Soil and water conservation monitoring method and system based on unmanned aerial vehicle remote sensing

Abstract

The application provides a soil and water conservation monitoring method and system based on unmanned aerial vehicle remote sensing, and relates to the technical field of image analysis. First, multispectral image data and laser point cloud data are collected to generate a first vegetation index change map and construct a digital surface model, and the digital surface model is used to obtain terrain change point cloud data. Then, the change map and the point cloud data are spatially superimposed and analyzed to obtain a spatially coincident region, and vegetation change map patches and terrain deformation map patches that meet a first preset condition in the spatially coincident region are extracted to obtain a potential erosion area. Subsequently, according to the three-dimensional point cloud features, an active erosion area is segmented from the terrain change point cloud data, and the soil loss amount is calculated according to the three-dimensional point cloud data. Finally, according to the spatial distribution information of the active erosion area and the soil loss amount, a soil and water conservation monitoring result is generated. The technical scheme provided by the application improves the accuracy and early warning capability of soil and water loss monitoring in complex slope surface scenarios.

Description

Technical Field

[0001] This application relates to the field of image analysis technology, and in particular to a method and system for monitoring soil and water conservation based on unmanned aerial vehicle (UAV) remote sensing. Background Technology

[0002] With increasingly refined requirements for the supervision of soil and water conservation in production and construction projects and natural slopes, the urgent need for current monitoring work is to quickly and accurately grasp the soil erosion dynamics of specific slopes, especially areas with historical erosion gullies.

[0003] Currently, the technical solution adopted is to use drones to acquire optical images and laser point cloud data of the monitoring area. This solution first analyzes the multispectral images and delineates the vegetation degradation range by comparing vegetation indices at different times. At the same time, the laser point cloud data is processed to generate a digital elevation model, and the surface deformation area is extracted by comparing it with historical models. Finally, the vegetation degradation range and the surface deformation range are directly spatially superimposed, and the overlapping area is identified as the soil erosion area. Based on the point cloud changes in this area, the amount of soil loss is estimated.

[0004] However, in practical applications, especially in complex slope scenarios with gully development, the accuracy and reliability of the monitoring results of this existing scheme are significantly limited. Because it does not fully consider the differences in mechanisms and spatiotemporal consistency between multispectral data and laser point cloud data in characterizing surface changes, simple overlay analysis is prone to misjudgment, such as misclassifying vegetation changes caused by crop harvesting as degradation caused by erosion. More importantly, this scheme fails to incorporate the historical erosion patterns and evolutionary laws of the monitored area as key prior knowledge into the analysis process, resulting in a lack of historical continuity constraints in the judgment of erosion activity, insensitivity to the identification of newly formed gullies, and difficulty in accurately assessing the activation level and development trend of existing gullies. Summary of the Invention

[0005] This application provides a soil and water conservation monitoring method and system based on UAV remote sensing, which solves the problems of poor accuracy in monitoring erosion on complex slopes caused by the coarse multi-source data fusion analysis and insufficient utilization of historical erosion prior information in the existing technology.

[0006] Firstly, this application provides a method for monitoring soil and water conservation based on unmanned aerial vehicle (UAV) remote sensing, including:

[0007] Based on the historical erosion gully distribution map of the target monitoring slope, a three-dimensional flight path of the UAV is generated to control the UAV to collect multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path;

[0008] Based on the multispectral image data, a first vegetation index change map of the target monitored slope is generated;

[0009] Based on the laser point cloud data, a digital surface model of the target monitoring slope is constructed, and the digital surface model is compared with a preset benchmark digital surface model to obtain topographic change point cloud data.

[0010] The first vegetation index change map and the topographic change point cloud data are spatially overlaid to obtain the spatially overlapping area. Vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area are extracted to obtain the potential erosion area.

[0011] Based on the three-dimensional point cloud features corresponding to the potential erosion area, the active erosion area is segmented from the topographic change point cloud data, and the soil loss is calculated based on the three-dimensional point cloud data of the active erosion area.

[0012] Based on the spatial distribution information of the active erosion area and the amount of soil loss, water and soil conservation monitoring results are generated.

[0013] Optionally, based on the multispectral image data, a first vegetation index change map of the target monitored slope is generated, including:

[0014] The first multispectral image and the second multispectral image of the target monitoring slope were collected at the first preset time point and the second preset time point, respectively.

[0015] Based on the red and near-infrared reflectance data in the first and second multispectral images, the first vegetation index distribution map of the target monitoring slope at the first preset time point and the second vegetation index distribution map at the second preset time point are calculated respectively.

[0016] The target monitoring slope is divided into multiple analysis units, and the dominant slope direction of each analysis unit is determined according to the slope area covered by the three-dimensional flight path.

[0017] Light condition compensation is performed on the vegetation index values ​​of the first vegetation index distribution map and the second vegetation index distribution map within the analysis unit. After the light condition compensation is completed, the vegetation index difference between the second preset time point and the first preset time point is calculated for each analysis unit.

[0018] The historical erosion gully distribution map of the target monitored slope is overlaid with the analysis unit to obtain the second analysis unit;

[0019] The vegetation index difference of the second analysis unit is adjusted by a preset first adjustment coefficient to generate a first vegetation index change map.

[0020] Optionally, based on the laser point cloud data, a digital surface model of the target monitoring slope is constructed, including:

[0021] The laser point cloud data is classified to obtain a ground point cloud subset and a non-ground point cloud subset;

[0022] Based on the flight angle of each strip in the three-dimensional flight path, calculate the point cloud density weight of each target point in the ground point cloud subset;

[0023] Based on the three-dimensional coordinates of each target point in the ground point cloud subset and the corresponding point cloud density weights, an initial triangular mesh model is constructed in the area where the target monitoring slope is located.

[0024] Identify the triangular facets in the initial triangular network model that spatially overlap with the historical erosion gully distribution map of the target monitored slope, and use them as facets to be processed.

[0025] Based on the direction of the erosion gullies in the historical erosion gully distribution map, the point cloud density weight of the target points contained in the surface to be processed is adjusted, and the surface to be processed is reconstructed using the adjusted point cloud density weight to obtain the target surface.

[0026] The target facet is merged with the triangular facets in the initial triangular mesh model, excluding the facet to be processed, to obtain a digital surface model.

[0027] Optionally, vegetation change patches and topographic deformation patches that meet the first preset conditions are extracted within the spatially overlapping area to obtain potential erosion areas, including:

[0028] Within the overlapping spatial region, multiple candidate sub-regions are determined;

[0029] The vegetation index change value corresponding to each candidate sub-region is obtained from the first vegetation index change map, and the terrain deformation value corresponding to each candidate sub-region is obtained from the terrain change point cloud data.

[0030] Candidate sub-regions whose vegetation index change values ​​are less than a first preset threshold are marked as vegetation degradation sub-regions;

[0031] Candidate sub-regions whose absolute values ​​of terrain deformation are greater than a second preset threshold are marked as surface deformation sub-regions;

[0032] Identify candidate sub-regions that are simultaneously marked as vegetation degradation sub-regions and surface deformation sub-regions as alternative erosion sub-regions;

[0033] Within the spatial range covered by the historical erosion gully distribution map, the candidate erosion sub-regions are identified as potential erosion areas;

[0034] Within the spatial range not covered by the historical erosion gully distribution map, potential erosion areas are determined from the candidate erosion sub-regions based on the consistency of the change direction of the vegetation index value and the topographic deformation value in the candidate erosion sub-regions.

