CN111598874A - Mangrove canopy density survey method based on intelligent mobile terminal - Google Patents
Mangrove canopy density survey method based on intelligent mobile terminal Download PDFInfo
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Abstract
The invention provides a mangrove forest canopy density survey method based on an intelligent mobile terminal, and aims to solve the problems of low efficiency, high error, strong subjectivity, and poor data comparability and precision of a manual method during canopy density survey. The mangrove forest canopy density survey system and method based on the intelligent mobile terminal are composed of a hardware unit, a software unit and a standard survey process, wherein the standard survey process comprises the following steps: step S101, configuration of software and hardware, step S102, design of a survey sample and an image acquisition standard, step S103, image data acquisition, and step S104, data calculation and management.
Description
Technical Field
The invention relates to the field of field investigation and ecosystem management, in particular to a mangrove forest canopy density investigation method based on an intelligent mobile terminal.
Background
Canopy density (Crown density/Shade density/Canopy closure) is an important index in forest resource investigation, and refers to the ratio of the vertical projection area of the forest Canopy to the area of the forest land. Expressed early in ten law, in recent years, with the wide development of resource investigation work, the precision requirement is increased, and the precision requirement can also be expressed in percentage (national forestry administration research and planning design institute, 2011 a). The canopy density is used as an important index for describing a forest ecosystem, and provides reference for defining a plurality of definitions such as forests, forest lands, forested lands, sparse lands, forest layers, shifts and the like (national forestry administration survey planning and design institute, 2011 b).
Currently, methods for measuring the degree of occlusion in the field include the following: visual inspection, crown projection, spline and sampling point. The highest precision is a crown projection method, and the measurement method accords with the definition of the canopy intensity, but the method has low efficiency and is difficult to carry out large-scale investigation; other methods require either manual estimation or random sampling to obtain the estimated occlusion degree. Because the methods all need to subjectively judge the degree of the canopy by people, the influence of human errors on the measurement result is great. In addition, methods for determining canopy density based on instruments/auxiliary equipment, such as tube observation, canopy density meter, spherical densitometer, canopy analyzer, etc., which avoid purely manual determination by instrument operation, but still require manual determination during reading, and problems of subjective error and inefficiency remain (department of encyclopedia of agriculture, china, 1989; lienning, etc., 2008); meanwhile, extra professional observation equipment and corresponding operators need to be carried when large-area field investigation is carried out, and workload and labor consumption are increased.
With the development of image processing technology, methods and techniques for extracting ecological environment parameters based on image information are receiving attention. Most of current image methods carry out the calculation of the degree of occlusion through fisheye images, and fisheye images have the characteristic of wide coverage and can contain more image information in the same shooting area (allowance, 2018). However, the fisheye image has the problem of image distortion, and the actual characteristics of an object in a shooting plane cannot be objectively reflected; in addition, when the canopy density is calculated based on the image processing method, the vertical projection area and the sampling area of the canopy are ignored, so that the difference of the statistical spatial scale exists only as an estimation method, the definition of the canopy density cannot be scientifically and accurately characterized, the comparability between values is not provided, and data support is difficult to provide for resource investigation and evaluation and subsequent planning and utilization.
The rapid popularization of the intelligent mobile terminal (such as a smart phone) can bring convenience to the determination of the canopy density, the characteristics of common use, portability, rapidness and high efficiency greatly reduce the workload of acquiring the canopy image in resource investigation, professional instrument operators and operation training are not needed, and the application value is very high. Meanwhile, by designing and standardizing the image acquisition flow and the image processing method, subjective errors caused by manual judgment can be effectively avoided, and the accuracy and the scientificity of survey data are improved. The intelligent mobile terminal, the standard investigation process and the rapid image processing technology are integrated, and efficient and accurate investigation of the mangrove forest canopy density is facilitated.
