Water stress monitoring method and device, storage medium and electronic equipment

Abstract

The invention provides a water stress monitoring method and device, a storage medium and electronic equipment, and relates to the field of ecological safety and environment. Wherein the electronic device obtains the sapwood areas of a plurality of trees in a research area, and the sapwood of each tree is a tissue used for conducting moisture; obtaining the water consumption of each tree in the research area according to the sapwood area of each tree in the research area; acquiring soil humidity of the research area; and determining whether water stress exists in the research area according to the water consumption and the soil humidity. Therefore, the water consumption and the soil humidity of each tree in the research area can be obtained, the moisture condition of the vegetation in the research area can be monitored and analyzed by comparing the water consumption and the soil humidity, and the moisture stress problem can be found and solved in time, so that healthy growth of the vegetation in the arid area is guaranteed, and the stability of the ecological environment is maintained.

Description

Technical Field

[0001] This application relates to the fields of ecological security and environment. Specifically, it relates to a method, device, storage medium, and electronic device for monitoring water stress. Background Art

[0002] Land desertification seriously threatens the ecological security and environmental conditions of inland river basins in arid regions. Therefore, it is particularly important to monitor the water stress of vegetation in arid regions. For example, Populus euphratica forests, as typical vegetation in desertified areas, maintain the ecological balance of the desertified area's river basin and are an ecological barrier for agricultural development. Populus euphratica forests play important roles such as wind prevention and sand fixation, and climate regulation, which are crucial for agricultural production and residents' lives within the river basin. However, existing technical methods are difficult to achieve refined monitoring of the water stress of Populus euphratica forests. Summary of the Invention

[0003] To overcome at least one deficiency in the prior art, this application provides a method, device, storage medium, and electronic device for monitoring water stress, specifically including:

[0004] In a first aspect, this application provides a method for monitoring water stress, the method including:

[0005] Obtain the sapwood area of multiple trees in the study area, where the sapwood of each tree is the tissue for transporting water;

[0006] Based on the sapwood area of each tree in the study area, obtain the water consumption of each tree in the study area;

[0007] Obtain the soil humidity of the study area;

[0008] Based on the water consumption and the soil humidity, determine whether there is water stress in the study area.

[0009] In a second aspect, this application further provides a device for monitoring water stress, the device including:

[0010] A sapwood area module, configured to obtain the sapwood area of multiple trees in the study area, where the sapwood of each tree is the tissue for transporting water;

[0011] A water consumption estimation module, configured to obtain the water consumption of each tree in the study area based on the sapwood area of each tree in the study area;

[0012] A soil humidity module, configured to obtain the soil humidity of the study area;

[0013] A water stress module, configured to determine whether there is water stress in the study area based on the water consumption and the soil humidity.

[0014] In a third aspect, the present application also provides a storage medium storing a computer program, which, when executed by a processor, implements the water stress monitoring method described above.

[0015] In a fourth aspect, the present application also provides an electronic device, which includes a processor and a memory. The memory stores a computing program, which, when executed by the processor, implements the water stress monitoring method described above.

[0016] Compared with the prior art, the present application has the following beneficial effects:

[0017] The present application provides a water stress monitoring method, device, storage medium and electronic device. Among them, the electronic device obtains the sapwood area of multiple trees in the research area, where the sapwood of each tree is the tissue for transporting water; according to the sapwood area of each tree in the research area, the water consumption of each tree in the research area is obtained; the soil humidity of the research area is obtained; according to the water consumption and the soil humidity, it is determined whether there is water stress in the research area. In this way, the water consumption and soil humidity of each tree in the research area can be obtained, and by comparing the two, the water condition of the vegetation in the research area can be monitored and analyzed, and the water stress problem can be discovered and solved in time, so as to ensure the healthy growth of the vegetation in the arid area and maintain the stability of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of the water stress monitoring method provided by the embodiment of the present application;

[0020] Figure 2 It is one of the detailed schematic diagrams of the water stress monitoring method provided by the embodiment of the present application;

[0021] Figure 3 It is the second detailed schematic diagram of the water stress monitoring method provided by the embodiment of the present application;

[0022] Figure 4 It is a schematic structural diagram of the water stress monitoring device provided by the embodiment of the present application;

[0023] Figure 5 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application. Detailed implementation manners

[0024] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0026] It should be noted that: like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0027] In the description of the present application, it should be noted that the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance. In addition, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0028] Based on the above statement, as introduced in the background art, land desertification seriously threatens the ecological security and environmental conditions of inland river basins in arid areas. Therefore, it becomes particularly important to monitor the water stress of vegetation in arid areas.