[0035] Optionally, based on the three-dimensional point cloud features corresponding to the potential erosion area, active erosion areas are segmented from the terrain change point cloud data, including:

[0036] Points located within the potential erosion area are extracted from the topographic change point cloud data to form a first point cloud set;

[0037] Calculate multiple feature parameters for each terrain change point in the first point cloud set. The multiple feature parameters include the tilt angle of the normal vector of the terrain change point, the point density within a preset range around the terrain change point, and the elevation change gradient of the terrain change point along the target monitoring slope direction.

[0038] Based on the multiple feature parameters, the terrain change points in the first point cloud set are determined, and the points whose normal vector tilt angle is greater than a preset tilt angle threshold, whose point density is less than a preset density threshold, and whose elevation change gradient is greater than a first preset gradient threshold are determined as erosion feature points.

[0039] Cluster the erosion feature points, and aggregate the erosion feature points whose three-dimensional spatial distance is less than a preset distance threshold into a point cloud cluster;

[0040] From all point cloud clusters, select those containing more than a preset threshold number of terrain change points as candidate erosion clusters;

[0041] The candidate erosion clusters located within the buffer zone of the gullies marked on the historical erosion gully distribution map are marked as active erosion areas;

[0042] From the remaining candidate erosion clusters, calculate the average elevation change gradient of all points within each candidate erosion cluster, and mark the candidate erosion clusters whose average elevation change gradient is greater than the second preset gradient threshold as active erosion areas.

[0043] Optionally, the soil loss is calculated based on the three-dimensional point cloud data of the active erosion area, including:

[0044] Based on the three-dimensional point cloud data of the active erosion area, a current terrain surface model of the active erosion area is generated;

[0045] Obtain the target baseline terrain surface model corresponding to the active erosion area;

[0046] The current terrain surface model is compared with the target reference terrain surface model to obtain the elevation change value of each sampling point in the active erosion area;

[0047] Based on the elevation change values, calculate the preliminary soil loss volume in the active erosion area;

[0048] Obtain the slope direction corresponding to the active erosion area, and determine the volume correction coefficient based on the slope direction;

[0049] The initial soil loss volume is corrected using the volume correction factor to obtain the intermediate soil loss volume;

[0050] Obtain the historical erosion intensity level of the active erosion area, and determine the soil bulk density parameter based on the historical erosion intensity level;

[0051] The amount of soil loss in the active erosion area is calculated based on the intermediate soil loss volume and the soil bulk density parameter.

[0052] Optionally, based on the spatial distribution information of the active erosion area and the soil loss, a soil and water conservation monitoring result is generated, including:

[0053] The soil loss amount of each active erosion area is obtained, and the active erosion area is divided into multiple loss levels according to a preset loss amount threshold range;

[0054] From the active erosion areas of the multiple erosion levels, the erosion level of the active erosion areas whose spatial location is within a preset range of the gully extension direction marked by the historical erosion gully distribution map is upgraded by one level;

[0055] Based on the center point coordinates of each active erosion area, the relative position of each active erosion area on the target monitoring slope is calculated. Based on the relative position, the active erosion areas after upgrading are sorted to generate a regional distribution sequence.

[0056] Based on the regional distribution sequence, active erosion areas of corresponding levels and locations are marked on the digital surface model of the target monitored slope, generating a soil erosion level distribution map.

[0057] The soil loss in each active erosion area is integrated with the soil and water loss level distribution map to output the soil and water conservation monitoring results.

[0058] Secondly, this application provides a soil and water conservation monitoring system based on unmanned aerial vehicle (UAV) remote sensing, comprising:

[0059] The acquisition module is used to generate a three-dimensional flight path of the UAV based on the historical erosion gully distribution map of the target monitoring slope, so as to control the UAV to acquire multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path;

[0060] The first calculation module is used to generate a first vegetation index change map of the target monitored slope based on the multispectral image data.

[0061] The construction module is used to construct a digital surface model of the target monitoring slope based on the laser point cloud data, and compare the digital surface model with a preset benchmark digital surface model to obtain topographic change point cloud data.

[0062] The analysis module is used to perform spatial overlay analysis on the first vegetation index change map and the topographic change point cloud data to obtain the spatially overlapping area, and extract the vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area to obtain the potential erosion area.

[0063] The second calculation module is used to segment active erosion areas from the topographic change point cloud data based on the three-dimensional point cloud features corresponding to the potential erosion areas, and to calculate soil loss based on the three-dimensional point cloud data of the active erosion areas.

[0064] The generation module is used to generate soil and water conservation monitoring results based on the spatial distribution information of the active erosion area and the amount of soil loss.

[0065] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a soil and water conservation monitoring method based on UAV remote sensing as described in the first aspect above.

[0066] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a soil and water conservation monitoring method based on UAV remote sensing as described in the first aspect.

[0067] This application plans a three-dimensional flight path by combining the historical erosion gully distribution information of the target monitoring slope, making the subsequently acquired multispectral image data and laser point cloud data more spatially targeted, and providing a precise data foundation for the deep fusion of multi-source data. On this basis, by spatially overlaying and extracting conditions from the first vegetation index change map and topographic change point cloud data, a strict spatial correlation and screening of vegetation degradation and surface deformation is achieved, effectively overcoming the spatiotemporal matching error caused by direct overlay of different data sources, thereby more accurately locating potential erosion areas.

[0068] Furthermore, throughout the entire process of identifying potential erosion areas, segmenting active erosion areas, and generating monitoring results, the distribution of historical erosion gullies was introduced as a key constraint and correction basis. This approach deeply integrates prior knowledge into the automated analysis process, enabling the identification of erosion areas to not only rely on instantaneous changes but also consider their intrinsic connection with historical geomorphological development. This significantly enhances the ability to identify the development trend of erosion activities, especially the reactivation of historical gullies and signs of new erosion. The final monitoring results integrate soil loss and water loss level distribution maps, providing results that combine quantitative assessment and spatial visualization features. Therefore, this application effectively improves the accuracy and early warning capability of soil and water loss monitoring in complex slope scenarios.

[0069] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 A flowchart of a soil and water conservation monitoring method based on UAV remote sensing provided in this application is shown;

[0072] Figure 2 This paper shows a schematic diagram of the structure of a soil and water conservation monitoring system based on UAV remote sensing provided in this application;

[0073] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0074] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0075] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0076] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] Figure 1 This application provides a flowchart of a soil and water conservation monitoring method based on UAV remote sensing, such as... Figure 1 As shown, the method includes:

[0078] Step 101: Based on the historical erosion gully distribution map of the target monitoring slope, generate a three-dimensional flight path for the UAV, and control the UAV to collect multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path.

[0079] In this step, the target monitoring slope refers to the specific slope area where soil and water conservation monitoring needs to be implemented. Its boundaries are delineated through on-site surveys and topographic maps to clarify the spatial scope for UAV data collection and subsequent analysis. This is obtained through on-site reconnaissance combined with analysis using a high-precision digital elevation model. The distribution of historical erosion gullies refers to the historical morphology and location information of existing erosion gullies on the monitoring slope. This information is usually presented in the form of vector map interpretation results and is used to guide UAVs in conducting focused monitoring. The three-dimensional flight path refers to the flight path of the UAV, which consists of a series of continuous location points in three-dimensional space. This path is used to control the UAV to fly at a specific altitude, angle, and coverage mode to comprehensively collect slope data. Multispectral image data refers to image data containing reflectance information of multiple specific wavelength bands, such as red, green, and near-infrared, which is used to analyze the surface vegetation cover and its changes.

