processed using SEADAS software to obtain Rrc data. The Algal Bloom Identification Index (AFAI) was calculated, and the algal bloom coverage (FAC) for each pixel was calculated based on the relationship model between FAC and AFAI.

[0044] Among them, the Rrc data of GOCI (Geostationary Ocean Color Imager) is the remote sensing reflectance after Rayleigh correction;

[0045]

[0046] Where, λ RED , λ NIR1 and λ NIR2The required wavelengths are 660, 745, and 865 nm.

[0047] Algal bloom coverage (FAC) is the percentage of algal bloom area within each pixel relative to the total pixel area.

[0048] Figure 1 The study showcased the intra-diurnal variations of representative algal blooms in Taihu Lake and Chaohu Lake, demonstrating that the significant differences in algal bloom coverage (FAC) across different lake regions are mainly caused by the combined horizontal and vertical movement of the algal blooms. Figure 2 The data shows the changes in FAC (Flaming Activity Coefficient) in different areas of Taihu Lake and Chaohu Lake. In Taihu Lake, it can be seen that the peak time of algal blooms is different. The peak time in the western part of Taihu Lake is around 10-11 am, while the peak time in the central part of the lake is around 12 pm, which also proves the horizontal movement of algal blooms.

[0049] 2) Calculate the horizontal movement speed of the algal bloom;

[0050] Calculate the centroid of the algal cluster for each GOCI image ( Figure 3 ).

[0051] Algal blooms were extracted from a GOCI image with an FAC value greater than 30% as the threshold. Algal clusters were defined as those with more than 50 consecutive pixels in space. A single image may contain multiple algal clusters of different sizes.

[0052] The centroid of the algal cluster is calculated using the following formula:

[0053]

[0054] In the formula, N is the total number of pixels in a certain algal bloom, and FAC(i) is the algal bloom coverage of pixel i.

[0055] By selecting a single algal cluster, tracking and recording its centroid position at different times, the horizontal movement rate of the algal cluster can be calculated based on the changes in the centroid distance at different times.

[0056]

[0057] n is the total number of images in a day, x i and y i They are respectively time i (t) i The position of the centroid of x i+1 and y i+1 They are respectively time i+1 (t) i+1 The position of the center of mass of ).

[0058] 3) Calculate the vertical velocity of the algal bloom;

[0059] When the wind speed is less than 3 m / s, it is assumed that the changes in algal bloom are mainly caused by vertical motion. The FAC of each pixel changes with time in three types: monotonically increasing (Type 1), first increasing and then decreasing (Type 2), and monotonically decreasing (Type 3). First, the type of each pixel is determined using a function fitting method. Type 1 is characterized by algal particles rising within the water column, i.e., the rising rate V+; Type 3 is characterized by algal particles continuously decreasing within the water column, with a decreasing rate V-; in Type 2, algal particles first rise within the water column, reaching the maximum FAC, and then decrease, with the rate in the first half of this process being V+ and the rate in the second half being V-. Figure 3 The formula for calculating the vertical velocity of algal particles is as follows:

[0060]

[0061] Z represents the water depth of a pixel, determined based on the underwater topography and water level; FACt1 represents the algal bloom coverage of the pixel at time t1, and FACt2 represents the algal bloom coverage of the pixel at time t2; for types 1 and 3, t1 and t2 represent the times of the first and last scenes within a day, respectively; for type 2, it is divided into rising and falling segments, with t1 and t2 representing the start and end times of the rising segment and the falling segment, respectively, used to calculate the rising rate and falling rate. Δt represents the difference between the start and end times.

[0062] Figure 4 The vertical rise and fall rates of algal particles in Taihu Lake and Chaohu Lake are shown for different seasons. Generally, the rise and fall rates are higher in summer and autumn than in winter and spring. Due to the later algal blooms in Chaohu Lake, there is limited data for winter and spring. In summer and autumn, the rise rate in the central lake area of ​​Taihu Lake is mostly greater than 1, while in winter it is mainly concentrated along the western shore. Areas prone to algal blooms have high rise rates and low fall rates. The rise and fall rates in Chaohu Lake are lower than in Taihu Lake, likely due to the greater water depth of Chaohu Lake.

Claims

1. A method for calculating the horizontal and vertical movement rates of cyanobacteria in lakes based on geostationary satellites, characterized in that, include: Acquire geostationary satellite remote sensing reflectance data and calculate algal bloom coverage, which is the percentage of algal bloom area in each pixel to the total pixel area. a. Calculate the horizontal movement rate of cyanobacteria; Extract a scene of a stationary satellite remote sensing image, extract algal bloom pixels based on the condition that the algal bloom coverage is greater than a preset threshold, and define an area with more than N consecutive pixels in space as an algal bloom pixel as an algal cluster. The horizontal movement rate of algae is calculated based on the distance and time of algal movement, and the horizontal movement rate of algae is used to characterize the horizontal movement rate of cyanobacteria. b. Calculate the vertical movement rate of cyanobacteria; The algal bloom coverage of a certain pixel is obtained. Based on the pixel water depth and the change in algal bloom coverage at different times, the vertical movement rate of algal particles in that pixel is obtained. The vertical movement rate of algal particles in a pixel represents the vertical movement rate of cyanobacteria. The calculation formula for the vertical movement rate of cyanobacteria is as follows: ； In the formula, Z represents the water depth of a certain pixel; FAC t1 Let FAC be the algal bloom coverage of the pixel at time t1. t2 The algal bloom coverage of this pixel at time t2; t is the difference between the start and end times; The algal bloom coverage of each pixel is divided into three types according to time variation: monotonically increasing, first increasing and then decreasing, and monotonically decreasing. For the monotonically increasing and monotonically decreasing types, t1 and t2 are the times of the first and last scenes within a day, respectively. For the first increasing and then decreasing type, it is divided into two segments: increasing and decreasing. t1 and t2 are the start and end times of the increasing segment, and the start and end times of the decreasing segment, respectively.

2. The method according to claim 1, characterized in that, Algal bloom coverage is calculated using a nonlinear decomposition model, the function of which is as follows: ； Where FAC represents algal bloom coverage, F refers to the algal bloom identification index, and m and n are fitting parameters.

3. The method according to claim 1, characterized in that, Geostationary satellite data with large-scale algal blooms and cloudless conditions were selected for calculation.

4. The method according to claim 3, characterized in that, The geostationary satellite remote sensing reflectance data are remote sensing reflectance data that have undergone Rayleigh correction.

5. The method according to claim 1, characterized in that, The algal bloom identification index is either the FAI index or the AFAI index.

6. The method according to claim 1, characterized in that, The position information of the center of mass of the algal floc at different times is obtained, and the horizontal movement rate of the algal floc is calculated based on the change in the distance between the center of mass and the algal floc at different times.

7. The method according to claim 6, characterized in that, The method for determining the centroid of the algal cluster is as follows: Calculate the mean value of the algal cluster pixel coverage and use it as the centroid of the algal cluster; The mean coverage of the algal bloom is the ratio of the sum of the algal bloom coverage of each pixel within the algal bloom to the total number of pixels within the algal bloom.

8. The method according to claim 1, characterized in that, The water depth of the pixel is determined based on the underwater topography and water level.

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