Based on the background, the invention establishes the mangrove forest canopy density survey method based on the intelligent mobile terminal, and is beneficial to improving the simplicity, accuracy and scientificity of forest survey data acquisition.
Disclosure of Invention
The invention aims to provide a mangrove forest canopy density survey method based on an intelligent mobile terminal, and aims to solve the problems of low efficiency, high error, strong subjectivity, poor data comparability and poor precision of a manual method during canopy density survey.
The invention provides a mangrove forest canopy density survey method based on an intelligent mobile terminal, which comprises a hardware unit, a software unit and a standard survey flow.
According to the definition of the degree of canopy, the calculation formula of the degree of canopy is as follows:
in the formula: c, degree of occlusion; si, the vertical projection area of the canopy of the single plant in the sample square; sj, surveying the area of the defined forest land. According to a formula, the method provided by the invention works according to the following principle when the canopy density is investigated and calculated: enabling a shooting device to be horizontal to the ground surface in an investigation sample plot, and collecting a canopy image from bottom to top, wherein the image comprises a forest canopy and a sky background; the method mainly utilizes an image binarization technology to separate the canopy pixels from the sky pixels, and the canopy pixels in the current picture can be obtainedThe value is proportional. And then, the 'fitting equation' method of the invention is used for obtaining the proportion of the pixel size of the current picture to the actual size, and the size of the area of the canopy in the shot picture can be further obtained. Because the shot picture is equivalent to compressing all contents in the picture to a virtual two-dimensional plane, the shot picture can be idealized as a vertical projection plane of a shot object, and the area of the forest canopy obtained by calculation is idealized Si; the area of the entire picture is idealized Si. The survey sample plot cannot be completely covered due to the limited area of the image shot at a time; therefore, in order to satisfy the requirement for the area of the sample land in the resource survey, it is necessary to perform the imaging n times to acquire the image within the area range specified as the requirement. In the invention, the image acquisition points in the sample are distributed by adopting a five-point method, so that the later image standardization processing and data extraction are facilitated.
Specifically, the hardware unit comprises an intelligent mobile terminal module, a shooting fixed module, a ranging module and a server module; the software unit comprises an intelligent mobile terminal program module and a server terminal program module and adopts a C/S framework.
Furthermore, the intelligent mobile terminal module in the hardware unit includes, but is not limited to, a smart phone carrying an android system and a mobile terminal program module capable of running.
Furthermore, the shooting fixing module in the hardware unit refers to a stabilizing device for fixing the android intelligent terminal. Preferably, the smart android phone can use a selfie stick.
Furthermore, the distance measuring module in the hardware unit refers to an instrument for measuring distance by an optical method; preferably, a portable hand-held laser rangefinder may be used.
Furthermore, the server module in the hardware unit refers to a computer loaded with a Windows system and capable of running a server-side program module.
Furthermore, a mobile terminal program module in the software unit refers to an application program running on the android system, and data acquisition, data transmission, data export and display are completed through the application program. The intelligent mobile terminal program module has a 'new image' function, a 'data management' function, a 'gloomy degree calculation' function and a 'terminal account management' function.
Further, the server-side program module in the software unit refers to a program running on a Windows system, establishes a connection with the mobile terminal through the internet, and completes operations such as image processing and data management. The server-side program module has a data management function, a data processing function (including the computation of the canopy density, etc.), and a user management function.
Specifically, the standard survey process is characterized in that the determination of the canopy density is realized by the following steps: s101, software and hardware configuration, S102 research sample and image acquisition standard design, S103 image data acquisition, and S104 data calculation and management.
The step S101 includes the following steps: (1) installing programs of a mobile terminal and a server terminal; (2) establishing a user account; (3) establishing a data template; (4) establishing a fitting equation; (5) and debugging the shooting fixing module and the ranging module.
Further, step S1012 refers to a user account applied and established by the mobile terminal.
Further, step S1013 is to create a custom data template through a "template management" sub-function in the "new image" function of the mobile terminal. Preferably, the required data template can be established with reference to a preset template.