[0080] Laser point cloud data refers to a set of discrete points with three-dimensional coordinates X, Y, Z and echo intensity information, which is used to construct high-precision digital terrain surface models.

[0081] In this step, the historical erosion gully distribution map of the target monitoring slope is first obtained. This map, in the form of vector polygons or line features, identifies the historical locations and approximate shapes of known erosion gullies on the monitoring slope. Next, the historical erosion gully distribution map, the three-dimensional boundary of the target monitoring slope, and the UAV's flight performance parameters (such as maximum flight time and minimum turning radius) and sensor parameters (such as the camera's field of view and the lidar's scanning angle) are input into the flight path planning algorithm. Based on the direction and density of the historical erosion gullies, the algorithm prioritizes planning denser flight strips over these key areas, ensuring that the lateral overlap between flight strips reaches a preset value (e.g., 80%) to guarantee data continuity and three-dimensional coverage. Simultaneously, the algorithm calculates a specific flight altitude and attitude angle for each flight strip, enabling the UAV to adjust its attitude to ensure the sensor lens faces the slope as directly as possible when flying over gully areas with significant slope changes. This generates a three-dimensional flight path file that covers the entire target monitoring slope and focuses on high-precision data acquisition of historical erosion gullies.

[0082] Next, the UAV flight control system loads and executes this 3D flight path file, controlling the UAV to take off autonomously and fly along the preset path. During flight, the onboard multispectral camera takes pictures at set time intervals or locations, acquiring multispectral image data covering the entire slope with information from multiple spectral bands. Simultaneously, the onboard lidar rotates and scans at a set scanning frequency, emitting laser pulses towards the slope and receiving their echoes. By measuring the pulse round-trip time, the distance is calculated. Combined with the UAV's real-time position and attitude information, a large number of high-precision 3D spatial points are calculated, thus forming laser point cloud data. Precise timestamps and location tags are recorded for both types of data, ensuring the spatiotemporal synchronization between the data and laying the foundation for subsequent collaborative analysis.

[0083] Step 102: Based on the multispectral image data, generate a first vegetation index change map of the target monitoring slope.

[0084] Optionally, step 102 may specifically include:

[0085] Step 1021: Collect the first multispectral image and the second multispectral image of the target monitoring slope at the first preset time point and the second preset time point, respectively.

[0086] Step 1022: Based on the red light band and near-infrared band reflectance data in the first multispectral image and the second multispectral image, calculate the first vegetation index distribution map of the target monitoring slope at the first preset time point and the second vegetation index distribution map at the second preset time point.

[0087] Step 1023: Divide the target monitoring slope into multiple analysis units, and determine the dominant slope direction of each analysis unit according to the slope area covered by the three-dimensional flight path.

[0088] Step 1024: Perform light condition compensation on the vegetation index values ​​of the first vegetation index distribution map and the second vegetation index distribution map within the analysis unit. After completing the light condition compensation, calculate the vegetation index difference between the second preset time point and the first preset time point for each analysis unit.

[0089] Step 1025: Overlay the historical erosion gully distribution map of the target monitored slope with the analysis unit to obtain the second analysis unit.

[0090] Step 1026: Adjust the vegetation index difference of the second analysis unit by a preset first adjustment coefficient to generate a first vegetation index change map.

[0091] In this step, the first vegetation index change map refers to a spatial distribution map used to represent the relative change in vegetation growth on the target monitoring slope between two different time points, visually displaying areas where vegetation degradation may occur; the first preset time point and the second preset time point refer to two specific times selected in advance for UAV data collection, with the second preset time point being later than the first preset time point, used to acquire multispectral image data reflecting changes in vegetation growth before and after; the first multispectral image and the second multispectral image refer to the target monitoring slope image data containing multiple band information collected by the UAV at the first preset time point and the second preset time point, respectively; the red light band and near-infrared band reflectance data refer to the intensity values ​​of solar radiation reflected from the ground surface extracted from the multispectral image, corresponding to the red visible light and near-infrared invisible light bands, respectively, and are the core data for calculating commonly used vegetation indices, obtained by reading the pixel values ​​of the corresponding bands in the multispectral image.

[0092] The first and second vegetation index distribution maps refer to spatial distribution maps reflecting the vegetation growth status of the target monitoring slope at the first and second preset time points, respectively, calculated using the first and second multispectral images. They are used to quantify the vegetation density at each location. An analysis unit refers to a basic grid of equal area into which the target monitoring slope is divided, used for local statistics and calculations. It is obtained by regularly gridding the entire slope area. The dominant slope aspect refers to the average tilt direction of the slope area represented by each analysis unit, used to assess the impact of topography on lighting conditions. It is calculated by analyzing the digital elevation model data corresponding to the analysis unit. The second analysis unit refers to the analysis units located within the historical erosion gully area selected after spatially comparing all analysis units with the historical erosion gully distribution map. It is used for special post-processing, obtained through spatial overlay analysis and conditional selection. The preset first adjustment coefficient is a value greater than zero, pre-set based on experience or experimentation. It is used to amplify and correct the vegetation change calculation results of analysis units falling within the historical erosion gully area, enhancing sensitivity to potential erosion signals. It is obtained through historical data analysis and model calibration.

[0093] In this step, the drone is first invoked to collect image data at a first preset time point to obtain a first multispectral image, and then collected again at a second preset time point to obtain a second multispectral image. Next, the first multispectral image is processed to extract reflectance data in the red band and near-infrared band. Using these two reflectance values, a vegetation index is calculated according to the definition formula: the reflectance value of the near-infrared band is subtracted from the reflectance value of the red band to obtain the difference. This difference is then divided by the sum of the reflectance values ​​of the near-infrared band and the red band, thus obtaining a first vegetation index distribution map of the target monitoring slope at the first preset time point. The value of each pixel in the first vegetation index distribution map represents the vegetation index at that location. The second multispectral image is calculated using the same formula and processing procedure to obtain a second vegetation index distribution map at the second preset time point.

[0094] Next, the map area of ​​the target monitoring slope is evenly divided into many small square grids, which are the analysis units. Then, based on the slope area covered by the 3D flight path and the high-precision terrain data of the slope area obtained in advance, the main tilt direction of the plot represented by each analysis unit is calculated, that is, the dominant slope aspect. Then, for each analysis unit, considering that the calculated vegetation index value may be affected by light and shadow at the first and second preset time points due to the difference in solar altitude angle and azimuth angle, as well as the influence of its own dominant slope aspect, it is necessary to perform a mathematical compensation adjustment on the vegetation index values ​​in the first and second vegetation index distribution maps within the unit according to the dominant slope aspect of the analysis unit and the solar position at the two shooting time points, so as to reduce the error caused by terrain shadows. After completing the light condition compensation for all analysis units, the vegetation index difference of each analysis unit is calculated by subtracting the compensated value in the first vegetation index map from the compensated value in the second vegetation index distribution map.

[0095] Then, the historical erosion gully distribution map of the target monitoring slope is overlaid with all the pre-divided analysis units to identify those analysis units whose geographical locations are completely or partially within the historical erosion gully range. These special units are marked as second analysis units. Finally, the vegetation index differences of all analysis units are processed. For special units marked as second analysis units, their vegetation index differences are multiplied by a preset first adjustment coefficient, which is usually greater than 1, to amplify their variation. For other ordinary analysis units, their vegetation index differences are kept unchanged. Finally, all the adjusted or unchanged vegetation index differences are rearranged and rendered according to their corresponding analysis unit spatial locations to generate a complete first vegetation index variation map.

