A method, device and medium for monitoring ecological restoration projects based on drones

Abstract

The present invention discloses a method, device, and medium for monitoring ecological restoration projects based on drones. The method comprises: controlling drones and satellites to simultaneously collect image data of the project's monitoring area, thereby obtaining drone data and satellite remote sensing data; wherein the drone's take-off and landing points are determined by identifying ecological change trends in the project's monitoring area; correcting satellite remote sensing data based on the drone data to obtain multispectral reflectance data; and evaluating the ecological restoration effect of the project's monitoring area based on the multispectral reflectance data to obtain ecological restoration project monitoring results. The present invention proposes a method, device, and medium for monitoring ecological restoration projects based on drones. By controlling drones and satellites to simultaneously collect remote sensing data of the project's monitoring area, and scientifically processing and analyzing this data, the method ensures the comprehensiveness of the data and improves the efficiency of data collection and analysis, thereby resolving the problem of difficulty in quickly and accurately monitoring ecological restoration projects.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to a method, device and medium for monitoring an ecological restoration project based on a drone. Background Art

[0002] Landscape and ecological restoration projects cover a wide range of sectors, with complex and diverse project types across different regions. These projects require long regulatory cycles and require high-precision, high-temporal-resolution data support. These projects have significantly improved regional environmental quality, effectively controlling dust and harmful gases in the air, purifying water quality, reducing noise pollution, and significantly improving residents' living environment. Multispectral remote sensing technology has extensive ecological applications. Multispectral image data acquired via satellites can be used to monitor sources of environmental pollution, including the atmosphere, water bodies, and soil. By analyzing the concentration and distribution of pollutants in these image data, the extent and scope of environmental pollution can be assessed, providing a basis for environmental protection and governance.

[0003] However, multispectral remote sensing technology is limited by factors such as resolution, cloud cover, light, temperature, soil and atmosphere, which can easily lead to data fluctuations. Even if ground measurement correction is performed later, the measurement data cannot be restored to the same conditions, which can easily lead to invalidation. In addition, the supervision of ecological restoration projects under conditions such as vegetation status, temperature, light, soil moisture and atmosphere takes a long time, which can easily lead to correction failure or low data credibility. Summary of the Invention

[0004] The present invention provides a method, device and medium for monitoring ecological restoration projects based on drones, so as to solve the problem that it is difficult to quickly and accurately monitor ecological restoration projects.

[0005] Controlling a drone and a satellite to simultaneously collect image data of the project supervision area to obtain drone data and satellite remote sensing data; wherein the take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area;

[0006] Correcting the satellite remote sensing data based on the drone data to obtain multispectral reflectance data;

[0007] The ecological restoration effect of the project supervision area is evaluated based on the multispectral reflectance data to obtain the ecological restoration project supervision results.

[0008] In the present invention, the high-resolution image data from the drone can clearly reflect surface details, helping to identify potential problem areas; while satellite remote sensing data can reveal changing trends in surface features through time series analysis; by combining the two data, they can be mutually verified and corrected, improving the accuracy and reliability of the data. Drones have the ability to be rapidly deployed and conduct real-time monitoring, and can monitor key areas multiple times in a short period of time, improving monitoring efficiency; while satellite remote sensing data, although it takes a long time to acquire, once acquired, it can cover a large area, reducing duplication of work; the combination of the two makes monitoring more efficient. Furthermore, by identifying ecological change trends and potential problem areas, it is possible to accurately determine the areas that the drone needs to focus on, thereby setting take-off and landing points in these areas. This precise positioning reduces aimless, large-scale flights, thereby maximizing the use of the drone's data collection capabilities and ensuring that key areas are quickly monitored and evaluated.

[0009] Compared with the existing technology, the present invention controls drones and satellites to simultaneously collect remote sensing data of the project supervision area, and scientifically processes and analyzes these data, which not only ensures the comprehensiveness of the data but also improves the efficiency of data collection and analysis. Therefore, it can solve the problem of difficulty in quickly and accurately supervising ecological restoration projects.

[0010] As a preferred solution, the take-off and landing points of the drone are determined by identifying the ecological change trends and potential problem areas in the project supervision area, specifically:

[0011] Dividing the project supervision area into a two-dimensional grid;

[0012] By performing curve compression and clustering processing on the two-dimensional grid, the coefficient of variation of the subclass grids in the two-dimensional grid is calculated;

[0013] defining subclass grids in the two-dimensional grid whose coefficient of variation exceeds a preset threshold as abnormal regions, and obtaining an abnormal region set;

[0014] The take-off and landing points of the UAV are obtained by performing spatial superposition processing on the abnormal areas concentrated in the abnormal areas.

[0015] This preferred solution accurately locates abnormal areas by calculating the coefficient of variation of subclass grids, providing an accurate basis for the subsequent selection of drone takeoff and landing points. Curve compression and clustering algorithms can quickly process large amounts of data and quickly identify abnormal areas where the coefficient of variation exceeds a preset threshold.

[0016] As a preferred solution, by performing curve compression and clustering processing on the two-dimensional grid, the coefficient of variation of the subclass grids in the two-dimensional grid is calculated, specifically:

[0017] Obtaining the normalized vegetation index of the subclass grid from the two-dimensional grid to obtain initial time series data;

[0018] Performing curve compression on the initial time series data to obtain first time series data;

[0019] Based on a first set of time series data points in the first time series data, connecting the first point and the last point into a straight line to obtain a target straight line;

[0020] Calculating the distances from all points in the first set of time series data points to the target straight line to obtain a distance set;

[0021] Eliminating target data from the time series data based on a maximum value in the distance set to obtain thinned-out second time series data;

[0022] The coefficient of variation of the subclass grid in the two-dimensional grid is calculated based on the second time series data.

[0023] This preferred solution compresses the initial time series data using curves, resulting in a more compact first time series, reducing the data volume and improving the efficiency of subsequent data processing. Thinning based on distance sets and target lines eliminates redundant data points and retains key information, thus reducing computational complexity while ensuring data accuracy.

[0024] As a preferred solution, based on the maximum value in the distance set, the target data of the initial time series data is eliminated to obtain the thinned second time series data, specifically:

[0025] Obtaining the maximum value in the distance set to obtain a maximum distance value;

[0026] If the maximum distance value is less than a preset threshold, then removing all points except the first and last points in the time series curve; wherein the time series curve is composed of the initial time series data;

[0027] If the maximum distance value is greater than or equal to the preset threshold, retaining the first data point corresponding to the maximum distance value, and dividing the first group of time series data points into two sub-point sets with the first data point as the boundary;

[0028] The two sub-point sets are recursively processed, and several groups of time series data point sets in the initial time series data are traversed to obtain the thinned-out second time series data.

