CN104036307A - Landscape pattern dimension recognition system based on response of zoobenthos community structure - Google Patents

Abstract

The invention is a landscape pattern scale recognition system based on the response of benthic animal community structure, which includes a sample collection device, a community structure calculation module, a landscape remote sensing device, a landscape pattern calculation module and a scale recognition module; The sample collection device collects macrobenthos samples at the survey point; the community structure calculation module counts and calculates the community structure index of the survey point; the landscape remote sensing device remotely senses the landscape of the survey point; the landscape pattern The calculation module determines the landscape type and calculates the landscape pattern index; the scale identification module performs correlation analysis on the community structure index and the landscape pattern index to determine the landscape pattern scale. In this way, the problem of the scale identification of the influence scale of the river corridor landscape pattern on the macrobenthic community structure is solved, and it has the advantages of fast, accurate, and simple operation, and provides a basis for quickly delineating the optimization range of the river corridor landscape with the goal of protecting aquatic organisms. Technical Support.

Description

A Landscape Pattern Scale Recognition System Based on the Response of Benthic Fauna Community Structure

technical field

The invention relates to a landscape pattern scale recognition system based on the response of benthos community structure.

Background technique

Macrobenthos are important indicator organisms that reflect the degree of river pollution and the quality of river habitats. Changes in the community structure index reflect river pollution and habitat destruction, which are ultimately due to land use patterns and landscapes caused by human activities. Caused by unreasonable layout. Studies have shown that the impact of landscape pattern on river benthos is mainly concentrated in the scale of river corridors, but there is no conclusion yet on how wide and how long river corridors have the most significant impact, and there is no best identification method . Therefore, it is of great significance to determine the scope of landscape pattern affecting the structure of benthic fauna for the delineation of terrestrial ecological red lines aimed at the protection of river aquatic organisms.

The impact mechanism of river corridor landscape patterns on river aquatic organisms is extremely complex. Using model simulation methods requires a large number of on-site experiments to determine parameters. The process is very complicated, and some tools for collecting samples are not fast enough and accurate enough.

In view of the above-mentioned defects, the author of the present invention, after a long period of research and experimentation, introduced a sampling shovel and other tools for collecting samples on the basis of the prior art, and finally obtained the present invention.

Contents of the invention

The purpose of the present invention is to overcome the above-mentioned technical defects and provide a landscape pattern scale recognition system based on the response of benthos community structure.

In order to achieve the above object, the technical solution adopted by the present invention is to provide a landscape pattern scale recognition system based on the response of benthos community structure, which includes a sample collection device, a community structure calculation module, a landscape remote sensing device, a landscape A pattern calculation module and a scale identification module; the sample collection device collects macrobenthic samples at the survey point; the community structure calculation module counts the samples and calculates the community structure index of the survey point; the landscape remote sensing device Carry out remote sensing of the landscape of the survey point; the landscape pattern calculation module determines the landscape type according to the results of the remote sensing, and calculates the landscape pattern index in the 6 river corridors; the scale identification module calculates the landscape pattern index of the survey point Calculate the community structure index and the landscape pattern index and carry out correlation analysis to determine the landscape pattern scale; the sample collection device is a sampling shovel, which includes a sampling bin, a handle and a cable reel; the rear part of the sampling bin It is the rear wall of the sampling chamber, and several pressure sensors and a control transmitter are distributed on the inner side of the rear wall of the sampling chamber. The pressure sensor measures the pressure on the rear wall of the sampling chamber, and the control transmitter converts the pressure The pressure data measured by the sensor is converted into a data signal and sent out; the cable reel is located at the rear of the handle, which includes a signal receiver and a computing device, and the signal receiver receives the data signal and transmits it to The calculation device, the calculation device calculates the pressure data, and the calculation formula is:

Z＝Z ₁ +|Z ₁ |+Z ₂ +|Z ₂ |

Among them, the calculation formulas of Z ₁ and Z ₂ are:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$

In the above formula, Z represents the contrast value, Z ₁ represents the pressure contrast value, Z ₂ represents the number contrast value, n represents the total number of pressure sensors, i represents the serial number of the pressure sensor, α represents the correction coefficient, β represents the correction value, F _i Indicates the pressure of the i-th pressure sensor, and N ₆ indicates the standard pressure of the pressure sensor at a water depth of 6m.

