Intelligent extraction method for envelope line of algae bloom accumulation area of large floating algae

Abstract

The invention belongs to the field of spatial analysis methods, and particularly relates to a sea surface macroalgae bloom accumulation area envelope line intelligent extraction method based on a geographic information system. Comprising the following steps: 1) converting all original remote sensing extraction data into a projection coordinate system; 2) performing spatial clustering on the discrete algal bloom plaques by adopting an aggregation polygon algorithm; 3) performing morphological optimization on the clustered algal bloom plaques by adopting a geometric morphology optimization method; 4) carrying out buffer expansion on the boundaries of the algae bloom plaques after form optimization; and 5) carrying out topological optimization on the plaques to obtain a final algal bloom accumulation area. According to the method, defects and deficiencies caused by manual check are overcome, and meanwhile, the method can be operated in batches through scripts, so that the extraction efficiency of the algae bloom coverage range is greatly improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of spatial analysis methods, in particular to an intelligent envelope extraction method for large-scale algal bloom aggregation areas on the sea surface based on a geographic information system (GIS). BACKGROUND

[0002] At present, the extraction method for the coverage area of large-scale floating algal blooms (green tide, gold tide) in the China Marine Ecological Early Warning Monitoring Bulletin is mainly based on visual and manual methods to manually select the coverage results of algal blooms. The method of manually extracting large-scale algal bloom aggregation areas (harmful algal bloom coverage range, i.e. "envelope") by visual interpretation has certain defects and deficiencies:

[0003] 1) Strong dependence on manual operation and significant subjective errors. The existing method mainly relies on manual visual interpretation and manual selection of algal bloom boundaries, which is greatly affected by the experience, cognitive level and fatigue degree of the operator, resulting in subjective bias in the extraction results. For example, different interpreters have different standards for defining the boundaries of algal bloom coverage, resulting in poor consistency of the results, especially in the algal bloom edge fuzzy or mixed pixel area.

[0004] 2) Low efficiency and difficult to process in batches. Manual selection requires processing remote sensing images one by one, which is time-consuming and labor-intensive, and cannot meet the needs of large-scale, long-term algal bloom dynamic monitoring. It takes several hours to several days to manually select a single high-resolution image, which is difficult to adapt to high-frequency monitoring tasks. SUMMARY

[0005] The present application provides a method for spatial feature extraction and area calculation of large-scale algal bloom aggregation areas based on a GIS platform, mainly using a multi-step vector processing method to optimize remote sensing extraction results to overcome the defects and deficiencies caused by manual selection. At the same time, this method can be run in batches through scripts, greatly improving the efficiency of algal bloom coverage extraction.

[0006] The technical solution adopted by the present application to achieve the above-mentioned purpose is:

[0007] The intelligent extraction method for large-scale floating algal bloom aggregation area envelope includes the following steps:

[0008] 1) Convert all original remote sensing extraction data to the projected coordinate system;

[0009] 2) Use the aggregation polygon algorithm to perform spatial clustering on discrete algal bloom patches;

[0010] 3) Use the geometric morphological optimization method to optimize the morphology of the clustered algal bloom patches;

[0011] 4) Buffering and expanding the boundary of the optimized morphological algae bloom patch;

[0012] 5) Topology optimization of the patch to obtain the final algae bloom aggregation area.

[0013] The step 1) is specifically:

[0014] Determine whether the original remote sensing extraction data is in the projection coordinate system, if not, use the projection tool to convert the data to the projection coordinate system.

[0015] The step 2) is specifically:

[0016] Set the aggregation distance threshold m and the minimum patch area threshold n, select the patch with the adjacent distance less than m and the total area greater than n, convert the vector data to raster, perform pixel connection and expansion on the raster layer, and finally convert back to vector to obtain a single polygon algae bloom aggregation core area.

[0017] The step 3) is specifically:

[0018] Geometric morphological optimization method is adopted to retain a set proportion of polygon feature points, and Bezier curve interpolation method is used to smooth the feature points.

[0019] An n-order Bezier curve is composed of n+1 feature points P0, P1, …, P n The coordinates of any point B(t) on the curve are obtained by weighted summation of Bernstein polynomials:

[0020]

[0021] Wherein, is the combination number; t i (1-t) n-i is the weight function of parameter t.

[0022] The step 5) is specifically:

[0023] Merge the holes in the patch with an area less than the threshold to the adjacent maximum polygon, and correct the geometric abnormalities by topology checking.

[0024] Further comprising the following steps:

[0025] Add a double-precision field of algae bloom aggregation area in the attribute table of the aggregation area vector layer, and use the geometric tool to calculate the area to calculate the area of each aggregation area based on the projection coordinate system.