[0029] This preferred solution significantly reduces the amount of data by removing points that contribute little to the shape of the time series curve, improving the efficiency of subsequent processing and analysis. By recursively processing the segmented subsets, the reconstructed time series curve can maintain the shape and characteristics of the original curve.

[0030] As a preferred solution, the coefficient of variation of the subclass grids in the two-dimensional grid is calculated based on the second time series data, specifically:

[0031] Controlling the subclass grids in the second time series data to cluster with the subclass grids within a preset radius to obtain a clustering result;

[0032] Based on the values of the preset time points in the clustering results, spatially clustering the data of several categories in the clustering results to obtain a comprehensive subclass grid set;

[0033] The coefficient of variation of the comprehensive subclass grid set in the two-dimensional grid is obtained by calculating the degree of discreteness of the data distribution of the comprehensive subclass grid set.

[0034] This preferred solution clusters time series data, grouping similar time series sub-grids into categories. This helps identify regions with similar dynamic change characteristics. Spatial clustering, based on the values at specific time points in the clustering results, further integrates spatially similar regions to form a comprehensive sub-grid set. Through these two clustering steps, the scope of clustering can be gradually narrowed, improving clustering accuracy. The coefficient of variation is calculated by calculating the degree of data distribution dispersion within the comprehensive sub-grid set. This indicator intuitively reflects the degree of difference between sub-grids within the comprehensive sub-grid set, providing strong support for subsequent analysis and decision-making.

[0035] As a preferred solution, the take-off and landing points of the UAV are obtained by spatially superimposing the abnormal areas concentrated in the abnormal areas, specifically:

[0036] According to the numerical value of the coefficient of variation, the abnormal region set is divided into several abnormal region categories;

[0037] Randomly extracting a number of abnormal regions from the several abnormal region categories to obtain data samples;

[0038] Simplifying the subclass grids in the data sample into a uniform lattice to obtain a lattice set;

[0039] Taking each point in the dot matrix as the center, a plurality of circular buffer zones are established according to a preset radius;

[0040] Spatially superimposing the plurality of circular buffer zones to obtain a numerical distribution of preset objective function values corresponding to each zone;

[0041] Based on the numerical distribution, several areas with the highest values are selected as the take-off and landing points of the UAV.

[0042] This preferred solution divides the set of abnormal regions according to the numerical value of the coefficient of variation, objectively and accurately identifying and categorizing regions of varying abnormality. Randomly selecting several abnormal regions from the divided abnormal region categories as data samples ensures sample representativeness while avoiding the subjectivity of human selection. The subclass grids in the data sample are simplified into a uniform dot matrix, simplifying the data complexity and improving processing efficiency. A circular buffer zone is established with each point in the dot matrix as the center and a preset radius, which intuitively reflects the spatial characteristics around each point, facilitating spatial overlay and analysis.

[0043] As a preferred solution, the drone and satellite are controlled to simultaneously collect image data of the project supervision area to obtain drone data and satellite remote sensing data. Specifically:

[0044] Adjusting the observation angle and camera parameters of the UAV to be consistent with the observation conditions of the data acquired by the satellite;

[0045] Controlling the UAV and the satellite to simultaneously collect image data of the project supervision area to obtain the UAV data and the satellite remote sensing data;

[0046] If the satellite remote sensing data shows an area with missing data or an area where the emissivity changes by more than a preset value, the drone is used to perform supplementary photography or encrypted measurement.

[0047] In this preferred solution, satellite remote sensing data has the advantages of macroscopicity, continuity and timeliness, but may be missing or have reduced quality due to factors such as cloud cover and ground cover. Drones can flexibly adjust their flight routes and altitudes to conduct supplementary photography or encrypted measurements of specific areas, thereby filling gaps in satellite data or improving data accuracy.

[0048] As a preferred solution, the project supervision area is specifically:

[0049] Obtain basic geographic information of the comprehensive project supervision area;

[0050] Based on the basic geographic information, by identifying the distribution of different land types and soil types, the key monitoring areas in the comprehensive project supervision area are determined to obtain the project supervision area.

[0051] This preferred solution determines the key monitoring areas by identifying the distribution of different land types and soil types, which can delineate the required regulatory areas and avoid the problem of wasting regulatory resources.

[0052] The present application also provides a drone-based ecological restoration project monitoring device, comprising a data module, a correction module, and an evaluation module;

[0053] The data module is used to control the drone and satellite to simultaneously collect image data of the project supervision area, thereby obtaining drone data and satellite remote sensing data; wherein the take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area;

[0054] The correction module is used to correct the satellite remote sensing data based on the UAV data to obtain multispectral reflectance data;

[0055] The evaluation module is used to evaluate the ecological restoration effect of the project supervision area based on the multispectral reflectance data to obtain the ecological restoration project supervision results.

[0056] The present application also provides a storage medium on which a computer program is stored. The computer program is called and executed by a computer to implement the above-mentioned drone-based ecological restoration project supervision method. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a method for monitoring an ecological restoration project based on a drone, as provided in an embodiment of the present application;

[0058] Figure 2 This is a schematic diagram of spatial superposition provided by an embodiment of the present application;

[0059] Figure 3 This is a schematic diagram of spatial buffering provided by an embodiment of the present application;

[0060] Figure 4 This is a schematic diagram of the cumulative result of the superposition value provided in the embodiment of the present application;

[0061] Figure 5 This is a structural diagram of an ecological restoration project monitoring device based on a drone provided in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0063] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, features specified as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise specified, "several" means two or more.

[0064] The embodiment of the present application provides a drone-based ecological restoration project supervision method, which is mainly used in situations where it is necessary to balance data accuracy, timeliness and cost to achieve full-cycle efficient supervision of landscape and ecological restoration projects.

[0065] Example 1:

[0066] See also Figure 1 The embodiment of the present application provides a method for monitoring ecological restoration projects based on drones, including S1 to S3. The specific implementation steps are as follows:

[0067] S1. Control the drone and satellite to simultaneously collect image data of the project supervision area to obtain drone data and satellite remote sensing data; the take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area.

