A method for identifying green tide surface subsidence area by remote sensing

Abstract

A method for identifying green tide surface subsidence area by remote sensing, comprising the following steps: S1, resampling multi-source remote sensing data of a target sea area to match a uniform resolution; extracting green tide activity tracks in a long time sequence from the multi-source remote sensing data; S2, mapping the ratio of total carbon and total nitrogen, i.e. CN ratio, in the green tide subsidence area using the multi-source remote sensing data; S3, calculating the growth rate of the green tide in the area and determining the specific value of the CN ratio in the remote sensing data; S4, based on the determined area with green tide activity tracks, the green tide growth rate being less than 1 and the CN ratio being greater than 0, calculating the average CN ratio in the area meeting the above conditions to construct a green tide surface subsidence area identification model to determine the green tide surface subsidence area. Compared with the prior art, the present application provides a fast, dynamic and large-scale remote sensing identification method which can make up for the shortcomings of the prior art, such as large cost and small range of green tide subsidence area monitoring.

Description

Technical Field

[0001] This invention relates to the field of remote sensing applications, and in particular to a remote sensing identification method for surface subsidence zones in green tides. Background Technology

[0002] Traditional methods for monitoring green tide deposition zones are mainly based on aerial surveys, including sample collection and sterol biomarker analysis.

[0003] 1) Sample collection

[0004] Sample collection primarily involved using a cassette sampler to collect sediment samples, followed by subsampling of surface sediments (0-5 cm) using a modified syringe, and storing the samples at -20°C before analysis. A total of 5 liters of seawater were collected for each phytoplankton sample.

[0005] 2) Analysis of sterol biomarkers

[0006] Since the dominant species of green tides in the Yellow Sea are mainly formed by Ulva prolifera, many techniques have been used to analyze 28-heterostosterol in surface sediments as a biomarker for Ulva prolifera.

[0007] The main implementation process for monitoring green tide subsidence areas is as follows: ① Aerial survey sample collection; ② Sample analysis (sterol biomarker analysis).

[0008] The sample collection process was as follows: A portion (2 liters) of the collected seawater sample was used for HTS analysis of micron- and nano-scale phytoplankton. The sample was pre-sieved (using a 20 μm pore size sieve) to remove large phytoplankton and zooplankton, and then filtered under 50 kPa vacuum onto a polycarbonate membrane (0.40 μm pore size, 47 mm diameter, Millipore Corporation, USA). Another portion of the seawater (1 liter) was passed through a 200 μm sieve and then filtered onto a 0.40 μm mesh polycarbonate membrane for phage analysis using qPCR. The remaining seawater was filtered through a Whitman GF / F glass fiber filter (25 mm diameter). The filtrate was collected for chlorophyll a measurement and stored in 60 mL polyethylene bottles to determine the concentration of dissolved inorganic nutrients. Approximately 0.1 mL of chloroform was added to the filtrate, and the sample was then stored at -20°C until analysis using a SKALAR flow analyzer (Skalar GmbH, Netherlands). Temperature and salinity were obtained using a conductivity temperature depth (CTD) recorder.

[0009] The sample analysis procedure was as follows: the final extract of the sample was derivatized using bis(trimethylsilyl)trifluoroacetamide (BSTFA) and trimethylchlorosilane (TMCS) (99% BSTFA + 1% TMCS), and sterols were analyzed using gas chromatography-mass spectrometry (Agilent 7890A-GC-5975C MS with autosampler). Sterols were separated using a silica capillary column (30 m × 0.25 mm, 0.25 μm film thickness SPE-50 column, SUPELCO, USA). Sterols were identified and quantified under selected ion scanning modes.

[0010] Currently, traditional methods for monitoring green tide subsidence areas mainly rely on aerial surveys and biochemical analysis. The distribution of sampling points is heavily dependent on aerial survey routes, and these surveys consume significant manpower and resources, are time-consuming, and are subject to complex and variable marine conditions. Furthermore, sample analysis depends on experimental equipment, and there is a lag between sample detection and sampling times. These issues limit the monitored area of ​​green tide subsidence areas. Moreover, due to the dynamic, floating, and massive nature of green tides, traditional monitoring methods are no longer sufficient for monitoring and preventing green tide disasters. In summary, traditional methods for monitoring green tide subsidence areas suffer from drawbacks such as limited monitoring range, high manpower and resource consumption, and monitoring lag.

