KR102118802B1 - Method and system for mornitoring dry stream using unmanned aerial vehicle - Google Patents

Abstract

Disclosed is a method and system for monitoring river dry stream using an unmanned aerial vehicle. According to the embodiment, the method for monitoring the dry-streaming, obtaining an aerial image of a river through an unmanned aerial vehicle, and correcting the aerial image using the location information of the unmanned aerial vehicle and the stream network of the river And determining whether or not to proceed with the stream building of the stream based on the corrected aerial image.

Description

METHOD AND SYSTEM FOR MORNITORING DRY STREAM USING UNMANNED AERIAL VEHICLE}

The embodiments below relate to a method and system for monitoring river dry stream using an unmanned aerial vehicle.

In the field of water resources, surveying equipment and measuring equipment, such as flowmeters, water gauges, and flowmeters, have been installed at specific points in the stream to acquire topographic information through water level, flow, and cross-section surveys of small watersheds. .

In order to build the terrain information acquisition equipment at the river site, it takes a lot of labor and time, and it is also economically expensive. Even if the terrain information acquisition equipment is built in the field, only the measurement value of the specific location where the equipment is built can be acquired due to the characteristics of the equipment.

Recently, aerial images acquired by using unmanned aerial vehicles have diversified information that can be acquired according to imaging techniques and advancement of imaging results. The aerial image acquired by using the unmanned aerial vehicle is not only applicable to various terrain analysis methods, but also competitive in terms of cost compared to on-site measurement, increasing interest and demand in the field.

Most of the stream image information using aerial images and satellite images is qualitative content that can only grasp the change of the flow state of the river flow and the distribution image of the water surface. A scientific analysis method is required to convert the monitoring results of the river's dry stream into objective and quantitative information.

Embodiments can provide a technique for monitoring whether the stream is progressing by using an aerial image of a river acquired through an unmanned aerial vehicle.

According to the embodiment, the method for monitoring the dry-streaming, obtaining an aerial image of a river through an unmanned aerial vehicle, and correcting the aerial image using the location information of the unmanned aerial vehicle and the stream network of the river And determining whether or not to proceed with the stream building of the stream based on the corrected aerial image.

The step of correcting may include obtaining a GPS value of the unmanned aerial vehicle at the time when the unmanned aerial vehicle acquires the aerial image of the river, and the GPS value as a virtual reference station and a ground control point. It may include the step of generating the position information of the unmanned aerial vehicle by correcting in Real Time Kinematic (RTK) method using.

The determining may include generating a digital surface model for the stream based on the corrected aerial image, and river characteristic factors for an arbitrary point in the stream based on the digital surface model. It may include the step of generating, and determining whether or not to proceed with the stream of the river using the characteristic factors of the river.

The determining may include comparing the stream characteristic factor with a past river characteristic factor for an arbitrary point in the river, and generating change information of the river for an arbitrary point in the stream. It may include the step of determining the level of progress of the stream building on the basis of.

The generating of the river characteristic factor includes generating a cross section for an arbitrary point in the stream based on the numerical surface model, stream type data, and a river drawing, and calculating a water level for an arbitrary point in the stream. , Calculating a flow rate and flow rate for an arbitrary point in the stream based on the water level, and the river characteristic factor may include a cross-section, flow rate, flow rate, and water level for an arbitrary point in the stream. .

The generating of the river characteristic factor further includes calculating a flow velocity using the slope of the cross section, and calculating a flow rate using the gradient and the flow velocity, wherein the river characteristic factor is arbitrary of the river. It may further include a river width and the water surface width for the point.

The method compares the digital surface model for the stream generated using the corrected aerial image to the previous numerical surface model, and colors the changed portion of the stream according to the degree to which the digital surface model has changed. The method may further include visualizing the change and providing the user with a change in the visualized river.

The stream type data and the river drawing data may be data based on a Geographic Information System (GIS).

The apparatus for monitoring dry stream according to an embodiment of the present invention includes a receiver for receiving an aerial image of a river obtained through an unmanned aerial vehicle, and using the location information of the unmanned aerial vehicle and the stream network of the river to view the aerial image. And a processor that corrects and determines whether or not the river is going to be dried based on the corrected aerial image.

The processor acquires the GPS value of the unmanned aerial vehicle at the time when the unmanned aerial vehicle acquires the aerial image of the river, and uses the GPS value in real time using a virtual reference station and a ground control point. Position information of the unmanned aerial vehicle may be generated by correcting by a real time kinematic (RTK) method.

The processor generates a digital surface model for the stream based on the corrected aerial image, and generates a river characteristic factor for an arbitrary point in the stream based on the numerical surface model, Using the river characteristic factor, the level of the river's consolidation progress can be determined.

