CN115993336A - Method for monitoring vegetation damage on two sides of water delivery channel and early warning method - Google Patents
Method for monitoring vegetation damage on two sides of water delivery channel and early warning method Download PDFInfo
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Abstract
The invention relates to a method for monitoring vegetation damage on two sides of a water channel and an early warning method, wherein the monitoring method comprises the following steps: s1, acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle; s2, acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image; and S3, determining the vegetation damage degree on the basis of the water area data on the currently monitored vegetation type and coverage. The unmanned aerial vehicle aerial photographing multispectral image is adopted to identify various vegetation types and corresponding coverage, water area data is used as a standard, whether the detected vegetation types and coverage near the water area meet the water area requirements or not is monitored on the damage condition of the vegetation, the vegetation protection capacity near the water area can be monitored in a targeted and characteristic mode, and the monitored damage degree meets the water area requirements. The monitoring mode is simple and easy to operate, and the obtained vegetation damage degree monitoring result is more accurate and more relevant, and has higher reference value.
Description
Technical Field
The invention relates to the technical field of ecology, in particular to a method for monitoring vegetation damage on two sides of a water delivery channel and an early warning method.
Background
The canal head sand settling pond generally adopts a sand settling mode combining self-flowing and water pumping, some engineering designs need to change the originally designed sand settling mode of self-flowing and water pumping into a digging and sinking mode, most of the cleared earthwork is piled up along the inner side of the separation dike by pasting a slope (internal pasting type dredging), and a small part of the cleared earthwork is discharged outside.
In the engineering reconstruction process, the current vegetation protection is inevitably destroyed. After the water delivery channel is rebuilt, the unlined section of the water-facing slope needs to be greened by adopting scattered grass planting; some vegetation is destroyed on two sides of the canal roof, and the need of planting and greening is also met; all the dredging soil and demolishd materials are abandoned, all the abandoned materials are transported to the abandoned soil pit outside the water delivery canal dike, and grass is required to be sowed in the soil piling area for protection in order to reduce water and soil loss. Because the long-time operation is carried out, the land use condition can also change due to the artificial influence, and the factors such as the reduced range of the plant protection belt, the degradation of the vegetation health condition and the like can lead to the change of the vegetation coverage, thereby having adverse effect on the control effect of water and soil loss.
After the engineering is completed, vegetation coverage on two sides of the canal needs to be monitored as required or periodically. If the monitoring technology based on the ground means is adopted, the monitoring technology is easily limited by natural conditions and is influenced by artificial subjective factors, convenience and safety cannot be considered, and monitoring on the actual vegetation coverage is difficult to obtain.
The above drawbacks are to be overcome by those skilled in the art.
Disclosure of Invention
First, the technical problem to be solved
In order to solve the problems in the prior art, the invention provides a method for monitoring vegetation damage on two sides of a water channel and an early warning method, and aims to solve the problem that the vegetation damage on two sides of the water channel cannot be effectively and conveniently monitored in the prior art.
(II) technical scheme
In order to solve the above problems, the present invention provides the following embodiments:
according to a first aspect of the present invention, an embodiment of the present invention provides a method for monitoring vegetation damage on two sides of a canal channel, the method comprising:
s1, acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2, acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
and S3, determining the vegetation damage degree on the basis of the water area data on the currently monitored vegetation type and coverage.
In an exemplary embodiment of the present invention, step S1 includes:
s11, acquiring an unmanned aerial vehicle flight path with a preset length according to the preset path;
s12, on a flight path of an unmanned aerial vehicle with a preset length, the unmanned aerial vehicle flies and generates a two-dimensional multispectral image in real time according to the acquired image data;
s13, identifying the boundary of the water area according to the collected multispectral image;
s14, when the shooting range of the unmanned aerial vehicle is identified to cross the boundary of the water area and enter the appointed water area, a water area detector is started in time, and water area data are obtained;
the water area data at least comprises one of water channel width, water depth, water flow velocity and water flow direction.
