CN111412899B - Method for monitoring and evaluating river by using unmanned aerial vehicle surveying and mapping - Google Patents
Method for monitoring and evaluating river by using unmanned aerial vehicle surveying and mapping Download PDFInfo
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Abstract
The invention provides a method for monitoring and evaluating a river by surveying and mapping of an unmanned aerial vehicle, which comprises the steps of acquiring image data by the unmanned aerial vehicle; reconstructing the image data by using a motion recovery shape algorithm to construct 3D terrain information; estimating the basic shape of the river streamline on the 3D topographic information according to the image data by using a Vextractor program, and fitting the basic shape into a river curvature index of the main river channel section; calculating an ortho-image spectral index by utilizing an RGB ortho-image measuring program to obtain river quantitative and qualitative information; and evaluating the river physical environment according to the curvature index and the river quantitative and qualitative information. According to the river monitoring and evaluating method provided by the invention, the unmanned aerial vehicle is used as basic equipment, and the motion recovery shape algorithm, the Vextractor program and the RGB ortho-image measurement program are utilized to realize the tracking of the eutrophication process in the large river, reflect the change process of eutrophication and vegetation overgrowth, and have very important application value for long-term monitoring and ecological restoration of the large river.
Description
Technical Field
The invention relates to the technical field of aquatic ecosystems, in particular to a method for monitoring and evaluating a river by surveying and mapping of an unmanned aerial vehicle.
Background
With the increasing influence of human activities on the ecosystem, rivers are facing more serious ecological problems: dynamic diversification of a river ecological system tends to be stable and homogenized due to the phenomena of no disturbance of the river, canalization, encroachment of a waterfront space and the like, so that biological diversity and ecological system service are degraded. In river restoration projects, post-project monitoring of the system and evaluation of further evolution of the restoration system are often lacking, and the lack of such information makes it difficult to evaluate the effectiveness of the restoration.
The habitat of the large river has the characteristics of long change period and relative stability, and the ground means is used as the environment monitoring technology, so that the limitation is more. For example, manually patrolling a river using a ship is a traditional way of evaluating. The method is not only laborious and laborious, but also can only observe the local water surface of the river, and is difficult to distinguish the color change of the water surface. In addition, when the ship is used for manual inspection, observation dead angles are easy to occur, and if the ship is too close to a side stall, the ship is easy to be stranded, and the safety is difficult to guarantee. The detection technology mainly based on the ground means is easily subjected to the dual influences of natural condition limitation and human subjective factors, and the ground monitoring based on the monitoring sample can not give consideration to representativeness, convenience and safety, and is difficult to comprehensively and accurately reflect the physical habitat conditions of rivers.
Disclosure of Invention
The invention provides a method for monitoring and evaluating a river by surveying and mapping an unmanned aerial vehicle, aiming at overcoming the technical defects that the existing evaluation mode for manually inspecting the river is easily limited by natural conditions and is influenced by man-made subjective factors, and the physical habitat condition of the river cannot be comprehensively and accurately reflected.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method of monitoring and evaluating a river using unmanned aerial vehicle surveying and mapping, comprising the steps of:
s1: presetting a flight route of the unmanned aerial vehicle, controlling the unmanned aerial vehicle to fly, and acquiring image data on the flight route;
s2: according to the image data, carrying out photogrammetric reconstruction on the lowest point image of the unmanned aerial vehicle by using a motion recovery shape algorithm to construct 3D topographic information;
s3: estimating the basic shape of the river streamline on the 3D topographic information according to the image data by using a Vextractor program, and fitting the basic shape into a river curvature index of the main river channel section;
s4: calculating an ortho-image spectral index by utilizing an RGB ortho-image measuring program and combining with 3D topographic information to obtain river quantitative and qualitative information;
s5: and evaluating the physical environment of the river according to the river curvature index of the main river channel section and the quantitative and qualitative river information.