The processor compares the river characteristic factor with a past river characteristic factor for an arbitrary point in the stream, generates change information of the river for an arbitrary point in the stream, and based on the change information You can judge whether or not to proceed with the consolidation.

The processor generates a cross section for an arbitrary point in the stream based on the numerical surface model, river type data, and a river drawing, calculates a water level for an arbitrary point in the stream, and based on the water level Calculate the flow rate and flow rate for any point of, and the river characteristic factors may include the cross-section, flow rate, flow rate and water level for any point of the stream.

The processor calculates a flow rate using the slope of the cross section, calculates a flow rate using the slope and the flow rate, and the river characteristic factor further includes a river width and a water surface width for an arbitrary point in the river. Can.

The processor compares the digital surface model for the stream generated using the corrected aerial image to the previous numerical surface model to color the changed portion of the stream according to the degree to which the digital surface model has changed. It can be visualized with the change of, and provide the user with the change of the visualized river.

The stream type data and the river drawing data may be data based on a Geographic Information System (GIS).

1 is a diagram of a global warming phenomenon caused by natural destruction.
Figure 2 is a block diagram showing a dry stream monitoring system according to an embodiment.
FIG. 3 is a block diagram specifically illustrating the device for monitoring dry stream shown in FIG. 2.
4 and 5 is a view showing the aerial image acquisition and processing of the dry stream monitoring system.
6 is a view for explaining a process of pre-processing the aerial image using the pitch, roll, and yaw of the drone monitoring device.
7 is a diagram for explaining a process of pre-processing aerial images through a VRS-RTK surveying method by a dry stream monitoring device.
8 and 9 show aerial images corrected by the dry-stream monitoring device using VRS-RTK surveying.
FIG. 10 is a diagram for explaining a process in which the dry sky monitoring device matches the corrected aerial image using the brightness correlation method.
FIG. 11 is a diagram for explaining an operation in which a dry stream monitoring device visualizes a changed portion of a stream.
12 and 13 are views for explaining an example of a characteristic factor of a stream generated by an aerial image of the stream by the dry stream monitoring device.
FIG. 14 is a view for explaining the effect of the dry stream monitoring device determining the stream dry stream using an aerial image of the stream.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, various modifications may be made to the embodiments, and the scope of the patent application right is not limited or limited by these embodiments. It should be understood that all modifications, equivalents, or substitutes for the embodiments are included in the scope of rights.

The terms used in the examples are for illustrative purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, the terms "include" or "have" are intended to indicate the presence of features, numbers, steps, actions, components, parts or combinations thereof described in the specification, one or more other features. It should be understood that the existence or addition possibilities of fields or numbers, steps, operations, components, parts or combinations thereof are not excluded in advance.

The terms first or second may be used to describe various components, but the components should not be limited by the terms. The terms are for the purpose of distinguishing one component from another component, for example, without departing from the scope of rights according to the concept of the embodiment, the first component may be referred to as the second component, and similarly The second component may also be referred to as the first component.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person skilled in the art to which the embodiment belongs. Terms, such as those defined in a commonly used dictionary, should be interpreted to have meanings consistent with meanings in the context of related technologies, and should not be interpreted as ideal or excessively formal meanings unless explicitly defined in the present application. Does not.

In addition, in the description with reference to the accompanying drawings, the same reference numerals are assigned to the same components regardless of reference numerals, and redundant descriptions thereof will be omitted. In describing the embodiments, when it is determined that detailed descriptions of related well-known technologies may unnecessarily obscure the subject matter of the embodiments, detailed descriptions thereof will be omitted.

1 is a diagram of a global warming phenomenon caused by natural destruction.

Referring to FIG. 1, climate change and global warming due to natural destruction of humans are becoming more and more problematic as they enter the 21st century, and may be the most important social and environmental problems faced by all humanity.

According to the fourth report of the Intergovernmental Panel on Climate Change (IPCC), the average temperature of the Earth will rise further up to 6.3 °C at the end of the 21st century and forecast frequent meteorological disasters.

Reduction of green space and increase in impermeable layer due to industrial development and urbanization can make it difficult to cultivate groundwater and cause hydrological problems such as groundwater reduction and groundwater level reduction. For example, a decrease in green space and an increase in the impermeable layer causes rainfall, which is a major source of river water, to be discharged within a short time, and accordingly, there is a lack of groundwater in the dry season, which may act as a cause of stream consolidation.

A dry stream may be a river that is deteriorating or has already deteriorated so that it cannot maintain its natural function. The dry stream can be determined by the flow rate that is unable to maintain its natural function and the period during which the flow rate continues. In addition, the river cheonhwahwa (河川乾川化) may be a state in which the nature of the river is changed to a property that satisfies the conditions of the stream due to various causes.

