Soil carbon stock monitoring method based on unmanned aerial vehicle and unmanned vehicle sampling

Abstract

The application provides a soil carbon storage monitoring method based on unmanned aerial vehicle and unmanned vehicle sampling, comprising the following steps: controlling the unmanned aerial vehicle to transport the unmanned vehicle to a sampling area of target soil; determining the variation coefficient of the soil organic carbon content space of each plot in the sampling area based on the carbon-related spatial data of the sampling area; screening the plot with the variation coefficient greater than a preset first threshold value as a first variation area, and screening the plot with the variation coefficient less than a preset second threshold value as a second variation area; wherein the first threshold value is greater than the second threshold value; increasing the sampling points of the first variation area and reducing the sampling points of the second variation area; controlling the unmanned vehicle to perform stratified soil sampling on each sampling point of the first variation area and the second variation area respectively to obtain soil samples corresponding to each stratified soil; and controlling the unmanned aerial vehicle to transport the unmanned vehicle loaded with the soil samples to the corresponding departure place, thereby solving the technical problems of low efficiency and high risk of manual sampling and realizing optimal allocation of sampling resources.

Description

Technical Field

[0001] This invention relates to the field of soil carbon storage monitoring technology, and in particular to a method for monitoring soil carbon storage based on sampling by unmanned aerial vehicles and unmanned vehicles. Background Technology

[0002] Soil carbon storage is a key component of the carbon pool in terrestrial ecosystems, and its accurate monitoring is crucial for assessing regional carbon sink functions. Current mainstream methods still rely on manual field sampling: undisturbed soil samples are collected at predetermined sampling points using soil drills or ring cutters at varying depths, and then carbon storage is calculated in the laboratory by measuring parameters such as bulk density and organic carbon content. While this method is accurate, it is labor-intensive, time-consuming, and costly, and difficult to implement in areas with complex terrain, poor transportation, or ecological sensitivity, making it unsuitable for supporting large-scale, high-frequency dynamic monitoring needs.

[0003] More importantly, traditional sampling typically employs a uniform sampling strategy, which fails to fully consider the spatial heterogeneity of soil carbon content. This results in insufficient sampling in areas with highly variable carbon distribution, while oversampling occurs in homogeneous areas, leading to resource waste and monitoring bias. Summary of the Invention

[0004] This invention provides a method for monitoring soil carbon storage based on sampling by drones and unmanned vehicles, in order to solve the technical problems of inefficient sampling and rigid sampling point layout in the prior art.

[0005] On the one hand, the present invention provides a method for monitoring soil carbon storage based on sampling by unmanned aerial vehicles and unmanned vehicles, comprising:

[0006] Control the drone to transport the unmanned vehicle to the target soil sampling area;

[0007] Based on the carbon-related spatial data of the sampling area, the spatial variation coefficient of soil organic carbon content in each plot within the sampling area is determined;

[0008] The plots with a coefficient of variation greater than a preset first threshold are selected as the first variation region, and the plots with a coefficient of variation less than a preset second threshold are selected as the second variation region; wherein the first threshold is greater than the second threshold.

[0009] Increase the number of sampling points in the first mutation region and decrease the number of sampling points in the second mutation region;

[0010] The unmanned vehicle is controlled to perform stratified soil sampling at each sampling point in the first and second variation regions to obtain soil samples corresponding to each stratified soil layer.

[0011] The drone is controlled to transport the unmanned vehicle carrying soil samples to the starting point corresponding to the sampling area.

[0012] This invention provides a soil carbon storage monitoring method based on UAV and unmanned vehicle sampling. The method uses UAVs to enable rapid remote delivery and retrieval of sampling equipment, and unmanned vehicles to perform precise and low-disturbance stratified sampling on the ground. Furthermore, it adopts an intelligent sampling point strategy based on the carbon spatial variation coefficient to dynamically adjust the sampling density in different heterogeneous areas. This effectively solves the technical problems of low efficiency and high risk of manual sampling, as well as the limited sampling depth and easy disturbance of samples by existing UAVs. Moreover, it achieves optimal allocation of sampling resources while ensuring monitoring accuracy, and significantly improves the monitoring efficiency of soil carbon storage in large-scale and complex terrain. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a schematic flowchart of the soil carbon storage monitoring method based on sampling by drones and unmanned vehicles provided in an embodiment of the present invention;

[0015] Figure 2 This is a schematic diagram of the soil carbon storage monitoring device based on sampling by drones and unmanned vehicles provided in an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0018] Figure 1 This is a schematic flowchart of a soil carbon storage monitoring method based on sampling by drones and unmanned vehicles provided in an embodiment of the present invention.

[0019] See Figure 1 The method for monitoring soil carbon storage based on sampling by drones and unmanned vehicles includes the following steps.

[0020] Step 101: Control the drone to transport the unmanned vehicle to the target soil sampling area.

