A method for identifying algal blooms in inland lake reservoir water bodies

Abstract

The application provides a kind of inland lake reservoir water body's algal bloom identification method, for water algae monitoring, comprising the following steps: the remote sensing reflectivity data obtained in advance is preprocessed, to obtain preprocessed result;Improved phytoplankton index formula is constructed, and satellite remote sensing data processing software is calculated to obtain improved phytoplankton index with the preprocessed result;The numerical image of the improved phytoplankton index is input into EVNI, with 0 as threshold value, extracts the coverage of algae, and calculates to obtain the area of phytoplankton;Wherein the improved phytoplankton index is the difference between phytoplankton index FAI and water body index EXPWI.The application can increase the standard deviation of data, expand the value range of data, increase the amount of information, enhance the cyanobacteria information, and increase the probability of identifying the area with small cyanobacteria density.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of remote sensing, and particularly relates to a method for identifying algal blooms in inland lake and reservoir water bodies. BACKGROUND

[0002] Remote sensing technology has the characteristics of large range, periodicity and rapid real-time, and has become one of the most effective measures for monitoring the water quality of large lakes. When cyanobacterial blooms occur, green algal organisms gather on the surface of the water body, are easily affected by the tidal current and wind direction, extend in strips, and usually have a flocculent texture structure on the remote sensing image, which is significantly different from the surrounding lake surface. At the same time, the content of chlorophyll in the water body is significantly increased, so that the absorption peaks of the water body reflectance spectrum in the blue and red wave bands are more obvious, and the water body reflectance spectrum has a similar vegetation spectral curve characteristic of “steep slope effect” in the near-infrared wave band. The reflectance spectrum characteristics of the water body change, and the spectral characteristics of the cyanobacterial bloom coverage area are quite different from those of the non-algal water surface. The spectral characteristics of cyanobacteria can be accurately recorded by satellite detectors. Therefore, satellite remote sensing data can be used to monitor the spatial distribution information of cyanobacterial blooms.

[0003] Based on this, how to improve the technical effect of remote sensing technology on cyanobacterial monitoring to better protect the environment becomes a technical problem that needs to be solved by those skilled in the art. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a technical solution that can solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical solution:

[0006] A method for identifying algal blooms in inland lake and reservoir water bodies, comprising the following steps:

[0007] Pretreating the pre-acquired remote sensing reflectivity data to obtain a pretreatment result;

[0008] Constructing an improved phytoplankton index formula and calculating an improved phytoplankton index in cooperation with satellite remote sensing data processing software based on the pretreatment result;

[0009] Inputting the numerical image of the improved phytoplankton index into EVNI, extracting the algal coverage range with 0 as the threshold value, and calculating to obtain the area of phytoplankton;

[0010] The improved phytoplankton index is the difference between the phytoplankton index FAI and the water body index EXPWI.

[0011] Preferably, the pre-acquired remote sensing reflectance data is pre-processed by using meteorological data and Rayleigh scattering lookup table, specifically including: ① removing the sample points of cloud coverage area combined with positioning data and satellite image; ② removing the sample points covered by algal blooms based on FAI > -0.004; ③ removing the sample points covered by solar flares based on satellite image fast view; ④ using a time window of (±3h) to match the data set combined with sampling time and satellite overpass time data.

[0012] Preferably, before constructing the improved phytoplankton index formula, it further includes:

[0013] The pre-processed results are subjected to the operation of removing water vapor absorption, ozone absorption and Rayleigh scattering.

[0014] Preferably, the phytoplankton index FAI acquisition step includes:

[0015] S1. The reflectance of the pre-processed results after Rayleigh correction is calculated by using the near-infrared light reflectance formula ;

[0016] S2. The satellite remote sensing data processing software is used to remove the image area covered by clouds and land in the pre-processed results, and extract the lake water body.

[0017] S3. The phytoplankton index FAI is calculated according to the phytoplankton index FAI formula and the satellite remote sensing data processing software.