In recent years, the phenomenon of stream building in rural areas is also progressing rapidly, and the function of providing abundant quantity of rivers and comfort of the environment that has been traditionally maintained may be deteriorated. According to a recent study, a number of small rivers and rural second-class rivers in rural areas as well as a number of local first-class rivers are in progress, and the tendency to increase is rapidly increasing every year. Provision of preventive measures may be urgent.

The causes affecting the consolidation of small rivers in rural areas can be diverse. Preventive measures may also need to be changed depending on the cause affecting each tendonization. The causes of small rivers in rural areas are estimated to be poor development of idle land, excessive use of groundwater, indiscriminate river improvement, river exit, etc., but it is not possible to accurately analyze the cause, and only outline measures have been made. It may be true.

Various studies on information construction based on geographic space have been actively conducted in order to identify the cause of consaturation and prepare preventive measures. For example, remote exploration using unmanned airborne vehicles (UAVs) can be used to build information based on terrain space. The photogrammetry method using an unmanned aerial vehicle can be actively applied to acquiring 3D terrain data, starting with the detection method for disaster observation.

In the field of water resources, a method of investigating the site by installing equipment such as a flow meter, a water level gauge, and a flowmeter at a specific point in a stream may be applied to acquire topographic information through surveying the water level, flow rate, and river cross section of a small watershed. In order to build the terrain information acquisition equipment at the river site, it takes a lot of labor and time and can be costly economically. In addition, even if the terrain information acquisition equipment is built in the field, only the measurement value of the specific location where the equipment is constructed may be acquired due to the characteristics of the equipment.

Recently, aerial images acquired using unmanned aerial vehicles have diversified information that can be acquired according to the shooting technique and the advancement of the results. The aerial image acquired by using the unmanned aerial vehicle is not only applicable to various terrain analysis methods, but also competitive in terms of cost compared to on-site measurement, increasing interest and demand in the field.

Most of the stream image information using aerial images and satellite images is qualitative contents that can only grasp the change of the flow state of the river flow and the distribution image of the water surface. A scientific analysis method is required to convert the monitoring results of the river's dry stream into objective and quantitative information.

According to an embodiment, a method for monitoring river dry stream using an unmanned aerial vehicle can quantify changes in river characteristics, and quantitatively provide results of disaster characteristics such as drought and flood of a river, and changes in river water level and flow rate. .

The embodiments may provide a stream monitoring system capable of analyzing the cause of the stream and determining whether the stream is progressing by monitoring the stream of the target area using drone image utilization technology in an area where the stream is in progress.

In addition, the embodiments can provide a technique that can be used as data for future tendonization prevention by temporally and temporally tracking a causal factor affecting tendonization.

Hereinafter, embodiments will be described with reference to FIGS. 2 to 14.

Figure 2 is a block diagram showing a dry stream monitoring system according to an embodiment.

Referring to FIG. 2, the dry sky monitoring system 10 includes an unmanned aerial vehicle 100 and a dry sky monitoring device 200.

The dry stream monitoring system 10 may determine whether the stream has been streamened based on the aerial image of the stream acquired by the unmanned aerial vehicle 100. The dry stream monitoring system 10 may provide data for use in river stream analysis by using an aerial image of a river acquired by the unmanned aerial vehicle 100 and geographic information system (GIS) data. GIS data may be data such as a numerical surface model (DSM), a digital elevation model (DEM), land use and soil map.

The dry stream monitoring system 10 automatically downloads rainfall and flow data for the target watershed for a period set by the user through routes such as the Korea Meteorological Administration, National Water Resources Management Information System (WAMIS), and the flood control station, and holds the actual flow rate of the station. It is possible to provide the user with a visual confirmation of the situation, rainfall, and timeliness of the flow and flow rate through graphs and tables.

The dry stream monitoring system 10 sets the start time and the end time of the rainfall event in the graph of the confirmed rainfall and flow data to automatically generate the rainfall event and the corresponding flow data as a weather input file, which is the input data of the model. It can be provided to be used to build weather data.

The dry stream monitoring system 10 can automatically extract the watershed characteristic parameters and parameters related to rainfall- runoff hydrological models from terrain and weather data and convert them into the input data format of the model.

The dry stream monitoring system (10) uses watershed area, average elevation, average slope/distribution through top and bottom post-processing tasks using GIS functions based on the river, basin boundary, DEM, land use, and soil map data of the target watershed. The watershed input file can be generated by estimating the watershed characteristic factors such as the mean flow path slope and the longest flow path, and model parameters such as NRCS-CN, arrival time, and storage constant. Through the implementation of this process, the dry stream monitoring system 10 can calculate the outflow hydrological curve for a desired point in the watershed at a time without separate control.

