CN116908114B - Remote sensing monitoring method for river basin granule organic carbon flux - Google Patents
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Abstract
The application discloses a remote sensing monitoring method for the organic carbon flux of river basin particles, which belongs to the technical field of the monitoring of the organic carbon flux of the river basin particles and comprises the following steps: obtaining the surface layer particle organic carbon concentration of the river under different water body optical categories by using a remote sensing reflectivity classification method; establishing a recurrence model of surface layer particle organic carbon concentration and each water level particle organic carbon concentration under different water optical categories; establishing a three-dimensional hydrodynamic model; and establishing a river basin granule organic carbon flux remote sensing monitoring model based on the three-dimensional hydrodynamic model under different water body optical categories. According to the application, more bayonet stations are not required to be established, or flow and particle organic carbon concentration monitoring equipment is installed in the existing bayonet stations, so that the cost is reduced, and the monitoring timeliness and the accuracy of the particle organic carbon flux in the river basin are improved by using the river basin particle organic carbon flux remote sensing monitoring model based on the three-dimensional hydrodynamic model.
Description
Technical Field
The application relates to a remote sensing monitoring method for the organic carbon flux of river basin particles, and in particular belongs to the technical field of the monitoring of the organic carbon flux of river basin particles.
Background
River systems are ligaments connecting land and sea and play an important role in the global carbon cycle. Particulate organic carbon (POC, particulate Organic Carbon) in rivers consists mainly of bio-organic carbon derived from soil and vegetation and fossil organic carbon derived from rock, and erosion, handling, oxidation and burial of POC are of great importance for carbon recycling at different time scales. In general, the flow field particle organic carbon flux is obtained mainly by arranging flow and POC concentration obtaining equipment at a bayonet station of a flow field outlet, and multiplying the flow and the POC concentration by each other at the bayonet station of the flow field outlet. This acquisition method has the following drawbacks: 1. the station arrangement cost and the personnel maintenance cost are high, the bayonet stations are generally arranged only in main river areas such as large rivers and important rivers, the station number is small, the river basin particle organic carbon flux at the large rivers and the important rivers can only be obtained, and the river basin particle organic carbon inside the river basin can not be obtained. 2. The cost of distributing a large number of bayonet station flows and POC concentration obtaining equipment in each river basin is high. 3. The timeliness is poor, when the main drainage basin is provided with a bayonet station, when the small drainage basin or the micro drainage basin changes in the organic carbon flux of the drainage basin particles due to environmental changes, the small drainage basin or the micro drainage basin can be discovered only after the small drainage basin or the micro drainage basin water body is injected into the main drainage basin, the organic carbon flux of the drainage basin or the micro drainage basin particles is difficult to monitor in real time, and meanwhile, the drainage basin places where the drainage basin particles change in the organic carbon flux are also difficult to change. 4. The monitoring precision is low, only bayonet stations are arranged in the main drainage basin, the drainage basin particle organic carbon flux of the small drainage basin or the micro drainage basin can be diluted by the main drainage basin, and the acquired monitoring result precision is seriously reduced.
Disclosure of Invention
In order to solve the problems of overhigh cost, poor timeliness, low monitoring precision and the like caused by determining POC flux only by means of a flow rate and POC concentration monitoring device of a bayonet station in the prior art, the application provides a remote sensing monitoring method for river basin granule organic carbon flux, which comprises the following steps:
in a first aspect, the application provides a remote sensing monitoring method for the flux of river basin granule organic carbon, comprising the following steps:
obtaining the river basin particle organic carbon concentration of the lower surface layer of different water body categories at the upper, middle and lower sides of the river by using a remote sensing reflectivity classification method;
obtaining the organic carbon concentration of river basin particles layer by layer under different water body categories at the upper, middle and lower streams of a river by utilizing a laser radar or layer by layer sampling method;
respectively establishing a recurrence model of the river basin granule organic carbon concentration from the surface layer to the bottom layer, and the surface layer and each level of the river basin granule organic carbon concentration under different water body categories of the upper, middle and lower sides of the river by using the river basin granule organic carbon concentration layer by layer under different water body categories of the upper, middle and lower sides of the river;
obtaining the river basin granule organic carbon concentration of the vertical three-dimensional distribution of the whole river by using the surface layer, the river basin granule organic carbon concentration of each level and the recurrence model;
obtaining measured flow data of the river gate station;
based on a hydrodynamic model, combining measured flow data of gate stations of all sub-gates of a river, river form data and vertical three-dimensional distributed organic carbon concentration of the gate particles to obtain simulation data of organic carbon flux of the gate particles of different gate stations;
optionally, using the actually measured data of the organic carbon flux of the river basin granule of the bayonet station, checking whether the analog data of the organic carbon flux of the river basin granule of the bayonet station meets the set monitoring requirement.
Alternatively, the set monitoring requirement means that the average absolute percentage error of the set basin granule organic carbon flux simulation data is less than 15%.
Optionally, reversely correcting the measured flow data, river form data and vertical three-dimensional distribution river basin granule organic carbon concentration of the bayonet station of each sub-river basin by using the river basin granule organic carbon flux simulation data and the river basin granule organic carbon flux measured data obtained for the first time to obtain granule organic carbon simulation flux of each sub-river basin in the river basin.