Further, in step S1014, a linear fitting equation of the shooting distance and shooting area relationship between the current mobile device and the shooting object is established through a "fitting equation" subfunction in the "new image" function of the mobile terminal. Through testing, the linear distance of the shot object and the pixel value of the shot object accord with a linear function relationship, namely:
in the formula: y, the linear distance from the shot object to the shot point; xpPixel values of a shot object in an image; a. b, a constant value determined by the mobile device. Because the length and width pixels of the picture shot by the mobile phone are fixed, the actual area of the shooting surface can be obtained through calculation after a fitting equation is established.
Further, step S1015, the module is in a good state, so that the shooting fixing module can normally fix the mobile device without sliding off, and the distance measuring module can accurately measure the linear distance.
The step S102 is a process of designing a scheme in advance and actually selecting a standard survey sample plot according to a five-point method image acquisition principle when forest resource survey is performed. Preferably, the area criteria are: one standard sample has a size of 600m2The method comprises the steps of forming 6 10m × 10m sample grids, reserving a 10m buffer area at the periphery of each sample grid, carrying out image acquisition in the sample grids according to a five-point sampling method, namely acquiring images at quartering points of 2 diagonal lines of the sample grids, acquiring the images at the center position only once, acquiring images at four corners in a clockwise sequence during image acquisition, acquiring images at the middle, sequentially numbering time-sample plot numbering-sample square numbering-sample grid numbering-image numbering (sequentially 1/2/3/4/5), and not applicable to the situation that the four corners are not suitable for the middle, namely, for some higher trees (more than 10 m), the number of the higher trees exceeds 100m in a single measurement2At the moment, the area of the sample grid can be met by only acquiring the image once, and in this case, the sampling is not carried out at four corners, and the sampling is carried out at the center of the sample grid.
The step S103 includes the following steps: (1) establishing a data group; (2) collecting an image; (3) inputting parameters; (4) and (5) primarily judging the total area covered by the image.
Further, step S1031 is to create a data storage group by the "select group" subfunction of the "new image" function of the mobile terminal, which can be used to manage individual pieces of data and to view the total measurement area in the current group. The data set naming can be done in a "date-like-square" format. When a data group is created, a data template is set, and then the data template exists as an inherent property of the group.
Further, step S1032 refers to the operation of acquiring image data for the survey pattern by the mobile terminal "new image" function. Preferably, a gyroscope carried by the mobile phone is used for correcting the angle, so that the X axis is less than or equal to +/-1.5 degrees and the Y axis is less than or equal to +/-1.5 degrees; by a five-point sampling method, image acquisition is carried out on acquisition points in a sample grid at four corners clockwise and then at the center, and the orientation of the mobile phone is kept consistent.
Further, in step S1033, all the custom data in the custom template are filled in through a "parameter entry" option of the photographing interface; meanwhile, the distance measuring module is used for measuring the vertical straight line distance from the mobile equipment to the vegetation canopy, and the vertical straight line distance is recorded as the height of the vegetation canopy. The selection of the ranging points needs to pay attention to the concentration condition of the canopy, and the most concentrated canopy region is selected for measurement.
Further, the step S1034 refers to checking whether the collected sampling points reach the sampling requirement of the sample grid through the "area in group" option or the "data query" subfunction — the "data group information" in the "data management" function of the shooting interface, that is, covering the investigation area of 10m × 10m, and processing the data in 100-120m2Most preferably.
The step S104 includes the following steps: (1) setting a working mode of a mobile terminal; (2) data transmission of a mobile terminal; (3) processing the server-side image and calculating the canopy intensity; (4) and returning and managing a result.
Further, step S1041 refers to selecting the working mode of the intelligent mobile terminal based on the field on-site communication state, and includes 2 kinds of networking working modes and network disconnection working modes.
