Method for obtaining green tide biomass based on multi-source satellite remote sensing and application

Abstract

The application discloses a green tide biomass acquisition method based on multi-source satellite remote sensing and application, relates to the technical field of satellite remote sensing green tide monitoring, and comprises a spectrum curve of different biomass densities based on field measurement and a satellite spectrum response function, and a biomass inversion model of different satellites is respectively constructed; satellite remote sensing images are processed, NDVI vegetation indexes are calculated, green tide information is extracted, and a distribution range of the green tide is acquired; green tide biomass densities of each pixel in green tide patches of each satellite source are calculated, and biomass and a barycenter in the green tide patches are counted; multi-source satellite green tide patch biomasses are fused, and the green tide biomass is acquired. Drift prediction is carried out based on the green tide biomass, a green tide patch information table to be salvaged is established, and a green tide salvage area is determined according to the total body capacity of a ship in a single day in order. The application directly empowers the front-line disaster disposal action with the remote sensing technology achievements, and realizes synchronous and substantial improvement of monitoring capacity and management efficiency.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing green tide monitoring technology, and in particular to a method and application for obtaining green tide biomass based on multi-source satellite remote sensing. Background Technology

[0002] Green tides are a crucial target for marine environmental monitoring, impacting not only marine ecosystems but also coastal environments, causing severe social and economic losses. Continuous and effective monitoring of green tides is essential for their prevention and control. In this process, satellite remote sensing technology, with its advantages of high timeliness, multispectral capabilities, large-scale data acquisition, and low acquisition cost, has become the primary means of monitoring, preventing, and quantitatively estimating green tide disasters.

[0003] In estimating green tide biomass using remote sensing, current technologies largely rely on single types of satellite data. For example, low- to medium-resolution data cannot effectively distinguish discrete, fragmented green tide patches, while high-resolution data can clearly identify patches but lacks spatiotemporal continuity. Furthermore, existing remote sensing monitoring results are mostly presented as distribution maps and area statistics reports, failing to directly translate into decision-making information for specific salvage operations. Salvage command departments need to clearly know "where and how much to salvage first," not just "where the green tide is." There is a gap between remote sensing information and salvage commands; a technical process is lacking to further process the estimated spatial distribution data of biomass into executable information such as salvage priority allocation, salvage volume prediction, and fleet route planning. Summary of the Invention

[0004] In order to overcome the above-mentioned problems in the existing technology, this invention proposes a method and application for obtaining green tide biomass based on multi-source satellite remote sensing.

[0005] The technical solution adopted by this invention to solve its technical problem is: a method for obtaining green tide biomass based on multi-source satellite remote sensing, comprising the following steps:

[0006] Step 1: Based on the spectral curves of different biomass densities measured in the field and the satellite spectral response function, construct biomass inversion models for different satellites respectively;

[0007] Step 2: Perform radiometric positioning and atmospheric correction on the satellite remote sensing image, and use land information to crop out land areas from the image;

[0008] Step 3: Calculate the NDVI vegetation index using the effects processed in Step 2, extract green tide information and obtain the distribution range of the green tide based on the NDVI threshold method;

[0009] Step 4: Each star source uses the corresponding biomass inversion model constructed in Step 1 to calculate the green tide biomass density of each pixel within the green tide patch, and then performs biomass statistics and centroid calculation within the green tide patch.

[0010] Step 5: Fuse the biomass of the multi-source satellite green tide patches obtained in Step 4 to obtain the green tide biomass.

[0011] The above-mentioned method for obtaining green tide biomass based on multi-source satellite remote sensing involves the same process in step 1 for constructing biomass inversion models for multiple different satellites. The specific process for constructing a biomass inversion model for a single satellite is as follows:

[0012] Step 1.1: Collect spectral information at different biomass densities on-site to obtain spectral curves;

[0013] Step 1.2: Using the spectral response function of the satellite sensor, the spectral reflectance data of different biomass densities measured in Step 1.1 are converted into the sensor's equivalent reflectance.

[0014] ;

[0015] Where Ref(λ) is the measured hyperspectral reflectance; SRF i (λ) is the spectral response function for the i-th band; Ref i Let be the sensor's equivalent reflectivity in the i-th band;

[0016] Step 1.3: Using the equivalent reflectance of different biomass densities obtained in Step 1.2, calculate the vegetation index corresponding to each biomass density;

[0017] Step 1.4: Using the vegetation index and corresponding biomass concentration scatter plot obtained in Step 1.3, form a biomass inversion model with linear, exponential, and power-law fitting.