[0029] Based on the discovery of the above technical problems, the inventor has put forward the following technical solutions through creative efforts to solve or improve the above problems. It should be noted that the defects existing in the above solutions in the prior art are all the results obtained by the inventor through practice and careful research. Therefore, the process of discovering the above problems and the solutions proposed by the embodiments of the present application below for the above problems should be the contributions made by the inventor to the present application during the invention creation process and should not be understood as the technical content known to those skilled in the art.

[0030] In view of this, this embodiment provides a method for monitoring water stress. As Figure 1 shown, the method includes:

[0031] S1. Obtain the sapwood area of multiple trees in the study area.

[0032] Among them, the sapwood of each tree is the tissue for transporting water.

[0033] S2. Obtain the water consumption of each tree in the study area according to the sapwood area of each tree in the study area.

[0034] S3. Obtain the soil moisture of the study area.

[0035] S4. Determine whether there is water stress in the study area according to the water consumption and the soil moisture.

[0036] In this way, the water consumption of each tree and the soil moisture in the study area can be obtained. By comparing the two, the water condition of the vegetation in the study area can be monitored and analyzed, and the water stress problem can be discovered and solved in time, so as to ensure the healthy growth of the vegetation in the arid area and maintain the stability of the ecological environment.

[0037] In this embodiment, the electronic device for implementing the water stress monitoring method may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, a server, etc. Among them, the server may be a single server or a server group. The server group may be centralized or distributed (for example, the server may be a distributed system). In some embodiments, the server may be local or remote relative to the user terminal. In some embodiments, the server may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, etc., or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.

[0038] To make the solution provided in this embodiment clearer, the following takes the server as the electronic device for implementing the water stress monitoring method, and Figure 1 elaborates on each step of the method shown in detail. However, it should be understood that the operations in the flowchart may not be implemented in sequence, and the steps without logical context may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of this application. Therefore, continuing to refer to Figure 1 , the method includes:

[0039] S1. Obtain the sapwood area of multiple trees in the study area.

[0040] For this, taking Populus euphratica forest as an example. When selecting the study area, representative profiles need to be chosen. Therefore, the topographic features and different age stages of Populus euphratica forest (such as young forest, middle-aged forest, over-mature forest) can be comprehensively considered, and then survey plots can be set on each profile. In the specific implementation process, a center line can be pulled with a measuring rope in the profile direction, and the survey range can be delimited within 10 meters on both sides of the center line. In the direction perpendicular to the center line, a 10 m × 10 m quadrat is divided every 10 meters, and each quadrat is further divided into a 10×10 grid, so as to ensure the systematicness and accurate matching of data and lay a foundation for subsequent data processing and analysis.

[0041] In addition, it should also be understood that the sapwood of each of the said trees is the tissue for water conduction. Sapwood refers to the part of a tree that transports water and nutrients, usually located near the bark. Sapwood is composed of xylem cells, which are responsible for transporting water and minerals from the roots to the crown, and at the same time transporting the nutrients produced by photosynthesis to various parts. Sapwood plays a crucial role in the growth and survival of trees because it directly affects the health status and growth ability of trees.

[0042] The sapwood area refers to the area of the sapwood on the cross-section of a tree. Specifically, the sapwood area is the two-dimensional area occupied by the sapwood part in the tree stem. The sapwood area can be obtained by scanning the cross-section of the tree and performing precise measurements. Since it is closely related to the water transport capacity of the tree, the sapwood area is an important biological parameter. Generally speaking, the larger the sapwood area, the stronger the water transport capacity of the tree, and vice versa. Therefore, by obtaining the sapwood area of each tree in the study area, the water consumption of each tree can be calculated.

[0043] In order to efficiently obtain the sapwood area of each tree, in this embodiment, the trees and their sizes in the study area are quickly counted through laser point cloud. Therefore, as Figure 2 shown, step S1 may include:

[0044] S1-1. Obtain the to-be-processed point cloud obtained by scanning the study area.

[0045] Optionally, the server can obtain the first point cloud collected from the air for the study area and the second point cloud collected from the ground for the study area; register the first point cloud and the second point cloud to obtain the corresponding relationship between the first point cloud and the second point cloud; fuse the first point cloud and the second point cloud according to the corresponding relationship to obtain the to-be-processed point cloud.

