Method and System for Extracting Mangrove Ecological Information Based on Unmanned Aerial Vehicle

Abstract

This application relates to the field of image processing technology, and discloses a method and system for extracting mangrove ecological information based on an unmanned aerial vehicle. The method includes: obtaining top-down and side-view photos through the unmanned aerial vehicle; correcting the top-down photos using coordinate information and overlaying them on the remote sensing image; generating a photo-taking direction layer based on the side-view photos; establishing a mangrove species interpretation marker library using the random forest algorithm; selecting samples accordingly for classification to generate a species distribution map; and finally evaluating the ecological functions of the mangroves to form ecological monitoring data. This application realizes the extraction of high-precision ecological information of mangroves from multiple angles and at multiple levels, thereby providing a more accurate and comprehensive scientific basis for the species classification, ecological system function evaluation, protection and management of mangroves.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and particularly to a method and system for extracting mangrove ecological information based on an unmanned aerial vehicle (UAV). Background Art

[0002] Mangroves are woody plant communities growing in the intertidal zones or river estuaries of tropical and subtropical coasts, and are important marine ecosystems, having important indicative significance for the global environment and climate change. In recent years, with the development of remote sensing technology, more and more researchers have studied the spatial distribution, area change, population classification, biomass and carbon storage estimation of mangroves based on remote sensing technology. Traditional mangrove monitoring methods mainly rely on ground surveys and the analysis of medium- and low-resolution satellite images. Ground survey methods include the quadrat method, belt transect method, and standard tree method, etc., and the growth status and ecological functions of mangroves are evaluated by on-site measurement of parameters such as the height, diameter at breast height, and density of mangroves; satellite remote sensing methods such as Landsat, SPOT, and Sentinel multispectral satellite images are widely used in large-scale mangrove distribution monitoring and change analysis. Combining field verification data and professional interpretation techniques, the macroscopic grasp of the spatial distribution of mangroves can be achieved.

[0003] However, the existing mangrove monitoring methods have obvious deficiencies. Mangroves grow in the intertidal shoals with harsh environmental conditions and dense mangrove vegetation, making it difficult for investigators to enter the interior of the forest area, resulting in low efficiency and certain destructiveness of traditional ground surveys. Due to the inaccessibility of most areas by field personnel, the distribution of survey data is uneven, affecting the representativeness and reliability of the data. Medium- and low-resolution satellite images have limitations in the fine classification of mangrove species due to limited spatial resolution and less spectral information, and it is difficult to accurately distinguish different mangrove species with similar morphologies. In addition, the existing technology lacks effective means to capture the multi-level structural characteristics of mangroves, resulting in an incomplete and inaccurate assessment of the ecological system functions of mangroves. Especially in the mangrove habitats with frequent tidal changes, traditional methods are difficult to achieve high-frequency and high-precision monitoring of mangroves, seriously restricting the protection and scientific management of mangrove resources. Summary of the Invention

[0004] This application provides a method and system for extracting mangrove ecological information based on an unmanned aerial vehicle (UAV), which can break through the limitations such as the difficulty for personnel to enter, uneven data distribution, and low satellite image resolution in traditional mangrove surveys, and achieve high-precision ecological information extraction of mangroves from multiple angles and at multiple levels, thereby providing a more accurate and comprehensive scientific basis for the species classification, ecological system function assessment, protection and management of mangroves.

[0005] In a first aspect, the present application provides a method for extracting mangrove ecological information based on an unmanned aerial vehicle. The method for extracting mangrove ecological information based on an unmanned aerial vehicle includes: taking low-altitude photos of mangroves from different angles by the unmanned aerial vehicle to obtain a top-down photo with a camera lens tilt angle of approximately 90° and a side-view photo with a camera lens tilt angle of approximately 30°, so as to obtain mangrove multi-angle image data; according to the mangrove multi-angle image data, calculating projection coordinates by using position coordinates, photo length and width, and flight height information, and performing geometric correction processing on the top-down photo to obtain a corrected image superimposed on a remote sensing image; based on the position and flight angle information of the side-view photo, generating a vector point layer and performing symbolization processing to obtain a photographing direction layer superimposed on the remote sensing image; based on the corrected image, the photographing direction layer, and the remote sensing image, extracting features and establishing an association relationship through a random forest algorithm to form a mangrove species interpretation marker library; according to the mangrove species interpretation marker library, selecting training samples and validation samples on the remote sensing image, performing supervised classification processing of mangrove species, and generating a mangrove species distribution map; based on the mangrove species distribution map, combining spectral characteristics and spatial distribution information, evaluating and analyzing the functions of the mangrove ecosystem to obtain mangrove ecological monitoring data.

[0006] In a second aspect, the present application provides a system for extracting mangrove ecological information based on an unmanned aerial vehicle. The system for extracting mangrove ecological information based on an unmanned aerial vehicle includes:

[0007] An acquisition module, configured to take low-altitude photos of mangroves from different angles by the unmanned aerial vehicle to obtain a top-down photo with a camera lens tilt angle of approximately 90° and a side-view photo with a camera lens tilt angle of approximately 30°, so as to obtain mangrove multi-angle image data;

[0008] A calculation module, configured to calculate projection coordinates according to the mangrove multi-angle image data by using position coordinates, photo length and width, and flight height information, and perform geometric correction processing on the top-down photo to obtain a corrected image superimposed on a remote sensing image;

[0009] A processing module, configured to generate a vector point layer and perform symbolization processing based on the position and flight angle information of the side-view photo to obtain a photographing direction layer superimposed on the remote sensing image;

[0010] An establishment module, configured to extract features and establish an association relationship through a random forest algorithm based on the corrected image, the photographing direction layer, and the remote sensing image to form a mangrove species interpretation marker library;

[0011] A selection module, configured to select training samples and validation samples on the remote sensing image according to the mangrove species interpretation marker library, perform supervised classification processing of mangrove species, and generate a mangrove species distribution map;

[0012] An analysis module, configured to evaluate and analyze the functions of the mangrove ecosystem based on the mangrove species distribution map, in combination with spectral characteristics and spatial distribution information, to obtain mangrove ecological monitoring data.

[0013] The third aspect of the present invention provides a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to cause the computer device to execute the above-mentioned method for extracting mangrove ecological information based on an unmanned aerial vehicle.

[0014] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when the instructions are run on a computer, the computer is caused to execute the above-mentioned method for extracting mangrove ecological information based on an unmanned aerial vehicle.

[0015] In the technical solution provided by this application, the mangroves are photographed at low altitude from different angles by an unmanned aerial vehicle (UAV) to obtain a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, forming multi-angle image data of the mangroves. This breaks through the limitation that it is difficult for traditional manual surveys to enter the interior of the mangroves, greatly improving the spatial coverage and data collection efficiency of mangrove surveys. The projection coordinates are calculated using the position coordinates, photo length and width, and flight height information, and geometric correction processing is performed on the top-down photo to obtain a corrected image superimposed on the remote sensing image, solving the problem of precise registration between UAV images and satellite remote sensing images and providing a basis for multi-source data fusion analysis. Based on the position and flight angle information of the side-view photo, a vector point layer is generated and symbolized to obtain a photo-taking direction layer superimposed on the remote sensing image, visually showing the relationship between the photo-taking direction and position of the side-view photo and facilitating the understanding of the side structure characteristics of the mangroves. It is particularly worth emphasizing that, based on the corrected image, photo-taking direction layer, and remote sensing image, this solution extracts features and establishes correlation relationships through the random forest algorithm to form a mangrove species interpretation mark library. The random forest algorithm shows excellent performance in feature selection and classification. It can not only process high-dimensional feature data but also evaluate the importance of features and handle the correlation between features, providing strong algorithm support for mangrove species identification. The algorithm has strong robustness to noise and outliers. By constructing a large number of decision trees and adopting a majority voting mechanism, the risk of overfitting is effectively reduced and the classification accuracy is improved. The interpretation mark library established in this way combines the advantages of the top-down view, side-view, and remote sensing data sources, achieving high-precision identification of mangrove species. According to the established mangrove species interpretation mark library, training samples and validation samples are selected on the remote sensing image for supervised classification processing of mangrove species to generate a mangrove species distribution map, providing an accurate basis for the study of the spatial distribution of mangroves. Finally, based on the mangrove species distribution map, combined with spectral characteristics and spatial distribution information, the functions of the mangrove ecosystem are evaluated and analyzed to obtain mangrove ecological monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of an embodiment of the method for extracting mangrove ecological information based on an unmanned aerial vehicle in an embodiment of this application;

[0018] Figure 2 It is a schematic diagram of the geometric correction of the top-down photo of the unmanned aerial vehicle in an embodiment of this application;

[0019] Figure 3 This is the superimposed image of the geometrically corrected overhead photo of the drone in the embodiment of the present application and the remote sensing image;