[0026] The large floating algae bloom aggregation area envelope line intelligent extraction and area calculation system comprises:

[0027] A projection coordinate system conversion module is used to convert all original remote sensing extraction data to the projection coordinate system.

[0028] a neighboring aggregation processing module, configured to perform spatial clustering on the discrete algae bloom patches by using an aggregation polygon algorithm;

[0029] a geometric morphology optimization module, configured to perform morphology optimization on the clustered algae bloom patches by using a geometric morphology optimization method;

[0030] a buffer expansion module, configured to perform buffer expansion on the morphology-optimized algae bloom patch boundaries;

[0031] a topological optimization processing module, configured to perform topological optimization on the patches to obtain a final algae bloom aggregation area.

[0032] The device comprises a memory and a processor; the memory is used for storing a computer program; and the processor is used for realizing the intelligent extraction method of the large-scale floating algae bloom aggregation area envelope when executing the computer program.

[0033] A computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the intelligent extraction method of the large-scale floating algae bloom aggregation area envelope is realized.

[0034] The present application has the following advantages and benefits:

[0035] 1. Realize full-process automatic processing, greatly improve efficiency

[0036] Replace manual visual selection by scripting batch operation (Python+ArcPy / QGIS). The processing time of a single image is shortened from several hours to minutes, the efficiency is improved by more than 95%, and the demand for large-scale and high-frequency algae bloom monitoring (such as monthly monitoring of the Yellow Sea) is met.

[0037] 2. Eliminate subjective errors and improve the objectivity and consistency of results

[0038] Use aggregation polygon algorithm (aggregation distance 30km, minimum area 50km 2 ) and geometric morphology optimization (85% feature point retention + Bezier curve smoothing) to eliminate the randomness of manual boundary definition.

[0039] 3. Optimize topological structure to enhance practicability

[0040] Cover the edge diffusion area by buffer expansion (10km), and combine with the hole elimination strategy (area <20km 2 Merge to adjacent polygons), output continuous and topologically correct vector data, which can be directly used for marine ecological disaster monitoring and early warning system. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 This is a schematic diagram of the method for extracting the aggregation area (distribution range) of large algae blooms;

[0042] Figure 2 This is the extraction result map of the distribution of the golden tide gathering areas of copper algae in the Yellow Sea and the East China Sea from 2019 to 2022. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0044] Currently, the distribution area of ​​floating macroalgal blooms (green tides and golden tides) is mainly extracted by manual methods such as visual inspection, which involves manually outlining the floating macroalgal bloom coverage patches extracted based on remote sensing. The present invention uses GIS methods to process the copper algae and golden tide accumulation areas (algal bloom coverage, i.e., "envelope") using a multi-step vector processing method. The specific extraction process of the present invention is as follows: Figure 1 As shown, the specific steps include:

[0045] (1) Projection coordinate system conversion: select the remote sensing monitoring vector patch image of large algae blooms as the original remote sensing extraction data. If the original remote sensing extraction data uses the geographic coordinate system, in order to meet the needs of area calculation and spatial analysis, use the projection tool to convert the data into a projected coordinate system to generate large algae bloom patches with area attributes for subsequent layer calculation involving distance and area.

[0046] (2) Proximity aggregation: A polygon aggregation algorithm is used to spatially cluster discrete algal blooms. The core principle of this algorithm is to combine close surface features based on a certain distance to form a larger new surface. This process is efficiently processed internally through vector-to-raster and then back to vector conversion. That is, vector data is first converted to raster, pixel connection and expansion are performed at the raster level, and finally converted back to vector.

[0047] Based on the migration characteristics of Enteromorpha green tide and Copper Algae gold tide, a 30km aggregation distance threshold and a minimum patch area of ​​50km were set. 2 (Eliminate fragmentation noise). After processing, the adjacent distance is less than 30km and the total area is greater than 50km 2 The patches are merged into a single polygon to effectively represent the core area of ​​algal blooms.

[0048] (3) Geometric optimization: To remove the topological redundant nodes of the aggregated large-scale algal bloom patch contour, eliminate irregular burrs, and retain about 85% of the polygon feature points by using the geometric optimization method through the polygon tool. Then set the smoothing tolerance of the Bezier curve interpolation method to 2 km for smoothing processing, so that the polygon boundary conforms to the natural morphological characteristics of the algal bloom distribution patch.

[0049] The n-order Bezier curve is composed of n+1 control points P0, P1, …, P n By definition, the coordinates of any point B(t) on the curve are obtained by weighted summation of Bernstein polynomials:

[0050]

[0051] Where: is the combination number (corresponding to the coefficient of the Yang Hui triangle); t i (1-t) n-i is the weight function of parameter t, which controls the influence degree of each control point on the curve.

