A forest wetland resource investigation unmanned aerial vehicle monitoring method and data management system

Abstract

The application discloses a forest wetland resource investigation unmanned aerial vehicle monitoring method and a data management system, and the monitoring method comprises the following steps: controlling an unmanned aerial vehicle carrying a multi-sensor group to fly in a forest wetland monitoring area, collecting multiple types of data such as crown layer stratification parameters and microtopographic relief degree; correcting the original flight height through a logarithmic wind speed profile correction model operation based on the microtopography and the wind speed; correcting the original spectral reflectance through a crown layer stratification shielding and water vapor scattering cooperative operation based on the compensated height and other parameters; quantifying the time-space matching effectiveness of the multi-source data through attitude and time-space synchronous coupling operation; and classifying and processing the data based on the effectiveness value. The application solves the problem that the prior art cannot accurately correct the data distortion caused by cooperative interference, guarantees the reliability of the monitoring data, and provides accurate data support for forest wetland resource investigation.

Description

Technical Field

[0001] This invention relates to the technical fields of unmanned aerial vehicle (UAV) remote sensing monitoring, forest and wetland resource surveys and data management systems, specifically to a UAV monitoring method and data management system for forest and wetland resource surveys. Background Technology

[0002] In forest and wetland resource surveys, UAV remote sensing monitoring is widely used due to its advantages such as wide coverage and high mobility. However, the reliability of monitoring data in complex forest and wetland environments is difficult to guarantee with existing technologies. The core problem lies in the environmental characteristics of forest and wetlands, where high-density layered canopies and high water vapor content coexist. This is compounded by the coupled effects of micro-topographic undulations and nonlinear low-altitude wind speed distribution. Existing technologies only use a single shading rate or a single water vapor correction method to process spectral data, failing to consider the synergistic interference of canopy layering shading and water vapor scattering. This results in severe distortion of the original spectral reflectance's representation of the underlying surface. Furthermore, processing flight altitude data only through fixed altitude correction or single wind speed compensation ignores the disturbance of the wind speed field by micro-topography and the combined effect of both on flight altitude, causing systematic deviations in the original flight altitude. More critically, existing technologies lack a synergistic consideration of compensated altitude, corrected spectrum, and UAV attitude, failing to accurately quantify the spatiotemporal matching effectiveness of multi-source monitoring data, thus significantly reducing the reliability of subsequent data applications.

[0003] Based on the above problems, there is an urgent need for a technical solution that can accurately solve the problems of collaborative interference and data validity quantification. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and proposes a method for monitoring forest wetland resources using unmanned aerial vehicles (UAVs), comprising the following steps:

[0005] S1 controls a drone equipped with a multi-sensor group to fly in the forest wetland monitoring area. The multi-sensor group collects forest wetland canopy layering parameters, micro-topographic relief, low-altitude wind speed, original spectral reflectance, original flight altitude, drone yaw angle deviation, drone pitch angle deviation, and time stamp.

[0006] S2, based on micro-topographic relief, low-altitude wind speed and original flight altitude, performs original flight altitude correction operation through logarithmic wind speed profile correction model calculation to generate compensated flight altitude;

[0007] S3, based on the compensated flight altitude, canopy layering parameters and original spectral reflectance, performs the original spectral reflectance correction operation through the collaborative operation of the canopy layering shading correction term and the water vapor scattering correction term, and generates the corrected spectral reflectance;

[0008] S4, based on the corrected spectral reflectance, compensated flight altitude, UAV yaw angle deviation, UAV pitch angle deviation and time stamp, performs spatiotemporal matching validity quantification operation on multi-source monitoring data through the coupling operation of attitude deviation correction term and spatiotemporal synchronization correction term, and generates spatiotemporal matching validity value;

[0009] S5 performs multi-source monitoring data classification processing based on spatiotemporal matching validity values.

[0010] Preferably, the multi-sensor group includes a lidar sensor, a terrain radar sensor, a wind speed sensor, a spectral sensor, a GPS positioning sensor, an attitude sensor, and a time-space stamp recording sensor. The lidar sensor is used to collect canopy layering parameters, the terrain radar sensor is used to collect micro-topographic undulations, the wind speed sensor is used to collect low-altitude wind speed, the spectral sensor is used to collect raw spectral reflectance, the GPS positioning sensor is used to collect raw flight altitude, the attitude sensor is used to collect the UAV yaw angle deviation and the UAV pitch angle deviation, and the time-space stamp recording sensor is used to collect time-space stamps.

[0011] In a further preferred embodiment, the flight routes for the forest wetland monitoring area are planned based on the forest wetland topographic distribution map and the forest wetland resource distribution map. The forest wetland topographic distribution map and the forest wetland resource distribution map are stored on the ground-based data storage device, and the UAV establishes a two-way wireless communication connection with the ground-based data storage device.

[0012] Further preferred, the classification processing includes valid data storage and invalid data re-sampling instruction sending. Valid data storage means storing multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standard in the ground-based data management system. Invalid data re-sampling instruction sending means sending re-sampling instructions to the UAV for the monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standard.

[0013] In a further preferred embodiment, the original flight altitude correction operation is implemented using a micro-topography-wind speed coupled flight altitude compensation formula. The calculation logic of the formula is to combine the influence of micro-topography undulations on altitude and the influence of wind speed field disturbances on altitude to collaboratively correct the original flight altitude.

[0014] In a further preferred embodiment, the original spectral reflectance correction operation is implemented using a canopy-altitude coordinated spectral correction formula. The calculation logic of the formula is to simultaneously consider the attenuation effect of canopy layering on the spectrum and the interference effect of water vapor scattering on the spectrum, and to accurately correct the original spectral reflectance by combining the compensated flight altitude.

[0015] In a further preferred embodiment, the spatiotemporal matching effectiveness quantification operation is implemented using a multi-source data spatiotemporal matching effectiveness quantification formula. The calculation logic of the formula is to integrate multiple factors such as spectral spatial consistency, altitude temporal stability, UAV attitude deviation, and spatiotemporal synchronization deviation to quantitatively evaluate the spatiotemporal matching degree of multi-source monitoring data.

[0016] A forest wetland resource survey drone monitoring data management system, applied to any of the above-described forest wetland resource survey drone monitoring methods, includes a multi-sensor group, a drone flight control module, a ground-end data receiving module, a ground-end altitude correction module, a ground-end spectral correction module, a ground-end validity quantification module, and a ground-end data processing module. The multi-sensor group is electrically connected to the drone flight control module. The drone flight control module and the ground-end data receiving module establish a data transmission relationship through a bidirectional wireless communication connection. The ground-end data receiving module is electrically connected to the ground-end altitude correction module, the ground-end altitude correction module is electrically connected to the ground-end spectral correction module, the ground-end spectral correction module is electrically connected to the ground-end validity quantification module, and the ground-end validity quantification module is electrically connected to the ground-end data processing module.

