CN118397558A - Soil restoration monitoring system and method - Google Patents
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Abstract
The invention relates to the technical field of soil restoration monitoring, in particular to a soil restoration monitoring system and a soil restoration monitoring method; acquiring images of a target saline-alkali soil at an initial time and a monitoring time, and identifying and analyzing soil areas of the images to obtain color coefficients and texture coefficients of the images at the initial time and the monitoring time; calculating a first change coefficient through a color coefficient and a texture coefficient; identifying and analyzing the vegetation areas of the images to obtain vegetation types and vegetation areas of the target saline-alkali soil at the initial moment and the monitoring moment, and calculating to obtain a second change coefficient; the first coefficient is weighted by the area change of the soil area, the second coefficient is weighted by the area change of the vegetation area, and the repair coefficient is calculated. According to the method, the restoration coefficient is obtained through calculation of the soil change and vegetation change of the saline-alkali soil, and the restoration condition of the saline-alkali soil is accurately monitored and measured.
Description
Technical Field
The invention relates to the technical field of soil restoration monitoring, in particular to a soil restoration monitoring system and a soil restoration monitoring method.
Background
Soil salinization refers to the process that salt in the bottom layer of soil or underground water rises to the surface of the ground along with capillary water, and after water is evaporated, the salt is accumulated in the surface layer soil. The soil salinization affects the human activity, and is mainly reflected in the effects of normal growth of plants, yield reduction of cash crops, ecological environment deterioration, corrosion damage to engineering facilities and the like.
In long-term exploration and experiments, a plurality of saline-alkali soil improvement methods are gradually mastered, including salt washing, gypsum improvement, organic matter addition, bioremediation and the like. In order to achieve a good saline-alkali soil improvement effect, a plurality of improvement methods are required to be combined according to actual conditions, and implementation details of the improvement methods are required to be dynamically adjusted, so that soil restoration conditions are required to be monitored.
In the prior art, a monitoring method and a monitoring device for soil remediation are provided in the patent with the application number of CN202310847861.2, and each pixel in a soil remote sensing image of a region to be monitored is marked as a soil pixel or a vegetation pixel; calculating a soil state value of each soil pixel, and calculating a vegetation state value of each vegetation pixel; calculating ecological restoration degree based on soil state values or vegetation state values of each pixel in the soil remote sensing image at the previous moment and the next moment; and generating an ecological restoration result of the area to be monitored according to the ecological restoration degree of each pixel. The patent with the application number of CN202211036522.8 provides a soil saline-alkali soil monitoring method based on data processing; setting an image segmentation threshold value which is updated continuously, acquiring node, length and width differences of a fracture binary image before and after the threshold value is updated, and calculating to obtain a threshold value grading value, thereby obtaining an optimal segmentation threshold value; and performing image processing through an optimal segmentation threshold value to obtain the crack rate of the gray level image of the salinized soil, and evaluating the salinized degree of the soil. The patent with the application number of CN202310542867.9 proposes a soil saline-alkali area identification and division method based on a remote sensing image; the method comprises the steps of obtaining a soil saline-alkali soil image through a remote sensor, dividing and extracting features of the image by utilizing region growth, establishing a soil saline-alkali content assessment model according to the extracted features, classifying each region by utilizing a support vector machine classifier, labeling the region with high soil saline-alkali content, and improving the accuracy and efficiency of recognition and division of the soil saline-alkali region by combining a remote sensing image technology.
In the salinization process of the soil, the soil can show color and shape changes along with the deep salinization degree, and meanwhile, the growth states of different plants in the saline-alkali soil can also show different changes; in the prior art, the soil and vegetation of the saline-alkali soil are mainly researched as integral factors, different reactions of different vegetation to the saline-alkali soil environment cannot be considered, and the condition of inaccurate monitoring exists.
Therefore, a soil remediation monitoring system and a soil remediation monitoring method are provided.
