Sea grass bed monitoring system and method

Abstract

The invention relates to the technical field of marine ecological environment monitoring, in particular to a seaweed bed monitoring system and method, and the method comprises the steps: obtaining image data of a target marine region and various environment parameters of each seaweed bed; according to the image data of different periods, determining the area change rate and the sea grass coverage degree of each sea grass bed in the target sea area; aiming at any one sea grass bed, taking various environmental parameters, area change rate and sea grass coverage of the sea grass bed as characteristic parameters influencing the health state of the sea grass bed, and carrying out weighted summation on all the characteristic parameters to obtain a comprehensive evaluation index of the sea grass bed; and substituting the comprehensive evaluation index and the various characteristic parameters into a pre-constructed sea grass bed health state evaluation model, and evaluating the health state of the sea grass bed. The method can achieve the accurate evaluation of the health state of the seaweed bed.

Description

Technical Field

[0001] The present invention relates to the technical field of marine ecological environment monitoring, and more specifically to a seagrass bed monitoring system and method. Background Art

[0002] Seagrass beds are an important part of the marine ecosystem and play an important role in maintaining biodiversity, preventing coastal erosion, and purifying water quality. However, due to the influence of human activities and natural factors, the health of seagrass beds is seriously threatened. Therefore, it is of great significance to monitor and evaluate the health of seagrass beds in real time.

[0003] However, there are currently few monitoring dimensions for seagrass beds, and the accuracy of assessments of the health status of seagrass beds is often insufficient. Summary of the invention

[0004] In view of this, the present invention provides a seagrass bed monitoring system and method, which can achieve accurate assessment of the health status of the seagrass bed.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a seagrass bed monitoring method, comprising:

[0007] Obtain image data of the target ocean area and various environmental parameters of each seagrass bed;

[0008] Based on the image data from different periods, determine the area change rate and seagrass coverage of each seagrass bed in the target ocean area;

[0009] For any seagrass bed, various environmental parameters, area change rate and seagrass coverage of the seagrass bed are used as characteristic parameters affecting its health status, and all characteristic parameters are weighted summed to obtain a comprehensive evaluation index of the seagrass bed;

[0010] The comprehensive evaluation index and various characteristic parameters are substituted into a pre-constructed seagrass bed health status evaluation model to evaluate the health status of the seagrass bed.

[0011] Furthermore, the environmental parameters of each seagrass bed obtained include: one or more of temperature, salinity, dissolved oxygen content, light intensity, biomass change, pH value, water flow velocity and water flow direction.

[0012] Furthermore, the determining of the area change rate and seagrass coverage of each seagrass bed in the target ocean area based on the image data of different periods includes:

[0013] Support vector machine is used to roughly divide the seawater, seagrass and sandy bottom in the image data of the target ocean area, identify the area with concentrated seagrass distribution as a seagrass bed, and mark the boundary of the seagrass bed;

[0014] Divide the current image data into blocks so that each image block contains a complete seagrass bed;

[0015] Different pixel values ​​are assigned to seawater and seagrass respectively. According to the change of pixel value of each seagrass bed image block, the seagrass is subdivided, and the area change rate and coverage of seagrass in each seagrass bed are calculated.

[0016] Furthermore, the step of assigning different pixel values ​​to the seawater and the seaweed, and dividing the seaweed into smaller parts according to the pixel value changes of each seaweed bed image block, includes:

[0017] Collect image information of various types of seaweed in advance, determine the pixel values ​​corresponding to various types of seaweed, classify the pixel values ​​representing seaweed into a unified pixel value; classify the pixel values ​​representing seawater into a single pixel value;

[0018] The pixel values ​​of each seagrass bed image block are reassigned to determine the pixel area of ​​the seagrass growth area and the pixel area of ​​the seagrass bed.

[0019] Furthermore, the calculation formula of the comprehensive evaluation index is:

[0020]

[0021] Among them, c k represents the kth seagrass bed in the target ocean area, s ki represents the i-th characteristic parameter of the i-th seagrass bed, ω ki represents the weight of the i-th characteristic parameter of the k-th seagrass bed, and n represents a total of n characteristic parameters.