[0017] In a further preferred embodiment, the ground-end altitude correction module has a built-in calculation program for the micro-topography-wind speed coupled flight altitude compensation formula, the ground-end spectral correction module has a built-in calculation program for the canopy-altitude coordinated spectral correction formula, and the ground-end effectiveness quantification module has a built-in calculation program for the multi-source data spatiotemporal matching effectiveness quantification formula. All calculation programs are pre-written and stored in the corresponding modules as computer executable programs.

[0018] In a further preferred embodiment, the ground-side data processing module includes an effective data storage unit and an invalid data resampling instruction sending unit. The effective data storage unit is electrically connected to the ground-side validity quantification module, and the invalid data resampling instruction sending unit is also electrically connected to the ground-side validity quantification module. The effective data storage unit is used to store multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standard, and the invalid data resampling instruction sending unit is used to send resampling instructions to the UAV flight control module for the monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standard.

[0019] The technical effects achieved by the above embodiments include:

[0020] The core inventive technologies of this invention are: highly coordinated correction of logarithmic wind speed profile correction model calculations; spectral coordinated correction of canopy layering and water vapor scattering; and effective coupling quantification of attitude and spatiotemporal synchronization. These technologies precisely solve the core problems in the background technologies, such as data distortion caused by canopy-water vapor coordinated interference and micro-topography-wind speed coupling effects, as well as the inability to quantify the effectiveness of multi-source data. This ensures the accuracy and reliability of forest wetland monitoring data, providing high-quality data support for resource surveys. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for monitoring forest wetland resources using unmanned aerial vehicles (UAVs).

[0022] Figure 2 This is a connection diagram of a data management system for a forest wetland resource survey drone according to this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] The core problem with traditional technical solutions is that in complex forest and wetland environments, drone monitoring data is easily affected by multiple collaborative interferences, and the effectiveness of spatiotemporal matching of multi-source data cannot be accurately assessed.

[0025] Based on this, this embodiment provides a monitoring method and data management system for forest wetland resource surveys using unmanned aerial vehicles (UAVs), including the following steps: First, controlling a UAV equipped with a multi-sensor group to fly in the forest wetland monitoring area, with the multi-sensor group simultaneously collecting forest wetland canopy layering parameters, micro-topographic relief, low-altitude wind speed, original spectral reflectance, original flight altitude, UAV yaw angle deviation, UAV pitch angle deviation, and time stamp; Second, based on the micro-topographic relief, low-altitude wind speed, and original flight altitude, performing an original flight altitude correction operation through a logarithmic wind speed profile correction model to generate a compensated flight altitude; The third step involves performing a correction operation on the original spectral reflectance based on the compensated flight altitude, canopy layering parameters, and original spectral reflectance through a collaborative operation of the canopy layering occlusion correction term and the water vapor scattering correction term, generating the corrected spectral reflectance. The fourth step involves performing a quantification operation on the spatiotemporal matching validity of multi-source monitoring data based on the corrected spectral reflectance, compensated flight altitude, UAV yaw angle deviation, UAV pitch angle deviation, and spatiotemporal stamp through a coupled operation of the attitude deviation correction term and the spatiotemporal synchronization correction term, generating a spatiotemporal matching validity value. The fifth step involves performing multi-source monitoring data classification processing based on the spatiotemporal matching validity value.

[0026] The core advantage of this technical solution lies in its progressive processing system, which includes height correction, spectral correction, and validity quantification. Through three core collaborative or coupled operations, it systematically solves the data distortion problem caused by multiple collaborative interferences, while simultaneously achieving accurate quantification of the validity of multi-source data. The first step, multi-sensor group acquisition, is the foundation of the entire solution, requiring all sensors within the multi-sensor group to maintain precise temporal and spatial synchronization. The lidar sensor scans the forest wetland canopy layer by layer by emitting laser pulses of a specific frequency. Utilizing the time difference between laser pulse emission and reception, it calculates layering parameters such as the thickness and density of the tree and herbaceous layers. The terrain radar sensor employs phased array scanning technology to perform high-resolution scanning of the monitored area's terrain, capturing minute changes in topography and generating micro-topographic relief data. The wind speed sensor uses a thermal anemometer principle to sense low-altitude wind speed changes along the flight path in real time, and its response speed must meet the wind speed acquisition requirements during the UAV's dynamic flight. The spectral sensor covers the characteristic spectral bands of the main underlying surface types of forest wetlands, capturing the original spectral reflectance of different underlying surfaces. The GPS positioning sensor accurately obtains the UAV's original flight altitude by receiving signals from multiple satellites. The attitude sensor uses a combination of gyroscopes and accelerometers to monitor the yaw and pitch angle deviations during the UAV's flight in real time. The spatiotemporal stamp recording sensor simultaneously records the acquisition time and spatial coordinates of each type of data, ensuring the spatiotemporal correlation of various data types. Steps two through five constitute a complete data processing loop. The logarithmic wind speed profile correction model calculation in step two can accurately eliminate the coupling effect of micro-topography and wind speed, providing reliable height parameters for subsequent spectral correction. The collaborative calculation in step three can simultaneously eliminate the interference of canopy stratification and water vapor scattering, restoring the true spectral characteristics of the underlying surface. The coupling calculation in step four integrates multiple influencing factors to quantify the validity of the data, providing a clear basis for the classification processing in step five. The classification processing in step five ensures the accurate application of valid data and the timely resampling of invalid data, guaranteeing the reliability of the entire monitoring process.

[0027] The problem with traditional technical solutions is that they do not specify the specific types of sensors required for UAV monitoring and their corresponding data acquisition functions, resulting in insufficient data acquisition targeting and an inability to provide accurate and matching data sources for subsequent correction and quantification calculations.

[0028] Based on this, the multi-sensor group includes a lidar sensor, a terrain radar sensor, a wind speed sensor, a spectral sensor, a GPS positioning sensor, an attitude sensor, and a spatiotemporal stamp recording sensor. The lidar sensor is used to collect canopy layering parameters, the terrain radar sensor is used to collect micro-topographic undulations, the wind speed sensor is used to collect low-altitude wind speeds, the spectral sensor is used to collect raw spectral reflectance, the GPS positioning sensor is used to collect raw flight altitude, the attitude sensor is used to collect UAV yaw angle deviation and UAV pitch angle deviation, and the spatiotemporal stamp recording sensor is used to collect spatiotemporal stamps.

