CN110618145B - Method for rapidly determining spring eye position in loess tableland area based on unmanned aerial vehicle - Google Patents

Method for rapidly determining spring eye position in loess tableland area based on unmanned aerial vehicle Download PDF

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CN110618145B
CN110618145B CN201910918503.XA CN201910918503A CN110618145B CN 110618145 B CN110618145 B CN 110618145B CN 201910918503 A CN201910918503 A CN 201910918503A CN 110618145 B CN110618145 B CN 110618145B
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赵思远
杜军凯
贾仰文
龚家国
牛存稳
王英
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China Institute of Water Resources and Hydropower Research
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Abstract

The invention discloses a method for rapidly determining the spring eye position of a loess tableland based on an unmanned aerial vehicle, which comprises the steps of determining a spring eye distribution area; acquiring radar microwave data and remote sensing image data of a target spring eye distribution area through an unmanned aerial vehicle; processing the obtained radar and remote sensing image data; rasterizing the spring eye distribution area, and calculating the soil water content of each grid of the target spring eye distribution area according to the obtained data; identifying and marking grids with maximum water content in the target spring eye distribution area, and screening to generate a spring eye distribution map of a single date; and repeating the calculation on a plurality of dates, and overlapping the spring eye distribution diagrams on all dates to determine the grid where the potential spring eyes are located, namely the positions of the spring eyes are obtained. The method can solve the problem that the prior art lacks a method for rapidly surveying the distribution of the dew point of the spring eye in a large-scale area, and has the advantages of high accuracy, high measuring speed and large range.

Description

Method for rapidly determining spring eye position in loess tableland area based on unmanned aerial vehicle
Technical Field
The invention relates to the technical field of hydrology and water resource remote sensing surveying, in particular to a method for rapidly determining the position of spring eyes in a loess highland area based on an unmanned aerial vehicle.
Background
The water resource planning and allocation are important links of national soil resource allocation, loess plateau is in arid and semi-arid regions of China, rainfall is concentrated in 7-9 months every year, the utilization rate of rainwater resources is low, water and soil loss is serious, and the utilization and allocation of water resources are particularly important.
The loess tableland is one of the main landforms in loess plateau area and has wide tableland area and weak surface runoff development, and underground water is the main water source for production and life of the residents in the tableland area. Because the aeration zone is vertically cut, the boundary area of the tableland, the ditch and the gully has more spring water dew points. Spring water is the main even only water source of the highland village, and villagers can meet the domestic water demand by storing water and guiding water near the spring water outlet point. The spatial position of the spring hole is rapidly measured, so that the natural discharge amount of local underground water can be quantitatively estimated, a key technical support can be provided for the research of an underground water replenishing and discharging mechanism, and the method has important significance for optimizing the utilization and configuration of underground water resources in the loess tableland.
The existing investigation method for the spring eye mainly depends on real-time exploration under the guidance of nearby villagers, is time-consuming and labor-consuming, and does not provide a method for rapidly investigating the distribution of the spring eye dew point in a large-scale area.
The method is a new technology for rapidly surveying the position of the spring eye by utilizing radar and remote sensing information. At present, the main source for acquiring radar and remote sensing information data is a satellite, the resolution of the obtained result is poor, the spring eye recognition is used for recognizing small-size targets in a large-scale range, and the distortion is serious by utilizing the satellite radar and the remote sensing data.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the method for rapidly measuring the spring hole position in the loess tableland, which can solve the problem that the prior art is lack of a method for rapidly surveying the spring hole dew point distribution in a large-scale area.
In order to solve the technical problems, the invention adopts the following technical scheme:
the method for rapidly determining the spring eye position in the loess tableland based on the unmanned aerial vehicle comprises the following steps:
s1, determining a spring eye distribution area;
s2, acquiring radar microwave data and remote sensing image data of a target spring eye distribution area through an unmanned aerial vehicle;
s3, processing the radar and remote sensing image data obtained in S2;
s4, rasterizing the spring eye distribution area, and calculating the soil water content m of each grid of the target spring eye distribution area according to the data obtained in S1 and S3;
s5, identifying and marking grids with maximum water content in the target spring eye distribution area, and screening to generate a spring eye distribution map of a single date;
and S6, repeating S1 to S5 on a plurality of dates, and overlapping the spring eye distribution diagrams of the dates to determine the grid where the potential spring eyes are located, namely the positions of the spring eyes are obtained.