[0018] The above-mentioned method for obtaining green tide biomass based on multi-source satellite remote sensing, specifically step 3 of extracting green tide information and obtaining the distribution range of green tide, involves: using a threshold method to extract green tides and generate a (0,1) raster image; saving the center points of green tide pixels as green tide point files; simultaneously saving green tide pixels as surface files; merging the surface pixels within the same patch into one surface; and using the green tide point files, performing buffer analysis with radius R to obtain the distribution range of green tides.

[0019] The above-mentioned method for obtaining green tide biomass based on multi-source satellite remote sensing, specifically the process of biomass statistics and centroid calculation within the green tide patch in step 4 is as follows: assign values ​​to the biomass attribute column of the generated green tide point file, read the green tide biomass density corresponding to each green tide point from the biomass raster file, calculate the green tide biomass contained in the pixel, and fill it into the biomass attribute of the green tide point.

[0020] Assign values ​​to the biomass attribute column of the produced green tide surface file, use spatial analysis to obtain the green tide points contained in the polygon of each green tide patch, and sum the biomass of the green tide points in each green tide patch to obtain the biomass of that green tide patch.

[0021] The formula for calculating the centroid of patchy green tides is:

[0022] ;

[0023] Where, x i Let be the x-coordinate of the i-th pixel within the patch, and b be the biomass of the patch. i Let r be the biomass density of the i-th pixel within the patch, and r be the pixel spatial resolution in meters. 2 The pixel area.

[0024] The above-mentioned method for obtaining green tide biomass based on multi-source satellite remote sensing, specifically step 5, involves: using the time of the satellite inversion results with full coverage and large range as a benchmark, drifting / tracing the green tide patches monitored by other satellite sources back to the benchmark time, performing overlay analysis on the biomass results of the time-synchronized multi-source satellite green tide patches, using the high-resolution image results as the basis for the overlapping area, cropping the low-resolution coverage and distribution using the distribution area of ​​the high-resolution image, and combining the high-resolution monitoring results with the cropped lower-resolution results to obtain the satellite fusion result.

[0025] The above-mentioned method for obtaining green tide biomass based on multi-source satellite remote sensing supplements the data when the satellite-fused biomass information fails to provide full coverage. The supplementary information includes green tide patches and the centroid of green tide patches. After fusion, the method generates green tide patch surface files, patch centroid point files, and green tide distribution areas for the current day.

[0026] An application of a green tide biomass acquisition method based on multi-source satellite remote sensing is proposed. The green tide biomass obtained by the above-mentioned method based on multi-source satellite remote sensing is used to predict the drift of the green tide biomass. The drift prediction results are overlaid with the sensitive areas that need protection for analysis to establish a green tide patch information table affecting the sensitive areas. Then, a green tide patch information table to be salvaged is established. Based on the overall daily salvage capacity of the ship, the patches to be salvaged on the same day are selected in order to determine the green tide salvage area.

[0027] The above application is characterized in that the specific process of establishing the green tide patch information table in the sensitive area is as follows: select green tide patches in the green tide fusion result, take the patch centroid as the initial field, use the Lagrange particle drift prediction method to obtain the green tide patch centroid drift path for the next N days, and store each patch path as a point file;

[0028] By overlaying the drift path point file with the sensitive area, if a point in the path point file falls into the sensitive area, the green tide patch corresponding to the path point file is selected as a candidate salvage patch. Among the points that fall into the sensitive area, the predicted time corresponding to the point with the smallest sequence number is the time required for the patch corresponding to the path to enter the sensitive area. The green tide patches to be salvaged are sorted according to the time when the patches affect the sensitive area, and an information table is established.

[0029] The above application is characterized in that the green tide salvage zone establishment process is as follows: according to the size of the biomass of the seed area patches, the patches are sequentially buffered by a centroid of d km, and superimposed analysis is performed on other patches. The green tide patch whose centroid falls on the current patch buffer zone is a salvage zone. The sum of the biomass of the green tide patches in the salvage zone is the biomass of the salvage zone. Based on this biomass, an appropriate number of salvage vessels are allocated. When the green tide biomass of the salvage zone is less than the salvage capacity of one vessel, the salvage zone is abandoned until all green tide patches have been traversed.