[0029] This solution, by clearly defining the composition of the multi-sensor group and the dedicated acquisition functions of each sensor, ensures a precise match between the acquired data and subsequent processing requirements, providing a reliable data foundation for subsequent height correction, spectral correction, and effectiveness quantification calculations. Specifically, the selection of the lidar sensor must fully consider the density characteristics of the forest wetland canopy, choosing a model with a pulse repetition frequency adapted to the canopy density to ensure accurate differentiation between the tree and herbaceous layers, avoiding data redundancy due to excessively high pulse repetition frequencies or inaccurate layer parameter identification due to excessively low frequencies. The topographic radar sensor adopts a high-resolution scanning mode, and its scanning accuracy must be able to capture micro-topographic undulations at the centimeter level to ensure the accuracy of micro-topographic relief data. The wind speed sensor selects a model with fast response speed and strong anti-interference capabilities, capable of tracking the dynamic changes in low-altitude wind speed in real time, avoiding subsequent height correction errors caused by lag in wind speed acquisition. The spectral sensor's band coverage... The data set must include typical underlying surfaces of forests and wetlands, such as the characteristic spectral bands of soil, vegetation, and water bodies, to ensure that the raw spectral reflectance data can effectively characterize the underlying surface type. The GPS positioning sensor must have centimeter-level positioning accuracy to ensure the accuracy of the raw flight altitude data. The attitude sensor must have high-precision angle measurement capabilities to accurately capture minute yaw and pitch deviations, providing precise attitude parameters for subsequent validity quantification. The spatiotemporal stamp recording sensor must have high-precision time synchronization capabilities, with its time synchronization error controlled within milliseconds, ensuring that the spatiotemporal stamps of various data types can accurately correspond, avoiding multi-source data association errors caused by spatiotemporal synchronization errors.

[0030] The problem with traditional technical solutions is that they do not clearly define the planning basis for UAV flight routes and the data transmission method, resulting in blind spots in the coverage of flight monitoring. Furthermore, the collected data cannot be transmitted to the ground in a stable and efficient manner, affecting the continuity and reliability of the entire monitoring process.

[0031] Based on this, the flight routes for the forest wetland monitoring area are planned based on the forest wetland topographic distribution map and the forest wetland resource distribution map. The forest wetland topographic distribution map and the forest wetland resource distribution map are stored on the ground-based data storage device, and the UAV establishes a two-way wireless communication connection with the ground-based data storage device.

[0032] This solution, by clearly defining the basis for flight route planning and data transmission methods, ensures that flight monitoring can comprehensively cover the survey area and that the collected data can be stably transmitted to the ground, guaranteeing the smooth operation of the monitoring process. Specifically, the forest and wetland topographic distribution map and the forest and wetland resource distribution map need to undergo preprocessing. The preprocessing process includes data cleaning, noise reduction, stitching, and coordinate calibration to remove invalid data from the distribution maps, ensuring their accuracy and completeness. When planning flight routes based on the two preprocessed distribution maps, the topographic relief characteristics and resource distribution density must be comprehensively considered. For areas with complex and undulating terrain, a low-altitude, low-speed flight mode is adopted, and the number of flight route nodes is increased to ensure the accuracy of the monitoring data. For areas with dense resource distribution, the number of flight route nodes also needs to be increased to improve monitoring resolution. For areas with flat terrain and sparse resource distribution, a high-altitude, high-speed flight mode can be used to reduce monitoring costs and improve monitoring efficiency. The ground-based data storage equipment needs to have large capacity and high read / write speeds to store the massive amounts of monitoring data transmitted by the UAV in real time, while also having data backup capabilities to prevent data loss. The two-way wireless communication connection established between the UAV and the ground-based data storage device employs a transmission protocol with strong anti-interference capabilities, enabling it to adapt to the complex electromagnetic environment of forest wetlands and avoid data transmission interruptions or loss due to electromagnetic interference. The two-way communication function not only enables data transmission from the UAV to the ground station but also allows the ground station to send commands to the UAV, such as flight path adjustment commands and re-data collection commands, ensuring real-time control of the UAV's flight monitoring process from the ground station.

[0033] The problem with traditional technical solutions is that they do not clearly define the classification and processing methods for multi-source monitoring data, which makes it impossible to identify invalid data in a timely manner. This results in invalid data being mixed into subsequent data application stages, affecting the quality of data application. At the same time, the storage and management of valid data are not standardized, making it impossible to quickly access the data.

[0034] Based on this, the classification process includes valid data storage and invalid data re-sampling instruction transmission. Valid data storage involves storing multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standard in the ground-based data management system. Invalid data re-sampling instruction transmission involves sending re-sampling instructions to the UAV for the monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standard.

[0035] This solution, by clearly defining specific classification and processing methods, achieves accurate storage of valid data and timely recollection of invalid data, ensuring the efficiency and reliability of data application. Specifically, the setting of quantitative standards must be based on the specific accuracy requirements of forest and wetland resource surveys, and calibrated using extensive experimental data. The quantitative standards must have clear numerical thresholds to ensure a clear distinction between valid and invalid data. The ground-based data management system must possess comprehensive data management functions, including data indexing, querying, statistics, and export. When storing valid data, it must be classified and stored according to dimensions such as monitoring time, monitoring area, and data type, establishing a clear data index to facilitate subsequent data retrieval and analysis. Simultaneously, the ground-based data management system must have data encryption capabilities to encrypt stored valid data, ensuring data security. The generation of invalid data recollection instructions must include specific monitoring point coordinates, flight path adjustment suggestions, and recollection parameter requirements, ensuring that the UAV can accurately travel to the corresponding monitoring point to perform the recollection operation. After the resampling command is sent to the UAV via a two-way wireless communication connection, the UAV needs to parse the command, then adjust its flight path to the target monitoring point, and perform the resampling operation according to the specified resampling parameters. After the resampling is completed, the newly collected data is transmitted to the ground terminal in a timely manner for re-quantification and classification of its effectiveness.

[0036] The problem with traditional technical solutions is that existing original flight altitude correction methods only use single altitude compensation or wind speed correction, without considering the coupling effect of micro-topographic undulations and low-altitude wind speed, resulting in low correction accuracy and failing to meet the high-precision requirements of subsequent spectral correction for altitude parameters.

[0037] Based on this, the original flight altitude correction operation is implemented using a micro-topography wind speed coupled flight altitude compensation formula. The calculation logic of the formula is to combine the influence of micro-topography undulations on altitude with the influence of wind speed field disturbances on altitude to collaboratively correct the original flight altitude. The micro-topography wind speed coupled flight altitude compensation formula is expressed as follows:

[0038] ;