[0020] Figure 4 This is a schematic diagram of an embodiment of the mangrove ecological information extraction system based on a drone in the embodiment of the present application;

[0021] Figure 5 This is a schematic block diagram of the structure of the computer device in the embodiment of the present invention. Detailed implementation manners

[0022] The embodiment of the present application provides a method and system for extracting mangrove ecological information based on a drone. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "include" or "have" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0023] For ease of understanding, the specific process of the embodiment of the present application will be described below. Please refer to Figure 1 An embodiment of the method for extracting mangrove ecological information based on a drone in the embodiment of the present application includes:

[0024] Step S101: Take low-altitude photos of the mangroves from different angles by the drone to obtain an overhead photo with a camera lens tilt angle of about 90° and a side view photo with a camera lens tilt angle of about 30°, and obtain mangrove multi-angle image data;

[0025] Step S102: According to the mangrove multi-angle image data, calculate the projection coordinates using the position coordinates, photo length and width, and flight height information, and perform geometric correction processing on the overhead photo to obtain a corrected image superimposed on the remote sensing image;

[0026] Step S103: Based on the position and flight angle information of the side view photo, generate a vector point layer and perform symbolization processing to obtain a photographing direction layer superimposed on the remote sensing image;

[0027] Step S104: According to the corrected image, the photographing direction layer and the remote sensing image, extract features and establish an association relationship through the random forest algorithm to form a mangrove species interpretation marker library;

[0028] Step S105: Select training samples and validation samples on the remote sensing image according to the mangrove species interpretation symbol library, perform supervised classification processing of mangrove species, and generate a mangrove species distribution map.

[0029] Step S106: Based on the mangrove species distribution map, combine spectral characteristics and spatial distribution information to evaluate and analyze the functions of the mangrove ecosystem, and obtain mangrove ecological monitoring data.

[0030] It can be understood that the execution subject of this application can be a mangrove ecological information extraction system based on an unmanned aerial vehicle (UAV), or it can also be a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0031] Specifically, the mangrove is photographed at low altitude by a UAV. During the flight of the UAV, two different shooting angles are adopted: one is that the tilt angle of the camera lens is about 90° (the lens is vertically downward) to obtain a top-down photo, the flight altitude is about 50 meters, and the photo shooting interval is about 40 meters; the other is that the tilt angle of the camera lens is about 30° to obtain a side view photo, the flight altitude is about 30 meters, and the photo shooting interval is about 30 meters. This shooting method forms mangrove multi-angle image data including the top and side views of the mangrove, which helps to capture the characteristics of different levels of the mangrove. After obtaining the mangrove multi-angle image data, geometric correction processing is performed on the top-down photos. The specific method is to extract data such as GPS coordinates, camera focal length, photo width and height dimensions, and flight altitude from the EXIF information of the top-down photos. Based on these parameters, calculate the projection range of the UAV top-down photo on the ground. Among them, the projection half-width W2 and projection half-height H2 of the photo are calculated by the formulas W2 = A×W / (2×f) and H2 = A×H / (2×f) respectively, where A is the scale factor, W is the photo width, H is the photo height, and f is the camera focal length. According to the photo center point coordinates and the heading angle, use the four-point projection coordinate formula to calculate the coordinates of the four corners of the photo on the ground, and form a photogrammetric control point. Through these control points, establish a spatial transformation matrix, perform affine transformation and resampling on the top-down photos, and finally generate a corrected image in the same coordinate system as the remote sensing image.

[0032] For the side view photos, the processing method is different from that of the top-down photos. Extract the GPS coordinates and flight heading angle information of the side view photos, and construct a position and angle information set for the side view photos. Filter these information to eliminate data points with repeated positions or excessive errors. Then use these coordinate information to create point features in the ArcGIS software to form a mangrove side view photo position point layer. Then assign the heading angle information to the point layer as the direction attribute, and represent the photo shooting direction by arrow symbolization. The arrow size is proportional to the flight altitude, and finally generate a photo shooting direction layer and overlay it on the remote sensing image.

[0033] After obtaining the corrected image and the photographing direction layer, a mangrove species interpretation symbol library can be established. In this step, representative sample areas of different mangrove species are first selected in the overlapping area of the corrected image, the photographing direction layer, and the remote sensing image. For each sample area, texture, color, and shape features are extracted from the corrected image, and spectral and spatial features are extracted from the remote sensing image to form a feature vector set. These feature data are processed by the random forest algorithm, where the number of decision trees is set to 500, the minimum number of samples per node is 5, and the random selection ratio of features is 0.7. Classification rules are extracted from the generated decision tree set, and these rules are associated with the corresponding photo and image features to form a mangrove species interpretation symbol library with a ternary association of "side-view photo - top-view photo - remote sensing image".

[0034] Species classification is carried out on the remote sensing image based on the mangrove species interpretation symbol library. First, the stratified sampling method is used to select training samples and validation samples. Feature parameters such as the normalized difference vegetation index, red-edge position index, and texture complexity index are extracted from the training samples to construct a sample feature matrix. This feature matrix is used to perform supervised classification on the remote sensing image to delineate the distribution ranges of different mangrove species. Patch merging and boundary optimization are performed on the preliminary classification results to eliminate isolated pixels and boundary noise. Then, the validation samples are used to evaluate the accuracy of the optimized classification results, calculate the overall accuracy and the classification accuracy of each species, and generate a classification accuracy report. According to the accuracy report, the classification results are graded for credibility and converted into vector format, and attribute information such as species names and areas is added to obtain a mangrove species distribution map. The area, density, and distribution form of each species are quantified from the mangrove species distribution map to generate a spatial structure parameter table. The normalized difference vegetation index and leaf area index are calculated using the near-infrared and visible light bands of the remote sensing image to construct a growth vitality evaluation index. Combining the species distribution map, spatial structure parameters, and growth vitality index, the mangrove carbon storage is calculated to obtain the quantified data of the carbon sink function. The spatial relationship between the mangroves and the coastline and their wave attenuation and sediment retention capabilities are analyzed to obtain the evaluation results of the coastal protection function. The species diversity index and niche overlap degree are calculated to obtain the data of the biodiversity maintenance function. Finally, these function evaluation data are integrated to form the mangrove ecological monitoring data.

[0035] In the embodiments of the present application, the mangroves are photographed at low altitude from different angles by a drone to obtain a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, forming multi-angle image data of the mangroves, breaking through the limitation that traditional manual surveys are difficult to enter the interior of the mangroves, and greatly improving the spatial coverage and data collection efficiency of mangrove surveys. The projection coordinates are calculated using the position coordinates, photo length and width, and flight height information, and geometric correction processing is performed on the top-down photo to obtain a corrected image superimposed on the remote sensing image, solving the problem of precise registration between the drone image and the satellite remote sensing image, and providing a basis for multi-source data fusion analysis. Based on the position and flight angle information of the side-view photo, a vector point layer is generated and symbolized to obtain a photographing direction layer superimposed on the remote sensing image, intuitively showing the relationship between the photographing direction and position of the side-view photo, and facilitating the understanding of the side structure characteristics of the mangroves. It is particularly worth emphasizing that, based on the corrected image, the photographing direction layer, and the remote sensing image, this solution extracts features and establishes correlation relationships through the random forest algorithm to form a mangrove species interpretation mark library. The random forest algorithm shows excellent performance in feature selection and classification. It can not only process high-dimensional feature data, but also evaluate the importance of features and cope with the correlation between features, providing strong algorithm support for mangrove species identification; this algorithm has strong robustness to noise and outliers. By constructing a large number of decision trees and adopting a majority voting mechanism, the risk of overfitting is effectively reduced, and the classification accuracy is improved. The interpretation mark library established in this way combines the advantages of the top-down, side-view, and remote sensing data sources, realizing high-precision identification of mangrove species. According to the established mangrove species interpretation mark library, training samples and verification samples are selected on the remote sensing image for supervised classification processing of mangrove species to generate a mangrove species distribution map, providing an accurate basis for the study of the spatial distribution of mangroves. Finally, based on the mangrove species distribution map, combined with spectral characteristics and spatial distribution information, the functions of the mangrove ecosystem are evaluated and analyzed to obtain mangrove ecological monitoring data.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] (1) Use a drone to plan a flight route above the mangroves, set the flight height for the outbound journey to about 50 meters to obtain a top-down photo, and set the flight height for the return journey to about 30 meters to obtain a side-view photo, forming a target acquisition flight route;

[0038] (2) Configure the photographing parameters of the drone. Set the camera lens tilt angle to 90° in the top-down photographing mode, with a photographing interval of about 40 meters, and set the camera lens tilt angle to 30° in the side-view photographing mode, with a photographing interval of about 30 meters;

[0039] (3) Record the shooting position coordinates, flight altitude, and heading angle of each photo through the GPS receiver carried by the drone to generate photo metadata information;

[0040] (4) Classify and organize the data of the overhead photos and side-view photos according to the photo metadata information, and construct a photo index database according to the shooting time sequence and spatial position relationship;

[0041] (5) Evaluate the image quality of the overhead photos and side-view photos and perform image screening to form mangrove multi-angle image data;

[0042] (6) Associate the mangrove multi-angle image data with the shooting position information to establish an image-position correspondence table.