[0011] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0012] The main objective of this invention is to overcome the deficiencies of the aforementioned background technology and provide a remote sensing identification method for surface settlement zones of green tides.

[0013] To achieve the above objectives, the present invention adopts the following technical solution:

[0014] A remote sensing identification method for surface subsidence zones in green tides includes the following steps:

[0015] S1. Resample the multi-source remote sensing data of the target area at sea to match a uniform resolution; extract the green tide activity trajectory from the long-term series of the multi-source remote sensing data;

[0016] S2. Use the multi-source remote sensing data to map the ratio of total carbon to total nitrogen, i.e., the CN ratio, within the green tide settling area;

[0017] S3. Calculate the growth rate of the green tide in the region and determine the specific value of the CN ratio in the remote sensing data;

[0018] S4. Based on the identified areas with green tide activity trajectories, green tide growth rates less than 1, and CN ratios greater than 0, calculate the average CN ratio within the areas that meet the above conditions, and use this to construct a green tide surface settlement zone identification model to determine the surface settlement zone of the green tide.

[0019] Furthermore:

[0020] In step S1, the multi-source remote sensing data includes ocean color, ocean physical, and ocean biochemical data; the resolution of the multi-source remote sensing data is resampled to 4km.

[0021] In step S1, the multi-source remote sensing data is preprocessed, and the floating algal index algorithm (FAI) is used to calculate the index. The calculated green tide pixels are decomposed into mixed pixels to obtain the green tide area, thereby extracting the green tide activity trajectory on the long-term series and counting the green tide "footprint" within every 4km pixel.

[0022] In step S1, the multi-source remote sensing data used for index calculation includes MODIS and / or GOCI remote sensing data.

[0023] In step S2, particulate organic carbon and nitrate remote sensing data are extracted from the multi-source remote sensing data and resampled and normalized. The particulate organic carbon and nitrate remote sensing data are then used to map the CN ratio.

[0024] Step S3 specifically includes:

[0025] (1) Calculate the absorption rates of nitrate and phosphate in the green tide algae;

[0026] (2) Calculate the effect function of sea surface temperature on the growth of green tide algae;

[0027] (3) Calculate the loss rate of nitrate and phosphate in the green tide algae;

[0028] (4) Calculate the change in carbon biomass of the green tide algae;

[0029] (5) Calculate the growth rate of the green tide algae based on the calculation results of (1)-(4);

[0030] Step S4 specifically includes:

[0031] For regions with green tide activity patterns, green tide growth rates less than 1, and CN ratios greater than 0, calculate the mean of all CN ratios in the region;

[0032] The region where the CN ratio is greater than the mean value is defined as the surface settlement zone of the green tide.

[0033] The multi-source remote sensing data refers to satellite remote sensing data.

[0034] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the remote sensing identification method for green tide surface subsidence zones.

[0035] The present invention has the following beneficial effects:

[0036] This invention provides a method for identifying surface settlement zones of green tides based on key biochemical factor mapping using remote sensing. In the monitoring and prevention of green tide disasters, this invention can accurately identify the main settlement areas of green tides, which are the primary areas of decay, decomposition, and death, causing severe damage to the marine ecological environment. Simultaneously, this invention overcomes the shortcomings of traditional aerial survey sampling and biochemical methods for finding settlement zones, such as limited coverage and high costs. By proposing a "remote sensing mapping of key biochemical factors in settlement zones," and based on multi-source remote sensing data, a green tide surface settlement zone identification model is constructed, providing technical support for the monitoring and prevention of green tide disasters. This invention also avoids interference from other marine organisms on the model, improving the accuracy of settlement zone identification.

[0037] Compared with traditional technologies, this invention provides a rapid, dynamic, and large-scale remote sensing identification method that can overcome the shortcomings of traditional green tide settlement zone monitoring, such as high cost and small range. This method greatly saves the cost of green tide disaster monitoring and solves the problem of lack of reasonable biological mechanism explanation in green tide remote sensing monitoring. It can be combined with traditional aerial survey sampling methods for green tide settlement zone verification and other work, significantly improving the real-time performance and effectiveness of green tide disaster monitoring and prevention.