[0039] The theoretical design of this formula is based on the logarithmic wind speed profile theory in fluid mechanics and the micro-topography analysis theory in geographic information science. The logarithmic wind speed profile theory reveals the logarithmic distribution law of near-surface wind speed with altitude, providing theoretical support for analyzing the impact of wind speed on flight altitude. The micro-topography analysis theory in geographic information science provides the theoretical basis for quantifying the impact of micro-topographic undulations on flight altitude. The coupled compensation formula, constructed by combining these two theories, can accurately quantify the synergistic effect of micro-topography and wind speed, achieving high-precision correction of the original flight altitude. The meaning and dimensions of each part of the formula are as follows: the compensated flight altitude is expressed in... This indicates that the dimension is length, the unit is meters, and it is the output of the formula, directly used in subsequent spectral reflectance correction operations. Its accuracy directly determines the effect of spectral correction; the original flight altitude is... The dimension is length, and the unit is meter. This serves as the reference parameter for correction, acquired by GPS positioning sensors, and is the fundamental data for the entire correction calculation. The micro-topographic influence weighting coefficient is... This is a dimensionless parameter, and its value range is determined based on the complexity of the forest wetland micro-topography. The more complex the micro-topography, the larger the value. Specific values ​​are derived through extensive experimental data calibration and are used to adjust the weight of the influence of micro-topographic undulation on height, ensuring the quantitative accuracy of the micro-topographic influence. The degree of micro-topographic undulation is expressed as... The dimension is length, the unit is meters, and it is acquired by topographic radar sensors. It reflects the height difference between the monitoring point and the surrounding terrain within a certain range, and is the core parameter for quantifying the impact of micro-topography; the micro-topography slope angle is... The value is expressed as a dimensionless parameter in radians, acquired by a terrain radar sensor, reflecting the slope of micro-topography, and is cosine. This is used to convert micro-topographic relief into effective terrain relief along the UAV's flight direction, ensuring a precise match between the micro-topographic influence and the flight direction; the wind speed influence weighting coefficient is used... This indicates that it is a dimensionless parameter, and its value range is determined based on the variation range of low-altitude wind speed. The greater the variation range of wind speed, the larger the value. The specific value is obtained through experimental calibration and is used to adjust the weight of the influence of wind speed field disturbance on altitude, ensuring the quantitative accuracy of the wind speed influence. Low-altitude wind speed is used... The unit of measurement is length and time, measured in meters per second. It is acquired by a wind speed sensor and reflects the wind speed at the drone's flight altitude; it is a core parameter for quantifying the impact of wind speed. The drone's dwell time at the monitoring point is expressed as... This indicates that the dimension is time, the unit is seconds, and it is recorded by the UAV flight control system, reflecting the duration of the UAV's stay at the monitoring point. The longer the stay, the more significant the cumulative impact of wind speed on flight altitude; the reference altitude is... This indicates that the dimension is length, the unit is meter, and it is an industry standard reference value of 1 meter. It is used to normalize the original flight altitude, making the logarithmic term a dimensionless parameter, thus ensuring the rationality of the formula calculation.

[0040] The logical derivation of the formula is as follows: First, analyze the impact of micro-topographic undulations on the original flight altitude, and the degree of micro-topographic undulation... cosine of the slope angle of the micro-topography Multiplying these yields the effective terrain undulation along the UAV's flight direction. This effective terrain undulation is then multiplied by the original flight altitude. The ratio reflects the proportion of influence of micro-topographic undulations relative to the original flight altitude, and is further compared with the micro-topographic influence weighting coefficient. Multiplying these results gives the relative proportion of the influence of micro-topographic undulations on the original flight altitude, i.e. Secondly, the influence of wind speed field disturbance on the original flight altitude is analyzed. Based on the logarithmic wind speed profile theory, the original flight altitude... Reference height The ratio is taken as the natural logarithm, i.e. This logarithm reflects the pattern of wind speed variation with altitude, specifically low-altitude wind speed. Duration of stay at drone monitoring points Multiplying these results in the cumulative effect of wind speed on the drone, which is then multiplied by the original flight altitude. The ratio reflects the proportion of the cumulative effect of wind speed relative to the original flight altitude, and is further compared with the wind speed influence weighting coefficient. Multiplying these results gives the relative proportion of the wind speed field disturbance to the original flight altitude, i.e. Finally, based on the original flight altitude Based on this, by adding the influence of micro-topographical undulations on altitude and subtracting the influence of wind speed field disturbances on altitude, the compensated flight altitude can be obtained. The derivation process strictly follows the basic principles of fluid mechanics and geographic information science, ensuring the theoretical rationality and scientific validity of the formula.

[0041] The formula is implemented by embedding it into the calculation program of the ground-end altitude correction module. When the ground-end altitude correction module receives the original flight altitude... Micro-topographic relief Micro-topography slope angle Low-altitude wind speed Drone monitoring point dwell time After obtaining the parameters, the calculation program automatically calls the formula to calculate the influence of micro-topography and wind speed, thereby obtaining the compensated flight altitude. The core innovation of this formula lies in breaking through the limitations of existing single corrections and achieving coupled and synergistic correction of micro-topography and wind speed. It can simultaneously eliminate the influence of micro-topography undulations and wind speed field disturbances on the original flight altitude, significantly improving the accuracy of flight altitude correction and providing a reliable guarantee for the subsequent accurate correction of spectral reflectance.

[0042] The problem with traditional technical solutions is that existing methods for correcting the original spectral reflectance only consider the effects of canopy shading or water vapor scattering, without taking into account the combined interference of the two. As a result, the corrected spectral data cannot accurately represent the characteristics of wetland ground surfaces.

[0043] Based on this, the original spectral reflectance correction operation is implemented using a canopy height-coordinated spectral correction formula. The formula's calculation logic simultaneously considers the attenuation effect of canopy layering on the spectrum and the interference effect of water vapor scattering, combining this with the compensated flight altitude to accurately correct the original spectral reflectance. The canopy height-coordinated spectral correction formula is expressed as:

[0044] ;

[0045] The theoretical design of this formula is based on the theories of vegetation canopy optical transmission and atmospheric water vapor scattering. The vegetation canopy optical transmission theory reveals the attenuation law of light transmission in the vegetation canopy, providing theoretical support for quantifying the impact of canopy shading on the spectrum. The atmospheric water vapor scattering theory elucidates the scattering mechanism of water vapor on light, providing a theoretical basis for analyzing the interference of water vapor scattering on the spectrum. The synergistic correction formula constructed by combining these two theories can accurately quantify the synergistic interference of canopy layering shading and water vapor scattering, achieving high-precision correction of the original spectral reflectance. The meaning and dimensions of each part of the formula are as follows: the corrected spectral reflectance is expressed in terms of... The parameter is a dimensionless parameter, representing the output of the formula. It accurately characterizes the spectral features of wetland surfaces, providing reliable spectral parameters for the effective quantification of spatiotemporal matching of subsequent multi-source data. The original spectral reflectance is used... This indicates that the parameter is dimensionless, acquired by a spectral sensor, and includes both canopy stratification and water vapor scattering interference; it serves as the fundamental data for correction calculations. The canopy stratification weighting coefficient is... It is indicated that is a dimensionless parameter, where Corresponding to the tree layer, Corresponding to the herbaceous layer, both satisfy Its value is determined based on the contribution of the tree layer and herbaceous layer to spectral attenuation, and is derived through extensive experimental data calibration. It is used to adjust the weights of different canopy layers on the influence of spectral attenuation, ensuring the quantitative accuracy of the canopy attenuation effect; the canopy layer shading rate is used... The extinction coefficient of the canopy is a dimensionless parameter acquired by a lidar sensor. It reflects the degree of light shading by each canopy layer and is a core parameter for quantifying the impact of canopy shading. It indicates that the dimension is the reciprocal of length, and the unit is the negative first power of meter. It reflects the attenuation ability of each canopy layer to light. Its value is determined based on the type, density, and other characteristics of the canopy. The extinction coefficient of the tree layer is usually greater than that of the herbaceous layer. The compensated flight altitude is expressed as... The dimension is length, the unit is meters, and it is calculated using the micro-topography wind speed coupled flight altitude compensation formula. It is used to determine the path length of light passing through the canopy and water vapor, and is a key parameter for correlating altitude and spectral correction; the canopy layer density is... The dimension is the reciprocal of volume, and the unit is negative one cubic meter. It is obtained from lidar sensors and reflects the density of the canopy; the higher the density, the more significant the attenuation effect on light. The water vapor scattering correction coefficient is... The value of is a dimensionless parameter, determined based on the water vapor content of the forest wetland. Higher water vapor content results in a larger value. It is obtained through experimental calibration and is used to adjust the weight of water vapor scattering on spectral interference, ensuring the quantitative accuracy of the water vapor interference effect. The wetland water vapor scattering coefficient is... It indicates that the dimension is the reciprocal of length, and the unit is the negative first power of meter. It reflects the ability of water vapor to scatter light and is calculated from temperature and humidity data collected by a temperature and humidity sensor; the water vapor path length is expressed in terms of... This indicates that the dimension is length, the unit is meters, and its value is related to the compensated flight altitude. A positive correlation exists; the higher the flight altitude, the longer the path of light through water vapor, and the more significant the effect of water vapor scattering. The cooperative correction constant is used... It is indicated as a dimensionless parameter used to correct the additional error caused by the combined effect of canopy stratification and water vapor scattering. This error cannot be eliminated by considering canopy shading or water vapor scattering alone. Its value is obtained through calibration of a large amount of experimental data to ensure the accuracy of the combined correction.