The dry stream monitoring system 10 may perform image analysis according to vegetation and topography, facilities, and groundwater usage around the stream using GIS data. The dry stream monitoring system 10 monitors stream changes and analyzes the causes of stream changes by comparing the aerial images of the unmanned aerial vehicle 100 acquired in the past with the current aerial images. And statistically analyzing and displaying changes in hydrological data.

The building monitoring system 10 is an unmanned aerial vehicle 100 image data management system and may include an open source GIS-based data processing function. The building monitoring system 10 can implement the GIS data processing function using MapWindow developed by Idaho State University, one of the Open GIS. MapWindow is a 2D GIS developer library based on ActiveX Component that supports GIS data management and editing functions in conjunction with Spatial Server.It can be developed in various languages that support ActiveX Components such as C#, VB, VC++, Delphi, etc. It may be possible to develop a GIS system easily and quickly. MapWindow supports various GIS data formats such as SHP, DXF, DGN, MDB, GRID, and ASC, and image raster data formats of aerial images and satellite images such as TIFF, JPG, GIF, so you can easily use existing data without additional data conversion. have. MapWindow can improve the development environment and shorten the development time by maximizing user convenience by applying the technology to quickly display the displayed map through the spatial indexing technology that can quickly search and process large amounts of spatial data.

The Geocheonhwa Monitoring System 10 is an ActiveX Component-based 2D GIS developer library that supports GIS data management and editing functions developed by Idaho State University to develop a framework for water resource geographic information systems for post-processing and display of data. It can be a system that uses MapWindow and Microsoft Visual Studio 2008 C#.NET language and does not rely on existing expensive commercial GIS software.

An unmanned aerial vehicle (100) can fly over a stream and acquire an aerial image of the stream. The unmanned aerial vehicle 100 may be a remotely controlled flight device that a pilot does not board, such as a drone.

The dry sky monitoring device 200 may correct the aerial image acquired by the drone 100.

The dry stream monitoring device 200 may generate a numerical surface model and stream characteristic parameters for a stream based on an aerial image acquired by the drone 100.

The dry stream monitoring apparatus 200 may determine whether the stream is dry or not based on a numerical surface model and a river characteristic factor.

The dry stream monitoring apparatus 200 may calculate the river width and the water surface width based on the aerial image acquired by the drone 100.

The dry stream monitoring apparatus 200 may visualize a changed portion of the river based on the aerial image acquired by the drone 100.

FIG. 3 is a detailed block diagram of the dry flower monitoring device illustrated in FIG. 2.

Referring to FIG. 3, the cheoncheon monitoring device 200 may include a receiver 210 and a processor 220.

The receiver 210 may receive an aerial image of a river acquired through the drone 100. The receiver 210 may receive data based on a Geographic Information System (GIS).

The processor 220 may correct the aerial image acquired by the unmanned aerial vehicle 100 by using the location information of the unmanned aerial vehicle 100 and the stream network of the river. The processor 220 acquires the GPS value of the unmanned aerial vehicle 100 at the time when the unmanned aerial vehicle 100 acquires the aerial image of the river, and generates location information of the unmanned aerial vehicle 100 including the obtained GPS value Can. For example, the processor 220 corrects the GPS value in a Real Time Kinematic (RTK) method using a virtual reference station and a ground control point to position the unmanned aerial vehicle 100. Information can be generated. The location information of the unmanned aerial vehicle 100 may include information about the pitch, roll, and yaw of the unmanned aerial vehicle 100. The process of acquiring and processing the aerial image (for example, the process includes a correction process) will be described in detail with reference to FIGS. 4 to 10.

The processor 220 may generate a digital surface model for the river based on the corrected aerial image.

The processor 220 may generate a river characteristic factor for an arbitrary point in the stream based on the numerical surface model. Stream characteristic factors can include cross-section, flow rate, flow rate, and water level for any point in the stream. The river characteristic factor may further include a river width and a water surface width for an arbitrary point in the stream calculated using a cross section for an arbitrary point in the stream.

The processor 220 may determine whether or not to stream the stream by using the river characteristic factor.

For example, the processor 220 compares the river characteristic factor for an arbitrary point in the river with the past river characteristic factor for an arbitrary point in the stream to generate information about the change of the river for an arbitrary point in the stream. Can. The processor 220 may determine whether or not to stream the stream based on the change information of the stream.

When the flow rate, flow rate, and water level change information for an arbitrary point in the stream is a negative value and is equal to or less than a predetermined value, the processor 220 may determine that the stream is in the stream.