Further, the step S1042 refers to uploading or saving the acquired image and the related data based on the selected working mode. And under the networking working mode, selecting corresponding image data, uploading the image data in batch or in real time, and transmitting the image data to a server for further processing and calculation. Under the working mode of network disconnection, the collected image data is stored in the mobile terminal in batches, and when the communication state is better, the transmission operation is carried out; meanwhile, transmission scenes such as immediate automatic transmission, batch manual transmission and the like when communication signals are recovered can be reserved/set.
Further, in the step S1043, the server end classifies, standardizes, and extracts the canopy intensity of the received field survey data. The data classification (1) refers to classifying and sorting image data and distance data of the same sample and a sample from a large batch of data received by a server based on a Python editor according to a set naming rule, and calculating and counting the actual area of each image unit pixel in the sample. (2) Data standardization, namely determining the magnification/reduction factor of image data based on the actual area of a unit pixel and resampling; further, based on longitude and latitude and image numbers, image splicing is realized through convolution operation; finally, an image of a 10m × 10m region is cropped based on the center point growing method. (3) The method comprises the following steps of extracting the canopy closure degree, namely calling matlab software and an image binarization processing method based on Python to realize quick and accurate extraction of the canopy closure degree; meanwhile, parameters such as the gloomy degree, mean value of the gloomy degree, standard deviation and the like are displayed step by step in percentage according to the levels of the sample, the sample and the region.
Further, in step S1044, after the server processes the data, the data is transmitted back to the intelligent mobile terminal, and the parameters such as the canopy density, the mean value of the canopy density, and the standard deviation are displayed step by step in a percentage manner according to the levels of the sample, the sample and the area at the mobile terminal. Meanwhile, visualization operation can be performed on the processing result, such as simple visualization display of a histogram, a scatter diagram and the like; furthermore, longitude and latitude coordinates can be matched, and spatial visual display and spatial interpolation operation of mangrove forest canopy density are carried out in the base map.
The invention has the beneficial effects that: the mangrove forest canopy density survey method based on the intelligent mobile terminal is established, the intelligent mobile terminal, the standard survey process and the rapid image processing technology are integrated, and efficient and accurate survey of the mangrove forest canopy density is facilitated. The survey is carried out based on the portable mobile intelligent terminal, the problems of inconvenience in carrying of professional equipment and training of professional operators can be effectively solved, and the portability has remarkable application advantages in forests and mangroves with poor accessibility. Meanwhile, two working modes of networking and network disconnection and a batch processing function of image files are provided, so that the method has prominent practical significance for large-scale field investigation in the field with poor communication signals. In addition, the quick extraction method of the canopy density based on the distance correction and the image processing can effectively avoid subjective errors caused by artificial judgment, can accurately and efficiently quantify the sampling area obtained during investigation, and can accurately and efficiently represent the characteristics of the canopy density in a sample plot.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. The invention has the following figures 6:
FIG. 1 is a software and hardware organizational diagram of the invention.
Fig. 2 is an overall work flow diagram of the present invention.
FIG. 3 is a schematic view of a placement reference in an embodiment of the present invention.
FIG. 4 is a schematic diagram of canopy image data acquired by mangrove forest field survey plots in the embodiment of the present invention.
FIG. 5 is a diagram of a binarization processing result of mangrove forest field survey plot to obtain canopy image data according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of the sample plot closing degree result of mangrove forest field survey in the embodiment of the present invention.
Detailed description of the invention
To facilitate understanding and implementation of the present invention by those of ordinary skill in the art, embodiments of the present invention will now be described with reference to the accompanying drawings (as shown in fig. 1 and 2). The specific embodiment is as follows:
taking a field survey of mangrove forest canopy density as an example for explanation, the implementation comprises the following operation steps:
and S101, configuring software and hardware, completing a series of operations of account establishment, data template establishment and fitting equation establishment at the mobile terminal, and completing the adjustment of the mobile phone support and the laser range finder.