[0096] Step 103: Based on the laser point cloud data, construct a digital surface model of the target monitoring slope, and compare the digital surface model with a preset benchmark digital surface model to obtain terrain change point cloud data.

[0097] Optionally, step 103 may specifically include:

[0098] Step 1031: Classify the laser point cloud data to obtain a ground point cloud subset and a non-ground point cloud subset.

[0099] Step 1032: Calculate the point cloud density weight of each target point in the ground point cloud subset based on the flight angle of each flight strip in the three-dimensional flight path.

[0100] Step 1033: Based on the three-dimensional coordinates of each target point in the ground point cloud subset and the corresponding point cloud density weights, construct an initial triangular network model in the area where the target monitoring slope is located.

[0101] Step 1034: Identify the triangular patches in the initial triangular network model that spatially overlap with the historical erosion gully distribution map of the target monitored slope, and use them as patches to be processed.

[0102] Step 1035: Based on the direction of the erosion gullies in the historical erosion gully distribution map, adjust the point cloud density weight of the target points contained in the surface to be processed, and use the adjusted point cloud density weight to reconstruct the surface of the surface to be processed to obtain the target surface.

[0103] Step 1036: Merge the target facet with the triangular facets in the initial triangular mesh model, excluding the facet to be processed, to obtain a digital surface model.

[0104] In this step, the digital surface model refers to a three-dimensional surface model composed of a large number of continuous triangular facets that can accurately represent the undulation of the target monitoring slope. It is used to calculate elevation, slope, and subsequent terrain change analysis. The preset benchmark digital surface model refers to a digital surface model of the same target monitoring slope established at an earlier time, used as a comparison benchmark to detect and quantify changes in the current terrain relative to its historical state. The terrain change point cloud data refers to the set of three-dimensional spatial points that record significant changes in surface elevation, identified by comparing the current digital surface model with the preset benchmark digital surface model. It is used to indicate specific locations where erosion or deposition may occur. The ground point cloud subset refers to the set of three-dimensional points separated from the original laser point cloud data that are considered to represent the real ground. The non-ground point cloud subset refers to the set of three-dimensional points separated from the original laser point cloud data that represent objects above the ground, such as vegetation and buildings. In the construction of surface models, points are usually discarded; flight angle refers to the angle between the flight direction of each flight strip of the UAV and the horizontal plane when executing a three-dimensional flight path, used to evaluate the geometric quality of point cloud data acquisition; point cloud density weight refers to a value assigned to each target point in the ground point cloud subset, used to reflect the density and reliability of the point cloud data at that location, and the higher the weight, the greater its proportion in the modeling; the initial triangular mesh model refers to the initial three-dimensional curved surface mesh formed by using the target points in the ground point cloud subset as vertices and connecting adjacent vertices to form triangular patches, which is the prototype of the digital surface model; the patches to be processed refer to those triangular patches in the initial triangular mesh model whose spatial location intersects with the distribution range of historical erosion gullies, which need to be specially optimized and identified through spatial location comparison; the target patch refers to the optimized triangular patch obtained after adjusting the vertex weights and recalculating the surface shape of the identified patches to be processed.

[0105] In this step, a point cloud classification algorithm is first run on the acquired laser point cloud data. Based on the characteristics of the points, such as height, density and the relationship between adjacent points, all laser points are automatically divided into two parts: one part is the ground point cloud subset that falls directly on the ground, and the other part is the non-ground point cloud subset that falls on vegetation, crops and other ground features. Next, the generated three-dimensional flight path is analyzed to obtain the flight angle of each flight strip.

[0106] Then, for each target point in the ground point cloud subset, a point cloud density weight is calculated based on the original point cloud distribution density at its location and the flight angle of the flight strip where the target point is located (e.g., a more inclined flight strip may cause uneven point density). Next, using the three-dimensional coordinates of all target points in the ground point cloud subset and the calculated point cloud density weight, a triangulation algorithm that considers point weights is run in the area where the target monitoring slope is located. When connecting points to form triangular patches, this triangulation algorithm will prioritize connecting points with higher point cloud density weights, thereby constructing an initial triangular mesh model.

[0107] Next, the historical erosion gully distribution map of the target monitoring slope is obtained, and it is compared with the spatial position of each triangular facet in the initial triangular network model one by one. Those triangular facests whose positions fall completely or partially within the distribution range of historical erosion gullies are marked. These marked triangular facests are the facests to be processed.

[0108] Subsequently, for each marked facet to be processed, the main direction of the erosion gully at that location is consulted in the historical erosion gully distribution map. Then, based on this direction, the point cloud density weights of several vertices belonging to this facet to be processed, i.e., the target points, are adjusted. The specific adjustment rule is that the weights of points along the direction of the erosion gully are appropriately increased to enhance the detail representation of the initial triangular mesh model in that direction. Using the adjusted new weights, a surface fitting algorithm is run only on the local area covered by these facets to be processed to recalculate and generate smoother triangular facets that better conform to the gully morphology. These newly generated facets are the target facets.

[0109] Finally, all the target faces obtained are combined with the triangular faces in the initial triangular mesh model that were not marked as faces to be processed by the model merging algorithm to form a seamless and continuous overall surface. This final surface is the required digital surface model.

[0110] Step 104: Spatially overlay the first vegetation index change map with the topographic change point cloud data to obtain the spatially overlapping area, and extract the vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area to obtain the potential erosion area.

[0111] Optionally, step 104 may specifically include:

[0112] Step 1041: Within the spatially overlapping area, determine multiple candidate sub-regions.

[0113] Step 1042: Obtain the vegetation index change value corresponding to each candidate sub-region from the first vegetation index change map, and obtain the terrain deformation value corresponding to each candidate sub-region from the terrain change point cloud data.

[0114] Step 1043: The candidate sub-regions whose vegetation index change values ​​are less than the first preset threshold are marked as vegetation degradation sub-regions.

[0115] Step 1044: Candidate sub-regions whose absolute values ​​of terrain deformation are greater than the second preset threshold are marked as surface deformation sub-regions.

[0116] Step 1045: Identify candidate sub-regions that are simultaneously marked as vegetation degradation sub-regions and surface deformation sub-regions as alternative erosion sub-regions.

[0117] Step 1046: Within the spatial range covered by the historical erosion gully distribution map, the candidate erosion sub-regions are identified as potential erosion areas.

[0118] Step 1047: Within the spatial range not covered by the historical erosion gully distribution map, potential erosion areas are determined from the candidate erosion sub-regions based on the consistency of the change direction of the vegetation index value and the topographic deformation value in the candidate erosion sub-regions.