[0046] Exemplarily, a drone can be used to scan the research area from the air to collect the first point cloud data. The drone is equipped with a high-precision lidar sensor (LiDAR) for generating high-density point cloud data. The principle of the lidar sensor is to emit laser pulses and receive the reflected signals, and then construct a three-dimensional point cloud model. Based on the lidar sensor carried by the drone, the drone is controlled to scan within the research area according to a predetermined flight path. The flight path usually adopts a grid pattern or a spiral pattern to ensure that the entire research area is fully covered. The flight altitude is generally set to a fixed value (e.g., 50 meters) to ensure the resolution and coverage of the point cloud data.

[0047] In this example, to ensure the accurate positioning of the drone, in an area with good signal, the continuous operating reference station (CORS) is used for joint measurement, and the basic positioning data of the Beidou satellite system is combined. In addition, to further improve the positioning accuracy, the real-time kinematic (RTK) GPS technology is adopted. RTK GPS corrects the position error of the drone in real time by sending the accurate position information of the reference station to the GPS receiver on the drone. The reference station is usually set at a fixed position with known coordinates and provides high-precision position correction data in real time by comparing with satellite signals.

[0048] When the signal status is poor or in a signal-free area, a temporary base station is set up in an open area of the survey plot. A custom coordinate system is adopted and signal relay is used to ensure the absolute position accuracy of the data obtained by the drone and the ground-based radar. To ensure the matching of multi-period data observations, control stakes are usually arranged near the base station to provide stable reference points.

[0049] Meanwhile, ground staff use backpack lidar equipment to walk on the ground along a predetermined route to collect the second point cloud data. Among them, backpack lidar equipment is equipped for users. The equipment integrates systems such as lidar sensors, GNSS, and IMU to ensure high precision; then, a "Z"-shaped walking path is designed, with a lateral spacing of 10 meters between each path to cover the entire research area; the operator walks along the planned path at a speed of 5 km / h. The lidar sensor continuously emits laser pulses and receives reflected signals during walking to generate three-dimensional point cloud data, ensuring that the point cloud density is greater than or equal to 6,000 points per square meter; finally, the generated point cloud data is stored in a portable storage device in real time. The data storage format is usually LAS or LAZ files, and can be instantaneously transmitted to the ground station via wireless transmission for real-time monitoring and quality inspection.

[0050] Therefore, these two kinds of point cloud data provide three-dimensional information about the research area from the air and the ground respectively. Then, the server needs to register the first point cloud data collected from the air with the second point cloud data collected from the ground. For this, this embodiment can register the point cloud data from two sources in two ways: coarse registration and fine registration successively.

[0051] (1) Coarse registration

[0052] First, the server calculates the bounding boxes of the two point clouds (i.e., the smallest cuboid enclosing the point cloud); then, by translating the point cloud entities, the central points of the two bounding boxes are made to coincide. This process ensures the rough alignment of the positions of the two point clouds. Next, the server uniformly selects more than three matching points in the two point clouds, and then completes the alignment of the two point clouds by aligning the selected points. This process is repeated until the RMS (root mean square error) value after alignment is less than 1. At this time, it can be considered that the coarse registration is completed. Specifically, the lower the RMS value, the higher the matching degree of the two point clouds. If the RMS value is lower than the set threshold, it means that the two point clouds have achieved a rough alignment and the purpose of coarse registration has been achieved. Among them, the calculation expression of RMS is:

[0053]

[0054] In the formula, N represents the number of point clouds, and x i represents the i-th point cloud.

[0055] (2) Fine registration

[0056] In this embodiment, the fine registration is implemented by the Iterative Closest Point (ICP) algorithm. The ICP algorithm continuously matches the points between the point clouds, gradually adjusts the position and pose (rotation and translation) of the point cloud, and calculates an optimal transformation matrix including rotation and translation parameters in each iteration to update the position and pose of the point cloud to make it closer to the target point cloud. The expression is as follows:

[0057]

[0058] In the formula, P and Q respectively represent the original point cloud set and the target point cloud set. The original point cloud set in this embodiment is the roughly aligned point cloud obtained by coarse registration, and the target point cloud set is the point cloud to be processed after fine registration. R represents the rotation relationship, and t represents the translation relationship.