[0043] Specifically, considering the shape, area of the mangrove distribution area and the surrounding terrain conditions, design a closed flight path through the DJI flight planning software, so that the drone can obtain overhead photos at a flight altitude of about 50 meters during the outbound flight, and obtain side-view photos at a flight altitude of about 30 meters during the return flight. This dual-altitude flight path design makes full use of a single flight mission, reduces battery consumption and operation time, and at the same time ensures the acquisition of image data from two different perspectives of the same area. In the photo parameter configuration session, accurately set the camera carried by the drone. In the overhead photo shooting mode, set the tilt angle of the camera lens to 90°, that is, the lens is perpendicular to the ground. This angle can obtain an image with a ground orthographic projection view, which is convenient for subsequent geometric correction and overlay with satellite remote sensing images. At the same time, set the photo shooting interval to about 40 meters to ensure that the images have an appropriate overlap. For the side-view photo shooting mode, adjust the tilt angle of the lens to 30°. This angle can capture the side structural features of the mangroves without being too parallel to the ground, resulting in an overly tilted perspective. The photo shooting interval in the side-view mode is set to about 30 meters, which is denser than the overhead mode to obtain more side detail information.

[0044] During the flight shooting process, the GPS receiver carried by the drone will automatically record the accurate shooting information of each photo. These information include key parameters such as longitude and latitude coordinates (WGS84 coordinate system), altitude, flight altitude (relative to the take-off point), and heading angle (the angle between the flight direction and the due north direction). These data will be written into the photo file as EXIF information to form photo metadata information. Photo metadata is an important basis for subsequent geometric correction and spatial registration, and directly determines the accuracy of image spatial positioning.

[0045] After obtaining the photos, process and organize the photo metadata. Divide all the photos into a top-down photo group and a side-view photo group according to the photo EXIF information. Then sort each group of photos according to the shooting timestamp to establish a chronological relationship. Next, construct a spatial index based on the GPS coordinate information to determine the spatial position relationship of each photo. Through the organization in two dimensions of time and space, a structured photo index database is formed, which is convenient for subsequent efficient query and processing of photos in a specific area or time period. Photo quality assessment is a key step in data screening. Detect the blur degree, evaluate the exposure, and analyze the cloud coverage for each photo. The blur degree detection is performed by calculating the variance value of the Laplace transform of the image. The larger the value, the clearer the image. The exposure evaluation is carried out by analyzing the distribution characteristics of the image histogram to determine whether there is overexposure or underexposure. The cloud coverage analysis is determined by calculating the proportion of high-brightness areas in the image. According to the evaluation results, eliminate the photos with unqualified quality, such as the photos with a blur degree score lower than the threshold, serious exposure problems, or a cloud coverage rate too high. Finally, high-quality multi-angle mangrove image data is selected. Associate the selected multi-angle mangrove image data with its shooting location information to establish an image-location correspondence table. This correspondence table contains the unique identifier, file path, shooting time, GPS coordinates, altitude information, heading angle information, and photo type (top-down or side-view) of each image. This structured data association lays a foundation for subsequent geometric correction, spatial analysis, and feature extraction.

[0046] For example, in a mangrove ecological survey, a drone is used to collect data for a mangrove area of about 2 square kilometers. The flight path is planned in a zigzag layout with a flight path spacing of 80 meters and a total length of about 15 kilometers. During the flight, the altitude on the way there is 50 meters, and 362 top-down photos are obtained; the altitude on the way back is 30 meters, and 410 side-view photos are obtained. After extracting the metadata from the photo EXIF information, it is found that some photos have quality problems: 25 photos have a blur degree score lower than the set threshold of 200 (calculated based on the variance of the Laplace transform), 18 photos have abnormal exposure (the histogram is concentrated in the gray value range of 0-50 or 205-255), and 15 photos have a cloud coverage rate exceeding 30%. After screening, finally 328 top-down photos and 386 side-view photos are retained, constituting high-quality multi-angle mangrove image data. Through the image-location correspondence table, each photo is associated with its spatial position.

[0047] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) Extract the EXIF information of the top-down photos from the multi-angle mangrove image data to obtain a set of photo parameters including the photo shooting position coordinates, camera focal length, photo pixel width and height, and relative flight altitude;

[0049] (2) Calculate the projected half-width and projected half-height of the overhead photo on the ground based on the camera focal length, photo pixel width and height, and relative flight height in the photo parameter set, and form the ground projection size data;

[0050] (3) Based on the shooting position coordinates and heading angle of the overhead photo, and combined with the ground projection size data, calculate the ground projection coordinates of the four corner points of the overhead photo, and establish a photogrammetric control point coordinate table;

[0051] (4) Use the photogrammetric control point coordinate table to analyze the corresponding relationship with the pixel coordinates of the overhead photo, calculate the six-parameter geometric correction parameter set, and construct a spatial transformation matrix;

[0052] (5) Apply the spatial transformation matrix to the overhead photo, perform affine transformation and resampling processing to generate a geographically corrected overhead photo;

[0053] (6) Import the geographically corrected overhead photo into ArcGIS software, perform geocoordinate registration and overlay operations with the remote sensing image to obtain the corrected image overlaid on the remote sensing image.

[0054] Specifically, extract the EXIF information of the overhead photo from the mangrove multi-angle image data. These information are metadata automatically recorded and embedded into the image file by the digital camera. These EXIF information can be read through the exifread library in Python or professional image processing software to obtain a photo parameter set containing the shooting position coordinates (longitude, latitude) of the photo, the camera focal length (in millimeters), the photo pixel width and height (number of pixels), and the relative flight height (height relative to the take-off point, in meters). These parameters are the basic data for subsequent geometric correction. Based on the camera focal length, photo pixel width and height, and relative flight height in the extracted photo parameter set, calculate the projection range of the overhead photo on the ground. According to the formula in the disclosure, the projected half-width and projected half-height are calculated as follows:

[0055] ;

[0056] Among them, represents the actual scale factor, which is related to the flight height; represents the photo pixel width; represents the photo pixel height; represents the camera focal length. Through these formulas, the camera parameters are converted into the ground coverage range to form the ground projection size data. This data represents the actual ground coverage range that a single overhead photo taken by the UAV can cover.

[0057] The third step is to calculate the ground projection coordinates of the four corner points of the overhead photo based on the shooting position coordinates and the heading angle of the overhead photo, in combination with the ground projection size data. The heading angle refers to the angle between the flight direction of the UAV and the true north direction. The coordinates of the four corner points are calculated as follows:

[0058] ;

[0059] where, is the ground coordinate of the center point of the photo, are the ground coordinates of the upper left corner, upper right corner, lower right corner and lower left corner respectively. Through these calculations, a coordinate table of photogrammetric control points is established. This table contains the exact positions of the four corners of the image in the ground coordinate system, as shown in Figure 2 which is the schematic diagram of geometric correction of the UAV overhead photo in the embodiment of the present application;

[0060] Establish the corresponding relationship between the coordinate table of photogrammetric control points and the pixel coordinates of the overhead photo. The pixel coordinates of the photo are a two-dimensional coordinate system in pixels. The origin is usually at the upper left corner of the photo, the X-axis is to the right, and the Y-axis is downward. Calculate the geometric correction parameters through the six-parameter model:

[0061] ;

[0062] where, are the pixel width and height of the photo respectively, A, B, D, E are the scaling and rotation parameters, and C, F are the translation parameters. These six parameters form a spatial transformation matrix, which defines the mapping relationship from the photo pixel coordinates to the ground coordinates.

[0063] Apply the calculated spatial transformation matrix to the overhead photo for affine transformation and resampling processing. Affine transformation is a mapping transformation that preserves points, lines and planes, and it is achieved through matrix multiplication. For each pixel point (i, j) in the photo, calculate its corresponding ground coordinate through the following formula :

[0064] ;

[0065] Through reverse mapping and resampling, the distorted original photo is converted into a corrected image that conforms to the ground coordinate system. Resampling methods include nearest neighbor interpolation, bilinear interpolation, cubic convolution interpolation, etc. Select a suitable method according to the accuracy requirements. Generate a geographically corrected overhead photo, which is consistent with the actual ground position in terms of spatial location. Import the geographically corrected overhead photo into ArcGIS software for georeferencing and overlay operations with the remote sensing image. ArcGIS is a professional geographic information system software with powerful spatial analysis and visualization functions. In ArcGIS, load the existing remote sensing image as the base map, and then import the corrected overhead photo, ensuring that both use the same coordinate system (such as WGS84 or UTM). By adjusting display parameters such as transparency and contrast, the overhead photo is naturally fused with the remote sensing image to form a corrected image superimposed on the remote sensing image, such as Figure 3 shown, which is the overlay map of the geometrically corrected drone overhead photo and the remote sensing image in the embodiment of the present application.