[0021] Among them, drones refer to unmanned aerial vehicles that generate lift through rotors, carry unmanned vehicles, and are controlled remotely or by preset programs. Their core function is to achieve long-distance, rapid, and cross-terrain aerial delivery and retrieval of sampling equipment (unmanned vehicles) and samples. Unmanned vehicles refer to unmanned ground vehicles that are transported to the sampling area by drones, can move autonomously or under controlled conditions on land, and are equipped with soil sampling devices. Their core task is to perform specific ground sampling operations such as stratified soil drilling, sample packaging, and numbering. Sampling areas are usually far from urban residential areas. Generally, equipment is transported to a nearby accessible road area via transportation (such as a car), and then a drone carrying an unmanned vehicle arrives at the designated sampling area to begin operations.

[0022] Step 102: Based on the pre-acquired carbon-related spatial data of the sampling area, determine the spatial variation coefficient of soil organic carbon content in each plot within the sampling area.

[0023] Carbon-related spatial data refers to remote sensing or geographic information data that can reflect or indirectly characterize the spatial distribution of soil organic carbon content, such as multispectral / hyperspectral imagery, vegetation indices (e.g., NDVI), land surface temperature, soil type maps, and historical measured carbon content point data, used to construct a spatial distribution model of carbon content. Carbon-related spatial data is acquired in advance before the sampling task through multi-source information such as remote sensing imagery (e.g., multispectral / hyperspectral satellite or UAV imagery), geographic information system (GIS) data (e.g., digital elevation models, soil type maps, land use maps), and historical measured soil organic carbon samples. Remote sensing and GIS data are used to extract proxy indicators related to soil organic carbon content (e.g., vegetation indices, land surface temperature, topographic humidity index, etc.), and then combined with historical measured samples to construct a carbon inversion model, thereby generating an estimate of the spatial distribution of soil organic carbon content covering the entire sampling area. This estimate is then used as carbon-related spatial data for subsequent coefficient of variation calculation and sampling point optimization.

[0024] The coefficient of variation of soil organic carbon content is a statistical measure used to quantify the degree of unevenness in the spatial distribution of soil organic carbon. A large coefficient of variation indicates that the carbon content within the plot or between it and surrounding plots varies significantly, indicating strong spatial heterogeneity; a small coefficient of variation indicates that the carbon content is relatively evenly distributed, indicating strong spatial homogeneity.

[0025] Step 103: Select plots with a coefficient of variation greater than a preset first threshold as the first variation region, and select plots with a coefficient of variation less than a preset second threshold as the second variation region; wherein the first threshold is greater than the second threshold.

[0026] Step 104: Increase the number of sampling points in the first mutation region and decrease the number of sampling points in the second mutation region.

[0027] Sampling points refer to specific geographical locations within the sampling area that are predetermined or dynamically determined for soil drilling. The carbon storage estimation in the first variation area has high uncertainty and is a key area affecting the overall monitoring accuracy; therefore, more sampling points are needed to improve representativeness. The carbon content in the second variation area is evenly distributed, and its impact on overall accuracy is relatively small; therefore, fewer sampling points can be used to improve efficiency.

[0028] Step 105: Control the unmanned vehicle to perform stratified soil sampling at each sampling point in the first and second variation areas to obtain soil samples corresponding to each stratified soil.

[0029] Among them, stratified soil sampling refers to the process of vertically drilling from the ground surface downwards and obtaining soil samples in sections, either in their original or non-original state, according to preset soil depth intervals (such as 0-10cm, 10-20cm, 20-30cm, etc.).

[0030] Step 106: Control the drone to transport the unmanned vehicle carrying soil samples to the starting point corresponding to the sampling area.

[0031] After the operation is completed, the unmanned vehicle is transported to the departure point by drone, and then transported to the laboratory or sample processing center by means of transportation (such as cars). Each sampling area can have a corresponding departure point, which is generally close to the sampling area and convenient for vehicles to return to the laboratory.

[0032] In this embodiment, the sampling equipment is rapidly and remotely delivered and retrieved using drones, and unmanned vehicles are used to perform precise and low-disturbance stratified sampling on the ground. Furthermore, an intelligent sampling strategy based on the carbon spatial variation coefficient is adopted to dynamically adjust the sampling density in different heterogeneous areas, effectively solving the technical problems of low efficiency and high risk of manual sampling. Moreover, while ensuring monitoring accuracy, the optimal allocation of sampling resources is achieved, significantly improving the monitoring efficiency of soil carbon storage in large-scale and complex terrain.

[0033] In one embodiment of this specification, based on pre-acquired carbon-related spatial data of the sampling area, the spatial variation coefficient of soil organic carbon content in various plots within the sampling area is determined, including:

[0034] Step 1: Divide the sampling area into multiple grid cells of equal area; each grid cell is considered a plot of land.

[0035] The grid cells are generally regular polygons, such as squares or regular hexagons.