In an exemplary embodiment of the present invention, step S1 further includes:
s15, constructing a three-dimensional image based on the two-dimensional multispectral image and the height data;
s16, detecting leakage points of the three-dimensional image, and correcting the route of the unmanned aerial vehicle in time and starting the supplementary shooting if the leakage points are found.
In an exemplary embodiment of the present invention, step S2 includes:
s21, dividing the collection area into three different areas of vegetation, bare land and water area according to the multispectral image and the classification standard;
s22, determining vegetation types including arbor, shrub and turf by analyzing chlorophyll content and distribution height of vegetation according to multispectral images corresponding to vegetation partitions;
s23, orthographic correction is carried out on the multispectral image in combination with the height standard values of different vegetation types;
s24, calculating normalized vegetation indexes NDVI for the multispectral images corrected by various vegetation types to obtain an NDVI spatial distribution diagram;
s25, inputting the NDVI spatial distribution map into a preset coverage evaluation model to obtain the coverage of vegetation.
In an exemplary embodiment of the present invention, step S22 includes:
s221, calculating the ground reflectivity according to the multispectral image corresponding to the vegetation partition;
s222, determining a preliminary analysis result of vegetation coverage according to the ground reflectivity;
s223, clustering the wave band spectrums reflected by different vegetation according to the primary analysis result;
s224, determining vegetation types and distribution according to the chlorophyll content of vegetation in the cluster-combined multispectral image and the collected distribution height.
In an exemplary embodiment of the present invention, step S25 includes:
s251, setting corresponding space distribution heights according to vegetation types planted on two sides of the canal;
s252, performing space synthesis by using vegetation NDVI space distribution recorded by a database;
s253, performing data training on the synthesized spatial distribution height and the distribution data by using a machine learning algorithm to obtain a preset coverage evaluation model;
s254, respectively inputting the NDVI spatial distribution diagrams corresponding to the vegetation of each type into a preset coverage evaluation model of the corresponding type, or uniformly inputting the NDVI spatial distribution diagrams into the preset coverage evaluation model to obtain the coverage of the vegetation of each type.
In an exemplary embodiment of the present invention, step S3 includes:
s31, calculating a required waterproof index according to water area data;
s32, determining a range value of vegetation water and soil conservation capability according to the type of the vegetation currently monitored;
s33, comparing the required waterproof index in the step S31 with the range value of the vegetation water and soil conservation capability in the step S32, and if the required waterproof index is not in the range value of the vegetation water and soil conservation capability, determining that the damage degree of the vegetation is serious;
s34, if the required waterproof index is within the range value of the vegetation water and soil conservation capability, updating the vegetation water and soil conservation capability according to the vegetation type and the corresponding coverage;
and S35, determining the damage degree of the vegetation according to the updated vegetation water and soil conservation capability.
In an exemplary embodiment of the present invention, step S3 includes:
s31', calculating a required waterproof index according to water area data;
s32', determining the corresponding water and soil conservation capability of the required vegetation according to the required waterproof index and referring to a preset water and soil protection standard;
s33', calculating vegetation water and soil conservation capacity according to the type of the currently monitored vegetation and the coverage;
s34', determining the vegetation damage degree according to the difference value between the vegetation water and soil conservation capability of the step S33' and the required vegetation waterproof conservation capability of the step S32 '.
According to a second aspect of the present invention, an embodiment of the present invention further provides a water and soil conservation early warning method for two sides of a water channel, including:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of water area data on the currently monitored vegetation type and coverage;
s4', judging whether the damage degree of vegetation reaches an early warning standard, and if so, sending out early warning.
According to a third aspect of the present invention, an embodiment of the present invention further provides a water and soil conservation early warning method for two sides of a canal channel, including:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of water area data on the currently monitored vegetation type and coverage;
s5', determining the self-repairing capability of the vegetation according to the vegetation type and the coverage and the vegetation growth characteristics;
s6', judging whether self-repair can be completed within a preset time according to the self-repair capability and the vegetation damage degree, and if not, sending out early warning.
