CN116645593B - Remote sensing method and device for monitoring seaweed bed distribution - Google Patents
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Abstract
The invention discloses a remote sensing method and a remote sensing device for monitoring seaweed bed distribution, wherein the method comprises the following steps: dividing a target water body area into seaweed, a sand substrate and seawater, and respectively extracting corresponding spectral information; calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the near infrared band remote sensing reflectivity observed by a satellite based on the spectral information of seaweed and seawater, and constructing a first spectral index; calculating the degree of remote sensing reflectivity reduction from a green light wave band to a red light wave band based on the spectrum information of seaweed and sand substrate, and constructing a second spectrum index; the seaweed is distinguished from seawater based on the first spectral index and the seaweed is distinguished from sandy substrate based on the second spectral index. By adopting the technical scheme, the remote sensing reflectivity of the water surface is directly utilized, and the method is applied to seaweed bed monitoring without a great deal of image training, and has stronger application value and higher result accuracy.
Description
Technical Field
The invention relates to the technical field of water remote sensing monitoring, in particular to a remote sensing method and device for monitoring seaweed bed distribution.
Background
Accounting of carbon sink the study of the blue carbon in the coastal zone is not left, and the coastal zone blue carbon ecosystem consists of three ecosystems of a seaweed bed, mangrove and salt marsh, wherein the seaweed bed ecosystem is an important carbon sink source. Although seaweed only accounts for 0.2% of the total area of the ocean worldwide, the annual carbon sequestration amount of seaweed accounts for 10% -18% of the total amount of ocean carbon sequestration worldwide, and is a globally important blue carbon sink. Therefore, the effective monitoring of the seaweed bed is important for finding out the background of blue carbon and the potential of carbon sink, and simultaneously, the seaweed bed also provides data support for marine ecology carbon sink.
At present, the remote sensing monitoring method of the seaweed bed mainly comprises a spectrum index method, wherein the spectrum index method is mainly used for constructing an effective spectrum index by analyzing the remote sensing reflectivity spectrum characteristics of the target object so as to maximize the difference between the target object and the non-target object, thereby realizing the extraction of the target object.
However, the spectral index method adopted in the prior art has a certain limitation, the index is mainly modeled aiming at vegetation spectral responses of red light wave bands and near infrared wave bands, but due to the strong absorption effect of water body on the red light wave bands and the near infrared wave bands, the reflection signal of the substrate becomes weak in the remote sensing reflectivity of the water body, and the effect of the index in the identification of aquatic vegetation is poor. Meanwhile, in the research of extracting seaweed beds in the prior art, such indexes are generally only used as an intermediate step of auxiliary drawing, but cannot be used for extracting the seaweed beds alone, for example, land vegetation at low tide can be masked by using the NDVI index or floating vegetation in the tidal zone and shallow water sub-tide zone can be obtained, shallow cement beaches or bare sand can be distinguished by using the red-green band ratio, and the like, non-seaweed areas can be masked by using such indexes in sequence, so that the area where seaweed may exist can be obtained step by step.
Disclosure of Invention
The invention aims to: the invention provides a remote sensing method and a remote sensing device for monitoring seaweed bed distribution, which mainly remove a sea water part and a small amount of sand substrate part by constructing a first spectrum index and a corresponding first optimal threshold value, remove the rest sand substrate part by a second spectrum index and a corresponding second optimal threshold value, finally obtain a seaweed bed region, and can be directly applied to seaweed bed monitoring by the first spectrum index and the second spectrum index, thereby having higher application value and higher result accuracy.
The technical scheme is as follows: the invention provides a remote sensing method for monitoring seaweed bed distribution, which comprises the following steps: acquiring a historical remote sensing image of a target area, and calculating to obtain a historical remote sensing reflectivity image of the target water area; collecting samples from the historical remote sensing reflectivity images, dividing a target water body area into seaweed, sandy bottom materials and seawater, and extracting corresponding spectral information from each sample respectively; based on the spectral information of seaweed and seawater, respectively calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the near infrared band remote sensing reflectivity observed by a satellite, and constructing a first spectral index of the seaweed and a first spectral index of the seawater; determining a first optimal threshold for distinguishing between seaweed and seawater in the overlapping portion of the first spectral index of seaweed and the first spectral index of seawater; based on the spectrum information of seaweed and sand substrate, respectively calculating the degree of remote sensing reflectivity reduction from green light wave band to red light wave band, and constructing a second spectrum index of the seaweed and a second spectrum index of the sand substrate; determining a second optimal threshold for distinguishing between seaweed and a sandy substrate in an overlapping portion of the second spectral index of seaweed and the second spectral index of the sandy substrate; and distinguishing seaweed from seawater based on a first optimal threshold for the remote sensing image to be monitored, and distinguishing seaweed from sand substrates based on a second optimal threshold to obtain a seaweed bed area.
