Method and device for measuring carbon dioxide flux in forest area based on unmanned aerial vehicle

Abstract

The invention provides a method and a device for measuring carbon dioxide flux in a forest area based on an unmanned aerial vehicle. The device comprises (1) a wind speed correction module; (2) a profile data processing module; (3) a horizontal data processing module; and (4) a carbon dioxide flux calculation module. The three-dimensional carbon dioxide flux algorithm model and system of the forest ecosystem are constructed based on the unmanned aerial vehicle, and the unmanned aerial vehicle system is adopted to measure the horizontal and vertical carbon dioxide flux above the forest canopy, so that the problems that a flux tower is limited by the tower height, the high-rise flux cannot be measured, and mobile measurement cannot be performed are solved; for a complex terrain such as a forest ecosystem, the horizontal flux change condition is considered in a non-horizontal, uniform and flat terrain region, so that the calculated carbon dioxide flux result is closer to the actual condition.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection measurement technology, and in particular to a method and device for measuring carbon dioxide flux in forest areas based on unmanned aerial vehicles. Background Art

[0002] As the impact of global climate change becomes increasingly serious, accurately predicting and controlling greenhouse gas emissions has become a top priority. Among them, carbon dioxide is one of the main greenhouse gases, and its source strength and sink weakness are of great significance for understanding and mitigating climate change. However, traditional carbon dioxide monitoring methods, such as ground observation stations and flux towers, are often restricted by geographical location and are difficult to cover large areas of forest ecosystems. Flux towers are limited by the height of the tower and cannot measure high-level fluxes and cannot be moved for measurement; for complex terrain such as forest ecosystems, there is no integrated algorithm research and equipment system in complex comprehensive terrains such as non-horizontal, uniform, and flat terrain areas. Therefore, how to efficiently and accurately measure carbon dioxide flux in forest areas has become a hot topic in current research. Summary of the invention