[0018] Preferably, the expression of the near-infrared light reflectance formula is:

[0019] ;

[0020] Among them, is the near-infrared light reflectance; is the radiance of the sensor after radiation calibration, is the vertical incident solar irradiance outside the atmosphere, which is is the solar zenith angle, is the reflectance of Rayleigh scattering estimated using the 6S model.

[0021] Preferably, the expression of the water body index EXPWI is:

[0022] ;

[0023] Among them, is the short-wave infrared band, which corresponds to band5 in TM, LISS-3 and MODIS image; ​For red light band, corresponding to band3 in TM and LISS-3 image, corresponding to band1 in MODIS image.

[0024] The method of the present application can increase the standard deviation of data, expand the value range of data, and increase the information amount. The cyanobacteria information is enhanced, and the probability of identifying the area with small cyanobacteria density is increased. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The present application is a method flow chart.

[0026] Fig. 2 is a schematic diagram of the results of an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions provided by the present application will be described in detail below in conjunction with the embodiments, but they should not be understood as limiting the scope of protection of the present application.

[0028] Example 1

[0029] The present embodiment discloses a method for identifying algal blooms in inland lake and reservoir water bodies. The method uses the difference between the floating algae index FAI (Floating Algae Index, formula (1)) and the water index EXPWI (EXP Water Index, formula (2)) to enhance the contrast between cyanobacteria water areas and non-cyanobacteria water areas. The method includes the following steps:

[0030] Pretreating the pre-acquired remote sensing reflectivity data to obtain a pretreatment result;

[0031] Constructing an improved floating algae index formula and calculating the improved floating algae index in combination with satellite remote sensing data processing software and the pretreatment result;

[0032] Inputting the numerical image of the improved floating algae index into EVNI, extracting the algae coverage range with 0 as the threshold value, and calculating to obtain the floating algae area;

[0033] The improved floating algae index is the difference between the floating algae index FAI and the water index EXPWI.

[0034] Specifically:

[0035] The pre-acquired remote sensing reflectivity data is pretreated by using meteorological data and Rayleigh scattering lookup table, specifically including: ① removing the sample points in the cloud coverage area by combining positioning data and satellite images; ② removing the algal bloom coverage sample points based on FAI >-0.004; ③ removing the sample points covered by solar flares based on the satellite image fast view; ④ matching the data set using a time window of (±3h) in combination with the sampling time and satellite overpass time data.

[0036] Before constructing the improved phytoplankton index formula, the following steps are included:

[0037] The pre-processed result is subjected to the operation of removing water vapor absorption, ozone absorption and Rayleigh scattering.

[0038] The obtaining step of the phytoplankton index FAI includes:

[0039] S1. The reflectivity of the pre-processed result after Rayleigh correction is calculated by using the near-infrared light reflectivity formula ;

[0040] S2. The satellite remote sensing data processing software is used to remove the image areas covered by clouds and land in the pre-processed result, and extract the lake water body;

[0041] S3. The phytoplankton index FAI is calculated according to the phytoplankton index FAI formula and the satellite remote sensing data processing software.

[0042] Formula (1);

[0043] Wherein and are calculated by using the following formulas respectively:

[0044] The expression of the near-infrared light reflectivity formula is: Formula (1-1);

[0045] Formula (1-2);

[0046] Wherein, is the near-infrared light reflectivity; is the radiance of the sensor after radiation calibration, is the solar irradiance vertically incident outside the atmosphere, which is is the solar zenith angle, is the reflectivity of Rayleigh scattering estimated by using the 6S model.

[0047] It should be noted that the FAI is not easily affected by environmental changes such as aerosol type and thickness, solar elevation angle and flare, can more effectively penetrate thin clouds, and monitor cyanobacterial blooms; the EXPWI can increase the reflectivity of water body in the short-wave infrared and suppress the reflectivity of non-water body in the red wave band, and can effectively reduce the interference of clouds on the extraction of water body information (Formula (2)). Compared with single FAI or EXPWI, this method can increase the standard deviation of data, expand the value range of data, and increase the information amount. The cyanobacterial information is enhanced, and the probability of identifying the area with small cyanobacterial density is increased. ​