[0036] Step 2: Extract proxy indicators related to soil organic carbon content from carbon-related spatial data;

[0037] Among them, proxy indicators refer to specific parameters or characteristic variables extracted from carbon-related spatial data that have a known or potential correlation with soil organic carbon content. For example, specific band reflectance extracted from remote sensing images, calculated vegetation indices, or topographic moisture indices derived from digital elevation models can all serve as proxy indicators for estimating soil organic carbon.

[0038] Step 3: Convert the proxy indicators into estimated soil organic carbon content values ​​for each plot;

[0039] Among them, the soil organic carbon content estimate refers to the quantitative prediction of the soil organic carbon content within a certain grid cell (plot) calculated based on a proxy index and through an established prediction model (such as a regression model, machine learning model, or geostatistical model).

[0040] Step 4: For each plot, based on the estimated soil organic carbon content of itself and adjacent plots, calculate the ratio of the standard deviation to the mean, which is used as the spatial variation coefficient of soil organic carbon content for that plot.

[0041] The standard deviation reflects the dispersion of the data. A larger ratio indicates more drastic spatial variation in carbon content within the plot and its neighboring areas; a smaller ratio indicates a more uniform spatial distribution. Adjacent plots refer to cells that are directly adjacent to the current grid cell. They are typically defined using four-neighborhoods (top, bottom, left, right) or eight-neighborhoods (including diagonals) for local statistical calculations. While this embodiment does not explicitly define the neighborhood boundaries, it is a common practice in geostatistics or spatial analysis.

[0042] In this embodiment, the soil organic carbon content of each grid cell is estimated by dividing the sampling area into grids and extracting proxy indicators using readily available carbon-related spatial data (such as remote sensing data), and finally calculating its spatial variation coefficient. This approach can quantitatively and efficiently identify the high and low distribution of soil carbon spatial heterogeneity within the sampling area.

[0043] In one embodiment of this specification, the sampling area is divided into multiple grid units of equal area, including:

[0044] Step 1: Obtain the spatial autocorrelation range of soil organic carbon content in the sampling area;

[0045] The spatial autocorrelation range of soil organic carbon content in the sampling area was pre-obtained using historical measured soil sample data. For example, it was achieved through spatial statistical analysis of existing soil organic carbon content sample data from the sampling area or its representative similar areas. Specifically, a certain number of historical measured soil organic carbon content data points with precise geographic coordinates were collected within the area as input samples. Geostatistical methods (e.g., calculating and fitting the empirical semi-variogram of these sample points) were used to establish a correlation model of soil organic carbon content varying with spatial distance. The maximum critical distance representing the existence of spatial correlation extracted from this model is the spatial autocorrelation range. The spatial autocorrelation range indicates that the soil attribute values ​​of two points with a spatial distance less than this range are spatially correlated; beyond this distance, the correlation between attribute values ​​becomes very weak or essentially independent. The range reflects the maximum spatial scale at which the soil attribute maintains continuity or similarity in space.

[0046] Step 2: Determine the side length of the grid cell based on the spatial autocorrelation range, so that the side length of a single grid cell is less than or equal to half of the spatial autocorrelation range.

[0047] The purpose of setting the side length to be less than or equal to half of the spatial autocorrelation range is to ensure that soil properties (in this case, organic carbon content) have high spatial homogeneity within each grid cell (because the cell size is smaller than the main spatial correlation range); and that the spatial variation structure of properties can be better captured between different grid cells (because the cell interval is smaller than the range, which can reflect spatial continuity).

[0048] Step 3: Based on the determined grid cell side length, divide the sampling area into multiple square grid cells of equal area.

[0049] In this embodiment, the grid division scale is determined by introducing the spatial autocorrelation range of soil organic carbon content, so that the side length of each grid cell is less than half of the range, thereby ensuring that the carbon content within the cell is relatively homogeneous and that spatial differences between cells can be effectively reflected.

[0050] In one embodiment of this specification, converting the proxy index into estimated soil organic carbon content for each plot includes:

[0051] Step 1: Construct a carbon inversion model based on historical measured sample sets; whereby the historical measured sample sets contain measured values ​​of soil organic carbon at multiple known locations and their corresponding proxy indices;

[0052] The historical measured sample set refers to a collection of soil sample data obtained in the target study area or similar regions prior to the implementation of this scheme through manual soil drilling and laboratory analysis. Each sample includes its known geographical coordinates, a precisely measured value of soil organic carbon content obtained in the laboratory, and various surrogate indicator data (such as corresponding remote sensing spectral values, vegetation indices, etc.) acquired or matched simultaneously at the sample location. This dataset is used to construct a statistical model for retrieving soil organic carbon content from surrogate indicators. The carbon inversion model is a model used to establish a mathematical relationship between surrogate indicators (independent variables) and soil organic carbon content (dependent variable). Its core function is to use the historical measured sample set for training or calibration, thereby predicting the corresponding estimated value of soil organic carbon content based on surrogate indicator data from new, unmeasured areas.

[0053] Step 2: Use the carbon inversion model to predict the proxy indicators for each plot and output the corresponding estimated values ​​of soil organic carbon content.

