A greenhouse gas observation system suitable for mangrove conservation

Abstract

The application discloses a greenhouse gas observation system suitable for mangrove conservation land, and relates to the field of greenhouse gas observation, and comprises a construction module, a tracking module, a control module and a coordinated processing module.The construction module carries out on-site environment survey on a target mangrove conservation land and constructs a three-dimensional environment map.The tracking module sets up a mobile observation network dynamically adapting to a tidal process according to the three-dimensional environment map, and tracks a water-gas exchange front driven by the tide according to the three-dimensional environment map and real-time water level data.The control module controls a bionic root system micro-electrode probe to obtain rhizosphere micro-environment data synchronous with a current water-gas exchange process while tracking and positioning the water-gas exchange front by the mobile observation platform.The coordinated processing module cooperatively processes macro-flux data and rhizosphere micro-environment data obtained by the mobile observation network to obtain a standard data set.The application realizes dynamic, multi-scale and mechanized cooperative observation on a greenhouse gas emission process of the mangrove wetland.

Description

Technical Field

[0001] This invention relates to the field of greenhouse gas observation, and in particular to a greenhouse gas observation system suitable for mangrove protected areas. Background Technology

[0002] In the field of greenhouse gas observation, the mainstream existing technologies for coastal wetlands such as mangroves are the eddy covariance method and the static chamber method. The former continuously measures gas exchange fluxes by setting up a fixed high tower, which has the advantage of automatic observation; the latter calculates fluxes by sampling at selected points, serving as a spatial supplement. These technologies provide the basic methodology and data support for assessing the carbon source and sink functions of mangroves.

[0003] Current technologies suffer from a key deficiency: the inability to conduct multi-scale coordinated observations of tidal-driven dynamic water-air exchange processes. Fixed flux towers cannot track instantaneous emission fronts that move with the tides and lack synchronous capture of key parameters at the sediment-water interface; static box methods are insufficient to reflect dynamic tidal changes. This makes it difficult for existing observations to quantify and analyze the contributions of different mechanisms, such as tidal pumping, microbial respiration, and root activity, to total flux, thus limiting breakthroughs in moving from phenomenological observation to mechanistic explanation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a greenhouse gas observation system suitable for mangrove protected areas to solve the problem that existing observation technologies are unable to simultaneously track dynamic tidal fronts and analyze their microscopic driving mechanisms.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a greenhouse gas monitoring system suitable for mangrove protected areas, comprising a construction module for conducting on-site environmental surveys of the target mangrove protected area and constructing a three-dimensional environmental map; The tracking module deploys a mobile observation network that dynamically adapts to the tidal process based on a three-dimensional environmental map, and tracks the water vapor exchange front driven by the tide based on the three-dimensional environmental map and real-time water level data. The control module, the mobile observation platform, tracks and locates the water-air exchange front, controls the biomimetic root microelectrode probe, and acquires rhizosphere microenvironment data synchronized with the current water-air exchange process. The coordination and processing module performs coordinated processing on the macroscopic flux data and rhizosphere microenvironment data acquired by the mobile observation network to obtain a standardized dataset; The analysis module uses tidal phase as the time frame to fuse and analyze the standard dataset. It uses a flux decoupling model to decompose the total greenhouse gas flux into tidal pumping flux, biological respiration flux and root pumping flux, and obtains the analysis results of flux source mechanisms. The optimization module adaptively optimizes the observation strategy of the mobile observation network based on the analysis results of the flux source mechanism.

[0007] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the construction of the three-dimensional environmental map includes: We acquire point cloud data of topography and canopy structure, underwater topography data of tidal channels, sediment characteristic data, and bioturbation distribution data, and construct a three-dimensional environmental map through data fusion and spatial interpolation.

[0008] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the mobile monitoring network includes: Based on underwater topographic data of tidal channels, a navigation channel is planned and deployed to form a mobile observation network that dynamically adapts to tidal processes. By utilizing underwater topographic data of tidal channels, the deployment locations of biomimetic resonant floating platforms are planned, and a dynamic mobile observation network composed of multiple biomimetic resonant floating platforms is deployed to adapt to tidal processes.

[0009] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the tracking of tidal-driven water vapor exchange fronts includes, Based on the 3D environmental map and real-time water level data, the predicted path of the tidal-driven water vapor exchange front is obtained by the tidal active tracking algorithm. The biomimetic resonant floating platform navigates to the front of the predicted path of the tidal-driven water-air exchange front according to the predicted path of the tidal-driven water-air exchange front. The biomimetic resonant floating platform activates the forced resonance mechanism to dynamically track the tidal-driven water-air exchange front in front of the predicted path of the tidal-driven water-air exchange front.

[0010] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the tracking and positioning includes: The biomimetic resonant floating platform measures the horizontal spatial gradient of dissolved carbon dioxide concentration in the surface water in real time using an onboard gas analyzer. The horizontal spatial gradient data of dissolved carbon dioxide concentration in surface water is overlaid with real-time water level data and a 3D environmental map. In the superimposed dynamic layer, the frontal line where the dissolved carbon dioxide concentration gradient vector converges is identified and determined as the instantaneous location of the water vapor exchange front.

[0011] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the rhizosphere microenvironment data includes: After reaching the water vapor exchange front, the biomimetic resonant floating platform locks in resonance with the periodic tidal flow, converting the horizontal kinetic energy of the biomimetic resonant floating platform into the deformation energy of the structure and dissipating it, thus achieving an adaptive and precise dwelling state. In the adaptive and precise residence state, the insertion speed and rotation angle of the biomimetic root microelectrode probe are dynamically adjusted based on the real-time measured sediment shear strength data. The biomimetic root microelectrode probe is implanted into the sediment in a biomimetic drilling manner. After implantation, the multi-channel microfluidic chip inside the probe begins to capture rhizosphere dissolved gas by pore water dialysis. In-situ real-time analysis was performed using a miniature membrane sample introduction mass spectrometer integrated into the probe handle to obtain a dissolved methane concentration profile. The analysis process is synchronized with the measurements of the redox potential microelectrode and solid pH microelectrode built into the probe. All profile data are embedded with timestamps synchronized with the macroscopic flux measurement equipment of the biomimetic resonant floating platform when they are generated, forming spatiotemporally synchronized rhizosphere microenvironment data.