[0038] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the extraction of green tide "footprints" based on multi-source remote sensing data in an embodiment of the present invention.

[0040] Figure 2 This is a framework diagram of the green tide surface settlement zone identification model according to an embodiment of the present invention. Detailed Implementation

[0041] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0042] To address the shortcomings of traditional methods for monitoring green tide settlement zones, such as limited monitoring range, high consumption of manpower and resources, and monitoring lag, this invention proposes a method for identifying surface settlement zones of green tides based on the mapping of key biochemical factors. This method proposes a technical approach of "first calculating the values ​​of key factors, then constructing a model." After finding the remote sensing mapping data of key settlement zone biochemical factors, it combines this with the green tide "footprint" monitored by multi-source remote sensing data, avoiding interference from other marine organisms and improving the accuracy of settlement zone identification. This invention includes finding the remote sensing mapping data of key settlement zone biochemical factors, determining the values ​​of settlement zone biochemical factors after mapping the remote sensing data, and constructing a settlement zone identification model.

[0043] See Figure 1 This invention provides a remote sensing identification method for surface subsidence zones in green tides, comprising the following steps:

[0044] S1. Resample the multi-source remote sensing data of the target area at sea to match a uniform resolution; extract the green tide activity trajectory (which can be called the green tide "footprint") from the multi-source remote sensing data over a long period of time.

[0045] S2. Use the multi-source remote sensing data to map the ratio of total carbon to total nitrogen, i.e., the CN ratio, within the green tide settling area;

[0046] S3. Calculate the growth rate of green tides within the region and determine areas unsuitable for green tide growth;

[0047] S4. Based on the identified areas with green tide activity trajectories, green tide growth rates less than 1, and CN ratios greater than 0, calculate the average CN ratio within these areas. Use this average CN ratio to construct a green tide surface settlement zone identification model to determine the surface settlement zone of the green tide. As an example, the constructed model can use the average CN ratio within the areas meeting preset conditions as the judgment criterion; areas with CN ratios greater than this average are determined to belong to the green tide surface settlement zone.

[0048] The multi-source remote sensing data in this invention may include remote sensing data obtained from satellites, space stations, aircraft, etc. In some embodiments, the multi-source remote sensing data may include ocean color, marine physical, and marine biochemical data. Specifically, ocean color data may include Rayleigh-corrected reflectance, photosynthetically active radiation, particulate organic carbon, etc. Marine physical data may include ocean currents and sea surface temperature, etc. Marine biochemical data may include nutrients, etc.

[0049] The following describes specific embodiments of the present invention.

[0050] In a specific embodiment, the process of the method for identifying surface settlement zones of green tides based on key biochemical factor mapping is as follows: Figure 2As shown. Its key aspects include multi-source remote sensing data mapping of crucial biochemical factors and the design of a green tide surface sedimentation zone identification model based on these factors. The main steps of this method are as follows:

[0051] 1. Multi-source remote sensing data processing and extraction of green tide "footprints"

[0052] To construct a model of the surface subsidence zone of green tides, the multi-source remote sensing data required for construction includes ocean color data, ocean physical data, and ocean biochemical data. The resolution problem between the multi-source data needs to be solved first. Therefore, the resolution of the multi-source remote sensing data is resampled to 4km.

[0053] Meanwhile, the extraction of the green tide "footprint" is also the foundation of model construction. This is achieved by preprocessing high temporal resolution remote sensing data such as MODIS (Moderate Resolution Imaging Spectroradiometer) and GOCI (Geostationary Ocean Color Imager). Taking Rayleigh-corrected reflectance data processing as an example, the data processing can include geometric correction, radiometric correction, Rayleigh correction, and cropping and masking of MODIS and GOCI remote sensing data to obtain Rayleigh-corrected data. Further, robust and accurate index calculations are performed. A preferred embodiment uses the Floating Algal Index (FAI) algorithm for index calculation. Finally, by performing mixed pixel decomposition on the calculated green tide pixels, the accurate green tide area is obtained, thereby extracting the green tide "footprint" over a long time series and statistically analyzing the green tide "footprint" within every 4 km pixel. The green tide "footprint" extraction can be expressed using the following formula:

[0054]

[0055] In the formula, It means Near-infrared reflectance at [location], the calculation within square brackets represents [the value]. and The linear baseline between them.