The processor 220 may calculate a stream width and a water surface width of the stream using a cross section for an arbitrary point in the stream. The processor 220 may provide a user with a river width and a water surface width.

The processor 220 may generate flow velocity information for an arbitrary point in the stream by using the slope of the cross section for an arbitrary point in the stream. The processor 220 may generate flow rate information for an arbitrary point in the river by using the slope of the cross-section for an arbitrary point in the stream and flow velocity information for an arbitrary point in the stream.

The processor 220 may calculate the flow rate of the river as Equation 1 below.

는 동수반경,

는 경사 h는 임의지점의 수위, n은 manning의 조도계수를 의미할 수 있다.V is the flow rate,

Is the same radius,

Where h is the water level at any point, and n is the manning roughness coefficient.

The processor 220 may calculate the flow rate of the river using Equation 2 below.

는 동수반경,

는 경사 h는 임의지점의 수위, n은 manning의 조도계수를 의미할 수 있다.Q is the flow rate, A is the cross-sectional area of the river, V is the flow rate,

Is the same radius,

Where h is the water level at any point, and n is the manning roughness coefficient.

The processor 220 may generate a cross section for an arbitrary point in the stream based on a numerical surface model, river type data, and a river drawing, and calculate a water level. Processor 220 may calculate the flow rate and flow rate for any point in the stream based on the water level information for any point in the stream. For example, stream type data and river drawing data may be data based on a Geographic Information System (GIS).

In addition, the processor 220 compares the numerical surface model with the previous numerical surface model, and visualizes the changed portion of the river as a color change according to the degree to which the numerical surface model is changed by comparing the numerical surface model with the previous numerical surface model. )can do. The processor 220 may provide the user with changes in visualized rivers. The details of the processor 220 visualizing the changed portion of the river will be described with reference to FIG. 11.

4 and 5 is a view showing the aerial image acquisition and processing of the dry stream monitoring system.

4 to 5, the aerial image acquisition and processing process of the dry sky monitoring system includes setting the shooting path (a) and image acquisition (b) of the unmanned aerial vehicle 100, and preprocessing the image of the dry sky monitoring device 200 (c). ), image matching (d), image extraction (e), terrain measurement (f), and terrain analysis (g).

In (a) of FIG. 4, in order to acquire an aerial image with the unmanned aerial vehicle 100, a river aerial photographing path of the unmanned aerial vehicle 100 may be first set. The altitude of photographing the unmanned aerial vehicle 100 may be calculated by Equation 3 as a factor for determining the spatial resolution (GSD) of the image.

GSD means spatial resolution, PS means pixel size, H means shooting altitude, and f means focal length.

The shooting altitude is an element that determines the sharpness of the image, and the user can determine the spatial resolution of the image according to the shooting altitude. If the shooting altitude is high, a large area can be photographed, while the spatial resolution is lowered. If the shooting altitude is low, the spatial resolution is increased, while a large area may not be photographed.

In addition, the spatial resolution may vary depending on the performance of the imaging sensor. At the same altitude, the 20 million pixel imaging sensor may have higher spatial resolution than the 10 million pixel imaging sensor. Therefore, the shooting altitude of an unmanned aerial vehicle image can be determined in consideration of the spatial resolution and the shooting area suitable for the purpose.

In (b) of FIG. 4, the unmanned aerial vehicle 100 may acquire an image of a river basin by setting a shooting altitude suitable for a purpose.

4( c) shows an aerial image preprocessing process acquired by the unmanned aerial vehicle 100. The aerial image pre-processing process uses the aerial image acquired by the unmanned aerial vehicle 100 using the information of the pitch, roll, and yaw of the unmanned aerial vehicle 100 and/or VRS (Virtual Reference Station)-RTK (network Real Time Kinematic) survey method. It can be a calibration process. In FIG. 6, the process of correcting using the information of the pitch, roll, and yaw of the aircraft 100 will be described in detail, and in FIG. 7, the process of correcting using the VRS-RTK survey method will be described in detail.

In (d) of FIG. 4, the dry stream monitoring apparatus 200 may perform a process of matching the aerial images of the corrected river to collect the aerial images of the entire river. For example, a matching process for collecting aerial images may use a brightness value correlation method. The matching process using the brightness value correlation method will be described in detail with reference to FIG. 10.

In (e), (f), and (g) of FIG. 5, the dry stream monitoring device 200 determines whether or not to dry stream through an aerial image extraction process, a terrain measurement process, and a terrain analysis process using the matched aerial image. Can generate data for

6 is a view for explaining a process of pre-processing aerial images using the pitch, roll, and yaw of the drone monitoring device.