Disclosure of Invention
The invention aims to provide a soil restoration monitoring system and a soil restoration monitoring method, which are used for acquiring images of a target saline-alkali soil at an initial moment and a monitoring moment; identifying and analyzing the soil area of the image to obtain the color coefficient and the texture coefficient of the image at the initial moment and the monitoring moment; calculating a first change coefficient through a color coefficient and a texture coefficient; identifying and analyzing the vegetation areas of the images to obtain vegetation types and vegetation areas of the images at the initial time and the monitoring time, and calculating to obtain a second change coefficient; the first coefficient is weighted through the area change of the soil area, the second coefficient is weighted through the area change of the vegetation area, the restoration coefficient is obtained through calculation, and the restoration condition of the saline-alkali soil is accurately monitored and measured.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a soil remediation monitoring system comprising:
The initial data acquisition module acquires a shooting image of the target saline-alkali soil at an initial moment to obtain an initial image data set; performing image recognition and division on the initial image data set to obtain a first initial image and a second initial image;
The monitoring data acquisition module is used for acquiring a shot image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; performing image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image;
The data preprocessing module is used for preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image;
The soil restoration monitoring module is used for identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; calculating to obtain a first change coefficient through the color coefficient and the texture coefficient;
the vegetation restoration monitoring module is used for identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; calculating a second change coefficient according to the vegetation type and the vegetation area;
And the restoration coefficient measuring and calculating module calculates the restoration coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
The first initial image is a soil area image of the target saline-alkali soil at the initial moment; the second initial image is a vegetation area image of the target saline-alkali soil at the initial moment;
The first monitoring image is a soil area image at the monitoring moment of the target saline-alkali soil; the second monitoring image is a vegetation area image at the monitoring moment of the target saline-alkali soil.
Obtaining an initial color coefficient and an initial texture coefficient of the target saline-alkali soil through the first initial image recognition; the acquisition steps are as follows:
converting the first initial image into a gray scale image to obtain a first initial gray scale image;
Setting a standard pixel threshold, identifying pixel values in the first initial gray image according to the standard pixel threshold, and calculating to obtain an initial color coefficient;
the calculation formula of the initial color coefficient is as follows:
Wherein Incf denotes an initial color coefficient; apv i denotes the actual gray value of pixel point i in the first initial gray image; PT represents a standard pixel threshold; n represents the total number of pixels of the first initial gray image;
acquiring a first initial gray image, and identifying a crack region in the first initial gray image;
calculating to obtain an initial texture coefficient according to the ratio of the number of pixel points of the crack area to the total number of pixel points in the first initial gray level image;
The calculation formula of the initial texture coefficient is as follows:
Wherein Iotf denotes the initial texture coefficients; crackP denotes the number of pixel points of the crack region in the first initial gray image; NP represents the total number of pixels of the first initial gray scale image;
and identifying the first monitoring image to obtain the monitoring color coefficient and the monitoring texture coefficient of the target saline-alkali soil.
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
The calculating step of the second change coefficient comprises the following steps:
Obtaining corresponding vegetation types and vegetation areas through the second initial image and the second monitoring image;
dividing vegetation types into three vegetation types, namely salt-loving vegetation, salt-tolerant vegetation and salt-free vegetation; the vegetation areas comprise a salt-tolerant vegetation area, a salt-tolerant vegetation area and an anaerobic vegetation area;
Obtaining an initial salt-tolerant vegetation area, an initial salt-tolerant vegetation type, an initial salt-free vegetation area and an initial salt-free vegetation type through the second initial image;
Obtaining a monitored salt-tolerant vegetation area, a monitored salt-tolerant vegetation type, a monitored salt-tolerant vegetation area and a monitored salt-tolerant vegetation type through the second monitoring image;
calculating a second change coefficient according to the obtained vegetation type and vegetation area data;
the calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mden i represents the monitored vegetation area of vegetation type i; iden i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
A soil remediation monitoring method comprising:
acquiring a shooting image of a target saline-alkali soil at an initial moment to obtain an initial image dataset; performing image recognition and division on the initial image data set to obtain a first initial image and a second initial image;
Acquiring a photographed image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; performing image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image;
Preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image;
Identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; calculating to obtain a first change coefficient through the color coefficient and the texture coefficient;
identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; calculating a second change coefficient according to the vegetation type and the vegetation area;
And calculating the repair coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
The calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mden i represents the monitored vegetation area of vegetation type i; iden i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the method, the monitoring color coefficient and the monitoring texture coefficient are obtained through the target saline-alkali soil image at the monitoring moment; obtaining an initial color coefficient and an initial texture coefficient through an image of a target saline-alkali soil at an initial moment; calculating to obtain a first change coefficient by monitoring the ratio of the color coefficient to the initial color coefficient and the ratio of the texture coefficient to the initial texture coefficient; and the color change and the crack change of the target saline-alkali soil area are accurately identified and measured.