[0012] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the standardized dataset includes: The timestamps of the macroscopic flux data recorded by the biomimetic resonance floating platform are aligned and matched with the timestamps of the spatiotemporally synchronized rhizosphere microenvironment dataset to form spatiotemporally paired macroscopic-microscopic data pairs. Data quality tags are added to each spatiotemporally paired macro-micro data pair, and all data in the spatiotemporally paired macro-micro data pair are standardized and converted according to unified greenhouse gas concentration units, temperature units, and pressure units to generate a standardized dataset with complete metadata description.

[0013] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the fusion analysis includes: Using tidal phase as the time frame, all spatiotemporal pairs of macro-micro data belonging to the same tidal phase are selected from the standard dataset; For all spatiotemporal pairs of macro-micro data belonging to the same tidal phase, sorting and data alignment are performed according to the time sequence within the tidal phase to form a fused data profile within the phase.

[0014] As a preferred embodiment of the greenhouse gas monitoring system for mangrove protected areas described in this invention, the analysis results of the flux source mechanism include: The flux decoupling model receives the intraphase fused data profile and separates the flux component that is linearly related to the rate of water level change and unrelated to the rhizosphere redox potential from the total greenhouse gas flux of the intraphase fused data profile to obtain the tidal pumping flux. The first residual flux is obtained by subtracting the tidal pumping flux from the total greenhouse gas flux. The biorespiration flux is obtained by extracting the flux component that is exponentially correlated with sediment temperature and negatively correlated with rhizosphere pH from the first residual flux. The flux decoupling model subtracts the biological respiration flux from the first residual flux to obtain the second residual flux. The second residual flux is then time-domain aligned with the minute-level negative jump events that occur in the rhizosphere redox potential. Flux pulses synchronized with the minute-level negative jump events are identified as root pumping fluxes. The analysis results of the flux source mechanism including tidal pumping flux, biological respiration flux, and root pumping flux are obtained.

[0015] As a preferred embodiment of the greenhouse gas observation system for mangrove protected areas described in this invention, the observation strategy for optimizing the mobile observation network includes: Based on the analysis of flux source mechanisms, hotspot areas dominated by root pumping flux were identified. Based on the distribution of hotspots dominated by root pumping flux, a new biomimetic resonant floating platform cruise path and stationary sampling point plan are generated and then distributed to the mobile observation network for execution.

[0016] The beneficial effects of this invention are as follows: A three-dimensional environmental map containing topographic, hydrological, and biological disturbance features is established through a construction module. Then, a tracking module deploys a dynamically adaptive tidal mobile observation network based on this map and water level data to track tidal-driven water-air exchange fronts. After achieving precise stationing at the front, the control module manipulates a biomimetic root microelectrode probe to simultaneously acquire rhizosphere microenvironment data. A coordination processing module performs spatiotemporal registration and standardization of macroscopic flux data and microscopic data to form a standardized dataset. An analysis module integrates and analyzes this dataset using tidal phase as a framework, decomposing the total flux into three components—tidal pumping, biological respiration, and root pumping—using a flux decoupling model to analyze the flux source mechanism. An optimization module adaptively adjusts the cruise path and sampling strategy of the observation network based on the analysis results, achieving dynamic, multi-scale, and mechanistic collaborative observation of greenhouse gas emission processes in mangrove wetlands. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a greenhouse gas monitoring system suitable for mangrove protected areas. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0022] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a greenhouse gas monitoring system suitable for mangrove protected areas, comprising the following steps: The module constructs a three-dimensional environmental map by conducting on-site environmental surveys of the target mangrove protected area.

[0023] We acquire point cloud data of topography and canopy structure, underwater topography data of tidal channels, sediment characteristic data, and bioturbation distribution data, and construct a three-dimensional environmental map through data fusion and spatial interpolation.

[0024] Furthermore, UAVs equipped with LiDAR scanners were used to acquire topographic and canopy structure point cloud data, multibeam sonar was used to acquire underwater topographic data of the tidal channel, sediment core samples were collected to determine sediment characteristic data, and survey plots were set up to record the number and size of crab burrows within the plots to obtain bioturbation distribution data. The data was then imported into a software platform with Geographic Information System (GIS) capabilities to unify and stitch the coordinates of the topographic and canopy structure point cloud data with the underwater topographic data of the tidal channel. The organic matter content distribution from the sediment characteristic data was overlaid onto the digital elevation model, and a spatial distribution surface of sediment organic matter content was generated using Kriging spatial interpolation. The crab burrow density information from the bioturbation distribution data was then overlaid onto the spatial distribution surface of sediment organic matter content, and Kriging spatial interpolation was again used to generate a three-dimensional environmental map comprehensively reflecting topography, sediment properties, and the intensity of biological activity.

[0025] The tracking module deploys a mobile observation network that dynamically adapts to tidal processes based on a three-dimensional environmental map, and tracks the water-air exchange front driven by tides based on the three-dimensional environmental map and real-time water level data.

[0026] Based on underwater topographic data of tidal channels, a navigation channel is planned and deployed to form a mobile observation network that dynamically adapts to tidal processes.

[0027] Furthermore, based on underwater topographic data of tidal channels, all continuous water areas with a depth greater than the draft of the biomimetic resonant floating platform were extracted from the 3D environmental map. These extracted continuous water areas were used as potential navigation channels for a mobile observation network dynamically adapting to tidal processes. Network topology analysis was performed on these extracted continuous water areas to identify the main tidal channel trunks and branches connecting the open sea, forest edge, and forest interior. These identified main tidal channel trunks and branches were marked as planned navigation channels for the mobile observation network dynamically adapting to tidal processes, thus completing the navigation channel planning.

