CN116818608A - Land gas flux detection method and device - Google Patents
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Abstract
The application provides a land gas flux detection method and a device, wherein the method comprises the following steps: detecting the gas concentration and the gas image factor by an unmanned aerial vehicle device, wherein the unmanned aerial vehicle device comprises a weather station and a gas analyzer; based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface; determining a land gas flux from the gas concentration and the turbulent diffusion coefficient; the turbulent diffusion coefficient model is established by: detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device; based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces; and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model. The application can realize accurate estimation of the flux of different types of underlying surfaces.
Description
Technical Field
The application relates to the technical field of observation, in particular to a land gas flux detection method and device.
Background
Flux refers to the vertical transport of energy and substances, including momentum, heat sensing, latent heat (steam), and carbon dioxide flux, near the surface due to turbulence, and is a quantitative way of mass energy exchange from ground loop to biosphere to atmosphere loop. Land-based processes refer to processes that involve the exchange of moisture, heat and substances between the atmosphere and the land-based mat, including all physical, chemical, biological and hydrologic processes on land. Global climate warming results in a sustained significant rise in atmospheric carbon dioxide concentration, while terrestrial and atmospheric interactions have a significant impact on atmospheric circulation and regional and global climate change. Therefore, it is well understood that the water, carbon and energy recycling processes of the terrestrial ecosystem are not only to discuss human intervention and regulation of global warming processes, and to alleviate strategic demands for shortage of fresh water resources, but also to human regulation of territory-biosphere-atmospheric interactions, maintenance of normal matter and energy recycling of the global ecosystem, assessment of ecological pathways and cut-in points for carbon peaks and carbon neutralization of the ecosystem.
The direct observation of flux is a key to accurately acquiring the exchange of substances and energy between the earth surface and the atmosphere, but the direct observation of flux is still limited to the traditional ground tower foundation observation of typical 'point' scale, the observation range of the direct observation of flux can only represent the region of tens to hundreds of meters around the underlying surface of the type where the observation tower is located, the observation result which is matched with the coarse grid level in the high spatial resolution and the land process model in remote sensing on the spatial scale is difficult to be provided, the flux observation data of a large number of underlying surfaces of atypical type are more difficult to acquire, and the direct observation of flux is one of the important reasons for restricting the flux research in the region and the global scale at present. Therefore, obtaining flux observation data of a large number of atypical undersides consistent with the spatial scale simulated by remote sensing or land process models to represent the direct observation of the earth surface flux of a larger spatial coverage is the current research focus and difficulty.
Disclosure of Invention
The embodiment of the application aims to provide a land gas flux detection method and a device, which can realize accurate observation of different types of underlying surface fluxes.
In order to achieve the above object, an embodiment of the present application provides a land gas flux detection method, including: detecting the gas concentration and the gas image factor by an unmanned aerial vehicle device, wherein the unmanned aerial vehicle device comprises a weather station and a gas analyzer; based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface; determining a land gas flux from the gas concentration and the turbulent diffusion coefficient; the turbulent diffusion coefficient model is established by: detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device; based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces; and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
Preferably, the meteorological factors include: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
Preferably, determining the land gas flux from the gas concentration and the turbulent diffusion coefficient comprises: the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, and K is the turbulence diffusion coefficient.
Preferably, determining the turbulence diffusion coefficient of the different types of underlying surfaces based on the flux towers of the different types of underlying surfaces in combination with the morning-obhuff similarity theory comprises: obtaining a universal function of friction wind speed and stability parameters based on the flux tower; the turbulence diffusion coefficient is determined by the following formula:
wherein k is Von-Karma constant, μ * For friction wind speed, z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
Preferably, obtaining a generic function of friction wind speed and stability parameters based on the flux tower comprises: detecting friction wind speed and momentum sensible heat flux based on the flux tower; the universal function of the stability parameter is determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,mu as a general function of the stability parameter * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights.
The embodiment of the application also provides a land gas flux detection device, which comprises: the device comprises a detection unit, a processing unit and a model building unit. The detection unit is used for detecting the gas concentration and the gas image factor through an unmanned aerial vehicle device, and the unmanned aerial vehicle device comprises a weather station and a gas analyzer; the processing unit is used for: based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface; determining a land gas flux from the gas concentration and the turbulent diffusion coefficient; wherein the model building unit builds a turbulent diffusion coefficient model by: detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device; based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces; and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
Preferably, the meteorological factors include: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
Preferably, the processing unit is configured to: the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, and K is the turbulence diffusion coefficient.