[0066] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0067] (1) Extract the GPS coordinates, flight heading angle, and shooting timestamp from the EXIF information of the side-view photo to form a side-view photo position and angle information set;

[0068] (2) Perform spatial filtering on the side-view photo position and angle information set to eliminate data points with repeated positions or excessive errors, and obtain an optimized position and angle data set;

[0069] (3) Use the GPS coordinate information in the optimized position and angle data set to create point features in ArcGIS software and construct a mangrove side-view photo position point layer;

[0070] (4) According to the flight heading angle data in the optimized position and angle data set, assign direction attributes to the mangrove side-view photo position point layer to generate a point layer with direction attributes;

[0071] (5) Based on the point layer with direction attributes, perform arrow symbolization design, where the arrow direction represents the shooting direction and the arrow size is proportional to the flight height, to form a mangrove side-view photo direction symbol layer;

[0072] (6) Spatially overlay the mangrove side-view photo direction symbol layer and the remote sensing image in the same coordinate system to obtain a shooting direction layer superimposed on the remote sensing image.

[0073] Specifically, different from the geometric correction of the top-down photos, the side-view photos are mainly used to represent the shooting position and direction, providing information support from the side perspective for the interpretation of mangrove species. Key data are extracted from the EXIF information of the side-view photos. The EXIF information of the side-view photos contains rich metadata. Through professional EXIF reading tools (such as ExifTool or the exifread library in Python), GPS coordinates (longitude, latitude), flight heading angle (indicating the angle between the flight direction of the drone and the due north direction, in degrees), and shooting timestamp (in the format of YYYY:MM:DD HH:MM:SS) can be extracted. These data combinations form the side-view photo position and angle information set, which is a data set representing when, where, and at what angle each side-view photo was taken.

[0074] Spatial filtering of the extracted side-view photo position and angle information set is a necessary step to ensure data quality. Due to GPS positioning errors or the drone may take multiple photos at certain positions in a short time, there may be duplicate positions or data points with excessive positioning errors in the data set. Two main strategies are adopted for spatial filtering: one is duplicate removal of adjacent points. By calculating the spherical distance between every two GPS points, duplicate points with a distance less than the distance threshold (usually set to 5 - 10 meters) are removed; the other is outlier detection. By calculating the average distance from each point to its K nearest neighbors (usually K = 5), comparing this value with the deviation of the overall distribution, outliers with a deviation degree exceeding the set deviation threshold (usually set to 3 times the standard deviation) are removed. After these two steps of processing, an optimized position and angle data set is obtained, which contains higher-quality and more evenly distributed side-view photo position and angle information.

[0075] Using the GPS coordinate information in the optimized position and angle data set, the next step is to create point features in the ArcGIS software. ArcGIS is a professional geographic information system software, providing powerful geographic data processing and visualization functions. Through the "XY Data Import" function of ArcGIS, the GPS coordinate (longitude, latitude) data are imported in CSV or Excel format, and an appropriate coordinate system (usually WGS84) is set to create an initial point layer. Then, through the "Feature Transformation" tool, the temporary layer is converted into a permanent geodatabase feature class, forming the mangrove side-view photo position point layer. This layer accurately represents the shooting position of each side-view photo in the geospatial space.

[0076] Based on the flight heading angle data in the optimized position - angle dataset, it is necessary to assign direction attributes to the layer of mangrove side - view photo position points. In ArcGIS, first, through the "Add Field" function, create a new field named "Direction" in the attribute table, with the data type being floating - point. Then use the "Calculate Field" function to assign the flight heading angle data to this field. For side - view photos with a camera lens tilt angle of approximately 30°, there is a certain angle difference between the photographing direction and the flight direction. Angle adjustment needs to be made according to the flight orientation of the UAV and the camera installation position. The adjusted angle value The calculation formula is:

[0077] ;

[0078] Among them, is the flight heading angle, is the camera installation offset angle (usually 90° for cameras installed on the right side and - 90° for those installed on the left side), and mod represents the modulo operation to ensure that the angle value is within the range of 0 - 360°. Through this processing, a point layer with direction attributes is generated. This layer not only contains position information but also the precise photographing direction of each shooting point.

[0079] Based on the point layer with direction attributes, next, arrow symbolization design is carried out. In ArcGIS, select the point layer, open the "Symbol Properties" dialog box, and select the "Arrow Marker" symbol type. Set the color, line width, and head style of the arrow, and set the rotation angle field of the arrow to the previously calculated "Direction" field to ensure that the arrow orientation is consistent with the photographing direction. To make the symbolization effect more intuitive, the arrow size can also be adjusted according to the flight height. The calculation formula is:

[0080] ;

[0081] Among them, is the final arrow size, is the reference arrow size (usually set to 2 - 5 points), is the actual flight height, is the reference flight height (usually taken as 30 meters). Through such a design, a mangrove side - view photo direction symbol layer is formed. This layer visually shows the shooting position and direction of the side - view photos, and the arrow size also reflects the flight height information.

[0082] The last step is to perform a spatial overlay of the mangrove side-looking photo direction symbol layer and the remote sensing image in the same coordinate system. First, load the existing remote sensing image in ArcGIS as the base map, ensuring that its coordinate system is consistent with that of the direction symbol layer. If necessary, use the "Projection" tool to perform coordinate system conversion. Then, load the mangrove side-looking photo direction symbol layer and adjust the layer order to ensure that the symbol layer is above the remote sensing image. By setting appropriate display parameters such as transparency and contrast, the two layers are naturally fused to form an intuitive visual effect. Finally, the photo-taking direction layer overlaid on the remote sensing image is obtained, which clearly shows the spatial relationship between the shooting positions and directions of the side-looking photos and the distribution of mangroves.

[0083] Take a specific mangrove survey example to illustrate: In the UAV survey conducted in a certain mangrove reserve, 285 side-looking photos were obtained using a UAV. The GPS coordinates, flight heading angles, and shooting timestamps were extracted from the EXIF information to form an initial set of side-looking photo position and angle information. Through spatial filtering, it was found that the mutual distances of 23 points were less than the set threshold of 8 meters, belonging to duplicate position points; another 7 points had positioning errors exceeding 3 times the standard deviation and were identified as abnormal points. After removing these points, 255 valid data points were obtained. These GPS coordinates were imported into ArcGIS to create a mangrove side-looking photo position point layer. Considering that the camera was installed on the right side of the UAV, a 90° offset was added to the flight heading angle. For example, for a point with a flight heading angle of 45°, its photo-taking direction angle was (45° + 90°) = 135°, indicating that the camera was facing the southeast direction. Based on the calculated direction angles and flight altitudes (ranging from 25 to 35 meters), arrow symbols of different sizes and directions were designed to intuitively show the photo-taking directions of each shooting point. Finally, the symbol layer was overlaid with the Sentinel-2 satellite image, clearly showing the spatial relationship between the side-looking photo shooting points and the mangrove distribution, providing important side-view information support for subsequent mangrove species interpretation.

[0084] In a specific embodiment, the process of performing step S104 may specifically include the following steps:

[0085] (1) In the overlapping area of the corrected image, the photo-taking direction layer, and the remote sensing image, select representative sample areas of different mangrove species to create a mangrove species sample point set;

[0086] (2) For each sample point in the mangrove species sample point set, extract texture features, color features, and shape features from the corrected image, and extract spectral features and spatial features from the remote sensing image to form a mangrove species feature vector set;

[0087] (3) Rank the importance of the mangrove species feature vector set through recursive feature elimination, retain the key features with high contribution, and construct an optimized feature subset;

[0088] (4) Based on the optimized feature subset, use the random forest algorithm to train the mangrove species classifier, where the number of decision trees is set to 500, the minimum number of samples per node is 5, and the random selection ratio of features is 0.7;

[0089] (5) Extract classification rules from the set of decision trees generated by the random forest algorithm, including the splitting thresholds of each feature and the species discrimination path, to generate a mangrove species discrimination rule set;

[0090] (6) Establish an associated mapping relationship between the mangrove species discrimination rule set and the corresponding side-view photos, top-view photos, and remote sensing image features to form a mangrove species interpretation sign library.