Specifically, images with cloud and solar flare influences exceeding a standard threshold are removed from the historical remote sensing images of the target area, and the historical remote sensing reflectivity images of the target area are obtained through atmospheric correction.
Specifically, an image is cut according to the vector boundary of the target water body region, and the land and water segmentation is carried out by utilizing the normalized difference water body index, so that the historical remote sensing reflectivity image of the target water body region is obtained.
Specifically, in the historical remote sensing reflectivity image, the remote sensing reflectivity of seaweed, sand substrate and seawater in a plurality of seasons in a plurality of years is selected as a sample.
Specifically, the formula of the first spectrum index is as follows:
，
wherein SSI-I represents a first spectral index, R rs (λ 2 )、R rs (λ 3 ) And R is rs (λ 4 ) Remote sensing reflectivities respectively representing red light wave band, near infrared wave band and short wave infrared wave band, lambda 2 、λ 3 And lambda (lambda) 4 The wavelengths of the red, near infrared and short wave infrared bands are respectively represented.
Specifically, the ratio of seaweed to seawater in the overlapping part of the first spectral exponential square distribution diagram of each seaweed sample and the first spectral exponential square distribution diagram of each seawater sample is counted, and a first spectral exponential value capable of distinguishing the seaweed and the seawater to the greatest extent is determined as a first optimal threshold.
Specifically, the formula of the second spectrum index is as follows:
，
wherein SSI-II represents a second spectral index, R rs (λ 1 ) And R is rs (λ 2 ) Remote sensing reflectivities, lambda, representing the green and red bands, respectively 1 And lambda (lambda) 2 Representing the wavelengths in the green and red bands, respectively.
Specifically, the proportion of seaweed and sand substrate in the overlapping part of the second spectrum index square distribution diagram of each seaweed sample and the second spectrum index square distribution diagram of each sand substrate sample is counted, and the second spectrum index value capable of distinguishing the seaweed and the sand substrate to the greatest extent is determined as a second optimal threshold.
Specifically, for a remote sensing image to be monitored, calculating a first spectrum index and a second spectrum index of the image pixel by pixel, extracting a part, wherein the first spectrum index value of the part is larger than the first optimal threshold value, as a seaweed and sand substrate area, and extracting a part, in the seaweed and sand substrate area, of which the second spectrum index value is larger than the second optimal threshold value, as a seaweed bed area.
The invention also provides a remote sensing device for monitoring seaweed bed distribution, which comprises: the device comprises a remote sensing acquisition unit, a sample collection unit, a first spectrum index calculation unit, a first optimal threshold value determination unit, a second spectrum index calculation unit, a second optimal threshold value determination unit and an application unit, wherein: the remote sensing acquisition unit is used for acquiring a historical remote sensing image of the target area and calculating to obtain a historical remote sensing reflectivity image of the target water body area; the sample collection unit is used for collecting samples from the historical remote sensing reflectivity images, dividing a target water body area into seaweed, sand substrates and seawater, and extracting corresponding spectrum information from each sample respectively; the first spectrum index calculation unit is used for respectively calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the near infrared band remote sensing reflectivity observed by a satellite based on the spectrum information of seaweed and sea water, and constructing a first spectrum index of the seaweed and a first spectrum index of sea water; the first optimal threshold determining unit is used for determining a first optimal threshold for distinguishing sea grass from sea water in an overlapped part of the first spectrum index of sea grass and the first spectrum index of sea water; the second spectrum index calculation unit is used for respectively calculating the degree of remote sensing reflectivity reduction from a green light wave band to a red light wave band based on spectrum information of the seaweed and the sand substrate, and constructing a second spectrum index of the seaweed and a second spectrum index of the sand substrate; the second optimal threshold determining unit is used for determining a second optimal threshold for distinguishing seaweed and the sandy bottom in the overlapping part of the second spectrum index of the seaweed and the second spectrum index of the sandy bottom; the application unit is used for distinguishing seaweed from seawater based on a first optimal threshold value and distinguishing seaweed from sandy bottom based on a second optimal threshold value for remote sensing images to be monitored, so as to obtain a seaweed bed area.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages: the method directly utilizes the remote sensing reflectivity of the water surface, can be directly applied to seaweed bed monitoring through the first spectrum index and the second spectrum index, does not need a large number of images to train, and has strong application value and high result accuracy.