[0030] The beneficial effects of this invention are that it constructs biomass inversion models using multi-source satellite remote sensing data and obtains green tide biomass based on the fusion of multi-source satellite remote sensing data. This overcomes the limitations of single data sources in existing technologies. For different sensor data, separate or adapted biomass inversion models are constructed, and then cross-validation and calibration are performed using in-situ spectral data, forming a multi-level, adaptive inversion model system. This method significantly reduces the inversion uncertainty caused by sensor differences, variations in environmental conditions (solar altitude angle, sea state), and the different physiological states of the green tide itself, greatly improving the robustness and accuracy of the model in different times and sea areas.

[0031] This invention directly transforms complex remote sensing quantitative products into clear and actionable guidance information for salvage operations, achieving a highly efficient closed loop between monitoring and response. Based on fused biomass data, salvage priorities can be scientifically determined (e.g., prioritizing areas with dense biomass or those approaching sensitive coastlines). Combined with green tide drift path prediction, it supports proactive deployment and dynamic path planning, guiding salvage forces to the most needed areas at the right time, thereby maximizing fleet utilization efficiency, shortening the overall response cycle, and reducing operating costs. The entire method provides a data-driven, dynamically optimized decision support system for green tide disaster emergency response. It transforms salvage operations from passive reaction to proactive planning, and from "area coverage" to "precise targeting," significantly improving the precision, scientific rigor, and intelligence of disaster management.

[0032] The technical framework of "multi-source data fusion and inversion - quantitative product generation - business decision support" established in this invention lays the core methodological foundation for building an operational, integrated air-space-ground monitoring and response support system for green tide disasters. This framework has good scalability and adaptability, and possesses long-term application value and promising prospects for widespread adoption.

[0033] In summary, this invention not only solves the technical bottleneck of existing remote sensing monitoring technology in the quantitative acquisition of green tide biomass, but also, through an innovative information transformation process, directly empowers front-line disaster response actions with cutting-edge remote sensing technology, achieving a simultaneous and substantial improvement in monitoring capabilities and governance effectiveness. Attached Figure Description

[0034] Figure 1 This is a flowchart of the satellite-based green tide biomass fusion process of this invention;

[0035] Figure 2 This is a schematic diagram of the biomass inversion model construction of the present invention;

[0036] Figure 3 These are the green tide spectral curves of different biomass densities according to the present invention;

[0037] Figure 4 This is the HY-1C biomass inversion model of the present invention;

[0038] Figure 5 This is a flowchart illustrating the fusion process of satellite green tide biomass patches, centroids, and distribution information with the fusion information from the previous day. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] This embodiment discloses a method for obtaining green tide biomass based on multi-source satellite remote sensing, including the following steps:

[0041] Step 1: Based on the spectral curves of different biomass densities measured in the field and the satellite spectral response function, construct biomass inversion models for different satellites respectively;

[0042] Step 2: Perform radiometric positioning and atmospheric correction on the satellite remote sensing image, and use land information to crop out land areas from the image;

[0043] Step 3: Calculate the NDVI vegetation index using the effects processed in Step 2, extract green tide information and obtain the distribution range of the green tide based on the NDVI threshold method;

[0044] Step 4: Each star source uses the corresponding biomass inversion model constructed in Step 1 to calculate the green tide biomass density of each pixel within the green tide patch, and then performs biomass statistics and centroid calculation within the green tide patch.

[0045] Step 5: Fuse the biomass of the multi-source satellite green tide patches obtained in Step 4 to obtain the green tide biomass. The specific fusion process is as follows: Figure 1 As shown.

[0046] In this embodiment, the influence of multiple satellite sources includes HY-1C / D (CZI), HY-1E (CZI / PMI), HJ-2A / B (CCD), GF-1 (WFV / PMS), etc.

[0047] The biomass retrieval models for various satellite imagery follow the same approach; the HY-1C model will be used as an example. First, the spectral curves of *Ulva prolifera* at different biomass densities are measured in-situ (the in-situ measured spectral curves are shown in...). Figure 3 (As shown in the image), then the equivalent reflectance of the satellite is calculated using the HY-1C satellite spectral response function. The vegetation index is then calculated using the equivalent reflectance, and a biomass inversion model is established using the vegetation index and the corresponding biomass density. The model establishment process is as follows: Figure 2 .

[0048] Using the spectral response function (SRF) of the HY-1C (CZI) satellite sensor, the measured spectral reflectance data for different biomass densities were converted into the sensor's equivalent reflectance; the calculation formula is as follows:

[0049] ;

[0050] Where Ref(λ) is the measured hyperspectral reflectance; SRF i (λ) is the spectral response function for the i-th band; Ref i Let be the sensor's equivalent reflectivity in the i-th band.