[0091] Specifically, select representative sample areas of different mangrove species in the overlapping area of the corrected image, the photographing direction layer, and the remote sensing image. Considering the spatial distribution of the three layers comprehensively, determine their common coverage area, which is generally realized by using the "layer overlay analysis" function of GIS software. In the overlapping area, according to expert knowledge or existing ground survey data, select typical sample areas for the main mangrove species (such as Kandelia obovata, Rhizophora stylosa, Avicennia marina, etc.). Each sample area should meet the requirements of moderate area (usually 5×5 meters to 10×10 meters), high species purity, and uniform spatial distribution. After determining the location of the sample area, use the point sampling tool to set multiple sampling points in each sample area to form a mangrove species sample point set, and record the coordinate position and the species category to which each point belongs.

[0092] For each sample point in the mangrove species sample point set, multi-source features need to be extracted. The features extracted from the corrected image mainly include three categories: Texture features are statistics that describe the pixel gray-scale distribution pattern in a local area of the image. Commonly used ones are gray-level co-occurrence matrix (GLCM) features, including contrast, correlation, entropy, etc.; Color features describe the color information of the area around the sample point, usually obtained by calculating statistics such as the mean, variance, and skewness of each channel in the RGB or HSV color space; Shape features describe the geometric characteristics of the mangrove canopy, such as area, perimeter, compactness, etc. The features extracted from the remote sensing image focus on spectral features and spatial features: Spectral features include the reflectance of each band and its combinations. Commonly used ones are the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil-adjusted vegetation index (SAVI), etc.; Spatial features include topographic features (such as elevation, slope) and surrounding environment features (such as the distance from the water body). For each sample point, these features are organized into a multi-dimensional vector to form a mangrove species feature vector set.

[0093] Due to the large number of extracted features, some features may be redundant or have a low correlation with the target species classification, and feature selection is required. Recursive Feature Elimination (RFE) is an effective feature selection method. Its basic idea is to iteratively train a base classifier (such as a random forest or a support vector machine), and each time delete the feature with the lowest importance until the preset number of features is reached. In the mangrove feature selection, RFE first trains a random forest model using all features, and calculates the importance score of each feature (usually based on the decrease in Gini index or information gain). Then, the features are removed in ascending order of importance. After removing one or a group of features each time, the model is retrained and the performance is evaluated. By comparing the classification performance (such as accuracy, F1 score) of different feature subsets, the optimal number of features is determined, and the key features with higher importance rankings are retained to construct an optimized feature subset.

[0094] Based on the optimized feature subset, a mangrove species classifier is trained using the random forest algorithm. Random forest is an ensemble learning method composed of multiple decision trees, and classification prediction is performed by majority voting. During the training process, the number of decision trees is set to 500, which is large enough to ensure the stability of the model; the minimum number of samples per node is set to 5 to prevent overfitting while maintaining sensitivity to minority classes; the random selection ratio of features is set to 0.7, that is, 70% of the features are randomly selected to participate in the decision when each node is split. The Bootstrap sampling method is used during training, that is, each decision tree uses a training set obtained by sampling with replacement, and about one-third of the samples are not selected. These samples are called "Out-of-Bag samples" and are used to evaluate the model performance.

[0095] Extracting classification rules from the trained random forest algorithm is a key step in establishing an interpretable interpretation library. Although the random forest is regarded as a "black box" model, rules can be extracted by analyzing its internal decision tree structure. The specific approach is to traverse each decision tree and record the complete path from the root node to the leaf node. Each path represents a classification rule. For example, for a path determined to be "Kandelia obovata", it may contain conditions such as "NDVI > 0.65 AND texture entropy < 1.2 AND red band reflectance < 0.15". These conditions include feature names (such as NDVI), comparison operators (>、<、=), and splitting thresholds (0.65). By counting the rules in all decision trees, the rules with high frequencies and high accuracies are selected to form a mangrove species discrimination rule set.

[0096] Establish an associated mapping relationship between the mangrove species discrimination rule set and multi-source image features. For each discrimination rule, it is necessary to trace back which data sources (side-looking photos, top-down photos, or remote sensing images) the features it uses come from, and record the feature extraction methods and parameter settings. For example, for texture features, record its window size, calculation method, etc.; for spectral indices, record its band combination formula, etc. This associated mapping enables the rule to tell not only "what it is", but also "why" and "how to judge". In this way, a ternary associated mangrove species interpretation symbol library containing species feature descriptions, discrimination rules, and image instances is formed. This symbol library combines expert experience with machine learning, having both strict quantitative criteria and intuitive visual explanations.

[0097] Illustrate this process with a specific case: In the study of a certain mangrove reserve, 120 representative sample areas were selected within the overlapping area of the corrected image, the photo-taking direction layer, and the remote sensing image, including Kandelia obovata (40), Rhizophora stylosa (35), Avicennia marina (30), and other species (15). For the 324 sample points set in each sample area, 18 texture features (such as GLCM contrast, homogeneity, correlation, etc.), 12 color features (such as the mean and standard deviation of each RGB channel), and 8 shape features (such as crown width, perimeter ratio, etc.) were extracted from the corrected image; 10 spectral features (such as NDVI, EVI, red edge position, etc.) and 7 spatial features (such as elevation, distance to water body, etc.) were extracted from the remote sensing image, totaling 55 feature dimensions. Through the recursive feature elimination method, features were removed in sequence according to the feature importance scores, and finally 22 optimal features were determined, including NDVI, texture contrast, NIR band reflectance, etc. The random forest classifier (500 decision trees, minimum node sample number 5, feature selection ratio 0.7) constructed based on these features achieved an accuracy of 94.2% on the training set. More than 1000 initial rules were extracted from the decision trees. After frequency screening and accuracy verification, 178 high-quality rules were retained, forming a detailed mangrove species discrimination rule set. These rules have established a clear associated relationship with multi-source image features. For example, a rule like "If NDVI > 0.72 and texture contrast < 1.5 and NIR reflectance > 0.45, then it is determined to be Kandelia obovata" associates the spectral features of the remote sensing image and the texture features of the corrected image at the same time, forming a complete mangrove species interpretation symbol library. This symbol library not only provides high-accuracy automatic classification ability, but also maintains good interpretability, facilitating experts to understand and verify the classification results.

[0098] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0099] (1)According to the species characteristic descriptions in the mangrove species interpretation mark library, representative areas of each mangrove species are selected by stratified sampling on the remote sensing image to generate a mangrove training sample set and a validation sample set;

[0100] (2)For each sample in the mangrove training sample set, the normalized difference vegetation index, red edge position index, and texture complexity index are extracted as mangrove characteristic parameters to construct a mangrove sample characteristic matrix;

[0101] (3)The mangrove species supervised classification of the remote sensing image is carried out through the mangrove sample characteristic matrix to divide the distribution ranges of different mangrove species and form a preliminary mangrove classification result;

[0102] (4)Using the spectral similarity and spatial continuity characteristics in the preliminary mangrove classification result, mangrove species patch merging and boundary optimization processing are carried out to eliminate isolated pixels and boundary noise, and an optimized mangrove classification result is obtained;

[0103] (5)Based on the mangrove validation sample set, the accuracy evaluation of the optimized mangrove classification result is carried out, the overall accuracy, Kappa coefficient, and classification accuracy of each species are calculated, and a mangrove classification accuracy report is generated;

[0104] (6)According to the accuracy indicators in the mangrove classification accuracy report, the credibility of the optimized mangrove classification result is graded and converted into a vector format, and species name, area, distribution characteristic attribute information are added to generate a mangrove species distribution map.

[0105] Specifically, sample points are selected on remote sensing images. The stratified sampling method is adopted instead of simple random sampling, mainly considering the obvious environmental gradients and community characteristics in the spatial distribution of mangrove species. Stratified sampling divides the entire study area into several relatively homogeneous strata according to factors such as different species types, growth environments, and altitudes, and then random sampling is carried out within each stratum to ensure that the samples can represent the mangrove characteristics under various environmental conditions. Usually, the total sample size is divided into a training sample set and a validation sample set in a ratio of 7:3. The training samples are used to construct a classification model, and the validation samples are used to evaluate the classification accuracy. For the selected mangrove training sample set, key characteristic parameters need to be extracted to characterize the spectral and texture characteristics of different mangrove species. The normalized difference vegetation index (NDVI) is one of the most commonly used vegetation indices, which is calculated by the reflectance difference between the near-infrared band and the red band, and reflects the biomass and vitality of vegetation. The red edge position index (REP) is the inflection wavelength position in the region where the vegetation spectral curve rapidly rises from the red light to the near-infrared, which is sensitive to the chlorophyll content and physiological state of vegetation and can effectively distinguish different mangrove species. The texture complexity index is a texture feature calculated based on the gray-level co-occurrence matrix (GLCM), which quantifies the complexity of pixel value changes in the local area of the image and reflects the complexity of the mangrove canopy structure. These three types of indices combine spectral information and spatial structure information, calculate for each sample point and organize them into a feature matrix. The rows of the matrix represent different sample points, and the columns represent different characteristic parameters.

