CN112686995B - Mangrove intelligent supervision system - Google Patents
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Abstract
The invention provides an intelligent mangrove supervision system, which comprises a service layer, a data layer and a presentation layer, wherein the service layer comprises a user system, a service system and an algorithm system, and the user system is mainly used for managing platform user operation behaviors and information management; the business system is used for managing business among the whole platform modules, including GIS map generation, correction of building inclined images and mangrove forest simulation; the algorithm system realizes the identification and monitoring of the supervision region, the building and the mangrove forest through a GIS map generation method, a correction method of the building inclined image and a mangrove forest simulation method. The invention combines satellite data, a relational database system and the relation between mangrove and environmental indexes to establish a base mangrove whitening prediction model, and realizes the assessment of the vulnerability of any specific phenomenon of the mangrove ecological system through high-resolution or medium-resolution remote sensing data.
Description
Technical Field
The invention belongs to the field of supervision systems, and particularly relates to an intelligent mangrove supervision system.
Background
Mangrove has important economic value. The asterias, crabs and shellfish in the region are all economic fish-catching objects, and the environment is good, so that the yield is high. Some cities optimize urban landscapes by repairing mangrove projects and also drive peripheral businesses to flourish. The Qing dynasty mangrove is always paid attention to by domestic experts, and in 2019, the Qing dynasty and shellfish culture coupling system is developed by more than 100 mu, and is a practice of the first mangrove in province and aquaculture coupling system, and the mangrove zone is planted on the periphery of the shellfish culture pond, so that coastlines are repaired, ecological barriers are provided for shellfish culture, the culture benefit is hopefully multiplied, and the strong support of local villages is obtained. The Wu Jiaping teaching of university of Zhejiang teaches that in recent years, the biomass of large benthonic animals in the tidal flat of the Qing Bay mangrove is 3 times that of the light beach, and that benthonic animals with high economy like the delicious tsfoot beetles and the like are significantly higher than the rice grass and the light beach. The Dragon bay tree sand island is originally an spartina alterniflora island, and a mangrove forest of more than 1000 mu is developed in 2014, and a national Katsujia national institute of agricultural and subtropical crops Chen Qiuxia research team introduces more than 30 parts of domestic Katsujia germplasm on the island to become a unique national mangrove plant Katsujia germplasm resource library at present. At present, 1000 mu of mangrove forests in the tree sand island are formed into a forest, and after 5 years of monitoring, the living diversity is obviously improved after the mangrove forests are planted and the flower-protecting rice grass is controlled, the aquatic plants and benthonic animals in the rural soil are regressed in a large number, a large number of birds are attracted to eat and inhabit, the area is currently listed in a provincial protection area, and a plurality of domestic mangrove forests are attracted to pay attention. It is known that the hole island of the secondary engineering of blue harbor remediation is currently being carried out, and a special color base for ecological development of the maximum mangrove wetland ecological industry demonstration garden and national mangrove forest ecological development in Zhejiang province is also created, which integrates ecological restoration and sightseeing and leisure. Experts show that the development of mangrove in Zhejiang province also needs to overcome a plurality of technical problems, such as the north boundary of mangrove forest distribution, the critical line of suitable forest, the cold-resistant seedling cultivation technology and the like. Ecological protection and remediation is not just a governmental act and responsibility. Only if a practical realization path of the economic value of mangrove is found, society and enterprises are more motivated to participate in protection and repair activities. The ecological environment is protected, namely the productivity is protected; improving the ecological environment, namely developing productivity. The mangrove forest is protected and repaired, the ecological system function of the mangrove forest is fully exerted, the ecological advantage can be changed into economic advantage, and the mangrove forest can be changed into 'golden tree forest'.
Mangrove grows on the beach shoals of land and juncture zones, is a special ecological system of land overmuch, is a special evergreen arbor community of the tropical, subtropical estuary beaches and estuary shoals, has about 55 mangrove varieties worldwide, and is mainly distributed in the coastal zones of Hainan, guangdong, guangxi, fujian, taiwan and Zhejiang. It is an ecological "star" in the eyes of many scientists and environmental protection people, and is a widely accepted "coastal guard". The method has the advantages of resisting tide waves, resolving storm, enriching ecology, absorbing pollution, ensuring biodiversity, fixing soil, regulating climate, providing resources and the like, and is unique to mangrove forest in the sense of miracle and has great importance worldwide. Mangroves are a home for many organisms and play a vital role in coastal protection, travel and fishery, thus supporting coastal communities. Mangrove is an ecosystem with a variety of ecological, environmental, and socioeconomic functions. This unique ecosystem is not at risk due to the increased frequency, diversity and scope of global human activities. However, mangrove is severely degraded, and about 30% of the mangrove area has been severely destroyed. By 2030, about 60% of mangrove may disappear. As the temperature increases, mangrove is losing most of its pigment microalgae endosymbionts, known as yellow worm algae, these cells are expelled from the body under pressure, making the appearance of mangrove lighter or whiter. If the thermal environment is severe and the duration is long, they may cause whitening and most mangroves may die. The increase in sea water temperature causes algae eruption in mangrove. There is a lack of monitoring of mangrove.
Currently, big data has become a fundamental and strategic resource for national economy and social development. The demands of management departments of all levels for real-time, visual presentation and analysis of big data are becoming stronger. The current various business management systems are independent of each other, lack of integration and sharing utilization of information resources, serious information island phenomenon, insufficient deep data application and imperfect data updating mechanism.