Drawings
Fig. 1 is a schematic flow chart of a remote sensing method for monitoring seaweed bed distribution.
Description of the embodiments
The technical scheme of the invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a schematic flow chart of a remote sensing method for monitoring seaweed bed distribution according to the present invention is shown.
The invention provides a remote sensing method for monitoring seaweed bed distribution.
In the embodiment of the invention, images with cloud and solar flare influences exceeding a standard threshold are removed from historical remote sensing images of a target area, the historical remote sensing reflectivity images of the target area are obtained through atmospheric correction, then the images are cut according to vector boundaries of the target water area, and land and water segmentation is carried out by utilizing normalized difference water indexes, so that the historical remote sensing reflectivity images of the target water area are obtained.
In particular embodiments, taking the Windsat's seaweed bed as an example, the long-time-series historical remote sensing image of the target area may be, for example, a long-time-series (2002-2022) Landsat satellite remote sensing image collected over the Windsat's seaweed bed, including Landsat-5 TM, landsat-7 ETM + Landsat-8 OLI, landsat-9 OLI2 and the like, images greatly influenced by cloud shadows and solar flares are removed from the images (standard threshold values can be set according to specific application scenes) through image recognition, visual observation and other modes, and atmospheric correction is carried out on the images by using an ACOLITE software module to obtain remote sensing reflectivityR rs (λ) Cutting the image according to the moon and lake vector boundary, realizing land and water segmentation by utilizing normalized difference water body indexes (Normalized Difference Moisture Index, NDMI), and finally obtaining R of the moon and lake water area rs (lambda) (remote sensing reflectivity) image.
In the embodiment of the invention, after the historical remote sensing reflectivity image of the target water body area is obtained, samples are collected from the historical remote sensing reflectivity image, the target water body area is divided into seaweed, sand substrate and seawater, and corresponding spectral information is extracted from each sample.
In the embodiment of the invention, seaweed, sand substrates and sea water pixels in a plurality of seasons in a plurality of years are selected as samples in the historical remote sensing reflectivity image.
In the specific implementation, taking the WiHairperson's moonpool seaweed bed as an example, the type of the substrate of the moonpool can be determined to be three types of seaweed, sand substrate and other seawater pixels by means of spectrum identification or visual observation, and the three types of pixels are respectively selected from different yearsThe pixels of three types of substrates in different seasons are used as samples>4000) and R for each sample was plotted rs (lambda) spectral curve and average spectral curve thereof for analysis and extraction of R of different substrates rs (lambda) spectral feature differences.
In a specific implementation, after the spectral information of the sample is obtained, a spectral index may be constructed.
In the embodiment of the invention, based on the spectral information of seaweed and seawater, the difference value between the near-infrared band remote sensing reflectivity obtained by linear interpolation of the red-wave band remote sensing reflectivity and the short-wave infrared band remote sensing reflectivity and the near-infrared band remote sensing reflectivity observed by a satellite is calculated respectively, so as to construct a first spectral index of the seaweed and a first spectral index of the seawater.