[0056]

[0057] In the formula, This indicates that the pixel contains a value of 0% green tide (minimum threshold). This represents the value (maximum threshold) indicating that a pixel contains 100% green tide. This represents the FAI value of the i-th pixel. It is important to note that... This represents the threshold value between seawater and algae pixels in each image. The value is set to 0.149.

[0058] 2. Remote sensing data mapping of the CN ratio, a key biochemical factor

[0059] Within the settling zone, the green tide is primarily characterized by decay and decomposition of the algae, which gradually sink to the seabed. During this process, the decomposition of the algae releases large amounts of biochemical elements such as carbon, nitrogen, and phosphorus. Previous studies have found that within the green tide settling zone identified using 28-heterofostellarin, the ratio of total carbon to total nitrogen is very high, and areas with higher ratios exhibit higher concentrations of 28-heterofostellarin.

[0060] For the key biochemical factor of the subsidence zone, the preferred embodiment uses remote sensing data of particulate organic carbon and nitrate to map the CN ratio. The acquired daily remote sensing data of particulate organic carbon and nitrate need to be resampled and normalized.

[0061] 3. Calculation of Green Tide Growth Rate

[0062] Based on the characteristics of the green tide settling zone, areas unsuitable for green tide growth were identified, and the green tide growth rate within these areas was calculated.

[0063] (1) Calculate the absorption rate of nitrate and phosphate in the green tide algae.

[0064]

[0065] In the formula, and These represent the maximum absorption rates of DIN and DIP, respectively. and These represent the contents of DIN and DIP, respectively. and These represent the half-saturation coefficients of DIN and DIP, respectively; and These represent the maximum allocation amounts for N and P, respectively.

[0066] (2) Calculate the effect function of sea surface temperature on the growth of green tide algae.

[0067]

[0068] In the formula, the temperature limit function , and They represent the temperature T The photosynthesis, respiration, and death processes under the skin.

[0069] (3) Calculate the loss rate of nitrate and phosphate in the green tide algae.

[0070]

[0071] In the formula, This indicates the carbon loss rate of the green tide algae. and The rates of nitrate and phosphate loss from algae are represented, respectively. This represents the rate of dark respiration.

[0072] (4) Calculate the change in carbon biomass of green tide algae.

[0073]

[0074] In the formula, This indicates the conversion rate between carbon and green wet weight, and its value is typically 8 mmol CgFW. -1 .

[0075] (5) Based on the calculation results of (1)-(4), calculate the growth rate of green tide algae.

[0076]

[0077] in, and The tables separately illustrate the absorption and loss of carbon by the algae. The carbon absorption by the algae depends on a function of photosynthesis. Subject to temperature function Nutrients Light attenuation due to self-shading by large algae Restrictions. Indicates the maximum photosynthetic rate. Photosynthetic efficiency. Nitrogen allocation quota. With phosphorus allocation quota They represent N:C and P:C, respectively. and These are the minimum allocation amounts for N and P, respectively.

[0078] 4. Establishment of surface settlement zone identification model

[0079] First, the CN ratio, a key biochemical factor in the settlement zone, needs to be determined based on three fundamental factors. Then, a model for identifying the surface settlement zone of green tides is constructed. The three fundamental factors for model construction are:

[0080] (1) Green Tide “Footprints”: Select areas where green tide “footprints” exist.

[0081] (2) Green tide growth rate: Select areas where the green tide growth rate is less than 1, indicating that the growth environment conditions in the area are no longer suitable for the growth of green tide.

[0082] (3) CN ratio: Select regions where the normalized CN ratio of remote sensing mapping is greater than 0.

[0083] (4) Green Tide Surface Settlement Zone Identification Model: The model is the mean of all CN ratios in the region that satisfies all conditions (1)-(3). The region with a value greater than this mean is the range of the green tide surface settlement zone. The model expression is:

[0084]

[0085] In the formula, The mean of the CN ratio, To satisfy the number of all cells in the three basics.