2. The invention identifies vegetation images of the target saline-alkali soil at the initial moment and the monitoring moment to obtain vegetation types and vegetation areas; dividing vegetation types into salt-tolerant vegetation and salt-tolerant vegetation; and calculating a second change coefficient according to the vegetation type, vegetation type and vegetation area change condition of the target saline-alkali soil at the initial time and the monitoring time, and accurately identifying and measuring the vegetation change condition of the target saline-alkali soil.
3. According to the method, the first change coefficient is weighted by the ratio of the area of the target saline-alkali soil area and the initial time, the second change coefficient is weighted by the ratio of the area of the target saline-alkali soil vegetation area, and the restoration coefficient is calculated; and accurately measuring the restoration condition of the target saline-alkali soil through the area change of the soil area and the area change of the vegetation area.
Drawings
FIG. 1 is a schematic illustration of test Tian Changjing according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a soil remediation monitoring system according to the present invention;
FIG. 3 is a schematic diagram of a first coefficient of variation measurement flow according to the present invention;
FIG. 4 is a flow chart of a second coefficient of variation measurement according to the present invention;
Fig. 5 is a schematic flow chart of a soil remediation monitoring method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to know how to treat the saline-alkali soil and to perform timely technical adjustment, the soil restoration condition needs to be monitored. In the salinization process of soil, the soil can show color and shape changes along with the deep salinization degree, meanwhile, the growth state of saline-alkali soil plants can also change, in the prior art, the research is mainly carried out through single characteristics of the saline-alkali soil, multidimensional characteristics cannot be fully considered, and the condition of inaccurate monitoring exists.
Therefore, a soil remediation monitoring system and a soil remediation monitoring method are provided.
Example 1
In order to explore a method for improving the saline-alkali soil, a scientific research institution sets a plurality of saline-alkali soil test fields for experiments, and in order to accurately understand the improvement effect of the test fields, the soil restoration monitoring system is adopted for monitoring. The scene of the saline-alkali soil test field is shown in figure 1.
The structure of the soil remediation monitoring system is shown in fig. 2, and comprises: the system comprises an initial data acquisition module, a monitoring data acquisition module, a data preprocessing module, a soil restoration monitoring module, a vegetation restoration monitoring module and a restoration coefficient measuring and calculating module.
The initial data acquisition module acquires a shooting image of the target saline-alkali soil at an initial moment to obtain an initial image data set; and carrying out image recognition and division on the initial image data set to obtain a first initial image and a second initial image.
The monitoring data acquisition module is used for acquiring a shot image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; and carrying out image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image.
The first initial image is a soil area image of the target saline-alkali soil at the initial moment; the second initial image is a vegetation area image of the target saline-alkali soil at the initial moment; the first monitoring image is a soil area image at the monitoring moment of the target saline-alkali soil; the second monitoring image is a vegetation area image at the monitoring moment of the target saline-alkali soil.
When the image of the target saline-alkali soil is identified, dividing the region type of the image into three types, namely a soil region, a vegetation region and other regions; and selecting a soil area image and a vegetation area image, and carrying out measurement and calculation on the restoration effect of the target saline-alkali soil.
The method comprises the steps of acquiring saline-alkali soil images at initial time and monitoring time, and identifying and dividing the images to obtain images of soil surfaces and images of vegetation coverage areas; and a data basis is provided for the subsequent change identification through both soil surface characteristics and vegetation cover characteristics.
And the data preprocessing module is used for preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image. The image preprocessing process comprises image noise processing and image enhancement processing.
The soil restoration monitoring module is used for identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; and calculating the first change coefficient through the color coefficient and the texture coefficient.