Natural disasters and accidents not only threaten personal safety and affect economy, but also pollute the environment. At present, a monitoring department monitors and tracks natural disasters and offshore accidents mainly through satellite remote sensing imaging. Because the drawing function of the general remote sensing image processing software is limited and the processing of specific business is not supported, the remote sensing image processing software is mainly used for the business-oriented working process. This can affect the efficiency of emergency response and decision support. With more and more people learning and using GIS, the GIS industry is widely applied and developed, especially when the GIS map function is compared with the map function of other traditional software. GIS drawing has obvious advantages.
As an important component of digital buildings, research and application of three-dimensional models has become a research hotspot of contemporary GIS and related disciplines. Acquisition and mapping of real textures are important steps in constructing three-dimensional models. Particularly, the development of digital photogrammetry technology promotes the extraction of spatial information and the extraction of real-world buildings to a great extent, and further promotes the construction and development of digital buildings. In the aspect of texture modeling, it should be noted that textures of some wall surfaces are invisible on these quasi-vertical photographic images.
Disclosure of Invention
In order to solve the problems, particularly how to monitor mangrove forests, the invention monitors mangrove forests by simulating the mangrove forests, and the specific scheme is as follows:
an intelligent mangrove supervision system comprises a business layer, a data layer and a presentation layer,
the business layer comprises a user system, a business system and an algorithm system, wherein the user system is mainly used for managing the operation behaviors of a platform user and information management; the business system is used for managing business among the whole platform modules, including GIS map generation, correction of building inclined images and mangrove forest simulation; the algorithm system realizes the identification and monitoring of the supervision region, the building and the mangrove forest through a GIS map generation method, a correction method of the building inclined image and a mangrove forest simulation method;
the data layer is used for data storage and is divided into a data center, a system database and an image database, and the data center is used for storing various business data; the system database stores business relation data between the system modules, including maps, image storage addresses and the like; the image database stores all remote sensing map data;
the display layer outputs the interactive returned result between the functional modules through the WEB end, and the developer of the open API interface calling method can call according to the provided calling rule through the relevant open interface address.
The system service adopts a lightweight flash Web application framework, a WSGI tool box adopts Werkzeug, the flash has a built-in server and unit test, and is adapted to RESTful to support safe cookies; the machine deep learning algorithm Keras artificial neural network and the Open CV machine learning visual algorithm capture dynamic images in real time for recognition; and automatically collecting data images to realize accurate and intelligent identification.
The mangrove forest simulation method comprises the following steps of;
step 1, parameter selection
Temperature, mangrove saturation, photosynthetically active radiation, wind speed, water depth and grade play a vital role in the bleaching vulnerability of mangrove, information about these parameter choices is as follows;
A. temperature (temperature)
B. Mangrove saturation
Surface water is supersaturated and mangrove saturation state Ω is defined as the product of the concentrations of dissolved calcium ions and carbonate ions divided by the calcium carbonate concentration at equilibrium:
when the saturation state is greater than 3, mangrove is easier to survive; when the saturation state is less than 3, mangrove is stressed and dies when 1 is reached.
C. Photosynthetically active radiation
Photosynthetically active radiation means the spectral range from 400 to 700nm of the sun reaching the surface, which is required for the algal yellow algae of mangrove in the photosynthesis process of mangrove, wherein photosynthetically active radiation is greater than 47 watts per square meter per day, conditions for survival of mangrove.
D. Wind speed
Wherein a maximum wind speed of less than 28 meters/second causes slight damage to the mangrove, but a wind speed of more than 30 meters/second causes damage to the near shore, and a wind speed of more than 40 meters/second causes catastrophic damage to the near water;
E. depth of water
The water depth measurement is selected as another parameter, and defines the survival depth of mangroves, wherein the mangroves exist at a certain depth, shallow water mangroves inhabit at a depth of 50 meters, and deep water mangroves inhabit at a depth of 150 meters;
F. gradient of slope
The gradient is derived from the water depth measurement data, the gradient is defined, and the mangrove forest grows in places with the gradient less than 4 degrees;
step 2, data processing method
Mangrove forest analyzed by multi-standard decision analysis, comprising the steps of:
step 2.1, identifying parameters: the procedure used in this model development process first selects parameters based on a priori knowledge known to affect mangrove forests. Six parameters are selected for the analytic hierarchy process, including temperature, photosynthetic effective radiation, mangrove saturation, wind speed, water depth and gradient;
step 2.2, data acquisition, re-projecting all layers, and extracting the range of an observation area;
step 2.3, using the six parameters of step 1.1 further as inputs to the overlap analysis, evaluating the performance of the six parameters based on comparisons in a set of inverse parameter matrices, the scale for the comparison parameters being 1 to 5, the correlation definition and description forming a comparison matrix as shown in the following table:
table 1 scale related definitions and illustrations
After the comparison matrix is formed, calculating a normalized feature vector, normalizing the comparison matrix by dividing the sum of each column by each element in the comparison matrix, and calculating the average value of each row of elements of the comparison matrix to obtain the normalized feature vector; when the uniformity ratio (CR) is less than 0.10, a reasonable level of uniformity of the parameters is acceptable. If the comparison matrix exceeds 0.10, the judgment is unreliable, the comparison matrix needs to be considered again, and the consistency ratio is calculated as follows:
wherein CR represents a consistency ratio, CI represents a consistency index, RI represents a randomness index, n represents a parameter number, λmax represents a principal eigenvalue (sum of products of respective elements of the eigenvector and a sum of reciprocal matrix columns), and the Randomness Index (RI) corresponds to the parameter number (n) as shown in the following table
TABLE 2 randomness index and parameter number correspondence table
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| RI | 0.0 | 0.0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
Step 3, hierarchical analysis
Using the six parameters of step 1 to determine the order of preference between each parameter, a universal scale is derived from the analytic hierarchy process as shown in the following table, wherein temperature is the most important parameter, its relative standard weight is 0.436, photosynthetic active radiation is 0.159, wind speed is 0.082, water depth is 0.082, mangrove saturation is 0.159, and slope is 0.082, the consistency of this determination is checked to show that the main eigenvalue λmax=6.005, the consistency index ci=0.001, for n=6, ri=1.24, the consistency ratio cr= >0.0008 (less than 0.1, indicating that consistency is feasible), and a comparison is made as shown in table 3 to determine whether each parameter is suitable for superposition;
table 3 weights of selected parameters
Step 4, weight superposition analysis
Determining mangrove bleaching susceptibility places according to priori knowledge, collecting data by adopting a grating format, reclassifying, providing a proportion value for each component, using scale values from 1 to 5, wherein 1 represents least sensitivity, 3 represents midness, 5 represents bleaching, and manufacturing a mangrove sensitivity graph after weighted superposition analysis. Realizes the simulation of mangrove forest.