[0028] By utilizing underwater topographic data of tidal channels, the deployment locations of biomimetic resonant floating platforms are planned, and a dynamic mobile observation network composed of multiple biomimetic resonant floating platforms is deployed to adapt to tidal processes.

[0029] Furthermore, underwater topographic data from tidal channels is used to plan the deployment locations of biomimetic resonant floating platforms. On the navigation channel of the planned dynamic tidal-adaptive mobile observation network, multiple points with moderate water depth, gentle currents, and coverage of different tidal inundation frequency areas are selected as pre-set anchorage points for the biomimetic resonant floating platforms. The biomimetic resonant floating platforms are deployed at these pre-set anchorage points. Each platform establishes communication connections with neighboring biomimetic resonant floating platforms and the shore-based control center via a wireless ad hoc network, forming a dynamic tidal-adaptive mobile observation network composed of multiple biomimetic resonant floating platforms, possessing collaborative navigation and data relay capabilities.

[0030] Based on the 3D environmental map and real-time water level data, the predicted path of the tidal-driven water vapor exchange front is obtained by the tidal active tracking algorithm. Specifically: ; in, To predict the path, This is the termination time for path planning. This is the start time for path planning. The first derivative of the path with respect to time. The tidal flow field vector, These are the coupling weight coefficients. Let the curvature of the path be... This represents the spatial gradient of the dissolved carbon dioxide concentration field at the path point. For distance measurement function, For the path function to be optimized itself, For the time derivative, The desired frontal path; Constrained by: ; in, This is a navigable water space domain. The threshold for the minimum effective water level change rate. Water level elevation Regarding time The partial derivatives; This item will determine the curvature of the path ( ) and frontal intensity The dynamic correlation and control logic is: when the algorithm senses that it is passing through a strong frontal region ( When it is large, it increases the path curvature. This allows the path to meander or circle around this region, increasing observation density and dwell time; when in a weak gradient region, the curvature is reduced, allowing the path to pass more straight and quickly in order to search for the next strong front.

[0031] Unlike gradient tracking, it doesn't simply climb a slope, but intelligently adjusts the search strategy based on the steepness of the slope.

[0032] Unlike fixed path planning, the curvature of the path is dynamic and adaptive to real-time environmental characteristics (frontal intensity).

[0033] Unlike passive advection: the path is not completely obscured. Instead of being coerced, it possesses the ability to actively explore and utilize trade-offs through curvature adjustment.

[0034] The significance of constraints: The path must be located within the navigable waters defined by the 3D environment map.

[0035] The rate of change of water level at the path point must be greater than the threshold. It only tracks areas that are experiencing active tidal inundation / exposure, which are the fronts where water vapor exchange is intense, automatically filtering out stagnant or slowly changing water bodies.

[0036] The biomimetic resonant floating platform navigates to the front of the predicted path of the tidal-driven water-air exchange front according to the predicted path of the tidal-driven water-air exchange front. The biomimetic resonant floating platform activates the forced resonance mechanism to dynamically track the tidal-driven water-air exchange front in front of the predicted path of the tidal-driven water-air exchange front.

[0037] Furthermore, the biomimetic resonant floating platform receives the predicted path of the tidal-driven water vapor exchange front from the control center. Based on the geometric relationship between the platform's own positioning and the predicted path, the navigation unit generates navigation commands to move ahead of the front. The platform's thrusters execute these commands, propelling the platform forward. When the platform reaches the area ahead of the predicted path, its control unit activates the biomimetic resonant structure at the bottom, which enters a forced resonance mode. In this mode, the structure resonates with the periodic tidal flow, effectively dissipating lateral water kinetic energy and maintaining a quasi-stationary state relative to the water mass ahead of the predicted path. In this state, the sensors on the platform continuously collect environmental data, enabling dynamic tracking of the tidal-driven water vapor exchange front.

[0038] Specifically, the principles of biomimetic structural dynamics are applied to the dynamic stability control of floating platforms to address the challenge of platforms being susceptible to water flow disturbances and unable to accurately remain stationary during mangrove tidal front tracking. The bottom structure of the biomimetic resonant floating platform mimics the morphology and mechanical properties of mangrove breathing roots, with its natural frequency preset to match the frequency band of typical tidal currents. When the platform sails to the front of the front, this biomimetic structure enters a forced resonance state under the influence of periodic water flow. Through the periodic elastic deformation of the structure, it actively absorbs and dissipates the lateral kinetic energy applied by the water flow. Based on the energy dissipation mechanism of resonance, the displacement fluctuations of the platform on the horizontal plane can be suppressed to a very small range with extremely low energy consumption, achieving adaptive and precise stationary status in dynamic water masses. This transforms environmental disturbance energy into a physical mechanism that maintains platform stability, allowing the platform to maintain a stable posture in tidal currents like a mangrove rooted in water. This solves the fundamental technical obstacle of spatiotemporal inaccuracies in observation data caused by platform swaying when mobile observation networks track high-speed, morphologically variable tidal fronts.

[0039] The control module, which is part of the mobile observation platform, tracks and locates the water-air exchange front, controls the biomimetic root microelectrode probe, and acquires rhizosphere microenvironment data synchronized with the current water-air exchange process.

[0040] The biomimetic resonant floating platform measures the horizontal spatial gradient of dissolved carbon dioxide concentration in surface water in real time using an onboard gas analyzer.