Optionally, the layers under different water body categories are a plurality of layers for dividing the surface layer under different water body categories to the bottom layer under different water body categories.
Optionally, the layers of different water body categories are positioned below the surface layer.
Optionally, establishing a recursive model of the water surface layer particle organic carbon concentration and each level particle organic carbon concentration includes:
selecting a plurality of remote sensing images under the same water body category;
setting a plurality of sampling points on each remote sensing image, and setting the sampling points at the same position of the corresponding river region according to the positions of the sampling points on each remote sensing image;
measuring the concentration of the granular organic carbon on the surface layer of the water body at each sampling point and each level;
calculating the concentration of the granular organic carbon on the surface layer of the water body;
establishing a granular organic carbon concentration recurrence equation of the water surface layer and each level, wherein the recurrence equation is as follows:
；
wherein,represents the i-th level particulate organic carbon concentration; />Expressing the slope of a granular organic carbon concentration recurrence equation of the surface layer of the water body and each level of the j-th remote sensing image; />Representing the depth of the ith level from the surface of the body of water; />Representing the concentration of organic carbon on the surface particles of the water body; i denotes the water body level designation, i=1, 2,3, g, g is a natural number; g represents the number of levels; j represents the remote sensing image number, j=1, 2,3, q, q is a natural number; q represents the number of remote sensing images.
Optionally, building a three-dimensional hydrodynamic model includes:
calculating the flow velocity of the surface layer of the water body;
according to the water surface flow velocity system of each level, a three-dimensional hydrodynamic model is established, and the three-dimensional hydrodynamic model is as follows:
；
wherein Q represents river cross-section flow; i denotes the water body level designation, i=1, 2,3, g, g is a natural number; g represents the number of levels;representing the surface flow velocity of river water body; />Represents the i-th level cross-sectional area; />And the water surface flow velocity coefficient corresponding to the ith layer is represented.
Optionally, a drainage basin granule organic carbon flux remote sensing monitoring model is established, which is as follows:
；
wherein F represents the flux of the watershed particles organic carbon;representing the total amount of particulate organic carbon flow for all water levels in the river cross section; a represents the total area of all water body level sections in the river section.
In a second aspect, the present application provides a remotely sensed monitoring system of river basin granule organic carbon flux, the system performing the steps of any of the methods described above, comprising:
the remote sensing module is used for acquiring a drainage basin remote image;
the particle organic carbon flux value acquisition module is used for calling the particle organic carbon flux value measured in the bayonet station;
the data acquisition module is used for setting sampling points on the remote sensing image, recording the concentration of the organic carbon of particles at each level of the sampling points, the concentration of the organic carbon of particles at the surface layer of the water body, the type of the water body, the depth of each level from the surface layer of the water body, the number of levels, the number of remote sensing images and the number of remote sensing images.
The data processing module is used for calling the data in the data acquisition module and calculating the gradient of the recursion equation of the concentration of the granular organic carbon on the surface layer of the water body and each level, the flow velocity of the surface layer of the river water body, the sectional area of the level and the flow velocity coefficient of the water surface;
the modeling module is used for constructing a recurrence model of the water surface layer particle organic carbon concentration and each level particle organic carbon concentration, a three-dimensional hydrodynamic model and a watershed particle organic carbon flux remote sensing monitoring model.
The application has the beneficial effects that:
1. realizing full-river basin monitoring, and for small tributaries or micro tributaries in a river basin, which cannot be directly shot with a remote sensing image, obtaining a calculation result by utilizing an obtained river basin particle organic carbon flux remote sensing monitoring model and combining a water optical type; or calculating the difference value of the calculated result of the measured data of the granular organic carbon flux obtained based on the bayonet station and the remote sensing monitoring model of the granular organic carbon flux based on the river basin, and obtaining the tributary granular organic carbon flux in the river basin which cannot be directly shot into the remote sensing image.
2. The method has timeliness, the particle organic carbon flux in the river basin is rapidly calculated by determining the hydrodynamic model and combining the hydrodynamic model and the particle organic carbon concentration model, and when the environment changes, the numerical value of the particle organic carbon flux in the river basin is rapidly calculated by only selecting different models or only adjusting independent variables, so that real-time monitoring is realized. Especially, when the optical type of the water body in a certain river basin is found to be changed based on the remote sensing image, the particle organic carbon flux of the river basin can be calculated immediately.
3. The monitoring precision is high, the hydrodynamic force model and the particle organic carbon concentration model are verified and adjusted, the model precision is guaranteed, and the combination of the hydrodynamic force model and the particle organic carbon concentration model is verified and adjusted, so that the accuracy of the watershed particle organic carbon flux remote sensing monitoring model is further improved.
4. The cost is reduced, the particle organic carbon flux of the detected river basin can be calculated based on the river basin particle organic carbon flux remote sensing monitoring model, the particle organic carbon flux value of each sub-river basin can be determined by combining the particle organic carbon flux actual measurement value of the existing bayonet station, a large number of bayonet station flow and POC concentration measuring equipment are not required to be newly arranged, and the installation cost and the maintenance cost are reduced.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a remote sensing monitoring method for the organic carbon flux of river basin particles, which is provided by the embodiment of the application;
FIG. 2 is a flowchart of a method for establishing a surface layer and a recursive model of the concentration of particulate organic carbon for each level according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for establishing a basin granule organic carbon monitoring model under a three-dimensional hydrodynamic model according to an embodiment of the present application;
fig. 4 is a schematic diagram of domains, minidomains, and micro-domains in some embodiments of the application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
It should be further noted that, for convenience of description, only some, but not all of the matters related to the present application are shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures.