[0046] The logical derivation of the formula is as follows: First, analyze the attenuation effect of canopy layering on the original spectral reflectance. For each canopy layer, the canopy layering extinction coefficient is... Compensated flight altitude With canopy layer density Multiplying these three factors together yields the degree of light attenuation as it passes through the canopy. Taking the negative exponent of the natural exponent, we get... The exponential decay term is obtained, which is related to the canopy stratification shading rate. and canopy stratification weighting coefficient Multiplying these two layers yields the attenuation effect of the canopy layer on the original spectral reflectance. Summing the attenuation effects of both canopy layers yields... The total attenuation effect of the two canopy layers on the original spectral reflectance is obtained. Subtracting this total attenuation effect from 1 yields... The correction factor for canopy stratification was obtained; secondly, the interference effect of water vapor scattering on the original spectral reflectance was analyzed, and the wetland water vapor scattering coefficient was determined. With water vapor path length Multiplying these gives the degree to which water vapor scatters light, and this scattering degree is then compared with the water vapor scattering correction factor. Multiplying these two components yields the interference effect of water vapor scattering on the original spectral reflectance. Subtracting this interference effect from 1 gives the result. The correction factor for water vapor scattering was obtained; finally, the original spectral reflectance was... With canopy stratification shading correction factor, water vapor scattering correction factor and co-correction constant Multiplying them together yields the corrected spectral reflectance. The derivation process strictly follows the basic principles of vegetation canopy optical transmission theory and atmospheric water vapor scattering theory, ensuring the theoretical rationality and scientific validity of the formula.

[0047] The formula is implemented by embedding it into the calculation program of the ground-side spectral correction module. When the ground-side spectral correction module receives the original spectral reflectance... Canopy layering parameters, compensated flight altitude Wetland water vapor scattering coefficient After obtaining the relevant parameters, the calculation program automatically calls the formula to calculate the canopy layering shading correction factor and the water vapor scattering correction factor in sequence, thereby obtaining the corrected spectral reflectance. The core innovation of this formula lies in breaking through the existing single correction mode and realizing the synergistic correction of canopy layering and water vapor scattering. It can simultaneously eliminate the synergistic interference of the two, significantly improve the accuracy of spectral reflectance correction, restore the true spectral characteristics of wetland ground surfaces, and provide high-quality spectral parameters for the subsequent quantification of the effectiveness of spatiotemporal matching of multi-source data.

[0048] The problem with traditional technical solutions is that existing technologies cannot comprehensively consider multiple influencing factors or quantify the spatiotemporal matching effectiveness of multi-source monitoring data, resulting in invalid data being mixed into subsequent application stages and affecting the quality of data application.

[0049] Based on this, the spatiotemporal matching effectiveness quantification operation is implemented using a multi-source data spatiotemporal matching effectiveness quantification formula. The calculation logic of the formula is to integrate multiple factors such as spectral spatial consistency, altitude temporal stability, UAV attitude deviation, and spatiotemporal synchronization deviation to quantitatively evaluate the spatiotemporal matching degree of multi-source monitoring data. The multi-source data spatiotemporal matching effectiveness quantification formula is expressed as follows:

[0050] ;

[0051] The theoretical basis of this formula comes from statistics and UAV attitude control theory. Statistics provides theoretical support for quantifying the spatial consistency and temporal stability of data; UAV attitude control theory provides a theoretical basis for analyzing the impact of attitude deviations on data validity. Combined with spatiotemporal synchronization error analysis, the constructed formula for quantifying the spatiotemporal matching validity of multi-source data can comprehensively consider multiple influencing factors and accurately quantify the spatiotemporal matching validity of multi-source monitoring data. The meanings and dimensions of each part of the formula are as follows: the spatiotemporal matching validity value is expressed as... The parameter is dimensionless, and the output value ranges from 0 to 1. The closer the value is to 1, the higher the spatiotemporal matching validity of the multi-source monitoring data and the better the data quality. It is used to directly judge the validity of multi-source data and provide a clear basis for subsequent classification processing. The corrected spectral spatial consistency coefficient is used... The spatial consistency coefficient is a dimensionless parameter reflecting the spatial consistency of the corrected spectral data. It is calculated by comparing the corrected spectral reflectance data of adjacent monitoring points; the closer the spectral data of adjacent monitoring points are, the greater the spatial consistency coefficient. The spatial consistency weighting coefficient is... The parameter denoted as is a dimensionless parameter used to adjust the weight of the influence of spectral spatial consistency on the effectiveness of spatiotemporal matching; its value is obtained through experimental calibration. The compensated altitude temporal stability coefficient is... The time stability coefficient is a dimensionless parameter reflecting the temporal stability of the compensated altitude data. It is calculated by comparing compensated flight altitude data from the same monitoring point at different times; the closer the altitude data are at different times, the greater the time stability coefficient. The time stability weighting coefficient is... The parameter is a dimensionless parameter used to adjust the weight of the influence of altitude temporal stability on the effectiveness of spatiotemporal matching. Its value is obtained through experimental calibration and satisfies the following conditions: UAV yaw angle deviation The parameter is dimensionless, measured in radians, and acquired by attitude sensors. It reflects the deviation in the UAV's flight direction; the larger the deviation, the more significant its impact on the effectiveness of data spatiotemporal matching. The UAV pitch angle deviation is expressed as... The value is a dimensionless parameter in radians, acquired by an attitude sensor, and reflects the pitch deviation of the UAV's flight attitude. The larger the deviation, the lower the effectiveness of the spatiotemporal matching of the data. The full-angle radian value is used as a normalization factor to make This becomes a dimensionless deviation ratio, ensuring the rationality of the quantification of the impact of attitude deviation; the time-space stamp synchronization deviation coefficient is used... This represents a dimensionless parameter reflecting the degree of synchronization deviation of spatiotemporal stamps for various data types. It is calculated by comparing spatiotemporal stamp data of different data types. The larger the synchronization deviation, the larger the coefficient value, and the lower the effectiveness of spatiotemporal matching of the data. The corrected spectral reflectance is used... This indicates that the parameter is dimensionless and is calculated using the canopy height-coordinated spectral correction formula; the compensated flight altitude is represented by... This indicates that the dimension is length, and the unit is meters; the reference flight altitude is... This indicates that the dimension is length, the unit is meters, and it is an industry standard reference value of 100 meters. It is used to normalize the compensated flight altitude into a dimensionless parameter to ensure the rationality of the formula calculation.