Preferably, the processing unit is configured to: obtaining a universal function of friction wind speed and stability parameters based on the flux tower; the turbulence diffusion coefficient is determined by the following formula:
wherein k is Von-Kama constant, mu * For friction wind speed, z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
Preferably, the processing unit is configured to: detecting friction wind speed and momentum sensible heat flux based on the flux tower; the universal function of the stability parameter is determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,mu as a general function of the stability parameter * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights.
Through the technical scheme, the embodiment of the application provides the land gas flux detection method and the land gas flux detection device, key variables are parameterized, the observed value of the near-ground gas concentration is obtained in a short time, and the accurate observation of the different types of underlying surface fluxes of the region can be realized by calculating by combining the flux estimation method.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 is a schematic perspective view of a gradient flux observation device according to an embodiment of the present application;
FIG. 2 is an enlarged view of a portion of FIG. 1;
FIG. 3 is a flow chart of a land gas flux detection method according to an embodiment of the present application;
FIG. 4 is a flow chart of a method for modeling a diffusion coefficient of turbulence according to an embodiment of the present application;
FIG. 5 is a schematic diagram comparing the observed data obtained by the method of the present application with the observed data obtained by the current prior art vorticity related observation system employing a flux tower;
fig. 6 is a block diagram of a land gas flux detection device according to an embodiment of the present application.
Description of the reference numerals
1. Unmanned plane; 11. a support leg; 12. a GPS module; 2. a gas analyzer; 3. a weather station; 4. an extension rod; 5. a fixing frame; 51. an upper plate body; 52. a lower plate body; 53. a connecting cylinder; 54. a connecting rod; 6. a data collector; 61. a detection unit; 62. processing unit 63, model building unit
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
According to the embodiment of the application, the unmanned plane device and the flux gradient observation method are combined together to carry out the application of the airborne gradient observation equipment, the unmanned plane device can obtain the observation values of the gas concentrations at different heights near the ground in a short time, and the flux estimation method is combined to carry out the calculation, so that the accurate estimation of the flux of the underlying surfaces of different types in the region can be realized, and the method has important significance for the development of the flux observation technology. The on-board flux observation system is developed, the standard, the method and the standard of mobile type carbohydrate flux observation equipment and observation products are established, technical support is provided for forming an ecological weather three-dimensional observation of an integration of space, earth and air, and the national carbon-to-peak carbon neutralization strategy is served.
Referring to fig. 1 and 2, the present application provides a unmanned aerial vehicle apparatus for implementing the present application, including: a hovering unmanned aerial vehicle 1, a gas analyzer 2 and a weather station 3. A gas analyzer 2 is connected to the hovering drone 1, the gas analyzer 2 being adapted to obtain CO 2 Concentration, moisture concentration and CH 4 Any one or more of the concentrations. The gas analyzer 2 has an air inlet pipe with a length of 1-2 m, and the air is filtered by the air inlet pipe and then enters the gas analyzer 2 for detection. The gas analyzer 2 may be used as a product on sale, for example, an ABB LGR greenhouse gas analyzer, but other gas analyzers may be used. The principle of the gas analyzer 2 is already known in the art, and the present disclosure is more numerous, and the description of the present application is omitted. The weather station 3 is connected to the unmanned aerial vehicle 1, and the weather station 3 is used for monitoring any one or more of wind speed, wind direction, temperature, humidity and atmospheric pressure. The weather station 3 may be a product on sale, and may be capable of detecting data required for calculation. For example, the weather station 3 may be selected from YS500 series ultrasonic anemometers, which are five-in-one digital environmental detectionThe device can accurately measure the data of the ambient wind speed, the wind direction, the temperature, the humidity and the atmospheric pressure. According to the application, the gas analyzer 2 and the weather station 3 are carried on the unmanned aerial vehicle 1, so that the gas concentration parameters and the corresponding weather parameters of different heights of any observation type or place can be obtained, and the mobility of flux measurement is improved. The movable airborne gradient flux observation device can acquire gas parameters and corresponding meteorological parameters at the same position, and the flux estimation accuracy is improved. The hovering unmanned aerial vehicle is adopted, so that the hovering height and position of the unmanned aerial vehicle 1 can be controlled on the ground conveniently by operators, and all gradient observation tasks can be completed smoothly. The unmanned aerial vehicle 1 is provided with a GPS module 12, and can acquire position data.