[0119] In this step, the spatially overlapping area refers to the geographical area covered by the first vegetation index change map and the geographical area covered by the topographic change point cloud data, the part that overlaps with each other on the horizontal ground. This area is used to define the common space where vegetation change and topographic change need to be analyzed collaboratively. The first preset condition refers to the dual standard used to determine whether a local area simultaneously exhibits significant vegetation degradation and surface deformation. This standard is used to screen high-risk erosion candidate areas from the spatially overlapping area and is defined by setting vegetation index change thresholds and topographic deformation thresholds. The vegetation change patch refers to the planar area formed by continuous pixels in the first vegetation index change map whose vegetation index change values ​​reach or exceed a certain significance level. This area is used to visualize vegetation changes. The terms "significant change" and "potential erosion zone" refer to the geographical areas that have undergone significant changes on the land surface. "Potential erosion zone" refers to the geographical areas that meet both vegetation degradation and surface deformation conditions, as initially determined through collaborative analysis, and require further verification. "Candidate sub-region" refers to multiple regular grid units formed by dividing spatially overlapping areas into fixed-size units; these are the basic units for local statistics and judgment. "Vegetation index change value" refers to the average value of all pixels within a candidate sub-region in the first vegetation index change map, used to quantify vegetation change in that area. The overall degree of vegetation degradation; the topographic deformation value refers to the average elevation change value of all points in a candidate sub-region in the topographic change point cloud data, used to quantify the overall magnitude of surface deformation in that region; the first preset threshold is a pre-set critical value used to determine whether vegetation has significantly degraded. When the vegetation index change value is lower than this threshold, the vegetation in that region is considered degraded, determined through historical data analysis and experiments; a vegetation degradation sub-region refers to a candidate sub-region where the vegetation index change value is less than the first preset threshold, indicating that the vegetation cover in that region has significantly deteriorated during the monitoring period; the second preset threshold is a pre-set critical value used to determine whether the surface has significantly deformed. When the absolute value of the topographic deformation value is greater than this threshold, the vegetation in that region is considered degraded. Significant changes in the land surface were determined through historical data analysis and experiments. A surface deformation sub-region refers to a candidate sub-region whose absolute value of topographic deformation exceeds a second preset threshold, indicating a significant decrease or increase in surface elevation during the monitoring period. A candidate erosion sub-region is one that simultaneously meets both vegetation degradation and surface deformation criteria; it is a candidate sub-region marked as both a vegetation degradation sub-region and a surface deformation sub-region, indicating that the area is a high-risk area for soil erosion. Consistency of change direction refers to the phenomenon where, within a candidate erosion sub-region, the direction of vegetation degradation indicated by the vegetation index change value matches the direction of surface subsidence indicated by the topographic deformation value. This is used to assist in assessing the likelihood of erosion in areas without historical erosion gully records.

[0120] In this step, the spatial analysis tools in the geographic information system are first used to perform an intersection operation on the coverage of the first vegetation index change map and the horizontal projection range of the topographic change point cloud data to obtain the spatially overlapping area. Then, within this spatially overlapping area, a spatial grid partitioning algorithm is run to regularly divide it into multiple square grids of equal area, and each grid is a candidate sub-region.

[0121] Secondly, for each divided candidate sub-region, it is spatially aligned with the first vegetation index change map. Then, the average value of the first vegetation index change image pixels contained within the boundary of the candidate sub-region is calculated. This average value is the vegetation index change value corresponding to the candidate sub-region. At the same time, the candidate sub-region is spatially aligned with the terrain change point cloud data, and all terrain change points whose horizontal coordinates fall within the range of the candidate sub-region are extracted. The average value of the elevation change values ​​of these terrain change points is calculated. This average value is the terrain deformation value corresponding to the candidate sub-region. Next, the calculated vegetation index change value of each candidate sub-region is compared with a pre-set first preset threshold that represents a critical value for significant vegetation degradation. All candidate sub-regions whose vegetation index change values ​​are less than this first preset threshold are marked as vegetation degradation sub-regions in their attributes.

[0122] Then, the topographic deformation value of each candidate sub-region is compared with a pre-set second preset threshold that represents the critical value of significant surface deformation. It should be noted that the absolute value of the topographic deformation value is compared here. Candidate sub-regions whose absolute values ​​of topographic deformation values ​​are greater than this second preset threshold are marked as surface deformation sub-regions in their attributes.

[0123] Furthermore, the labeling attributes of all candidate sub-regions are traversed and checked to identify those candidate sub-regions that are simultaneously labeled as vegetation degradation sub-regions and surface deformation sub-regions. All candidate sub-regions that meet this dual condition are extracted separately as candidate erosion sub-regions. Then, the historical erosion gully distribution map of the target monitoring slope is obtained, and the spatial location of all candidate erosion sub-regions is superimposed and compared with the range of the historical erosion gully distribution map. Candidate erosion sub-regions whose geographical location falls entirely within the gully range marked by the historical erosion gully distribution map are selected. Since they are located in the known high-risk erosion zone, they are directly identified as potential erosion areas.

[0124] Finally, for those candidate erosion sub-regions whose spatial location is not covered by the historical erosion gully distribution map, further discrimination is needed to check whether the change direction of vegetation index value and topographic deformation value in these regions is consistent. Specifically, only when the change value of vegetation index is negative, indicating vegetation degradation, and the topographic deformation value is also negative, indicating surface subsidence, is it considered that the change direction is consistent and meets the physical law of erosion. Candidate erosion sub-regions that meet this consistency of change direction are supplemented and identified as potential erosion areas, and finally the set of potential erosion areas is obtained.

[0125] Step 105: Based on the three-dimensional point cloud features corresponding to the potential erosion area, segment the active erosion area from the topographic change point cloud data, and calculate the soil loss based on the three-dimensional point cloud data of the active erosion area.

[0126] Optionally, step 105 may specifically include:

[0127] Step 1051: Extract points located within the potential erosion area from the topographic change point cloud data to form a first point cloud set.

[0128] Step 1052: Calculate multiple feature parameters for each terrain change point in the first point cloud set. The multiple feature parameters include the tilt angle of the normal vector of the terrain change point, the point density within a preset range around the terrain change point, and the elevation change gradient of the terrain change point along the target monitoring slope direction.

[0129] Step 1053: Determine the terrain change points in the first point cloud set according to the multiple feature parameters, and determine the points whose normal vector tilt angle is greater than a preset tilt angle threshold, whose point density is less than a preset density threshold, and whose elevation change gradient is greater than a first preset gradient threshold as erosion feature points.

[0130] Step 1054: Cluster the erosion feature points and aggregate the erosion feature points whose three-dimensional spatial distance is less than a preset distance threshold into a point cloud cluster.

[0131] Step 1055: Select point cloud clusters from all point cloud clusters whose number of terrain change points is greater than a preset threshold as candidate erosion clusters.

[0132] Step 1056: The candidate erosion clusters located in the buffer zone of the gullies marked by the historical erosion gully distribution map are marked as active erosion areas.

[0133] Step 1057: Calculate the average elevation change gradient of all points within each candidate erosion cluster from the remaining candidate erosion clusters, and mark the candidate erosion clusters with an average elevation change gradient greater than the second preset gradient threshold as active erosion areas.

[0134] Step 1058: Based on the three-dimensional point cloud data of the active erosion area, generate the current terrain surface model of the active erosion area.

[0135] Step 1059: Obtain the target reference terrain surface model corresponding to the active erosion area; compare the current terrain surface model with the target reference terrain surface model to obtain the elevation change value of each sampling point in the active erosion area.

[0136] Step 10510: Calculate the preliminary soil loss volume of the active erosion area based on the elevation change value.

[0137] Step 10511: Obtain the slope direction corresponding to the active erosion area, and determine the volume correction coefficient based on the slope direction.

[0138] Step 10512: Correct the initial soil loss volume using the volume correction coefficient to obtain the intermediate soil loss volume.

[0139] Step 10513: Obtain the historical erosion intensity level of the active erosion area, and determine the soil bulk density parameter based on the historical erosion intensity level.

[0140] Step 10514: Calculate the soil loss in the active erosion area based on the intermediate soil loss volume and the soil bulk density parameter.

[0141] In this step, 3D point cloud features refer to attribute parameters extracted from topographic change point cloud data that can describe the local 3D geometric shape and intensity of change. They are used to finely distinguish between eroded and non-eroded areas and are obtained by calculating the spatial geometric attributes of each point.