The invention has the beneficial effects that:
the invention has certain advancement, foresight and expansibility in design ideas, system architecture, adoption technology and selection platform. Advancement is one of the main goals of system construction. The advancement is mainly characterized in that: based on fully understanding and grasping the development trend of the information technology and adopting the current advanced database technology, the technology such as data exchange among distributed databases, multi-source heterogeneous data integration and the like is realized, the data maintenance cost is reduced, the data management efficiency is improved, and the system can represent the main flow development direction of the application when the fishery production safety environment is ensured.
Therefore, the selected software platform is not only an advanced product mature at the present stage, but also the main stream of international similar products, and accords with the development direction in the future; in the software development concept, the design, management and development must be strictly in accordance with the standard of software engineering and object-oriented theory, so as to ensure the high starting point of system development.
The invention fully considers the convenience and flexibility of application and maintenance, provides a concise and convenient operation mode and a visual operation interface, and enables a user to easily grasp and use the operation mode and the visual operation interface. Many software systems often have contradictions between powerful functions and easy use, i.e. software with complete functions and powerful functions is often difficult to master due to too many menus; on the contrary, the function of the easy-to-use software is not perfect. The system should overcome the two tendencies and achieve the purposes of easy use and powerful functions.
The invention establishes scientific and reasonable data standard, establishes and perfects related data operation technical rules, ensures compatibility and openness of basic geographic data, improves interoperability of data layers, and can effectively support and expand data platform services.
The invention has flexible and convenient secondary development interface, and can be customized service based on components so as to ensure the expandability of the system. The specific expression is as follows: in order to meet the demands of users for expanding the system capacity and expanding the application range in the future, the system should fully consider the function expansion in various aspects such as system structure, function design, management object and the like; upgrading of software: the system should fully consider the scalability of the platform and the load balancing mechanism. The system has flexible and smooth expansion capability; the system is designed and developed by adopting the current popular technology, and the module packaging of the business logic is realized, so that the system has excellent reconfigurable capability and expandability.
The design and development of the invention follow the principles of safety, confidentiality and sharing, and the relationship between data resource sharing and data safety confidentiality is processed. The project database is designed by fully considering the overall design and planning of the fishery production safety environment guarantee informatization construction, and the project database is shared with all relevant departments and units on the premise of ensuring safety and confidentiality.
According to the invention, the application requirements of monitoring departments and businesses are analyzed, the purpose that the distribution of technical support GIS data can be conveniently and rapidly set according to different sizes, scales and scales is provided for the remote sensing monitoring of natural disasters and accidents according to the characteristics of remote sensing data monitoring, the sizes of geographic information system symbols and maps can be kept consistent on the maps, errors can be avoided by utilizing the digital data of GIS, the drawing precision is improved, and unnecessary map elements such as text messages, compass, scales and legends are generated, so that the monitoring is changed from the initial emergency and accident monitoring to the daily monitoring. According to the characteristics of remote sensing monitoring information and the working requirements of the service, the remote sensing monitoring drawing method makes the monitoring department more important and effective;
the invention accurately registers the two-dimensional straight line projected by the three-dimensional model and the extracted two-dimensional characteristic line. Then, the external azimuth parameters with high precision are obtained by using the method. Projecting the three-dimensional model onto an image through the refined external azimuth parameters, thereby estimating the correct texture expansion, and then obtaining the real texture of the three-dimensional model through image resampling. Registration of building two-dimensional edge line segments between three-dimensional line segment projection lines is a key issue in determining the accuracy of unknown parameters of the results. The invention provides a set of practical value method for automatically acquiring the texture of the three-dimensional model based on the inclined image.
In the invention, a mangrove whitening prediction model based on satellite data, a relational database system and the relation between mangrove and environmental indexes is established. Mangrove systems are highly sensitive in middle and north areas. High-resolution or medium-resolution telemetry data allows the ecosystem approach to be applied to vulnerability assessment of any particular phenomenon within an area. By combining mangrove mortality data with field observations, the assessment of this model will become more useful.
Drawings
FIG. 1 is a flow chart of a method of remote sensing monitoring mapping of the present invention;
FIG. 2 is a flow chart of a method for correcting an oblique image according to the present invention;
FIG. 3 is a flow chart of a method of simulating mangrove forest of the present invention;
Detailed Description
The invention is further described below with reference to the drawings and examples.
Embodiments of the present invention are illustrated with reference to fig. 1-3.