In a specific embodiment, R according to different substrates rs (lambda) remote sensing reflectance spectrum variability, an Index that can maximally highlight the spectrum differences of seaweed and other targets was constructed as the extracted seaweed bed spectrum Index (SSI). By analyzing the spectrum curves of sea weed and sea water, the sea weed presents a certain reflection peak characteristic in a near infrared band, the sea water presents an obvious absorption valley characteristic due to strong absorption, and accordingly, a first spectrum index of sea weed and a first spectrum index of sea water are constructed, and the formulas of the first spectrum indexes SSI-I of sea weed and sea water are the same and are expressed as follows:
，
wherein SSI-I represents a first spectral index, R rs (λ 2 )、R rs (λ 3 ) And R is rs (λ 4 ) Respectively representing the remote sensing reflectivities of red light wave band, near infrared wave band and short wave infrared wave band,λ 2 、λ 3 andλ 4 the wavelengths of the red, near infrared and short wave infrared bands are respectively represented.
In a specific implementation, the first spectral index is defined as a difference between the near-infrared band remote sensing reflectivity obtained by linear interpolation of the red-wave band and short-wave infrared band remote sensing reflectivity and the near-infrared band remote sensing reflectivity observed by the satellite. According to the remote sensing reflectivity spectral characteristics of the seaweed and the seawater, the first spectral index numerical distribution of the seaweed and the seawater shows obvious difference, so that the seaweed and the seawater can be distinguished from each other.
In an embodiment of the present invention, a first optimal threshold for distinguishing between seaweed and seawater is determined in an overlapping portion of a first spectral index of the seaweed and a first spectral index of the seawater.
In a specific implementation, the first spectral indexes (continuous values) of the respective samples of seaweed, sea water and sand substrates are calculated respectively, and the first spectral index square distribution map distribution of the three substrates is counted, wherein the seaweed and the sea water have obvious distinction, but still a small part of "fuzzy" pixels (the first spectral index values of the seaweed and the sea water pixels overlap). The distribution characteristics of sea weed and sea water pixels in the interval where the fuzzy pixels are located are analyzed by counting the proportion of the sea weed and the sea water pixels, a first spectrum index value (point value) capable of distinguishing the sea weed and the sea water pixels to the greatest extent is determined, the first spectrum index value is used as a first optimal threshold value of the first spectrum index, and the sea water pixels are removed.
In particular implementations, the threshold is determined from the histogram distribution of the whole sample, statistics from the histogram, and then the minimum threshold for the two types of error cases, such as the histogram thresholding method, is determined.
In a specific implementation, statistics find that the first spectrum index square distribution diagram of the sand matrix pixels and the seaweed pixels are seriously overlapped, and the sand matrix pixels and the seaweed pixels are difficult to distinguish well. Therefore, another spectral index needs to be further constructed to distinguish seaweed from sandy pixels. For this purpose, seaweed and sand substrate samples were selected and analyzed for R rs (lambda) spectral curve difference, a second spectral index is constructed.
In the embodiment of the invention, based on the spectrum information of seaweed and sand substrate, the degree of remote sensing reflectivity reduction from green light wave band to red light wave band is calculated respectively, and the second spectrum index of seaweed and the second spectrum index of sand substrate are constructed, and the formulas of the second spectrum indexes SSI-II are the same and expressed as:
，
wherein SSI-II represents a second spectral index, R rs (λ 1 ) And R is rs (λ 2 ) Remote sensing reflectivities, lambda, representing the green and red bands, respectively 1 And lambda (lambda) 2 Representing the wavelengths in the green and red bands, respectively.
In a specific implementation, the remote sensing reflectance value of the seaweed in the green light band is far smaller than that of the sandy substrate, the remote sensing reflectance value in the red light band is greatly attenuated compared with that in the green light band, the sandy substrate is attenuated less, and the second spectral index is defined as the intensity of the decrease of the remote sensing reflectance from the green light band to the red light band according to the remote sensing reflectance spectrum difference of the seaweed and the sandy substrate as described above. According to the remote sensing reflectivity spectral characteristics of the seaweed and the sand matrix, the second spectral index numerical distribution of the seaweed and the sand matrix shows obvious difference, so that the seaweed and the sand matrix can be used for distinguishing the pixels of the seaweed and the sand matrix.
In an embodiment of the present invention, a second optimal threshold for distinguishing between seaweed and sandy background is determined in the overlapping portion of the second spectral index of seaweed and the second spectral index of sandy background.