[0041] Furthermore, the biomimetic resonant floating platform, equipped with a submersible spectroscopic gas analyzer, continuously measures the dissolved carbon dioxide concentration in surface water using a high-frequency sampling mode, while simultaneously recording the high-precision spatiotemporal coordinates corresponding to each concentration measurement. The acquired sequence of dissolved carbon dioxide concentration measurements is then used to generate a two-dimensional distribution field of the dissolved carbon dioxide concentration at the current moment through Kriging space interpolation. Gradient calculation is performed on this two-dimensional distribution field to obtain the spatial rate of change vector of the dissolved carbon dioxide concentration in the horizontal direction, i.e., the horizontal spatial gradient data of the dissolved carbon dioxide concentration in the surface water.

[0042] The horizontal spatial gradient data of dissolved carbon dioxide concentration in surface water is overlaid with real-time water level data and a 3D environmental map.

[0043] Furthermore, within a unified geographic coordinate system and temporal framework, the horizontal spatial gradient data of dissolved carbon dioxide concentration in surface water is imported into the geographic information system (GIS) as a vector layer. Real-time water level data is interpolated into a water level elevation isosurface layer based on the coordinates of its measurement points and water level elevation values. A 3D environmental map containing topographic, vegetation, and sediment information is used as the base geographic map layer. The GIS performs coordinate registration and spatiotemporal synchronization overlay on the vector layer, isosurface layer, and base geographic map layer to generate a dynamic integrated layer that combines the concentration gradient field, water level field, and static geographic environmental information.

[0044] In the superimposed dynamic layer, the frontal line where the dissolved carbon dioxide concentration gradient vector converges is identified and determined as the instantaneous location of the water vapor exchange front.

[0045] Furthermore, in the overlaid dynamic layer, vector field analysis and image processing algorithms are applied to the dissolved carbon dioxide concentration gradient vector map layer. The curl and divergence fields of the gradient vectors are obtained, and regions with negative divergence and low curl values ​​are identified. These regions characterize strong convergence of the vector field and low turbulence. An active contour model or streamline tracing algorithm based on gradient vector flow is applied to extract continuous gradient vector maximum modulus ridges connecting high divergence negative value regions from the convergence areas. This ridge is then spatially validated against rapidly changing areas in real-time water level data and specific habitat boundaries in the 3D environmental map. Ridges that pass the validation are ultimately determined as the instantaneous location of the water-air exchange front.

[0046] After reaching the water vapor exchange front, the biomimetic resonant floating platform locks in resonance with the periodic tidal flow, converting the horizontal kinetic energy of the biomimetic resonant floating platform into the deformation energy of the structure and dissipating it, thus achieving an adaptive and precise dwelling state. Furthermore, the bottom of the biomimetic resonant floating platform is equipped with a resonant structure that mimics the morphology and mechanical properties of mangrove breathing roots, with its natural frequency preset to match the dominant frequency of the tidal current. When the biomimetic resonant floating platform reaches the water-air exchange front, the periodic tidal current periodically excites the resonant structure, causing it to enter a forced resonance state. Its reciprocating deformation converts the lateral kinetic energy applied to the biomimetic resonant floating platform by the tidal current into the deformation energy and heat energy of the damping material inside the structure, which is then dissipated. This significantly suppresses the horizontal displacement fluctuations of the biomimetic resonant floating platform, achieving an adaptive and precise dwelling state with centimeter-level positioning accuracy.

[0047] Specifically, the system utilizes the physical mechanism of forced resonance to achieve energy conversion-based stability rather than antagonistic stability. The biomimetic resonant structure converts harmful lateral water flow energy into a form that can be dissipated by structural damping, mimicking the principle of mangrove breathing roots flexibly dissipating wave energy, thus making the platform itself a dynamic vibration damper. When the water flow frequency approaches the structure's natural frequency, a small energy input can trigger a large resonant response, efficiently dissipating energy and achieving ultra-stable residence of the platform in dynamic water flow at extremely low active energy consumption.

[0048] In the adaptive and precise residence state, the insertion speed and rotation angle of the biomimetic root microelectrode probe are dynamically adjusted based on the real-time measured sediment shear strength data. The biomimetic root microelectrode probe is implanted into the sediment in a biomimetic drilling manner. After implantation, the multi-channel microfluidic chip inside the probe begins to capture rhizosphere dissolved gas by pore water dialysis. Furthermore, in an adaptive and precise dwelling state, the biomimetic resonant floating platform extends a contact-type sediment shear strength probe to measure the sediment shear strength at the insertion point in real time. Based on the sediment shear strength measurement, the insertion and advancement speed of the hydraulic servo mechanism of the biomimetic root microelectrode probe, as well as the speed and direction of the rotary drive motor, are dynamically adjusted through preset control logic. A slow-advance, fast-rotation biomimetic drilling strategy is used at points with high sediment shear strength, while a fast-advance, slow-rotation strategy is used at points with low sediment shear strength, implanting the biomimetic root microelectrode probe into the sediment to a preset depth with minimal disturbance. After implantation, the internal multi-channel microfluidic chip activates a micro-pump, slowly and continuously extracting pore water from the rhizosphere sediment through a semi-permeable membrane interface using the principle of dialysis, achieving undisturbed capture of dissolved gases in the rhizosphere.

[0049] Specifically, mimicking the biological intelligence of mangrove roots adjusting their growth strategies based on soil resistance, the probe dynamically adjusts insertion kinetic parameters using real-time feedback data on sediment shear strength. The slow-advance, fast-rotation approach at high shear strength simulates the root system's evasive penetration strategy when encountering obstacles, while the fast-advance, slow-rotation approach at low shear strength simulates the rapid extension strategy of roots in loose media. Adaptive control ensures the probe reaches the target depth with minimal structural disturbance, maximizing the preservation of the original rhizosphere sediment structure and microenvironment, thus providing a prerequisite for obtaining authentic in-situ pore water and dissolved gas samples.