Preferably, the community structure index includes the number of species, the number of EPT family-level taxa, the percentage of EPT species, ASPT index, Berger Parker index and Shannon-Wiener index.

Preferably, the landscape pattern index includes patch density, maximum patch index, landscape shape index, fractal dimension index, aggregation index, separation index, connectivity index, patch richness density, and Shannon diversity index.

Preferably, the six river corridors take the river section 10km upstream of the survey point as the center line to generate six river section buffer zones with left and right widths of 50m, 100m, 200m, 300m, 400m and 500m.

Preferably, the landscape pattern calculation module is a Fragstats software.

Preferably, the landscape remote sensing device is an aerial photography remote sensing balloon. Preferably, the sampling shovel also includes a sampling compartment cover, a compartment cover hinge, a control cable and a connecting end of the control cable; the compartment cover is located at the front end of the sampling compartment; the compartment cover hinge connects the The bin cover and the sampling bin are convenient to open and close; the connecting end of the control cable is a protrusion arranged on the part near the connecting seam of the bin cover for fixing the control cable; one end of the control cable is fixed on The connecting end of the control cable passes through the inside of the handle, and is connected to the cable reel at the bottom of the handle.

Preferably, the sampling shovel also includes a cover closing spring, one end of the cover closing spring is fixed on the far connecting seam part of the cover, and the other end is fixed on the lower part of the sampling compartment, and under the action of elastic force, the The lid is closed.

Preferably, the cable reel further includes a motor, a central controller and a power supply, the central controller receives the result of the computing device and makes a judgment, and controls the motor to rotate; the motor and the The control cable is connected, and the control cable is moved by rotation; the power supply provides electric energy for the cable reel.

Preferably, the sampling shovel also includes a handle angle adjustment bolt, the handle angle adjustment bolt connects the sampling bin and the handle, and if loosened, the angle between the sampling bin and the handle can be adjusted , tighten the angle to lock the sampling chamber and the handle.

Compared with the prior art, the beneficial effect of the present invention is that it provides a landscape pattern scale identification system based on the response of the benthos community structure, which solves the impact scale identification of the landscape pattern of the river corridor domain on the macrobenthos community structure of the river The problem has the advantages of being fast, accurate, and easy to operate, and provides technical support for quickly delineating the optimal range of river corridor landscapes with the goal of protecting aquatic organisms; the new calculation formula uses the logarithmic method to combine the pressure of the pressure sensor with the The problem of the size of the standard pressure is transformed into a problem of positive and negative values, which simplifies the calculation process, reduces the amount of calculation, saves system resources, and shortens the reaction time, so that the sampling chamber cover can be opened and closed automatically, and it is faster and more convenient; The shovel can more effectively control the number and location of collected samples. If you need to collect deep samples, you don’t need to go into the water, which can realize easy and accurate collection; and the automatic opening and closing of the sampling shovel cover makes the whole collection process more convenient, faster, and more efficient. Intelligent, which solves the problem that sampling personnel do not know the underwater situation, and can more effectively control the number and location of collected samples.

Description of drawings

Fig. 1 is the structural diagram of the landscape pattern scale recognition system based on the benthos community structure response of the present invention;

Fig. 2 is the structural diagram of the sampling shovel of the landscape pattern scale identification system based on the benthos community structure response of the present invention;

Fig. 3 is the structural diagram inside the rear wall of the sampling shovel of the landscape pattern scale identification system based on the benthic animal community structure response of the present invention;

Fig. 4 is the structural diagram of the sampling shovel and cable reel of the landscape pattern scale identification system based on the benthos community structure response of the present invention;

Fig. 5 is a flow chart of the scale identification of the landscape pattern scale identification system based on the response of benthos community structure in the present invention.

Detailed ways

Explanation of reference signs

Sample collection device 1, community structure calculation module 2, landscape remote sensing device 3, landscape pattern calculation module 4, scale recognition module 5, sampling bin 11, sampling bin rear wall 111, pressure sensor 1112, control transmitter 1113, bin cover 12, Handle 13, cover closing spring 14, cover hinge 15, control cable connection end 16, control cable 17, handle angle adjustment bolt 18, cable reel 19, signal receiver 191, computing device 192, motor 193, center Controller 194, power supply 195.