Referring to fig. 1, a remote sensing monitoring method for the organic carbon flux of the river basin granule provided by the embodiment of the application is suitable for monitoring the organic carbon flux of the river basin granule of all the river basins, and comprises the following steps:
s110, determining the concentration of the granular organic carbon flux on the surface layer of the water body under the conditions of different optical types of the water body in different flow areas of the river by utilizing a remote sensing reflectivity analysis method.
The method aims to obtain the river basin granule organic carbon concentration of the surface layer under different water body categories in the upper, middle and lower streams of the river by using a remote sensing reflectivity classification method, and obtain the river basin granule organic carbon concentration layer by layer under different water body categories in the upper, middle and lower streams of the river by using a laser radar or layer by layer sampling method, so as to obtain the data base of the river basin granule organic carbon flux monitoring model.
The optical type of the water body is the water body color displayed by the remote sensing image, and classification is carried out based on different colors;
in some embodiments, the optical type of the water body is a gray-scale value of a gray-scale image of the remote sensing image after pretreatment, and the classification is performed based on the gray-scale value of the image.
In the method for acquiring the concentration of the granular organic carbon on the surface layer of the water body, the clustering algorithm is adopted for processing, and the specific steps are as follows:
step one, collecting water remote sensing reflectivity data: and acquiring water remote sensing image data, and ensuring the quality and accuracy of the data.
The method comprises the steps of acquiring a remote sensing image of a river in a sub-river basin which can be identified by a remote sensing satellite, identifying the optical type of a water body in the remote sensing image, classifying the remote sensing image with the same optical type of the water body into the same database according to the classification of the optical type of the water body;
in some embodiments, the remote sensing image of the river in the same sub-river basin is subjected to water body optical type identification, the remote sensing image is subjected to block processing according to the water body optical type, and the remote sensing image blocks with the same water body optical type are classified into the same database;
in some embodiments, similarity detection is performed on the optical type of the water body, and two or more remote sensing images with higher similarity or with a similarity calculation result falling into a merging threshold value are classified into the same database;
in some embodiments, displaying the concentration of the organic carbon particles is realized through gray processing of the remote sensing image, in the specific processing, the remote sensing image of a certain place in the flow field is obtained, and the atmospheric correction is carried out to obtain the remote sensing reflectivity value after the atmospheric correction of each wave band; and collecting the organic carbon concentration of the particles on the surface layer of the water body at the same place, marking the obtained remote sensing image subjected to gray scale treatment to obtain a relation model of the organic carbon concentration of the particles and gray scale values, and determining the organic carbon concentration of the particles based on the pixel values of the gray scale images.
The water body optical type comprises the water body color of the remote sensing image, the gray level of the gray level map of the remote sensing image, the hyperspectral image of the remote sensing image and the like.
And performing noise reduction processing on the acquired remote sensing image.
The method has the advantages that the optical type of the water body can reflect the concentration of the granular organic carbon in the water body, remote sensing images with the same optical type of the water body are brought into the same database, one of the databases is built for remote sensing image databases of different watercourses and different types of water bodies, and the remote sensing images are conveniently processed in a large scale in real-time monitoring; 2. judging the position, the organic carbon concentration of the water surface particles, the water type and other information by utilizing the water optical type, such as green eutrophication lakes, yellow rivers with more sediment content and the like; 3. the image preprocessing is realized, and the difficulty and complexity of the subsequent model construction are reduced.
And secondly, selecting sample points for the concentration of the granular organic carbon on the surface layer of the water body.
And identifying the river bank from the acquired remote sensing image to acquire the river region in the remote sensing image.
Wherein, n sample points are randomly arranged in a river region in the remote sensing image and in the same water body optical type region;
in some embodiments, in the first step of S110, n sample points are directly and randomly distributed on the remote sensing image located in the same database without determining the optical type of the water body.
The method is characterized in that on the one hand, based on the obtained sample points, water samples, especially water samples with different depths, can be obtained at the sample points so as to detect the concentration of the granular organic carbon, and the method is used for verifying the concentration of the granular organic carbon of a remote sensing image; on the other hand, the set sample points are directly used for a clustering algorithm to obtain the organic carbon concentration of the water body surface particles under the remote sensing image.
And thirdly, clustering the particle organic carbon concentration sample points on the surface layer of the water body.
K clustering centers are randomly arranged in the remote sensing image, the clustering centers are not overlapped with the sample points, and the K value is the set clustering number;
in some embodiments, K sample points are randomly selected from the n sample points, the K sample points are set as cluster centers, and the K value is the set number of clusters.
In some embodiments, determining Euclidean distances between each sample point and K clustering centers respectively, and distributing each sample point to a clustering center category closest to the Euclidean distance to each sample point to obtain K clusters;
in some embodiments, the Euclidean distance between the rest (n-K) sample points and K clustering centers is determined, and each rest sample point is distributed into the clustering center category closest to the Euclidean distance, so that (n-K) clusters are obtained.