[0068] Step S1 of the embodiment of the present application includes S1.1 to S1.7; wherein S1.1 is a process of obtaining a project supervision area, S1.2 is a process of establishing a distance set based on the project supervision area, S1.3 is a process of obtaining second time series data based on the distance set, S1.4 is a process of calculating a coefficient of variation based on the second time series data, S1.5 is a process of selecting the best airport placement candidate area based on the coefficient of variation, S1.6 is a process of determining a drone take-off and landing point by combining the best airport placement candidate area and road network data, and S1.7 is a process of controlling the drone and satellite to collect data, specifically:

[0069] S1.1. Obtain basic geographic information of the comprehensive project supervision area, including topography, landforms, water systems, and vegetation cover;

[0070] Collect land planning and soil type data provided by relevant departments, and establish a unified database management system to store and manage all data by integrating records and performance data of historical ecological restoration projects;

[0071] The comprehensive data, including the basic geographic information of the comprehensive project supervision area and data in the database management system, were imported into the GIS platform (Geographic Information System). GIS technology was used to perform spatial overlay analysis on the comprehensive data. By identifying the distribution of different land types and soil types, dividing the supervision area according to NDVI data (Normalized Difference Vegetation Index) and ecological sensitivity, and determining key monitoring areas, the project supervision area was obtained.

[0072] In this embodiment S1.1, by identifying the distribution of different land types and soil types, key monitoring areas can be determined, and the required regulatory areas can be delineated to avoid the problem of wasting regulatory resources.

[0073] S1.2. Divide the project supervision area into a two-dimensional grid. The size of each sub-grid in the two-dimensional grid corresponds to a pixel of the multispectral satellite. That is, a sub-grid is the same size as a pixel of NDVI. In addition, "Prow,col[n]" represents the NDVI value of the grid corresponding to row th and column th at the time of measurement n, with a sampling interval of T.

[0074] Obtain the NDVI values of the subclass grids from the two-dimensional grid to obtain the initial time series data;

[0075] Using a curve compression algorithm (such as the Douglas-Peucker algorithm and the Visvalingam-Whyatt algorithm) to perform curve compression on the initial time series data to obtain the first time series data;

[0076] Based on the first set of time series data points P in the first time series data, the first point p0 and the last point p n Connect them into a straight line to get the target straight line AB; where P={p0,p1,...,p n}, and for each point: p i =(x i ,y i ), for the target line AB: A(x0,y0), B(x n ,y n );

[0077] Calculate all points p in the first set of time series data points i The distance d from (i=1,2,...,n-1) to the target line AB i , and get the distance set.

[0078] The distance formula from a point to the target line is:

[0079]

[0080] The general equation of the target line is:

[0081] Ax + By + C = 0

[0082] For parameters A, B, and C, there are:

[0083] A = y n - y0;

[0084] B = x0 - x n ;

[0085] C = x n y0 - x0y n

[0086] Where x and y respectively represent the abscissa and ordinate of a certain point on the target line.

[0087] In this embodiment S1.2, due to the high time resolution, large amount of data and data redundancy, direct clustering will consume too much time. Therefore, curve compression is performed before clustering, which can improve the clustering efficiency on the premise of retaining the shape of the time curve.

[0088] S1.3. Obtain the maximum value in the distance set to get the maximum distance value d max ;

[0089] If the maximum distance value d max is less than the preset threshold D, that is, d max < D, then剔除 other points except the first point p0 and the last point p n from the time series curve; where the time series curve is composed of initial time series data;

[0090] If the maximum distance value d max is greater than or equal to the preset threshold D, that is, d max ≥ D, then retain the first data point p max corresponding to the maximum distance value d max_index , and divide the first set of time series data point set P into two sub-point sets with the first data point p max_index as the boundary; where the two sub-point sets are respectively P1 = {p0,..., p max_index} and P2 = {p max_index ,..., p n};

[0091] Repeat the above curve clustering and data elimination calculation operations on the two sub-point sets for recursive processing; by traversing several groups of time series data point sets in the initial time series data, after the recursive processing ends, merge all the retained points in order to obtain the thinned second time series data.

[0092] In this embodiment, S1.3, by removing points that have little contribution to the shape of the time series curve, the amount of data can be significantly reduced, thereby improving the efficiency of subsequent processing and analysis. By recursively processing the segmented sub-point set, it can be ensured that the reconstructed time series curve can better maintain the shape and characteristics of the original curve.

[0093] Therefore, the thinning process based on the distance set and the target line can eliminate redundant data points and retain key information, thereby reducing the amount of calculation while ensuring data accuracy.

[0094] S1.4. Control the subclass grids in the second time series data to cluster with the subclass grids within a preset radius R1 to obtain a clustering result;

[0095] Based on the value of the last time point in the clustering result, spatial clustering is performed on the data of several categories in the clustering result to obtain a comprehensive subclass grid set consisting of many spatially clustered small classes under each major class;

[0096] By calculating the discrete degree of data distribution of the comprehensive subclass grid set, the coefficient of variation CV of the comprehensive subclass grid set in the two-dimensional grid is obtained.

[0097] The calculation formula for the coefficient of variation is:

[0098] CV = (SD / Mean) × 100%

[0099] Among them, SD is the standard deviation of the NDVI value of each subclass grid, which is used to reflect the fluctuation of the NDVI value within the subclass grid; Mean is the average value of all NDVI values in the comprehensive subclass grid set, which is used to reflect the average vegetation coverage of the subclass grid at a specific time point.

[0100] In this embodiment, S1.4, time series data is clustered to group similar time series subclass grids into categories, helping to identify regions with similar dynamic change characteristics. Spatial clustering, based on the values at specific time points in the clustering results, can further integrate similar regions to form a comprehensive subclass grid set. This two-step clustering process can gradually narrow the scope of clustering and improve clustering accuracy. The coefficient of variation is calculated by calculating the degree of data distribution dispersion within the comprehensive subclass grid set. This metric intuitively reflects the degree of difference between subclass grids within the comprehensive subclass grid set, providing strong support for subsequent analysis and decision-making.

[0101] S1.5. defining the subclass grids whose coefficient of variation (CV) in the two-dimensional grid exceeds a preset threshold as abnormal regions, and obtaining an abnormal region set;

[0102] According to the value of the coefficient of variation CV and the importance of the region, the abnormal region set is divided into several abnormal region categories; wherein, the several abnormal region categories include three categories: strong, medium and weak;

[0103] Randomly sample several abnormal regions from several abnormal region categories to obtain data samples S = {s1, s2, s3}; where a sample point in the data sample S corresponds to a sample point in a subclass grid of several abnormal region categories; and the sample point has a certain degree of flexibility within the subclass grid, that is, it can move within the grid;

[0104] The minimum bounding circle algorithm is used to plan the flight path of the drone to ensure that the drone can cover as many different types of sample points as possible. This allows the drone to collect samples in a short time under the condition of a certain number of samples, ensuring data synchronization.