[0059] Therefore, the server uses the roughly aligned point cloud obtained by coarse registration as the initial state and iterates through the ICP algorithm to obtain the point cloud to be processed. For this ICP algorithm, the maximum number of iterations of the algorithm can be set to 400 times, and the RMS difference threshold can be set to 0.5. Only when the RMS difference between the two point clouds is less than this threshold is the fine registration considered complete. In this way, after fine registration, the point clouds from the two sources will be merged into a high-precision point cloud to be processed, which not only contains the canopy structure information of the poplar forest but also detailed three-dimensional information on the vegetation structure under the canopy.

[0060] Based on the above introduction of the point cloud to be processed in the embodiment, the following continues to describe Figure 2 the steps S1-2 in

[0061] S1-2. Cluster the point cloud to be processed to obtain multiple point cloud clusters.

[0062] Among them, each point cloud cluster represents a tree. It can be understood that the clustering algorithm will group similar point cloud data into one category to form different point cloud clusters. Exemplarily, continuing with the poplar forest as an example. The server can use a clustering algorithm (such as the K-means clustering algorithm) to process the above point cloud data to be processed to obtain multiple point cloud clusters. The clustering algorithm will automatically identify and separate the point cloud data belonging to the same tree according to the geometric characteristics and spatial distribution of the point cloud data. Therefore, in this process, each point cloud cluster represents a poplar tree.

[0063] In addition, it should also be understood that in the study area, in addition to the poplar forest, there is also vegetation under the canopy of the poplar forest (referred to as under-canopy vegetation). In this embodiment, the cloth simulation filtering algorithm can also be used to distinguish the point cloud belonging to the under-canopy vegetation from the point cloud to be processed.

[0064] In the specific implementation, first, the server uses the Cloth Simulation Filter algorithm for ground extraction. This algorithm is based on a simple physical simulation process, that is, placing a soft cloth above the terrain, and its model after falling under gravity is the DSM (Digital Surface Model). Reversing the terrain and assuming the cloth is rigid forms the DTM (Digital Terrain Model). The expression of the algorithm is as follows:

[0065]

[0066] In the formula, X(t) represents the position of the particle at time t, and F ext (X,t) represents the external force acting on the particle at position X and time t, which is specifically composed of gravity and collision force (generated by the particle colliding with some objects in its moving direction); F int(X,t) represents the internal force exerted on a particle at position X and time t, which is specifically generated by the interaction between particles.

[0067] Among them, in the parameter setting of this algorithm, the grid resolution and the number of point cloud particles are input respectively according to the actual calculation requirements and the actual situation of data acquisition. After the algorithm is executed, the ground and the poplar point cloud segmented in the previous step are filtered out, and the remaining point cloud is the three-dimensional data of the vegetation structure under the canopy.

[0068] Based on the above introduction of the point cloud set in the embodiment, the following continues to Figure 2 describe the steps S1-3 in

[0069] S1-3, respectively obtain the sapwood area of each tree according to each point cloud set.

[0070] It should be understood that since each point cloud set represents a tree, therefore, by processing each point cloud set, the basic parameters of each tree can be extracted, such as the diameter at breast height (DBH). These parameters can be estimated from the point cloud density and geometric shape in the point cloud set.

[0071] Exemplarily, continuing with the example of poplar, in this embodiment, the maximum spacing of the tree trunk is set as the maximum distance between the trunk points, while the maximum spacing of the tree crown is set as the maximum point spacing of the point cloud in the crown part. Therefore, two spacing values are used to guide the algorithm to distinguish the trunk and crown parts.

[0072] In addition, the straightness of the tree trunk (i.e., the opposite number of the difference between the ratio of the maximum bending radius of the tree trunk to the tree trunk height and 1) and the curvature of the trunk points (the thickness of the trunk point cloud divided by 0.1 plus the thickness of the trunk point cloud) are also calculated according to the actual point cloud data. The minimum tree spacing and the maximum crown width (the maximum crown width divided by the average tree spacing) are also set according to the actual point cloud acquisition situation.

[0073] After these parameter settings are completed, the point cloud set representing a poplar can be segmented into individual trees, so as to obtain the height (Height), diameter at breast height (DBH) and maximum crown width (CD) of each tree.

[0074] This embodiment also provides the relationship between the diameter at breast height and the sapwood area of the tree as:

[0075] As=a*DBH b

[0076] In the formula, As represents the sapwood area of the tree, DBH represents the diameter at breast height of the tree, and a and b represent the coefficients pre-fitted.