And determining a cluster center again for the generated clusters, wherein the new cluster center can be obtained by calculating the sample points in the clusters and determining the Euclidean distance mean value of the cluster centers.
And repeatedly determining Euclidean distance between each sample point and a new cluster center, and distributing each sample point into a new cluster center category closest to the sample point in Euclidean distance to obtain a new cluster.
And repeating the steps until the obtained clustering center and/or the obtained clustering position are unchanged.
The method aims at reducing the calculation complexity and improving the calculation speed by adopting a K-means clustering algorithm, ensures that the remote sensing graph can be rapidly processed in a large batch in real-time monitoring, and meets the technical requirements of the method in terms of precision.
And S120, layering the water body surface layer to the bottom layer in the vertical direction under different water body categories, and obtaining a granular organic carbon concentration recurrence model of the water body surface layer and each level.
The method aims at respectively establishing a recursive model of the organic carbon concentration of the river basin particles from the surface layer to the bottom layer under different water body categories of the upper, middle and lower sides of the river by utilizing the organic carbon concentration of the river basin particles layer by layer under different water body categories of the upper, middle and lower sides of the river, and establishing a recursive model under different water body optical types by utilizing the organic carbon concentration of the river basin particles of the surface layer and each layer and the recursive model to obtain the vertical three-dimensional distributed organic carbon concentration of the river basin particles of the whole river and measured flow data of a river bayonet station.
Referring to fig. 2, a flowchart of a method for establishing a surface layer and a particulate organic carbon concentration recurrence model of each level is provided for an embodiment of the present application, and is suitable for establishing a particulate organic carbon flux of a surface layer of a water body and a particulate organic carbon concentration recurrence model of each level of the water body.
Wherein, a sampling area is defined in the sub-basin, and sampling points are randomly set for sampling, or sampling points randomly set in step S110 are sampled.
And a plurality of layering layers are arranged between the surface and the bottom surface of the water body in the sampled area, and the layering thickness can be kept the same or personalized adjustment of the layering thickness is carried out according to actual requirements.
For the sampling mode, a laser radar and/or a mode of collecting a water sample can be used for sampling, and for the laser radar, the sampling area is irradiated; for water sampling, samples were taken in each of the tiers and the particulate organic carbon concentration was determined.
The method comprises the following specific steps of establishing a recurrence model of the particle organic carbon concentration of each level and the particle organic carbon concentration of the surface layer of the water body:
step one, determining clustering of the water surface layer based on the positions of the sampling points, and determining the concentration of the granular organic carbon on the water surface layer.
Calculating the average value of the concentration of the organic carbon particles in each cluster, wherein the obtained calculation result is considered as the concentration of the organic carbon particles in the cluster;
in some embodiments, the mean square error calculation is performed on the particle organic carbon concentration of the sample point in each cluster, a threshold value is set, when the mean square error of the particle organic carbon concentration of a certain sampling point exceeds the set threshold value, the mean square error calculation is performed on the particle organic carbon concentration of the sample point and the particle organic carbon concentrations of other clusters, when the mean square error does not exceed the threshold value, the sample point is included in the cluster, the method is used for adjusting the cluster until the cluster is unchanged, and the average value of the particle organic carbon concentrations of the sample points in the cluster is calculated.
Calculating the average value of the organic carbon concentration of all clustered particles in the river with the same optical type of the water body, wherein the obtained calculation result is the organic carbon concentration of the particles on the surface layer of the water body under the same optical type of the water body;
in some embodiments, calculating the average value of the organic carbon concentration of particles containing sample points in each cluster in the river with the same optical type of the water body, obtaining the organic carbon concentration of the clustered particles, calculating the mean square error of the organic carbon concentration of each clustered particle, setting a mean square error threshold, and if the organic carbon concentration of the clustered particles exceeds the mean square error threshold in a certain proportion of clusters, not calculating the average value of the organic carbon concentration of the particles of all the clusters, wherein the organic carbon concentration of the particles of the surface layer of the water body is displayed according to the clustering range.
The method aims at obtaining the concentration of the granular organic carbon on the surface layer of the water body in a certain area by calculating the average value in the sub-watershed with the same optical type of the water body, so that the operation step is simplified. In addition, the variance of the concentration of the organic carbon of the particles at the sample points is calculated, double verification based on Euclidean distance and the concentration of the organic carbon of the particles in the clustering can be realized, and the quality of the clustering setting is improved. And the mean square error calculation of the concentration of the clustered particles and the organic carbon is supported, whether the concentration of the clustered particles and the organic carbon is uniform or not is judged, if the concentration of the clustered particles and the organic carbon is not uniform, the mean value is not calculated, and the accuracy of the water surface particle and the organic carbon concentration recurrence model of each level particle is further improved.
Step two, establishing a recurrence model of the organic carbon concentration of the water surface particles and the organic carbon concentration of each level particle.
Selecting a plurality of remote sensing images under the same water body optical type;
in some embodiments, a remote sensing image is selected that contains different optical types of the body of water, and the image is segmented into image blocks according to the optical types of the body of water.