[0054] The carbon inversion model includes at least one of the following: machine learning regression model, geostatistical interpolation model, or empirical statistical relation.

[0055] In this embodiment, a carbon inversion model is constructed using historical measured samples to convert proxy indicators in remote sensing or geospatial data into estimated values ​​of soil organic carbon content for each plot. This avoids relying entirely on uniform sampling or subjective experience, making the sampling design more targeted.

[0056] In one embodiment of this specification, the sampling points for the first mutation region are increased, including:

[0057] Step 1: Within each plot in the first variation region, simulate multiple candidate sampling points based on the spatial distribution of the estimated soil organic carbon content of the plot and its neighboring areas;

[0058] In this context, "neighborhood" refers to the pre-defined spatial range surrounding the plot, typically centered on the plot and defined according to certain rules (such as a range within a certain radius or adjacent grid cells). "Neighborhood" can be understood as a four-neighborhood or an eight-neighborhood. "Candidate sampling points" refer to potential, selectable sampling locations generated through spatial analysis simulation within the plot requiring intensive sampling (i.e., the plot in the first variation region), based on the spatial distribution characteristics (such as high-value areas, low-value areas, and gradient change zones) of the estimated soil organic carbon content of the plot and its neighborhood.

[0059] Spatial distribution refers to the numerical differences, trends, and patterns of soil organic carbon content at different geographical locations. Spatial distribution can be predicted for the entire region by combining proxy indicators such as remote sensing with historical measured samples and using carbon inversion models (such as machine learning or geostatistical methods), generating an attribute distribution map covering the entire area.

[0060] Step 2: For each candidate sampling point, calculate the expected reduction in the variance of the carbon storage estimate for the entire sampling area after it is collected;

[0061] The variance of carbon storage estimation refers to the statistical measure of the uncertainty or error in the estimation results obtained when using sampling point data to estimate the soil carbon storage of the entire sampling area. A larger variance indicates higher uncertainty and lower reliability in the estimation results. The expected reduction refers to the expected reduction in the variance of the carbon storage estimation results for the entire sampling area after assuming the collection of soil samples from a candidate sampling point and obtaining its true organic carbon content data. This indicator is evaluated using geostatistical kriging variance reduction calculations or other spatial optimization algorithms. A larger expected reduction means a greater contribution from collecting samples from that point to reducing overall estimation uncertainty and improving monitoring accuracy.

[0062] Step 3: Select the candidate sampling points with the largest expected reduction as the new sampling points.

[0063] In this embodiment, in the first variation region, by simulating multiple candidate points and evaluating their expected reduction effect on the variance of carbon storage estimation, the location with the highest information gain is selected for additional sampling, avoiding the waste of resources caused by simple uniform encryption, and concentrating the limited number of samplings on the location that can best improve the estimation accuracy.

[0064] In one embodiment of this specification, reducing the number of sampling points in the second variation region includes:

[0065] Step 1: Within each plot in the second variation region, determine the sensitivity of that plot to the carbon storage estimation results for the entire sampling area;

[0066] Step 2: When the sensitivity is lower than the preset sensitivity threshold, cancel the sampling points deployed in the plot;

[0067] Sensitivity is the proportion of the estimated soil carbon storage of this plot to the estimated soil carbon storage of the entire sampling area.

[0068] In this embodiment, when the sensitivity of a plot is lower than a preset sensitivity threshold, its sampling points can be omitted without significantly affecting the overall accuracy. By calculating the contribution ratio of each plot in the second variation region to the total carbon storage of the region, areas with minimal impact on the overall estimation results are identified, and sampling points in these areas are proactively cancelled, effectively reducing the amount of fieldwork and the operational burden of unmanned vehicles, and improving the efficiency of the entire monitoring process.

[0069] In one embodiment of this specification, controlling an unmanned vehicle to perform stratified soil sampling at each sampling point in the first and second variation regions includes:

[0070] Step 1: Based on the estimated soil organic carbon content and its spatial covariance structure of each plot in the sampling area, determine the uncertainty value of carbon storage estimation for each planned sampling point;

[0071] The soil organic carbon content estimate was obtained by constructing and applying a carbon inversion model. This model uses a historical measured sample set (containing laboratory measurements of soil organic carbon at known locations and their corresponding remote sensing or geographic proxy indicators, such as vegetation index, spectral reflectance, and topographic humidity index) as training data. It is fitted using machine learning regression models (such as random forest or XGBoost), geostatistical interpolation models (such as Kriging), or empirical statistical relationships. Subsequently, the proxy indicators corresponding to each plot within the sampling area are input into the model, which outputs the soil organic carbon content estimate for each plot, forming a spatial distribution map of carbon content covering the entire region.