(III) beneficial effects
The beneficial effects of the invention are as follows: according to the vegetation damage monitoring method and the early warning method for the two sides of the water channel, the unmanned aerial vehicle aerial multispectral image is adopted to identify various vegetation types and corresponding coverage degrees, water area data are used as standards, whether the detected vegetation types and coverage degrees near the water area meet the water area requirements or not is monitored, the vegetation damage condition near the water area can be monitored in a targeted and characteristic mode, and the monitored damage degree meets the water area requirements. The monitoring mode is simple and easy to operate, and the obtained vegetation damage degree monitoring result is more accurate and more relevant, and has higher reference value. Furthermore, whether the vegetation water and soil conservation capability meets the requirements is judged according to the monitored vegetation damage degree so as to perform early warning in time.
Drawings
FIG. 1 is a flow chart of a method for monitoring vegetation damage on two sides of a canal channel according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating step S1 of FIG. 1 according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating step S2 of FIG. 1 according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S22 in FIG. 3 according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating step S25 in FIG. 3 according to an embodiment of the present invention;
FIG. 6 is a flowchart illustrating step S3 of FIG. 1 according to an embodiment of the present invention;
FIG. 7 is a flowchart illustrating the step S3 of FIG. 1 according to an embodiment of the present invention;
FIG. 8 is a flow chart of a method for pre-warning water and soil conservation on two sides of a canal channel according to another embodiment of the present invention;
fig. 9 is a flowchart of a method for early warning of water and soil conservation on two sides of a canal channel according to another embodiment of the present invention.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
In addition, descriptions such as those related to "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the amount of data of the indicated technical feature in the present invention. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In order to effectively prevent and treat water and soil loss, the damage condition of vegetation on two sides of a channel needs to be effectively and conveniently monitored in time, and the embodiment of the invention provides a method for monitoring and early warning damage of vegetation on two sides of a water delivery channel by using an unmanned aerial vehicle.
Fig. 1 is a flowchart of a method for monitoring vegetation damage on two sides of a canal channel according to an embodiment of the present invention, wherein data required for monitoring is collected during the flight of an unmanned aerial vehicle, as shown in fig. 1, and the monitoring method includes the following steps:
as shown in fig. 1, in step S1, multispectral images and water area data are acquired on a preset path of the unmanned aerial vehicle;
as shown in fig. 1, in step S2, vegetation types and coverage on both sides of the canal are obtained according to the multispectral image;
as shown in fig. 1, in step S3, a vegetation damage level is determined for the currently monitored vegetation type and coverage based on the water area data.
The method can judge the growth condition and the health level condition of vegetation on two sides of the channel, thereby realizing dynamic monitoring and interpretation of the vegetation, taking targeted measures such as early warning in time, scientific supplementing planting, optimizing plant types, improving irrigation modes and the like based on the dynamic monitoring and interpretation of the vegetation, and realizing fine management.
The following describes the specific steps of the monitoring method in connection with specific embodiments:
in step S1, multispectral images and water area data are acquired on a preset path of the unmanned aerial vehicle.
In an exemplary embodiment of the invention, an unmanned aerial vehicle is used for carrying a multispectral imager in the step, so that the application of the multispectral influence technology in the aspect of monitoring water and soil conservation vegetation in the water conservancy industry is realized. The digital orthophoto map (Digital Orthophoto Map, abbreviated as DOM) obtained by the unmanned aerial vehicle utilizes the DEM to carry out radiation correction, differential correction and mosaic on the digital aerial photograph or remote sensing image (monochromatic or color) which is subjected to scanning treatment, cuts the generated image data according to a specified map range, and has a planar map of kilometer grids, figure profile decoration and annotation), and the spatial resolution of a digital surface model (DigitalSurface Model, abbreviated as DSM, a ground elevation model comprising the heights of surface buildings, bridges, trees and the like) can reach the centimeter level generally, and the water and soil conservation monitoring work requirement can be completely met.
The preparation type of land cover can be accurately classified and the ground object can be accurately identified based on the multispectral imager, so that bare land, vegetation, water area and the like can be distinguished. As an important means of low-altitude remote sensing, the multispectral unmanned aerial vehicle can collect abundant information at one time, and the natural resource refined investigation monitoring capability is greatly improved. The visible light and the multispectral camera of the spectrum version unmanned aerial vehicle are 200 ten thousand pixels, when the flying height is 100 meters, the ground resolution reaches 5.3cm, and the spectrum version unmanned aerial vehicle has higher spatial resolution both in RGB images and multispectral images, thereby providing high-precision data for quantitative investigation.