Referring to FIG. 6, even if the unmanned aerial vehicle 100 photographs a river vertically, accumulation of images may not be the same when there are undulations on the surface of the river. Therefore, the image may have a radial displacement around the vertical point in the image plane. In addition, when the drone 100 is shaken and photographed, a tilted image may be photographed.

The dry stream monitoring apparatus 200 may need information about the pitch, roll, and yaw of the unmanned aerial vehicle 100 to correct the aerial image acquired by the unmanned aerial vehicle 100. It is possible to perform a 3D rotation conversion of an image with information of the Picth(ω), Roll(ψ), and Yaw(χ) of the unmanned aerial vehicle 100.

The Pitch(ω) conversion equation can be calculated by Equation 4.

The Pitch(ω) conversion equation can be calculated by Equation 5.

The Yaw(χ) conversion equation can be calculated by Equation 6.

The rotation conversion equation of the aerial image of the unmanned aerial vehicle 100 may be calculated by Equation 7.

7 is a diagram for explaining a process of pre-processing aerial images through a VRS-RTK surveying method by a dry stream monitoring device.

Referring to FIG. 7, the location of the aerial image of the unmanned aerial vehicle 100 may be represented as coordinates of the image through the GPS value of the unmanned aerial vehicle 100 when photographing. In general, since the positioning accuracy of GPS is about 2 to 6m, it is possible to improve the accuracy of the image by measuring the GCP (Ground Control Point). In particular, the positional accuracy of the image of the unmanned aerial vehicle 100 periodically monitoring the same area may require precise correction.

The dry stream monitoring device 200 may perform a Virtual Reference Station (VRS)-network Real Time Kinematic (RTK) survey to improve the location accuracy of the aerial image acquired by the drone 100. As a method of network RTK, VRS can separate and model systematic errors using a reference station network composed of GPS stations, and generate surveys by generating virtual reference points as observed at any location in the network. The VRS-RTK survey may be a survey method for determining a precise mobile station location through a virtual reference point (VRS) and an RTK between the mobile station.

In the VRS-RTK survey method, the reference station may receive GPS data and transmit it to the control station (801). The control station may generate a correction value through the collected reference station data (802). The user may transmit the current location information to the control station (803). The control station may transmit a correction value corresponding to the location requested by the user (804). The user may obtain precise coordinates through the received correction value (805).

The VRS-RTK surveying method can obtain high-quality surveying performance by obtaining positioning results with accuracy of 1 to 2 cm in real time from a mobile station (Rover) using a correction value for a carrier phase of a reference station having precise location information.

8 and 9 show aerial images corrected by the dry-stream monitoring device using VRS-RTK surveying.

8 to 9 are aerial images of Sinheungcheon, which are members of Cheongmicheon that were obtained by the unmanned aerial vehicle 100 at different times and went through the calibration process of the dry stream monitoring device 200.

The dry stream monitoring device 200 improves the positioning accuracy of the unmanned aerial vehicle 100 image through VRS-RTK surveying, thereby photographing the shape of a specific point by time in an aerial image created with a spatial resolution of 8 cm as shown in FIGS. Recognition may also be possible.

If the same area is periodically monitored in agricultural disaster assessment using remote sensing, accurate location accuracy correction using VRS-RTK may be necessary. The position correction process of the unmanned aerial vehicle 100 can be regarded as an important process because information such as a farm road or a farm water channel is acquired in the image where the position is not corrected when obtaining information from the remote sensing image.

FIG. 10 is a diagram for explaining a process in which the dry sky monitoring device matches the corrected aerial image using the brightness correlation method.

Referring to FIG. 10, the most basic process in aerial photography of the unmanned aerial vehicle 100 may be to find a conjugate point in an overlapping region.

The corrected aerial image matching method may be a method of finding an actual object corresponding to a position of one image of an aerial image at a position of another image and matching the corresponding location.

The aerial image matching method may go through a process of moving a target area defined in one image by one point in the search area of another image to calculate statistically similar observation values for the key point. This method can be referred to as gray value correlation. The dry sky monitoring device 200 may perform a matching process of aerial images using a brightness value correlation method.

The dry stream monitoring apparatus 200 may generate the aerial video of the river, which is the basis for determining whether the dry stream is in progress by matching the corrected aerial images.

The dry stream monitoring apparatus 200 may generate a digital surface model (DSM) of a stream based on the generated aerial image of the stream.

FIG. 11 is a diagram for explaining an operation in which the dry stream monitoring device visualizes a changed portion of a stream.

In FIG. 11, the aerial image of the river visualized through comparison of the current numerical surface model generated by the dry-surface monitoring device 200 based on the past numerical surface model and the aerial image of the river is shown.