An intelligent mangrove supervision system comprises a business layer, a data layer and a presentation layer,
the business layer comprises a user system, a business system and an algorithm system, wherein the user system is mainly used for managing the operation behaviors of a platform user and information management; the business system is used for managing business among the whole platform modules, including GIS map generation, correction of building inclined images and mangrove forest simulation; the algorithm system realizes the identification and monitoring of the supervision region, the building and the mangrove forest through a GIS map generation method, a correction method of the building inclined image and a mangrove forest simulation method;
the data layer is used for data storage and is divided into a data center, a system database and an image database, and the data center is used for storing various business data; the system database stores business relation data between the system modules, including maps, image storage addresses and the like; the image database stores remote sensing map data;
the display layer outputs the interactive returned result between the functional modules through the WEB end, and the developer of the open API interface calling method can call according to the provided calling rule through the relevant open interface address.
The system service adopts a lightweight flash Web application framework, a WSGI tool box adopts Werkzeug, the flash has a built-in server and unit test, and is adapted to RESTful to support safe cookies; the machine deep learning algorithm Keras artificial neural network and the Open CV machine learning visual algorithm capture dynamic images in real time for recognition; and automatically collecting data images to realize accurate and intelligent identification.
The GIS map generation method is remote sensing monitoring and drawing, and specifically comprises the following steps: the remote sensing monitoring drawing relies on an ArcGIS engine, aiming at the characteristics of remote sensing monitoring data, the requirements of business-oriented work and drawing are met, various map elements are automatically drawn in the screen range of interest of a user, map output and business-oriented work are realized, the graphic object of the ArcGIS engine is a view object PageLayout, and the view object PageLayout is provided with a modification class and an object for drawing output.
The remote sensing monitoring drawing needs to be processed rapidly and effectively, the convenient and rapid mapping requirement in the satellite remote sensing monitoring process is met, the information release efficiency is guaranteed, the object drawing is similar to the object page layout, graphic elements are displayed, and corresponding graphic information comprising a map, geographic coordinates, marks, characters, legends, compass and scale is added on the basis of the object map.
According to a specific embodiment of the present invention, map support in an object map provides detailed information on how to map these elements below the geographic scale, scale and legend limited company of the object and class, map, comprising the steps of;
step 1, selecting and preprocessing a drawing range
Through interpersonal interaction, a user manually drags a mouse to draw a rectangular frame on a screen, each trigger event is temporarily recorded in a buffer memory, when the event is ended and the rectangle disappears, the buffer memory can be automatically released, each new man-machine interaction temporarily records the range of interest of the user, and the recording of the range is temporary and random. When the drawn range exceeds the normal length-width ratio, the shape is too wide or too high, the drawing range is modified according to a certain proportion, so that the drawing range accords with visual perception, the center point of the rectangle is used as the standard of the length and the width, a lower value is recorded, and the lower value is used for recalculating a higher value under a certain length-width ratio.
Step 2, geographical division
Map geographic division is mainly to draw horizontal and vertical line elements by the extent of rectangular boxes. Fig. 2 shows the structure of the work:
step 2.1, recording a rectangular frame drawing range;
step 2.2, judging whether the coordinates are geographic coordinates, if yes, entering step 2.4; otherwise, enter step 2.3;
step 2.3, converting the coordinates into geographic coordinates;
step 2.4, calculating the integer value interval_x of the warp and weft spacing by the piecewise integer function
The warp and the weft are drawn on accurate and complete positions, and the longitude and latitude intervals in the rectangular frame range are different due to different scales;
for the warp, the longitude difference IntervalX of the rectangular frame range is recorded, a number n is set to indicate how many warp lines are displayed in the rectangular frame range, and the warp line distance value is calculated as follows:
interval_x is the Interval value of the longitude line, and may not be an integer, and a longitude component is setSegment integer function:the integer value of interval_x calculated.
For the weft, the latitude difference interval of the rectangular frame range is recorded, a number m is set to indicate how many weft lines are displayed in the rectangular frame range, and the weft pitch value is calculated as follows:where interval_y is the Interval value of the latitude line, interval_y may not be an integer, and a latitude segmentation integer function is set:the integer value of interval_y calculated.
Step 2.5, finding the longitude and latitude of the first integer, and respectively adding interval_x and interval_y; finding a first integer longitude line and a first integer latitude line in the rectangular frame range, adding and drawing straight lines and annotation elements, and sequentially increasing and decreasing intervals on the basis of the first integer longitude line and the first integer latitude line;
step 2.6, judging whether the range is exceeded or not, otherwise, entering step 2.7, and if yes, entering step 2.2;
step 2.7, drawing warps and wefts;
the coordinate system of the remote sensing monitoring data is a WGS84 coordinate system, the projection is a mercator projection, the conversion of longitude and latitude and unit length is calculated, and the map coordinate information is dynamically acquired.
Step 3, drawing a compass, a scale and a legend,
the compass exists in the form of characteristic elements, a legend and a scale are drawn through the combination of the elements, legend information is from a layer, information comprises colors and symbol patterns, dynamic search information is carried out according to the characteristics, the drawing of the scale is to set two points as a starting point and an ending point respectively, the segment number of the map scale is set, the segment calibration of the scale is an integer value, the end points of the scale are dynamically adjusted, and the length of the scale is an integer.
The invention analyzes the application requirements of monitoring departments and businesses. According to the characteristics aiming at remote sensing data monitoring, the method different from the traditional drawing is researched by combining the ArcGIS engine drawing technology, and technical support is provided for remote sensing monitoring.
The correction of the building inclination image specifically comprises the following steps:
step 1, linear feature extraction
Edges are regions in an image where the intensity function or the spatial derivative of the intensity function changes rapidly, and carry a lot of information, which is important in analyzing, describing and understanding the image at a high level. The edge extraction is always a hotspot of research of domestic and foreign scholars, in the research field of digital image processing and computer vision, the polygonal edge operator is a new edge detection operator, and three evaluation indexes of an edge detection result are proposed by the polygonal edge, namely: the judgment rate is low, namely the misjudgment rate of the edge points and the non-edge points is low; positioning accuracy, namely positioning edge points on pixels with the largest gray level variation; suppressing the occurrence of false edges; in the two-dimensional image space, the polygonal edge operator can generate gradient strength and direction information through better edge estimation, and the gradient strength and direction information are used for linear feature extraction;
step 2, accurate registration
Registration is a coarse-to-fine process that involves establishing a set of line features that describe the top contours of the building, and evaluating the line features to determine the best candidate line features in the image.