Taking the organic carbon concentration of the water surface particles as the intercept of an equation, taking the depth of the water level as an independent variable, and taking the organic carbon concentration of the level particles as an independent variable, thereby obtaining the following equation:
（1）
wherein,represents the i-th level particulate organic carbon concentration; a represents a slope; />Representing the depth of the ith level from the surface of the body of water; />Representing the concentration of organic carbon on the surface particles of the water body; i denotes the water body level designation, i=1, 2,3,g, g is a natural number.
Randomly selecting p remote sensing images with the same optical type of the water body, ensuring that the division mode of the depth of the water body level is the same in the region of the watershed corresponding to the selected remote sensing images, ensuring that the variance of the concentration of the granular organic carbon of each level with the same depth is smaller, substituting the values of the concentration of the granular organic carbon of the ith level, the depth of the ith level from the water body surface layer and the concentration of the granular organic carbon of the water body surface in each remote sensing image into an equation (1), and obtaining p values of a;
in some embodiments, clusters on a single remote sensing image are obtained, the organic carbon concentration of particles on the surface of the water body of each cluster is obtained, and the concentration of the organic carbon of the particles of the clusters, the concentration of the organic carbon of the particles of the ith level below the clusters and the depth value of the ith level from the surface layer of the water body are substituted into equation (1), so that K a can be obtained ’ Value of a ’ And (5) representing the gradient of the water surface layer clustering particle organic carbon concentration and the i-th level particle organic carbon concentration recurrence model.
Wherein the obtained value a or a is obtained ’ And (3) the value average value and simultaneously recording the depth of the ith layer to obtain the following equation:
（2）
wherein q represents the obtained a value or a ’ The number of the values is p or K; j is the value of a, j=1, 2,3, the terms p and K.
Wherein equations (1) and (2) are combined, namely: a recursive model of the water surface layer particle organic carbon concentration and each level particle organic carbon concentration. The equation is as follows:
（3）。
in some embodiments, all data is recorded in a tabular mode including water optical type, water surface particulate organic carbon concentration, water layer progression and/or depth values of water layer level, recurrence equation slope, particulate organic carbon concentration of the level, and the like.
The method comprises the steps of establishing a recurrence model between the organic carbon concentration of the water surface particles and the organic carbon concentration of each water level particle under different water optical types, obtaining a plurality of slopes by adopting a plurality of groups of data, and improving model accuracy by calculating the average value of the slopes.
And thirdly, verifying the water surface layer particle organic carbon concentration and the water level particle organic carbon concentration recurrence equation according to the bayonet station measured data in the flow field.
And acquiring the concentration of the granular organic carbon on the surface layer of the water body under different optical types of the water body by using the remote sensing image.
And calculating the particle organic carbon concentration of different levels of the water body based on the obtained particle organic carbon concentration of the surface layer of the water body and the particle organic carbon concentration of the water body level.
And after the particle organic carbon concentrations of different levels of the water body are obtained, superposing the obtained particle organic carbon concentrations to obtain a particle organic carbon concentration result based on a recurrence equation.
And obtaining the concentration of the granular organic carbon in the watershed under the optical type of the water body based on the bayonet station monitoring data.
And calculating error percentages of the particle organic carbon concentration measured by the bayonet station and the particle organic carbon concentration obtained by calculation of a recurrence equation, and if the deviation of the particle organic carbon concentration and the particle organic carbon concentration is found to be larger, adjusting by using an empirical formula.
The method aims at verifying the recurrence equation of the organic carbon concentration of the water surface particles and the organic carbon concentration of each level particle, and guaranteeing the correctness and the accuracy of the equation.
S130, establishing a basin granule organic carbon monitoring model under the three-dimensional hydrodynamic model.
The method aims at obtaining river basin granule organic carbon flux simulation data of different river basin stations based on a hydrodynamic model by combining measured flow data, river morphology data and river basin granule organic carbon concentration of each river basin bayonet station of a river and finally establishing a river basin granule organic carbon monitoring model.
In the step, a three-dimensional hydrodynamic model is firstly established, the optical type of the water body is combined to obtain a river basin granule organic carbon monitoring model, meanwhile, the percentage error value of river basin granule organic carbon flux simulation data of a river basin bayonet station and actual measurement river basin granule organic carbon flux data of the bayonet station is calculated, an error threshold value is set, and if the percentage error value falls within the error threshold value range, the hydrodynamic model is considered to be correct; and if the percentage error value exceeds the error threshold value, adjusting parameters in the hydrodynamic model, and improving the model precision.
The method comprises the steps of collecting climatic environments, geological environments and humane environments of different sub-watercourses, wherein the climatic environments comprise precipitation, precipitation time periods and the like; the geological environment comprises mountain height, land flatness, vegetation coverage area, mountain angle and the like around the sub-river basin; the humane environment includes land types around the sub-river basin, such as classification into farmland, woodland, city, etc.
The method comprises the steps of preprocessing acquired weather environment, geological environment and humane environment data, dividing rainfall time periods, rainfall and the like in the weather environment, and selecting a proper rainfall processing method from the weather environment; the geological environment needs to acquire mountain angle, mountain height, vegetation coverage, river underwater topography, river potential data and the like; the human environment needs to obtain the farmland area, the connection relation between the farmland water supply and drainage system and the river basin, the forest area and place, the connection relation between the city water supply and drainage system and the river basin, and the like.