[0142] Active erosion areas refer to specific spatial ranges currently experiencing strong soil erosion, further filtered from potential erosion areas, exhibiting significant 3D point cloud features and verified by historical information or change intensity. These areas are the direct targets for soil loss calculation. Soil loss refers to the mass of soil material lost from active erosion areas within a monitoring period, used to quantitatively assess the severity of soil erosion. The first point cloud set refers to the collection of all topographic change points whose spatial locations fall within the potential erosion area, filtered from the topographic change point cloud data. This serves as the input data for refined analysis. Multiple feature parameters refer to three specific values ​​calculated for each topographic change point in the first point cloud set, including the dip angle of the normal vector, the point density within a preset range, and the elevation. The gradient variation is used to characterize the local erosion features of a point; the inclination angle of the normal vector refers to the angle between the tilt direction of the small plane fitted through a topographic change point and its neighboring points and the vertical direction, reflecting the local steepness of the surface at that point; the point density within the preset range refers to the number of other topographic change points contained within a specified three-dimensional sphere radius centered on a topographic change point, reflecting the sparseness or density of the point cloud data at that location; the elevation change gradient refers to the rate of change of the elevation value of a topographic change point along the overall downhill direction of the target monitored slope, used to quantify the severity of surface changes along the slope aspect; the preset inclination angle threshold is a critical angle value used to determine whether the surface is steep enough, through analysis... The slope characteristics of typical erosion landforms are analyzed to determine the density threshold. A preset density threshold is a critical density value used to judge whether the point cloud is sufficiently sparse, potentially indicating surface damage; it is determined by analyzing the point cloud characteristics of typical erosion areas. A first preset gradient threshold is a critical gradient value used to judge whether the elevation drop along the slope direction is sufficiently drastic. Erosion feature points refer to topographic change points that simultaneously meet three conditions: a normal vector inclination angle greater than a preset inclination angle threshold, a point density less than a preset density threshold, and an elevation change gradient greater than the first preset gradient threshold. These are considered suspicious points representing active erosion. Candidate erosion clusters refer to point cloud clusters selected from all point cloud clusters that contain more than a preset number of erosion feature points; these are considered to be suspected erosion areas of a certain scale. A second preset gradient... The threshold is a critical average gradient value used to determine the overall erosion activity intensity of candidate erosion clusters when there are no historical erosion gullies as constraints. It is determined by analyzing the average change intensity of typical erosion areas. The current terrain surface model refers to a triangular mesh surface model that reflects the latest landform of an active erosion area, constructed based on 3D point cloud data, and is used for volume calculation. The target reference terrain surface model refers to a triangular mesh surface model that is completely corresponding to the spatial range of the active erosion area and is obtained by cutting a preset reference digital surface model, serving as a comparison benchmark for volume calculation. The elevation change value refers to the difference between the elevation of the current terrain surface model and the corresponding elevation of the target reference terrain surface model at each regular sampling point location within the active erosion area, and is used to calculate the volume change.The preliminary soil loss volume refers to the total estimated volume of soil loss obtained by summing up the elevation changes (negative values ​​indicate descent) of all sampling points within the active erosion area and multiplying this sum by the horizontal area represented by each sampling point. The volume correction factor is a multiplier used to correct the preliminary soil loss volume based on its topographical value. Its value is determined according to the overall aspect of the slope where the active erosion area is located, and it corrects for volume calculation errors caused by slope inclination. The intermediate soil loss volume is the topographically corrected soil loss volume obtained by multiplying the preliminary soil loss volume by the volume correction factor. The historical erosion intensity level refers to the classification of the location of the active erosion area based on long-term observation data or historical remote sensing interpretation, characterizing the intensity of its historical erosion activity (e.g., light, moderate, severe), used to determine the soil physical state. The soil bulk density parameter refers to the mass of a unit volume of naturally occurring dry soil including pores; it is a key parameter for converting soil loss volume into mass, and its value is related to the historical erosion intensity level.

[0143] In this step, the boundaries of the potential erosion area polygons are first read, and then the spatial range is queried in the terrain change point cloud data to extract all terrain change points whose horizontal coordinates fall within these polygons. These points constitute the first point cloud set.

[0144] Next, for each terrain change point in the first point cloud set, a series of geometric calculations are performed. First, the terrain change point and a certain number of neighboring points are selected. A small plane is fitted using the three-dimensional coordinates of these neighboring points. The normal vector of the plane is calculated, and the angle between the normal vector and the vertical upward direction is obtained to get the inclination angle of the normal vector of the terrain change point. Then, with the terrain change point as the center, a three-dimensional sphere radius is preset, and the total number of other terrain change points contained in the sphere is counted to obtain the point density within the preset range around the terrain change point.

[0145] Then, the downslope direction of the target monitoring slope is determined, and the rate of change of elevation value along this direction with horizontal distance is calculated, i.e., elevation change gradient. These three values ​​constitute multiple characteristic parameters of the terrain change point. Next, the dip angle, point density, and elevation change gradient of the normal vector calculated for each terrain change point are compared with preset dip angle threshold, density threshold, and first gradient threshold, respectively. Only when the dip angle of the normal vector of a terrain change point is greater than the preset dip angle threshold, its terrain change point density is less than the preset density threshold, and its elevation change gradient is greater than the first preset gradient threshold, is this terrain change point determined as an erosion feature point.

[0146] Furthermore, a spatial clustering algorithm is run on all identified erosion feature points to calculate the three-dimensional straight-line distance between any two erosion feature points. Points with a distance less than a preset distance threshold are grouped into the same group, and finally all erosion feature points are divided into several point cloud clusters. Then, the number of erosion feature points contained in each point cloud cluster is counted. Point cloud clusters with a number of erosion feature points less than a preset threshold and too small in size are removed, while point cloud clusters with a number of erosion feature points greater than the preset threshold are retained as candidate erosion clusters.

[0147] Next, a historical erosion gully distribution map of the target monitoring slope is obtained, and a buffer zone of a specific width is created for each historical gully marked on the map. The spatial range of all candidate erosion clusters is overlaid with these historical gully buffer zones for analysis. Candidate erosion clusters whose spatial location falls completely or partially within any historical gully buffer zone are directly marked as active erosion areas. Subsequently, for the remaining candidate erosion clusters that do not fall within any historical gully buffer zone, further judgment is required, and the average elevation change gradient of all original topographic change points within each such candidate erosion cluster is calculated. Candidate erosion clusters whose average elevation change gradient is greater than a second preset gradient threshold are also marked as active erosion areas. At this point, the active erosion areas are completely determined.

[0148] Next, for each marked active erosion area, all original or processed terrain change points within its boundary are extracted. Using the 3D coordinates of these original terrain change points, a triangulation algorithm is run to construct the current terrain surface model of that active erosion area. Simultaneously, a portion perfectly matching the spatial extent of each active erosion area is cropped from a pre-defined baseline digital surface model and used as the corresponding target baseline terrain surface model. The current terrain surface model and the target baseline terrain surface model are then precisely aligned spatially. A regular grid of sampling points is laid out on the model surface, and interpolation is performed to calculate the position of each sampling point in both models. The elevation values ​​are subtracted from the elevation values ​​to obtain the elevation change value for each sampling point. Then, the negative elevation change values ​​of all sampling points, representing the surface subsidence, are summed. This sum is multiplied by the horizontal ground area represented by a single sampling point to obtain the preliminary soil loss volume of the active erosion area. Next, the average slope direction of the overall slope of the active erosion area is determined, i.e., the slope direction. According to a pre-set lookup table that maps different slope directions to different coefficients, the corresponding volume correction coefficient is found. Then, this volume correction coefficient is multiplied by the preliminary soil loss volume to obtain the intermediate soil loss volume after topographic tilt correction.