[0050] In-situ real-time analysis was performed using a miniature membrane sample introduction mass spectrometer integrated into the probe handle to obtain a dissolved methane concentration profile. The analysis process is synchronized with the measurements of the redox potential microelectrode and solid pH microelectrode built into the probe. All profile data are embedded with timestamps synchronized with the macroscopic flux measurement equipment of the biomimetic resonant floating platform when they are generated, forming spatiotemporally synchronized rhizosphere microenvironment data.

[0051] Furthermore, the captured rhizosphere dissolved gases are transported via a microfluidic path to the vacuum injection chamber of a miniature membrane mass spectrometer integrated into the handle of the biomimetic root microelectrode probe. The dissolved gases permeate through a selectively permeable membrane into the ionization region of the mass spectrometer, where they are ionized by electron bombardment and then separated and detected by a quadrupole mass analyzer based on their mass-to-charge ratio. The concentration values ​​of dissolved methane and other gases are output in real time, forming a dissolved methane concentration profile. The analysis process is synchronously triggered with the measurement circuits of the redox potential microelectrode and solid pH microelectrode built into the biomimetic root microelectrode probe, ensuring that the measurements of redox potential, pH value, and dissolved methane concentration are performed at strictly identical times. All measurement data are embedded with a high-precision BeiDou satellite time stamp the instant they are generated. This time stamp is strictly synchronized with the time reference of the macroscopic flux data recorded by the three-dimensional ultrasonic anemometer and gas analyzer at the top of the biomimetic resonant floating platform, thus forming spatiotemporally synchronized rhizosphere microenvironment data.

[0052] Specifically, integrating a miniature membrane mass spectrometer sensor into the probe handle enables in-situ, real-time, and continuous analysis of dissolved gases, avoiding errors caused by sample storage and transport. Rigorous synchronous measurement of redox potential, pH, and dissolved methane concentration allows for the capture of the coupled changes of these key biogeochemical parameters on timescales of minutes or even seconds, crucial for understanding the instantaneous kinetics of methanogenesis. Embedding all microscopic and macroscopic flux data with a unified, high-precision timestamp fundamentally solves the challenge of causal relationship analysis using multi-source asynchronous data. This allows microscopic mechanism data to be directly correlated and compared with macroscopic flux data on the same time axis, providing an unprecedented high-quality, highly consistent chain of evidence for establishing quantitative models of microscopic processes driving macroscopic fluxes.

[0053] The coordination and processing module performs collaborative processing on the macroscopic flux data and rhizosphere microenvironment data acquired by the mobile observation network to obtain a standardized dataset.

[0054] The timestamps of the macroscopic flux data recorded by the biomimetic resonance floating platform are aligned and matched with the timestamps of the spatiotemporally synchronized rhizosphere microenvironment dataset to form spatiotemporally paired macroscopic-microscopic data pairs.

[0055] Furthermore, the UTC timestamps recorded by the high-precision time reference source used by the biomimetic resonant floating platform when recording macroscopic flux data, and the BeiDou satellite time synchronization timestamps used for each data record in the spatiotemporally synchronized rhizosphere microenvironment dataset, are imported into a time series database. In the time series database, using a preset time window tolerance as the matching condition, the timestamps of the macroscopic flux data recorded by the biomimetic resonant floating platform are retrieved and compared with the timestamps of the spatiotemporally synchronized rhizosphere microenvironment dataset. Macroscopic flux data records whose timestamp differences fall within the time window tolerance range are associated with the same observation event as rhizosphere microenvironment data records. A successfully associated macroscopic flux data record is then packaged with one or more rhizosphere microenvironment data records into a single data unit; this data unit constitutes the spatiotemporally paired macroscopic-microscopic data pair.

[0056] Data quality tags are added to each spatiotemporally paired macro-micro data pair, and all data in the spatiotemporally paired macro-micro data pair are standardized and converted according to unified greenhouse gas concentration units, temperature units, and pressure units to generate a standardized dataset with complete metadata description.

[0057] Furthermore, data quality tags are added to each spatiotemporally paired macro-micro data pair. These tags are generated based on sensor status indicators, data integrity verification results, and outlier detection results exceeding reasonable physical ranges. All greenhouse gas concentration data in the spatiotemporally paired macro-micro data pairs are uniformly converted to dry air mole fractions in micromoles per cubic meter, all temperature data are uniformly converted to thermodynamic temperature in Kelvin, and all pressure data are uniformly converted to atmospheric pressure in hectopascals. After completing the data quality tagging and unit conversion, the spatiotemporally paired macro-micro data pairs are supplemented with metadata description information including observation platform number, latitude and longitude coordinates, tidal phase, water depth, and data version number, generating a standardized dataset with complete metadata descriptions.

[0058] The analysis module uses tidal phase as the time frame to fuse and analyze the standard dataset. It uses a flux decoupling model to decompose the total greenhouse gas flux into tidal pumping flux, biological respiration flux, and root pumping flux, and obtains the analysis results of flux source mechanisms.

[0059] Using tidal phase as the time frame, we selected all spatiotemporal pairs of macro-micro data belonging to the same tidal phase from the standard dataset.

[0060] Furthermore, based on the precise timestamps recorded in each spatiotemporally paired macro-micro data pair in the standardized dataset, and combined with the pre-defined tide table of the target mangrove protected area or the tidal phase function derived from water level data, the specific tidal phase at the time of occurrence is labeled for each spatiotemporally paired macro-micro data pair. Tidal phases are typically divided into discrete periods such as pre-high tide, mid-high tide, post-high tide, high tide, pre-low tide, mid-low tide, post-low tide, and low tide. Through database querying, all spatiotemporally paired macro-micro data pairs labeled with the same tidal phase are retrieved and extracted from the standardized dataset based on the tidal phase label, thus completing the filtering of all spatiotemporally paired macro-micro data pairs belonging to the same tidal phase.