[0086] In summary, addressing the limitations of traditional aerial surveying and biochemical methods for identifying settlement zones, such as limited coverage and high costs, this invention proposes a method for identifying surface settlement zones in green tides based on the mapping of key biochemical factors. This method, based on the concept of "remote sensing mapping of key biochemical factors in settlement zones," utilizes multi-source remote sensing data to construct a model for identifying surface settlement zones in green tides, providing technical support for the monitoring and prevention of green tide disasters.

[0087] Compared with traditional technologies, the remote sensing identification method of this invention overcomes the shortcomings of traditional green tide settlement zone monitoring, such as high cost and small range. It is fast, dynamic and wide-ranging, which greatly saves the cost of green tide disaster monitoring. At the same time, it solves the problem of lack of reasonable biological mechanism explanation in green tide remote sensing monitoring. It can be combined with traditional aerial survey sampling methods to verify green tide settlement zones, and significantly improves the real-time performance and effectiveness of green tide disaster monitoring and prevention.

[0088] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0089] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0090] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0091] The storage medium can be implemented by any type of volatile or non-volatile storage device, or a combination thereof. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0092] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0093] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0094] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0095] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0097] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0098] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0099] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0100] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A remote sensing identification method for surface subsidence zones in green tides, characterized in that, Includes the following steps: S1. Resample the multi-source remote sensing data of the target area at sea to match a uniform resolution; extract the green tide activity trajectory from the long-term series of the multi-source remote sensing data; S2. The ratio of total carbon to total nitrogen (CN) in the green tide settling area is mapped using the multi-source remote sensing data; wherein, particulate organic carbon and nitrate remote sensing data are extracted from the multi-source remote sensing data and resampled and normalized, and the CN ratio is mapped using the particulate organic carbon and nitrate remote sensing data. S3. Calculate the growth rate of green tides within the region and determine areas unsuitable for green tide growth; S4. Based on the identified areas with green tide activity trajectories, green tide growth rates less than 1, and CN ratios greater than 0, calculate the average CN ratio within the areas that meet the above conditions, and use this to construct a green tide surface settlement zone identification model to determine the surface settlement zone of the green tide.

2. The remote sensing identification method for surface subsidence zones of green tides as described in claim 1, characterized in that, In step S1, the multi-source remote sensing data includes ocean color, ocean physical, and ocean biochemical data; the resolution of the multi-source remote sensing data is resampled to 4km.

3. The remote sensing identification method for surface subsidence zones of green tides as described in claim 1, characterized in that, In step S1, the multi-source remote sensing data is preprocessed, and the floating algal index algorithm (FAI) is used to calculate the index. The calculated green tide pixels are decomposed into mixed pixels to obtain the green tide area, thereby extracting the green tide activity trajectory on the long-term series and statistically analyzing the green tide activity trajectory within every 4km pixel.

4. The remote sensing identification method for surface subsidence zones of green tides as described in claim 3, characterized in that, In step S1, the multi-source remote sensing data used for index calculation includes MODIS and / or GOCI remote sensing data.

5. The remote sensing identification method for surface subsidence zones of green tides as described in claim 1, characterized in that, Step S3 specifically includes: (1) Calculate the absorption rates of nitrate and phosphate in the green tide algae; (2) Calculate the effect function of sea surface temperature on the growth of green tide algae; (3) Calculate the loss rate of nitrate and phosphate in the green tide algae; (4) Calculate the change in carbon biomass of the green tide algae; (5) Based on the calculation results of (1)-(4), calculate the growth rate of the green tide algae.

6. The remote sensing identification method for surface subsidence zones of green tides as described in any one of claims 1 to 5, characterized in that, Step S4 specifically includes: For regions with green tide activity patterns, green tide growth rates less than 1, and CN ratios greater than 0, calculate the mean of all CN ratios in the region; The region where the CN ratio is greater than the mean value is defined as the surface settlement zone of the green tide.

7. The remote sensing identification method for surface subsidence zones of green tides as described in any one of claims 1 to 5, characterized in that, The multi-source remote sensing data refers to satellite remote sensing data.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the remote sensing identification method for green tide surface subsidence areas as described in any one of claims 1 to 7.

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