The flow of the measurement and calculation of the first change coefficient is shown in fig. 3, and includes:
The method comprises the steps of obtaining an initial color coefficient and an initial texture coefficient of a target saline-alkali soil through first initial image identification, wherein the obtaining steps are as follows:
converting the first initial image into a gray scale image to obtain a first initial gray scale image;
Setting a standard pixel threshold, identifying pixel values in the first initial gray image according to the standard pixel threshold, and calculating to obtain an initial color coefficient;
Along with the change of the salinization degree of the soil, the color of the soil surface also shows a certain change; for example, when the salt concentration in the soil is high, white salt particles or crystals may precipitate, resulting in a transition of the soil surface to white and off-white; thus, as the soil salinization level changes, the color of the soil surface also changes.
According to the method, the gray average value of the normal soil gray image of the area where the saline-alkali soil is located is used as a standard pixel threshold value, and the pixel gray values of the saline-alkali soil gray images at different moments are measured and compared.
The calculation formula of the initial color coefficient is as follows:
Wherein Incf denotes an initial color coefficient; apv i denotes the actual gray value of pixel point i in the first initial gray image; PT represents a standard pixel threshold; n represents the total number of pixels of the first initial gray image.
Acquiring a first initial gray image, and identifying a crack region in the first initial gray image;
calculating to obtain an initial texture coefficient according to the ratio of the number of pixel points of the crack area to the total number of pixel points in the first initial gray level image;
The calculation formula of the initial texture coefficient is as follows:
Wherein Iotf denotes the initial texture coefficients; crackP denotes the number of pixel points of the crack region in the first initial gray image; NP represents the total number of pixels of the first initial gray scale image;
The monitoring color coefficient and the monitoring texture coefficient of the target saline-alkali soil are obtained through the first monitoring image identification, and specifically:
Converting the first monitoring image into a gray scale image to obtain a first monitoring gray scale image; according to a standard pixel threshold value, identifying a pixel gray value in a first initial monitoring image according to the standard pixel threshold value, and calculating to obtain a monitoring color coefficient;
acquiring a first monitoring gray level image, and identifying a crack region in the first monitoring gray level image; and calculating to obtain a monitoring texture coefficient according to the ratio of the number of the pixel points of the crack area to the total number of the pixel points in the first monitoring gray level image.
The method comprises the steps of performing image processing on a first initial image to obtain a first initial gray level image; recognizing the pixel gray value in the first initial gray image according to the standard pixel threshold value, and calculating to obtain an initial color coefficient; calculating to obtain an initial texture coefficient through the ratio of the pixel points of the crack area to the total pixel points in the first initial gray level image; and processing the first monitoring image according to the same method, calculating to obtain a monitoring color coefficient and a monitoring texture coefficient, and providing data support for measuring and calculating the first variation coefficient.
And displaying the color coefficient and the texture coefficient of the soil area according to the data of the experimental field of the scientific research institution.
Table 1 table of soil variation data for test fields
| Initial color coefficient | Initial texture coefficient | Monitoring color coefficients | Monitoring texture coefficients | |
| Test field 1 | 1.25 | 0.06 | 1.18 | 0.04 |
| Test field 2 | 1.19 | 0.10 | 1.02 | 0.08 |
| Test field 3 | 0.76 | 0.04 | 0.84 | 0.05 |
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
The color weight and the texture weight are obtained through calculation and verification according to historical data and are used for reflecting the importance degree of the soil color and the soil texture in soil change.
According to the invention, a monitoring color coefficient and a monitoring texture coefficient are obtained through a first monitoring image, and an initial color coefficient and an initial texture coefficient are obtained through a first initial image; then, calculating to obtain a first change coefficient by monitoring the ratio of the color image to the initial color image and the ratio of the texture coefficient to the initial texture coefficient; and the color change and crack change of the soil surface of the target saline-alkali soil are accurately identified and measured.
The vegetation restoration monitoring module is used for identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; and calculating to obtain a second change coefficient through the vegetation type and the vegetation area.