In the application, the unmanned plane 1, the gas analyzer 2 and the weather station 3 are integrated, and can fly for a plurality of times in one day. The operator controls the hover height and position of the unmanned aerial vehicle 1 on the ground. For example, 3 gradient observations can be set, after the unmanned aerial vehicle 1 reaches the first observation height, gas is filtered by the gas inlet pipe with the length of 1.5 meters and then enters the gas analyzer 2, gradient switching is performed on the observation height after the height observation is finished, the height switching is completed within 10 seconds to reach a stable state, and a second gradient observation is performed until the gradient observations are sequentially arranged and observed. After all gradient observations are completed, the operating personnel operate the unmanned aerial vehicle 1 to return. Wherein during gradient observation the instrument needs to be able to reach a steady state after about 25s of switching. Therefore, the first 25s of data is first rejected at the data processing period. And calculating the underlying flux of different types of areas according to the observed data of different ecological system types.
In one possible embodiment, the gradient flux observation device comprises an extension rod 4, one end of the extension rod 4 being connected to the drone 1. The weather station 3 is connected to the other end of the extension rod 4.
In the embodiment of the application, the weather station 3 is far away from the main body of the unmanned aerial vehicle 1 by the arrangement of the extension rod 4, so that the fan blades on the main body of the unmanned aerial vehicle 1 have small influence on the work of the weather station 3, and the data detection accuracy is improved. The length of the extension rod 4 can be adjusted according to practical conditions.
In one possible embodiment, the weather station 3 is located at the top of the drone 1 and the gas analyzer 2 is located at the bottom of the drone 1.
The gas analyzer 2 weight is great, and the volume is also big, and for reducing unmanned aerial vehicle 1 flight to the influence of gas analyzer 2, gas analyzer 2 preferably sets up in unmanned aerial vehicle 1's bottom, and set up a plurality of extension arms in the unmanned aerial vehicle 1 main part, and each extension arm extends to unmanned aerial vehicle 1 periphery side, and the end of extension arm sets up the flabellum, and the flabellum rotation is less to gas analyzer 2 influence.
The unmanned aerial vehicle 1 comprises a host machine and at least two supporting legs 11 connected with the host machine, and an accommodating space is formed between the supporting legs 11. The gas analyzer 2 is located in the accommodating space, and the gas analyzer 2 is connected to the bottom of the host through a connecting piece. The gas analyzer 2 is suspended from the bottom of the host while the legs 11 are supported on the ground. The embodiment of the application does not limit the specific structure of the connecting piece.
In one possible embodiment, the gas analyzer 2 has a weather station interface, the extension rod 4 has a hollow cavity, the weather station 3 is connected with a cable, the cable part is positioned in the hollow cavity, and the cable part extends out of the extension rod 4 and is electrically connected with the weather station interface. In this embodiment, the weather station 3 is connected to the weather station interface through a cable, and can transmit the monitored weather parameters to the gas analyzer 2, so that the weather parameters and the gas parameters detected by the gas analyzer 2 are synchronized in time, and accurate estimation of the fluxes of different physiological system types in the area can be realized.
The gas analyzer 2 has a wireless module for communication connection with a terminal. A calculation unit may be provided at the gas analyzer 2 or at the terminal. Taking the terminal setting calculation unit as an example, the synchronous data of the meteorological parameters and the gas parameters acquired by the gas analyzer 2 can be directly sent to the terminal in a wireless manner, and the data is processed by the terminal.
As shown in fig. 1 and 2, the gradient flux observation device includes a fixing frame 5, the fixing frame 5 is connected to the unmanned aerial vehicle 1, and the extension rod 4 is connected to the fixing frame 5. Through the setting of mount 5, can be with the stable assembly of weather station 3 on unmanned aerial vehicle 1.
Specifically, the fixing frame 5 includes an upper plate body 51, a lower plate body 52, and a communicating tube. The lower plate body 52 and the upper plate body 51 are arranged at intervals, and the upper plate body 51 or the lower plate body 52 is connected to the unmanned aerial vehicle 1. The connecting cylinder 53 is disposed between the upper plate 51 and the lower plate 52, and the connecting cylinder 53 connects the upper plate 51 and the lower plate 52, respectively. The extension rod 4 passes through the upper plate 51 and is inserted into the connecting cylinder 53. The extension rod 4 and the connecting cylinder 53 are in plug-in connection, the connecting cylinder 53 can cover the bottom of the extension rod 4 for a circle, and the stability of the assembly structure is good.