The dry sky monitoring device 200 may include an aerial image synthesis and update function. The aerial video synthesis and update function may be a function of combining and updating two different grid data into one grid data.

The aerial image synthesis and update function can be used to maintain the latest grid data by updating the cell value, resolution and spatial range based on the grid data for application when the cell values, resolution and spatial range of the two grid data are different.

The aerial video synthesis and update function can be applied to update the grid data such as DEM and land use data to the latest grid data when there is a difference.

The aerial image synthesis and update function changes the runoff characteristics according to changes in the watershed characteristic factors and parameters that can be extracted from time series grid data such as DEM and land use among input data of hydrological models used to analyze stream outlook. It can be used to analyze.

The dry stream monitoring device 200 compares errors of each cell of grid data, such as the past numerical surface model and the current numerical surface model (or numerical elevation model), or past land use and current land use. You can do

As shown in FIG. 11, the aerial image of the visualized river may indicate that the error value of each cell is greater as the color is darker and the error value is smaller as the color is lighter.

Visualized aerial images of rivers can be used as basic data to determine the extent of changes in a range of areas in past and present numerical surface models or land use grid data, and to analyze the causes of them.

12 and 13 are views for explaining an example of a characteristic parameter of a stream generated by an aerial image of the stream by the dry stream monitoring device.

12 and 13 are cross-sections of arbitrary points of the stream as characteristic parameters of the stream. In order to examine the utilization of the dry stream monitoring system 10, a cross-section data including a high water section and a low water section of each stream was extracted assuming an arbitrary cross-section line in the stream constituted by the dry stream monitoring device 200.

The cross section of the river in the upper part of the bridge, which is essential information for the basic plan for river maintenance and facility planning in the river, was extracted, and the longitudinal section of the river was extracted along the center line and the core line.

As shown in (a) of FIG. 12, a lateral line for extracting a longitudinal cross-section of a river including a stream topography and a coriander was displayed.

If a facility is planned on a crossing line that does not match the lateral line established in the river basic plan, a separate local survey is essential, but if the stream topography constructed by the dry stream monitoring system 10 is used, as shown in FIG. 12(b) With a simple task like this, you can easily obtain survey results such as local surveys.

The stream topography extracted from the dry stream monitoring system 10 includes information on various facilities, such as roads and buildings located in the high water, as well as the river topography, so it can be highly utilized for facility planning, land use change, and river facility database.

When the method of extracting stream topography through the dry stream monitoring system 10 is very economical and time-efficient compared to the existing stream surveying method, it can shorten the time interval of long-term stream information collection, so when it is used in connection with the stream information database It can be effectively used in the field of observing and managing long- and short-term changes in river topography through accumulated river crossing information.

As illustrated in FIGS. 1 to 8 of FIG. 13, the user can extract stream cross-section information at a cross-section point previously registered with the dry-stream monitoring device 200. In addition, the user may perform river cross-section information extraction at an arbitrary point designated by the user with the dry sky monitoring device 200 according to an embodiment.

14 is a view for explaining the effect of determining the dry stream of the stream using the aerial image of the stream monitoring device.

In FIG. 14, for convenience of description, the dry stream monitoring device 200 compares the measured value of the stream width with the stream width calculated by the dry stream monitoring device based on the aerial image of the stream.

The accuracy of the accuracy by comparing and analyzing the readings of river information that is recognizable in the aerial image of the unmanned aerial vehicle 100 and the length of the actual river width before and after aerial photographing of the river region to be tested through the drying monitoring device 200 Judging and assuming arbitrary species and cross-sections in the stream, river section data including road, building, vegetation, and facility information was extracted.

Streaming monitoring device 200 automatically calculated the river width according to the outflow after rainy days (at least 20 days) through stream cross-section data, and basic data for conducting research on the stream drying effect factors using aerial photography It was used for construction.

As shown in the graph of FIG. 14, the image readout performed by the dry stream monitoring device 200 is data having an error of about 10 cm or more when compared with the measured value of the stream width, and thus can be sufficiently used to construct basic data for dry stream monitoring.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, or the like alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiments or may be known and usable by those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs, DVDs, and magnetic media such as floptical disks. Includes hardware devices specifically configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language code that can be executed by a computer using an interpreter, etc., as well as machine language codes produced by a compiler. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may include a computer program, code, instruction, or a combination of one or more of these, and configure the processing device to operate as desired, or process independently or collectively You can command the device. Software and/or data may be interpreted by a processing device, or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodied in the transmitted signal wave. The software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described by the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the components of the described system, structure, device, circuit, etc. are combined or combined in a different form from the described method, or other components Alternatively, proper results can be achieved even if replaced or substituted by equivalents.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (16)