In the scheme, the unmanned aerial vehicle is used as basic equipment, and a specified route is set to shoot a large river regularly and quantitatively; on the basis, a Motion recovery shape algorithm (SFM) is implanted into an unmanned aerial vehicle executive program, the position, the coverage area and the overlapping part of the image are deduced according to the SFM, the obtained image is used for photogrammetric reconstruction, and 3D terrain information reconstruction of various terrains such as a river corridor, a coastal zone and a peripheral habitat is realized; extracting river flow lines from the high-resolution images through a Vextractor program; and finally, the quantitative and qualitative information of the river is calculated by utilizing an RGB ortho-image measuring program, the eutrophication process in the large river is tracked, the change process of eutrophication and vegetation overgrowth is reliably reflected, and the method has very important application value for long-term monitoring and ecological restoration of the large river.
Wherein, the step S1 specifically includes: and the control terminal is used for planning and editing the flight path of the unmanned aerial vehicle in advance, the coverage range of the flight path edited in advance is 70% of the front and side overlapped area, and image data on the flight path is obtained.
In the scheme, the unmanned aerial vehicle one-time imaging activity comprises 2-3 flights, each flight lasts for 15 minutes, the flight height is about 50-70 meters, and the position, the coverage range and the overlapping part of the image obtained by the unmanned aerial vehicle flight track are used for photogrammetric analysis according to the weather condition of the day.
Wherein, the step S2 specifically includes the following steps:
s21: acquiring images with sufficient front and side overlap;
s22: monitoring corresponding aerial triangulation and image alignment points, calculating the distance between each point by using a relatively small number of points obtained by three-dimensional coordinate measurement, and constructing a sparse point cloud by using a 3D spherical algorithm;
s23: repeatedly cruising for multiple times by using an unmanned aerial vehicle, continuously increasing the density of the sparse cloud, adding the multiple sparse cloud results, and finally generating a dense cloud;
s24: and carrying out imaging calculation on the dense cloud by using the 3D digital surface model to construct 3D terrain information.
Wherein, the step S3 specifically includes the following steps:
s31: loading a Vextractor program in an unmanned aerial vehicle executive program;
s32: extracting a river streamline from high-resolution image data of 3D terrain information, describing a vector graphic file, deducing and calculating basic hydrological indexes of river geometric properties, and manually correcting the obtained river streamline;
s33: and calculating the curve number of the river channel in the vector graphic file, deducing the values of the average curve amplitude and the wavelength, and fitting the deducted result to the river curvature index of the main river channel section.
Wherein, the step S4 specifically includes: loading an RGB (red, green and blue) ortho-image measuring program in an unmanned aerial vehicle, and automatically calculating an ortho-image spectral index, wherein the index is calculated as follows:
GRVI＝(G-R)/(G+R)
VDVI＝(2×G-G-B)/(2×G+R+B)
wherein GRVI is a green-red vegetation index, and the uncalibrated RGB unmanned aerial vehicle sensor is used for monitoring crop coverage biomass; VARI is a visible atmospheric pressure index, indicating promising performance in grass monitoring; VDVI is the index of vegetation with difference, and the accuracy in vegetation extraction is positive correlation; and calculating the index of each monitoring during the monitoring period, and statistically analyzing the minimum value, the maximum value, the average value and the quartile value of the three indexes.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a method for monitoring and evaluating a river by mapping an unmanned aerial vehicle, which comprises the steps of taking the unmanned aerial vehicle as basic equipment to shoot a large river regularly and quantitatively, constructing 3D topographic information of the river by utilizing a motion recovery shape algorithm, extracting river flow lines from a high-resolution image by utilizing a Vextractor program, and finally calculating quantitative and qualitative information of the river by utilizing an RGB ortho-image measuring program, thereby realizing the tracking of the eutrophication process in the large river, reliably reflecting the change process of eutrophication and vegetation overgrowth, and having very important application value for long-term monitoring and ecological restoration of the large river.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is an imaging activity diagram of the UAV; wherein: (a) planning a flight path; (b) schematic of the position, coverage and overlap of the 292 images produced by the flight trajectory; (c) a study region assignment control point and checkpoint schematic;
fig. 3 is a schematic diagram of a monitoring evaluation result according to an embodiment.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, a method for monitoring and evaluating a river by mapping with an unmanned aerial vehicle comprises the following steps:
s1: presetting a flight route of the unmanned aerial vehicle, controlling the unmanned aerial vehicle to fly, and acquiring image data on the flight route;
s2: according to the image data, carrying out photogrammetric reconstruction on the lowest point image of the unmanned aerial vehicle by using a motion recovery shape algorithm to construct 3D topographic information;
s3: estimating the basic shape of the river streamline on the 3D topographic information according to the image data by using a Vextractor program, and fitting the basic shape into a river curvature index of the main river channel section;
s4: calculating an ortho-image spectral index by utilizing an RGB ortho-image measuring program and combining with 3D topographic information to obtain river quantitative and qualitative information;
s5: and evaluating the physical environment of the river according to the river curvature index of the main river channel section and the quantitative and qualitative river information.