[0106] Through the established mangrove sample feature matrix, supervised classification of mangrove species is carried out for the entire remote sensing image. In the classification process, algorithms such as Maximum Likelihood Classification, Support Vector Machine (SVM), or Random Forest are used to compare each pixel in the remote sensing image with the sample feature matrix, and it is classified into different mangrove species categories according to the principle of feature similarity. The Maximum Likelihood Method assumes that the feature distribution of each category conforms to a multivariate normal distribution, calculates the probability that a pixel belongs to each category, and selects the category with the highest probability as the classification result of the pixel. The Support Vector Machine realizes classification by constructing an optimal separation hyperplane in a high-dimensional feature space. Each pixel in the remote sensing image is assigned a specific mangrove species category to form a preliminary mangrove classification result. There are usually problems such as isolated pixels and boundary noise in the preliminary classification result, which need to be post-processed and optimized. The post-processing first analyzes based on spectral similarity, calculates the spectral distance between each pixel and its surrounding pixels. If the spectral distance between the isolated pixel and the dominant category of the surrounding area is less than the set threshold, it is merged into the surrounding dominant category. At the same time, the spatial continuity feature is used for patch merging and boundary smoothing. The Majority Filter is used to perform window filtering on the classification result, and the window size is usually set to 3×3 or 5×5 pixels. The category of the central pixel is determined by the most common category within the window. For isolated patches smaller than the specified area threshold (usually 4 - 9 pixels), they are merged into the largest and most similar surrounding patches. Boundary optimization uses morphological processing methods to smooth the category boundary through dilation and erosion operations, generating an optimized mangrove classification result that better conforms to the natural distribution law.

[0107] The accuracy of the optimized classification result is evaluated based on the reserved mangrove validation sample set. The accuracy evaluation first constructs a Confusion Matrix, where the rows of the matrix represent the classification results and the columns represent the reference truth values (i.e., the actual categories of the validation samples). A series of statistical indicators in the matrix are calculated to evaluate the classification accuracy, including Overall Accuracy, Kappa coefficient, Producer's Accuracy, and User's Accuracy. The Overall Accuracy is the number of correctly classified samples divided by the total number of samples; the Kappa coefficient takes into account the influence of random classification and reflects the degree of consistency between the classification result and the reference data; the Producer's Accuracy represents the proportion of a certain category that is correctly identified; the User's Accuracy represents the proportion of pixels classified as a certain category that actually belong to that category. These indicators comprehensively form a mangrove classification accuracy report, providing a quantitative evaluation of the reliability of the results.

[0108] Optimize and output the results according to the mangrove classification accuracy report. First, classify the results based on the classification accuracy indicators. Usually, the classification accuracy is divided into three levels: high, medium, and low, and the corresponding thresholds are 85%, 70%, and 60% respectively. Then, convert the raster-format classification results into vector format. During the conversion process, set an area threshold to remove small patches and perform boundary smoothing. For each vector polygon, add attribute information, including species name (based on the classification results), area (obtained by calculating the polygon area), distribution characteristics (such as the average elevation and slope of the distribution area), etc. The resulting mangrove species distribution map contains both spatial distribution information and rich attribute information, providing basic data for subsequent ecosystem function assessment.

[0109] For example: In a study of a certain mangrove reserve, 430 sample points were selected on the remote sensing image based on the mangrove species interpretation symbol library, including 150 points of Kandelia obovata, 120 points of Rhizophora stylosa, 90 points of Avicennia marina, and 70 points of other species. They were divided into 300 training samples and 130 validation samples according to a 7:3 ratio. For each training sample, the NDVI value (reflecting biomass), REP value (reflecting chlorophyll content), and GLCM-based texture complexity index (reflecting canopy structure) were extracted to construct a 300×3 feature matrix. The maximum likelihood method was used to classify the remote sensing image. In the preliminary results, the total area of mangroves identified was approximately 250 hectares, but there were a large number of isolated pixels and uneven boundaries. Through the majority filter with a 3×3 window and patch merging with a minimum area threshold of 4 pixels, the isolated pixels and small patches were eliminated, and the optimized area was 243 hectares, with a more continuous and reasonable distribution. Using 130 validation sample points to evaluate the accuracy, the overall accuracy reached 87.6%, and the Kappa coefficient was 0.84. Among them, the user accuracy of Kandelia obovata was 92.1%, Rhizophora stylosa was 86.3%, and Avicennia marina was 88.7%, indicating that the classification results were reliable. Finally, the classification results were vectorized to generate a distribution map containing 48 mangrove patches, and each patch contained attribute information such as species type and area, providing an accurate spatial information basis for mangrove ecological monitoring and protection management.

[0110] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0111] (1) Based on the mangrove species distribution map, extract the spatial distribution patterns of different mangrove species, quantify the area, density, and distribution form of each species, and generate a mangrove spatial structure parameter table;

[0112] (2) Use the near-infrared band and visible band data in the remote sensing image to calculate the normalized difference vegetation index and leaf area index of the mangrove area, and construct a mangrove growth vitality evaluation index;

[0113] (3)Combined with the mangrove species distribution map, the mangrove spatial structure parameter table, and the mangrove growth vitality evaluation indicators, the biomass conversion coefficient is used to calculate the mangrove carbon storage, generating the quantified data of the mangrove carbon sink function;

[0114] (4)Extract the spatial relationship between the mangrove and the coastline from the mangrove species distribution map and the mangrove spatial structure parameter table, and combine with the tide level and terrain data to analyze the wave attenuation and sediment retention capabilities of the mangrove, forming the evaluation results of the mangrove coastal protection function;

[0115] (5)Analyze the species composition in the mangrove species distribution map and the distribution pattern in the mangrove spatial structure parameter table, calculate the species diversity index and the niche overlap degree, and obtain the data of the mangrove biodiversity maintenance function;

[0116] (6)Integrate and integrate the quantified data of the mangrove carbon sink function, the evaluation results of the mangrove coastal protection function, and the data of the mangrove biodiversity maintenance function to obtain the mangrove ecological monitoring data.

[0117] Specifically, based on the obtained mangrove species distribution map, a quantitative analysis is carried out on the spatial distribution pattern of different species. In the GIS environment, a spatial statistical analysis is performed on the mangrove species distribution map to calculate the area (obtained through the vector data area calculation function), density (the number of individuals per unit area), and distribution form (described by patch shape index, aggregation index, etc.) of each species. For the area, calculate the total area of each mangrove species and its proportion in the entire study area; for the density, through the high-resolution image data obtained by the unmanned aerial vehicle, combined with the canopy recognition algorithm, calculate the number of tree individuals per unit area; for the distribution form, use landscape pattern indices, such as patch size variation coefficient, patch shape index, and aggregation index, etc., to quantify the spatial distribution characteristics of the mangrove. These parameters comprehensively form the mangrove spatial structure parameter table, which details the spatial distribution characteristics of each mangrove species.

[0118] Use the spectral information in the remote sensing image to calculate the mangrove growth vitality indicators. The Normalized Difference Vegetation Index (NDVI) is an important indicator to characterize the vegetation growth status, which is calculated by the reflectance of the near-infrared band (NIR) and the red band (R): NDVI = (NIR - R) / (NIR + R). The higher the NDVI value, the higher the vegetation coverage and biomass. The Leaf Area Index (LAI) represents the leaf area size per unit ground area and is an important parameter to describe the vegetation canopy structure. LAI can be converted from NDVI through an empirical model or directly calculated after three-dimensional reconstruction of the multi-view images obtained by the unmanned aerial vehicle. These two indicators together constitute the mangrove growth vitality evaluation indicators, reflecting the growth status and photosynthetic ability of the mangrove.

[0119] The third step is the calculation of the mangrove carbon storage, which comprehensively utilizes the three datasets obtained previously: the mangrove species distribution map, the mangrove spatial structure parameter table, and the mangrove growth vitality evaluation indicators. The carbon storage calculation is divided into aboveground and underground parts. For the calculation of aboveground carbon storage, biomass conversion equations based on species are used. Corresponding conversion equations are selected for different mangrove species to convert parameters such as tree height and diameter at breast height into biomass, and then multiplied by the carbon content coefficient (usually 0.5) to obtain the carbon storage. The underground carbon storage is estimated according to the root-shoot ratio, and different mangrove species have different root-shoot ratio values. In the UAV data processing, tree height can be extracted from LiDAR data or three-dimensional point cloud models, and diameter at breast height can be estimated through the combination of tree height and crown width using species-specific related equations. In this way, the carbon storage is calculated for each mangrove patch in the study area, generating the quantified data of the mangrove carbon sink function.