In step S1011, the installation of the mobile terminal and the server terminal programs is completed.
Step S1012, establishing a mobile terminal account, operating on a login interface, and completing operation of filling in an account name and a password;
and step S1013, establishing a data template, operating in the function bar of the 'newly-built image', selecting 'template management', and customizing the table content filled in during the survey. If no special requirement exists, a preset template can be selected, and the preset template contains: sample name, serial number, main tree species, canopy height, area measurement, calculated canopy intensity, picture (click can be opened), shooting time, longitude and latitude, and person recording. Wherein the latitude and longitude and the time can be automatically acquired.
Step S1014, the establishment of the "fitting equation" needs to be performed according to the following steps: (1) setting up a reference object: an object of known length is placed in front of a selected flat wall surface so that the object is parallel to the wall surface. Preferably, as shown in fig. 3, a tape measure can be placed with a length set to 1m or 2 m. (2) Using the "fitting equation" function of the mobile end: firstly, selecting a 'fitting equation' subfunction in a 'newly-built image' function bar, selecting a 'reference object image' subfunction option in a new window, and entering a reference object shooting mode; secondly, the mobile phone is horizontally arranged, the mobile phone is kept vertical to the ground through the auxiliary function of the gyroscope, and the shooting surface of the mobile phone is kept parallel to the wall surface. And determining the linear distance between the mobile phone and the wall surface by using another measuring tape or a laser range finder, and taking a group of equidistant pictures by maintaining the spatial relationship. The set distance range is usually 2m to 10m, and the shooting is carried out according to a 1m picture; obtaining the relation between the actual length of the reference object and the pixel under different distances by determining the pixel value of the reference object in the group of pictures; and fourthly, fitting the equation relation by using the sub-function option of checking the reference object data. After fitting, the current fitting equation can be checked in the "check fitting equation" sub-function option, and checked for correctness by checking.
Through testing, the linear distance of the shot object and the pixel value of the shot object accord with a linear function relationship, namely:
in the formula: y, the linear distance from the shot object to the shot point; xpPixel values of a shot object in an image; a. b, a constant value determined by the mobile device. Because the length and width pixels of the picture shot by the mobile phone are fixed, the actual area of the shooting surface can be obtained through calculation after a fitting equation is established.
Step S102, survey the design of the sample and the image acquisition standard. Designing a sample prescription, a sample and an image acquisition method according to the requirements of forest resource investigation, and carrying out the following steps:
step S1021, area standard: typically, a sample needs to investigate 600m2The position of the sample can be adjusted according to the field situation, but the size of 10m × m must be kept, and a buffer zone of 10m needs to be reserved around each sample.
Step S1022, image acquisition standard: and (3) image acquisition is carried out in the sample grid according to a five-point sampling method, namely, images are acquired at quartering points of 2 diagonal lines of the sample grid, and the central position is acquired only once. When the image is collected, the images of the four corners are collected firstly according to the clockwise sequence, and then the image in the middle is collected; sequentially numbered time-sample number-image number (sequentially 1/2/3/4/5), e.g., 20191223-1-2-6-5; when the image is collected, the orientation of the collected person and the orientation of the mobile phone are kept consistent in order to facilitate post-processing. Sampling should avoid the basal trunk part of the arbor, and the influence of the arbor on image acquisition is reduced; meanwhile, a representative position should be selected for sampling, so that the accidental forest window is prevented from interfering sampling.
And (5) image data acquisition. Image acquisition and parameter input are carried out on acquisition points selected in a sample according to ordered steps, and the method specifically comprises the following steps:
step S1031, shooting fixing module assembly (optional): install android mobile phone on the cell-phone from rapping bar, the rear camera is placed up, presss from both sides tight cell-phone and ensures can not drop. This step is optional and is used when investigating inconvenient access areas or shorter tree species.