The flow of the second change coefficient measurement is shown in fig. 4, and includes:
obtaining vegetation types and vegetation areas through images of saline-alkali soil; dividing vegetation types into three vegetation types, namely salt-loving vegetation, salt-tolerant vegetation and salt-free vegetation; the halophilic vegetation is a plant which can normally survive and reproduce in saline-alkali soil and is limited in growth in non-saline-alkali soil; the salt-tolerant vegetation is a plant with the characteristic of tolerating saline-alkali soil and can survive in saline-alkali soil and non-saline-alkali environment simultaneously; anaerobic vegetation is plants that cannot survive in saline-alkali soil environments.
Halophilic vegetation includes suaeda plants, which survive and reproduce in saline-alkali soil and are growth-limited in other soil-type environments.
The vegetation areas comprise a salt-tolerant vegetation area, a salt-tolerant vegetation area and an anaerobic vegetation area;
obtaining an initial salt-tolerant vegetation area, an initial salt-tolerant vegetation type, an initial salt-tolerant vegetation area and an initial salt-tolerant vegetation type through a second initial image;
obtaining a monitored salt-tolerant vegetation area, a monitored salt-tolerant vegetation type, a monitored salt-tolerant vegetation area and a monitored salt-tolerant vegetation type through the second monitoring image;
calculating a second change coefficient according to vegetation type and vegetation area data obtained through the initial image and the monitoring image;
the calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mten i represents the monitored vegetation area of vegetation type i; iten i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
The vegetation type coefficients represent the importance degree of different vegetation types on the improvement of the saline-alkali soil, the vegetation area coefficients and the vegetation type coefficients represent the importance degree of vegetation types and vegetation areas on the improvement of the saline-alkali soil respectively, and the vegetation type coefficients and the vegetation area coefficients are obtained through verification of historical data.
The monitoring data of the initial time and the monitoring time of the test field 1 are selected and displayed. The total vegetation area of the test field 1 at the initial moment is 35 square kilometers, and 15 total vegetation types are provided; the total vegetation area at the monitoring time is increased to 42 square kilometers, and 18 vegetation total varieties are obtained.
Table 2 table of test field vegetation change data
The invention identifies vegetation images of the target saline-alkali soil at the initial moment and the monitoring moment to obtain vegetation types and vegetation areas; dividing vegetation types into salt-tolerant vegetation and salt-tolerant vegetation; and calculating a second change coefficient according to the vegetation type, vegetation type and vegetation area change condition of the target saline-alkali soil at the initial time and the monitoring time, and accurately identifying and measuring the vegetation change condition of the target saline-alkali soil.
And the restoration coefficient measuring and calculating module calculates the restoration coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
The first weight represents the importance of soil change to saline-alkali soil improvement, and the second weight represents the importance of vegetation change to saline-alkali soil improvement, which are obtained through verification through historical data.
According to the method, the first change coefficient is weighted by the soil surface area ratio of the target saline-alkali soil through monitoring the moment and the initial moment, the second change coefficient is weighted by the vegetation coverage area ratio of the target saline-alkali soil, and the restoration coefficient is calculated; and accurately measuring the restoration condition of the target saline-alkali soil through the soil surface change and the vegetation coverage change.
By the aid of the soil restoration monitoring system, a scientific research organization can accurately measure restoration effects of an experimental field of the saline-alkali soil, and speed and accuracy of research of the scientific research organization are promoted.
Obtaining a monitoring color coefficient and a monitoring texture coefficient through a target saline-alkali soil image at the monitoring moment; obtaining an initial color coefficient and an initial texture coefficient through an image of a target saline-alkali soil at an initial moment; calculating to obtain a first change coefficient by monitoring the ratio of the color image to the initial color image and the ratio of the texture coefficient to the initial texture coefficient; identifying a vegetation image of the target saline-alkali soil at the initial moment to obtain vegetation types and vegetation areas; dividing vegetation types into salt-tolerant vegetation and salt-tolerant vegetation; calculating a second change coefficient according to the vegetation type, vegetation type and vegetation area change condition of the target saline-alkali soil at the initial time and the monitoring time; the first change coefficient is weighted by the soil surface area ratio of the target saline-alkali soil through monitoring the moment and the initial moment, the second change coefficient is weighted by the vegetation coverage area ratio of the target saline-alkali soil, the restoration coefficient is obtained through calculation, and the restoration condition of the target saline-alkali soil is accurately measured.