[0105] Simplify the subclass grid in the data sample S into a uniform lattice to obtain a lattice set;

[0106] With each point in the dot matrix as the center, a number of circular buffers are established according to the preset radius R2; wherein the value of each buffer in the number of circular buffers is the CV value (coefficient of variation) of the sample point;

[0107] Several circular buffers are spatially superimposed to divide each other. All values are added together, ensuring that the same subclass ID (Identifier) is only added once, to obtain the numerical distribution of the preset objective function value F(C) corresponding to each area.

[0108] Based on the numerical distribution, the N areas with the highest values are selected as the best airport placement candidate areas for drones.

[0109] Among them, the objective function value is:

[0110]

[0111] Among them, α i is the weight of the importance of the i-th subclass of the sample, β i is the CV value of the sample point, K is the number of all subclasses; covered i A Boolean variable that is 1 if the subclass is overridden and 0 otherwise.

[0112] To apply this application example, please refer to Figure 2-4 ;

[0113] Figure 2This is a schematic diagram of spatial superposition provided by an embodiment of the present application, illustrating the principle of spatial superposition of several circular buffer zones, namely: when two graphics are superimposed, the intersection becomes an independent graphic;

[0114] Figure 3 is a schematic diagram of spatial buffering provided in an embodiment of the present application, illustrating a process of spatially superimposing several circular buffer zones;

[0115] Figure 4 This is a schematic diagram of the result of the accumulation of superposition values provided in an embodiment of the present application, which shows the result of the accumulation of superposition values obtained after spatially superimposing several circular buffer zones.

[0116] In this embodiment, S1.5 divides the set of abnormal regions according to the numerical value of the coefficient of variation, objectively and accurately identifying and categorizing regions of varying abnormality. Randomly extracting several abnormal regions from the divided abnormal region categories as data samples ensures the representativeness of the samples while avoiding the subjectivity of human selection. Simplifying the subclass grid in the data sample into a uniform dot matrix simplifies the data complexity and improves processing efficiency. Establishing a circular buffer zone with each point in the dot matrix as the center and a preset radius intuitively reflects the spatial characteristics around each point, facilitating spatial overlay and analysis.

[0117] S1.6. Obtain detailed road network data within a pre-defined area, including road geometry, type, and traffic restrictions;

[0118] Overlay the location of the best airport placement candidate area with the road network data to determine the optimal drone takeoff point;

[0119] Using an efficient path search algorithm (such as the Dijkstra algorithm) combined with the UAV flight path planned using the minimum circle cover algorithm, calculate the shortest path from the selected optimal UAV takeoff point to all destination sample points;

[0120] Based on the shortest path calculation results and the actual operation requirements of the UAV, the final layout of the UAV airport is determined by comprehensively considering factors such as the location of the take-off point, the surrounding environment, and traffic conditions, thereby obtaining the take-off and landing points of the UAV;

[0121] Fixed drone airports and mobile drone airports are set up based on the take-off and landing points of drones; and ensure that the facilities of drone airports are complete and can meet the needs of drone take-off and landing and maintenance; among them, fixed drone airports are set up in preset key supervision areas, and mobile drone airports are set up according to actual needs.

[0122] S1.7. Before the satellite passes overhead, calibrate the satellite's multispectral camera using radiation correction plates with different reflectivity of 25%, 50%, and 75% to ensure stable performance and high accuracy; and record the calibration results for subsequent data processing and analysis. The satellite is a multispectral satellite.

[0123] Control the drone to depart from the drone airport (take-off and landing point) and arrive at the destination sample point; adjust the drone's observation angle (azimuth) and camera parameters (camera pitch angle) to be consistent with the observation conditions of the data obtained by the satellite;

[0124] Control the drone and satellite to simultaneously collect image data of the project supervision area, obtaining drone data and high-resolution satellite remote sensing data; the image data is multispectral image data;

[0125] If there are areas with missing data in the satellite remote sensing data or areas where the emissivity changes exceed the preset value, supplementary photography or encrypted measurements will be carried out using drones to ensure the integrity and accuracy of the data.

[0126] In this embodiment S1.7, satellite remote sensing data has the advantages of macroscopicity, continuity, and timeliness, but may be missing or have degraded quality due to factors such as cloud cover and ground cover. Drones can flexibly adjust their flight paths and altitudes to perform supplementary photography or intensive measurements of specific areas, thereby filling gaps in satellite data or improving data accuracy.

[0127] S2. Correct satellite remote sensing data based on UAV data to obtain multispectral reflectance data.

[0128] Step S2 of the embodiment of the present application is specifically as follows:

[0129] Perform preliminary preprocessing on drone data, such as denoising and enhancement, and extract reflectivity information from drone data for subsequent data analysis;

[0130] Time-synchronize drone data with satellite remote sensing data to ensure data consistency and comparability;

[0131] Using the actual radiation reflectivity of each band in the drone data as a benchmark, the satellite remote sensing data was corrected to eliminate the differences caused by atmospheric correction, thereby obtaining multispectral reflectivity data. The accuracy and reliability of the multispectral reflectivity data were also evaluated.

[0132] The relevant index (such as NDVI) is calculated based on the multispectral reflectance data, and the latest relevant index is used as the basis for the next sampling.

[0133] S3. Evaluate the ecological restoration effect of the project supervision area based on multispectral reflectance data to obtain the ecological restoration project supervision results.

[0134] Step S3 of the embodiment of the present application includes S3.1 to S3.2; wherein S3.1 is the process of obtaining the supervision results of the ecological restoration project, and S3.2 is the process of continuous monitoring and feedback, specifically:

[0135] S3.1. Conduct time series analysis and spatial distribution analysis on the NDVI data, which is a time series fusion of the multispectral reflectance data, to evaluate the ecological restoration effect in the project supervision area and obtain the first supervision result;

[0136] The first supervision results are combined with the field investigation results of the project supervision area and the preset expert knowledge to identify existing problems and challenges and obtain the supervision results of the ecological restoration project.