Step S1032 is that ① selects the option of "New image" in the function bar- "template management" subfunction- "selection group" in the function bar to create new data group or use the existing data group, preferably, name the data group according to the format of "date-sample" and set the data template when the data group is new, then it exists as the inherent attribute of the group. ② enters the shooting interface to check the selection of "area in group". The method of creating new image in the intelligent mobile terminal application③ image acquisition, using a manual or automatic photographing mode, wherein the mobile phone feeds back the current spatial position by means of a gyroscope, when the mobile phone is in an X-axis 0 DEG or a Y-axis 0 DEG, emits a corresponding sound, when both are at 0 DEG, the mobile phone keeps a long ring to enter a shooting countdown, automatically after the shooting is finished, the mobile phone needs to keep a stable posture when the shooting is finished, and if any axial deviation exceeds 3 DEG, the mobile phone stops long, the mobile phone needs to enter a shooting countdown to reach a preset time, the mobile phone keeps a shooting countdown to automatically start the shooting when the shooting is finished, and if the mobile phone keeps a shooting countdown to reach a preset time, the mobile phone keeps a stable posture when the shooting is started, and if the mobile phone keeps a vertical sampling point acquisition mode, a central distance measurement module, a distance measurement module2Above, at 100-2Most preferably. Not applicable to the case of four corners first and then the middle: for some higher trees (above 10 m), it is possible that a single measurement exceeds 100m2The ⑦ area list may be a "data query" sub-function in the "area in group" option or "data management" function of the capture interface"data set information" is found.
And step S104, calculating and managing data. The method comprises the steps that data strips or data groups are transmitted to a server for operation through the working mode selection and the data selection and transmission of a mobile terminal; and rapidly acquiring the canopy density based on an image processing method, and returning the result to the mobile terminal to realize data visual display. Meanwhile, data can be inquired through the server side and exported, and follow-up analysis and decision making are facilitated. The method comprises the following specific steps:
step S1041, selecting a working mode of the mobile terminal. The working mode of the intelligent mobile terminal is selected based on the field communication state, and comprises 2 networking working modes and 2 disconnected network working modes.
Step S1042, data transmission of the mobile terminal. And uploading or saving the acquired images and related data based on the selected working mode. And under the networking working mode, selecting corresponding image data, uploading the image data in batch or in real time, and transmitting the image data to a server for further processing and calculation. Under the working mode of network disconnection, the collected image data is stored in the mobile terminal in batches, and when the communication state is better, the transmission operation is carried out; meanwhile, transmission scenes such as immediate automatic transmission, batch manual transmission and the like when communication signals are recovered can be reserved/set.
And step S1043, performing image processing and canopy density calculation on the server side, namely classifying and standardizing the received field survey data and extracting the canopy density at the server side. (1) And (6) classifying the data. Specifically, classifying the image data in the same sample and corresponding height data into one class in a database of a server based on a Python editor according to image names; and calculating and counting the actual area of each image unit pixel in the sample by combining the prior 'fitting equation' data.
(2) And (6) standardizing data. Specifically, the reduction multiple of the rest of images in the same grid is determined by taking the maximum unit area as a standard through the actual area of the unit pixel, and resampling is performed. Determining the relative position of the image in the sample based on the longitude and latitude mark and the image name; and traversing 1/4 areas close to the center of the resampled images at the four corners in the sample and all areas of the central acquisition points by adopting convolution operation, and carrying out image splicing. And finally, based on the spliced image, adopting a central point growing method, based on the actual size of the unit pixel, growing the image to a region of 10m multiplied by 10m around, and cutting the region to be used as a canopy density extraction image of the sample.
(3) Extracting the degree of occlusion. Specifically, matlab software is called through Python, the quick and accurate extraction of the canopy closure degree is completed based on the standardized image, and the processing flow and the corresponding program are as follows:
reading and graying the image.