Example two
A certain agricultural planting agency holds a piece of saline-alkali soil, the saline-alkali soil needs to be improved to be used for planting crops normally, and in order to accurately know the restoration condition of the saline-alkali soil, the soil restoration monitoring method is adopted for monitoring.
The flow of the soil remediation monitoring method is shown in fig. 5, and the method comprises the following steps:
acquiring a shooting image of a target saline-alkali soil at an initial moment to obtain an initial image dataset; performing image recognition and division on the initial image data set to obtain a first initial image and a second initial image;
Acquiring a photographed image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; performing image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image;
Preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image;
Identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; calculating to obtain a first change coefficient through the color coefficient and the texture coefficient;
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
The following is the data of the saline-alkali soil at the initial time and the monitoring time after a period of improvement.
Table 3 saline-alkali soil variation data table
| Initial color coefficient | Initial texture coefficient | Monitoring color coefficients | Monitoring texture coefficients | |
| Saline-alkali soil | 0.89 | 0.08 | 0.75 | 0.05 |
Identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; calculating a second change coefficient according to the vegetation type and the vegetation area;
the calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mden i represents the monitored vegetation area of vegetation type i; iden i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
The following are data relating to vegetation change in saline-alkali soil.
Table 4 saline-alkali soil vegetation change data sheet
And calculating the repair coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
Acquiring images of a target saline-alkali soil at an initial moment and a monitoring moment; identifying and analyzing the soil area of the image to obtain the color coefficient and the texture coefficient of the image at the initial moment and the monitoring moment; calculating a first change coefficient through a color coefficient and a texture coefficient; identifying and analyzing the vegetation areas of the images to obtain vegetation types and vegetation areas of the images at the initial time and the monitoring time, and calculating to obtain a second change coefficient; the first coefficient is weighted through the area change of the soil area, the second coefficient is weighted through the area change of the vegetation area, the restoration coefficient is obtained through calculation, and the restoration condition of the saline-alkali soil is accurately measured.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1.A soil remediation monitoring system, comprising:
The initial data acquisition module acquires a shooting image of the target saline-alkali soil at an initial moment to obtain an initial image data set; performing image recognition and division on the initial image data set to obtain a first initial image and a second initial image;
The monitoring data acquisition module is used for acquiring a shot image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; performing image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image;
The data preprocessing module is used for preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image;
The soil restoration monitoring module is used for identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; calculating to obtain a first change coefficient through the color coefficient and the texture coefficient;
the vegetation restoration monitoring module is used for identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; calculating a second change coefficient according to the vegetation type and the vegetation area;
And the restoration coefficient measuring and calculating module calculates the restoration coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
2. A soil remediation monitoring system according to claim 1 wherein:
the first initial image is a soil area image of the target saline-alkali soil at the initial moment; the second initial image is a vegetation area image of the target saline-alkali soil at the initial moment;
The first monitoring image is a soil area image at the monitoring moment of the target saline-alkali soil; the second monitoring image is a vegetation area image at the monitoring moment of the target saline-alkali soil.
3. A soil remediation monitoring system according to claim 1 wherein:
obtaining an initial color coefficient and an initial texture coefficient of the target saline-alkali soil through the first initial image recognition; the acquisition steps are as follows:
converting the first initial image into a gray scale image to obtain a first initial gray scale image;
setting a standard pixel threshold, identifying a pixel gray value in the first initial gray image according to the standard pixel threshold, and calculating to obtain an initial color coefficient;
the calculation formula of the initial color coefficient is as follows:
Wherein Incf denotes an initial color coefficient; apv i denotes the actual gray value of pixel point i in the first initial gray image; PT represents a standard pixel threshold; n represents the total number of pixels of the first initial gray image;
acquiring a first initial gray image, and identifying a crack region in the first initial gray image;
calculating to obtain an initial texture coefficient according to the ratio of the number of pixel points of the crack area to the total number of pixel points in the first initial gray level image;
The calculation formula of the initial texture coefficient is as follows:
Wherein Iotf denotes the initial texture coefficients; crackP denotes the number of pixel points of the crack region in the first initial gray image; NP represents the total number of pixels of the first initial gray scale image;
and identifying the first monitoring image to obtain the monitoring color coefficient and the monitoring texture coefficient of the target saline-alkali soil.