[0149] Finally, based on the spatial location of the active erosion area, the historical erosion intensity level of the area is queried. According to a preset reference table that maps different historical erosion intensity levels to different soil bulk density parameters, the soil bulk density parameter used for the calculation of the active erosion area is determined. Finally, the calculated intermediate soil loss volume is multiplied by the determined soil bulk density parameter to obtain the soil loss amount of the active erosion area during this monitoring period.

[0150] Step 106: Generate soil and water conservation monitoring results based on the spatial distribution information of the active erosion area and the amount of soil loss.

[0151] Optionally, step 106 may specifically include:

[0152] Step 1061: Obtain the soil loss amount of each active erosion area, and divide the active erosion area into multiple loss levels according to the preset loss amount threshold range.

[0153] Step 1062: From the active erosion areas of the multiple erosion levels, the erosion level of the active erosion areas whose spatial location is within a preset range of the gully extension direction marked by the historical erosion gully distribution map is increased by one level.

[0154] Step 1063: Calculate the relative position of each active erosion area on the target monitoring slope based on the center point coordinates of each active erosion area, sort the active erosion areas after upgrading based on the relative positions, and generate a regional distribution sequence.

[0155] Step 1064: Based on the regional distribution sequence, mark the active erosion areas with corresponding levels and locations on the digital surface model of the target monitoring slope to generate a soil erosion level distribution map.

[0156] Step 1065: Integrate the soil loss amount of each active erosion area with the soil and water loss level distribution map, and output the soil and water conservation monitoring results.

[0157] In this step, spatial distribution information refers to the set of information describing the specific geographical locations of multiple active erosion areas on the target monitoring slope, including the boundary range and center point of each area, used to grasp the spatial pattern of erosion from a macroscopic perspective; soil and water conservation monitoring results refer to a comprehensive and visualized report on the soil and water loss status of the target monitoring slope during the monitoring period, used to guide governance decisions; the preset loss threshold range refers to a pre-defined range of values ​​used to divide continuously changing soil loss values ​​into different severity intervals, used to classify the erosion area, determined through historical data statistics; multiple loss levels refer to categories representing different degrees of soil and water loss severity, such as mild, moderate, and severe, divided according to the preset loss threshold range, used to classify the risk of erosion areas; the center point coordinates refer to the plane coordinates X corresponding to the arithmetic mean of the coordinates of all boundary points of an active erosion area. Y represents the approximate central location of the area; relative position refers to the distance and direction relationship between the center point of each active erosion area and the downstream outlet, slope top, etc., of the target monitoring slope boundary, used to describe the ordinal position of the erosion points on the slope; regional distribution sequence refers to the sequential list formed by arranging all the upgraded active erosion areas according to a certain rule, such as from the top of the slope to the bottom of the slope, used to reflect the spatial development sequence of erosion; soil and water loss level distribution map refers to a spatial thematic map on the digital surface model or base map of the target monitoring slope, clearly marked with different colors or symbols for each level of active erosion areas and their corresponding levels, used to intuitively display the monitoring results.

[0158] In this step, the calculated soil loss value for each active erosion area is first read, and these soil loss values ​​are compared with a preset loss threshold range table. This preset loss threshold range table defines several value intervals, each interval corresponding to a loss level. Based on the interval into which the soil loss of each active erosion area falls, a corresponding loss level is assigned to it, thereby initially dividing all active erosion areas into multiple loss levels.

[0159] Next, obtain the historical erosion gully distribution map of the target monitoring slope, extract the main axis of each historical gully from the historical erosion gully distribution map, and generate a strip area of ​​a specific width along its extension direction as a preset range. Then, check each active erosion area that has been initially divided into loss levels and determine whether its spatial location is within the preset range of the extension direction of any historical gully. For all active erosion areas that meet this condition, upgrade their loss level by one level on the original basis, such as upgrading the original moderate level to severe.

[0160] Next, the core location of the active erosion area after each level is calculated, which is the average of the coordinates of all its boundary points. The center point coordinates of the active erosion area are obtained. Then, a reference point is set, such as the top line or the highest point of the target monitoring slope. The straight-line distance and orientation of each center point coordinate relative to the reference are calculated to determine the relative position of each active erosion area on the slope, such as the upper slope position, middle slope position, and lower slope position. Then, based on this relative position information, the active erosion areas after all levels are sorted. The common sorting rule is to follow the order from the top of the slope to the bottom of the slope, thereby generating an ordered regional distribution sequence.

[0161] Subsequently, the digital surface model of the target monitoring slope is loaded and used as a three-dimensional base map. Based on the generated regional distribution sequence, the active erosion areas after each level are processed in sequence. On the digital surface model, a closed polygon is drawn on the corresponding surface position according to the boundary coordinates of the active erosion area. At the same time, according to the final determined loss level, a specific color or filling pattern is assigned to the polygon, such as yellow for light, orange for moderate, and red for heavy. After all areas are marked on the base map according to this rule, an intuitive water and soil loss level distribution map is generated.

[0162] Finally, all the information obtained will be compiled and integrated, including the specific soil loss values ​​for each active erosion area, its final determined loss level, its spatial boundary information, and the generated soil and water loss level distribution map. This information will be organized into a structured report, which may include data tables, maps, and text descriptions, as the final output of the soil and water conservation monitoring results.

[0163] Figure 2 This application provides a schematic diagram of the structure of a soil and water conservation monitoring system based on UAV remote sensing, as shown below. Figure 2 As shown, the system includes:

[0164] The acquisition module 21 is used to generate a three-dimensional flight path of the UAV based on the historical erosion gully distribution map of the target monitoring slope, so as to control the UAV to acquire multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path;

[0165] The first calculation module 22 is used to generate a first vegetation index change map of the target monitoring slope based on the multispectral image data.

[0166] The construction module 23 is used to construct a digital surface model of the target monitoring slope based on the laser point cloud data, and compare the digital surface model with a preset benchmark digital surface model to obtain topographic change point cloud data.

[0167] Analysis module 24 is used to perform spatial overlay analysis on the first vegetation index change map and the topographic change point cloud data to obtain the spatially overlapping area, and extract the vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area to obtain the potential erosion area.

[0168] The second calculation module 25 is used to segment active erosion areas from the topographic change point cloud data based on the three-dimensional point cloud features corresponding to the potential erosion areas, and to calculate soil loss based on the three-dimensional point cloud data of the active erosion areas.

[0169] The generation module 26 is used to generate soil and water conservation monitoring results based on the spatial distribution information of the active erosion area and the amount of soil loss.

[0170] Figure 2 The aforementioned soil and water conservation monitoring system based on UAV remote sensing can perform... Figure 1 The implementation principle and technical effects of the soil and water conservation monitoring method based on UAV remote sensing described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the soil and water conservation monitoring system based on UAV remote sensing in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0171] In one possible design, Figure 2 The soil and water conservation monitoring system based on UAV remote sensing in the illustrated embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0172] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0173] The processing component 32 is used for the above Figure 1 The embodiment describes a method for monitoring soil and water conservation based on unmanned aerial vehicle (UAV) remote sensing.