[0077] Therefore, it can be understood that the relationship between the diameter at breast height (DBH) and sapwood area of the above-mentioned trees is obtained by fitting through establishing a regression model. Continuing with the example of Populus euphratica forest, first, a large number of actual measurement data of the DBH and sapwood area of Populus euphratica trees need to be collected. Based on these data, the Box-Cox transformation is used to optimize the fitting effect of the regression model, making the residuals closer to the normal distribution, thereby improving the accuracy of the model. After fitting, the parameter values of regression parameters a and b can be obtained, which reflect the relationship between the DBH and sapwood area.

[0078] Based on the introduction of the sapwood area in the above embodiments, the following continues to describe Figure 1 step S2 in

[0079] S2. According to the sapwood area of each tree in the study area, obtain the water consumption of each tree in the study area.

[0080] Optionally, for each tree, the server can obtain the sap flow rate of the tree; determine the product of the sap flow rate and the sapwood area of the tree as the water consumption of the tree.

[0081] For the above sap flow rate, it can be achieved by using a plant stem sap flow observation system based on the heat dissipation probe (TDP) technology. Continuing with the example of Populus euphratica forest, find the position of the diameter at breast height on the selected Populus euphratica tree, and then insert the probe into the tree trunk. The probe will measure the temperature change inside the tree trunk, and by calculating the difference between the maximum temperature difference value measured without sap flow and the actually measured temperature difference value, the sap flow rate of the tree trunk can be deduced. In actual operation, researchers will set multiple measurement points in Populus euphratica forests with different growth forms, such as young forest, middle-aged forest, near-mature forest, mature forest, and over-mature forest. For each growth form, three Populus euphratica trees are measured, and the average value is taken as the sap flow rate of this growth form. Through this method, the water transport situation of Populus euphratica trees at different growth stages can be comprehensively grasped, so as to better understand their water stress status.

[0082] Due to the adoption of a plant stem sap flow observation system based on the heat dissipation probe (TDP) technology, the expression of the sap flow rate is:

[0083] V = 0.0119 × K α

[0084] K = (d TM -d T ) / d T

[0085] In the formula, V represents the sap flow rate, d TM represents the maximum temperature difference value measured by the probe without sap flow, d TΔT represents the temperature difference value measured at that time, and α represents the specific adjustment parameter of the sap flow observation system of different models. It can be understood that five measurement groups are set according to the growth form of Populus euphratica in the research area, namely young forest, middle-aged forest, near-mature forest, mature forest, and over-mature forest, and the α values obtained for different measurement groups are different.

[0086] Finally, multiplying the sap flow velocity by the sapwood area obtained in the above embodiment can obtain the water consumption of Populus euphratica:

[0087] Fs = As × V

[0088] In the formula, Fs represents the water consumption, also known as the sap flow flux (cm 3 / s), V represents the sap flow rate, and As represents the sapwood area (cm 2 ).

[0089] Based on the description of the water consumption of each tree in the above embodiment, the following continues to describe Figure 1 step S3 in:

[0090] S3, obtain the soil moisture of the research area.

[0091] In this embodiment, the soil moisture of the research area is inverted through the normalized difference vegetation index of the research area. Therefore, as an alternative implementation, as Figure 3 shown, step S3 may include:

[0092] S3-1, obtain the normalized difference vegetation index of the research area.

[0093] In this regard, in this embodiment, first collect the multispectral image data of the research area. These multispectral image data are usually obtained from the air by a drone carrying a multispectral sensor to ensure that the light of a specific wavelength can be captured. After the multispectral image data is collected, the server starts to preprocess these multispectral images, including removing noise and correcting the image brightness, to ensure the quality and accuracy of the data.

[0094] Then, the server extracts the data of the red band and the near-infrared band from the preprocessed multispectral images. The red band and the near-infrared band are very sensitive to the health status of vegetation and can reflect the growth status and water status of vegetation. Specifically, the reflectance of the red band is relatively low, while the reflectance of the near-infrared band is relatively high. This difference is the basis for calculating the normalized difference vegetation index (NDVI). The specific calculation expression is:

[0095]

[0096] In the formula, NDVI represents the Normalized Difference Vegetation Index, NIR represents the reflectance in the near-infrared band, and Red represents the reflectance in the red band.