[0072] Spatial covariance structure is used to describe the statistical correlation of soil organic carbon content at different locations in geospatial space as a function of distance. It reflects the spatial dependence that points that are closer together have more similar carbon content. Based on historical measured soil organic carbon data (including precise coordinates), its semivariogram can be calculated and fitted by theoretical models (such as spherical models and exponential models) to quantify the relationship between spatial covariance and distance.

[0073] The uncertainty value in carbon reserve estimation refers to a quantitative evaluation index of the reliability or error range of the estimation result when estimating the carbon reserves at a specific sampling point based on existing information in the surrounding area (such as the estimated values ​​of surrounding plots and the spatial covariance structure). It is typically calculated using spatial statistical methods such as Kriging variance; a higher value indicates greater uncertainty in the estimation result for that point.

[0074] Step 2: Extract each sampling point located within the first variation region and its corresponding uncertainty value to form the uncertainty sequence of sampling points in the first variation region;

[0075] Step 3: Within the first variation region, sort the sampling points from highest to lowest uncertainty value according to the uncertainty sequence of the sampling points in the first variation region, and control the unmanned vehicle to sample each sampling point according to this sorting.

[0076] Step 4: After completing the sampling of the first variation area, control the unmanned vehicle to traverse the second variation area in a straight line, and sample only the sampling points located on the straight line.

[0077] In this embodiment, the sampling points in the first variation region (high heterogeneity region) are subjected to uncertainty quantification and sorting, so as to prioritize the collection of samples with the highest information value and the greatest contribution to reducing the overall estimation error, thereby quickly improving the reliability of regional carbon storage estimation with the fewest sampling actions; at the same time, a straight path traversal sampling is adopted in the second variation region (low heterogeneity region), which significantly shortens the invalid movement path and operation time of the unmanned vehicle while basically maintaining the representativeness of the samples in this region.

[0078] In one embodiment of this specification, the straight path is determined in the following manner:

[0079] Step 1: Obtain the position of the last sampling point in the first mutation region as the starting point;

[0080] Step 2: Obtain the boundary plot in the second variation region whose geographical coordinates are closest to the starting point and which retains the sampling point as the endpoint;

[0081] Step 3: Fit a straight line between the starting point and the ending point, and use this line as the straight path through the second variation region;

[0082] Among them, the sampling points located within the preset lateral tolerance range on both sides of the straight path are the sampling points on the straight path.

[0083] In this embodiment, the optimal travel path for the autonomous vehicle when moving from a high-value area to a low-value area is ensured, avoiding unnecessary backtracking or detours and minimizing empty runs between areas. By setting a lateral tolerance range, it is guaranteed that necessary samples from low-heterogeneity areas can be collected (sampling points are retained).

[0084] In one embodiment of this specification, the preset lateral tolerance range on both sides of the straight path is set in the following manner:

[0085] Step 1: Divide the straight path into multiple continuous sub-straight line segments;

[0086] Among them, continuous sub-straight segments refer to multiple shorter, connected straight segments formed by dividing the overall straight path that traverses the second variation area according to a preset fixed length or based on terrain feature points (such as slope turning points).

[0087] Step 2: For each sub-straight line segment, obtain its corresponding terrain slope and surface roughness;

[0088] Among them, terrain slope refers to the degree of inclination of the surface area traversed by a certain sub-straight line segment, usually expressed as the angle (in degrees or percentage) between the horizontal plane and the tangent plane of the surface, and is used to describe the steepness of the terrain. Surface roughness refers to the degree of unevenness of the surface area traversed by a certain sub-straight line segment, and is usually calculated by the standard deviation of surface elevation data or a specific index (such as undulation).

[0089] Step 3: Dynamically set the lateral tolerance range of the sub-straight line segment based on the terrain slope and surface roughness.

[0090] The lateral tolerance range refers to a distance range on both sides of the aforementioned fitted straight path as the center line, allowing for lateral deviation. Sampling points within this range are considered valid. On gentle, smooth road sections (small slope, low roughness), the tolerance can be set smaller to improve path accuracy; on steep or rugged road sections (large slope, high roughness), the tolerance can be widened to avoid vehicle slippage, jamming, or missing nearby sampling points due to strict alignment.

[0091] In this embodiment, by segmenting the straight path and dynamically adjusting the lateral tolerance range based on the actual terrain slope and surface roughness of each segment, the unmanned vehicle can adapt to the traffic restrictions of complex terrain when traversing the second variation area, and can flexibly collect retained sampling points near the path. This avoids the problem of path tracking failure caused by using a uniform fixed tolerance in rugged areas or sampling omissions in flat areas.

[0092] In one embodiment of this specification, during the process of the unmanned vehicle performing stratified soil sampling at each sampling point, the method further includes:

[0093] Step 1: Record sampling operation images in real time using the camera equipment mounted on the unmanned vehicle;

[0094] The camera equipment carried by the unmanned vehicle refers to the image acquisition equipment (such as a visible light camera, an infrared camera, or an optical sensor with video recording function) that is fixedly installed on the top of the sampling vehicle device, and is used to acquire and record visual information of the on-site environment in real time during the sampling operation.