Further, the method for determining the spring eye distribution area comprises the following steps:
and extracting the trench line and the trench by analyzing the topographic data of the region to be researched.
Furthermore, the terrain data of the area to be researched is obtained through DEM image data with the precision of 30m, and the terrain data of the area to be researched comprises a slope, a slope direction, a ground curvature, a ditch line and a valley line.
Further, a specific method for acquiring radar and remote sensing image data of a target spring eye distribution area through an unmanned aerial vehicle comprises the following steps:
planning the flight path of the unmanned aerial vehicle according to the spring eye distribution area, and setting course overlapping degree of 80%, side direction overlapping degree of 70%, flight height of 50m, and controlling the space resolution within 1 m; selecting meteorological conditions of no wind or wind power less than 2 grade, clear and no cloud or cloud amount less than 2, and performing the following steps of 10: and flying between 00 and 14:00 to acquire remote sensing image data.
Further, standard reference white board correction is carried out before and after each set flight of the unmanned aerial vehicle.
Further, the processing of the radar and remote sensing image data comprises the following steps:
s1, detecting the image quality of the radar and the remote sensing image data through manual visual inspection, and removing the deformation image caused by gust;
s2, carrying out image radiation correction on the remote sensing image data, eliminating radiation distortion or distortion generated in the data acquisition and transmission process, and obtaining real reflectivity data of ground objects;
s3, performing image noise reduction by adopting a linear smooth Gaussian filtering method;
and S4, resampling the radar radiation data and the remote sensing image data to the same spatial resolution respectively, and then outputting the processed data.
Further, the method for calculating the soil water content m of each grid of the target spring hole distribution area comprises the following steps:
s1, according to the near infrared band reflectivity rho in the remote sensing image dataNIRAnd mid-infrared band reflectivity ρMIRCalculating vegetation water content data m of each grid through datavegThe calculation formula is as follows:
Figure BDA0002216815960000031
s2, according to the vegetation water content data mvegAnd radar wave incidence angle theta, calculating double-layer attenuation factor gamma of the vegetation layer2(θ) having the formula:
γ2(θ)＝exp(-2·0.137mveg·secθ)；
s3, double-layer attenuation factor gamma according to vegetation layer2(theta), calculating the back scattering coefficient of the vegetation layer
Figure BDA0002216815960000032
The calculation formula is as follows:
Figure BDA0002216815960000033
s4 finding the backscattering coefficient of the vegetation layer
Figure BDA0002216815960000034
And the total radar backscattering coefficient under the vegetation covered ground
Figure BDA0002216815960000035
Calculating direct surface backscattering coefficient
Figure BDA0002216815960000036
The calculation formula is as follows:
Figure BDA0002216815960000041
s5 direct surface backscattering coefficient
Figure BDA0002216815960000042
And (3) calculating the water content m of the soil, wherein the calculation formula is as follows:
Figure BDA0002216815960000043
further, the method for identifying and marking the grids with the maximum water content in the target spring eye distribution area and screening the grids to generate the spring eye distribution map with the single date comprises the following steps:
s1, carrying out extreme value analysis on the water content of each grid in the target area, marking the grid as a potential spring eye grid, and marking the corresponding grid water content as mi；
S2, water content m of potential spring eye gridiThe water capacity m of the soil on the ground surface all year roundWater capacity in fieldFor comparison, if mi≤mWater capacity in fieldJudging the potential spring eye grid to be a pseudo spring eye grid; if mi＞mWater capacity in fieldJudging the potential spring eye grid to be a real spring eye grid;
and S3, extracting the real spring eye grids in the S2 and generating a spring eye distribution diagram of the single date.
Further, the method for determining the grid where the potential spring eyes are located by superposing the spring eye distribution maps of all dates comprises the following steps: and the grid marked as the potential position of the spring eye in the spring eye distribution diagram of each date is the position of the spring eye.