In a specific implementation, the second spectral indexes (continuous values) of the seaweed and the sand substrate are calculated respectively, the distribution situation of the second spectral index square distribution diagram of the two substrates is counted, the distribution characteristics are analyzed, the seaweed and the sand substrate pixels are obviously distinguished, but a small part of the "fuzzy" pixels (the second spectral index values of the seaweed and the sand substrate pixels overlap). And determining a second spectrum index value (point value) capable of distinguishing the seaweed and the sand matrix pixels to the greatest extent by counting the proportion of the seaweed and the sand matrix pixels in the interval where the fuzzy pixels are located, taking the second spectrum index value as a second optimal threshold value of the second spectrum index, and further removing the sand matrix pixels.
In a specific implementation, similarly, the threshold may be determined according to the square distribution map distribution of the whole sample, statistics of the square distribution map, and then the threshold with the smallest error percentage of the two types, such as the square distribution map threshold method, are determined.
In the embodiment of the invention, for the remote sensing image to be monitored, sea weed and sea water are distinguished based on a first optimal threshold value, sea weed and sand substrate are distinguished based on a second optimal threshold value, and a sea weed bed area is obtained.
In the embodiment of the invention, for the remote sensing image to be monitored, a first spectrum index and a second spectrum index of the image are calculated pixel by pixel, a part, in which the first spectrum index value is larger than the first optimal threshold value, is extracted to be used as a seaweed and sand substrate area, and a part, in which the second spectrum index value is larger than the second optimal threshold value, is extracted to be used as a seaweed bed area.
The invention also provides a remote sensing device for monitoring seaweed bed distribution, which comprises: the device comprises a remote sensing acquisition unit, a sample collection unit, a first spectrum index calculation unit, a first optimal threshold value determination unit, a second spectrum index calculation unit, a second optimal threshold value determination unit and an application unit, wherein: the remote sensing acquisition unit is used for acquiring a historical remote sensing image of the target area and calculating to obtain a historical remote sensing reflectivity image of the target water body area; the sample collection unit is used for collecting samples from the historical remote sensing reflectivity images, dividing a target water body area into seaweed, sand substrates and seawater, and extracting corresponding spectrum information from each sample respectively; the first spectrum index calculation unit is used for respectively calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the observed near infrared band remote sensing reflectivity based on the spectrum information of the seaweed and the seawater, and constructing a first spectrum index of the seaweed and a first spectrum index of the seawater; the first optimal threshold determining unit is used for determining a first optimal threshold for distinguishing sea grass from sea water in an overlapped part of the first spectrum index of sea grass and the first spectrum index of sea water; the second spectrum index calculation unit is used for respectively calculating the degree of remote sensing reflectivity reduction from a green light wave band to a red light wave band based on spectrum information of the seaweed and the sand substrate, and constructing a second spectrum index of the seaweed and a second spectrum index of the sand substrate; the second optimal threshold determining unit is used for determining a second optimal threshold for distinguishing seaweed and the sandy bottom in the overlapping part of the second spectrum index of the seaweed and the second spectrum index of the sandy bottom; the application unit is used for distinguishing seaweed from seawater based on a first optimal threshold value and distinguishing seaweed from sandy bottom based on a second optimal threshold value for remote sensing images to be monitored, so as to obtain a seaweed bed area.
In the embodiment of the invention, the remote sensing acquisition unit is used for removing the images with the cloud and solar flare influence exceeding the standard threshold value from the historical remote sensing images of the target area, and obtaining the historical remote sensing reflectivity images of the target area through atmospheric correction.
In the embodiment of the invention, the remote sensing acquisition unit is used for cutting out images according to the vector boundary of the target water body area, and performing amphibious segmentation by utilizing the normalized difference water body index to obtain the historical remote sensing reflectivity image of the target water body area.
In the embodiment of the invention, the sample collection unit is used for selecting the remote sensing reflectivity of seaweed, sand substrate and sea water pixels in a plurality of seasons in a plurality of years from the historical remote sensing reflectivity image as a sample.
In the embodiment of the present invention, the formula of the first spectrum index is as follows:
，
wherein SSI-I represents a first spectral index, R rs (λ 2 )、R rs (λ 3 ) And R is rs (λ 4 ) Remote sensing reflectivities respectively representing red light wave band, near infrared wave band and short wave infrared wave band, lambda 2 、λ 3 And lambda (lambda) 4 The wavelengths of the red, near infrared and short wave infrared bands are respectively represented.