[0095] Step 2: When each layered soil sample is packaged, a unique electronic tag is automatically generated; the unique electronic tag includes the geographical coordinates of the sampling point, the sampling time, and the soil depth layer.

[0096] Step 3: Associate and store the electronic tags with the corresponding sampling operation images, and upload them to the remote monitoring system.

[0097] Among them, the remote monitoring system refers to the software platform deployed in a remote command center or cloud, which is used to receive, display, store and manage various status data, operation data and multimedia data transmitted back from on-site operation equipment (drones, unmanned vehicles) in real time.

[0098] In this embodiment, by automatically acquiring images during the sampling process and generating electronic tags with precise spatiotemporal and stratigraphic information, the entire process of digital recording and accurate traceability of each soil sample from collection to packaging is achieved. The images are associated with and stored using structured tags and transmitted back in real time, enabling back-end personnel to remotely and intuitively verify the standardization of the sampling operation and the authenticity of the samples.

[0099] The present invention will be described in detail below through a specific embodiment.

[0100] The unmanned aerial vehicle (UAV) serves as the system's aerial delivery and recovery platform, with a hexacopter UAV at its core. The fuselage is constructed from carbon fiber composite materials, combining high strength with lightweight properties to enhance payload capacity and flight stability. An openable payload bay is located in the lower fuselage, providing space to house and secure the UAV. The payload bay door is driven by a servo motor and controlled by the flight controller. The UAV is equipped with a high-power brushless motor and propellers (rotors), providing ample lift and maneuverability to ensure takeoff, landing, and hovering capabilities in complex terrains such as hills and forest edges. The landing gear features a four-point cushioning design with built-in shock-absorbing springs to absorb landing impacts and protect the fuselage and internal equipment. Furthermore, the UAV integrates a high-precision GPS / RTK positioning module, an inertial measurement unit (IMU), a data radio, and obstacle avoidance radar, forming the basis of its navigation and flight control subsystem. The UAV can also be equipped with camera equipment.

[0101] The sampling trolley consists of a body, wheels, a sealer, and a camera. The body is also made of high-strength, lightweight materials; the wheels have a hollow mesh structure with elastic shock-absorbing materials, which can effectively adapt to complex surface environments such as grasslands, slopes, and sandy soils; the sealer is located inside the body and is used to receive and seal layered soil samples to prevent contamination or loss during transportation; the camera is fixed on the top of the body and is used to record images or videos of the entire sampling process in real time.

[0102] The vehicle body features a quick-installation and disassembly interface for connecting the sampling device. The sampling device includes a robotic arm, a sampling head, and a hydraulic power unit. The robotic arm is a two-degree-of-freedom arm used to precisely position the sampling head relative to the ground, both vertically and horizontally. The sampling head consists of a hollow drill bit and a multi-section telescopic sampling tube. The hollow drill bit is connected to the bottom of the sampling tube and is driven to rotate and drill downwards by the hydraulic power unit. The sampling tube consists of at least five coaxially nestable sections, which can be extended and retracted to a maximum sampling depth of 50 cm via a hydraulic mechanism. A disposable liner is fitted inside the sampling tube, and the outer wall of the liner is marked with graduations at 10 cm intervals and a unique number. Inside the sampling tube, a ring-shaped slice is fixed every 10 cm. When the sampling tube is full of soil, the slice can contract along the tube diameter, cutting the soil column layer by layer. A hydraulic drag bar is connected to the top of the sampling tube for ejecting the liner and the layered soil sample inside as a whole after sampling is completed.

[0103] The hydraulic power unit, integrated within the unmanned vehicle, provides power for the rotation and drilling / lifting of the hollow drill bit, the extension and retraction of the sampling tube, the slicing action, and the deployment of the hydraulic drag bar. The sealer, located inside the vehicle, receives the liner sample pushed out from the sampling tube. The sealer contains a mechanism that automatically tightens the spiral sealing films and sealing rings at both ends of the liner, completing the sample sealing. After sealing, the sample is stored in a dedicated sample rack inside the vehicle, its position automatically associated with the liner number. Camera equipment is mounted on the roof of the vehicle for recording video and taking photos during the sampling process.

[0104] The ground control and data processing center comprises the hardware and software components of a control system, a storage system, and a remote monitoring system. The control system includes multiple software subsystems such as UAV control, unmanned vehicle control, sampling device control, and navigation control, supporting both fully automated execution of preset tasks and remote manual intervention. The storage system stores sampling plans, sampling process data (coordinates, time, status), and audio / video materials. The remote monitoring system provides a visual interface that displays the real-time status, location, video feed, and sampling progress of the UAVs and unmanned vehicles.