[0022] Furthermore, the seagrass bed health status evaluation model is a multivariate linear regression model, and its expression is:

[0023] H k =β0+β1s k1 +β2s k2 +L+β n s kn +β'c k

[0024] Among them, H k represents the health status assessment value of the kth seagrass bed, β0,β1,β2,L,β n and β' both represent model parameters.

[0025] In a second aspect, the present invention provides a seagrass bed monitoring system, comprising: a data acquisition subsystem and a health status assessment subsystem;

[0026] The data acquisition subsystem includes: an underwater monitoring platform and a drone; the underwater monitoring platform is deployed at different monitoring points of each seagrass bed in the target ocean area, and is equipped with various types of sensors for collecting various environmental parameters of each monitoring point; the drone is used to collect image data of the target ocean area;

[0027] The health status assessment subsystem is used to assess the health status of the seagrass bed using the method described above.

[0028] Furthermore, the underwater monitoring platform is also equipped with a first camera; the first camera is used to collect video information of a preset underwater area of ​​the seagrass bed;

[0029] The underwater monitoring platform is also connected to a buoy via a rope, and the buoy is equipped with a second camera, a solar power generation device and a communication device;

[0030] The second camera is used to collect video information of a preset area above the seagrass bed;

[0031] The solar power generation device supplies power to various underwater sensors, the first camera, the second camera and the communication device;

[0032] The communication device is used to send the environmental parameters, video information and operating status information of each device collected at the current monitoring point to the health status assessment subsystem in real time.

[0033] Furthermore, the health status assessment subsystem is also used to display and store underwater video information, above-water video information, various environmental parameters, equipment operating status and health assessment results of each monitoring point of any seagrass bed in the target ocean area according to preset rules.

[0034] Furthermore, the health status assessment subsystem is also used to set up an electronic fence based on the boundary of each seagrass bed, and determine whether there is any illegal ship intrusion or illegal operation based on the water video information. If so, an alarm message is generated.

[0035] It can be seen from the above technical solutions that, compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention calculates a comprehensive evaluation index by weighted summation of multiple environmental parameters, area change rate and seagrass coverage of seagrass beds, which can more comprehensively reflect the ecological status of seagrass beds. At the same time, weights are assigned according to the degree of influence of each characteristic parameter on the health status, ensuring that the comprehensive evaluation index pays more attention to factors that have a greater impact on the health status, thereby improving the accuracy of the evaluation results.

[0037] The present invention also uses the calculated comprehensive evaluation index and other characteristic parameters as characteristic inputs of the seagrass bed health status evaluation model, thereby reflecting the ecological status of the seagrass bed more comprehensively and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 A flow chart of the seagrass bed monitoring method provided by the present invention;

[0040] Figure 2 A flowchart of processing image data provided by the present invention;

[0041] Figure 3 This is a structural schematic diagram of the seagrass bed monitoring system provided by the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, an embodiment of the present invention discloses a seagrass bed monitoring method, comprising:

[0044] S1. Obtain image data of the target ocean area and various environmental parameters of each seagrass bed;

[0045] S2. Determine the area change rate and seagrass coverage of each seagrass bed in the target ocean area based on image data from different periods;

[0046] S3. For any seagrass bed, various environmental parameters, area change rate and seagrass coverage of the seagrass bed are taken as characteristic parameters affecting its health status, and all characteristic parameters are weighted summed to obtain a comprehensive evaluation index of the seagrass bed;

[0047] S4. Substitute the comprehensive evaluation index and various characteristic parameters into the pre-built seagrass bed health status evaluation model to evaluate the health status of the seagrass bed.

[0048] The following is a further explanation of the above steps.

[0049] S1. Various sensors are deployed underwater in each seagrass bed in the target ocean area to collect various environmental parameters of the seagrass bed, and each seagrass bed and the sensors deployed thereon are numbered to form a sensor network to achieve group transmission of various data. The specific environmental parameters are one or more of temperature, salinity, dissolved oxygen content, light intensity, light duration, biomass change, pH value, water flow velocity and water flow direction.