The above and other technical features and advantages of the present invention will be described in more detail below in conjunction with the accompanying drawings.

As shown in Figure 1, it is the structural diagram of the landscape pattern scale identification system based on the benthos community structure response of the present invention, wherein, the landscape pattern scale identification system based on the benthos community structure response includes a sample collection device 1, a Community structure calculation module 2 , a landscape remote sensing device 3 , a landscape pattern calculation module 4 and a scale identification module 5 .

The sample collection device 1 collects macrobenthic samples at the survey point; the community structure calculation module 2 counts the collected macrobenthos samples and calculates the community structure index; the landscape remote sensing device 3 performs remote sensing of the landscape of the survey point; the landscape pattern calculation module 4 Determine the landscape type based on remote sensing data, and calculate the landscape pattern index; scale identification module 5 conducts correlation analysis on the calculated community structure index and landscape pattern index, and determines the landscape pattern scale.

The sample collection device 1 is a sampling shovel.

As shown in Figure 2, it is a structural diagram of the sampling shovel of the landscape pattern scale recognition system based on the response of benthic animal community structure in the present invention, wherein the sampling shovel includes: a sampling bin, a handle, a handle angle adjustment bolt, and a closing device : Cover, cover hinge, cover closing spring, control cable, cable reel.

The sampling bin 11 is a steel box with one side opening, and the bottom surface is longer than the top surface, which is convenient for scooping samples. The sampling bin 11 is connected with the handle 13 through the handle angle adjusting bolt 18, the proper angle can be adjusted by loosening the handle angle adjusting bolt 18, and the angle can be locked when tightened.

Sampling bin 11 is connected with bin lid 12 with bin lid hinge 15 to facilitate opening and closing, and is connected by bin lid closing spring 14 to facilitate bin lid can close automatically under natural state. The cover 12 is provided with a protrusion near the joint seam for fixing the control cable 17 as the connection end 16 of the control cable. One end of control cable 17 is fixed on the connecting end 16 of the cover surface, and the other end is a cable reel 19. Control cable 17 passes through the inside of handle 13 and is connected with cable reel 19 at the bottom for easy pulling.

As shown in Figure 3, it is the structural diagram of the inside of the sampling shovel sampling bin rear wall of the landscape pattern scale recognition system based on the benthos community structure response of the present invention; wherein, the rear portion of the sampling bin 11 is the sampling bin rear wall 111, and the sampling Several pressure sensors 1112 and a control transmitter 1113 are distributed on the inner side of the warehouse rear wall 111. The pressure sensors 1112 can measure the pressure on the sampling warehouse rear wall 111. The control transmitter 1113 provides electric energy for the pressure sensor 1112 and measures the pressure sensor 1112. The output data is converted into a data signal and transmitted.

As shown in Figure 4, it is the structural diagram of the sampling shovel reel of the present invention based on the landscape pattern scale identification system response of benthos community structure; Wherein, there is a reel 19 at the handle 13 rear, and the reel 19 includes A signal receiver 191, a computing device 192, a motor 193, a central controller 194 and a power supply 195; the signal receiver 191 receives the data signal that controls the transmitter 1113 to transmit, and transmits to the computing device 192; the computing device 192 pairs The pressure data is calculated, and the calculation result is transmitted to the central controller 194; the central controller 194 judges the result, and controls the motor 193 to rotate; the motor 193 is connected with the control cable 17, and the control cable 17 is moved by rotation; the power supply 195 Provide electrical energy for the cable reel 19.

When the collection shovel is in the natural state, the sampling bin 11 is empty, the pressure on the pressure sensor 1112 is 0, the calculation device 192 calculates the pressure data, and transmits the calculation result to the central controller 194, and the central controller 194 performs a calculation on the result. Judgment, control the motor 193 to rotate in the forward direction, tighten the control cable 17, after the control cable 17 is tightened, lift the sampling compartment cover 12 through the traction control cable connection end 16, and the sampling compartment cover 12 will rotate around the sampling compartment cover hinge 15 On, ready for sample collection.