[0052] The logical derivation of the formula is as follows: First, calculate the combined influence of spectral spatial consistency and high temporal stability, and then apply the corrected spectral spatial consistency coefficient. Spatial consistency weighting coefficient Multiplying these yields the contribution of spectral spatial consistency to effectiveness, and the compensated height temporal stability coefficient is then calculated. With time stability weighting coefficient Multiplying them yields the contribution of high time stability to effectiveness; adding the two together gives the total contribution. First, we obtain the influence quantity that comprehensively reflects the spatial and temporal characteristics of the data; second, we calculate the influence quantity of the UAV attitude deviation, including the UAV yaw angle deviation. pitch angle deviation Summing yields the total attitude deviation, which is then summed to obtain the total angular deviation in radians. The ratio of these values ​​yields the attitude deviation ratio, which reflects the degree of attitude deviation relative to the full angle. Subtracting this attitude deviation ratio from 1 gives the attitude deviation percentage. The attitude deviation correction factor is obtained. The smaller the attitude deviation, the closer the correction factor is to 1, and the smaller the impact on the spatiotemporal matching effectiveness. The impact of the spatiotemporal stamp synchronization deviation is calculated again by subtracting the spatiotemporal stamp synchronization deviation coefficient from 1. ,Right now The correction factor for the spatiotemporal synchronization deviation is obtained. The smaller the spatiotemporal synchronization deviation, the closer the correction factor is to 1, and the higher the spatiotemporal matching effectiveness of the data. Finally, the co-matching amount of the spectrum and altitude is calculated, and the compensated flight altitude is then used. Reference flight altitude The ratio, i.e. The normalized compensated height is obtained, which is related to the corrected spectral reflectance. The sum of the squares of 1 and the normalized compensated height is calculated by taking the square root of the sum. The numerator is obtained by taking the square root of the sum of the squares of 1 and the normalized height. The denominator is the ratio of the numerator to the denominator. The spectral and altitude matching quantity is obtained, which reflects the consistency between the corrected spectral data and the compensated altitude data. Finally, the above four parts, namely the comprehensive influence quantity, attitude deviation correction factor, spatiotemporal synchronization deviation correction factor, and spectral matching quantity, are multiplied together to obtain the spatiotemporal matching effectiveness value. The derivation process strictly follows the basic principles of statistics and UAV attitude control theory, ensuring the theoretical rationality and scientific validity of the formula.

[0053] The formula is implemented by embedding it into the calculation program of the ground-end effectiveness quantification module. When the ground-end effectiveness quantification module receives the corrected spectral reflectance... Compensated flight altitude UAV yaw angle deviation UAV pitch angle deviation After obtaining parameters such as spatiotemporal stamp data, the calculation program automatically calls the formula to calculate the influence of each part and the correction factor in sequence, thereby obtaining the spatiotemporal matching validity value. The core innovation of this formula lies in breaking through the limitations of existing single-factor evaluations. It comprehensively considers multiple influencing factors, including spectral spatial consistency, altitude temporal stability, UAV attitude deviation, and spatiotemporal synchronization deviation. This achieves a comprehensive and accurate quantification of the spatiotemporal matching effectiveness of multi-source data, providing a scientific and reliable basis for the classification and processing of multi-source data, and effectively preventing invalid data from being mixed into subsequent application stages.

[0054] The problem with traditional technical solutions is that the existing forest wetland drone monitoring data management system has an unclear module structure, and the connection relationship and data transmission path between modules are unclear, which leads to unstable system operation, unsmooth data transmission, and affects the efficiency and reliability of the entire monitoring process.

[0055] Based on this, this embodiment provides a forest wetland resource survey UAV monitoring data management system, including a multi-sensor group, a UAV flight control module, a ground-end data receiving module, a ground-end altitude correction module, a ground-end spectral correction module, a ground-end validity quantification module, and a ground-end data processing module. The multi-sensor group is electrically connected to the UAV flight control module. The UAV flight control module and the ground-end data receiving module establish a data transmission relationship through a bidirectional wireless communication connection. The ground-end data receiving module is electrically connected to the ground-end altitude correction module, the ground-end altitude correction module is electrically connected to the ground-end spectral correction module, the ground-end spectral correction module is electrically connected to the ground-end validity quantification module, and the ground-end validity quantification module is electrically connected to the ground-end data processing module.