[0050] Among them, temperature, pH value, and dissolved oxygen reflect the environmental temperature of the seaweed, as well as the acidity and oxygen content of the water, which are crucial to the growth of seaweed. Salinity reflects the concentration of nutrients such as nitrogen and phosphorus in seawater, affecting the growth of seaweed. Light intensity and light duration affect the photosynthesis of seaweed. Water flow speed and direction affect the physical environment and material exchange of the seagrass bed. Biomass changes reflect the productivity of the seagrass bed ecosystem.

[0051] At the same time, the present invention uses a drone to take aerial photos of the target ocean area to obtain image data. During the specific aerial photography, the drone maintains an appropriate height to shoot the target ocean area on a predetermined flight route, ensuring that a reasonable overlap (front and back overlap and side overlap) is set, usually requiring 60%-80% front and back overlap and 30%-50% side overlap, so that the image has sufficient resolution to observe the details of the seagrass bed.

[0052] Afterwards, the image is dedistorted to correct the distortion caused by the camera lens, and the image is radiometrically corrected to eliminate the impact of lighting changes and ensure the consistency of image color.

[0053] Several bottom control points with known coordinates can be selected in advance near the seagrass bed and marked with colors to calibrate the scale of the image. Use geographic information system (GIS) software or remote sensing image processing software to calibrate the scale and direction of the image based on the control points.

[0054] S2. Calculate the seagrass bed area change rate and seagrass coverage. The specific implementation steps are as follows: Figure 2 As shown, including:

[0055] S21. Use support vector machine to roughly divide the seawater, seagrass and sandy bottom in the image data of the target ocean area, identify the area where seagrass is concentrated as a seagrass bed, and mark the boundary of the seagrass bed; this step is a rough division of the seagrass bed in the target ocean area, and it is only necessary to roughly divide the boundary area of ​​the seagrass bed.

[0056] S22, dividing the current image data into blocks so that each image block contains a complete seagrass bed; image block division can not only ensure the display accuracy of a single seagrass bed, but also improve the subsequent image processing speed.

[0057] S23, assign different pixel values ​​to seawater and seagrass respectively, divide the seagrass into smaller parts according to the pixel value changes of each seagrass bed image block, and calculate the area change rate and coverage of the seagrass in each seagrass bed. Since the previous rough division of the seagrass bed is used, but the area of ​​each seagrass bed is not completely covered with seagrass, there may be some empty space. In order to estimate the seagrass area, it needs to be divided into smaller parts. Specifically, it includes:

[0058] Image information of various types of seaweed is collected in advance, and the pixel values ​​corresponding to various seaweeds are determined. The pixel values ​​representing seaweed are classified into a unified pixel value (such as green); the pixel values ​​representing seawater are classified into a pixel value (such as blue); or, the seaweed pixel values ​​are assigned black and the seawater pixel values ​​are assigned white to achieve a more obvious division.

[0059] The pixel values ​​of each seagrass bed image block are reassigned to determine the pixel area of ​​the seagrass growth area and the pixel area of ​​the seagrass bed.

[0060] The actual area is then calculated based on the pixel area, image scale and image resolution, specifically: actual area = (pixel area / image resolution)*scale.

[0061] Finally, according to the changes in the actual area of ​​seagrass growing areas in different periods, the editing change rate of the seagrass bed was calculated, and the area of ​​the seagrass growing area was divided by the area of ​​the seagrass bed to obtain the seagrass coverage of the seagrass bed.

[0062] S3. Calculation of comprehensive evaluation indicators.

[0063] For each seagrass bed, the calculation method of its comprehensive evaluation index is the same. Since the types of sensors deployed in each seagrass bed may be different during actual monitoring, the calculation can be based on the actual characteristic parameters.

[0064] First, the collected data are cleaned to remove outliers and missing values, and the characteristic parameters of different dimensions are converted into dimensionless standardized values ​​to eliminate the impact of the dimension.

[0065] Afterwards, the weight of each characteristic parameter is determined through expert scoring, hierarchical analysis or principal component analysis to reflect the degree of its impact on the health of the seagrass bed.

[0066] Finally, the weighted sum of each characteristic parameter is performed to obtain a comprehensive evaluation index, and its calculation formula is:

[0067]

[0068] Among them, c k represents the kth seagrass bed in the target ocean area, s ki represents the i-th characteristic parameter of the i-th seagrass bed, ω ki represents the weight of the i-th characteristic parameter of the k-th seagrass bed, and n represents a total of n characteristic parameters.