Fig. 2 is a flowchart of step S1 in fig. 1 according to an embodiment of the present invention, and as shown in fig. 2, step S1 includes the following steps:
step S11, acquiring an unmanned aerial vehicle flight path with a preset length according to the preset path;
step S12, on the flight path of the unmanned aerial vehicle with a preset length, the unmanned aerial vehicle flies and generates a two-dimensional multispectral image in real time according to the acquired image data;
s13, identifying the boundary of the water area according to the collected multispectral image;
step S14, when the shooting range of the unmanned aerial vehicle is identified to cross the boundary of the water area and enter the appointed water area, a water area detector is started in time, and water area data are obtained; the water area data at least comprises one of water channel width, water depth, water flow velocity and water flow trend;
s15, constructing a three-dimensional image based on the two-dimensional multispectral image and the height data;
and S16, detecting the leakage points of the three-dimensional image, and correcting the route of the unmanned aerial vehicle in time and starting the supplementary shooting if the leakage points are found.
In an embodiment of the invention, a segmented shooting mode is adopted, namely, the unmanned aerial vehicle is provided with a flashing map on a path with a preset length, so that problems can be found in time on an operation site, and more targeted countermeasures can be flexibly adopted. Immediately after the drawing, the multispectral image is identified, boundaries of vegetation and a water area are distinguished, and when the unmanned aerial vehicle is detected to enter the appointed water area, an independent water area detector is started to acquire water area data. On the one hand, the water area identified by the multispectral image only contains static information such as the width, the depth and the like of the ditch, so that the water area analysis is not comprehensive enough, and the water area detector can further acquire dynamic information such as the flow velocity, the trend and the like of water flow, so that the comprehensive analysis of the water area is facilitated. On the other hand, due to timely drawing, missing photographing or storage faults (or faults of storage equipment, or reasons that an image cannot be taken due to shielding in flight, or deviation of a path occurs) existing in photographing can be timely found according to the three-dimensional images, for example, grid division can be conducted according to the three-dimensional images within a preset length aerial photographing range, the identification of the number of point cloud data can be conducted by taking small grids as units, and the problem of missing points can be timely found. If the problem of missing points is found, the unmanned aerial vehicle route is changed in time to carry out the supplementary shooting, so that the time and cost waste caused by repeated shooting after the whole aerial shooting is found out of the way after the whole aerial shooting is completed. The data after the supplementary shooting is timely supplemented to the three-dimensional image data, so that the data integrity is ensured, and the subsequent analysis is convenient.
In step S2, vegetation types and coverage at two sides of the canal are obtained according to the multispectral image.
According to the spectral characteristics of vegetation, the spectra of different narrow-side wavebands are combined in a nonlinear mode to form various vegetation indexes, and the vegetation indexes of different wavebands can reflect different characteristics of the vegetation. The vegetation is strong in red light wave band absorption and near infrared wave band reflection, and the normalized vegetation index (Normalized Difference Vegetation Index, NDVI for short) can be obtained by calculating the sum of the difference ratio of the reflection value of the near infrared wave band and the reflection value of the red light wave band. The calculation formula of the NDVI index is as follows:
wherein the method comprises the steps ofρ NIR Is the near infrared band spectrum reflectivity value of the remote sensing image,ρ R is the spectral reflectance value of the red band.
Fig. 3 is a flowchart of step S2 in fig. 1 according to an embodiment of the present invention, and as shown in fig. 3, step S2 includes the following steps:
s21, dividing the acquisition area into three different areas of vegetation, bare land and water area according to the multi-spectral images and the classification standard;
s22, determining vegetation types including arbor, shrub and turf by analyzing chlorophyll content and distribution height of vegetation according to multispectral images corresponding to vegetation areas;
s23, orthographic correction is carried out on the multispectral image in combination with the height standard values of different vegetation types;
s24, calculating a normalized vegetation index NDVI for the multispectral images corrected by various vegetation types to obtain an NDVI spatial distribution diagram;
and S25, inputting the NDVI spatial distribution map into a preset coverage evaluation model to obtain the coverage of vegetation.