In the embodiment of the invention, the first optimal threshold determining unit is configured to count the first spectrum index square distribution diagram of each seaweed sample and the proportion of seaweed and seawater in the overlapping portion of the first spectrum index square distribution diagram of each seawater sample, and determine, as the first optimal threshold, a first spectrum index value capable of distinguishing the seaweed and the seawater to the greatest extent.
In the embodiment of the present invention, the formula of the second spectrum index is as follows:
，
wherein SSI-II represents a second spectral index, R rs (λ 1 ) And R is rs (λ 2 ) Remote sensing reflectivities, lambda, representing the green and red bands, respectively 1 And lambda (lambda) 2 Representing the wavelengths in the green and red bands, respectively.
In the embodiment of the present invention, the second optimal threshold determining unit is configured to count a second spectrum index square distribution diagram of each seaweed sample and a proportion of the seaweed and the sand matrix in an overlapping portion of the second spectrum index square distribution diagram of each sand matrix sample, and determine a second spectrum index value capable of distinguishing the seaweed and the sand matrix to the greatest extent as the second optimal threshold.
In the embodiment of the invention, the application unit is configured to calculate, for a remote sensing image to be monitored, a first spectrum index and a second spectrum index of the image pixel by pixel, extract a portion in which the first spectrum index value is greater than the first optimal threshold value as a seaweed and sand matrix area, and extract a portion in which the second spectrum index value is greater than the second optimal threshold value as a seaweed bed area in the seaweed and sand matrix area.
Claims (8)
1. A remote sensing method for monitoring seaweed bed distribution, comprising:
acquiring a historical remote sensing image of a target area, and calculating to obtain a historical remote sensing reflectivity image of the target water area;
collecting samples from the historical remote sensing reflectivity images, dividing a target water body area into seaweed, sandy bottom materials and seawater, and extracting corresponding spectral information from each sample respectively;
based on the spectral information of seaweed and seawater, respectively calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the near infrared band remote sensing reflectivity observed by a satellite, and constructing a first spectral index of the seaweed and a first spectral index of the seawater; the formula of the first spectral index is as follows:
，
wherein SSI-I represents a first spectral index, R rs (λ 2 )、R rs (λ 3 ) And R is rs (λ 4 ) Remote sensing reflectivities respectively representing red light wave band, near infrared wave band and short wave infrared wave band, lambda 2 、λ 3 And lambda (lambda) 4 Respectively representing the wavelengths of a red light wave band, a near infrared wave band and a short wave infrared wave band;
determining a first optimal threshold for distinguishing between seaweed and seawater in the overlapping portion of the first spectral index of seaweed and the first spectral index of seawater;
based on the spectrum information of seaweed and sand substrate, respectively calculating the degree of remote sensing reflectivity reduction from green light wave band to red light wave band, and constructing a second spectrum index of the seaweed and a second spectrum index of the sand substrate;
determining a second optimal threshold for distinguishing between seaweed and a sandy substrate in an overlapping portion of the second spectral index of seaweed and the second spectral index of the sandy substrate;
and for the remote sensing image to be monitored, calculating a first spectrum index and a second spectrum index of the image pixel by pixel, extracting a part, wherein the first spectrum index value of the part is larger than the first optimal threshold value, as a seaweed and sand substrate area, and extracting a part, in the seaweed and sand substrate area, of which the second spectrum index value is larger than the second optimal threshold value, as a seaweed bed area.
2. The method of claim 1, wherein the calculating a historical remote sensing reflectance image of the target water region comprises:
removing images with cloud and solar flare influences exceeding a standard threshold from the historical remote sensing images of the target area, and obtaining the historical remote sensing reflectivity images of the target area through atmospheric correction.
3. The remote sensing method for monitoring sea grass bed distribution according to claim 2, wherein the calculating to obtain the historical remote sensing reflectivity image of the target water body area comprises:
cutting the image according to the vector boundary of the target water body region, and carrying out amphibious segmentation by utilizing the normalized difference water body index to obtain the historical remote sensing reflectivity image of the target water body region.