[0003] The object of the present invention is to provide a method for measuring forest carbon dioxide flux based on an unmanned aerial vehicle, the method comprising: (1) Wind speed correction module: corrects the wind speed changes caused by the rotation, movement and attitude (pitch, roll and yaw) of the drone propeller; (2) Profile data processing module: Obtain the vertical profile information of the UAV, perform Gaussian weighted average fitting on the wind profile, carbon dioxide concentration, and water vapor concentration; calculate the stability correction function based on the above wind profile using the empirical constant judgment formula; calculate the Obukhov length and turbulence characteristic quantity based on the above Richardson number; calculate the turbulence diffusion coefficient based on the above turbulence characteristic quantity; (3) Horizontal data processing module: obtain horizontal data information of the drone at different altitudes, and perform Gaussian weighted average fitting on horizontal wind speed, carbon dioxide concentration, and water vapor concentration; calculate the normal wind speed in the longitude and latitude directions according to the flight trajectory based on the above horizontal wind speed; perform linear integration of carbon dioxide flux based on the above longitude and latitude directions, that is, multiply the normal wind speed by the carbon dioxide concentration gradient to obtain the inflow and outflow carbon dioxide flux in the longitude and latitude directions respectively; (4) CO2 flux calculation module: For profile data, based on the flux gradient method, the vertical CO2 flux is obtained by multiplying the turbulent diffusion coefficient at different heights by the CO2 concentration gradient; for horizontal data, based on the mass balance method, the CO2 flux outflow in the longitude and latitude directions is subtracted from the CO2 flux inflow to obtain the horizontal CO2 flux. Furthermore, if the temperature of the two layers is known, and With two layers of wind speed and The overall Richardson number can be calculated, and the expression for the overall Richardson number is as follows: in, is the acceleration due to gravity; The average temperature of the two layers; is the height of the lower wind speed. Further, different wind speed profile and temperature profile stability correction functions are selected according to different overall Richardson numbers. and , the stability correction function can generally be written as an empirical function: Among them, the empirical constant and , The determination of is based on the different Richardson numbers and is selected accordingly. Further, if it is known that , and Average wind speed at different altitudes , and , according to the overall Richardson number and stability parameters The relationships are: in, , are the integral forms of the stability correction functions of the wind speed profile and temperature profile, respectively. , ; is the surface roughness, is the Monin-Obukhov length. For steep, non-streamlined rough surfaces, according to the zero plane displacement ( ) and the positive area coefficient ( )Fitting curve expression: in is the canopy height; and is a general coefficient that is a positive area coefficient. In addition, according to the relationship between zero plane displacement and surface roughness ( ) gives an empirical formula: . is a general coefficient. Substituting into the above relationship, the surface roughness is calculated as , and then according to the overall Richardson number and stability parameters The relationship between the Morning-Obukhov length is calculated. Furthermore, according to the functional relationship between the average meteorological element profile of the near-surface layer and the turbulent flux, the surface roughness is uniformly taken as As the lower boundary condition, the integral expressions of wind speed profile and temperature profile are obtained: in, is the von Karman constant; is the friction speed; is the potential temperature characteristic scale; is the average potential temperature near the ground. According to the integral expression of the wind speed profile and temperature profile, the turbulence characteristic scale can be calculated: . Furthermore, the turbulent viscosity rate ( ) and turbulent thermal conductivity ( ) is a physical quantity that characterizes the strength of turbulence development in the near-surface layer. It is expressed by the following flux profile relationship: Furthermore, the calculation formula of the vertical carbon dioxide flux is as follows: in, Air density; is the turbulent diffusion coefficient; is the carbon dioxide mixing ratio. Further, the horizontal data information of the drone at different altitudes is obtained, and the horizontal wind speed, carbon dioxide concentration, and water vapor concentration are fitted by Gaussian weighted average; the normal wind speed in the longitude and latitude directions is calculated according to the flight trajectory, and the carbon dioxide flux line integral is performed according to the above longitude and latitude directions, that is, the normal wind speed is multiplied by the carbon dioxide concentration gradient to obtain the inflow and outflow carbon dioxide flux in the longitude and latitude directions respectively; according to the mass balance method, the outflow carbon dioxide flux in the longitude and latitude directions is subtracted from the inflow carbon dioxide flux to obtain the horizontal carbon dioxide flux, and the calculation formula is as follows: , It is the change in carbon dioxide flux corresponding to a data point in the longitude or latitude direction. In addition, the present invention also provides a device for measuring forest carbon dioxide flux based on an unmanned aerial vehicle, and the device includes: an unmanned aerial vehicle system, a three-dimensional ultrasonic wind speed system, a data transmission system, a gas sampling system and an RTK positioning system. Among them, the gas sampling system is mounted on the bottom of the unmanned aerial vehicle, and the air inlet of the sampling tube is fixed at the same height as the sensor of the three-dimensional ultrasonic wind speed system to ensure that the data collected by the gas sampling system and the three-dimensional ultrasonic wind speed system have the same spatiality and avoid the influence caused by the downwash airflow of the unmanned aerial vehicle; the ultrasonic anemometer is fixed above the propeller of the unmanned aerial vehicle; the data transmission system is located under the rectangular base under the unmanned aerial vehicle; the RTK system installs two GPS systems behind the nose of the unmanned aerial vehicle, and adds a high-precision GNSS receiver on the ground to ensure continuous tracking and observation of satellites and the quality of satellite signals. Furthermore, the UAV system is a multi-purpose medium-sized four-rotor or six-rotor UAV (such as Shenzhen DJI M300, Tianjin Feiyan F-10L, F-30L, etc.). The UAV system is equipped with a three-dimensional ultrasonic analyzer system, a data transmission system, a gas sampling system, etc. for flight, and ensures that the single flight time is greater than half an hour. Furthermore, the three-dimensional ultrasonic wind speed system is small in size and low in power consumption. It is mounted above the UAV to measure meteorological parameters such as wind speed, wind direction, and temperature during the flight of the UAV. Furthermore, the gas sampling system samples the surrounding environmental gases during the flight of the UAV and outputs the carbon dioxide and water vapor concentration values. Since it is mounted on the UAV, it needs to be small in size and light in weight. Furthermore, the RTK positioning system, the real-time dynamic measurement system (RTK, Real Time Kinematic) (RTK, Haixingda, CN) adopts a multi-star multi-frequency GNSS unit, supports BDS, GPS, GLONASS and other systems for navigation and positioning; uses a UBase receiver installed on a tripod, fixed on relatively flat ground, receives satellite signals, and forms a three-point positioning with the two GPS positioning systems on the drone, so as to achieve high-precision measurement of the forest ecosystem. Since the canopy height data needs to be measured for forest measurement, the RTK positioning system can accurately measure the flight altitude of the drone to prevent the drone from colliding with the trees when measuring the canopy of the forest. Furthermore, the data transmission system mainly uses two data radios for data transmission, namely the drone's data transmission radio and the second data transmission radio, which transmit the drone's flight parameters and the measurement data of the meteorological sensor to the ground computer at the same time and save them locally for further data processing and analysis.The drone's data transmission radio communicates with the drone's transmitter and the ground receiver, so that the ground receiver can receive decoded GPS information and flight attitude data from the drone through the serial port, and can send control data to the drone through the ground station software to achieve the mission setting and route planning of the drone. The second data transmission radio can also transmit various measurement data of the meteorological sensor to the local computer through the serial port through the communication between its drone transmitter and the ground receiver.