The remote sensing image is utilized to obtain the optical types of the water bodies of different rivers in the river basin under the actions of the climate environment, the geological environment and the humane environment.
And obtaining the total flux of the water body in the river or the flux of different water body layering according to the corresponding relation between the water body surface flow velocity and the different water body layering flow. As shown in fig. 3, a flowchart of a method for establishing a three-dimensional hydrodynamic model for monitoring organic carbon in a river basin according to an embodiment of the present application is specifically shown:
step one, segmenting the river in the sub-stream area, wherein the upstream, the middle and the downstream of the river at least comprise one segmentation section.
Acquiring each tributary connected with the main stream in the stream domain, and shooting for multiple times to obtain a plurality of river surface images;
in some embodiments, river surface images of the tributaries are taken at different wind speeds;
in some embodiments, without distinguishing main and sub-streams, all the rivers within the entire stream are photographed, resulting in multiple river surface images.
Determining the optical type of the water body in the image according to the river surface image, and taking the difference of the optical type of the water body as a segmentation line if obvious difference of the optical type of the water body exists in the image; if the optical type difference of the water body does not exist in the image, the upstream end and the downstream end of the river covered in the image are taken as the segmentation lines.
If the whole river has no difference in optical type of the water body, the river is randomly divided into a plurality of sections.
The purpose of this step is: firstly, by establishing the segments, the segments can be used as objects, and the whole river is not required to be used as the objects, so that the data volume is reduced, and the calculation complexity is reduced; secondly, when river segmentation is carried out based on the optical type of the water body, the particle organic carbon flux in the river segmentation section can be obtained and used for analyzing the change of the optical type of the water body caused by factors such as meteorological environment, human environment, geological environment and the like in the surrounding area of the river segmentation, so that the application range of the technology is widened.
And step two, calculating the surface flow velocity of river water along the river direction in each segmented interval.
Wherein markers in the river surface image, such as floats, bubbles, etc., are selected.
Wherein, confirm the shooting interval time of a plurality of river surface layer pictures, and discern the marker selected in each river surface layer picture.
According to the acquired multiple river surface images of the same place and the actual length of the shot river segmentation section, calculating the length relation between the pixels in the river surface images and the actual river segmentation section.
And performing marker position superposition processing on the obtained river surface images, and measuring the moving path length of the markers in the river surface images.
According to the length of a moving route of the marker in the river surface image, the shooting interval or total shooting time of the river surface, and the length relation between pixels in the river surface image and an actual river segmentation interval, the moving speed of the marker in the river surface is calculated, wherein the moving speed is the surface flow velocity of the river water body, and an equation is as follows:
（4）
wherein T represents the total time for capturing a plurality of river surface images;representing the actual length of the photographed river segmentation section; />The number of the pixels of the axis of the river subsection interval is shown; />Representing the number of pixels on the marker moving path in the multiple river surface images; />Representing an included angle between the moving path of the marker and the central axis of the river segmentation section; />Representing the surface flow velocity of river water body;
in some embodiments, the moving route of the marker in a certain time is determined by means of image recording.
In some embodiments, the wind speed is measured and recorded while a plurality of river surface images are shot, and the moving speed of the marker on the river surface is calculated according to the moving path of the marker displayed in the river surface images, the shooting interval or total shooting time of the river surface images, and the length relation between the pixels in the river surface images and the actual river segmentation interval, wherein the moving speed is the river water surface flow speed under the condition of the wind speed.
The purpose of this step is that by taking a river surface image to calculate the river surface velocity, the data acquisition and processing progress can be improved based on the mode of field shooting, and the sampling degree of freedom is improved and the sampling cost is reduced.
And thirdly, calculating the water surface flow velocity coefficient in each segmented interval.
The water flow velocity coefficient is the ratio of the measured flow of the section to the virtual flow calculated by simultaneously measuring the water flow velocity.
Measuring the measured flow rate of each level according to the mode of the step S120, and calculating the average value, wherein the average value is the section flow rate in the river sectional area;
in some embodiments, the measured flow for each level is measured and the measurement values are recorded in the manner of step S120.
Calculating the ratio of the section flow in the river segment interval to the surface flow rate of the river water body to obtain a water surface flow rate coefficient;
in some embodiments, the ratio of the measured flow rate of each level to the surface flow rate of the river water body is calculated to obtain the water surface flow rate coefficient.
According to the surface flow velocity and the water surface flow velocity coefficient of the river water body, calculating the section flow of the river or the flow of each level, wherein the equation is as follows:
（5）
wherein Q represents river cross-section flow; i denotes the water body level designation, i=1, 2,3, g, g is a natural number;representing the surface flow velocity of river water body; />Represents the i-th level cross-sectional area; />And the water surface flow velocity coefficient corresponding to the ith layer is represented. If the cross-sectional flow of the ith level of the river is directly calculated, the +.>And->The value is obtained.
And fourthly, establishing a drainage basin granule organic carbon flux remote sensing monitoring model based on the three-dimensional hydrodynamic model.
The three-dimensional hydrodynamic model and the water body level particle organic carbon concentration recurrence equation are combined to obtain the following equation:
（6）
wherein the method comprises the steps ofThe flow of particulate organic carbon over the cross section of the river is shown.