In the specific implementation process, an unmanned aerial vehicle is used as basic equipment, and a specified route is set to shoot a large river regularly and quantitatively; on the basis, a Motion recovery shape algorithm (SFM) is implanted into an unmanned aerial vehicle executive program, the position, the coverage area and the overlapping part of the image are deduced according to the SFM, the obtained image is used for photogrammetric reconstruction, and 3D terrain information reconstruction of various terrains such as a river corridor, a coastal zone and a peripheral habitat is realized; extracting river flow lines from the high-resolution images through a Vextractor program; and finally, the quantitative and qualitative information of the river is calculated by utilizing an RGB ortho-image measuring program, the eutrophication process in the large river is tracked, the change process of eutrophication and vegetation overgrowth is reliably reflected, and the method has very important application value for long-term monitoring and ecological restoration of the large river.
More specifically, the step S1 specifically includes: and the control terminal is used for planning and editing the flight path of the unmanned aerial vehicle in advance, the coverage range of the flight path edited in advance is 70% of the front and side overlapped area, and image data on the flight path is obtained.
In a specific implementation, the single imaging activity of the drone comprises 2 to 3 flights, each flight being 15 minutes, the flight height being about 50 to 70 meters, depending on the weather conditions of the day, the position, coverage and overlap of the images obtained by the drone flight trajectory, for photogrammetric analysis.
More specifically, the step S2 specifically includes the following steps:
s21: acquiring images with sufficient front and side overlap;
s22: monitoring corresponding aerial triangulation and image alignment points, calculating the distance between each point by using a relatively small number of points obtained by three-dimensional coordinate measurement, and constructing a sparse point cloud by using a 3D spherical algorithm;
s23: repeatedly cruising for multiple times by using an unmanned aerial vehicle, continuously increasing the density of the sparse cloud, adding the multiple sparse cloud results, and finally generating a dense cloud;
s24: and carrying out imaging calculation on the dense cloud by using the 3D digital surface model to construct 3D terrain information.
More specifically, the step S3 specifically includes the following steps:
s31: loading a Vextractor program in an unmanned aerial vehicle executive program;
s32: extracting a river streamline from high-resolution image data of 3D terrain information, describing a vector graphic file, deducing and calculating basic hydrological indexes of river geometric properties, and manually correcting the obtained river streamline;
s33: and calculating the curve number of the river channel in the vector graphic file, deducing the values of the average curve amplitude and the wavelength, and fitting the deducted result to the river curvature index of the main river channel section.
More specifically, the step S4 specifically includes: loading an RGB (red, green and blue) ortho-image measuring program in an unmanned aerial vehicle, and automatically calculating an ortho-image spectral index, wherein the index is calculated as follows:
GRVI＝(G-R)/(G+R)
VDVI＝(2×G-G-B)/(2×G+R+B)
wherein GRVI is a green-red vegetation index, and the uncalibrated RGB unmanned aerial vehicle sensor is used for monitoring crop coverage biomass; VARI is a visible atmospheric pressure index, indicating promising performance in grass monitoring; VDVI is the index of vegetation with difference, and the accuracy in vegetation extraction is positive correlation; and calculating the index of each monitoring during the monitoring period, and statistically analyzing the minimum value, the maximum value, the average value and the quartile value of the three indexes.