[0137] S3.2. Develop a regular monitoring plan to ensure continuous monitoring of the project supervision area; conduct irregular spot checks of the project supervision area based on actual needs and monitoring results; dynamically adjust the sampling plan and supervision priorities based on monitoring results and the progress of the ecological restoration project;

[0138] Establish a real-time data transmission system to transmit monitoring data to the supervision system in real time; use data visualization technology to convert monitoring data into intuitive charts and images; and use visualization to display the progress and results of ecological restoration projects in real time;

[0139] Establish an early warning system, set warning thresholds and trigger conditions; conduct real-time monitoring and analysis of monitoring data to identify abnormal situations; once an abnormal situation is discovered, issue a warning signal in a timely manner and respond accordingly;

[0140] Collect and analyze monitoring data to evaluate the progress and effectiveness of ecological restoration projects; regularly prepare monitoring reports to summarize monitoring results and issues found; submit monitoring reports to project management departments and relevant policy-making agencies to provide a scientific basis for project management and policy-making.

[0141] Overall, this embodiment has the following beneficial effects:

[0142] In the present invention, the high-resolution multispectral imaging data of the UAV can clearly reflect the surface details and help to discover potential problem areas; while the satellite remote sensing data can reveal the changing trends of surface features through time series analysis; by combining the data of the two, they can be verified and corrected to improve the accuracy and reliability of the data. UAVs have the ability to be quickly deployed and monitored in real time, and can monitor key areas multiple times in a short period of time, improving monitoring efficiency; and although the acquisition cycle of satellite remote sensing data is longer, once acquired, it can cover a large area and reduce duplication of work; the combination of the two makes monitoring more efficient. Moreover, by identifying ecological change trends and potential problem areas, it is possible to accurately determine the areas that the UAV needs to focus on, so that take-off and landing points can be set in these areas. This precise positioning reduces aimless and large-scale flights, so the data collection capabilities of the UAV can be maximized to ensure that key areas are quickly monitored and evaluated;

[0143] Through the deep integration and calibration of drone and satellite remote sensing data, we have achieved full-cycle, high-precision, and low-cost supervision of landscape and ecological restoration projects. This approach not only significantly improves data accuracy and timeliness, but also effectively reduces manual verification costs and enhances the intelligence and automation of supervision. Furthermore, through continuous monitoring and feedback mechanisms, problems that arise during ecological restoration can be promptly identified and resolved, ensuring smooth project implementation and achieving the desired results.

[0144] Example 2:

[0145] See also Figure 5 , an embodiment of the present application provides an ecological restoration project supervision device based on a drone, comprising a data module 10, a correction module 20 and an evaluation module 30;

[0146] The data module 10 is used to control the drone and satellite to simultaneously collect image data of the project supervision area, thereby obtaining drone data and satellite remote sensing data. The take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area.

[0147] Correction module 20, for correcting satellite remote sensing data based on UAV data to obtain multispectral reflectance data;

[0148] The evaluation module 30 is used to evaluate the ecological restoration effect of the project supervision area based on the multispectral reflectance data to obtain the ecological restoration project supervision results.

[0149] In one embodiment, the data module 10 includes an area unit, a distance unit, a sequence unit, a variation unit, a candidate unit, a take-off and landing unit, and a collection unit. The area unit is a process for obtaining a project supervision area, the distance unit is a process for establishing a distance set based on the project supervision area, the sequence unit is a process for obtaining second time series data based on the distance set, the variation unit is a process for calculating a coefficient of variation based on the second time series data, the candidate unit is a process for selecting the best airport placement candidate area based on the coefficient of variation, the take-off and landing unit is a process for determining the take-off and landing points of a drone by combining the best airport placement candidate area and road network data, and the collection unit is a process for controlling the drone and satellite to collect data, specifically:

[0150] Regional unit, used to obtain basic geographic information of the comprehensive project supervision area, including topography, landforms, water systems and vegetation cover;

[0151] The regional unit is also used to collect land planning and soil type data provided by relevant departments, and to establish a unified database management system for storing and managing all data by integrating records and performance data of historical ecological restoration projects;

[0152] The regional unit is also used to import comprehensive data, including basic geographic information of the comprehensive project supervision area and data in the database management system, into the GIS platform (Geographic Information System); GIS technology is used to perform spatial overlay analysis on the comprehensive data, and the project supervision area is obtained by identifying the distribution of different land types and soil types, dividing the supervision area according to NDVI data (Normalized Difference Vegetation Index) and ecological sensitivity, and determining key monitoring areas.

[0153] The regional unit of this embodiment determines the key monitoring area by identifying the distribution of different land types and soil types, and can delineate the required supervision area to avoid the problem of wasting supervision resources.

[0154] Distance units are used to divide the project supervision area into a two-dimensional grid. The size of each sub-grid in the two-dimensional grid corresponds to a pixel of the multispectral satellite, that is, a sub-grid is the same size as a pixel of NDVI. In addition, "Prow,col[n]" represents the NDVI value of the grid corresponding to row th and column th at the time of measurement n, with a sampling interval of T.

[0155] The distance unit is also used to obtain the NDVI values of the subclass grid from the two-dimensional grid to obtain the initial time series data;

[0156] The distance unit is also used to perform curve compression on the initial time series data using a curve compression algorithm (such as the Douglas-Peucker algorithm and the Visvalingam-Whyatt algorithm) to obtain the first time series data;

[0157] The distance unit is also used to separate the first point p0 and the last point p based on the first set of time series data points P in the first time series data. n Connect them into a straight line to get the target straight line AB; where P={p0,p1,...,p n}, and for each point: p i =(x i ,y i ), for the target line AB: A(x0,y0), B(x n ,y n );

[0158] The distance unit is also used to calculate the distance of all points p in the first set of time series data points. i The distance d from (i=1,2,...,n-1) to the target line AB i , and get the distance set.

[0159] The distance formula from a point to the target line is:

[0160]

[0161] The general equation of the target line is:

[0162] Ax+By+C=0

[0163] For parameters A, B and C:

[0164] A=y n -y0;

[0165] B=x0-x n ;

[0166] C=x n y0-x0y n

[0167] Where x and y represent the horizontal and vertical coordinates of a point on the target line, respectively.

[0168] In the distance unit of this embodiment, due to the high time resolution, large data volume and data redundancy, direct clustering will consume too much time. Therefore, curve compression is performed before clustering to improve clustering efficiency while preserving the shape of the time curve.