[0051] Using the HY-1C equivalent reflectance curves for different biomass densities, six vegetation indices corresponding to each biomass density were calculated. The calculation formulas for the vegetation indices are as follows:

[0052] Normalized Difference Vegetation Index (NDVI), formula: NDVI = (NIR - Red) / (NIR + Red)

[0053] Ratio vegetation index, formula: RVI = NIR / Red

[0054] Enhanced Vegetation Index (EVI), formula: EVI = G * (NIR - Red) / (NIR + C1 * Red - C2 * Blue + L), common parameters: G = 2.5, L = 1, C1 = 6, C2 = 7.5

[0055] Difference Vegetation Index (DVI), formula: DVI = NIR - Red

[0056] Virtual baseline floating macroalgae height index, formula: VB-FAH=(NIR - GREEN)+(GREEN - Red)*(NIR - GREEN) / (2NIR - Red -GREEN)

[0057] The red, green, and blue floating algae index, calculated using the formula: RGB-FAI = (GREEN-BLUE) + (Red-BLUE) * (GREEN-BLUE) / (Red -BLUE)

[0058] NIR: Reflectance of the Earth's surface in the near-infrared band; Red: Reflectance of the Earth's surface in the red band.

[0059] Using the calculated vegetation indices and corresponding biomass concentration scatter plots, linear, exponential, and power-law fitted biomass inversion models were developed. r was selected. 2 The exponential model with the largest EVI value is used as the biomass inversion model for HY-1C images, such as... Figure 4 .

[0060] Where the EVI inversion model corresponds to R 2 The value is the largest, and its corresponding inversion model is used as the biomass inversion model for HY-1C.

[0061] y=0.2086e 6.0599x

[0062] Where y represents biomass density, in kg / m³ 2 x is the EVI value.

[0063] Steps for extracting green tide information:

[0064] Image preprocessing

[0065] Radiometric calibration and atmospheric correction were performed on satellite remote sensing images, and land areas were cropped from the images using land information.

[0066] Extraction of green tide coverage and distribution information

[0067] Calculate the NDVI (normalized difference vegetation index) using the processed imagery:

[0068] NDVI = (ρ NIR - ρ Red ) / (ρ NIR + ρ Red )

[0069] Where, ρ nir , ρ red , where represents the reflectivity in the near-infrared and red bands, respectively.

[0070] Green tide information is extracted using the NDVI thresholding method, with the threshold typically set to 0. Fine-tuning is performed for different images based on specific circumstances. The thresholding method is used to extract the green tide and generate a (0,1) raster image. The center points of the green tide pixels are saved as green tide point files, and the green tide pixels are also saved as polygon files. Pixels within the same patch are merged into one polygon.

[0071] Using the green tide point file, a buffer analysis is performed with a radius R (the buffer analysis radius is usually set to 1km-3km) to obtain the distribution range of the green tide.

[0072] Biomass inversion.

[0073] Each satellite source uses its corresponding biomass inversion model to calculate the green tide biomass density of each pixel within the green tide patch, generating a biomass raster map.

[0074] 4) Biomass statistics and centroid calculation of green tide patches

[0075] Assign values ​​to the biomass attribute column of the generated green tide point file. Read the green tide biomass density y corresponding to each green tide point from the biomass raster file (.tif format), then calculate the green tide biomass contained in that pixel using the formula b=y*r*r, in kg, and fill it into the biomass attribute of that green tide point.

[0076] Assign values ​​to the biomass attribute column of the generated green tide surface file. Using spatial analysis methods, obtain the green tide points contained in the polygon of each green tide patch, and then sum the biomass of the green tide points in each green tide patch to obtain the biomass of that green tide patch.

[0077] ;

[0078] Where b is the biomass of the patch, in kg. i The biomass density of the i-th pixel within the patch, in kg / m³. 2 r is the pixel spatial resolution, in meters (m). 2 The pixel area.

[0079] The formula for calculating the centroid (X, Y) of patchy green tide is as follows:

[0080] ;

[0081] Where x i Let be the x-coordinate of the i-th pixel within the patch, and b be the biomass of the patch in kg. i The biomass density of the i-th pixel within the patch, in kg / m³. 2 r is the pixel spatial resolution, in meters (m). 2 The pixel area.