[0120] Next, the spatial relationship between the mangroves and the coastline is extracted from the mangrove species distribution map and the spatial structure parameter table to evaluate its coastal protection function. Among them, the coastline data is obtained (which can be obtained from satellite images or existing geographical databases), and then the distance, width, and continuity between the mangrove belt and the coastline are calculated. The wave attenuation ability of mangroves mainly depends on the belt width, density, and species composition, and can be calculated through an empirical model: wave energy attenuation rate = T (belt width, tree density, root system complexity). The sediment retention ability is related to the root development degree and the spatial structure of mangroves, and different mangrove species have different root characteristics and soil fixation abilities. Combining the tidal level and terrain data, the wave action scenarios under different tidal level conditions are simulated, and the protection efficiency index of mangroves is calculated to form the evaluation results of the mangrove coastal protection function.

[0121] Based on the species composition in the mangrove species distribution map and the distribution pattern in the spatial structure parameter table, various biodiversity indices are calculated. Commonly used ones include the Shannon-Wiener diversity index, Simpson diversity index, Pielou evenness index, etc. The calculation formula for the Shannon-Wiener index is: H' = -∑(Po × lnPo), where Po is the relative abundance of species o. The niche overlap degree describes the similarity of resource utilization between different species and is calculated through the Pianka index or Morisita index. High species diversity and moderate niche overlap usually indicate the stability of ecosystem functions and strong anti-interference ability. These indicators comprehensively form the data on the mangrove biodiversity maintenance function. Integrate the quantified data of the mangrove carbon sequestration function, the evaluation results of the coastal protection function, and the data on the biodiversity maintenance function to form comprehensive mangrove ecological monitoring data. The integration process uses a multi-criteria evaluation method to standardize different function indicators, unifying each indicator value within the range of 0-1 for easy comparison. Then, set the weights of each function and obtain the comprehensive evaluation value through weighted summation or the analytic hierarchy process (AHP). This comprehensive data set not only contains the original evaluation data of each function but also includes the overall evaluation of the mangrove ecosystem service function, providing a scientific basis for formulating mangrove protection strategies and determining ecological compensation standards.

[0122] The above describes the method for extracting mangrove ecological information based on drones in the embodiments of the present application. Next, the system for extracting mangrove ecological information based on drones in the embodiments of the present application will be described. Please refer to Figure 4 , an embodiment of the system for extracting mangrove ecological information based on drones in the embodiments of the present application includes:

[0123] An acquisition module, configured to take low-altitude photos of the mangrove from different angles through a drone, obtain a top-down photo with a camera lens tilt angle of approximately 90° and a side-view photo with a camera lens tilt angle of approximately 30°, and obtain mangrove multi-angle image data;

[0124] A calculation module, configured to calculate projection coordinates based on the mangrove multi-angle image data, using position coordinates, photo length and width, and flight height information, and perform geometric correction processing on the top-down photo to obtain a corrected image superimposed on the remote sensing image;

[0125] A processing module, configured to generate a vector point layer and perform symbolization processing based on the position and flight angle information of the side-view photo to obtain a photo-taking direction layer superimposed on the remote sensing image;

[0126] A building module, configured to extract features and establish an association relationship through a random forest algorithm based on the corrected image, the photo-taking direction layer, and the remote sensing image to form a mangrove species interpretation symbol library;

[0127] A selection module, configured to select training samples and validation samples on the remote sensing image according to the mangrove species interpretation signature library, perform supervised classification processing of mangrove species, and generate a mangrove species distribution map;

[0128] An analysis module, configured to evaluate and analyze the functions of the mangrove ecosystem based on the mangrove species distribution map, combined with spectral characteristics and spatial distribution information, to obtain mangrove ecological monitoring data.

[0129] Through the collaborative cooperation of the above-mentioned various components, low-altitude photos of the mangrove are taken from different angles by an unmanned aerial vehicle, obtaining a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, forming multi-angle image data of the mangrove, breaking through the limitation that it is difficult for traditional manual surveys to enter the interior of the mangrove, and greatly improving the spatial coverage and data collection efficiency of mangrove surveys. The projection coordinates are calculated using the position coordinates, photo length and width, and flight height information, and geometric correction processing is performed on the top-down photo to obtain a corrected image superimposed on the remote sensing image, solving the problem of accurate registration between the unmanned aerial vehicle image and the satellite remote sensing image, and providing a basis for multi-source data fusion analysis. Based on the position and flight angle information of the side-view photo, a vector point layer is generated and symbolized to obtain a photo-taking direction layer superimposed on the remote sensing image, intuitively showing the relationship between the photo-taking direction and position of the side-view photo, and facilitating the understanding of the side structure characteristics of the mangrove. It is particularly worth emphasizing that according to the corrected image, the photo-taking direction layer, and the remote sensing image, this solution extracts features and establishes correlation relationships through the random forest algorithm, forming a mangrove species interpretation signature library. The random forest algorithm shows excellent performance in feature selection and classification. It can not only process high-dimensional feature data, but also evaluate the importance of features and handle the correlation between features, providing strong algorithm support for mangrove species identification; the algorithm has strong robustness to noise and outliers. By constructing a large number of decision trees and adopting a majority voting mechanism, the risk of overfitting is effectively reduced, and the classification accuracy is improved. The interpretation signature library established in this way combines the advantages of the top-down view, side-view, and remote sensing data sources, realizing high-precision identification of mangrove species. According to the established mangrove species interpretation signature library, training samples and validation samples are selected on the remote sensing image, supervised classification processing of mangrove species is performed, and a mangrove species distribution map is generated, providing an accurate basis for the study of the spatial distribution of mangroves. Finally, based on the mangrove species distribution map, combined with spectral characteristics and spatial distribution information, the functions of the mangrove ecosystem are evaluated and analyzed to obtain mangrove ecological monitoring data.

[0130] Refer to Figure 5 , in an embodiment of the present invention, a computer device is further provided. The computer device may be a server, and its internal structure may be as Figure 5As shown in the figure. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0131] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0132] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0133] Those of ordinary skill in the art can understand that all or part of the process of implementing the method in the above embodiment can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database, or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0134] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0135] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0136] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for extracting mangrove ecological information based on drones, characterized in that, The method for extracting mangrove ecological information based on an unmanned aerial vehicle includes: Taking low-altitude photos of the mangrove from different angles by the unmanned aerial vehicle, obtaining a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, and obtaining mangrove multi-angle image data; According to the mangrove multi-angle image data, calculating projection coordinates by using position coordinates, photo length and width, and flight height information, and performing geometric correction processing on the top-down photo to obtain a corrected image superimposed on the remote sensing image; Based on the position and flight angle information of the side-view photo, generating a vector point layer and performing symbolization processing to obtain a photographing direction layer superimposed on the remote sensing image, including: extracting GPS coordinates, flight heading angle, and shooting timestamp from the EXIF information of the side-view photo to form a side-view photo position angle information set; performing spatial filtering on the side-view photo position angle information set to eliminate data points with repeated positions or excessive errors, and obtaining an optimized position angle data set; using the GPS coordinate information in the optimized position angle data set to create point features in ArcGIS software and constructing a mangrove side-view photo position point layer; assigning direction attributes to the mangrove side-view photo position point layer according to the flight heading angle data in the optimized position angle data set to generate a point layer with direction attributes; based on the point layer with direction attributes, performing arrow symbolization design, where the arrow direction represents the photographing direction and the arrow size is proportional to the flight height, to form a mangrove side-view photo direction symbol layer; spatially superimposing the mangrove side-view photo direction symbol layer and the remote sensing image in the same coordinate system to obtain a photographing direction layer superimposed on the remote sensing image; Based on the corrected image, the photographing direction layer, and the remote sensing image, extracting features and establishing an association relationship through a random forest algorithm to form a mangrove species interpretation mark library; According to the mangrove species interpretation mark library, selecting training samples and validation samples on the remote sensing image, and performing supervised classification processing of mangrove species to generate a mangrove species distribution map; Based on the mangrove species distribution map, combining spectral characteristics and spatial distribution information, evaluating and analyzing the functions of the mangrove ecosystem to obtain mangrove ecological monitoring data.