Photo ═ image (image _ name); % read-in normalized image
Photo rgb2gray (Photo); % processing image into grayscale
Photo1 ═ imresize (Photo, 0.2); % resizing the original image down to its 0.2
figure, imshow (Photo 1); % displays the adjusted original image (as shown in fig. 4).
And exchanging black and white areas of the standardized image. In order to accurately calculate the projection area of the canopy, black and white areas of the preprocessed pictures are exchanged. The procedure was as follows:
photo2 ═ compensation (Photo 1); % converting black and white area of adjusted original image
figure, imshow (Photo 2); % displays the adjusted image (as shown in fig. 5).
Thirdly, calculating the total pixels of the white area, and the procedure is as follows:
b＝sum(sum(Photo2＞0))；
% statistics of white area pixel sum in picture, i.e. total pixel sum of canopy projection area.
Fourthly, calculating the proportion of the white area to the total image, wherein the program is as follows:
ratio＝b/numel(Photo2)；
% the value of the proportion of white area to the total image is calculated by the pixel value proportion (as shown in fig. 6).
And step S1044, returning and managing the result. The method comprises the steps of processing data by a server, transmitting the data to an intelligent mobile terminal, and displaying parameters such as the gloomy degree, mean value of the gloomy degree, standard deviation and the like step by step in a percentage mode according to a sample, a sample and the level of an area at the mobile terminal. Meanwhile, visualization operation can be performed on the processing result, such as simple visualization display of a histogram, a scatter diagram and the like; furthermore, longitude and latitude coordinates can be matched, and spatial visual display and spatial interpolation operation of mangrove forest canopy density are carried out in the base map.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The mangrove forest canopy density survey method based on the intelligent mobile terminal comprises the following steps of: the method is characterized by comprising the following steps:
s101, configuring software and hardware;
step S102, designing a survey sample prescription and an image acquisition standard, namely, designing a scheme in advance and selecting a standard survey sample prescription on the spot according to a five-point method image acquisition principle when forest resource survey is carried out; when the images are collected, the images at four corners are collected in a clockwise sequence, then the images in the middle are collected, and the images are numbered as time, sample plot number, sample square number, sample grid number and image number in sequence; not applicable to the case of four corners first and then the middle: for some higher trees (above 10 m), it is possible that a single acquisition exceeds 100m2At the moment, the area of the sample grid can be met by acquiring the image once, and in the case, the sampling is not carried out at four corners, and the sampling is directly carried out at the center of the sample grid;
step S103, image data acquisition;
step S104, data calculation and management.
2. The mangrove forest canopy density survey method according to claim 1, wherein the step S101 comprises the steps of:
step S1011, installing programs of the mobile terminal and the server terminal;
step S1012, establishing a user account;
step S1013, establishing a data template;
step S1014, establishing a fitting equation;
step S1015, the shooting fixing module and the ranging module are debugged.
3. The mangrove forest canopy density survey method of claim 1, wherein the area criteria are: one standard sample is 600m2The method is characterized by comprising 6 samples of 10m × 10m, wherein a buffer area of 10m is reserved on the periphery of each sample, and the image acquisition standard is that image acquisition is carried out in the samples according to a five-point sampling method, namely, images are acquired at quartering points of 2 diagonals of the samples, and the image acquisition is carried out only once at the center position.
4. The mangrove forest canopy density survey method of claim 1, wherein said step S103, referring to the image acquisition and parameter entry of the selected acquisition points in the sample according to the ordered steps, further comprising the steps of:
step S1031, establishing a data group;
step S1032, image acquisition;
step S1033, parameter entry;
step S1034, primarily judging the total area covered by the image.