When collecting, put the collecting shovel into the collecting area and push it forward, and the sediment and water entering the sampling chamber 11 will press the pressure sensor 1112, the pressure on the pressure sensor 1112 will change, and the computing device 192 will calculate the pressure data. And the calculation result is transmitted to the central controller 194, and the central controller 194 judges the result. If it is not necessary to close the lid, then the control motor 193 does not act. After the cable 17 and the control cable 17 are loosened, the control cable connection end 16 loses traction, and the sampling compartment cover 12 is subjected to the reverse traction of the sampling compartment cover closing spring 14, and the sampling compartment cover 12 will rotate and close with the sampling compartment cover hinge 15 as the axis. At this time, the samples to be collected are gathered in the sampling bin 11, and the collection shovel can be drawn out.

Place the collection shovel vertically, and the sampling chamber 11 is at the lower end. Under the action of gravity, the pressure on the pressure sensor 1112 is 0. Open the collection chamber cover according to the above process, and the sample falls.

The calculation formula for the calculation device 192 to calculate the pressure data is:

Z＝Z ₁ +|Z ₁ |+Z ₂ +|Z ₂ | (1)

Among them, the calculation formulas of Z ₁ and Z ₂ are:

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In the above formula, Z represents the contrast value, Z ₁ represents the pressure contrast value, Z ₂ represents the number contrast value, n represents the total number of pressure sensors, i represents the serial number of the pressure sensor, α represents the correction coefficient, β represents the correction value, F _i Indicates the pressure of the i-th pressure sensor, and N ₆ indicates the standard pressure of the pressure sensor at a water depth of 6m.

The basic idea is to use the logarithm method to convert the pressure of the pressure sensor and the standard pressure into a problem of positive and negative values, and then count the sum of all values in the positive and negative values and the difference between the positive and negative values. Finally, the "or" operation is converted into a numerical calculation. Only when the two conditions of "the sum of all values in positive and negative values is positive" and "the number of positive values is more than negative values" are not true at the same time, can the sum of the comparison values obtained be 0.

The above calculation method converts the pressure of the pressure sensor and the standard pressure into positive and negative values by taking the logarithm method, which simplifies the calculation process, reduces the amount of calculation, saves system resources, shortens the response time, and makes The sampling chamber cover can be opened and closed automatically, which is faster and more convenient.

If the calculation result Z value is 0, it means that the pressure in the sampling chamber has not reached the standard at this time, and the motor needs to rotate forward to open the sampling chamber cover; if the calculation result Z value is greater than 0, it means that the pressure in the sampling chamber has reached the standard at this time. The motor needs to rotate in reverse to close the sampling compartment cover.

The opening and closing of the cover of the sampling shovel can more effectively control the number and location of collected samples. If you need to collect deep samples, you don’t need to go into the water, which can achieve easy and accurate collection.

The automatic opening and closing of the cover makes the whole collection process more convenient, faster, and more intelligent, which solves the problem that the sampling personnel do not know the underwater situation, and can more effectively control the number and location of the collected samples.

The community structure calculation module 2 calculates the community structure index, including the number of species, the number of EPT family-level taxa, the percentage of EPT species, ASPT index, Berger Parker index, Shannon-Wiener index, etc.

The community structure calculation module 2 counts the samples collected by the sample collection device 1, counts the number and types of benthic organisms in the samples, and calculates the number of species, the number of EPT family-level taxa, the percentage of EPT species, ASPT index, Berger Parker index and Community structure indices such as the Shannon-Weener index.

The landscape remote sensing device 3 remotely senses the landscape of the survey point, and the landscape remote sensing device can be a remote sensing satellite, or other aircraft equipped with remote sensors such as an aerial photographic remote sensing balloon.

The landscape pattern calculation module 4 determines the landscape type according to the remote sensing data, and calculates the landscape pattern index, including patch density (PD), largest patch index (LPI), landscape shape index (LSI), fractal dimension index (FRAC), aggregation degree Index (AI), Separation Index (SPLIT), Connectivity Index (COHESION), Patch Rich Density (PRD), Shannon Diversity Index (SHDI), etc.

Taking the river section 10km upstream of the survey point as the center line, generate six buffer zones with widths of 50m, 100m, 200m, 300m, 400m and 500m as river corridors; calculate the landscape pattern in the six river corridors index.

The data needed for the landscape pattern calculation module 4 also includes the field survey of the landscape types near the survey point to assist remote sensing images and land use interpretation.