Step 2.1, establishing a candidate characteristic line set
The initial projection of the three-dimensional model deviates from the actual position, the three-dimensional model line corresponding to the actual image line characteristic should display main characteristics in a local area of the three-dimensional model line, under an optimization strategy, the characteristic of an accurate or approximate main line in an image is determined by utilizing a straight line in a possible candidate line characteristic set, the candidate line characteristic of the three-dimensional model projection is determined, the determination process is a process of extracting the straight line characteristic by using the projection line of the three-dimensional model as a central line and multi-edge operators in a rectangular area with d buffer width, d=30-50 is set as the buffer width by using the projection line as the central line;
step 2.2, evaluating candidate line feature set
Because the number of line features corresponding to projection lines of each three-dimensional model line is different, a judgment standard is established, the best candidate line feature is determined as a target feature, the external azimuth parameter is refined, and the reliability is used for evaluating the line feature. Let l be p For projection of three-dimensional model lines in an image, the candidate line feature set is l i (i＝1,2,...,n)，Is l i Length of->Is l p And/l i Included angle between->Is l i The vertical distance from the reference point P (x, y), P (x, y) being the geometric center of the projection of the top line of the building in the three-dimensional model, is used to calculate the reliability of the candidate line feature as a function of:
where MAX is the maximum value of the candidate line feature, P j (j=1, 2, 3) is the contribution rate of different geometric features to reliability, because the deviation distance between the projection line and the actual position is large, the length of the candidate line feature is taken as a main judgment standard, the contribution rate of P1 is 0.8, the contribution rates of P2 and P3 are 0.1, when the projection line approaches to the actual position in the image, the length, the angle and the distance are simultaneously selected as judgment standards, the contribution rate of P1 is 0.4, the contribution rates of P2 and P3 are 0.3, and the best candidate feature line is determined by evaluation according to the judgment standards;
step 3, refining external azimuth parameters
After extracting the corresponding optimal candidate characteristic line, refining external azimuth parameters by using coplanarity condition, wherein the requirement is that a two-dimensional line segment in an image space is a conjugate line of a three-dimensional line segment on a three-dimensional model, O (X) 0 ,Y 0 ,Z 0 ) For the exposure center, the starting point of the line segment, A (X 1 ,Y 1 ,Z 1 ) And B (X) 2 ,Y 2 ,Z 2 ) Is the end point of the line segment, a (x 1 ,y 1 ) And b (x) 2 ,y 2 ) A two-dimensional representation of points on the plane of line segments OA and OB; o (X) 0 ,Y 0 ,Z 0 )、a(x 1 ,y 1 )、b(x 2 ,y 2 )、A(X 1 ,Y 1 ,Z 1 ) And B (X) 2 ,Y 2 ,Z 2 ) Should lie on the same plane, determined by the imaging geometry, i.e. the central perspective projection, the coplanarity condition is:
represents a (x) 1 ,y 1 ) And b (x) 2 ,y 2 ) Respectively with O (X) 0 ,Y 0 ,Z 0 ) All coordinates are identical in a common coordinate system, a three-dimensional coordinate system of the image space is used, the coordinate system originates from the exposure center O (X 0 ,Y 0 ,Z 0 ) Conversion of coplanarity condition to constraint F 1 And F 2 ：
Wherein a, b and c are constants, r ij Representing the correlation coefficient, the system of nonlinear equations described above requires linearization with a taylor series,and solving the azimuth parameters through iterative calculation. After linearization, the equation above can be expressed as:
wherein F represents a restriction function, ω,Kappa represents azimuth, pitch and roll angles, respectively, e represents systematic errors, and the difference between the conditions is minimized by the least square method, and the error equation is:
and->Representing an n×m order coefficient matrix and an n×n order feature matrix, respectively,/for>Respectively representing a solution vector, a difference vector and an error vector,
according to the parameters after obtaining the difference vector and the error vector refinement, projecting a three-dimensional model into an image, evaluating the accuracy of the derived parameters, using a covariance matrix M, and assuming sigma 0 Is m 0 Q is covariance matrix, then
Wherein (1)>
Step 4, texture acquisition
Building a realistic three-dimensional model, obtaining texture data of a sense of reality, wherein the three-dimensional model data is a three-dimensional coordinate set of a building, obtaining target coordinates of the three-dimensional model by adopting a linear interpolation method, calculating positions of pixels on an image by utilizing a collineation equation, automatically obtaining textures by adopting an indirect correction method, obtaining texture images, and obtaining textures with different resolutions by setting different resampling intervals.
And (3) accurately registering the two-dimensional straight line projected through the three-dimensional model and the extracted two-dimensional characteristic line. Then, the external azimuth parameters with high precision are obtained by using the method. Projecting the three-dimensional model onto an image through the refined external azimuth parameters, thereby estimating the correct texture expansion, and then obtaining the real texture of the three-dimensional model through image resampling. Registration of building two-dimensional edge line segments between three-dimensional line segment projection lines is a key issue in determining the accuracy of unknown parameters of the results.