[0097] The Normalized Difference Vegetation Index obtained using the above expression can be further used to obtain the vegetation coverage of the study area:

[0098]

[0099] In the formula, FVC represents the vegetation coverage, NDVI represents the Normalized Difference Vegetation Index, NDVI min represents the NDVI value in the area with no vegetation coverage, and NDVI max represents the NDVI value in the area completely covered by vegetation.

[0100] Based on the above description of the Normalized Difference Vegetation Index, the following continues to describe Figure 3 step S3-2 in

[0101] S3-2: Obtain the lowest surface temperature and the highest surface temperature of the study area according to the Normalized Difference Vegetation Index.

[0102] In this embodiment, after obtaining the Normalized Difference Vegetation Index, it is necessary to combine ground observation data and thermal infrared data for temperature information extraction. Among them, ground observation can directly measure the surface temperature through temperature sensors installed in the study area. At the same time, the thermal infrared sensor on the unmanned aerial vehicle can also obtain the surface temperature data of the study area. These temperature data will be correlated with the NDVI values to determine the relationship with the surface temperature.

[0103] Exemplarily, researchers can draw a scatter plot between the NDVI value and the surface temperature, and fit the correlation between the NDVI value and the surface temperature through linear regression. This step includes drawing a relationship curve between NDVI and temperature, so as to determine the lowest surface temperature and the highest surface temperature corresponding to different NDVI values. The expression is as follows:

[0104] Ts max = a1 + b1 × NDVI

[0105] Ts min = a2 + b2 × NDVI

[0106] In the formula, a1, b1, a2, and b2 represent the fitted coefficients, and Ts min represents the calculated lowest temperature, and Ts max represents the calculated highest temperature.

[0107] Based on the lowest surface temperature and the highest surface temperature obtained from the above embodiments, the following continues to Figure 3The description of step S3-3 in it is as follows:

[0108] S3-3. Take the ratio between the first difference and the second difference as the vegetation drought index.

[0109] Among them, the first difference is the difference between the measured temperature in the study area and the lowest surface temperature, and the second difference is the difference between the lowest surface temperature and the highest surface temperature.

[0110]

[0111] In the formula, TVDI represents the vegetation drought index, Ts represents the measured temperature, for example, the temperature obtained through thermal infrared data.

[0112] Based on the description of the vegetation drought index in the above embodiments, next, continue to describe Figure 3 step S3-4 in it:

[0113] S3-4. Obtain the soil moisture of the study area according to the vegetation drought index.

[0114] It should be understood that the lower the value of the vegetation drought index, the drier the soil; on the contrary, the higher the value of the vegetation drought index, the higher the soil moisture. Therefore, the calculated vegetation drought index can be fitted with the measured soil moisture to obtain a regression model. This regression model can associate the vegetation drought index with the actual soil water content, so as to invert the soil water content of the entire study area.

[0115] Based on the above introduction of water consumption and soil moisture in the embodiment, continue to refer to Figure 1 , and the following is the description of step S4 in the figure:

[0116] S4. Determine whether there is water stress in the study area according to the water consumption and soil moisture.

[0117] It can be understood that if the water consumption of the trees exceeds the amount of water that the current soil moisture can provide, then it can be judged that there is a water stress phenomenon in this area.

[0118] To sum up, in this embodiment, by monitoring the sap flow rate (i.e., the water flow rate) of each tree and combining with the sapwood area of the tree, the water consumption of each tree is calculated. In this way, the total amount of water consumed by each tree within a certain period of time can be understood.

[0119] Then, through the drone thermal infrared sensor and the ground soil moisture measuring instrument, the soil moisture data of the study area is obtained. This step includes calculating the normalized difference vegetation index (NDVI) and the temperature vegetation drought index (TVDI), and combining with the measured soil moisture value to invert the soil water content of the entire study area.

[0120] Finally, the calculated water consumption of each tree is compared with the soil moisture in the study area. If the water consumption of the trees exceeds the amount of water that the soil can provide, it can be determined that the area is under water stress.