[0169] Sequence unit, used to obtain the maximum value in the distance set and obtain the maximum distance value d max ;

[0170] The sequence unit is also used to, if the maximum distance value d max is less than the preset threshold D, that is, d max < D, then remove all points in the time series curve except the first point p0 and the last point p n ; wherein, the time series curve is composed of initial time series data;

[0171] The sequence unit is also used to, if the maximum distance value d max is greater than or equal to the preset threshold D, that is, d max ≥ D, then retain the first data point p max corresponding to the maximum distance value d max_index , and divide the first set of time series data point set P into two sub-point sets with the first data point p max_index as the boundary; wherein, the two sub-point sets are respectively P1 = {p0,..., p max_index} and P2 = {p max_index ,..., p n};

[0172] The sequence unit is also used to repeat the above curve clustering and data removal calculation operations on the two sub-point sets for recursive processing; by traversing several sets of time series data point sets in the initial time series data, after the recursive processing ends, all the retained points are merged in order to obtain the thinned second time series data.

[0173] In this embodiment, by removing the points with less contribution to the curve shape in the time series curve, the sequence unit can significantly reduce the data volume and improve the efficiency of subsequent processing and analysis. By recursively processing the divided sub-point sets, it can ensure that the reconstructed time series curve can better maintain the shape and characteristics of the original curve;

[0174] Therefore, the thinning process based on the distance set and the target line can remove redundant data points and retain key information, thereby reducing the calculation amount while ensuring the data accuracy.

[0175] The mutation unit is used to control the clustering of the sub-class grids in the second time series data with the sub-class grids within the preset radius R1 to obtain the clustering result;

[0176] The mutation unit is also used to perform spatial clustering on the data of several categories in the clustering result based on the value of the last time point in the clustering result to obtain a comprehensive sub-class grid set composed of many spatially aggregated small classes under each large class;

[0177] The mutation unit is also used to calculate the data distribution dispersion degree of the comprehensive sub-class grid set to obtain the coefficient of variation CV of the comprehensive sub-class grid set in the two-dimensional grid.

[0178] The calculation formula for the coefficient of variation is:

[0179] CV = (SD / Mean) × 100%

[0180] Among them, SD is the standard deviation of the NDVI value of each subclass grid, which is used to reflect the fluctuation of the NDVI value within the subclass grid; Mean is the average value of all NDVI values in the comprehensive subclass grid set, which is used to reflect the average vegetation coverage of the subclass grid at a specific time point.

[0181] The variation unit in this embodiment clusters time series data, grouping similar time series subclass grids into categories. This helps identify regions with similar dynamic change characteristics. Spatial clustering, based on the values at specific time points in the clustering results, further integrates spatially similar regions to form a comprehensive subclass grid set. This two-step clustering process gradually narrows the scope of clustering and improves clustering accuracy. The coefficient of variation is calculated by calculating the degree of data distribution dispersion within the comprehensive subclass grid set. This metric intuitively reflects the degree of difference between subclass grids within the comprehensive subclass grid set, providing strong support for subsequent analysis and decision-making.

[0182] The candidate unit is used to define the subclass grids whose coefficient of variation CV in the two-dimensional grid exceeds a preset threshold as abnormal regions, and obtain an abnormal region set;

[0183] The candidate unit is further used to divide the abnormal region set into several abnormal region categories according to the value of the coefficient of variation CV and the importance of the region; wherein the several abnormal region categories include three categories: strong, medium and weak;

[0184] The candidate unit is further used to randomly extract several abnormal regions from several abnormal region categories to obtain data samples S = {s1, s2, s3}; wherein a sample point in the data sample S corresponds to a sample point in a subclass grid of several abnormal region categories; and the sample point has a certain degree of flexibility within the subclass grid, that is, it can move within the grid;

[0185] Candidate units are also used to plan the flight path of the drone using the Minimum Bounding Circle Algorithm, ensuring that the drone can cover as many different types of sample points as possible. This allows the drone to collect samples in a shorter time under certain conditions, ensuring data synchronization.

[0186] The candidate unit is also used to simplify the subclass grid in the data sample S into a uniform point matrix to obtain a point matrix set;

[0187] The candidate unit is further used to establish a number of circular buffers with each point in the point matrix as the center and a preset radius R2; wherein the value of each buffer in the number of circular buffers is the CV value (coefficient of variation) of the sample point;

[0188] The candidate unit is also used to spatially superimpose several circular buffers so that the buffers are divided from each other. Under the condition that the same subclass ID (Identifier) is only added once, all values are added together to obtain the numerical distribution of the preset objective function value F(C) corresponding to each area.

[0189] The candidate unit is also used to select the N areas with the highest values as the best airport placement candidate areas for drones based on the numerical distribution.

[0190] Among them, the objective function value is:

[0191]

[0192] Among them, α i is the weight of the importance of the i-th subclass of the sample, β i is the CV value of the sample point, K is the number of all subclasses; covered i A Boolean variable that is 1 if the subclass is overridden and 0 otherwise.

[0193] To apply this application example, please refer to Figure 2-4 ;

[0194] Figure 2 This is a schematic diagram of spatial superposition provided by an embodiment of the present application, illustrating the principle of spatial superposition of several circular buffer zones, namely: when two graphics are superimposed, the intersection becomes an independent graphic;

[0195] Figure 3 is a schematic diagram of spatial buffering provided in an embodiment of the present application, illustrating a process of spatially superimposing several circular buffer zones;

[0196] Figure 4 This is a schematic diagram of the result of the accumulation of superposition values provided in an embodiment of the present application, which shows the result of the accumulation of superposition values obtained after spatially superimposing several circular buffer zones.

[0197] This embodiment divides the candidate unit into sets of abnormal regions based on the numerical value of the coefficient of variation, objectively and accurately identifying and categorizing regions of varying abnormality. Randomly selecting several abnormal regions from the divided abnormal region categories as data samples ensures sample representativeness while avoiding the subjectivity of human selection. The subclass grid in the data sample is simplified into a uniform dot matrix, simplifying data complexity and improving processing efficiency. A circular buffer zone is established with each point in the dot matrix as the center and a preset radius, intuitively reflecting the spatial characteristics around each point, facilitating spatial overlay and analysis.