In order to further enhance the stability of the connection structure of the connection cylinder 53 and the extension rod 4. A plurality of first fasteners may be prepared, each of which is connected to the extension rod 4 through the connection cylinder 53, respectively. Thereby limiting the extension rod 4 in the length direction of the extension rod 4 and preventing the extension rod 4 from sliding off the connecting cylinder 53. The first fastener may be a screw.
The fixing frame 5 includes a plurality of connecting rods 54 and a plurality of second fasteners (not shown), and each connecting rod 54 passes through the upper plate 51 and the lower plate 52 and is connected to the unmanned aerial vehicle 1. The second fastening members have one ends connected to the corresponding links 54 through the upper plate 51 to fix the upper plate 51 to the links 54. The screw hole can be set up at unmanned aerial vehicle 1 top, and the lower edge threaded connection of connecting rod 54 sets up the screw thread groove in the screw hole on unmanned aerial vehicle 1, the top of connecting rod 54, and the second fastener can be the screw, and the double-screw bolt of screw is connected in the screw thread groove at the top of connecting rod 54, and the nut of screw then is limited in the upper surface of upper plate body 51 to realize connecting upper plate body 51 and connecting rod 54 fixedly.
In one possible embodiment, the gradient flux observation device further comprises a data collector 6, the data collector 6 is disposed between the upper plate 51 and the lower plate 52, the cable leading out of the extension rod 4 is connected to the first input port of the data collector 6, and the weather station interface on the gas analyzer 2 is connected to the second input port of the data collector 6 through the cable. The meteorological station sends collected meteorological data to the data collector 6, a time module is arranged on the gas analyzer 2, the time data and corresponding gas parameters can be sent to the data collector 6, and the data collector can store the data, because the gas analyzer 2 sends the time data, the gas parameters are synchronous in time with the gas parameters detected by the gas analyzer 2, and accurate estimation of the flux of different physiological system types in the area is facilitated. A data memory card, such as an SD card, may be disposed within the data collector. After the flight mission is finished, the data memory card can be detached and installed on a terminal (a computer, a mobile phone, a pad and the like), and the data is processed and analyzed through the terminal.
It should be noted that the connecting barrel 53 may be provided with a wire hole, and the cable extending from the extension rod 4 may be connected to the data collector 6 through the wire hole.
The gas analyzer 2 is provided with an unmanned aerial vehicle interface and is used for obtaining flight data of the unmanned aerial vehicle 1 through communication connection between a cable and the unmanned aerial vehicle 1. The gas analyzer 2 is provided with a power supply plug for connecting with a power supply through a cable. The power source may be a power source on the drone 1.
Fig. 3 is a flowchart of a land gas flux detection method according to an embodiment of the present application. As shown in fig. 3, the method includes:
step S301, detecting the gas concentration and the meteorological factors by an unmanned aerial vehicle device, wherein the unmanned aerial vehicle device comprises a weather station and a gas analyzer;
the structure of the unmanned aerial vehicle may be described above, but is not limited thereto. The meteorological factors may include: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time. The gas concentration may be CO 2 Or CH (CH) 4 Etc., the gas concentration of which gas is detected in order to determine the land gas flux of which gas.
Step S302, based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface;
the turbulence diffusion coefficient model is a parameterized representation of turbulence diffusion coefficients, and specifically, the correspondence between the turbulence diffusion coefficients and different meteorological factors and different vegetation remote sensing parameters. By substituting the model into meteorological factors and vegetation remote sensing parameters of the current type of underlying surface, the turbulence diffusion coefficient can be directly obtained, and the turbulence diffusion coefficient is determined without obtaining all parameters through a flux tower. The vegetation remote sensing parameters comprise the surface reflectivity of 10 wave bands and 26 vegetation indexes constructed based on the surface reflectivity of a single wave band, which are known to those skilled in the art, and are not described herein. The method of creating the turbulent diffusion coefficient model will be described in detail below.
Step S303, determining a land gas flux according to the gas concentration and the turbulent diffusion coefficient.
Wherein the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, in CO 2 For example, a value of 1.52 is preferred, K being the turbulence diffusion coefficient.