Further, the unmanned aerial vehicle is equipped with image measurement module and global navigation satellite positioning module, loads synthetic aperture radar and high spectrum imager.
The method for rapidly measuring the spring eye position in the loess tableland based on the unmanned aerial vehicle has the main beneficial effects that:
according to the method, by means of the unmanned aerial vehicle, the water content of the earth surface soil in the research area is analyzed by utilizing radar and remote sensing information, the distribution position of the spring holes in the loess tableland area is quickly determined by searching the soil humidity image element, the distribution position of the spring holes can be quickly determined in a large-scale area range, and the efficiency of hydrogeological exploration work is greatly improved; by analyzing the data of the radar and the remote sensing image with higher spectral, spatial and temporal resolutions, the limitation of external factors such as weather, terrain and the like is broken, and the accuracy and precision of the spring eye identification are obviously improved.
Meanwhile, the working sensors can be matched as required by means of an unmanned aerial vehicle system, multi-purpose investigation can be achieved, and compared with satellite radar remote sensing data, the space resolution is greatly improved, the result is accurate, and the reliability is higher.
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Fig. 1 is a flowchart of the method for rapidly determining the spring eye position in the loess tableland based on the unmanned aerial vehicle of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
fig. 1 is a flowchart illustrating a method for rapidly determining the location of a spring hole in a loess tableland according to the present invention.
The method for rapidly measuring the spring eye position in the loess tableland based on the unmanned aerial vehicle comprises the following steps:
and S1, determining a spring eye distribution area.
Specifically, the spring holes in the loess plateau region are mainly generated by exposing the water-containing layer due to cutting of the air-entrained zone by the gully, and therefore the spring hole distribution region is mainly near the gully line of the loess plateau region and upstream of the valley line.
And extracting data from DEM image data with the precision of 30m in the research area and analyzing the data to obtain ditch lines and valley lines in the research area. The terrain data includes slope, direction of slope, curvature of the ground, etc.
And S2, acquiring radar microwave data and satellite remote sensing data of the spring eye distribution area through an unmanned aerial vehicle.
Further, the specific method comprises the following steps:
planning the flight path of the unmanned aerial vehicle according to the spring eye distribution area, and setting course overlapping degree of 80%, side direction overlapping degree of 70%, flight height of 50m, and controlling the space resolution within 1 m; selecting meteorological conditions of no wind or wind power less than 2 grade, clear and no cloud or cloud amount less than 2, and performing the following steps of 10: and (5) flying between 00 and 14:00 to acquire radar microwave and remote sensing image data. And (4) carrying out standard reference white board correction before and after each frame flight of the unmanned aerial vehicle.
Wherein, unmanned aerial vehicle adopts many rotor unmanned aerial vehicle, including image measurement module and global navigation satellite positioning module to be used for accurate positioning and flight attitude adjustment. The unmanned aerial vehicle is also provided with a synthetic aperture radar and a hyperspectral imager so as to be used for collecting radar microwaves and remote sensing data of a spring eye distribution area.
And S3, processing the remote sensing image data obtained in S2.
Further, the specific method comprises the following steps:
and S3-1, removing the deformation image caused by gust by manually and visually checking the image quality of the radar and the remote sensing image data.
S3-2, carrying out image radiation correction on the remote sensing image data, eliminating radiation distortion or distortion generated in the data acquisition and transmission process, and obtaining the real reflectivity data of the ground object.
Image radiation correction can be performed using SpecView software.
And S3-3, performing image noise reduction by adopting a linear smooth Gaussian filtering method.
And S3-4, resampling the radar radiation data and the remote sensing image data to the same spatial resolution respectively, and then outputting the processed data.
And S4, rasterizing the spring eye distribution area, and calculating the soil water content m of each grid of the target spring eye distribution area according to the data obtained in S1 and S3.