In step S21, different partitions may be divided according to the threshold value of the classification standard, and after the vegetation partition is identified, which vegetation type is determined as arbor, shrub and turf by further analyzing the chlorophyll content and the distribution height subdivision of the vegetation, so that orthographic projection correction may be performed according to different height standards, and a more accurate NDVI spatial distribution map may be obtained. Wherein the height of arbor (such as Pinus massoniana, cryptomeria fortunei, cedrus cedar, fir, duchesnea shanensis, michelia figo, flos Nelumbinis, cinnamomum camphora, ligustrum lucidum, murraya koenigii, magnolia juniper, platycladus orientalis, cephalotaxus fortunei, etc.) is about 7-10 m, the height of shrubs (such as Lauraria latifolia, cyberon and Magnolia duodenum Leguminosae, etc.) is about 1-1.5 m, and the height of turf (such as Cyberon, baixi grass, ledebouriella, etc.) is 0.5 or less than 0.2 m. Different types of vegetation have different distances from the vicinity of the water area, different root systems have different ranges, and different water and soil fixing capacities.
For example, three unmanned aerial vehicle image near-infrared band simulation models of vegetation, bare land and water are respectively constructed based on three types of basis function spaces of vegetation, bare land and water. Let the multidimensional basis function space be { xm1, xm2, … …, xmn }, the coordinate coefficients (β1, β2, … …, βn) under the basis function space, where m is the number of pixels of the three-dimensional image and n is the number of basis functions. Each type of basis function space can be obtained according to calculation of green light wave band reflectivity data, red light wave band reflectivity data and blue light wave band reflectivity data in the unmanned aerial vehicle image.
Fig. 4 is a flowchart of step S22 in fig. 3 according to an embodiment of the present invention, and as shown in fig. 4, step S22 includes the following steps:
step S221, calculating the ground reflectivity according to the multispectral image corresponding to the vegetation partition;
step S222, determining a preliminary analysis result of vegetation coverage according to the ground reflectivity;
step S223, clustering the wave band spectrums reflected by different vegetation according to the preliminary analysis result;
and S224, determining vegetation types and distribution according to the chlorophyll content of vegetation in the cluster-combined multispectral image and the acquired distribution height.
In order to obtain subdivided vegetation types and distribution, in the specific implementation process, preliminary analysis is performed according to the ground reflectivity, namely different types can be marked out, but for simplicity, different types can be divided and boundaries can be marked out in a clustering mode due to the fact that different types are adjacent and overlap is present, and therefore subsequent analysis is facilitated.
Fig. 5 is a flowchart of step S25 in fig. 3 according to an embodiment of the present invention, and as shown in fig. 5, step S25 includes the following steps:
s251, setting corresponding space distribution heights according to vegetation types planted on two sides of the canal;
s252, performing space synthesis by using vegetation NDVI space distribution recorded by a database;
s253, performing data training on the synthesized spatial distribution height and the distribution data by using a machine learning algorithm to obtain a preset coverage evaluation model;
s254, respectively inputting the NDVI spatial distribution diagrams corresponding to the vegetation of each type into a preset coverage evaluation model of the corresponding type, or uniformly inputting the NDVI spatial distribution diagrams into the preset coverage evaluation model to obtain the coverage of the vegetation of each type.
In the first three steps in step S25, a preset coverage evaluation model is built according to the historical data, and specifically, data training can be performed according to the spatial height corresponding to each vegetation and the corresponding relationship between the spatial distribution height and the corresponding distribution data (such as plant density, chlorophyll content, etc.) and coverage. The vegetation coverage is used as the accuracy and reliability of main indexes for measuring vegetation growth conditions and regional ecological system restoration conditions, and is used as the accuracy of important parameters of models such as hydrology, climate, ecological assessment and the like, so that the limitation of ground conditions is avoided.
In step S3, a vegetation damage level is determined for the currently monitored vegetation type and coverage based on the water area data.