[0198] A take-off and landing unit is used to obtain detailed road network data within a preset area, including information such as road geometry, type, and traffic restrictions;

[0199] The take-off and landing unit is also used to overlay the location of the best airport placement candidate area with the road network data to determine the optimal drone take-off point;

[0200] The take-off and landing unit is also used to use an efficient path search algorithm (such as the Dijkstra algorithm) combined with the UAV flight path planned using the minimum circle cover algorithm to calculate the shortest path from the selected optimal UAV take-off point to all destination sample points;

[0201] The take-off and landing unit is also used to determine the final layout of the drone airport based on the shortest path calculation results and the actual operation requirements of the drone, taking into account factors such as the location of the take-off point, the surrounding environment and traffic conditions, thereby obtaining the take-off and landing points of the drone;

[0202] The take-off and landing unit is also used to set up fixed drone airports and mobile drone airports based on the take-off and landing points of drones; and ensure that the facilities of drone airports are complete and can meet the needs of drone take-off and landing and maintenance; among them, fixed drone airports are set up in preset key supervision areas, and mobile drone airports are set according to actual needs.

[0203] The acquisition unit is used to calibrate the satellite's multispectral camera using radiation correction plates with different reflectivity of 25%, 50%, and 75% before the satellite passes overhead to ensure its stable performance and high accuracy; and record the calibration results for subsequent data processing and analysis. The satellite is a multispectral satellite;

[0204] The acquisition unit is also used to control the UAV to depart from the UAV airport (take-off and landing point) and arrive at the destination sampling point; adjust the UAV's observation angle (azimuth) and camera parameters (camera pitch angle) to be consistent with the observation conditions of the data acquired by the satellite;

[0205] The acquisition unit is also used to control the UAV and satellite to simultaneously collect image data of the project supervision area, thereby obtaining UAV data and high-resolution satellite remote sensing data; wherein the image data is multispectral image data;

[0206] The acquisition unit is also used to perform supplementary photography or encrypted measurements through drones if there are areas with missing data in the satellite remote sensing data or areas where the radiation rate changes exceed the preset value, to ensure the integrity and accuracy of the data.

[0207] In the acquisition unit of this embodiment, satellite remote sensing data has the advantages of macroscopicity, continuity and timeliness, but may be missing or have reduced quality due to factors such as cloud cover and ground cover. UAVs can flexibly adjust their flight routes and altitudes to perform supplementary photography or encrypted measurements of specific areas, thereby filling gaps in satellite data or improving data accuracy.

[0208] In one embodiment, the correction module 20 is specifically:

[0209] Perform preliminary preprocessing on drone data, such as denoising and enhancement, and extract reflectivity information from drone data for subsequent data analysis;

[0210] Time-synchronize drone data with satellite remote sensing data to ensure data consistency and comparability;

[0211] Using the actual radiation reflectivity of each band in the drone data as a benchmark, the satellite remote sensing data was corrected to eliminate the differences caused by atmospheric correction, thereby obtaining multispectral reflectivity data. The accuracy and reliability of the multispectral reflectivity data were also evaluated.

[0212] The relevant index (such as NDVI) is calculated based on the multispectral reflectance data, and the latest relevant index is used as the basis for the next sampling.

[0213] In one embodiment, the evaluation module 30 includes a supervision unit and a feedback unit. The supervision unit is the process of obtaining the supervision results of the ecological restoration project, and the feedback unit is the process of continuous monitoring and feedback, specifically:

[0214] The supervision unit is used to conduct time series analysis and spatial distribution analysis on the NDVI data obtained by time series fusion of the multispectral reflectance data, so as to evaluate the ecological restoration effect of the project supervision area and obtain the first supervision result;

[0215] The supervision unit is also used to combine the first supervision results with the field investigation results of the project supervision area and preset expert knowledge to identify existing problems and challenges and obtain the supervision results of the ecological restoration project.

[0216] The feedback unit is used to formulate regular monitoring plans to ensure continuous monitoring of the project supervision area; conduct irregular spot checks of the project supervision area based on actual needs and monitoring results; and dynamically adjust the sampling plan and supervision priorities based on monitoring results and the progress of the ecological restoration project;

[0217] The feedback unit is also used to establish a real-time data transmission system to transmit monitoring data to the supervision system in real time; using data visualization technology, the monitoring data is converted into intuitive charts and images; through visual display, the progress and results of the ecological restoration project are displayed in real time;

[0218] The feedback unit is also used to establish an early warning system, set warning thresholds and trigger conditions; monitor and analyze monitoring data in real time to identify abnormal situations; once an abnormal situation is found, a warning signal is issued in a timely manner and a response is taken;

[0219] The feedback unit is also used to collect and analyze monitoring data, evaluate the progress and effectiveness of ecological restoration projects; regularly prepare monitoring reports to summarize monitoring results and problems found; and submit monitoring reports to project management departments and relevant policy-making agencies to provide a scientific basis for project management and policy-making.

[0220] Overall, this embodiment has the following beneficial effects:

[0221] In the present invention, the high-resolution multispectral imaging data of the UAV can clearly reflect the surface details and help to discover potential problem areas; while the satellite remote sensing data can reveal the changing trends of surface features through time series analysis; by combining the data of the two, they can be verified and corrected to improve the accuracy and reliability of the data. UAVs have the ability to be quickly deployed and monitored in real time, and can monitor key areas multiple times in a short period of time, improving monitoring efficiency; and although the acquisition cycle of satellite remote sensing data is longer, once acquired, it can cover a large area and reduce duplication of work; the combination of the two makes monitoring more efficient. Moreover, by identifying ecological change trends and potential problem areas, it is possible to accurately determine the areas that the UAV needs to focus on, so that take-off and landing points can be set in these areas. This precise positioning reduces aimless and large-scale flights, so the data collection capabilities of the UAV can be maximized to ensure that key areas are quickly monitored and evaluated;

[0222] Through the deep integration and calibration of drone and satellite remote sensing data, we have achieved full-cycle, high-precision, and low-cost supervision of landscape and ecological restoration projects. This approach not only significantly improves data accuracy and timeliness, but also effectively reduces manual verification costs and enhances the intelligence and automation of supervision. Furthermore, through continuous monitoring and feedback mechanisms, problems that arise during ecological restoration can be promptly identified and resolved, ensuring smooth project implementation and achieving the desired results.