The mangrove forest simulating method includes the following steps;
step 1, parameter selection
Temperature, mangrove saturation, photosynthetically active radiation, wind speed, water depth and grade play a vital role in the bleaching vulnerability of mangrove, information about these parameter choices is as follows;
A. temperature (temperature)
B. Mangrove saturation
Surface water is supersaturated with respect to mangrove (CaCO 3) and other carbonate minerals, mangrove saturation state Ω being defined as the product of the concentrations of dissolved calcium ions and carbonate ions divided by the calcium carbonate concentration at equilibrium:
when the saturation state is greater than 3, mangrove is easier to survive; when the saturation state is less than 3, mangrove is stressed and dies when 1 is reached.
C. Photosynthetically active radiation
Photosynthetically active radiation means the spectral range from 400 to 700nm of the sun reaching the surface, which is required for the algal yellow algae of mangrove in the photosynthesis process of mangrove, wherein photosynthetically active radiation is greater than 47 watts per square meter per day, conditions for survival of mangrove.
D. Wind speed
Wherein a maximum wind speed of less than 28 meters/second causes slight damage to the mangrove, but a wind speed of more than 30 meters/second causes damage to the near shore, and a wind speed of more than 40 meters/second causes catastrophic damage to the near water;
E. depth of water
The water depth measurement is selected as another parameter, and defines the survival depth of mangroves, wherein the mangroves exist at a certain depth, shallow water mangroves inhabit at a depth of 50 meters, and deep water mangroves inhabit at a depth of 150 meters;
F. gradient of slope
The gradient is derived from the water depth measurement data, the gradient is defined, and the mangrove forest grows in places with the gradient less than 4 degrees;
step 2, data processing method
Mangrove forest analyzed by multi-standard decision analysis, comprising the steps of:
step 2.1, identifying parameters: the procedure used in this model development process first selects parameters based on a priori knowledge known to affect mangrove forests. Six parameters are selected for the analytic hierarchy process, including temperature, photosynthetic effective radiation, mangrove saturation, wind speed, water depth and gradient;
step 2.2, data acquisition, re-projecting all layers, and extracting the range of an observation area;
step 2.3, using the six parameters of step 1.1 further as inputs to the overlap analysis, evaluating the performance of the six parameters based on comparisons in a set of inverse parameter matrices, the scale for the comparison parameters being 1 to 5, the correlation definition and description forming a comparison matrix as shown in the following table:
table 1 scale related definitions and illustrations
| Scale with a scale bar | Definition of the definition | Description of the invention |
| 1 | Equal importance | The two parameters contribute equally to the target. |
| 2 | Equal to moderate degree | When a compromise value between 1 and 3 is required |
| 3 | Moderately important | It is slightly inclined to one parameter than another |
| 4 | Moderate bias strength | When a compromise value between 3 and 5 is required |
| 5 | Strong strength | Strongly favoring one parameter over another |
After the comparison matrix is formed, calculating a normalized feature vector, normalizing the comparison matrix by dividing the sum of each column by each element in the comparison matrix, and calculating the average value of each row of elements of the comparison matrix to obtain the normalized feature vector; when the uniformity ratio (CR) is less than 0.10, a reasonable level of uniformity of the parameters is acceptable. If the comparison matrix exceeds 0.10, the judgment is unreliable, the comparison matrix needs to be considered again, and the consistency ratio is calculated as follows:
/>
wherein CR represents a consistency ratio, CI represents a consistency index, RI represents a randomness index, n represents a parameter number, λmax represents a principal eigenvalue (sum of products of respective elements of the eigenvector and a sum of reciprocal matrix columns), and the Randomness Index (RI) corresponds to the parameter number (n) as shown in the following table
TABLE 2 randomness index and parameter number correspondence table
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| RI | 0.0 | 0.0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.49 |
Step 3, hierarchical analysis
The six parameters of step 1 were used to determine the order of preference between each parameter, a general scale was derived from the analytic hierarchy process as shown in the following table, wherein temperature was the most important parameter, its relative standard weight was 0.436, photosynthetically active radiation was 0.159, wind speed was 0.082, water depth was 0.082, mangrove saturation was 0.159, and grade was 0.082, and the consistency of this determination was checked to show that the main eigenvalue λmax=6.005, the consistency index ci=0.001, and for n=6, ri=1.24, the consistency ratio cr= >0.0008 (less than 0.1, indicating that consistency was feasible), and a comparison was made as shown in table 3 to determine whether each parameter was suitable for superposition.
Table 3 weights of selected parameters
Step 4, weight superposition analysis
Determining mangrove bleaching susceptibility places according to priori knowledge, collecting data by adopting a grating format, reclassifying, providing a proportion value for each component, using scale values from 1 to 5, wherein 1 represents least sensitivity, 3 represents midness, 5 represents bleaching, and manufacturing a mangrove sensitivity graph after weighted superposition analysis. Realizes the simulation of mangrove forest.