[0069] S4. Substituting the comprehensive evaluation index and various characteristic parameters into a pre-constructed seagrass bed health status evaluation model to evaluate the health status of the seagrass bed.

[0070] The seagrass bed health status assessment model is a multivariate linear regression model, which can be fitted with historical data. Its expression is:

[0071] H k =β0+β1s k1 +β2s k2 +L+β n s kn +β'c k

[0072] Among them, H k represents the health status assessment value of the kth seagrass bed, β0,β1,β2,L,β n and β' both represent model parameters.

[0073] The calculated health status assessment value reflects the comprehensive health status of the seagrass bed. The comprehensive health status of the seagrass bed is rated based on this value to determine whether it is healthy, sub-healthy, unhealthy, etc.

[0074] The present invention can also analyze the health of the seagrass bed in various aspects according to the independent numerical values ​​of each characteristic parameter, and provide corresponding countermeasures according to the health status of each aspect.

[0075] like Figure 3 As shown, the present invention also provides a seagrass bed monitoring system, including: a data acquisition subsystem and a health status assessment subsystem;

[0076] The data collection subsystem includes: underwater monitoring platforms and drones; the underwater monitoring platforms are deployed at different monitoring points of each seagrass bed in the target ocean area, and are equipped with various types of sensors to collect various environmental parameters at each monitoring point; the drones are used to collect image data of the target ocean area;

[0077] The health status assessment subsystem is used to assess the health status of the seagrass bed using the method described above.

[0078] Specifically, the underwater monitoring platform is also equipped with a first camera; the first camera is used to collect video information of a preset underwater area of ​​the seagrass bed;

[0079] The underwater monitoring platform is also connected to a buoy via a rope, which carries a second camera, a solar power generation device and a communication device;

[0080] The second camera is used to collect video information of a preset area above the seagrass bed;

[0081] The solar power generation device supplies power to various underwater sensors, the first camera, the second camera and the communication device;

[0082] The communication device is used to send the environmental parameters, video information and operating status information of each device collected by the current monitoring point to the health status assessment subsystem in real time.

[0083] The health status assessment subsystem is also used to display and store underwater video information, surface video information, various environmental parameters, equipment operation status and health assessment results of each monitoring point of any seagrass bed in the target ocean area according to preset rules. For example, it can be displayed in partitions or individually. Since video information requires a large storage space, a video frame image can be captured and stored in each time period.

[0084] The present invention can conduct cross-analysis and historical statistics on various types of data, comprehensively grasp the natural environment conditions in the restoration area, and provide an evaluation basis and research foundation to ensure the restoration effect. At the same time, the operating information of each device in the system can be checked, including location, energy, posture, operating time, water tightness, etc., to achieve in-depth monitoring of the main equipment and facilities of the system, ensure its long-term reliable operation, timely discover fault trends or hidden dangers, and improve the system management level.

[0085] The underwater monitoring platform of the present invention is also equipped with lighting, cleaning brushes, pan-tilt heads and other equipment. Users can obtain water quality conditions by viewing underwater videos of seagrass beds. They can also remotely control the rotation of the underwater pan-tilt head, the switch of lighting, the movement of the cleaning brush, the zoom and magnification of the camera, etc., to achieve efficient and continuous underwater monitoring of the seagrass bed area, fully grasp the development process of its ecological conditions, and provide guarantees for the sustainable development of restoration work.

[0086] In one embodiment, the health status assessment subsystem is also used to set up an electronic fence based on the boundary of each seagrass bed, and determine whether there is any illegal ship intrusion or illegal operation based on the water video information. If so, an alarm message is generated.

[0087] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0088] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring seagrass beds, characterized in that: include: Obtain image data of the target ocean area and various environmental parameters of each seagrass bed; Based on the image data from different periods, determine the area change rate and seagrass coverage of each seagrass bed in the target ocean area; For any seagrass bed, various environmental parameters, area change rate and seagrass coverage of the seagrass bed are used as characteristic parameters affecting its health status, and all characteristic parameters are weighted summed to obtain a comprehensive evaluation index of the seagrass bed; The comprehensive evaluation index and various characteristic parameters are substituted into a pre-constructed seagrass bed health status evaluation model to evaluate the health status of the seagrass bed.