The landscape pattern calculation module 4 may be a Fragstats software, or other tools for calculating the landscape pattern index.

The scale identification module 5 performs correlation analysis on the calculated community structure index and landscape pattern index to determine the landscape pattern scale.

As shown in Figure 5, it is a flow chart of the scale identification of the landscape pattern scale identification system based on the response of benthic animal community structure in the present invention, wherein the process of scale identification is,

In step a, the landscape pattern index and community structure index corresponding to the 6 widths of river corridors were counted respectively.

River corridors with different widths correspond to different landscape pattern indexes and the same community structure index. The width is used as the distinguishing feature in the statistics, and all data are divided into six parts, corresponding to the six widths.

In step b, correlation analysis is carried out in 6 widths of river corridors.

Correlation analysis is to analyze the linear correlation between two variables to obtain the correlation coefficient. The correlation coefficient is represented by r, and r describes the degree of linear correlation between two variables. The larger the absolute value of r, the stronger the correlation. powerful.

For each width, there are m community structure indexes and n landscape pattern indexes, and the community structure index and landscape pattern index correspond one-to-one, and m×n correlation analysis is carried out.

Step c: Comparing the correlation coefficient between the landscape pattern index and the benthos community structure index in river corridors of different widths, and determining the river corridor width that has a significant impact on the benthos community structure.

Comparing the 6 correlation coefficients of the same community structure index and landscape pattern index within 6 widths, if none of the 6 correlation coefficients are significantly correlated, then the community structure index and the landscape pattern index do not have corresponding significant river corridors Width, if at least one of the 6 correlation coefficients is a significant correlation coefficient, then take the largest correlation coefficient among the significant correlation coefficients, and the corresponding river corridor width is the pair of benthos community structure corresponding to the community structure index and the landscape pattern index Affects significant river corridor width.

Traverse the correlation analysis of m×n corresponding community structure index and landscape pattern index, and determine the river corridor width corresponding to each community structure index and landscape pattern index that has a significant impact on the benthos community structure.

Step d, on the basis of the significant river corridor width, set the length of 6 river sections starting from the survey point as the river section to be identified: 2km, 3km, 5km, 10km, 20km, from the sampling point to the source .

On the basis of width identification, analyze the length of the river section where the community structure index has the greatest impact on the benthos community structure index. In order to ensure the amount of data, it is necessary to re-select several survey points for identification of the length of the river section. The sampling point to the source means that the entire river section from the sampling point to the river source is taken as the river section to be identified.

Step e, recalculate the landscape pattern index in the 6 river sections.

If the survey points are reselected, the landscape pattern index in the six river sections also needs to be recalculated, and the landscape remote sensing device 3 and the landscape pattern calculation module 4 need to be used in the calculation process.

Step f: Use linear regression analysis to establish a fitting curve between the benthos community structure index and the landscape pattern index, and the one with the highest fitting degree is the most significant river corridor length.

Among the present invention, linear regression analysis is to quantitatively analyze the benthos community structure index and the landscape pattern index corresponding to one to one, and draw a fitting curve that can express its interrelationship, and the degree of fitting of the fitting curve is determined by the correlation coefficient r The greater the absolute value of r, the better the curve fit.

The width and length of the river corridor that have a significant impact on the benthos community structure are determined, and the scale of the river corridor that has a significant impact on the benthos community structure is also determined.

This landscape pattern scale identification system based on the response of benthos community structure solves the problem of scale identification of the impact of river corridor landscape pattern on river macrobenthos community structure. It has the advantages of fast accuracy and simple operation. Provided technical support for determining the scope of river corridor landscape optimization with the goal of protecting aquatic organisms.

Example:

1. Identification of river corridor width

45 sampling points were selected in the Taizi River Basin, and the 45 sampling points were located in 10 tributaries: 5 in the Hebei branch of Taizi, 5 in the Henan branch of Taizi, 3 in the Xiaotang River, 7 in the Xihe River, 4 in the Lanhe River, and 5 in the Tanghe River. There are 4 in Erdao River, 3 in Tanghe Xiada River, 5 in Beisha River, 3 in Nansha River and 6 in Haicheng River. Macrobenthos surveys were carried out in October 2009 and June 2010. Taking the river section 10km upstream of the sampling point as the center line, respectively, generate river section buffer zones with widths of 50m, 100m, 200m, 300m, 400m, and 500m, as candidate corridors for significance identification. Land use interpretation uses SPOT5 satellite remote sensing images in August 2009, with a resolution of 2.5m. Land use types are divided into 10 types: paddy field, dry land, industrial and mining construction land, residential area, forest land, grassland, river canal, lake, reservoir, pit and pond, beach swamp, and unused land.