[0056] This solution constructs a well-structured and stable UAV monitoring data management system by clearly defining the system's module composition, connection relationships, and data transmission paths, ensuring the efficient and smooth operation of the entire monitoring process. The functions of each module and the interaction process between modules are as follows: The multi-sensor group, as the data acquisition unit, is responsible for collecting various raw data required for forest wetland monitoring, including canopy layering parameters, micro-topographic relief, low-altitude wind speed, raw spectral reflectance, raw flight altitude, UAV yaw angle deviation, UAV pitch angle deviation, and time stamps. The multi-sensor group and the UAV flight control module are electrically connected using shielded wires to effectively reduce electromagnetic interference and ensure stable transmission of the collected data to the UAV flight control module. The UAV flight control module, as the data relay and control unit, receives the raw data transmitted from the multi-sensor group, performs preliminary integration and formatting of the raw data to meet the requirements of subsequent transmission and processing, and receives control commands from the ground, such as flight path adjustment commands and resampling commands, and controls the UAV's flight status according to these commands. The UAV flight control module and the ground-based data receiving module establish a data transmission relationship through a two-way wireless communication connection. This two-way wireless communication connection uses an industrial-grade wireless communication module, supporting long-distance, high-bandwidth data transmission and adapting to the complex outdoor environment of forests and wetlands. This ensures that the integrated raw data can be stably transmitted to the ground, while the ground control commands can be accurately transmitted to the UAV flight control module. The ground-based data receiving module, as the ground-based data receiving unit, is responsible for receiving the integrated raw data transmitted from the UAV flight control module, verifying and denoising the data, and removing invalid and noisy data generated during transmission to ensure the integrity and accuracy of the transmitted data. The ground-based data receiving module and the ground-based altitude correction module are electrically connected, with the processed raw data being transmitted to the ground-based altitude correction module. The ground-based altitude correction module is responsible for performing the original flight altitude correction operation. It has a built-in calculation program for the micro-topography wind speed coupled flight altitude compensation formula. After receiving parameters such as the original flight altitude, micro-topography undulation, and low-altitude wind speed from the ground-based data receiving module, it calls the calculation program to complete the correction calculation and generate the compensated flight altitude. The ground-based altitude correction module is electrically connected to the ground-based spectral correction module, transmitting the compensated flight altitude and related parameters such as the original spectral reflectance to the ground-based spectral correction module. The ground-based spectral correction module is responsible for performing the original spectral reflectance correction operation. It has a built-in calculation program for the canopy height-coordinated spectral correction formula. After receiving relevant parameters, it calls the calculation program to complete the correction calculation and generate the corrected spectral reflectance. The ground-based spectral correction module is electrically connected to the ground-based effectiveness quantification module, transmitting parameters such as the corrected spectral reflectance, compensated flight altitude, UAV yaw angle deviation, UAV pitch angle deviation, and time stamp to the ground-based effectiveness quantification module.The ground-based validity quantification module is responsible for performing the spatiotemporal matching validity quantification operation of multi-source monitoring data. It has a built-in calculation program for the multi-source data spatiotemporal matching validity quantification formula. After receiving relevant parameters, it calls the calculation program to complete the quantification calculation and generate the spatiotemporal matching validity value. The ground-based validity quantification module and the ground-based data processing module are electrically connected, transmitting the spatiotemporal matching validity value and the corresponding multi-source monitoring data to the ground-based data processing module. The ground-based data processing module is responsible for performing the classification processing of multi-source monitoring data, storing valid data, and sending instructions to re-collect invalid data. The connections between the modules are stable and reliable, and the data transmission path is clear, forming a complete closed loop from data acquisition, transmission, correction, quantification to classification processing, ensuring the stable and efficient operation of the system.

[0057] The problem with traditional technical solutions is that the correction and quantization modules of existing data management systems lack corresponding calculation programs, making it impossible to achieve accurate correction and quantization calculations. This results in insufficient accuracy of the corrected parameters and quantized validity values, affecting the quality of subsequent data applications.

[0058] Based on this, the ground-end altitude correction module has a built-in calculation program for the micro-topography wind speed coupled flight altitude compensation formula, the ground-end spectral correction module has a built-in calculation program for the canopy height coordinated spectral correction formula, and the ground-end effectiveness quantification module has a built-in calculation program for the multi-source data spatiotemporal matching effectiveness quantification formula. All calculation programs are pre-written and stored in the corresponding modules as computer executable programs.

[0059] This solution ensures accurate correction and quantization calculations by equipping each correction and quantization module with dedicated computational programs, providing high-quality parameters and validity assessment results for subsequent data applications. The writing, storage, and retrieval process of each computational program is as follows: Each program is written based on its corresponding formula logic using an efficient and stable industrial-grade programming language to ensure computational efficiency and stability. During the writing process, the real-time requirements of forest wetland monitoring were fully considered, and the programs were optimized to reduce computational redundancy and improve computational speed, meeting the requirements for real-time processing of multi-source data. The calculation program for the micro-topography wind speed coupled flight altitude compensation formula was written strictly according to the logical derivation process of the formula, sequentially calculating the influence of micro-topography and wind speed, and finally outputting the compensated flight altitude. The calculation program for the canopy height collaborative spectral correction formula, based on the derivation logic of the formula, progressively calculates the canopy layering shading correction factor and water vapor scattering correction factor, and outputs the corrected spectral reflectance. The calculation program for the multi-source data spatiotemporal matching effectiveness quantification formula, following the derivation steps of the formula, sequentially calculates the comprehensive influence, attitude deviation correction factor, spatiotemporal synchronization deviation correction factor, and collaborative matching quantity, and finally outputs the spatiotemporal matching effectiveness value. After each calculation program was written, it underwent extensive testing and verification. The testing process combined simulated data and actual monitoring data to comprehensively test the program's computational accuracy, stability, and computational speed. Problems discovered during testing were repeatedly optimized to ensure that the program can accurately process various input data and output accurate calculation results. Each calculation program is stored in the corresponding module's storage unit, which uses non-volatile storage media to ensure that the program is not lost in the event of power failure. After the ground-end altitude correction module, ground-end spectral correction module, and ground-end effectiveness quantification module are started, they will automatically load the corresponding calculation programs and enter a standby state. Upon receiving the corresponding input parameters, they will immediately call the program to execute the calculation. After the calculation is completed, the output results will be transmitted to the next level module. Each calculation program adopts a modular design, and each functional module of the program is relatively independent, which facilitates subsequent maintenance, upgrades, and modifications. If it is necessary to adjust the formula parameters or optimize the formula logic in the future, only the corresponding functional module needs to be modified, without the need to reconstruct the entire program, which significantly reduces maintenance costs.

[0060] The problem with traditional technical solutions is that the functions of the ground-based data processing module in existing data management systems are unclear, making it impossible to accurately store effective data and promptly recollect invalid data. This leads to chaotic management of effective data and the inability to promptly compensate for invalid data, affecting the integrity and reliability of the entire monitoring data.

[0061] Based on this, the ground-side data processing module includes an effective data storage unit and an invalid data resampling instruction sending unit. The effective data storage unit is electrically connected to the ground-side validity quantification module, and the invalid data resampling instruction sending unit is also electrically connected to the ground-side validity quantification module. The effective data storage unit is used to store multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standard. The invalid data resampling instruction sending unit is used to send resampling instructions to the UAV for monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standard.