Further, the specific method comprises the following steps:
s4-1, according to the reflectivity rho of the near infrared band in the remote sensing image dataNIRAnd mid-infrared band reflectivity ρMIRCalculating vegetation water content data m of each grid through datavegThe unit is kg/m3, and the calculation formula is as follows:
Figure BDA0002216815960000061
s4-2, according to the water content data m of the vegetationvegAnd radar wave incidence angle theta, calculating double-layer attenuation factor gamma of the vegetation layer2(θ) having the formula:
γ2(θ)＝exp(-2·0.137mveg·secθ)；
s4-3, double-layer attenuation factor gamma according to vegetation layer2(theta), calculating the back scattering coefficient of the vegetation layer
Figure BDA0002216815960000062
The calculation formula is as follows:
Figure BDA0002216815960000071
s4-4, back scattering coefficient according to vegetation layer
Figure BDA0002216815960000072
And the total radar backscattering coefficient under the vegetation covered ground
Figure BDA0002216815960000073
Calculating direct surface backscattering coefficient
Figure BDA0002216815960000074
The calculation formula is as follows:
Figure BDA0002216815960000075
s4-5, direct surface backscattering coefficient
Figure BDA0002216815960000076
And (3) calculating the water content m of the soil, wherein the calculation formula is as follows:
Figure BDA0002216815960000077
and exporting the calculated soil weight water content m of the target spring hole distribution area according to the grid.
S5, calculating the average value of the soil moisture content of the target spring hole distribution area
Figure BDA0002216815960000078
And marking the grid with the maximum water content value to generate a spring eye distribution diagram of a single date.
Further, the specific method comprises the following steps:
s5-1, carrying out extreme value analysis on the water content of each grid in the target area, marking the grid as a potential spring eye grid, and marking the corresponding grid water content as mi。
S5-2, water content m of potential spring eye gridiThe water capacity m of the soil on the ground surface all year roundWater capacity in fieldFor comparison, if mi≤mWater capacity in fieldJudging the potential spring eye grid to be a pseudo spring eye grid; if mi＞mWater capacity in fieldAnd judging the potential spring eye grid to be a real spring eye grid.
For convenience of explanation, taking the data of the spring hole distribution area of a certain area as an example, the soil water content m of a soil layer of 0-1 m in the areaWater capacity in fieldThe range is large and is 18.65-21.76%, and the range is calculated by taking an average value, wherein the average value is 20.40%.
And S5-3, extracting the real spring eye grids in the S5-2 and generating a spring eye distribution diagram of the single date.
And S6, repeating S1 to S5 on a plurality of dates, and overlapping the spring eye distribution diagrams of the dates to determine the grid where the potential spring eyes are located, namely the positions of the spring eyes are obtained.
Specifically, the method for determining the grid where the potential spring eyes are located by superposing the spring eye distribution maps of all dates comprises the following steps: and the grid marked as the potential position of the spring eye in the spring eye distribution diagram of each date is the position of the spring eye.
The above description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.

Claims (7)

1. A method for rapidly determining the spring eye position in the loess tableland based on an unmanned aerial vehicle is characterized by comprising the following steps:
s1, determining a spring eye distribution area, wherein the method for determining the spring eye distribution area comprises the following steps:
extracting a ditch line and a ditch by analyzing topographic data of an area to be researched;
s2, acquiring radar microwave data and remote sensing image data of a spring eye distribution area through an unmanned aerial vehicle;
s3, processing the radar microwave data and the remote sensing image data obtained in the S2;
s4, rasterizing the spring eye distribution area, and calculating the soil water content m of each grid of the spring eye distribution area according to the data obtained in S1 and S3;
s5, identifying and marking grids with maximum water content in the spring eye distribution area, and screening to generate a spring eye distribution map of a single date, wherein the method comprises the following steps:
s5-1, carrying out extreme value analysis on the water content of each grid in the spring eye distribution area, marking the grid as a potential spring eye grid, and marking the corresponding grid water content as mi；
S5-2, water content m of potential spring eye gridiThe water capacity m of the soil on the ground surface all year roundWater capacity in fieldFor comparison, if mi≤mWater capacity in fieldJudging the potential spring eye grid to be a pseudo spring eye grid; if mi＞mWater capacity in fieldJudging the potential spring eye grid to be a real spring eye grid;
s5-3, extracting the real spring eye grids in the S5-2 and generating a spring eye distribution diagram of a single date;
s6, repeating S1 to S5 on a plurality of dates, and superposing the spring eye distribution diagrams of the dates to determine the grid where the potential spring eyes are located, namely the location of the spring eyes, wherein the method comprises the following steps:
and the grid marked as the potential position of the spring eye in the spring eye distribution diagram of each date is the position of the spring eye.