The 7 community structure indexes SP, %E-sp, %T-sp, EPT-fa, ASPT, BP and H' of 45 sampling points were counted and calculated respectively. Nine landscape pattern indices of river corridors with different widths were calculated: PD, LPI, LSI, FRAC, AI, SPLIT, COHESION, PRD, and SHDI.

Using SPSS18.0 software, the Pearson correlation analysis was carried out on the 9 landscape pattern indexes and 7 benthos community structure indexes in river corridors with different widths, and the significant correlation results were listed (Table 3).

Table 3 Pearson correlation analysis of landscape pattern index and benthos community structure index under different river corridors (** means significant correlation at 0.01 level, * means significant correlation at 0.05 level. Those with insignificant correlation are not listed in the table)

Further comparing the phase coefficients, the scale with the largest absolute value of the correlation coefficient is the width of the most significant river corridor. The scales of river corridors at which landscape patterns have a significant impact on benthos community structure are summarized in Table 4. It can be seen that FRAC has a significant impact on most benthos community structure indexes in the 100m river corridor; LPI and SHDI have a significant impact on the benthos community structure in wider river corridors (400m to 500m). Impact. However, PD, LSI, AI, SPLIT, COHESION and PRD did not have a strong impact on benthos community structure, and only had a significant impact on a single scale.

Table 4 The width of the river corridor landscape pattern that has a significant impact on the benthos community structure

2. Identification of river corridor length

On the basis of width identification, the FRAC index with a width of 100m and the SHDI index with a width of 500m were selected to analyze the length of the river section at which the most significant impact on the benthos community structure index was.

In order to ensure the amount of data, it is planned to re-select several points for identification of the length of the river section. Select 31 tributary points in the Taizi River Basin, and set 6 lengths of river sections in the 100m and 500m wide river corridors: 2km, 3km, 5km, 10km, 20km, from the sampling point to the source.

In the 100m river corridor, the benthos community structure indexes significantly affected by the landscape FRAC index include SP, EPT-fa, ASPT, BP and H', and the 5 benthos community structure indexes of 31 points were calculated. At the same time, the landscape FRAC index of the length of 6 river sections in the 100m river corridor was calculated. In the 500m river corridor, the EPT-fa, BP and H' indexes of benthic animals significantly affected by the landscape SHDI index were calculated. At the same time, the landscape SHDI index of the length of 6 river sections in the 500m river corridor was calculated.

Carry out linear regression analysis on each benthos community structure index and landscape pattern index respectively, and establish a regression curve. By comparing the correlation coefficient r value, the absolute value of r is the largest, that is, the benthos community structure index is in a river with a width of 100m. Length of reach with significant response to FRAC under the corridor and to SHDI under the 500m wide river corridor.

The results show that under the 100m river corridor, the landscape FRAC index has the most significant impact on benthos SP, EPT-fa, ASPT and H' index. Under the river corridor, the most significant impact of landscape SHDI index on benthos EPT-fa, BP and H' index is the river section from the sampling point to the river source (Table 5).

Table 5 The length of the river section where the landscape pattern has a significant impact on the benthos community structure

The above descriptions are only preferred embodiments of the present invention, and are only illustrative rather than restrictive to the present invention. Those skilled in the art understand that many changes, modifications, and even equivalents can be made within the spirit and scope defined by the claims of the present invention, but all will fall within the protection scope of the present invention.