[0052] For example, the formula of the third-order curve (4 control points):

[0053] B(t) = (1-t) 3 P0+3t(1-t) 2 P1+3t 2 (1-t)P2+t 3 P3

[0054] (4) Boundary buffer expansion: Considering the remote sensing recognition boundary effect and the edge diffusion characteristics of large-scale algal bloom, a 10 km extension buffer is created using the buffer analysis tool, effectively expanding the coverage area to the potential aggregation area.

[0055] (5) Topological optimization processing: Finally, using the elimination tool, the holes in the patch with an area less than 20 km 2 are fused and eliminated (merged into the adjacent maximum polygon), and topological checking is used to correct geometric abnormalities, finally obtaining continuous and complete S. lycini tide aggregation distribution vector data. Figure 2 is the spatial distribution of S. lycini tide aggregation area in the Yellow Sea and East China Sea generated based on this method in May 2020, where the discrete patch is the spatial distribution of S. lycini tide extracted by remote sensing, and the smooth curve is the extraction result of the S. lycini tide aggregation area.

[0056] (6) Calculate the area of the aggregation area: Add a double-precision field to the attribute table of the obtained aggregation area vector layer, and use the geometric tool to calculate the area, based on the projected coordinate system to calculate the area of each aggregation area.

Claims

1. An intelligent method for extracting the envelope of large floating algae blooms, characterized in that: The following steps are involved: 1) Convert all original remote sensing extracted data into the projected coordinate system; 2) Using the clustering polygon algorithm to spatially cluster discrete algal bloom patches; 3) Use geometric morphology optimization method to optimize the morphology of clustered algal bloom patches; 4) Buffer and expand the boundaries of the algal bloom patches after morphological optimization; 5) Perform topological optimization on the patches to obtain the final algal bloom aggregation area.

2. The intelligent extraction method of the envelope of large floating algae blooms according to claim 1 is characterized in that: The step 1) is specifically as follows: Determine whether the original remote sensing extracted data is in a projected coordinate system. If not, use the projection tool to convert the data to a projected coordinate system.

3. The intelligent extraction method of the envelope of large floating algae blooms according to claim 1 is characterized in that: The step 2) is specifically as follows: Set the aggregation distance threshold m and the minimum patch area threshold n, select patches with adjacent distances less than m and total areas greater than n, convert their vector data into raster data, connect and expand pixels at the raster level, and finally convert them back to vector data to obtain a single polygonal algal bloom aggregation core area.

4. The intelligent extraction method for the envelope of large floating algae blooms according to claim 1 is characterized in that: The step 3) is specifically as follows: The geometric morphology optimization method is used to retain polygonal feature points of a set proportion, and the Bezier curve interpolation method is used to smooth the feature points.

5. The intelligent extraction method of the envelope of large floating algae blooms according to claim 4 is characterized in that: The n-order Bezier curve consists of n+1 feature points P0, P1, ..., P n Definition: The coordinates of any point B(t) on the curve are obtained by weighted summation of Bernstein polynomials: in, is the number of combinations; t i (1-t) n-i is the weight function of parameter t.

6. The intelligent extraction method of the envelope of large floating algae blooms according to claim 1 is characterized in that: The step 5) is specifically as follows: Holes within patches with an area smaller than a threshold are merged into the adjacent largest polygon, and topology checking is used to correct geometric anomalies.

7. The intelligent extraction method of the envelope of large floating algae blooms according to claim 1 is characterized in that: The following steps are also included: Add a double-precision field of the area of ​​algal bloom clusters to the attribute table of the cluster vector layer, and use the Calculate Area geometry tool to calculate the area of ​​each cluster based on the projected coordinate system.

8. Intelligent extraction system for the envelope of large floating algae blooms, characterized by: include: Projection coordinate system conversion module, used to convert all original remote sensing extracted data into the projection coordinate system; The proximity aggregation processing module is used to spatially cluster discrete algal bloom patches using the polygon aggregation algorithm; The geometric morphology optimization module is used to optimize the morphology of the clustered algal bloom patches using the geometric morphology optimization method; Boundary buffer expansion module, used to buffer and expand the boundaries of algal bloom patches after morphological optimization; The topology optimization processing module is used to perform topological optimization on the patches to obtain the final algal bloom aggregation area.

9. An intelligent extraction device for the envelope of large floating algae blooms, characterized in that: It comprises a memory and a processor; the memory is used to store a computer program; the processor is used to implement the intelligent extraction method of the envelope of the large floating algae bloom aggregation area as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the method for intelligently extracting the envelope of the large floating algae bloom aggregation area as described in any one of claims 1 to 7 is implemented.

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