Example 2
More specifically, on the basis of example 1, Bragg
The brook study area was a 1 km long urbanized area, and in 2015 a complex repair was performed to evaluate that the river was located at the northeast boundary of the bragg metropolitan area, the area between the old housing and the newly developed area. The flow was channelized during the 80's 20 th century during the new construction of roads and highway infrastructure in this area. Due to the development of new residential areas, the 2015 bragg city realized a complex regional revival project not only aimed at restoring the tortuous system of rivers, but also established entertainment infrastructure for the public, including bicycle tracks, amusement parks and parks. The complexity of the whole repair project, including the stabilization of the newly-built channel by the riparian zone grassland and the planting of trees.
The monitoring design for the river restoration area comprises the following steps:
1. unmanned aerial vehicle monitoring device
After the repair work, monitoring with unmanned aerial vehicles in the study area was performed for three years. There were 29 imaging sessions from 9 months 2015 to 4 months 2018. Two preliminary activities were done by model DJI Phantom 2Vision plus platform (DJI, Shenzen, China) drones, followed by 27 activities by model DJI Inspire 1Pro system drones. The unmanned aerial vehicle flight orbit is planned and edited in advance, and 70% of the overlapped area of the front surface and the side surface is covered as much as possible. Imaging sessions were performed at two month intervals, with one imaging session comprising approximately 2-3 flights and each taking time being 15 minutes. The shooting height is 50-71 meters, and about 290-320 pictures are taken each time.
As shown in fig. 2, imaging activities of the drone. (a) Planning a flight route; (b) the position, coverage and overlap of the 292 images produced by the flight trajectory for photogrammetric analysis; (c) the study area is assigned a control point and an inspection point.
2. Photogrammetric processing
Photogrammetric reconstruction of the nadir image of the drone using a motion recovery shape algorithm (SFM), comprising the steps of:
s1, acquiring an image by adopting enough front and side overlapping;
s2, monitoring corresponding aerial triangulation and image alignment points to form a sparse cloud;
s3, generating a dense cloud and obtaining two key deliverable results;
s4, using a digital surface model;
and S5, obtaining quantitative and qualitative information by using an orthoimage measuring tool.
3. Description of channel geometry
And extracting a river streamline from the ortho-image for evaluating the conformity of river restoration and a plan and monitoring the river evolution condition after restoration. The basic shape of the streamlines was inferred using the Vextractor tracking software and the obtained stream streamlines were then corrected manually. From the depicted vector, shapefile, a basic hydrological indicator for calculating the geometric properties of the river is derived. And calculating the curve number of the river channel, deducing values of average curve amplitude and wavelength, and deducing a river curvature index of the main river channel section by using a river measuring tool in QGIS software.
4. Monitoring river habitat changes by applying spectral indexes calculated by RGB ortho-images comprises various measures implemented in a complex repair framework, and aims to restore natural organism habitats and increase biological diversity. The index is calculated as follows:
GRVI＝(G-R)/(G+R
VDVI＝(2×G-G-B)/(2×G+R+B)
wherein GRVI is a green-red vegetation index, and the uncalibrated RGB unmanned aerial vehicle sensor is used for monitoring crop coverage biomass; VARI is a visible atmospheric pressure index, indicating promising performance in grass monitoring; VDVI is the index of vegetation with difference in zone, and the accuracy in vegetation extraction is positive correlation. For all indices, there were five sessions of post-recovery spring and summer calculations during 2016 from month 1 to month 9. For the analysis, the minimum, maximum, mean and quartile values of the area under the area were calculated. And (4) completing index calculation and grid statistics by using a SAGAGIS grid calculator and a grid statistics tool. As shown in figure 3, the pond built in the river has no hydrological connection and is eutrophic.