Claims (10)

1. A landscape pattern scale identification system based on the response of benthic animal community structure, which includes a sample collection device, a community structure calculation module, a landscape remote sensing device, a landscape pattern calculation module and a scale identification module; the sample The collection device collects macrobenthos samples at the survey point; the community structure calculation module counts the samples and calculates the community structure index of the survey point; the landscape remote sensing device remotely senses the landscape of the survey point; the The landscape pattern calculation module determines the landscape type according to the remote sensing results, and calculates the landscape pattern index in the six river corridors; the scale identification module performs correlation analysis on the calculated community structure index and landscape pattern index of the survey points , to determine the scale of the landscape pattern; it is characterized in that the sample collection device is a sampling shovel, which includes a sampling bin, a handle and a cable reel; the rear part of the sampling bin is the rear wall of the sampling bin, and the Several pressure sensors and a control transmitter are distributed on the inner side of the rear wall of the sampling chamber, the pressure sensor measures the pressure on the rear wall of the sampling chamber, and the control transmitter converts the pressure data measured by the pressure sensor into The data signal is transmitted; the cable reel is located at the rear of the handle, and it includes a signal receiver and a computing device, the signal receiver receives the data signal and transmits it to the computing device, and the computing device The device calculates the pressure data, and the calculation formula is: Z＝Z ₁ +|Z ₁ |+Z ₂ +|Z ₂ | Among them, the calculation formulas of Z ₁ and Z ₂ are: ${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; ln</span>}\frac{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; 6</span>}}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; )</span>$ In the above formula, Z represents the contrast value, Z ₁ represents the pressure contrast value, Z ₂ represents the number contrast value, n represents the total number of pressure sensors, i represents the serial number of the pressure sensor, α represents the correction coefficient, β represents the correction value, F _i Indicates the pressure of the i-th pressure sensor, and N ₆ indicates the standard pressure of the pressure sensor at a water depth of 6m. 2. The landscape pattern scale identification system based on the response of benthic animal community structure according to claim 1, wherein said community structure index comprises species number, EPT family-level taxon number, EPT species percentage, ASPT index, Berger Parker index and Shannon-Wiener index. 3. The landscape pattern scale recognition system based on the response of benthic animal community structure according to claim 2, wherein the landscape pattern index comprises patch density, maximum patch index, landscape shape index, fractal dimension index, Aggregation index, separation index, connectivity index, patch richness density, Shannon diversity index. 4. The landscape pattern scale identification system based on the response of benthos community structure according to claim 3, characterized in that, the 6 river corridors are respectively centered on the river section 10 km upstream of the survey point , generate 6 river section buffers with left and right widths of 50m, 100m, 200m, 300m, 400m and 500m. 5. The landscape pattern scale identification system based on the response of benthos community structure according to claim 4, characterized in that, the landscape pattern calculation module is a Fragstats software. 6 . The landscape pattern scale recognition system based on the response of benthos community structure according to claim 4 , wherein the landscape remote sensing device is an aerial photography remote sensing balloon. 6 . 7. The landscape pattern scale identification system based on the response of benthos community structure according to claim 4, wherein said sampling shovel also comprises a sampling bin cover, a bin cover hinge, a control cable and a control cable The connecting end of the bin; the bin cover is located at the front end of the sampling bin; the bin cover is hinged to connect the bin cover and the sampling bin for easy opening and closing; the connecting end of the control cable is a protrusion arranged on the The part near the connecting seam of the cover is used to fix the control cable; one end of the control cable is fixed to the connecting end of the control cable, passes through the inside of the handle, and connects the bottom of the handle with the Reel connection described above. 8. The landscape pattern scale identification system based on the response of benthic animal community structure according to any one of the preceding claims, wherein the sampling shovel also includes a cover closing spring, one end of which is fixed on the The cover is far away from the seam part, and one end is fixed on the lower part of the sampling compartment, and the cover is closed under the action of elastic force. 9. The landscape pattern scale identification system based on the response of benthic animal community structure according to claim 8, characterized in that, the reel also includes a motor, a central controller and a power supply, and the central controller The result of the calculation device is received and judged, and the motor is controlled to rotate; the motor is connected to the control cable, and the control cable moves through rotation; the power supply provides electric energy for the cable reel. 10. The landscape pattern scale identification system based on the response of benthic animal community structure according to claim 9, wherein the sampling shovel also includes a handle angle adjustment bolt, and the handle angle adjustment bolt is connected to the If the sampling bin and the handle are loosened, the angle between the sampling bin and the handle can be adjusted, and if tightened, the angle between the sampling bin and the handle can be locked.