5. The mangrove forest canopy density survey method of claim 4, wherein the step S103 further comprises the steps of:
step S1031, establishing a data storage group by the "selection group" subfunction of the "new image" function of the mobile terminal, wherein the group can be used for managing single data and checking the total measurement area in the current group;
step S1032, the acquisition operation of image data is carried out on the check sample plot through the function of 'newly building an image' of the mobile terminal, and preferably, the gyroscope of the mobile terminal is used for correcting the angle, namely the X axis is less than or equal to +/-1.5 degrees and the Y axis is less than or equal to +/-1.5 degrees; by a five-point sampling method, image acquisition is carried out on acquisition points in a sample grid at four corners clockwise and then at the center, and the orientation of the mobile phone is kept consistent;
step S1033, all the custom data in the custom template are filled in through a parameter entry option of the photographing interface; meanwhile, a distance measuring module is used for measuring the distance between the mobile equipment and the perpendicular line of the vegetation canopy, the height of the canopy is recorded, the concentration condition of the canopy needs to be noticed when the distance measuring points are selected, and the most concentrated canopy area is selected for measurement;
step S1034, check whether the collected sampling points meet the sampling requirement of the sample through the "area in group" option of the shooting interface or the "data query" subfunction — the "data group information" in the "data management" function.
6. The mangrove forest canopy density survey method of claim 5, wherein the "select group" sub-function may name the data group in a "date-sample" format, and the data group will set a data template when created and then exist as an inherent property of the group.
7. The mangrove forest canopy density survey method of claim 1, wherein the step S104 is to transmit the data strip or data group to the server for operation through the working mode selection, data selection and transmission of the mobile terminal; the method is characterized in that the gloomy degree is rapidly acquired based on an image processing method, the result is returned to a mobile terminal, data visualization is realized, meanwhile, data can be inquired through a server terminal and are exported, and follow-up analysis and decision making are facilitated, and the method comprises the following steps:
step S1041, setting a working mode of the mobile terminal;
step S1042, data transmission of the mobile terminal;
step S1043, server side image processing and canopy density calculation;
and step S1044, returning and managing the result.
8. The mangrove forest canopy density survey method of claim 7, wherein the step S1041 refers to selecting the working mode of the intelligent mobile terminal based on the field on-site communication state, comprising 2 working modes of networking and network disconnection:
under the networking working mode, corresponding image data is selected, uploaded in batch or in real time and transmitted to a server for further processing and calculation;
under the working mode of network disconnection, the collected image data is stored in the mobile terminal in batches, and when the communication state is better, the transmission operation is carried out; meanwhile, transmission scenes such as immediate automatic transmission and batch manual transmission when communication signals are recovered can be reserved/set.
9. The mangrove forest canopy density survey method of claim 7, wherein the step S1043 is to classify, standardize and extract the canopy density of the received field survey data at the server, comprising:
(1) data classification, namely classifying and sorting image data and distance data of the same sample and a sample from a large batch of data received by a server based on a Python editor according to a set naming rule, and calculating and counting the actual area of each image unit pixel in the sample;
(2) data standardization, namely determining the magnification/reduction factor of image data based on the actual area of a unit pixel and resampling; further, based on longitude and latitude and image numbers, image splicing is realized through convolution operation; finally, cutting an image of a 10m multiplied by 10m area based on a central point growing method;
(3) the method comprises the following steps of extracting the canopy closure degree, namely calling matlab software and an image binarization processing method based on Python to realize quick and accurate extraction of the canopy closure degree; meanwhile, the parameters of the gloomy degree, the mean value of the gloomy degree and the standard deviation are displayed step by step in percentage according to the levels of the sample grid, the sample square and the area.
10. The mangrove forest canopy density survey method of claim 7, wherein the step S1044 is that after the server processes the data, the data is transmitted back to the intelligent mobile terminal, and the canopy density, the mean value of the canopy density, and the standard deviation parameters are displayed step by step in percentage form at the mobile terminal according to the levels of the sample lattice, the sample square, and the area; meanwhile, visualization operation can be performed on the processing result, such as simple visualization display of a histogram, a scatter diagram and the like; the longitude and latitude coordinates can be matched, and spatial visual display and spatial interpolation operation of mangrove forest canopy density are carried out in the base map.
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