[0061] Specifically, the biogeochemical processes of mangrove ecosystems, particularly greenhouse gas exchange directly related to hydrodynamics, are strictly modulated by tidal rhythms in terms of intensity and pattern. Using tidal phases as a screening framework, the data organization follows the endogenous driving rhythms of ecological processes from the outset. For example, this ensures that when analyzing methane pulses at the high tide front, the associated rhizosphere redox potential data all originate from the same dynamic driving phase, avoiding the erroneous correlation between the slow diffusion process during the calm high tide period and the scouring and release process during the high tide period. This process-based rather than clockwork-based data organization is fundamental to the subsequent effective separation of different physical and biological driving mechanisms, synchronizing data analysis with natural processes and enhancing the potential to reveal causal relationships.

[0062] For all spatiotemporal pairs of macro-micro data belonging to the same tidal phase, sorting and data alignment are performed according to the time sequence within the tidal phase to form a fused data profile within the phase.

[0063] Furthermore, all spatiotemporally paired macro-micro data pairs belonging to the same tidal phase are sorted in ascending order according to the chronological order of their timestamps within the tidal phase. Using an interpolation algorithm, the parameter sequences of each item in the sorted spatiotemporally paired macro-micro data pairs, discretely distributed within the tidal phase, are resampled to a unified time grid point on the time axis of the tidal phase. On the unified time grid, the macroscopic flux parameter values ​​and microscopic environmental parameter values ​​corresponding to each time point are aligned and combined to generate a comprehensive data record containing all observed variables at that moment. The comprehensive data records of all time grid points within the tidal phase are then connected in chronological order to form a fused data profile within the phase.

[0064] Specifically, by sorting and resampling within a phase, this method logically constructs a standardized tidal phase time axis and maps discrete observations onto this axis. It simulates a super-observer perspective, continuously and synchronously observing all variables within that tidal phase. For example, this allows the peak flux measured by platform A at location 1 during the early high tide and the rhizosphere reduction condition changes measured by platform B at location 2 during the middle of the high tide to be arranged in their relative order on the unified dynamic stage of the high tide phase, thus revealing dynamic characteristics such as the hysteretic response of flux to changes in the rhizosphere environment. This integrates spatially distributed, asynchronous observations into a continuous, multi-dimensional process profile of a specific ecological process (such as a complete high tide).

[0065] The flux decoupling model receives intraphase fused data profiles and separates flux components that are linearly correlated with the rate of water level change and uncorrelated with the rhizosphere redox potential from the total greenhouse gas fluxes in the intraphase fused data profiles to obtain tidal pumping fluxes.

[0066] Furthermore, the flux decoupling model performs multiple linear regression analysis on the intra-phase fused data profile. In the multiple linear regression analysis, the total greenhouse gas flux time series of the intra-phase fused data profile is used as the dependent variable, and the water level change rate time series and rhizosphere redox potential time series of the intra-phase fused data profile are used as the two independent variables. The flux decoupling model obtains the regression coefficient of total greenhouse gas flux on the water level change rate, as well as the partial correlation coefficient between total greenhouse gas flux and rhizosphere redox potential. The portion of flux change that has a significant linear regression relationship with the water level change rate but no significant partial correlation coefficient with the rhizosphere redox potential, i.e., the predicted value obtained by multiplying the regression coefficient by the water level change rate series, is separated and defined as the tidal pumping flux.

[0067] Specifically, partial correlation analysis in multivariate statistics is used to statistically separate physical and biochemical driving forces. The rate of water level change is a direct quantitative indicator of tidal physical driving forces, while rhizosphere redox potential is a key indicator of microbial metabolic activity. A partial correlation coefficient with redox potential is introduced as a filter, requiring that the flux components classified as tidal pumping remain significantly correlated with water level changes after excluding the influence of redox potential changes. The flux is purely caused by the release of dissolved gases due to water level changes, bubble overflow due to pressure changes, or advection transport in the water body, and is statistically independent of the concurrent microbial gas production activity.

[0068] The first residual flux is obtained by subtracting the tidal pumping flux from the total greenhouse gas flux. The biorespiration flux is obtained by extracting the flux component that is exponentially correlated with sediment temperature and negatively correlated with rhizosphere pH from the first residual flux.

[0069] Furthermore, the calculated tidal pumping flux time series is subtracted point-by-point from the total greenhouse gas flux time series of the phase-merged data profile to obtain the first residual flux time series. A nonlinear fit based on the Arrhenius equation is then performed between the first residual flux time series and the sediment temperature time series of the phase-merged data profile. The Arrhenius equation describes the exponential dependence of the chemical reaction rate constant on temperature, and the negative correlation coefficient between the first residual flux time series and the rhizosphere pH time series of the phase-merged data profile is obtained. The flux changes that show a significant exponential correlation (i.e., high goodness of fit) with the sediment temperature time series and a negative correlation with the rhizosphere pH time series are identified as biorespiratory fluxes generated by the catabolism (respiration) of sediment organic matter microorganisms.

[0070] Specifically, two biogeochemical fingerprints—temperature sensitivity index and pH correlation—were combined to identify respiratory flux. Microbial respiration (especially methanogenesis) strictly follows Arrhenius's law, exhibiting an exponential response to temperature changes, distinct from the temperature response patterns of physical or other biological processes. Typical methanogenesis consumes protons, leading to an increase in ambient pH, thus showing a negative correlation with rhizosphere pH. Double verification is required: firstly, the pattern of residual flux change must conform to the universal thermodynamics of microbial metabolism (exponential); secondly, this change must be accompanied by expected environmental chemical changes (negative pH correlation). This improves the specificity of biological respiratory flux determination. For example, this helps distinguish flux resulting from decreased solubility of physically dissolved gases in sediments due to diurnal warming from genuine enhanced microbial respiration, as the former may also show flux increases with temperature but will not necessarily be accompanied by characteristic changes in rhizosphere pH.