[0223] Example 3:

[0224] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device containing the computer-readable storage medium is controlled to execute the method for supervising an ecological restoration project based on a drone;

[0225] Among them, if the described method of ecological restoration project supervision based on drones is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The described computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the described computer program includes computer program code, and the described computer program code can be in source code form, object code form, executable file or some intermediate form. The described computer-readable medium may include: any entity or device that can carry the described computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0226] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for monitoring ecological restoration projects based on drones, characterized in that: include: Control drones and satellites to simultaneously collect image data of the project supervision area, and obtain drone data and satellite remote sensing data; Correcting the satellite remote sensing data based on the drone data to obtain multispectral reflectance data; Evaluate the ecological restoration effect of the project supervision area based on the multispectral reflectance data to obtain the ecological restoration project supervision results; The take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area, specifically: Dividing the project supervision area into a two-dimensional grid; By performing curve compression and clustering processing on the two-dimensional grid, the coefficient of variation of the subclass grids in the two-dimensional grid is calculated; defining subclass grids in the two-dimensional grid whose coefficient of variation exceeds a preset threshold as abnormal regions, and obtaining an abnormal region set; Obtaining the take-off and landing points of the UAV by performing spatial superposition processing on the abnormal areas where the abnormal areas are concentrated; The coefficient of variation of the subclass grids in the two-dimensional grid is calculated by performing curve compression and clustering processing on the two-dimensional grid, specifically: Obtaining the normalized vegetation index of the subclass grid from the two-dimensional grid to obtain initial time series data; performing curve compression on the initial time series data to obtain first time series data; connecting the first point and the last point of a first set of time series data points in the first time series data into a straight line to obtain a target straight line; calculating the distance from all points in the first set of time series data points to the target straight line to obtain a distance set; and eliminating the target data of the initial time series data based on the maximum value in the distance set to obtain a thinned-out second time series data; The subclass grids in the second time series data are controlled to cluster with the subclass grids within a preset radius to obtain a clustering result; based on the values of the preset time points in the clustering result, several categories of data in the clustering result are spatially clustered to obtain a comprehensive subclass grid set; and the coefficient of variation of the comprehensive subclass grid set in the two-dimensional grid is obtained by calculating the degree of discreteness of the data distribution of the comprehensive subclass grid set.

2. The method for monitoring ecological restoration projects based on drones according to claim 1, characterized in that: Based on the maximum value in the distance set, the target data of the initial time series data is eliminated to obtain the thinned second time series data, specifically: Obtaining the maximum value in the distance set to obtain a maximum distance value; If the maximum distance value is less than a preset threshold, then removing all points except the first and last points in the time series curve; wherein the time series curve is composed of the initial time series data; If the maximum distance value is greater than or equal to the preset threshold, retaining the first data point corresponding to the maximum distance value, and dividing the first group of time series data points into two sub-point sets with the first data point as the boundary; The two sub-point sets are recursively processed, and several groups of time series data point sets in the initial time series data are traversed to obtain the second time series data after thinning.

3. The method for monitoring ecological restoration projects based on drones according to claim 1, characterized in that: By performing spatial superposition processing on the abnormal areas where the abnormal areas are concentrated, the take-off and landing points of the UAV are obtained, specifically: According to the numerical value of the coefficient of variation, the abnormal region set is divided into several abnormal region categories; Randomly extracting a number of abnormal regions from the several abnormal region categories to obtain data samples; Simplifying the subclass grids in the data sample into a uniform lattice to obtain a lattice set; Taking each point in the dot matrix as the center, a plurality of circular buffer zones are established according to a preset radius; Spatially superimposing the plurality of circular buffer zones to obtain a numerical distribution of preset objective function values corresponding to each zone; Based on the numerical distribution, several areas with the highest values are selected as the take-off and landing points of the UAV.

4. The method for monitoring ecological restoration projects based on drones according to claim 1, characterized in that: Control the drone and satellite to simultaneously collect image data of the project supervision area, and obtain drone data and satellite remote sensing data, specifically: Adjusting the observation angle and camera parameters of the UAV to be consistent with the observation conditions of the data acquired by the satellite; Controlling the UAV and the satellite to simultaneously collect image data of the project supervision area to obtain the UAV data and the satellite remote sensing data; If the satellite remote sensing data shows an area with missing data or an area where the emissivity changes by more than a preset value, the drone is used to perform supplementary photography or encrypted measurement.

5. The method for monitoring ecological restoration projects based on drones according to claim 1, characterized in that: The project supervision areas are specifically: Obtain basic geographic information of the comprehensive project supervision area; Based on the basic geographic information, by identifying the distribution of different land types and soil types, the key monitoring areas in the comprehensive project supervision area are determined to obtain the project supervision area.

6. An ecological restoration project monitoring device based on drones, characterized in that: Includes data module, correction module and evaluation module; The data module is used to control the drone and satellite to simultaneously collect image data of the project supervision area to obtain drone data and satellite remote sensing data; The correction module is used to correct the satellite remote sensing data based on the UAV data to obtain multispectral reflectance data; The evaluation module is used to evaluate the ecological restoration effect of the project supervision area based on the multispectral reflectance data to obtain the ecological restoration project supervision results; The take-off and landing points of the drone are determined by identifying ecological change trends and potential problem areas in the project supervision area, specifically: Dividing the project supervision area into a two-dimensional grid; By performing curve compression and clustering processing on the two-dimensional grid, the coefficient of variation of the subclass grids in the two-dimensional grid is calculated; defining subclass grids in the two-dimensional grid whose coefficient of variation exceeds a preset threshold as abnormal regions, and obtaining an abnormal region set; Obtaining the take-off and landing points of the UAV by performing spatial superposition processing on the abnormal areas where the abnormal areas are concentrated; The coefficient of variation of the subclass grids in the two-dimensional grid is calculated by performing curve compression and clustering processing on the two-dimensional grid, specifically: Obtaining the normalized vegetation index of the subclass grid from the two-dimensional grid to obtain initial time series data; performing curve compression on the initial time series data to obtain first time series data; connecting the first point and the last point of a first set of time series data points in the first time series data into a straight line to obtain a target straight line; calculating the distance from all points in the first set of time series data points to the target straight line to obtain a distance set; and eliminating the target data of the initial time series data based on the maximum value in the distance set to obtain a thinned-out second time series data; The subclass grids in the second time series data are controlled to cluster with the subclass grids within a preset radius to obtain a clustering result; based on the values of the preset time points in the clustering result, several categories of data in the clustering result are spatially clustered to obtain a comprehensive subclass grid set; and the coefficient of variation of the comprehensive subclass grid set in the two-dimensional grid is obtained by calculating the degree of discreteness of the data distribution of the comprehensive subclass grid set.

7. A storage medium, characterized in that: The storage medium stores a computer program, which is called and executed by a computer to implement a drone-based ecological restoration project supervision method as described in any one of claims 1 to 5 above.

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