4. The method of claim 1, wherein collecting samples from historical remote sensing reflectance images to divide a target water body area into seaweed, sandy bottom and sea water comprises:
and selecting the remote sensing reflectivities of seaweed, sandy bottom materials and seawater in a plurality of seasons in a plurality of years from the historical remote sensing reflectivity images as samples.
5. The method of claim 1, wherein determining a first optimal threshold for distinguishing between seaweed and seawater in the overlapping portion of the first spectral index of seaweed and the first spectral index of seawater comprises:
and counting the proportion of the seaweed and the seawater in the overlapped part of the first spectral index square distribution diagram of each seaweed sample and the first spectral index square distribution diagram of each seawater sample, and determining a first spectral index value capable of distinguishing the seaweed and the seawater to the greatest extent as a first optimal threshold.
6. The method of claim 1, wherein the second spectral index is formulated as follows:
，
wherein SSI-II represents a second spectral index, R rs (λ 1 ) Remote sensing reflectivity lambda representing green light wave band 1 Indicating the wavelength of the green band.
7. The method of claim 6, wherein determining a second optimal threshold for distinguishing between seaweed and sand matrix in the overlapping portion of the second spectral index of seaweed and the second spectral index of sand matrix comprises:
and counting the proportion of the seaweed and the sand substrate in the overlapped part of the second spectrum index square distribution diagram of each seaweed sample and the second spectrum index square distribution diagram of each sand substrate sample, and determining the second spectrum index value capable of distinguishing the seaweed and the sand substrate to the greatest extent as a second optimal threshold.
8. A remote sensing device for monitoring the distribution of a seaweed bed, comprising: the device comprises a remote sensing acquisition unit, a sample collection unit, a first spectrum index calculation unit, a first optimal threshold value determination unit, a second spectrum index calculation unit, a second optimal threshold value determination unit and an application unit, wherein:
the remote sensing acquisition unit is used for acquiring a historical remote sensing image of the target area and calculating to obtain a historical remote sensing reflectivity image of the target water body area;
the sample collection unit is used for collecting samples from the historical remote sensing reflectivity images, dividing a target water body area into seaweed, sand substrates and seawater, and extracting corresponding spectrum information from each sample respectively;
the first spectrum index calculation unit is used for respectively calculating the difference value between the near infrared band remote sensing reflectivity obtained by linear interpolation of the red light band remote sensing reflectivity and the short wave infrared band remote sensing reflectivity and the near infrared band remote sensing reflectivity observed by a satellite based on the spectrum information of seaweed and sea water, and constructing a first spectrum index of the seaweed and a first spectrum index of sea water; the formula of the first spectral index is as follows:
，
wherein SSI-I represents a first spectral index, R rs (λ 2 )、R rs (λ 3 ) And R is rs (λ 4 ) Remote sensing reflectivities respectively representing red light wave band, near infrared wave band and short wave infrared wave band, lambda 2 、λ 3 And lambda (lambda) 4 Respectively representing the wavelengths of a red light wave band, a near infrared wave band and a short wave infrared wave band;
the first optimal threshold determining unit is used for determining a first optimal threshold for distinguishing sea grass from sea water in an overlapped part of the first spectrum index of sea grass and the first spectrum index of sea water;
the second spectrum index calculation unit is used for respectively calculating the degree of remote sensing reflectivity reduction from a green light wave band to a red light wave band based on spectrum information of the seaweed and the sand substrate, and constructing a second spectrum index of the seaweed and a second spectrum index of the sand substrate;
the second optimal threshold determining unit is used for determining a second optimal threshold for distinguishing seaweed and the sandy bottom in the overlapping part of the second spectrum index of the seaweed and the second spectrum index of the sandy bottom;
the application unit is used for calculating a first spectrum index and a second spectrum index of an image pixel by pixel for a remote sensing image to be monitored, extracting a part, wherein the first spectrum index value of the part is larger than the first optimal threshold value, of the remote sensing image to be monitored to serve as a seaweed and sand substrate area, and extracting a part, in the seaweed and sand substrate area, of the part, the second spectrum index value of the part is larger than the second optimal threshold value, of the part, serving as a seaweed bed area.
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