[0082] The fusion of biomass information from multi-source satellite inversion of green tide patches mainly includes the following steps:

[0083] For time-asynchronous results, the time of the comprehensive and wide-ranging satellite inversion results is used as the benchmark. Other satellite-sourced green tide patches are drifted / traced back to the benchmark time. Then, the biomass results of green tide patches from time-synchronized multi-source satellites are overlaid and analyzed. Overlapping areas are based on high-resolution imagery. During operation, low-resolution coverage and distribution are cropped using the distribution area of ​​the high-resolution imagery. Finally, the high-resolution monitoring results are combined with the cropped lower-resolution results to obtain the satellite fusion result, such as... Figure 1 As shown.

[0084] When satellite-fused biomass information fails to provide full coverage, the prediction results of the previous day's green tide biomass fusion information (which is based on the previous day's on-site salvage situation, excluding salvaged green tide patches) are used to supplement the data. The supplementary information includes green tide patches and their centroids. After fusion, a green tide patch surface file, a patch centroid point file, and a green tide distribution area for the current day are generated, such as... Figure 5 As shown.

[0085] Based on the fusion results obtained from the above-mentioned green tide biomass acquisition method, this embodiment also provides the application of the fusion results. An information table of salvaged green tide patches is established based on the fusion results, and the salvage area is determined. The specific process is as follows:

[0086] By overlaying the drift prediction results of green tide fusion information with the sensitive areas requiring protection, an information table of green tide patches affecting sensitive areas is established:

[0087] 1) Drift prediction: Green tide patches west of 121°E and north of 35°N from the green tide fusion results are selected. Using the patch centroid as the initial field, the Lagrange particle drift prediction method is used to obtain the centroid drift path of the green tide patch over the next N days (N ranges from 7 to 10). Each patch path is stored as a point shapefile, named with the patch's FID number in the fusion file, and the time interval between each time point in the point file is 1 hour.

[0088] 2) Establishment of a candidate green tide patch information table: This involves overlaying drift path point files with sensitive areas. If a point in the path point file falls into a sensitive area, the corresponding green tide patch is considered a candidate for retrieval. Among the points falling into the sensitive area, the predicted time corresponding to the point with the smallest sequence number is the time required for the patch to enter the sensitive area. The green tide patches to be retrieved are then sorted according to the order in which they affect the sensitive area, creating a green tide patch information table. This table includes the patch's FID (Fluid ID), centroid, biomass, time of arrival at the sensitive area, name of the affected sensitive area, location of the sensitive area, and path to the sensitive area in the fusion file.

[0089] A daily information table for green tide patches was established. Based on the vessel's overall daily salvage capacity, patches were selected from the candidate salvage information table in chronological order to create the daily information table for green tide patches. This table includes the green tide patch number, center of gravity, biomass, time of arrival at the sensitive area, name of the sensitive area, location of the sensitive area, and path taken to reach the sensitive area.

[0090] The salvage zone is constructed by first sorting the patches according to their biomass size in the seed region. Then, using the centroid of each patch as a buffer zone (d km, 3km~5km), the patches are overlaid with other patches for analysis. A green tide patch whose centroid falls within the buffer zone is considered a salvage zone. The sum of the biomass of the green tide patches within this salvage zone is the total biomass of that zone. The appropriate number of salvage vessels is then allocated based on this biomass. If the biomass of the green tide forming a salvage zone is less than the salvage capacity of a single vessel (approximately 30 tons per day), the salvage zone is abandoned. This process continues until all green tide patches have been traversed.