Fig. 4 is a flowchart of a method for modeling a turbulent diffusion coefficient according to an embodiment of the present application. As shown in fig. 4, the method includes:
step S401, detecting weather factors of different heights of different types of underlying surfaces by using an unmanned aerial vehicle device;
the meteorological factors may include: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
Step S402, based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces;
here, a flux tower is taken as an example, and the turbulence diffusion coefficient is determined. The turbulence diffusion coefficients at each location are similar and will not be described in detail herein. The method specifically comprises the following steps:
firstly, based on the flux tower, detecting friction wind speed and momentum sensible heat flux, which can be obtained from vorticity related observation data;
the universal function of the stability parameter is then determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,mu as a general function of the stability parameter * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights can be calculated by the formula: z g ＝(z 1 z 2 ) 1/2 。
Next, the turbulence diffusion coefficient is determined by the following formula:
where k is a Von-Karma constant, preferably a value of 0.4, μ * For friction wind speed，z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
Step S403, establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
After weather factors of different heights of the different types of underlying surfaces are determined, and turbulence diffusion coefficients of the different types of underlying surfaces are calculated, regression relations of the three types of underlying surfaces can be established based on vegetation remote sensing parameters of the different types of underlying surfaces, and a turbulence diffusion coefficient model is obtained.
The method of the application comprises the following specific working processes: the method comprises the steps of connecting a large-scale M600 six-rotor unmanned aerial vehicle with a gas analyzer, hanging a weather station on the upper part of the gas analyzer, and carrying out multiple flights in one day. The control personnel control the hovering height and the position of the unmanned aerial vehicle on the ground, 3 gradient observations are set, after the unmanned aerial vehicle reaches the first observation height, gas enters the gas analyzer after being filtered by the gas inlet pipe with the length of 1.5 meters, gradient switching is carried out on the observation height after the height observation is finished, the height switching is finished within 10 seconds to reach a stable state, and the second gradient observation is carried out until the gradient observation is sequentially finished. After all gradient observations are completed, the operating personnel operate the unmanned aerial vehicle to return voyage.
The embodiment of the application can be used for conveniently and rapidly installing and observing the working process, greatly improving the portability and being completely suitable for field work.
Fig. 5 is a schematic diagram comparing observations obtained by the method of the present application (on-board flux system) with observations obtained by current prior art vorticity-related observation systems employing flux towers. As shown in fig. 5, the comparison is as follows:
the unmanned aerial vehicle of the application is used for carrying out continuous 4-day observation at a fixed city station from 24 days of 2022 to 27 days of 9 months, each 30-minute observation is carried out, each 3-minute hovering time is 3 minutes respectively at 3 heights (the difference of 3 m), 180 data can be obtained at each height, and then the flux data obtained by calculating the obtained data as 30 minutes is compared with the data of 30 minutes of a vorticity-related flux system. The result shows that the onboard flux observation system has good correlation with the carbon flux data of the vorticity correlation observation system, and R2 reaches 0.82.
Fig. 6 is a block diagram of a land gas flux detection device according to an embodiment of the present application. As shown in fig. 6, the apparatus includes: a detection unit 61, a processing unit 62, and a model building unit 63, wherein the detection unit 61 is configured to detect a gas concentration and a gas image factor by an unmanned aerial vehicle device including a weather station and a gas analyzer; the processing unit 62 is configured to: based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current position; determining a land gas flux from the gas concentration and the turbulent diffusion coefficient; wherein the model building unit 63 builds a turbulent diffusion coefficient model by: detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device; based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces; and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
Preferably, the meteorological factors include: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
Preferably, the processing unit 2 is configured to: the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, and K is the turbulence diffusion coefficient.
Preferably, the processing unit 2 is configured to: obtaining a universal function of friction wind speed and stability parameters based on the flux tower; the turbulence diffusion coefficient is determined by the following formula:
wherein k is Von-Kama constant, mu * For friction wind speed, z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
Preferably, the processing unit 2 is configured to: detecting friction wind speed and momentum sensible heat flux based on the flux tower; the universal function of the stability parameter is determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,is the stability parameterGeneral function of number, mu * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights.
The embodiments of the land gas flux detection device described above are similar to the embodiments of the land gas flux detection method described above, and are not described here again.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A land gas throughput detection method, comprising:
detecting the gas concentration and the gas image factor by an unmanned aerial vehicle device, wherein the unmanned aerial vehicle device comprises a weather station and a gas analyzer;
based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface;
determining a land gas flux from the gas concentration and the turbulent diffusion coefficient;
wherein the turbulent diffusion coefficient model is established by:
detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device;
based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces;
and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
2. The land gas flux detection method according to claim 1, wherein said meteorological factors comprise: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
3. The land gas flux detection method according to claim 1, wherein determining land gas flux from the gas concentration and the turbulent diffusion coefficient comprises:
the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, and K is the turbulence diffusion coefficient.