2. The method for extracting mangrove ecological information based on an unmanned aerial vehicle according to claim 1, wherein The step of taking low-altitude photos of the mangrove from different angles by the unmanned aerial vehicle, obtaining a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, and obtaining mangrove multi-angle image data includes: Planning a flight route above the mangrove by the unmanned aerial vehicle, setting the flight height for the outbound journey to about 50 meters to obtain a top-down photo, and setting the flight height for the return journey to about 30 meters to obtain a side-view photo, to form a target acquisition route; Configuring the photo-taking parameters of the unmanned aerial vehicle, setting the camera lens tilt angle to 90° in the top-down photo-taking mode, with a photo-taking interval of about 40 meters, and setting the camera lens tilt angle to 30° in the side-view photo-taking mode, with a photo-taking interval of about 30 meters; The GPS receiver carried by the UAV records the shooting position coordinates, flight altitude, and heading angle of each photo to generate photo metadata information; Classify and organize the data of the top-down photos and the side-view photos according to the photo metadata information, and construct a photo index database according to the shooting time sequence and spatial position relationship; Evaluate the image quality of the top-down photos and the side-view photos and perform image screening to form mangrove multi-angle image data; Associate the mangrove multi-angle image data with the shooting position information to establish an image-position correspondence table.

3. The method for extracting mangrove ecological information based on an unmanned aerial vehicle according to claim 1, wherein According to the mangrove multi-angle image data, calculate the projection coordinates using the position coordinates, photo length and width, and flight altitude information, and perform geometric correction processing on the top-down photos to obtain corrected images superimposed on the remote sensing images, including: Extract the EXIF information of the top-down photos from the mangrove multi-angle image data to obtain a photo parameter set containing the photo shooting position coordinates, camera focal length, photo pixel width and height, and relative flight altitude; Calculate the projected half-width and projected half-height of the top-down photo on the ground according to the camera focal length, photo pixel width and height, and relative flight altitude in the photo parameter set to form ground projection size data; Based on the photo shooting position coordinates and heading angle of the top-down photo, combine the ground projection size data to calculate the ground projection coordinates of the four corner points of the top-down photo, and establish a photogrammetric control point coordinate table; Use the correspondence analysis between the photogrammetric control point coordinate table and the pixel coordinates of the top-down photo to calculate the six-parameter geometric correction parameter set and construct a spatial transformation matrix; Apply the spatial transformation matrix to the top-down photo, perform affine transformation and resampling processing to generate a geographically corrected top-down photo; Import the geographically corrected top-down photo into ArcGIS software, perform geocoordinate registration and overlay operations with the remote sensing image to obtain a corrected image superimposed on the remote sensing image.

4. The method for extracting mangrove ecological information based on an unmanned aerial vehicle according to claim 1, wherein Based on the corrected image, the photo-taking direction layer, and the remote sensing image, extract features and establish association relationships through the random forest algorithm to form a mangrove species interpretation symbol library, including: In the overlapping area of the corrected image, the photo-taking direction layer, and the remote sensing image, select representative sample areas of different mangrove species to create a mangrove species sample point set; For each sample point in the mangrove species sample point set, extract texture features, color features, and shape features from the corrected image, and extract spectral features and spatial features from the remote sensing image to form a mangrove species feature vector set; Perform importance ranking on the mangrove species feature vector set through recursive feature elimination, retain the key features with high contribution, and construct an optimized feature subset; Based on the optimized feature subset, use the random forest algorithm to train a mangrove species classifier, where the number of decision trees is set to 500, the minimum number of samples per node is 5, and the feature random selection ratio is 0.7; Extract classification rules from the decision tree set generated by the random forest algorithm, including the splitting threshold of each feature and the species discrimination path, to generate a mangrove species discrimination rule set; Establish an associated mapping relationship between the mangrove species discrimination rule set and the corresponding side-view photos, top-view photos, and remote sensing image features to form a mangrove species interpretation symbol library.

5. The method for extracting mangrove ecological information based on an unmanned aerial vehicle according to claim 1, wherein According to the mangrove species interpretation symbol library, select training samples and validation samples on the remote sensing image, and perform supervised classification processing of mangrove species to generate a mangrove species distribution map, including: Based on the species feature descriptions in the mangrove species interpretation symbol library, use stratified sampling on the remote sensing image to select representative areas of each mangrove species to generate a mangrove training sample set and a validation sample set; For each sample in the mangrove training sample set, extract the normalized difference vegetation index, red edge position index, and texture complexity index as mangrove feature parameters, and construct a mangrove sample feature matrix; Perform supervised classification of mangrove species on the remote sensing image through the mangrove sample feature matrix, divide the distribution ranges of different mangrove species, and form a preliminary mangrove classification result; Utilize the spectral similarity and spatial continuity features in the preliminary mangrove classification result to perform mangrove species patch merging and boundary optimization processing, eliminate isolated pixels and boundary noise, and obtain an optimized mangrove classification result; Based on the mangrove validation sample set, evaluate the accuracy of the optimized mangrove classification result, calculate the overall accuracy, Kappa coefficient, and classification accuracy of each species, and generate a mangrove classification accuracy report; According to the accuracy indicators in the mangrove classification accuracy report, perform credibility grading on the optimized mangrove classification result and convert it into a vector format, and add species name, area, distribution characteristic attribute information to generate the mangrove species distribution map.

6. The method for extracting mangrove ecological information based on an unmanned aerial vehicle according to claim 1, wherein Based on the mangrove species distribution map, combine spectral features and spatial distribution information to evaluate and analyze the functions of the mangrove ecosystem to obtain mangrove ecological monitoring data, including: Based on the mangrove species distribution map, extract the spatial distribution patterns of different mangrove species, quantify the area, density, and distribution form of each species, and generate a mangrove spatial structure parameter table; Use the near-infrared band and visible band data in the remote sensing image to calculate the normalized difference vegetation index and leaf area index of the mangrove area, and construct a mangrove growth vitality evaluation index; Combining the mangrove species distribution map, the mangrove spatial structure parameter table, and the mangrove growth vitality evaluation index, calculate the mangrove carbon storage using the biomass conversion coefficient to generate mangrove carbon sink function quantification data; Extract the spatial relationship between the mangroves and the coastline from the mangrove species distribution map and the mangrove spatial structure parameter table, and combine tide level and terrain data to analyze the wave attenuation and sediment retention capabilities of the mangroves to form a mangrove coastal protection function evaluation result; Analyze the species composition in the mangrove species distribution map and the distribution pattern in the mangrove spatial structure parameter table, calculate the species diversity index and niche overlap degree, and obtain mangrove biodiversity maintenance function data; Integrate the mangrove carbon sink function quantification data, the mangrove coastal protection function evaluation result, and the mangrove biodiversity maintenance function data to obtain mangrove ecological monitoring data.

7. A mangrove ecological information extraction system based on an unmanned aerial vehicle, which is used to implement the mangrove ecological information extraction method based on an unmanned aerial vehicle according to any one of claims 1-6, characterized in that, The UAV-based mangrove ecological information extraction system includes: An acquisition module, which is used to take low-altitude photos of the mangrove from different angles by using a UAV, obtain a top-down photo with a camera lens tilt angle of about 90° and a side-view photo with a camera lens tilt angle of about 30°, and obtain mangrove multi-angle image data; A calculation module, which is used to calculate projection coordinates according to the mangrove multi-angle image data by using position coordinates, photo length and width, and flight height information, and perform geometric correction processing on the top-down photo to obtain a corrected image superimposed on the remote sensing image; A processing module, which is used to generate a vector point layer and perform symbolization processing based on the position and flight angle information of the side-view photo to obtain a photographing direction layer superimposed on the remote sensing image, including: extracting GPS coordinates, flight heading angle and shooting timestamp from the EXIF information of the side-view photo to form a side-view photo position angle information set; performing spatial filtering on the side-view photo position angle information set to remove data points with repeated positions or excessive errors to obtain an optimized position angle data set; using the GPS coordinate information in the optimized position angle data set to create point features in ArcGIS software to construct a mangrove side-view photo position point layer; assigning direction attributes to the mangrove side-view photo position point layer according to the flight heading angle data in the optimized position angle data set to generate a point layer with direction attributes; based on the point layer with direction attributes, performing arrow symbolization design, where the arrow direction represents the photographing direction and the arrow size is proportional to the flight height, to form a mangrove side-view photo direction symbol layer; spatially superimposing the mangrove side-view photo direction symbol layer and the remote sensing image in the same coordinate system to obtain a photographing direction layer superimposed on the remote sensing image; A building module, which is used to extract features and establish an association relationship through a random forest algorithm based on the corrected image, the photographing direction layer and the remote sensing image to form a mangrove species interpretation mark library; A selection module, which is used to select training samples and validation samples on the remote sensing image according to the mangrove species interpretation mark library, perform supervised classification processing of mangrove species, and generate a mangrove species distribution map; An analysis module, which is used to evaluate and analyze the mangrove ecosystem function based on the mangrove species distribution map, combined with spectral characteristics and spatial distribution information, to obtain mangrove ecological monitoring data.

8. A computer device, characterized in that, It includes a memory and a processor, and the memory stores a computer program that can run on the processor. The characteristic is that when the processor executes the computer program, it implements the UAV-based mangrove ecological information extraction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the processor is caused to execute the UAV-based mangrove ecological information extraction method according to any one of claims 1 to 6.

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