2. The seagrass bed monitoring method according to claim 1, characterized in that: The environmental parameters of each seagrass bed obtained include: one or more of temperature, salinity, dissolved oxygen content, light intensity, biomass change, pH value, water flow speed and water flow direction.

3. The seagrass bed monitoring method according to claim 1, characterized in that: Determining the area change rate and seagrass coverage of each seagrass bed in the target ocean area based on image data from different periods includes: Support vector machine is used to roughly divide the seawater, seagrass and sandy bottom in the image data of the target ocean area, identify the area with concentrated seagrass distribution as a seagrass bed, and mark the boundary of the seagrass bed; Divide the current image data into blocks so that each image block contains a complete seagrass bed; Different pixel values ​​are assigned to seawater and seagrass respectively. According to the change of pixel value of each seagrass bed image block, the seagrass is subdivided, and the area change rate and coverage of seagrass in each seagrass bed are calculated.

4. The seagrass bed monitoring method according to claim 3, characterized in that: The method of assigning different pixel values ​​to the seawater and the seaweed respectively and dividing the seaweed into smaller parts according to the pixel value changes of each seaweed bed image block includes: Collect image information of various types of seaweed in advance, determine the pixel values ​​corresponding to various types of seaweed, classify the pixel values ​​representing seaweed into a unified pixel value; classify the pixel values ​​representing seawater into a single pixel value; The pixel values ​​of each seagrass bed image block are reassigned to determine the pixel area of ​​the seagrass growth area and the pixel area of ​​the seagrass bed.

5. The seagrass bed monitoring method according to claim 1, characterized in that: The calculation formula of the comprehensive evaluation index is: Among them, c k represents the kth seagrass bed in the target ocean area, s ki represents the i-th characteristic parameter of the i-th seagrass bed, ω ki represents the weight of the i-th characteristic parameter of the k-th seagrass bed, and n represents a total of n characteristic parameters.

6. The seagrass bed monitoring method according to claim 1, characterized in that: The seagrass bed health status evaluation model is a multivariate linear regression model, and its expression is: H k =β0+β1s k1 +β2s k2 +L+β n s kn +β'c k Among them, H k represents the health status assessment value of the kth seagrass bed, β0,β1,β2,L,β n and β' both represent model parameters.

7. A seagrass bed monitoring system, characterized in that: include: Data acquisition subsystem and health status assessment subsystem; The data acquisition subsystem includes: an underwater monitoring platform and a drone; The underwater monitoring platform is deployed at different monitoring points of each seagrass bed in the target ocean area, and is equipped with various types of sensors for collecting various environmental parameters of each monitoring point; the drone is used to collect image data of the target ocean area; The health status assessment subsystem is used to assess the health status of the seagrass bed using the method described in any one of claims 1-6.

8. The seagrass bed monitoring system according to claim 7, characterized in that: The underwater monitoring platform is also equipped with a first camera; the first camera is used to collect video information of a preset underwater area of ​​the seagrass bed; The underwater monitoring platform is also connected to a buoy via a rope, and the buoy is equipped with a second camera, a solar power generation device and a communication device; The second camera is used to collect video information of a preset area above the seagrass bed; The solar power generation device supplies power to various underwater sensors, the first camera, the second camera and the communication device; The communication device is used to send the environmental parameters, video information and operating status information of each device collected at the current monitoring point to the health status assessment subsystem in real time.

9. The seagrass bed monitoring system according to claim 8, characterized in that: The health status assessment subsystem is also used to display and store underwater video information, above-water video information, various environmental parameters, equipment operating status and health assessment results of each monitoring point of any seagrass bed in the target ocean area according to preset rules.

10. The seagrass bed monitoring system according to claim 8, characterized in that: The health status assessment subsystem is also used to set up an electronic fence based on the boundary of each seagrass bed, and determine whether there is any illegal ship intrusion or illegal operation based on the water video information. If so, an alarm message is generated.

Images