[0062] This solution, by clearly defining the composition and function of the ground-based data processing module, achieves accurate storage of valid data and timely re-collection of invalid data, ensuring the integrity, reliability, and standardization of monitoring data. The specific functions and working processes of the valid data storage unit and the invalid data re-collection instruction sending unit are as follows: The valid data storage unit is electrically connected to the ground-based validity quantification module. After receiving the spatiotemporal matching validity value and corresponding multi-source monitoring data transmitted by the ground-based validity quantification module, it first compares the spatiotemporal matching validity value with a preset quantification standard to determine whether the corresponding multi-source monitoring data is valid. For valid data whose spatiotemporal matching validity value meets the quantification standard, the valid data storage unit classifies and stores it according to preset storage rules. These rules are based on dimensions such as monitoring time, monitoring area, and data type. For example, monitoring time is divided by year, quarter, or month; monitoring areas are divided by different forest and wetland zones; and data types are divided by spectral data, altitude data, and attitude data. Simultaneously, a detailed data index is established for the valid data. The data index includes information such as monitoring time, monitoring area, data type, and data acquisition parameters, facilitating subsequent data query, retrieval, and analysis. The effective data storage unit utilizes high-capacity, high-read / write-speed storage media to meet the storage needs of massive amounts of effective data. It also features data backup capabilities, employing a combination of local and off-site backups to periodically back up stored effective data, preventing data loss due to storage media failures, natural disasters, or other factors. Furthermore, the effective data storage unit incorporates data encryption, using high-strength encryption algorithms to encrypt stored effective data, preventing unauthorized access, tampering, or leakage, thus ensuring data security. The invalid data resampling instruction sending unit is also electrically connected to the ground-based validity quantification module. After receiving relevant data, it assesses the spatiotemporal matching validity value. For invalid data whose spatiotemporal matching validity value does not meet the quantification standard, the invalid data resampling instruction sending unit extracts the corresponding monitoring point coordinates. Combining this with the terrain features, resource distribution, and the UAV's current flight status, it generates a resampling instruction. The resampling instruction includes detailed information such as monitoring point coordinates, flight path adjustment parameters, resampling data type, and acquisition parameter requirements, ensuring the UAV can accurately and efficiently complete the resampling operation. The invalid data resampling command sending unit transmits the resampling command to the UAV flight control module via a two-way wireless communication connection. After transmission, the unit monitors the UAV's response status in real time. If no response is received from the UAV within a preset time, the resampling command is resent to ensure successful delivery. After the UAV completes the resampling operation, it transmits the newly collected multi-source data to the ground terminal. The ground terminal processes the newly collected data according to the normal monitoring process, including transmission, correction, quantification, and classification, until valid data that meets the quantification standards is obtained. This ensures timely compensation for invalid data and guarantees the integrity of the monitoring data.

[0063] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for monitoring forest wetland resources using unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: S1 controls a drone equipped with a multi-sensor group to fly in the forest wetland monitoring area. The multi-sensor group collects forest wetland canopy layering parameters, micro-topographic relief, low-altitude wind speed, original spectral reflectance, original flight altitude, drone yaw angle deviation, drone pitch angle deviation, and time stamp. S2, based on micro-topographic relief, low-altitude wind speed and original flight altitude, performs original flight altitude correction operation through logarithmic wind speed profile correction model calculation to generate compensated flight altitude; S3, based on the compensated flight altitude, canopy layering parameters and original spectral reflectance, performs the original spectral reflectance correction operation through the collaborative operation of the canopy layering shading correction term and the water vapor scattering correction term, and generates the corrected spectral reflectance; S4, based on the corrected spectral reflectance, compensated flight altitude, UAV yaw angle deviation, UAV pitch angle deviation and time stamp, performs spatiotemporal matching validity quantification operation on multi-source monitoring data through the coupling operation of attitude deviation correction term and spatiotemporal synchronization correction term, and generates spatiotemporal matching validity value; S5 performs multi-source monitoring data classification processing based on spatiotemporal matching validity values.

2. The method for monitoring forest and wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The multi-sensor group includes a lidar sensor, a terrain radar sensor, a wind speed sensor, a spectral sensor, a GPS positioning sensor, an attitude sensor, and a time-space stamp recording sensor. The lidar sensor is used to collect canopy layering parameters, the terrain radar sensor is used to collect micro-topographic undulations, the wind speed sensor is used to collect low-altitude wind speeds, the spectral sensor is used to collect raw spectral reflectance, the GPS positioning sensor is used to collect raw flight altitude, the attitude sensor is used to collect the UAV's yaw angle deviation and pitch angle deviation, and the time-space stamp recording sensor is used to collect time-space stamps.

3. The method for monitoring forest wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The flight routes for the forest and wetland monitoring area are planned based on the forest and wetland topographic distribution map and the forest and wetland resource distribution map. The forest and wetland topographic distribution map and the forest and wetland resource distribution map are stored on the ground-based data storage device, and the UAV establishes a two-way wireless communication connection with the ground-based data storage device.

4. The method for monitoring forest wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The classification process includes valid data storage and invalid data re-sampling instruction transmission. Valid data storage involves storing multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standard in the ground-based data management system. Invalid data re-sampling instruction transmission involves sending re-sampling instructions to the UAV for the monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standard.

5. The method for monitoring forest wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The original flight altitude correction operation is implemented using a micro-topography-wind speed coupled flight altitude compensation formula. The calculation logic of the formula is to combine the influence of micro-topography undulations on altitude and the influence of wind speed field disturbance on altitude to perform a coordinated correction of the original flight altitude.

6. The method for monitoring forest wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The original spectral reflectance correction operation is achieved using a canopy-altitude coordinated spectral correction formula. The calculation logic of the formula is to simultaneously consider the attenuation effect of canopy layering on the spectrum and the interference effect of water vapor scattering on the spectrum, and to accurately correct the original spectral reflectance by combining the compensated flight altitude.

7. The method for monitoring forest and wetland resources using unmanned aerial vehicles according to claim 1, characterized in that, The spatiotemporal matching effectiveness quantification operation is implemented using a multi-source data spatiotemporal matching effectiveness quantification formula. The calculation logic of the formula is to integrate multiple factors such as spectral spatial consistency, altitude temporal stability, UAV attitude deviation, and spatiotemporal synchronization deviation to quantitatively evaluate the spatiotemporal matching degree of multi-source monitoring data.

8. A monitoring data management system for forest wetland resource survey drones, applied to the monitoring method for forest wetland resource survey drones as described in any one of claims 1-7, characterized in that, It includes a multi-sensor group, a UAV flight control module, a ground-end data receiving module, a ground-end altitude correction module, a ground-end spectral correction module, a ground-end effectiveness quantification module, and a ground-end data processing module. The multi-sensor group is electrically connected to the UAV flight control module. The UAV flight control module and the ground-end data receiving module establish a data transmission relationship through a two-way wireless communication connection. The ground-end data receiving module is electrically connected to the ground-end altitude correction module, the ground-end altitude correction module is electrically connected to the ground-end spectral correction module, the ground-end spectral correction module is electrically connected to the ground-end effectiveness quantification module, and the ground-end effectiveness quantification module is electrically connected to the ground-end data processing module.

9. The forest wetland resource survey UAV monitoring data management system according to claim 8, characterized in that, The ground-end altitude correction module has a built-in calculation program for the micro-topography-wind speed coupled flight altitude compensation formula, the ground-end spectral correction module has a built-in calculation program for the canopy-altitude coordinated spectral correction formula, and the ground-end effectiveness quantification module has a built-in calculation program for the multi-source data spatiotemporal matching effectiveness quantification formula. All calculation programs are pre-written and stored in the corresponding modules as computer executable programs.

10. The forest wetland resource survey UAV monitoring data management system according to claim 8, characterized in that, The ground-based data processing module includes an effective data storage unit and an invalid data resampling instruction sending unit. The effective data storage unit is electrically connected to the ground-based validity quantification module, and the invalid data resampling instruction sending unit is also electrically connected to the ground-based validity quantification module. The effective data storage unit is used to store multi-source monitoring data whose spatiotemporal matching validity values ​​meet the quantification standards. The invalid data resampling instruction sending unit is used to send resampling instructions to the UAV flight control module for the monitoring points corresponding to multi-source monitoring data whose spatiotemporal matching validity values ​​do not meet the quantification standards.

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