[0048] FAI and EXPWI have similar value range. The new image is obtained by subtracting the two in ENVI using band operation. When the result of [FAI-EXPWI] is positive, it indicates that the water body is covered by blue-green algae bloom. The threshold value is set to 0, and the spatial distribution information of blue-green algae bloom in Taihu Lake can be extracted. In the images of different periods, the value range and standard deviation of [FAI-EXPWI] are larger than those of FAI (Table 1), which indicates that the information of [FAI-EXPWI] is more abundant. This method can effectively identify the spatial distribution of blue-green algae bloom on medium and high spatial resolution images (such as Landsat TM, IRS P6 LISS-3). After being applied to the algae bloom monitoring of Taihu Lake, the index shows more abundant details in texture and higher accuracy in area (Figure 2). Among them, Figure 2 improved floating algae method (FAI-EXPWI) extracts blue-green algae bloom in Taihu Lake: (a) Landsat TM 30m; (b) EOS MODIS 500m; (c) IRS P6 LISS-3 23.5m; (d) EOS MODIS 500m.

[0049] Table 1 Comparison of statistical information of FAI and [FAI-EXPWI]

[0050]

[0051] In this embodiment, the expression of the water body index EXPWI is:

[0052] Formula (2);

[0053] wherein, is a short-wave infrared band, which corresponds to band 5 in TM, LISS-3 and MODIS images. is a red light band, which corresponds to band 3 in TM and LISS-3 images, and corresponds to band 1 in MODIS images.

[0054] The above only describes the preferred embodiments of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, several improvements and refinements can be made, which should also be considered as the protection scope of the present application.

Claims

1. A method of identifying a bloom in an inland lake reservoir water body, characterized by, The method comprises the following steps: preprocessing remote sensing reflectivity data to obtain a preprocessing result; constructing an improved phytoplankton index formula and cooperating with satellite remote sensing data processing software to calculate an improved phytoplankton index based on the preprocessing result; inputting a numerical image of the improved phytoplankton index into EVNI, taking 0 as a threshold value, extracting an algae coverage range, and calculating to obtain a phytoplankton area; wherein the improved phytoplankton index is a difference between a phytoplankton index FAI and a water body index EXPWI; The water body index The expression of the water body index is: ； wherein, is short-wave infrared band, corresponding to band 5 in TM, LISS-3 and MODIS images; is red light band, corresponding to band 3 in TM and LISS-3 images, and corresponding to band 1 in MODIS images.

2. A method of identifying a bloom in an inland lake or reservoir water body according to claim 1, wherein, the preprocessing of the remote sensing reflectivity data is performed by using meteorological data and a Rayleigh scattering lookup table, and specifically comprises: ① removing sample points in a cloud coverage area in combination with positioning data and satellite images; ② removing sample points covered by algal blooms based on FAI>-0.004; ③ removing sample points covered by solar flares based on a satellite image fast view; ④ matching a data set by using a time window of (±3h) in combination with sampling time and satellite transit time data.

3. A method of identifying a bloom in an inland lake or reservoir water body according to claim 1, wherein, Before constructing the improved phytoplankton index formula, the preprocessing result is further subjected to an operation of removing water vapor absorption, ozone absorption and Rayleigh scattering.

4. A method of identifying a bloom in an inland lake or reservoir water body according to claim 1, wherein, the obtaining step of the phytoplankton index FAI comprises: S1. Calculate the reflectivity of the pre-treatment result after Rayleigh correction by using the near-infrared light reflectivity formula ; S2. Using the satellite remote sensing data processing software, and , removing the cloud and land covered image area in the pretreatment result, extracting the lake water body; S3. calculating the phytoplankton index FAI according to a phytoplankton index FAI formula and the satellite remote sensing data processing software.

5. A method of identifying a bloom in an inland lake reservoir water body as claimed in claim 4, wherein, an expression of the near-infrared light reflectivity formula is: ； wherein, is the near infrared reflectance; is the radiance of the sensor after radiometric calibration, is the solar irradiance at normal incidence outside the atmosphere, is is the solar zenith angle, is the reflectance of Rayleigh scattering estimated using the 6S model.

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