In some embodiments, each hierarchical level of particulate organic carbon flow is calculated.
Wherein, according to the relation of flow and flux, the following equation is obtained:
（7）
wherein F represents a particulate organic carbon flux; a represents the total area of all water body level sections in the river section. If the particulate organic carbon flux of a specific water level is to be determined, g=1 at this time, and the independent variable is selected based on the step of S120.
The method comprises the steps of establishing an association relation among the particle organic carbon concentration of each level of the water body, the cross-sectional area of each level and the flow, obtaining the particle organic carbon concentration flux of the level, realizing the monitoring of the particle organic carbon concentration of a specific depth in the water body, improving the technical implementation flexibility, and having wider technical application range.
And fifthly, verifying a particle organic carbon flux monitoring simulation model.
The river connection relation in the flow field, the connection relation between the river and the surrounding environment are determined, and the optical type of the water body under different environments is determined.
The method comprises the steps of obtaining measured data of the particle organic carbon flux obtained by a bayonet station and a calculation result obtained by an equation (7), calculating an average absolute percentage error of the measured data and the calculation result, wherein the average absolute percentage error is preferably set to be 15%, and if the average absolute percentage error exceeds 15%, adjusting a particle organic carbon monitoring simulation model based on an empirical formula;
in some embodiments, the organic carbon concentration of each level in each river segment is measured, the simulation result of the particulate organic carbon flux of each level in the river segment (where g=1 and the optical type of the body of water is known) is obtained according to equation (7), the comparison of the simulation result and the actual measurement result is performed, and the particulate organic carbon flux simulation equation of each level in the segment is adjusted until all river segment segments are traversed.
The method aims at verifying the established particle organic carbon flux monitoring simulation model so as to improve the model precision.
The method comprises the steps of determining the change of the optical type of the water body caused by different environments according to the climate environment, the geological environment and the human environment, determining the optical type of the water body based on different environmental factors, and selecting a corresponding particle organic carbon flux monitoring simulation model based on the optical type of the water body, wherein the river basin particle organic carbon flux monitoring simulation model based on the climate environment, the geological environment and the human environment is obtained.
And S140, monitoring the particle organic carbon flux based on the obtained river basin particle organic carbon flux remote sensing monitoring model.
The method comprises the steps of determining the optical type of the water body according to a remote sensing image, selecting a corresponding drainage basin particle organic carbon flux remote sensing monitoring model based on the optical type of the water body, and calculating the particle organic carbon flux.
If the simulation model for monitoring the granular organic carbon flux of the river basin based on the climate environment, the geological environment and the humane environment is obtained, the granular organic carbon flux can be calculated according to the environmental parameters of the river basin.
Wherein, as shown in fig. 4, the flux of the organic carbon particles is displayed in a drainage basin chart according to the numerical value.
In an embodiment of the present application, there is provided a remote sensing monitoring system for a river basin granule organic carbon flux, the system performing the steps of any one of the methods described above, including:
the remote sensing module is used for acquiring a drainage basin remote image;
the particle organic carbon flux value acquisition module is used for calling the particle organic carbon flux value measured in the bayonet station;
the data acquisition module is used for setting sampling points on the remote sensing image, recording the concentration of the organic carbon of particles at each level of the sampling points, the concentration of the organic carbon of particles at the surface layer of the water body, the type of the water body, the depth of each level from the surface layer of the water body, the number of levels, the number of remote sensing images and the number of remote sensing images.
The data processing module is used for calling the data in the data acquisition module and calculating the gradient of the recursion equation of the concentration of the granular organic carbon on the surface layer of the water body and each level, the flow velocity of the surface layer of the river water body, the sectional area of the level and the flow velocity coefficient of the water surface;
the modeling module is used for constructing a recurrence model of the water surface layer particle organic carbon concentration and each level particle organic carbon concentration, a three-dimensional hydrodynamic model and a watershed particle organic carbon flux remote sensing monitoring model.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
The foregoing detailed description of the application has been presented for purposes of illustration and description, and it should be understood that the foregoing is by way of illustration and description only, and is not intended to limit the scope of the application.