[0004] The present invention has the following beneficial effects: the present invention establishes a three-dimensional carbon dioxide flux algorithm model of a forest ecosystem based on a drone, and uses the drone system to measure the horizontal and vertical carbon dioxide fluxes on the forest canopy, thereby solving the problem that the traditional flux tower cannot observe high-level fluxes due to height limitations and is difficult to move and measure. For forest ecosystems in complex non-horizontal, uniform and flat terrain areas, the model also takes into account the factors of horizontal flux changes, so that the calculated carbon dioxide flux results are more in line with the actual environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 Measurement calculation flow chart. DETAILED DESCRIPTION The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0007] Embodiment 1, as Figure 1 As shown, A method for measuring forest carbon dioxide flux based on drones, the method comprising: (S1) Wind speed correction module: corrects the wind speed changes caused by the rotation, movement and attitude (pitch, roll and yaw) of the drone propeller; (S2) Profile data processing module: obtain the vertical profile information of the UAV, perform Gaussian weighted average fitting on the wind profile, carbon dioxide concentration, and water vapor concentration; calculate the stability correction function based on the above wind profile through the empirical constant judgment formula; calculate the Obukhov length and turbulence characteristic quantity based on the above Richardson number; calculate the turbulence diffusion coefficient based on the above turbulence characteristic quantity; (S3) Horizontal data processing module: obtain horizontal data information of the drone at different altitudes, perform Gaussian weighted average fitting on horizontal wind speed, carbon dioxide concentration, and water vapor concentration; calculate the normal wind speed in the longitude and latitude directions according to the flight trajectory based on the above horizontal wind speed; perform line integration of carbon dioxide flux based on the above longitude and latitude directions, that is, multiply the normal wind speed by the carbon dioxide concentration gradient to obtain the inflow and outflow carbon dioxide flux in the longitude and latitude directions respectively; (S4) CO2 flux calculation module: For profile data, based on the flux gradient method, the vertical CO2 flux is obtained by multiplying the turbulent diffusion coefficient at different heights by the CO2 concentration gradient; for horizontal data, based on the mass balance method, the CO2 flux outflow in the longitude and latitude directions is subtracted from the CO2 flux inflow to obtain the horizontal CO2 flux. and With two layers of wind speed and The overall Richardson number can be calculated, and the expression for the overall Richardson number is as follows: in, is the acceleration due to gravity; The average temperature of the two layers; is the height of the lower wind speed. Different wind speed profiles and temperature profile stability correction functions are selected according to different overall Richardson numbers. and , the stability correction function can generally be written as an empirical function: Among them, the empirical constant and , The determination of is based on the different Richardson numbers. , and Average wind speed at different altitudes , and , according to the overall Richardson number and stability parameters The relationships are: in, , are the integral forms of the stability correction functions of the wind speed profile and temperature profile, respectively. , ; is the surface roughness, is the Monin-Obukhov length. For steep, non-streamlined rough surfaces, according to the zero plane displacement ( ) and the positive area coefficient ( )Fitting curve expression: in, is the canopy height; and is a general coefficient that is a positive area coefficient. In addition, according to the relationship between zero plane displacement and surface roughness ( ) gives an empirical formula: . is a general coefficient. Substituting into the above relationship, the surface roughness is calculated as , and then according to the overall Richardson number and stability parameters The relationship between the Morning-Obukhov length is calculated. According to the functional relationship between the average meteorological element profile near the surface layer and the turbulent flux, the surface roughness is uniformly taken as As the lower boundary condition, the integral expressions of wind speed profile and temperature profile are obtained: in, is the von Karman constant; is the friction speed; is the potential temperature characteristic scale; is the average potential temperature near the ground. According to the integral expression of the wind speed profile and temperature profile, the turbulence characteristic scale can be calculated: . Turbulent viscosity rate ( ) and turbulent thermal conductivity ( ) is a physical quantity that characterizes the strength of turbulence development in the near-surface layer. It is expressed by the following flux profile relationship: Furthermore, the calculation formula of the vertical carbon dioxide flux is as follows: in, is the air density; is the turbulent diffusion coefficient; is the carbon dioxide mixing ratio. The horizontal data information of the drone at different altitudes is obtained, and the horizontal wind speed, carbon dioxide concentration, and water vapor concentration are fitted by Gaussian weighted average. The normal wind speed in the longitude and latitude directions is calculated according to the flight trajectory, and the carbon dioxide flux line integral is performed according to the longitude and