4. A soil remediation monitoring system according to claim 3 wherein:
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
5. A soil remediation monitoring system according to claim 1 wherein:
the calculating step of the second change coefficient comprises the following steps:
Obtaining corresponding vegetation types and vegetation areas through the second initial image and the second monitoring image;
dividing vegetation types into three vegetation types, namely salt-loving vegetation, salt-tolerant vegetation and salt-free vegetation; the vegetation areas comprise a salt-tolerant vegetation area, a salt-tolerant vegetation area and an anaerobic vegetation area;
Obtaining an initial salt-tolerant vegetation area, an initial salt-tolerant vegetation type, an initial salt-free vegetation area and an initial salt-free vegetation type through the second initial image;
Obtaining a monitored salt-tolerant vegetation area, a monitored salt-tolerant vegetation type, a monitored salt-tolerant vegetation area and a monitored salt-tolerant vegetation type through the second monitoring image;
calculating a second change coefficient according to the obtained vegetation type and vegetation area data;
the calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mden i represents the monitored vegetation area of vegetation type i; iden i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
6. A soil remediation monitoring system according to claim 1 wherein:
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
7. A soil remediation monitoring method is characterized in that:
acquiring a shooting image of a target saline-alkali soil at an initial moment to obtain an initial image dataset; performing image recognition and division on the initial image data set to obtain a first initial image and a second initial image;
Acquiring a photographed image of the target saline-alkali soil at the monitoring moment to obtain a monitoring image data set; performing image recognition and division on the monitoring image data set to obtain a first monitoring image and a second monitoring image;
Preprocessing the first initial image, the second initial image, the first monitoring image and the second monitoring image;
Identifying the soil change condition of the target saline-alkali soil at the monitoring moment according to the first initial image and the first monitoring image to obtain a color coefficient and a texture coefficient; calculating to obtain a first change coefficient through the color coefficient and the texture coefficient;
identifying vegetation change conditions of the target saline-alkali soil at the monitoring moment according to the second initial image and the second monitoring image to obtain vegetation types and vegetation areas; calculating a second change coefficient according to the vegetation type and the vegetation area;
And calculating the repair coefficient of the target saline-alkali soil according to the first change coefficient and the second change coefficient.
8. The method for monitoring soil remediation according to claim 7, wherein:
The calculation formula of the first change coefficient is as follows:
Wherein Fcoe denotes a first coefficient of variation; alpha 1 represents color weight; incf denotes an initial color coefficient; mncf denotes a monitor color coefficient; alpha 2 represents texture weight; iotf denotes the initial texture coefficients; motf denotes a monitor texture coefficient; exp represents an exponential function that bases on a natural constant.
9. The method for monitoring soil remediation according to claim 7, wherein:
the calculation formula of the second change coefficient is as follows:
Wherein Scoe denotes a second coefficient of variation; mnvs represents the total number of monitored vegetation types of the target saline-alkali soil; invs represents the initial vegetation type total number of the target saline-alkali soil; beta i represents the weight of vegetation type i; mdem i represents the monitored vegetation area of vegetation type i; iden i denotes an initial vegetation area of vegetation type i; mcla i represents the monitored vegetation type of vegetation type i; icla i denotes the initial vegetation type of vegetation type i; mu 1 represents a vegetation area coefficient; mu 2 represents a vegetation type coefficient; exp represents an exponential function with a base of natural constant; i represents vegetation type, when i=1, salt-tolerant vegetation; when i=2, salt tolerant vegetation is represented; when i=3, anaerobic vegetation is represented.
10. The method for monitoring soil remediation according to claim 7, wherein:
The formula for calculating the repair coefficient is as follows:
Wherein Repf denotes a repair coefficient; θ 1 represents a first weight; msoilA represents the soil area at the time of monitoring; isoilA represents the soil area at the initial time; fcoe denotes a first coefficient of variation; θ 2 represents a second weight; mvegA denotes the vegetation area at the time of monitoring; ivegA denotes the vegetation area at the initial time; scoe denotes a second coefficient of variation; exp represents an exponential function that bases on a natural constant.
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