Claims (7)
1. A method for remotely sensing and monitoring the flux of organic carbon in a river basin granule, which is characterized by comprising the following steps:
obtaining the river basin particle organic carbon concentration of the lower surface layer of different water body categories at the upper, middle and lower sides of the river by using a remote sensing reflectivity classification method;
obtaining the organic carbon concentration of river basin particles layer by layer under different water body categories at the upper, middle and lower streams of a river by utilizing a laser radar or layer by layer sampling method;
by utilizing the river basin granule organic carbon concentration layer by layer under the different water categories in the upper, middle and lower streams of the river, respectively establishing a recurrence model from the water surface layer to the bottom layer under the different water categories in the upper, middle and lower streams of the river, the water surface layer and the river basin granule organic carbon concentration of each level, comprising:
selecting a plurality of remote sensing images under the same water body category;
setting a plurality of sampling points on each remote sensing image, and setting the sampling points at the same position of the corresponding river region according to the positions of the sampling points on each remote sensing image;
measuring the concentration of the river basin granule organic carbon of the surface layer of the water body at each sampling point and each level;
calculating the concentration of the river basin granule organic carbon on the surface layer of the water body;
establishing a recurrence model of the water surface layer and the river basin granule organic carbon concentration of each level, wherein the recurrence model is as follows:
；
wherein,represents the i-th level basin particle organic carbon concentration; />A recurrence model slope representing the water surface layer of the j-th remote sensing image and the river basin granule organic carbon concentration of each level; />Representing the depth of the ith level from the surface of the body of water;representing the concentration of the organic carbon in the watershed particles on the surface of the water body; i denotes the water body level designation, i=1, 2,3, g, g is a natural number; g represents the number of levels; j represents the remote sensing image number, j=1, 2,3, q, q is a natural number; q represents the number of remote sensing images; obtaining the river basin granule organic carbon concentration of the vertical three-dimensional distribution of the whole river by using the water body surface layer and a recursive model of the river basin granule organic carbon concentration of each level;
obtaining measured flow data of the river gate station;
establishing a river basin granule organic carbon flux remote sensing monitoring model based on the three-dimensional hydrodynamic model, and combining measured flow data, river morphology data and vertical three-dimensional distribution of river basin granule organic carbon concentration of each sub-river basin of the river to obtain river basin granule organic carbon flux simulation data of different river basin stations;
establishing a three-dimensional hydrodynamic model, comprising:
calculating the flow velocity of the surface layer of the water body; according to the water surface flow velocity system of each level, a three-dimensional hydrodynamic model is established, and the three-dimensional hydrodynamic model is as follows:
；
wherein Q represents river cross-section flow; i denotes the water body level designation, i=1, 2,3, g, g is a natural number; g represents the number of levels;representing the surface flow velocity of river water body; />Represents the i-th level cross-sectional area; />Representing the water surface flow velocity coefficient corresponding to the ith layer; establishing a drainage basin granule organic carbon flux remote sensing monitoring model, which is as follows:
；
wherein F represents the flux of the watershed particles organic carbon;representing the total amount of particulate organic carbon flow for all water levels in the river cross section; a represents the total area of all water level sections in the river section; i denotes the water body level designation, i=1, 2,3, g, g is a natural number; />Represents the ith level distance water body tableThe depth of the face; />Representing the concentration of the organic carbon in the watershed particles on the surface of the water body; />Representing the surface flow velocity of river water body; />Representing the water surface flow velocity coefficient corresponding to the ith layer; />Represents the i-th level cross-sectional area; a represents a recursive model slope mean value of the river basin granule organic carbon concentration of the water surface layer and the ith level of the plurality of remote sensing images, and a calculation equation is as follows:
；
wherein j represents the remote sensing image number, j=1, 2,3, the terms, q, q is a natural number; q represents the number of remote sensing images;and (5) representing the slope of a recursive model of the water surface layer of the j-th remote sensing image and the organic carbon concentration of the river basin particles of each level.
2. The method for remotely sensing and monitoring the organic carbon flux of the river basin granule according to claim 1,
and verifying whether the simulation data of the organic carbon flux of the river basin particles of the bayonet station meets the set monitoring requirements or not by using the actual measurement data of the organic carbon flux of the river basin particles of the bayonet station.
3. The method for remotely sensing and monitoring the organic carbon flux of the river basin particles according to claim 2, wherein,
the set monitoring requirement means that the average absolute percentage error of the simulation data of the organic carbon flux of the particles in the river basin is set to be less than 15 percent.
4. The method for remotely sensing and monitoring the organic carbon flux of the river basin granule according to claim 1,
and reversely correcting the measured flow data of the bayonet stations of each sub-river basin, river form data and the vertical three-dimensional distribution of the organic carbon concentration of the river basin particles by using the first obtained river basin particle organic carbon flux simulation data and the river basin particle organic carbon flux measured data to obtain the river basin particle organic carbon simulation flux of each sub-river basin in the river basin.
5. The method for remotely sensing and monitoring the organic carbon flux of the river basin granule according to claim 1,
the layers under different water body categories are a plurality of layers which divide the surface layer under different water body categories to the bottom layer under different water body categories.
6. The method for remotely sensing and monitoring the organic carbon flux of the river basin granule according to claim 1,
the layers of different water body categories are positioned below the surface layer.
7. A system for remotely sensing the flux of watershed particulate organic carbon, the system performing the steps of the method of any one of claims 1-6, comprising:
the remote sensing module is used for acquiring a drainage basin remote sensing image;
the drainage basin granule organic carbon flux value acquisition module is used for calling the drainage basin granule organic carbon flux value measured in the bayonet station;
the data acquisition module is used for setting sampling points on the remote sensing image, recording the concentration of the organic carbon of the particles in each level of the drainage basin of the sampling points, the concentration of the organic carbon of the particles in the surface layer of the water body, the type of the water body, the depth of each level from the surface layer of the water body, the number of levels, the number of remote sensing images and the number of remote sensing images;
the data processing module is used for calling the data in the data acquisition module and calculating the recurrence model slope of the water body surface layer and the river basin granule organic carbon concentration of each level, the river water body surface flow rate, the level cross-section area and the water surface flow rate coefficient;
the modeling module is used for constructing a recurrence model of the water body surface layer and the river basin granule organic carbon concentration of each level, a three-dimensional hydrodynamic model and a river basin granule organic carbon flux remote sensing monitoring model.
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