The above-described embodiment represents only one embodiment of the present invention, and is not to be construed as limiting the scope of the present invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (1)
1. An intelligent mangrove supervision system comprises a business layer, a data layer and a presentation layer,
the business layer comprises a user system, a business system and an algorithm system, wherein the user system is mainly used for managing the operation behaviors of a platform user and information management; the business system is used for managing business among the whole platform modules, including GIS map generation and mangrove forest simulation; the algorithm system realizes the identification and monitoring of the supervision region and mangrove forest through a GIS map generation method and a mangrove forest simulation method;
the data layer is used for data storage and is divided into a data center, a system database and an image database, and the data center is used for storing various business data; the system database stores business relation data between the system modules, including maps and image storage addresses;
the display layer outputs the interactive return result among the functional modules through the WEB end, and an open API interface calling method developer can call according to the provided calling rule through the relevant open interface address;
the system service adopts a lightweight flash Web application framework, a WSGI tool box adopts Werkzeug, the flash has a built-in server and unit test, and is adapted to RESTful to support safe cookies; the machine deep learning algorithm Keras artificial neural network and the Open CV machine learning visual algorithm capture dynamic images in real time for recognition; automatically collecting data images to realize accurate and intelligent identification;
the GIS map generation method is remote sensing monitoring and drawing, and specifically comprises the following steps:
the remote sensing monitoring drawing relies on an ArcGIS engine, aiming at the characteristics of remote sensing monitoring data, the requirements of business-oriented work and drawing are met, various map elements are automatically drawn in the screen range of interest of a user, map output and business-oriented work are realized, the graphic object of the ArcGIS engine is a view object PageLayout, and the view object PageLayout is provided with a modification class and an object for drawing output;
the remote sensing monitoring drawing needs to be processed rapidly and effectively, the convenient and rapid mapping requirement in the satellite remote sensing monitoring process is met, the information release efficiency is guaranteed, the object drawing is similar to the object page layout, graphic elements are displayed, and corresponding graphic information comprising a map, geographic coordinates, marks, characters, legends, compass and scale is added on the basis of an object map;
comprises the following steps of;
step 1, selecting and preprocessing a drawing range
Through interpersonal interaction, a user manually drags a mouse to draw a rectangular frame on a screen, each trigger event is temporarily recorded in a cache, when the event is ended and the rectangle disappears, the cache can be automatically released, each new interaction temporarily records the range of interest of the user, and the recording of the range is temporary and random; when the drawn range of the user exceeds the normal length-width ratio, the shape of the drawn range is too wide or too high, the drawing range is selected to be modified according to a certain proportion so that the drawing range accords with visual perception, a center point of the rectangle is used as a reference of the length and the width, a lower value is recorded, and the lower value is used for recalculating a higher value under a certain length-width ratio;
step 2, geographical division
Map geographic division mainly draws horizontal and vertical line elements according to the range of a rectangular frame:
step 2.1, recording a rectangular frame drawing range;
step 2.2, judging whether the coordinates are geographic coordinates, if yes, entering step 2.4; otherwise, enter step 2.3;
step 2.3, converting the coordinates into geographic coordinates;
step 2.4, calculating integer value interval_x of the warp pitch and the weft pitch through a piecewise integer function, wherein the interval_y warp and the weft are drawn on accurate and complete positions, and the longitude and latitude intervals in the rectangular frame range are different due to different scales;
for the warp, the longitude difference IntervalX of the rectangular frame range is recorded, a number n is set to indicate how many warp lines are displayed in the rectangular frame range, and the warp line distance value is calculated as follows:
interval_x is the Interval value of the longitude line, and may not be an integer, a longitude piecewise integer function is set:
the integer value of the interval_x obtained by calculation;
for the weft, the latitude difference interval of the rectangular frame range is recorded, a number m is set to indicate how many weft lines are displayed in the rectangular frame range, and the weft pitch value is calculated as follows:
interval_y is the Interval value of the latitude line, and a latitude segmentation integer function is set:
the integer value of the interval_y obtained through calculation;
step 2.5, finding the longitude and latitude of the first integer, and respectively adding interval_x and interval_y;
finding a first integer longitude line and a first integer latitude line in the rectangular frame range, adding and drawing straight lines and annotation elements, and sequentially increasing and decreasing intervals on the basis of the first integer longitude line and the first integer latitude line;
step 2.6, judging whether the range is exceeded or not, otherwise, entering step 2.7, and if yes, entering step 2.2;
step 2.7, drawing warps and wefts;
the coordinate system of the remote sensing monitoring data is a WGS84 coordinate system, the projection is a mercator projection, the conversion of longitude and latitude and unit length is calculated, and map coordinate information is dynamically acquired;
step 3, drawing a compass, a scale and a legend,
the compass exists in the form of characteristic elements, a legend and a scale are drawn through the combination of the elements, legend information is from a layer, information comprises colors and symbol patterns, dynamic search information is carried out according to the characteristics, the drawing of the scale is to set two points as a starting point and an ending point respectively, the segment number of the map scale is set, the segment calibration of the scale is an integer value, the end points of the scale are dynamically adjusted, and the length of the scale is an integer;
the mangrove forest simulation method comprises the following steps of;
step 1, parameter selection
Temperature, mangrove saturation, photosynthetically active radiation, wind speed, water depth and grade play a vital role in the bleaching vulnerability of mangrove, information about these parameter choices is as follows;
A. temperature (temperature)
B. Mangrove saturation