latitude directions, that is, the normal wind speed is multiplied by the carbon dioxide concentration gradient to obtain the inflow and outflow carbon dioxide flux in the longitude and latitude directions respectively. According to the mass balance method, the outflow carbon dioxide flux in the longitude and latitude directions is subtracted from the inflow carbon dioxide flux to obtain the horizontal carbon dioxide flux. The calculation formula is as follows: Example 2 In addition, the technical solution of the present invention also includes a UAV carbon dioxide flux measurement device, which mainly includes the following parts: UAV system, three-dimensional ultrasonic wind speed monitoring system, data transmission system, gas sampling system and RTK positioning system. Among them, the gas sampling system is designed as a detachable module at the bottom of the UAV, which sets the opening position of the measured gas inlet at the same height as the three-dimensional ultrasonic wind speed system sensor to ensure that the data acquired by the gas sampling system has the same spatial properties and avoids the impact caused by the descent of the UAV. The ultrasonic anemometer is installed on the top of the UAV propeller. The data transmission system is placed under the rectangular base of the UAV base. The RTK system consists of two GPS systems, which are installed behind the head of the UAV, and a high-precision GNSS receiver is added on the ground to achieve continuous tracking of satellites and improve the quality of satellite signals. For the UAV system, a multifunctional four-rotor or six-rotor UAV (such as Shenzhen's DJI M300, Tianjin Feiyan F-10L, F-30L, etc.) is selected. The UAV system is equipped with a three-dimensional ultrasonic wind speed system, a data transmission system, a gas sampling system, and a flight to ensure that each flight time exceeds half an hour. The three-dimensional ultrasonic wind speed system is a compact and energy-saving device. It is installed on the drone and can measure meteorological information such as wind speed, wind direction and temperature in real time during the flight of the drone. During the flight of the drone, the gas sampling system is responsible for collecting air samples around it and outputting the values ​​of carbon dioxide and water vapor concentration. This design makes the weight and volume of the equipment small and easy to carry by the drone. The RTK positioning system uses a real-time dynamic measurement system (RTK, such as Haixingda, a Chinese product), which can use multi-star multi-frequency GNSS units to support multiple systems such as BDS, GPS, GLONASS, etc. for navigation and positioning. It is installed behind the head of the drone, and a high-precision GNSS receiver is added on the ground to ensure continuous tracking of satellites and improve the quality of satellite signals. Finally, the data transmission system is mainly responsible for data transmission through two data radios, which can be used for data transmission of the drone itself or as a second data radio. The main function of the data transmission system is to simultaneously transmit the flight parameters of the drone and the measurement data of the meteorological sensor to the ground computer for processing and analysis. Specifically, the data transmission radio of the drone communicates with the ground receiving end, allowing the ground receiving end to receive the decoded GPS information and flight attitude data of the drone, and then send these control data to the drone through the ground station software, so as to realize the mission setting and route planning of the drone. The second data radio can also transmit all the measurement data of the meteorological sensor to the ground computer through the serial port. A three-dimensional carbon dioxide flux algorithm model and hardware system based on drones for forest ecosystems was invented and designed.With the help of the drone system, the model can accurately measure the lateral and longitudinal carbon dioxide fluxes in the forest canopy, which can solve the difficult problem of the inability to observe the top flux and the difficulty in migration measurement caused by the height limitation of the traditional flux tower. In addition, for those non-horizontal, uniform and flat terrain areas, the model also takes into account the influence of lateral flux changes, so that the calculated carbon dioxide flux results can be closer to the actual environment. The embodiments of the present invention are given for the purpose of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and changes are obvious to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. A method for calculating forest carbon dioxide flux based on drone measurement, characterized in that: The method comprises: (1) Wind speed correction: Correct the wind speed changes caused by the rotation, movement and attitude of the drone propeller; (2) Profile data processing: Obtain the vertical profile information of the UAV and perform Gaussian weighted average fitting on the wind profile, carbon dioxide concentration, and water vapor concentration; (3) Horizontal data processing: Obtain horizontal data information at different altitudes of the drone and perform Gaussian weighted average fitting on horizontal wind speed, carbon dioxide concentration, and water vapor concentration; (4) Calculation of CO2 flux: For profile data, based on the flux gradient method, the vertical CO2 flux is obtained by multiplying the turbulent diffusion coefficient at different heights by the CO2 concentration gradient. For horizontal data, based on the mass balance method, the horizontal CO2 flux is obtained by subtracting the inflow CO2 flux from the outflow CO2 flux in the longitude and latitude directions, respectively.