4. The land gas flux detection method according to claim 1, wherein determining turbulence diffusion coefficients of different types of underlying surfaces based on flux towers of different types of underlying surfaces in combination with a morning-obhuff similarity theory comprises:
obtaining a universal function of friction wind speed and stability parameters based on the flux tower;
the turbulence diffusion coefficient is determined by the following formula:
wherein k is Von-Karma constant, μ * For friction wind speed, z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
5. The land gas flux detection method according to claim 4, wherein obtaining a generic function of friction wind speed and stability parameters based on said flux tower comprises:
detecting friction wind speed and momentum sensible heat flux based on the flux tower;
the universal function of the stability parameter is determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,mu as a general function of the stability parameter * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights.
6. A land gas throughput detection device, comprising:
the device comprises a detection unit, a processing unit and a model building unit, wherein,
the detection unit is used for detecting the gas concentration and the gas image factor through an unmanned aerial vehicle device, and the unmanned aerial vehicle device comprises a weather station and a gas analyzer;
the processing unit is used for:
based on a turbulence diffusion coefficient model, determining a turbulence diffusion coefficient according to the meteorological factors and vegetation remote sensing parameters of the current type of underlying surface;
determining a land gas flux from the gas concentration and the turbulent diffusion coefficient;
wherein the model building unit builds a turbulent diffusion coefficient model by:
detecting weather factors of different heights of different types of underlying surfaces through an unmanned aerial vehicle device;
based on flux towers of different types of underlying surfaces and combined with a Morning-Obuhuff similarity theory, determining turbulence diffusion coefficients of the different types of underlying surfaces;
and establishing regression relations among meteorological factors of different heights of different types of underlying surfaces, turbulence diffusion coefficients of different types of underlying surfaces and vegetation remote sensing parameters of different types of underlying surfaces to obtain a turbulence diffusion coefficient model.
7. The land gas flux detection device according to claim 6, wherein said meteorological factors comprise: one or more of wind speed, wind direction, temperature, humidity, atmospheric pressure, and precipitation for a predetermined period of time.
8. The land gas throughput detection device of claim 6, wherein said processing unit is configured to:
the Liu Qi flux is determined by the following formula:
wherein F is land gas flux, r 1 and r2 Respectively represent at z 1 and z2 Height measured gas concentration. ρa is the air density, c is the unit conversion constant of the gas, and K is the turbulence diffusion coefficient.
9. The land gas throughput detection device of claim 6, wherein said processing unit is configured to:
obtaining a universal function of friction wind speed and stability parameters based on the flux tower;
the turbulence diffusion coefficient is determined by the following formula:
wherein k is Von-Kama constant, mu * For friction wind speed, z g Is z 1 、z 2 The geometric mean height of the two measured heights,is a generic function of the stability parameter.
10. The land gas throughput detection device of claim 9, wherein said processing unit is configured to:
detecting friction wind speed and momentum sensible heat flux based on the flux tower;
the universal function of the stability parameter is determined by the following formula:
(ζ > 0, stable condition)
(ζ<0, neutral or unstable conditions
wherein ,mu as a general function of the stability parameter * For friction wind speed, ζ is stability parameter, L is Morning-Obuhuff length, g is gravitational acceleration, θ v The deficiency syndrome is the condition of the kidney qi>Is momentum sensible heat flux, z g Is z 1 、z 2 The geometric mean height of the two measured heights.
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| CN110308023A (en) * | 2019-07-08 | 2019-10-08 | 连云港市气象局 | Particulate Vertical Observation system and the method for sampling based on unmanned aerial vehicle onboard |
| US20210055180A1 (en) * | 2018-02-01 | 2021-02-25 | Bridger Photonics, Inc. | Apparatuses and methods for gas flux measurements |
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| US20210055180A1 (en) * | 2018-02-01 | 2021-02-25 | Bridger Photonics, Inc. | Apparatuses and methods for gas flux measurements |
| US20220082495A1 (en) * | 2019-01-15 | 2022-03-17 | Bridger Photonics, Inc. | Apparatuses, systems, and methods for gas flux measurements with mobile platforms |
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