Obtaining an aerial image of a river and location information of the unmanned aerial vehicle through an unmanned aerial vehicle;
Correcting the aerial image using location information of the unmanned aerial vehicle and a stream network of the river; And
Determining whether or not to proceed with the stream building on the basis of the corrected aerial image.
Including,
The determining step,
Generating a digital surface model for the river based on the corrected aerial image;
Generating a river characteristic factor for an arbitrary point of the stream based on the numerical surface model; And
Determining whether or not to proceed with the stream building by using the river characteristic factor
A method for monitoring cheonseonghwa that includes.
According to claim 1,
The correction step,
Obtaining a GPS value of the unmanned aerial vehicle at the time when the unmanned aerial vehicle acquires the aerial image of the river; And
Generating location information of the unmanned aerial vehicle by correcting the GPS value in a Real Time Kinematic (RTK) method using a virtual reference station and a ground control point
A method for monitoring cheonseonghwa that includes.
delete According to claim 1,
The step of determining whether or not to proceed with the stream building is performed using the river characteristic factor,
Comparing the river characteristic factor with a past river characteristic factor for an arbitrary point in the stream to generate information about a change in the river for an arbitrary point in the stream; And
Determining the level of the river's stream building progress based on the change information
A method for monitoring cheonseonghwa that includes.
According to claim 4,
The step of generating the river characteristic factor,
Generating a cross section for an arbitrary point in the stream based on the numerical surface model, stream type data, and a river drawing, and calculating a water level for an arbitrary point in the stream; And
Calculating flow rates and flow rates for any point in the stream based on the water level
Including,
The river characteristic factor is a stream monitoring method comprising a cross-section, flow rate, flow rate and water level for any point of the stream.
The method of claim 5,
The step of generating the river characteristic factor,

Calculating a flow velocity using the slope of the cross section; And
Calculating a flow rate using the slope and the flow rate
Further comprising,
The river characteristic factor further includes a river width and a water surface width for any point of the stream monitoring monitoring.
According to claim 1,
The digital surface model for the stream generated using the corrected aerial image is compared with the previous numerical surface model to visualize the changed part of the stream as a change in color according to the degree to which the numerical surface model has changed. (visualization); And
Steps to provide users with visible stream changes
A method for monitoring the dry flowers further comprising.
The method of claim 5,
The stream type data and the river drawing data are based on GIS (Geographic Information System).
How to monitor dry-scaling.
A receiver that receives aerial images of rivers obtained through an unmanned aerial vehicle and location information of the unmanned aerial vehicle; And
A processor that corrects the aerial image by using the location information of the unmanned aerial vehicle and the river network map of the river, and determines whether the river is proceeded to be hardened based on the corrected aerial image
Including,
The processor,
A digital surface model for the stream is generated based on the corrected aerial image, and a river characteristic factor is generated for an arbitrary point in the stream based on the numerical surface model, and the river characteristic factor is generated. A dry stream monitoring device for determining whether to stream dry stream using the method.
The method of claim 9,
The processor,
Real-time mobile surveying (RTK) that acquires the GPS value of the unmanned aerial vehicle at the time when the unmanned aerial vehicle acquires the aerial image of the river and uses the GPS value as a virtual reference station and a ground control point. , Real Time Kinematic) method to generate location information of the drone
Dry Stream Monitoring Device.
delete The method of claim 9,
The processor,
Compare the river characteristic factor with the past river characteristic factor for an arbitrary point in the stream, generate change information of the river for an arbitrary point in the stream, and generate a stream of progress of the stream based on the change information. Judging the level
Dry Stream Monitoring Device.
The method of claim 12,
The processor,
Based on the numerical surface model, river type data and river drawings, a cross section is generated for any point in the stream, and the water level is calculated for any point in the stream,
Calculate the flow rate and flow rate for any point in the stream based on the water level,
The river characteristic factor is a stream monitoring device comprising a cross-section, flow rate, flow rate and water level for any point of the stream.
The method of claim 13,
The processor,
The flow rate is calculated using the slope of the cross section, and the flow rate is calculated using the slope and the flow rate,
The river characteristic factor further includes a river width and a water surface width for any point of the stream monitoring monitoring device.
The method of claim 9,
The processor,
The digital surface model for the stream generated using the corrected aerial image is compared with the previous numerical surface model to visualize the changed part of the stream as a change in color according to the degree to which the numerical surface model has changed. (visualization), providing users with visualized changes in the river
Dry Stream Monitoring Device.
The method of claim 13,
The stream type data and the river drawing data are based on GIS (Geographic Information System).
Dry Stream Monitoring Device.

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