2. The method according to claim 1, characterized in that The wind speed correction is as follows: and With wind speed and The overall Richardson number is calculated, and the expression of the overall Richardson number is as follows: in, is the acceleration due to gravity; The average temperature of the two layers; is the height of the lower layer wind speed.

3. The method according to claim 2, characterized in that: Select different stability correction functions of wind speed profile and temperature profile according to different overall Richardson numbers and , the stability correction function can generally be written as an empirical function: Among them, the empirical constant and , The determination is based on the different Richardson numbers and the corresponding selection is made.

4. The method according to claim 3, characterized in that: according to , and Average wind speed at different altitudes , and , according to the overall Richardson number and stability parameters The relationships are: in, , are the integral forms of the stability correction functions of the wind speed profile and temperature profile, respectively. , ; is the surface roughness, is the Morning-Obukhov length.

5. The method according to claim 1, characterized in that The carbon dioxide flux is calculated as follows: Based on the functional relationship between the average meteorological element profile near the surface layer and the turbulent flux, the surface roughness is uniformly taken as As the lower boundary condition, the integral expressions of wind speed profile and temperature profile are obtained: in, is the von Karman constant; is the friction speed; is the potential temperature characteristic scale; is the average potential temperature near the ground; according to the integral expression of the wind speed profile and temperature profile, the turbulence characteristic scale can be calculated .

6. The method according to claim 5, characterized in that: The calculation formula of the carbon dioxide vertical flux is as follows: in, Air density; is the turbulent diffusion coefficient; is the carbon dioxide mixing ratio.

7. A device for measuring forest carbon dioxide flux based on an unmanned aerial vehicle, the device comprising: An unmanned aerial vehicle system, a three-dimensional ultrasonic wind speed system, a data transmission system, a gas sampling system and an RTK positioning system, wherein the gas sampling system is mounted on the bottom of the unmanned aerial vehicle system, the air inlet of the sampling tube is fixed at the same height as the sensor of the three-dimensional ultrasonic wind speed system, the data transmission system is located under a rectangular base under the unmanned aerial vehicle, and the RTK system is to install two GPS systems behind the nose of the unmanned aerial vehicle, and further add a high-precision GNSS receiver on the ground to ensure continuous tracking and observation of satellites and the quality of satellite signals.

8. The device according to claim 7, characterized in that: The UAV system is a multi-purpose medium-sized four-rotor or six-rotor UAV. The UAV system is equipped with a three-dimensional ultrasonic analyzer system, a data transmission system, and a gas sampling system for flight. The three-dimensional ultrasonic wind speed system is mounted above the UAV to measure the wind speed, wind direction, and temperature meteorological parameters during the flight of the UAV. The gas sampling system samples the surrounding environmental gas during the flight of the UAV and outputs the carbon dioxide and water vapor concentration values.

9. The device according to claim 7, characterized in that: The UAV system and the data transmission system mainly use two data radios for data transmission, namely the UAV data radio and the second data radio, which transmit the flight parameters of the UAV and the measurement data of the meteorological sensor to the ground computer at the same time and save them locally; the UAV data radio communicates with the UAV transmitting end and the ground receiving end; the second data radio communicates with the UAV transmitting end and the ground receiving end, and transmits various measurement data of the meteorological sensor to the local computer via the serial port.

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