In the specific implementation process, the method takes a commercial unmanned aerial vehicle with the functions of photographing and shooting as basic equipment, and sets a specified route to perform timed and quantitative photographing on the large river. On the basis, the invention firstly implants the motion recovery shape algorithm into the unmanned aerial vehicle executive program, deduces the position, the coverage area and the overlapping part of the image by the algorithm, and uses the acquired image for photogrammetric reconstruction, thereby realizing the 3D reconstruction of various terrains such as river galleries, coastal zones, peripheral habitats and the like by means of data measured by the unmanned aerial vehicle.
In a specific implementation process, the Vextractor program is loaded in the post-imaging operation of the unmanned aerial vehicle, and the river line is extracted from the high-resolution image, so that the method can be used for evaluating the conformity of river restoration and plan and monitoring the river evolution condition after restoration. Furthermore, the invention loads the RGB ortho-imaging semi-automatic program in the later imaging operation to draw the river, can monitor the change of the river habitat, can track the eutrophication process in the large river according to the calculation of the spectral index, and can reliably reflect the changes of eutrophication and vegetation overgrowth.
In the specific implementation process, the data imaging algorithm and the image extraction program are integrated, the method for monitoring and evaluating the river by using unmanned aerial vehicle surveying and mapping is updated, multiple processing technologies are combined, qualitative and quantitative spatial information can be quickly and excellently extracted from the basic image shot by the traditional unmanned aerial vehicle, and the spatial information is processed into a geographical spatial surveying and mapping result which can be directly utilized. The method provides new thought reference and technical support for the river monitoring method taking scientific experiments as means at present, and has very important application value for long-term monitoring and ecological restoration of large rivers.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (3)
1. A method for monitoring and evaluating a river using unmanned aerial vehicle surveying and mapping, comprising the steps of:
s1: presetting a flight route of the unmanned aerial vehicle, controlling the unmanned aerial vehicle to fly, and acquiring image data on the flight route;
s2: according to the image data, carrying out photogrammetric reconstruction on the lowest point image of the unmanned aerial vehicle by using a motion recovery shape algorithm to construct 3D topographic information;
s3: estimating the basic shape of the river streamline on the 3D topographic information according to the image data by using a Vextractor program, and fitting the basic shape into a river curvature index of the main river channel section;
s4: calculating an ortho-image spectral index by utilizing an RGB ortho-image measuring program and combining with 3D topographic information to obtain river quantitative and qualitative information;
s5: evaluating the physical environment of the river according to the river curvature index of the main river channel section and the quantitative and qualitative river information;
the step S3 specifically includes the following steps:
s31: loading a Vextractor program in an unmanned aerial vehicle executive program;
s32: extracting a river streamline from high-resolution image data of 3D terrain information, describing a vector graphic file, deducing and calculating basic hydrological indexes of river geometric properties, and manually correcting the obtained river streamline;
s33: calculating the number of curves of the river channel in a vector graphic file, deducing the values of the average curve amplitude and wavelength, and fitting the deducing result into a river curvature index of a main river channel section;
the step S4 specifically includes: loading an RGB (red, green and blue) ortho-image measuring program in an unmanned aerial vehicle, and automatically calculating an ortho-image spectral index, wherein the index is calculated as follows:
GRVI＝(G-R)/(G+R)
VDVI＝(2×G-G-B)/(2×G+R+B)
wherein GRVI is a green-red vegetation index, and the uncalibrated RGB unmanned aerial vehicle sensor is used for monitoring crop coverage biomass; VARI is a visible atmospheric pressure index, indicating promising performance in grass monitoring; VDVI is a visible light wave band difference vegetation index, and the accuracy in vegetation extraction is positive correlation; calculating indexes of each monitoring during the monitoring period, and statistically analyzing the minimum value, the maximum value, the average value and the quartile value of the three indexes; and then completing index calculation and grid statistics by using an SAGAGIS grid calculator and a grid statistics tool to obtain river quantitative and qualitative information.