In the step, the water and soil protection capability is not evaluated simply according to the growth conditions such as the visual monitoring vegetation coverage, the water and soil conservation capability of the vegetation is comprehensively evaluated according to the requirements of the water-proof index required by the water area environment, the damage degree of the vegetation is judged, a certain unified standard is not carried simply, the fine evaluation is realized by combining the actual requirements, and the method provides very powerful and effective support for the subsequent maintenance work of the vegetation.
Fig. 6 is a flowchart of step S3 in fig. 1 according to an embodiment of the present invention, and as shown in fig. 6, step S3 includes the following steps:
step S31, calculating a required waterproof index according to water area data;
s32, determining a range value of vegetation water and soil conservation capability according to the type of the vegetation currently monitored;
step S33, comparing the required waterproof index of the step S31 with the range value of the vegetation water and soil conservation capability of the step S32, and if the required waterproof index is not in the range value of the vegetation water and soil conservation capability, determining that the damage degree of the vegetation is serious;
step S34, if the required waterproof index is within the range value of the vegetation water and soil conservation capability, updating the vegetation water and soil conservation capability according to the vegetation type and the corresponding coverage;
and step S35, determining the vegetation damage degree according to the updated vegetation water and soil conservation capability.
The implementation mode adopts a two-step strategy, in the step S32, the range value of the overall water and soil conservation capability of the vegetation on two sides of the channel is initially calculated and evaluated through weighting according to the vegetation type and vegetation distribution, if the range value of the water and soil conservation capability can not meet the required waterproof index, the protection capability of the vegetation cannot meet the requirement of a water area and is seriously damaged, so that the situation of serious damage of the vegetation can be recognized as soon as possible, early warning and vegetation maintenance operations can be timely fed back to a management platform, and the spread of the vegetation damage is reduced. If the range of the water and soil conservation capability can meet the required waterproof index, the water and soil conservation capability of the whole vegetation needs to be updated by further continuously combining with specific coverage calculation, so that the vegetation damage degree is determined according to the difference value between the updated water and soil conservation capability of the vegetation and the required waterproof index. For example, if the difference range is less than 60, it is determined that the vegetation damage level is low; if the difference value range is 61-100, determining that the vegetation damage degree is medium; and if the difference value ranges from 101 to 150, determining that the vegetation damage degree is high. The range of differences herein may be set according to engineering specific needs.
Fig. 7 is a flowchart of step S3 in fig. 1 according to an embodiment of the present invention, and in another implementation manner, as shown in fig. 7, step S3 includes the following steps:
step S31', calculating a required waterproof index according to water area data;
step S32', determining the corresponding required vegetation water and soil conservation capacity according to the required waterproof index and referring to the preset water and soil protection standard;
step S33', calculating vegetation water and soil conservation capacity according to the currently monitored vegetation type and the coverage;
step S34', determining the vegetation damage degree according to the difference value between the vegetation water and soil conservation capability of the step S33' and the required vegetation waterproof conservation capability of the step S32 '.
The implementation mode is different from the implementation mode, the vegetation type and the coverage are directly calculated and evaluated through weighting and comprehensive calculation, the overall water and soil conservation capacity of the vegetation on two sides of the channel is not divided into two steps, then the vegetation damage degree is determined according to the difference value between the overall water and soil conservation capacity of the vegetation and the required waterproof conservation capacity, and the specific determination mode is the same as the above.
In summary, according to the method for monitoring vegetation damage in the ditch channel provided by the embodiment of the invention, the unmanned aerial vehicle aerial multispectral image is adopted to identify various vegetation types and corresponding coverage, the water area data is taken as a standard, whether the detected vegetation types and coverage near the water area meet the water area requirements or not is monitored, the vegetation protection capacity near the water area can be monitored in a targeted and characteristic manner, and the monitored damage degree meets the water area requirements. The monitoring mode is simple and easy to operate, and the obtained vegetation damage degree monitoring result is more accurate and more relevant, and has higher reference value.