[0121] Based on the same inventive concept as the water stress monitoring method provided in this embodiment, this embodiment also provides a water stress monitoring device, which includes at least one software function module that can be stored in a memory or fixed in an electronic device in the form of software. The processor in the electronic device is used to execute the executable module stored in the memory. For example, the software function module and computer program included in the device. Please refer to Figure 4 , functionally speaking, the device may include:

[0122] A sapwood area module 11 is used to obtain the sapwood area of ​​multiple trees in the study area, wherein the sapwood of each tree is a tissue used to conduct water;

[0123] A water consumption estimation module 12, for obtaining the water consumption of each tree in the study area according to the sapwood area of ​​each tree in the study area;

[0124] A soil moisture module 13 is used to obtain the soil moisture of the study area;

[0125] The water stress module 14 is used to determine whether there is water stress in the study area according to the water consumption and the soil moisture.

[0126] In this embodiment, the sapwood area module 11 is used to implement Figure 1 In step S1, the water consumption estimation module 12 is used to implement Figure 1 In step S2, the soil moisture module 13 is used to implement Figure 1 In step S3, the water stress module 14 is used to implement Figure 1 Therefore, for the detailed description of each of the above modules, please refer to the specific implementation of the corresponding step.

[0127] In addition, since the invention concept is the same as that of the above-mentioned water stress monitoring method, the water stress monitoring device can also implement other steps or sub-steps of the method through the above-mentioned modules.

[0128] Optionally, the sapwood area module 11 is further specifically used for:

[0129] Obtaining a point cloud to be processed obtained by scanning the study area;

[0130] Clustering the point cloud to be processed to obtain a plurality of point cloud sets, wherein each point cloud set represents a tree;

[0131] Based on each of the point cloud sets, the sapwood area of each of the trees is obtained.

[0132] Optionally, the sapwood area module 11 is further specifically configured to:

[0133] Obtain a first point cloud collected from the air for the study area and a second point cloud collected from the ground for the study area;

[0134] Register the first point cloud and the second point cloud to obtain the correspondence between the first point cloud and the second point cloud;

[0135] Fuse the first point cloud and the second point cloud according to the correspondence to obtain the point cloud to be processed.

[0136] Optionally, the sapwood area module 11 is further specifically configured to:

[0137] Based on each of the point cloud sets, obtain the diameter at breast height of each of the trees;

[0138] Based on the diameter at breast height of each of the trees, obtain the sapwood area of each of the trees.

[0139] Optionally, the water consumption estimation module 12 is further specifically configured to:

[0140] For each of the trees, obtain the sap flow rate of the tree;

[0141] Determine the product of the sap flow rate and the sapwood area of the tree as the water consumption of the tree.

[0142] Optionally, the soil moisture module 13 is further specifically configured to:

[0143] Obtain the normalized difference vegetation index of the study area;

[0144] Based on the normalized difference vegetation index, obtain the lowest surface temperature and the highest surface temperature of the study area;

[0145] Take the ratio between the first difference and the second difference as the vegetation drought index, where the first difference is the difference between the measured temperature of the study area and the lowest surface temperature, and the second difference is the difference between the lowest surface temperature and the highest surface temperature;

[0146] Based on the vegetation drought index, obtain the soil moisture of the study area.

[0147] In addition, in each embodiment of the present application, the functional modules can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0148] It should also be understood that when the above embodiments are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this 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 for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application.

[0149] Therefore, this embodiment also provides a storage medium that stores a computer program. When the computer program is executed by a processor, it implements the moisture stress monitoring method provided in this embodiment. Among them, the storage medium can be various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc.

[0150] An electronic device for implementing the moisture stress monitoring method provided in this embodiment. As Figure 5 shown, the electronic device may include a processor 22 and a memory 21. And, the memory 21 stores a computer program. The processor reads and executes the computer program corresponding to the above embodiments in the memory 21 to implement the moisture stress monitoring method provided in this embodiment.

[0151] Continue to refer to Figure 5 , the electronic device further includes a communication unit 23. The memory 21, the processor 22, and the communication unit 23 are directly or indirectly electrically connected to each other through a system bus 24 to achieve data transmission or interaction.

[0152] Among them, the memory 21 can be an information recording device based on any electronic, magnetic, optical, or other physical principles for recording execution instructions, data, etc. In some embodiments, the memory 21 can be, but is not limited to, a volatile memory, a non-volatile memory, a storage drive, etc.

[0153] In some embodiments, the volatile memory may be a Random Access Memory (RAM); in some embodiments, the non-volatile memory may be a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electric Erasable Programmable Read-Only Memory (EEPROM), a flash memory, etc.; in some embodiments, the storage drive may be a disk drive, a solid state drive, any type of storage disk (such as an optical disk, a DVD, etc.), or a similar storage medium, or a combination thereof, etc.