[0091] The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art can make various modifications or equivalent substitutions to the present invention within its scope and spirit, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for obtaining green tide biomass based on multi-source satellite remote sensing, characterized in that, It comprises the following steps: Step 1, based on the spectral curve of different biomass density measured on site and the satellite spectral response function, the biomass inversion model of different satellites is respectively constructed; Step 2, the satellite remote sensing image is carried out radiation positioning and atmospheric correction, and the land area in the image is cut off by using land information; Step 3, the NDVI vegetation index is calculated by using the image processed in step 2, the green tide information is extracted according to the NDVI threshold method, and the distribution range of the green tide is obtained; Step 4, each satellite source uses the corresponding biomass inversion model constructed in step 1 to calculate the green tide biomass density of each pixel in the green tide patch, and then the biomass statistics and gravity center calculation of the green tide patch are carried out; Step 5, the multi-source satellite green tide patch biomass obtained in step 4 is fused to obtain the green tide biomass. The specific process of biomass statistics and gravity center calculation of the green tide patch in step 4 is that the generated green tide point file biomass attribute column is assigned value, the green tide biomass density corresponding to each green tide point is read from the biomass grid file, the green tide biomass contained in the pixel is calculated, and the green tide biomass density corresponding to each green tide point is filled into the biomass attribute of the green tide point; The generated green tide surface file biomass attribute column is assigned value, the green tide points contained by each green tide patch polygon are obtained by using spatial analysis method, the green tide biomass in each green tide patch is added, and the biomass of the green tide patch is obtained; The patch green tide gravity center calculation formula is: wherein x i is the horizontal coordinate of the i-th pixel within the patch, b is the biomass of the patch, b i is the biomass density of the i-th pixel within the patch, r is the pixel spatial resolution in m, and r 2 is the pixel area; The specific process of step 5 is that for the results not synchronized in time, the time of the satellite inversion result covering all and the range is taken as the benchmark, the monitoring green tide patch of other satellite sources is drifted / traceable to the benchmark time, the multi-source satellite green tide patch biomass results synchronized in time are superimposed and analyzed, the coincident area is taken as the high-resolution image result, the distribution area of the high-resolution image is cut off from the low-resolution coverage and distribution, the high-resolution monitoring result and the low-resolution result after cutting are combined to obtain the satellite fusion result. 2.The method according to claim 1, wherein, The biomass inversion model construction process of multiple different satellites in step 1 is the same, and the specific process of constructing the biomass inversion model of one satellite is as follows: Step 1.1, field collection of spectral information of different biomass densities to obtain spectral curve; Step 1.2, using the spectral response function of satellite sensor, converting the spectral reflectance data of different biomass densities measured in step 1.1 into sensor equivalent reflectance: Wherein, Ref(λ) is the measured hyperspectral reflectance; SRF i (λ) is the spectral response function of the ith waveband; Ref i is the sensor equivalent reflectance of the ith waveband; Step 1.3, using the equivalent reflectance of different biomass densities obtained in step 1.2, calculating the vegetation index corresponding to each biomass density; Step 1.4, using the vegetation index obtained in step 1.3 and the corresponding biomass concentration scatter plot, forming the linear, exponential and power fitting biomass inversion model. 3.The method according to claim 1, wherein, In step 3, the green tide information is extracted and the distribution range of the green tide is obtained, which is that the threshold method is used to extract the green tide generation (0, 1) grid map, the green tide pixel center point is saved as a green tide point file, and the green tide pixel is saved as a surface file, and the pixels in the same patch are merged into a surface; the green tide point file is used to carry out buffer analysis with a radius R to obtain the distribution range of the green tide. 4.The method according to claim 1, wherein, When the satellite fusion biomass information fails to cover completely, the prediction result of the previous day's green tide biomass fusion information is used for supplement, and the supplement content includes green tide patches and green tide patch barycenters. After fusion, the green tide patch surface file, patch barycenter point file and green tide distribution area of the day are generated.

5. The application of a method for obtaining green tide biomass based on multi-source satellite remote sensing, characterized in that, The green tide biomass obtained by using the green tide biomass acquisition method based on multi-source satellite remote sensing according to any one of claims 1-4 is used for drift prediction, the drift prediction result is superimposed and analyzed with a sensitive area needing protection, an impact sensitive area green tide patch information table is established, and then a green tide patch information table to be salvaged is established. According to the total salvaging capacity of a ship on a single day, the patches salvaged on the day are selected in order of priority, and the green tide salvaging area is determined.

6. Use according to claim 5, characterized in that, The specific process of establishing the impact sensitive area green tide patch information table is as follows: selecting a green tide patch in the green tide fusion result, taking the patch barycenter as the initial field, and using the Lagrangian particle drift prediction method to obtain the drift path of the green tide patch barycenter in the future N days. Each patch path is stored as a point file; The drift path point file is superimposed and analyzed with the sensitive area. If the path point file has a point falling into the sensitive area, the green tide patch corresponding to the path file is a candidate salvaged patch. The point with the smallest serial number among the points falling into the sensitive area corresponds to the time required for the patch corresponding to the path to enter the sensitive area. According to the time sequence of the patch affecting the sensitive area, a green tide patch information table to be salvaged is established.

7. Use according to claim 5, characterized in that, The process of establishing the green tide salvaging area is as follows: the seed area patch biomass is sorted according to size, and the barycenter is sequentially buffered d km, and superimposed and analyzed with other patches. The barycenter falling in the buffer area of the current patch is a salvaging area. The green tide patch biomass in the salvaging area is the biomass of the salvaging area. According to the biomass, the number of salvaging ships is adjusted. When the green tide biomass of the salvaging area is less than the salvaging capacity of a ship, the salvaging area is abandoned, and the process is repeated until all green tide patches are traversed.

Images