According to a second aspect of the present invention, an embodiment of the present invention further provides a water and soil conservation pre-warning method for two sides of a water channel, and fig. 8 is a flowchart of a water and soil conservation pre-warning method for two sides of a water channel according to another embodiment of the present invention, as shown in fig. 8, including the following steps:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of the current monitored vegetation type and coverage of the water area data;
and S4', judging whether the damage degree of vegetation reaches an early warning standard, and if so, sending out early warning.
In summary, whether the vegetation water and soil conservation capability meets the requirements is judged according to the monitored vegetation damage degree so as to perform early warning in time. The early warning method can be combined with different vegetation types and corresponding coverage to carry out comprehensive evaluation, so that the overall water and soil conservation capability of the vegetation is obtained, and the situation that the one-sided evaluation result is not accurate enough is avoided. The method directly judges whether early warning needs to be sent as soon as possible according to the vegetation damage degree, has rapid response and provides data support for vegetation maintenance.
According to a third aspect of the present invention, an embodiment of the present invention further provides a water and soil conservation pre-warning method for two sides of a water channel, and fig. 9 is a flowchart of a water and soil conservation pre-warning method for two sides of a water channel according to another embodiment of the present invention, as shown in fig. 9, including the following steps:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of the current monitored vegetation type and coverage of the water area data;
s5', determining the self-repairing capability of the vegetation according to the vegetation type and the coverage and the vegetation growth characteristics;
and S6', judging whether self-repair can be completed within a preset time according to the self-repair capability and the vegetation damage degree, and if not, sending out early warning.
In summary, besides considering the monitored damage degree of the vegetation, since the vegetation generally has a certain self-repairing capability, the self-repairing capability of the whole vegetation is further judged according to the growth characteristics of the vegetation, and if the conditions are met, the water and soil conservation capability of the vegetation can be maintained through self-repairing within a limited time. Therefore, whether the vegetation can finish self-repair within a preset time can be judged, and early warning is not required to be sent out when the vegetation can finish self-repair; if the restoration cannot be completed, an early warning is sent out, and vegetation recovery is assisted by adopting an intervention mode so as to ensure the water and soil conservation capability.
It should be noted that, the embodiment of the invention can form a water and soil conservation monitoring integrated platform based on collected multispectral influence, monitoring, early warning data and the like, can realize the combination of real-time and long-term monitoring, and provides a data management platform with simple operation, high automation and high integration level for a water and soil conservation monitoring unit and an operation management unit.
It should be understood that the above description of the specific embodiments of the present invention is only for illustrating the technical route and features of the present invention, and is for enabling those skilled in the art to understand the present invention and implement it accordingly, but the present invention is not limited to the above-described specific embodiments. All changes or modifications that come within the scope of the appended claims are intended to be embraced therein.
Claims (10)
1. The utility model provides a method for monitoring vegetation damage on two sides of a canal channel, which is characterized by comprising the following steps:
s1, acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2, acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
and S3, determining the vegetation damage degree on the basis of the water area data on the currently monitored vegetation type and coverage.
2. The method of monitoring damage to vegetation on both sides of a raceway channel of claim 1, wherein step S1 comprises:
s11, acquiring an unmanned aerial vehicle flight path with a preset length according to the preset path;
s12, on a flight path of an unmanned aerial vehicle with a preset length, the unmanned aerial vehicle flies and generates a two-dimensional multispectral image in real time according to the acquired image data;
s13, identifying the boundary of the water area according to the collected multispectral image;
s14, when the shooting range of the unmanned aerial vehicle is identified to cross the boundary of the water area and enter the appointed water area, a water area detector is started in time, and water area data are obtained;
the water area data at least comprises one of water channel width, water depth, water flow velocity and water flow direction.
3. The method of monitoring damage to vegetation on both sides of a raceway channel of claim 2, wherein step S1 further comprises:
s15, constructing a three-dimensional image based on the two-dimensional multispectral image and the height data;
s16, detecting leakage points of the three-dimensional image, and correcting the route of the unmanned aerial vehicle in time and starting the supplementary shooting if the leakage points are found.