[0105] The specific work process is as follows:

[0106] Step 1: Task Planning and Data Preparation

[0107] Before the sampling operation, the coefficient of variation (COP) of soil organic carbon content in each plot within the sampling area was determined based on carbon-related spatial data (such as satellite remote sensing imagery, topographic data, and historical measured data). According to preset first and second thresholds, plots with COPs greater than the first threshold were selected as the first variation region (high heterogeneity), and plots with COPs less than the second threshold were selected as the second variation region (low heterogeneity). In the sampling plan, the sampling point density in the first variation region was increased, and the sampling point density in the second variation region was decreased, resulting in an optimized sampling point layout. This plan clearly defines the geographical coordinates of each sampling point and the required stratified soil sampling depth, and is uploaded to the system.

[0108] Step 2: Equipment Deployment

[0109] The drone, carrying an unmanned vehicle, takes off from the take-off and landing site and automatically flies to the target sampling area according to a preset route or remote command. After landing and coming to a stop at a suitable location in the sampling area, the drone opens its payload compartment and releases the unmanned vehicle to the ground.

[0110] Step 3: Autonomous Sampling Operation

[0111] The unmanned vehicle (RV) proceeds to the first variation area, arriving at each sampling point sequentially according to a certain priority order (e.g., based on the contribution of each sampling point to the uncertainty of carbon storage estimation). At each point, the RV stops, and its onboard sampling device performs the following operations: a robotic arm positions the sampling head, and a hydraulic power unit drives a hollow drill bit and multiple sampling tubes to drill to a predetermined depth (e.g., 50 cm); a slicing device inside the sampling tube cuts the soil column into layers at preset depth intervals (e.g., every 10 cm); after sampling, a hydraulic tow bar pushes the liner containing the layered samples out to a sealing device for sealing and numbering. The entire process is recorded by onboard cameras. After completing sampling in the first variation area, the RV plans an efficient path to traverse the second variation area. Along this path, it only stops to sample preset sampling points located within the vicinity of the path, thus quickly completing the sampling task in this area.

[0112] Step 4: Sample Recovery and Return

[0113] After all sampling operations are completed, the unmanned vehicle autonomously drives to the agreed-upon rendezvous coordinates with the drone. The drone flies over the rendezvous point and lands, while the unmanned vehicle drives into its payload compartment and secures itself. The drone then returns to its origin, carrying the unmanned vehicle and all soil samples, either to the take-off and landing site or the designated soil receiving area.

[0114] Step 5: Data Feedback and Traceability

[0115] Throughout the entire operation, data such as the status, trajectory, sampling action logs of the drones and unmanned vehicles, as well as the associated environmental images and sample packaging information of each sampling point, are continuously transmitted back to the ground control center for storage, enabling remote monitoring and data traceability of the entire operation process.

[0116] Based on the same general inventive concept, this invention also protects a soil carbon storage monitoring device based on sampling by drones and unmanned vehicles, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of the soil carbon storage monitoring device based on UAV and unmanned vehicle sampling provided in an embodiment of the present invention. The soil carbon storage monitoring device based on UAV and unmanned vehicle sampling provided by the present invention will be described below. The soil carbon storage monitoring device based on UAV and unmanned vehicle sampling described below can be referred to in correspondence with the soil carbon storage monitoring method based on UAV and unmanned vehicle sampling described above.

[0117] Soil carbon storage monitoring devices based on drone and unmanned vehicle sampling include:

[0118] The unmanned delivery module 201 controls the drone to transport the unmanned vehicle to the target soil sampling area;

[0119] The carbon spatial variation module 202 determines the coefficient of variation of soil organic carbon content in each plot within the sampling area based on carbon-related spatial data of the sampling area.

[0120] The region identification module 203 filters out plots of land with a coefficient of variation greater than a preset first threshold as first variation regions, and filters out plots of land with a coefficient of variation less than a preset second threshold as second variation regions; wherein the first threshold is greater than the second threshold.

[0121] The sampling point optimization module 204 increases the sampling points in the first mutation region and decreases the sampling points in the second mutation region;

[0122] The stratified sampling module 205 controls the unmanned vehicle to perform stratified soil sampling at each sampling point in the first and second variation regions, and obtain soil samples corresponding to each stratified soil.

[0123] The sample transfer module 206 controls the drone to transport the unmanned vehicle carrying soil samples to the departure point corresponding to the sampling area.

[0124] Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0125] like Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions from the memory 330 to execute a soil carbon storage monitoring method based on UAV and unmanned vehicle sampling.

[0126] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the soil carbon storage monitoring method based on UAV and unmanned vehicle sampling provided by the above methods.