2. The method for monitoring and evaluating river according to claim 1, wherein the step S1 is specifically as follows: and the control terminal is used for planning and editing the flight path of the unmanned aerial vehicle in advance, the coverage range of the flight path edited in advance is 70% of the front and side overlapped area, and image data on the flight path is obtained.
3. The method for monitoring and evaluating a river according to claim 1, wherein the step S2 specifically comprises the following steps:
s21: acquiring images with sufficient front and side overlap;
s22: monitoring corresponding aerial triangulation and image alignment points, calculating the distance between each point by using a relatively small number of points obtained by three-dimensional coordinate measurement, and constructing a sparse point cloud by using a 3D spherical algorithm;
s23: repeatedly cruising for multiple times by using an unmanned aerial vehicle, continuously increasing the density of the sparse cloud, adding the multiple sparse cloud results, and finally generating a dense cloud;
s24: and carrying out imaging calculation on the dense cloud by using the 3D digital surface model to construct 3D terrain information.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101192300B1 (en) * | 2011-09-27 | 2012-12-24 | 계명대학교 산학협력단 | Water level detection method using k-means clustering |
| CN106403904A (en) * | 2016-10-19 | 2017-02-15 | 中国林业科学研究院 | Landscape-scale vegetation coverage calculation method and system based on unmanned aerial vehicle |
| CN108363983A (en) * | 2018-03-06 | 2018-08-03 | 河南理工大学 | A kind of Urban vegetation classification method based on unmanned plane image Yu reconstruction point cloud |
| CN109117811A (en) * | 2018-08-24 | 2019-01-01 | 颜俊君 | A kind of system and method based on low-altitude remote sensing measuring technique estimation urban vegetation coverage rate |
| CN109902571A (en) * | 2019-01-23 | 2019-06-18 | 中交信息技术国家工程实验室有限公司 | A kind of maintenance process of highway planting engineering and system based on unmanned plane |
| CN109945853A (en) * | 2019-03-26 | 2019-06-28 | 西安因诺航空科技有限公司 | A kind of geographical coordinate positioning system and method based on 3D point cloud Aerial Images |
| CN110779498A (en) * | 2019-09-19 | 2020-02-11 | 中国科学院测量与地球物理研究所 | Shallow river water depth mapping method and system based on unmanned aerial vehicle multi-viewpoint photography |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109781191A (en) * | 2018-12-05 | 2019-05-21 | 北京师范大学 | A method of utilizing the unmanned plane image fast inversion discharge of river |
-
2020
- 2020-03-09 CN CN202010158040.4A patent/CN111412899B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101192300B1 (en) * | 2011-09-27 | 2012-12-24 | 계명대학교 산학협력단 | Water level detection method using k-means clustering |
| CN106403904A (en) * | 2016-10-19 | 2017-02-15 | 中国林业科学研究院 | Landscape-scale vegetation coverage calculation method and system based on unmanned aerial vehicle |
| CN108363983A (en) * | 2018-03-06 | 2018-08-03 | 河南理工大学 | A kind of Urban vegetation classification method based on unmanned plane image Yu reconstruction point cloud |
| CN109117811A (en) * | 2018-08-24 | 2019-01-01 | 颜俊君 | A kind of system and method based on low-altitude remote sensing measuring technique estimation urban vegetation coverage rate |
| CN109902571A (en) * | 2019-01-23 | 2019-06-18 | 中交信息技术国家工程实验室有限公司 | A kind of maintenance process of highway planting engineering and system based on unmanned plane |
| CN109945853A (en) * | 2019-03-26 | 2019-06-28 | 西安因诺航空科技有限公司 | A kind of geographical coordinate positioning system and method based on 3D point cloud Aerial Images |
| CN110779498A (en) * | 2019-09-19 | 2020-02-11 | 中国科学院测量与地球物理研究所 | Shallow river water depth mapping method and system based on unmanned aerial vehicle multi-viewpoint photography |
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