[0071] The flux decoupling model subtracts the biological respiration flux from the first residual flux to obtain the second residual flux. The second residual flux is then time-domain aligned with the minute-level negative jump events that occur in the rhizosphere redox potential. Flux pulses synchronized with the minute-level negative jump events are identified as root pumping fluxes. The analysis results of the flux source mechanism including tidal pumping flux, biological respiration flux, and root pumping flux are obtained.

[0072] Furthermore, the biological respiration flux time series was subtracted point-by-point from the first residual flux time series to obtain the second residual flux time series. Abrupt change point detection was performed on the rhizosphere redox potential time series of the phase-fused data profile to identify all minute-level negative jump events with short durations and significant decreases, and the start and peak times of each negative jump event were precisely recorded. The second residual flux time series was time-domain aligned with the occurrence times of these negative jump events to obtain the impulse response intensity of the second residual flux time series within the negative jump event time window. Flux pulse sequences that were statistically verified to be strictly synchronized with the occurrence times of minute-level negative jump events and showed a positive correlation between pulse intensity and negative jump amplitude were identified as root pumping fluxes. Integrating tidal pumping flux, biological respiration flux, and root pumping flux yielded the analysis results of flux source mechanisms including quantitative contributions from each mechanism.

[0073] Specifically, the key biophysical process of root activity is characterized as a synchronous flux pulse triggered by a minute-level negative jump in rhizosphere redox potential. Mangrove root oxygen secretion is intermittent, leading to a sharp decrease in redox potential in a small rhizosphere region within a short period. This negative potential jump is a direct signal of the formation of a locally strong reducing microenvironment driven by root activity. This establishes an event-driven decoupling logic: it does not seek continuous correlations, but rather seeks a precise temporal lock between flux release and characteristic root activity events. When a minute-level negative jump in rhizosphere redox potential (root activity event) is detected, the flux data is searched for a flux pulse occurring at the same time. If the synchronicity is repeated and statistically significant, the flux pulse is considered to be pumped or triggered by this root activity. This may be due to root activity altering pore water chemistry, instantaneously releasing dissolved gases, or triggering microbubbles. The event synchronicity-based determination method specifically captures the discontinuous flux contribution dominated by instantaneous root physiological activities, which is something that no previous model based on continuous variable correlation could achieve. This fully reveals the last part of the total flux, which is often the most ecologically significant but also the most difficult to observe.

[0074] The optimization module adaptively optimizes the observation strategy of the mobile observation network based on the analysis results of the flux source mechanism.

[0075] Based on the analysis of flux source mechanisms, hotspot areas dominated by root pumping flux were identified. Furthermore, based on the analysis results of the flux source mechanism, the root pumping flux contribution ratio calculated for each observation location is used to spatially mark all observation locations whose root pumping flux contribution ratio exceeds a preset threshold on a 3D environment map. The density and connectivity of these marked locations are analyzed using a spatial clustering algorithm. Clustered regions that are spatially adjacent and have a consistently high root pumping flux contribution ratio are identified as hotspot regions dominated by root pumping flux. The geographical boundaries and core location coordinates of each hotspot region dominated by root pumping flux are recorded.

[0076] Based on the distribution of hotspots dominated by root pumping flux, a new biomimetic resonant floating platform cruise path and stationary sampling point plan are generated and then distributed to the mobile observation network for execution.

[0077] Furthermore, based on the spatial distribution of identified hotspots dominated by root pumping flux, especially the core location coordinates and geographical boundaries of these hotspots, and combined with underwater topographic data of tidal channels in a 3D environmental map, a path planning algorithm is used to obtain an optimized navigation sequence connecting representative points at the core locations or boundaries of each hotspot. This navigation sequence constitutes a new cruise path for the biomimetic resonant floating platform. At the same time, specific stationary sampling points and suggested stationary durations are specified for each hotspot along the cruise path. A complete task instruction file containing the cruise path and stationary sampling point planning is generated. The new biomimetic resonant floating platform cruise path and stationary sampling point planning are then distributed to the control units of each biomimetic resonant floating platform in the mobile observation network via a wireless communication network, and the biomimetic resonant floating platform executes the updated observation tasks.

[0078] In summary, this invention establishes a three-dimensional environmental map containing topographic, hydrological, and biological disturbance features through a construction module. A tracking module then deploys a dynamically adaptive tidal mobile observation network based on this map and water level data to track tidal-driven water-air exchange fronts. A control module, after achieving precise stationing at the front, manipulates biomimetic root microelectrode probes to simultaneously acquire rhizosphere microenvironment data. A coordination processing module performs spatiotemporal registration and standardization of macroscopic and microscopic data to form a standardized dataset. An analysis module integrates and analyzes this dataset using tidal phase as a framework, decomposing the total flux into three components—tidal pumping, biological respiration, and root pumping—using a flux decoupling model to analyze the flux source mechanism. An optimization module adaptively adjusts the observation network's cruise path and sampling strategy based on the analysis results, achieving dynamic, multi-scale, and mechanistic collaborative observation of greenhouse gas emissions from mangrove wetlands.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A greenhouse gas monitoring system suitable for mangrove protected areas, characterized in that: include, The module constructs a three-dimensional environmental map by conducting on-site environmental surveys of the target mangrove protected area. The tracking module deploys a mobile observation network that dynamically adapts to the tidal process based on a three-dimensional environmental map, and tracks the water vapor exchange front driven by the tide based on the three-dimensional environmental map and real-time water level data. The control module, the mobile observation platform, tracks and locates the water-air exchange front, controls the biomimetic root microelectrode probe, and acquires rhizosphere microenvironment data synchronized with the current water-air exchange process. The coordination and processing module performs coordinated processing on the macroscopic flux data and rhizosphere microenvironment data acquired by the mobile observation network to obtain a standardized dataset; The analysis module uses tidal phase as the time frame to fuse and analyze the standard dataset. It uses a flux decoupling model to decompose the total greenhouse gas flux into tidal pumping flux, biological respiration flux and root pumping flux, and obtains the analysis results of flux source mechanisms. The optimization module adaptively optimizes the observation strategy of the mobile observation network based on the analysis results of the flux source mechanism.