4. A method of monitoring damage to vegetation on both sides of a raceway channel as claimed in any one of claims 1 to 3 wherein step S2 comprises:
s21, dividing the collection area into three different areas of vegetation, bare land and water area according to the multispectral image and the classification standard;
s22, determining vegetation types including arbor, shrub and turf by analyzing chlorophyll content and distribution height of vegetation according to multispectral images corresponding to vegetation partitions;
s23, orthographic correction is carried out on the multispectral image in combination with the height standard values of different vegetation types;
s24, calculating normalized vegetation indexes NDVI for the multispectral images corrected by various vegetation types to obtain an NDVI spatial distribution diagram;
s25, inputting the NDVI spatial distribution map into a preset coverage evaluation model to obtain the coverage of vegetation.
5. The method of monitoring damage to vegetation on both sides of a raceway channel of claim 4, wherein step S22 comprises:
s221, calculating the ground reflectivity according to the multispectral image corresponding to the vegetation partition;
s222, determining a preliminary analysis result of vegetation coverage according to the ground reflectivity;
s223, clustering the wave band spectrums reflected by different vegetation according to the primary analysis result;
s224, determining vegetation types and distribution according to the chlorophyll content of vegetation in the cluster-combined multispectral image and the collected distribution height.
6. The method of monitoring damage to vegetation on both sides of a raceway channel of claim 4, wherein step S25 comprises:
s251, setting corresponding space distribution heights according to vegetation types planted on two sides of the canal;
s252, performing space synthesis by using vegetation NDVI space distribution recorded by a database;
s253, performing data training on the synthesized spatial distribution height and the distribution data by using a machine learning algorithm to obtain a preset coverage evaluation model;
s254, respectively inputting the NDVI spatial distribution diagrams corresponding to the vegetation of each type into a preset coverage evaluation model of the corresponding type, or uniformly inputting the NDVI spatial distribution diagrams into the preset coverage evaluation model to obtain the coverage of the vegetation of each type.
7. A method of monitoring damage to vegetation on both sides of a raceway channel as claimed in any one of claims 1 to 3 wherein step S3 comprises:
s31, calculating a required waterproof index according to water area data;
s32, determining a range value of vegetation water and soil conservation capability according to the type of the vegetation currently monitored;
s33, comparing the required waterproof index in the step S31 with the range value of the vegetation water and soil conservation capability in the step S32, and if the required waterproof index is not in the range value of the vegetation water and soil conservation capability, determining that the damage degree of the vegetation is serious;
s34, if the required waterproof index is within the range value of the vegetation water and soil conservation capability, updating the vegetation water and soil conservation capability according to the vegetation type and the corresponding coverage;
and S35, determining the damage degree of the vegetation according to the updated vegetation water and soil conservation capability.
8. A method of monitoring damage to vegetation on both sides of a raceway channel as claimed in any one of claims 1 to 3 wherein step S3 comprises:
s31', calculating a required waterproof index according to water area data;
s32', determining the corresponding water and soil conservation capability of the required vegetation according to the required waterproof index and referring to a preset water and soil protection standard;
s33', calculating vegetation water and soil conservation capacity according to the type of the currently monitored vegetation and the coverage;
s34', determining the vegetation damage degree according to the difference value between the vegetation water and soil conservation capability of the step S33' and the required vegetation waterproof conservation capability of the step S32 '.
9. The water and soil conservation early warning method for the two sides of the water conveying channel is characterized by comprising the following steps of:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of water area data on the currently monitored vegetation type and coverage;
s4', judging whether the damage degree of vegetation reaches an early warning standard, and if so, sending out early warning.
10. The water and soil conservation early warning method for the two sides of the water conveying channel is characterized by comprising the following steps of:
s1', acquiring multispectral images and water area data on a preset path of an unmanned aerial vehicle;
s2', acquiring vegetation types and coverage of two sides of the ditch according to the multispectral image;
s3', determining vegetation damage degree on the basis of water area data on the currently monitored vegetation type and coverage;
s5', determining the self-repairing capability of the vegetation according to the vegetation type and the coverage and the vegetation growth characteristics;
s6', judging whether self-repair can be completed within a preset time according to the self-repair capability and the vegetation damage degree, and if not, sending out early warning.
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