2. The unmanned aerial vehicle-based method for rapidly measuring the position of the spring eyes in the loess plateau area, according to claim 1, wherein the topographic data of the area to be studied, which includes a slope, a sloping direction, a ground curvature, a channel, a furrow line and a valley line, is obtained through DEM image data with a precision of 30 m.
3. The method for rapidly determining the spring eye position in the loess plateau area based on the unmanned aerial vehicle as claimed in claim 1, wherein the specific method for acquiring the radar microwave data and the remote sensing image data of the spring eye distribution area by the unmanned aerial vehicle comprises the following steps:
planning the flight path of the unmanned aerial vehicle according to the spring eye distribution area, and setting course overlapping degree of 80%, side direction overlapping degree of 70%, flight height of 50m and control spatial resolution within 1 m; selecting meteorological conditions of no wind or wind power less than 2 grade, clear and no cloud or cloud amount less than 2, and performing the following steps of 10: and (5) flying between 00 and 14:00 to acquire radar microwave data and remote sensing image data.
4. The unmanned aerial vehicle-based method for rapidly measuring the position of the spring eyes in the loess plateau area, according to claim 1, wherein the unmanned aerial vehicle is calibrated by a standard reference whiteboard before and after each set flight.
5. The unmanned aerial vehicle-based method for rapidly measuring the spring eye position in the loess plateau area, according to claim 1, wherein the processing of the radar microwave data and the remote sensing image data comprises the steps of:
s1, detecting the image quality of the radar microwave data and the remote sensing image data through manual visual inspection, and removing the deformation image caused by gust;
s2, carrying out image radiation correction on the remote sensing image data, eliminating radiation distortion or distortion generated in the data acquisition and transmission process, and obtaining real reflectivity data of ground objects;
s3, performing image noise reduction by adopting a linear smooth Gaussian filtering method;
and S4, resampling the radar microwave data and the remote sensing image data to the same spatial resolution respectively, and then outputting the processed data.
6. The unmanned aerial vehicle-based method for rapidly measuring the spring hole position in the loess tableland area according to claim 5, wherein the method for calculating the soil water content m of each grid in the spring hole distribution area comprises the steps of:
s1, according to the near infrared band reflectivity rho in the remote sensing image dataNIRAnd mid-infrared band reflectivity ρMIRCalculating vegetation water content data m of each grid through datavegThe calculation formula is as follows:
Figure FDA0002750871710000031
s2, according to the vegetation water content data mvegAnd radar wave incidence angle theta, calculating double-layer attenuation factor gamma of the vegetation layer2(θ) having the formula:
γ2(θ)＝exp(-2·0.137mveg·secθ)；
s3, double-layer attenuation factor gamma according to vegetation layer2(theta), calculating the back scattering coefficient of the vegetation layer
Figure FDA0002750871710000032
The calculation formula is as follows:
Figure FDA0002750871710000033
s4 finding the backscattering coefficient of the vegetation layer
Figure FDA0002750871710000034
And vegetation covered subsurface general radarCoefficient of backscattering
Figure FDA0002750871710000035
Calculating direct surface backscattering coefficient
Figure FDA0002750871710000036
The calculation formula is as follows:
Figure FDA0002750871710000037
s5 direct surface backscattering coefficient
Figure FDA0002750871710000038
And (3) calculating the water content m of the soil, wherein the calculation formula is as follows:
Figure FDA0002750871710000039
7. the method for rapidly surveying the quaysian area spring eye position based on the unmanned aerial vehicle of claim 1, wherein the unmanned aerial vehicle is equipped with an image measuring module and a global navigation satellite positioning module, and is loaded with a synthetic aperture radar and a hyperspectral imager.
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