[0154] The communication unit 23 is configured to transmit and receive data via a network. In some embodiments, the network may include a wired network, a wireless network, an optical fiber network, a telecommunication network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Networks (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a Bluetooth network, a ZigBee network, or a Near Field Communication (NFC) network, etc., or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include a wired or wireless network access point, such as a base station and / or a network switching node, and one or more components of the service request processing system may be connected to the network via the access point to exchange data and / or information.

[0155] The processor 22 may be an integrated circuit chip with signal processing capabilities, and the processor may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the aforementioned processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction-set Processor (ASIP), a Graphics Processing Unit (GPU), a Physics Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computing (RISC), or a microprocessor, etc., or any combination thereof.

[0156] It can be understood that Figure 5 The structure shown is only illustrative. The electronic device may also have more or fewer components than Figure 5 shown, or have a configuration different from that Figure 5 shown. Figure 5 Each of the components shown may be implemented using hardware, software, or a combination thereof.

[0157] It should be understood that the devices and methods disclosed in the above embodiments can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0158] As described above, the above are only various embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A water stress monitoring method, characterized in that: The method comprises: Obtaining sapwood areas of a plurality of trees in a study area, wherein the sapwood of each of the trees is a tissue used to conduct water; According to the sapwood area of ​​each tree in the study area, the water consumption of each tree in the study area is obtained; Obtaining soil moisture in the study area; Determine whether there is water stress in the study area based on the water consumption and the soil moisture.

2. The water stress monitoring method according to claim 1, characterized in that: Get the sapwood area of ​​multiple trees in the study area, including: Obtaining a point cloud to be processed obtained by scanning the study area; Clustering the point cloud to be processed to obtain a plurality of point cloud sets, wherein each point cloud set represents a tree; The sapwood area of ​​each of the trees is obtained according to each of the point cloud sets.

3. The water stress monitoring method according to claim 2, characterized in that: Obtaining a point cloud to be processed obtained by scanning the study area, including: Acquire a first point cloud collected from the air and a second point cloud collected from the ground; Registering the first point cloud with the second point cloud to obtain a corresponding relationship between the first point cloud and the second point cloud; The first point cloud and the second point cloud are fused according to the corresponding relationship to obtain the point cloud to be processed.

4. The water stress monitoring method according to claim 2, characterized in that: According to each of the point cloud sets, the sapwood area of ​​each of the trees is obtained, including: According to each of the point cloud sets, the diameter at breast height of each of the trees is obtained; The sapwood area of ​​each tree is obtained according to the breast diameter of each tree.

5. The water stress monitoring method according to claim 4, characterized in that: For each of the trees, the relationship between the DBH and sapwood area of ​​the tree is: As=a*DBH b Wherein, As represents the sapwood area of ​​the tree, DBH represents the diameter at breast height of the tree, and a and b represent pre-fitted coefficients.

6. The water stress monitoring method according to claim 1, characterized in that: According to the sapwood area of ​​each tree in the study area, the water consumption of each tree in the study area is obtained, including: For each of the trees, obtaining the sap flow rate of the tree; The product of the sap flow rate and the sapwood area of ​​the tree is determined as the water consumption of the tree.

7. The water stress monitoring method according to claim 1, characterized in that: Obtain the soil moisture of the study area, including: Obtaining the Normalized Difference Vegetation Index of the study area; According to the normalized difference vegetation index, the minimum surface temperature and the maximum surface temperature of the study area are obtained; The ratio between the first difference and the second difference is used as the vegetation drought index, wherein the first difference is the difference between the measured temperature of the study area and the lowest surface temperature, and the second difference is the difference between the lowest surface temperature and the highest surface temperature; The soil moisture of the study area is obtained according to the vegetation drought index.

8. A water stress monitoring device, characterized in that: The device comprises: A sapwood area module is used to obtain the sapwood area of ​​multiple trees in the study area, wherein the sapwood of each tree is a tissue used to conduct water; A water consumption estimation module, for obtaining the water consumption of each tree in the study area according to the sapwood area of ​​each tree in the study area; A soil moisture module, used for obtaining the soil moisture of the study area; The water stress module is used to determine whether there is water stress in the study area according to the water consumption and the soil moisture.

9. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program, when executed by a processor, implements the water stress monitoring method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements the water stress monitoring method according to any one of claims 1 to 7.

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