[0128] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the soil carbon storage monitoring methods based on UAV and unmanned vehicle sampling provided by the above methods.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring soil carbon storage based on sampling by unmanned aerial vehicles (UAVs) and unmanned vehicles, characterized in that, include: Control the drone to transport the unmanned vehicle to the target soil sampling area; Based on the pre-acquired carbon-related spatial data of the sampling area, the spatial variation coefficient of soil organic carbon content in each plot within the sampling area is determined; The plots with a coefficient of variation greater than a preset first threshold are selected as the first variation region, and the plots with a coefficient of variation less than a preset second threshold are selected as the second variation region; wherein the first threshold is greater than the second threshold. Increase the number of sampling points in the first mutation region and decrease the number of sampling points in the second mutation region; The unmanned vehicle is controlled to perform stratified soil sampling at each sampling point in the first and second variation regions to obtain soil samples corresponding to each stratified soil layer. The drone is controlled to transport the unmanned vehicle carrying soil samples to the departure point corresponding to the sampling area; The increase of sampling points in the first variant region includes: Within each plot in the first variation region, multiple candidate sampling points are simulated based on the spatial distribution of the estimated soil organic carbon content of the plot and its neighborhood; For each of the candidate sampling points, calculate the expected reduction in the variance of the carbon storage estimate for the entire sampling area after it is collected; Select the preset number of candidate sampling points that have the largest expected reduction as the new sampling points; The reduction of sampling points in the second variant region includes: Within each plot in the second variation region, determine the sensitivity of that plot to the carbon storage estimation results for the entire sampling area; When the sensitivity is lower than a preset sensitivity threshold, the sampling points deployed in that plot of land are cancelled. Sensitivity is the proportion of the estimated soil carbon storage of this plot to the estimated soil carbon storage of the entire sampling area.

2. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 1, characterized in that, The determination of the spatial variation coefficient of soil organic carbon content for each plot within the sampling area, based on pre-acquired carbon-related spatial data of the sampling area, includes: The sampling area is divided into multiple grid units of equal area; each grid unit is considered a plot of land. Extract proxy indicators related to soil organic carbon content from the aforementioned carbon-related spatial data; The proxy indicators are converted into estimated values ​​of soil organic carbon content for each plot. For each plot, the ratio of the standard deviation to the mean is calculated based on the estimated soil organic carbon content of the plot itself and its neighboring plots, and is used as the spatial variation coefficient of soil organic carbon content for that plot.

3. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 2, characterized in that, The step of dividing the sampling area into multiple grid units of equal area includes: Obtain the spatial autocorrelation range of soil organic carbon content in the sampling area; The side length of the grid cell is determined based on the spatial autocorrelation range, such that the side length of a single grid cell is less than half of the spatial autocorrelation range. Based on the determined grid cell side length, the sampling area is divided into multiple square grid cells of equal area.

4. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 2, characterized in that, The process of converting the proxy index into estimated soil organic carbon content values ​​for each plot includes: A carbon retrieval model is constructed based on a historical measured sample set; wherein, the historical measured sample set contains measured values ​​of soil organic carbon at multiple known locations and their corresponding proxy indices; Using the carbon inversion model, the proxy indicators of each plot are predicted, and the corresponding estimated values ​​of soil organic carbon content are output. The carbon inversion model includes at least one of a machine learning regression model, a geostatistical interpolation model, or an empirical statistical relation.

5. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 1, characterized in that, The control of the unmanned vehicle to perform stratified soil sampling at each sampling point in the first variation region and the second variation region includes: Based on the estimated soil organic carbon content and its spatial covariance structure of each plot within the sampling area, the uncertainty value of carbon storage estimation corresponding to each planned sampling point is determined. Extract each sampling point located within the first variation region and its corresponding uncertainty value to form an uncertainty sequence of sampling points in the first variation region; Within the first variation region, the sampling points are sorted from high to low uncertainty value according to the uncertainty sequence of the sampling points in the first variation region, and the unmanned vehicle is controlled to sample each sampling point according to the sorting. After completing the sampling of the first variation region, the unmanned vehicle is controlled to traverse the second variation region in a straight path, and sampling is performed only on the sampling points located on the straight path.

6. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 5, characterized in that, The straight path is determined in the following way: The position of the last sampling point in the first mutation region is taken as the starting point; The boundary plot in the second variation region whose geographical coordinates are closest to the starting point and which retains the sampling point is taken as the endpoint; A straight line is fitted between the starting point and the ending point, and this straight line is used as the straight path through the second variation region; Among them, the sampling points located within the preset lateral tolerance range on both sides of the straight path are the sampling points on the straight path.

7. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 6, characterized in that, The preset lateral tolerance range on both sides of the straight path is set in the following way: The straight path is divided into multiple continuous sub-straight line segments; For each sub-straight line segment, obtain its corresponding terrain slope and surface roughness; The lateral tolerance range of the sub-straight line segment is dynamically set based on the terrain slope and the surface roughness.

8. The method for monitoring soil carbon storage based on UAV and unmanned vehicle sampling according to claim 1, characterized in that, The process of the unmanned vehicle performing stratified soil sampling at each sampling point also includes: The unmanned vehicle records images of the sampling operation in real time using its onboard camera equipment. When each layered soil sample is packaged, a unique electronic tag is automatically generated; the unique electronic tag includes the geographical coordinates of the sampling point, the sampling time, and the soil depth layer. The electronic tags are associated with and stored in relation to the corresponding sampling operation images, and then uploaded to the remote monitoring system.

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