Surface water is supersaturated and mangrove saturation state Ω is defined as the product of the concentrations of dissolved calcium ions and carbonate ions divided by the calcium carbonate concentration at equilibrium:
when the saturation state is greater than 3, mangrove is easier to survive; when the saturation state is less than 3, mangrove is stressed and dies when reaching 1;
C. photosynthetically active radiation
Photosynthetically active radiation means the spectral range from 400 to 700nm of the sun reaching the surface, which is required by the algae yellow algae of the mangrove during photosynthesis of the mangrove, wherein photosynthetically active radiation is greater than 47 watts per square meter per day, conditions for survival of the mangrove;
D. wind speed
Wherein a maximum wind speed of less than 28 meters/second causes slight damage to the mangrove, but a wind speed of more than 30 meters/second causes damage to the near shore, and a wind speed of more than 40 meters/second causes catastrophic damage to the near water;
E. depth of water
The water depth measurement is selected as another parameter, and defines the survival depth of mangroves, wherein the mangroves exist at a certain depth, shallow water mangroves inhabit at a depth of 50 meters, and deep water mangroves inhabit at a depth of 150 meters;
F. gradient of slope
The gradient is derived from the water depth measurement data, the gradient is defined, and the mangrove forest grows in places with the gradient less than 4 degrees;
step 2, data processing method
Mangrove forest analyzed by multi-standard decision analysis, comprising the steps of:
step 2.1, identifying parameters: the procedure used in this model development process first selects parameters based on a priori knowledge known to affect mangrove forests; six parameters are selected for the analytic hierarchy process, including temperature, photosynthetic effective radiation, mangrove saturation, wind speed, water depth and gradient;
step 2.2, data acquisition, re-projecting all layers, and extracting the range of an observation area;
step 2.3, using the six parameters of step 1.1 further as inputs to the overlap analysis, evaluating the performance of the six parameters based on comparisons in a set of inverse parameter matrices, the scale for the comparison parameters being 1 to 5, the correlation definition and description forming a comparison matrix as shown in the following table:
table 1 scale related definitions and illustrations
After the comparison matrix is formed, calculating a normalized feature vector, normalizing the comparison matrix by dividing the sum of each column by each element in the comparison matrix, and calculating the average value of each row of elements of the comparison matrix to obtain the normalized feature vector; when the uniformity ratio is less than 0.10, a reasonable uniformity level of the parameter is acceptable; if the comparison matrix exceeds 0.10, the judgment is unreliable, the comparison matrix needs to be considered again, and the consistency ratio is calculated as follows:
wherein CR represents the consistency ratio, CI represents the consistency index, RI represents the randomness index, n represents the parameter number, λmax represents the main characteristic value, and the randomness index and the parameter number correspond to each other as shown in the following table
TABLE 2 randomness index and parameter number correspondence table
Step 3, hierarchical analysis
Determining the priority order among the six parameters in the step 1, obtaining a universal scale from a hierarchical analysis method, wherein the temperature is the most important parameter, the relative standard weight is 0.436, the photosynthetic effective radiation is 0.159, the wind speed is 0.082, the water depth is 0.082, the mangrove saturation is 0.159 and the gradient is 0.082, checking the consistency of the judgment, namely, the main characteristic value lambda max=6.005, the consistency index CI=0.001, and the consistency ratio CR= >0.0008 is compared for n=6 and RI=1.24, and judging whether each parameter is suitable for superposition or not as shown in the table 3;
table 3 weights of selected parameters
Step 4, weight superposition analysis
Determining mangrove bleaching susceptibility places according to priori knowledge, collecting data by adopting a grating format, reclassifying, providing a proportion value for each component, using scale values from 1 to 5, wherein 1 represents least sensitivity, 3 represents middleness, 5 represents bleaching, and manufacturing a mangrove sensitivity map after weighted superposition analysis to simulate the mangrove.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100885246B1 (en) * | 2008-02-13 | 2009-02-25 | (주) 지오씨엔아이 | Forest arrangement method to use database establish method and this for forest arrangement that use gis |
| CN103000077A (en) * | 2012-11-27 | 2013-03-27 | 中国科学院东北地理与农业生态研究所 | Method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method |
| CN104573654A (en) * | 2015-01-08 | 2015-04-29 | 中国林业科学研究院资源信息研究所 | Mangrove extraction method based on hierarchical decision tree |
| CN108986116A (en) * | 2018-07-16 | 2018-12-11 | 中国科学院东北地理与农业生态研究所 | A kind of mangrove extracting method and system based on remote sensing image |
| CN110596008A (en) * | 2019-09-06 | 2019-12-20 | 中国科学院遥感与数字地球研究所 | Plot-based soil nutrient digital mapping method for agricultural region of Chinese Hongsheng plain |
| CN110926430A (en) * | 2019-11-22 | 2020-03-27 | 海南省林业科学研究所 | Air-ground integrated mangrove forest monitoring system and control method |
| CN111767865A (en) * | 2020-06-30 | 2020-10-13 | 北部湾大学 | Method for inverting mangrove forest biomass by using aerial image and laser data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11481972B2 (en) * | 2018-01-17 | 2022-10-25 | United States Of America As Represented By The Secretary Of Agriculture | Method of performing dendrometry and forest mapping |
| CN108319733B (en) * | 2018-03-29 | 2020-08-25 | 华中师范大学 | Map-based education big data analysis method and system |
-
2020
- 2020-12-25 CN CN202011566014.1A patent/CN112686995B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100885246B1 (en) * | 2008-02-13 | 2009-02-25 | (주) 지오씨엔아이 | Forest arrangement method to use database establish method and this for forest arrangement that use gis |
| CN103000077A (en) * | 2012-11-27 | 2013-03-27 | 中国科学院东北地理与农业生态研究所 | Method for carrying out mangrove forest map making on intermediate resolution remote sensing image by utilizing object-oriented classification method |
| CN104573654A (en) * | 2015-01-08 | 2015-04-29 | 中国林业科学研究院资源信息研究所 | Mangrove extraction method based on hierarchical decision tree |
| CN108986116A (en) * | 2018-07-16 | 2018-12-11 | 中国科学院东北地理与农业生态研究所 | A kind of mangrove extracting method and system based on remote sensing image |
| CN110596008A (en) * | 2019-09-06 | 2019-12-20 | 中国科学院遥感与数字地球研究所 | Plot-based soil nutrient digital mapping method for agricultural region of Chinese Hongsheng plain |
| CN110926430A (en) * | 2019-11-22 | 2020-03-27 | 海南省林业科学研究所 | Air-ground integrated mangrove forest monitoring system and control method |
| CN111767865A (en) * | 2020-06-30 | 2020-10-13 | 北部湾大学 | Method for inverting mangrove forest biomass by using aerial image and laser data |
Non-Patent Citations (1)
| Title |
|---|
| 红树林图谱信息系统设计及关键技术研究;李峥;兰樟仁;张东水;张伟;;福建林业科技(第04期);82-85 * |
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