2. The greenhouse gas monitoring system for mangrove protected areas as described in claim 1, characterized in that: The construction of the three-dimensional environment map includes, We acquire point cloud data of topography and canopy structure, underwater topography data of tidal channels, sediment characteristic data, and bioturbation distribution data, and construct a three-dimensional environmental map through data fusion and spatial interpolation.

3. The greenhouse gas monitoring system for mangrove protected areas as described in claim 2, characterized in that: The mobile observation network includes, Based on underwater topographic data of tidal channels, a navigation channel is planned and deployed to form a mobile observation network that dynamically adapts to tidal processes. By utilizing underwater topographic data of tidal channels, the deployment locations of biomimetic resonant floating platforms are planned, and a dynamic mobile observation network composed of multiple biomimetic resonant floating platforms is deployed to adapt to tidal processes.

4. The greenhouse gas monitoring system for mangrove protected areas as described in claim 3, characterized in that: The tracking of tidal-driven water vapor exchange fronts includes Based on the three-dimensional environmental map and real-time water level data, the prediction of the tidal-driven water vapor exchange front is obtained by the tidal active tracking algorithm. The biomimetic resonant floating platform navigates to the front of the predicted path of the tidal-driven water-air exchange front according to the predicted path of the tidal-driven water-air exchange front. The biomimetic resonant floating platform activates the forced resonance mechanism to dynamically track the tidal-driven water-air exchange front in front of the predicted path of the tidal-driven water-air exchange front.

5. The greenhouse gas monitoring system for mangrove protected areas as described in claim 4, characterized in that: The tracking and positioning includes, The biomimetic resonant floating platform measures the horizontal spatial gradient of dissolved carbon dioxide concentration in the surface water in real time using an onboard gas analyzer. The horizontal spatial gradient data of dissolved carbon dioxide concentration in surface water is overlaid with real-time water level data and a 3D environmental map. In the superimposed dynamic layer, the frontal line where the dissolved carbon dioxide concentration gradient vector converges is identified and determined as the instantaneous location of the water vapor exchange front.

6. The greenhouse gas monitoring system for mangrove protected areas as described in claim 5, characterized in that: The rhizosphere microenvironment data includes, After reaching the water vapor exchange front, the biomimetic resonant floating platform locks in resonance with the periodic tidal flow, converting the horizontal kinetic energy of the biomimetic resonant floating platform into the deformation energy of the structure and dissipating it, thus achieving an adaptive and precise dwelling state. In the adaptive and precise residence state, the insertion speed and rotation angle of the biomimetic root microelectrode probe are dynamically adjusted based on the real-time measured sediment shear strength data. The biomimetic root microelectrode probe is implanted into the sediment in a biomimetic drilling manner. After implantation, the multi-channel microfluidic chip inside the probe begins to capture rhizosphere dissolved gas by pore water dialysis. In-situ real-time analysis was performed using a miniature membrane sample introduction mass spectrometer integrated into the probe handle to obtain a dissolved methane concentration profile. The analysis process is synchronized with the measurements of the redox potential microelectrode and solid pH microelectrode built into the probe. All profile data are embedded with timestamps synchronized with the macroscopic flux measurement equipment of the biomimetic resonant floating platform when they are generated, forming spatiotemporally synchronized rhizosphere microenvironment data.

7. The greenhouse gas monitoring system for mangrove protected areas as described in claim 6, characterized in that: The canonical dataset includes, The timestamps of the macroscopic flux data recorded by the biomimetic resonance floating platform are aligned and matched with the timestamps of the spatiotemporally synchronized rhizosphere microenvironment dataset to form spatiotemporally paired macroscopic-microscopic data pairs. Data quality tags are added to each spatiotemporally paired macro-micro data pair, and all data in the spatiotemporally paired macro-micro data pair are standardized and converted according to unified greenhouse gas concentration units, temperature units, and pressure units to generate a standardized dataset with complete metadata description.

8. The greenhouse gas monitoring system for mangrove protected areas as described in claim 7, characterized in that: The fusion analysis includes, Using tidal phase as the time frame, all spatiotemporal pairs of macro-micro data belonging to the same tidal phase are selected from the standard dataset; For all spatiotemporal pairs of macro-micro data belonging to the same tidal phase, sorting and data alignment are performed according to the time sequence within the tidal phase to form a fused data profile within the phase.

9. The greenhouse gas monitoring system for mangrove protected areas as described in claim 8, characterized in that: The analysis results of the flux source mechanism include, The flux decoupling model receives the intraphase fused data profile and separates the flux component that is linearly related to the rate of water level change and unrelated to the rhizosphere redox potential from the total greenhouse gas flux of the intraphase fused data profile to obtain the tidal pumping flux. The first residual flux is obtained by subtracting the tidal pumping flux from the total greenhouse gas flux. The biorespiration flux is obtained by extracting the flux component that is exponentially correlated with sediment temperature and negatively correlated with rhizosphere pH from the first residual flux. The flux decoupling model subtracts the biological respiration flux from the first residual flux to obtain the second residual flux. The second residual flux is then time-domain aligned with the minute-level negative jump events that occur in the rhizosphere redox potential. Flux pulses synchronized with the minute-level negative jump events are identified as root pumping fluxes. The analysis results of the flux source mechanism including tidal pumping flux, biological respiration flux, and root pumping flux are obtained.

10. The greenhouse gas monitoring system for mangrove protected areas as described in claim 9, characterized in that: The optimized observation strategy for the mobile observation network includes, Based on the analysis of flux source mechanisms, hotspot areas dominated by root pumping flux were identified. Based on the distribution of hotspots dominated by root pumping flux, a new biomimetic resonant floating platform cruise path and stationary sampling point plan are generated and then distributed to the mobile observation network for execution.

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