[0174] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0175] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0176] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0177] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0178] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for monitoring soil and water conservation based on unmanned aerial vehicle (UAV) remote sensing, characterized in that, include: Based on the historical erosion gully distribution map of the target monitoring slope, a three-dimensional flight path of the UAV is generated to control the UAV to collect multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path; Based on the multispectral image data, a first vegetation index change map of the target monitored slope is generated; Based on the laser point cloud data, a digital surface model of the target monitoring slope is constructed, and the digital surface model is compared with a preset benchmark digital surface model to obtain topographic change point cloud data. The first vegetation index change map and the topographic change point cloud data are spatially overlaid to obtain the spatially overlapping area. Vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area are extracted to obtain the potential erosion area. Based on the three-dimensional point cloud features corresponding to the potential erosion area, the active erosion area is segmented from the topographic change point cloud data, and the soil loss is calculated based on the three-dimensional point cloud data of the active erosion area. Based on the spatial distribution information of the active erosion area and the amount of soil loss, water and soil conservation monitoring results are generated. The step of segmenting active erosion areas from the terrain change point cloud data based on the three-dimensional point cloud features corresponding to the potential erosion areas includes: Points located within the potential erosion area are extracted from the topographic change point cloud data to form a first point cloud set; Calculate multiple feature parameters for each terrain change point in the first point cloud set. The multiple feature parameters include the tilt angle of the normal vector of the terrain change point, the point density within a preset range around the terrain change point, and the elevation change gradient of the terrain change point along the target monitoring slope direction. Based on the multiple feature parameters, the terrain change points in the first point cloud set are determined, and the points whose normal vector tilt angle is greater than a preset tilt angle threshold, whose point density is less than a preset density threshold, and whose elevation change gradient is greater than a first preset gradient threshold are determined as erosion feature points. Cluster the erosion feature points, and aggregate the erosion feature points whose three-dimensional spatial distance is less than a preset distance threshold into a point cloud cluster; From all point cloud clusters, select those containing more than a preset threshold number of terrain change points as candidate erosion clusters; The candidate erosion clusters located within the buffer zone of the gullies marked on the historical erosion gully distribution map are marked as active erosion areas; From the remaining candidate erosion clusters, calculate the average elevation change gradient of all points within each candidate erosion cluster, and mark the candidate erosion clusters whose average elevation change gradient is greater than the second preset gradient threshold as active erosion areas.

2. The method according to claim 1, characterized in that, Based on the multispectral image data, a first vegetation index change map of the target monitored slope is generated, including: The first multispectral image and the second multispectral image of the target monitoring slope were collected at the first preset time point and the second preset time point, respectively. Based on the red and near-infrared reflectance data in the first and second multispectral images, the first vegetation index distribution map of the target monitoring slope at the first preset time point and the second vegetation index distribution map at the second preset time point are calculated respectively. The target monitoring slope is divided into multiple analysis units, and the dominant slope direction of each analysis unit is determined according to the slope area covered by the three-dimensional flight path. Light condition compensation is performed on the vegetation index values ​​of the first vegetation index distribution map and the second vegetation index distribution map within the analysis unit. After the light condition compensation is completed, the vegetation index difference between the second preset time point and the first preset time point is calculated for each analysis unit. The historical erosion gully distribution map of the target monitored slope is overlaid with the analysis unit to obtain the second analysis unit; The vegetation index difference of the second analysis unit is adjusted by a preset first adjustment coefficient to generate a first vegetation index change map.

3. The method according to claim 1, characterized in that, Based on the laser point cloud data, a digital surface model of the target monitoring slope is constructed, including: The laser point cloud data is classified to obtain a ground point cloud subset and a non-ground point cloud subset; Based on the flight angle of each strip in the three-dimensional flight path, calculate the point cloud density weight of each target point in the ground point cloud subset; Based on the three-dimensional coordinates of each target point in the ground point cloud subset and the corresponding point cloud density weights, an initial triangular mesh model is constructed in the area where the target monitoring slope is located. Identify the triangular facets in the initial triangular network model that spatially overlap with the historical erosion gully distribution map of the target monitored slope, and use them as facets to be processed. Based on the direction of the erosion gullies in the historical erosion gully distribution map, the point cloud density weight of the target points contained in the surface to be processed is adjusted, and the surface to be processed is reconstructed using the adjusted point cloud density weight to obtain the target surface. The target facet is merged with the triangular facets in the initial triangular mesh model, excluding the facet to be processed, to obtain a digital surface model.

4. The method according to claim 1, characterized in that, Extract vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area to obtain potential erosion areas, including: Within the overlapping spatial region, multiple candidate sub-regions are determined; The vegetation index change value corresponding to each candidate sub-region is obtained from the first vegetation index change map, and the terrain deformation value corresponding to each candidate sub-region is obtained from the terrain change point cloud data. Candidate sub-regions whose vegetation index change values ​​are less than a first preset threshold are marked as vegetation degradation sub-regions; Candidate sub-regions whose absolute values ​​of terrain deformation are greater than a second preset threshold are marked as surface deformation sub-regions; Identify candidate sub-regions that are simultaneously marked as vegetation degradation sub-regions and surface deformation sub-regions as alternative erosion sub-regions; Within the spatial range covered by the historical erosion gully distribution map, the candidate erosion sub-regions are identified as potential erosion areas; Within the spatial range not covered by the historical erosion gully distribution map, potential erosion areas are determined from the candidate erosion sub-regions based on the consistency of the change direction of the vegetation index value and the topographic deformation value in the candidate erosion sub-regions.

5. The method according to claim 1, characterized in that, Soil loss is calculated based on the three-dimensional point cloud data of the active erosion area, including: Based on the three-dimensional point cloud data of the active erosion area, a current terrain surface model of the active erosion area is generated; Obtain the target baseline terrain surface model corresponding to the active erosion area; The current terrain surface model is compared with the target reference terrain surface model to obtain the elevation change value of each sampling point in the active erosion area; Based on the elevation change values, calculate the preliminary soil loss volume in the active erosion area; Obtain the slope direction corresponding to the active erosion area, and determine the volume correction coefficient based on the slope direction; The initial soil loss volume is corrected using the volume correction factor to obtain the intermediate soil loss volume; Obtain the historical erosion intensity level of the active erosion area, and determine the soil bulk density parameter based on the historical erosion intensity level; The amount of soil loss in the active erosion area is calculated based on the intermediate soil loss volume and the soil bulk density parameter.

6. The method according to claim 1, characterized in that, Based on the spatial distribution information of the active erosion areas and the soil loss, water and soil conservation monitoring results are generated, including: The soil loss amount of each active erosion area is obtained, and the active erosion area is divided into multiple loss levels according to a preset loss amount threshold range; From the active erosion areas of the multiple erosion levels, the erosion level of the active erosion areas whose spatial location is within a preset range of the gully extension direction marked by the historical erosion gully distribution map is upgraded by one level; Based on the center point coordinates of each active erosion area, the relative position of each active erosion area on the target monitoring slope is calculated. Based on the relative position, the active erosion areas after upgrading are sorted to generate a regional distribution sequence. Based on the regional distribution sequence, active erosion areas of corresponding levels and locations are marked on the digital surface model of the target monitored slope, generating a soil erosion level distribution map. The soil loss in each active erosion area is integrated with the soil and water loss level distribution map to output the soil and water conservation monitoring results.

7. A soil and water conservation monitoring system based on unmanned aerial vehicle (UAV) remote sensing, applied to the soil and water conservation monitoring method based on UAV remote sensing according to any one of claims 1-6, characterized in that, include: The acquisition module is used to generate a three-dimensional flight path of the UAV based on the historical erosion gully distribution map of the target monitoring slope, so as to control the UAV to acquire multispectral image data and laser point cloud data of the target monitoring slope along the three-dimensional flight path; The first calculation module is used to generate a first vegetation index change map of the target monitored slope based on the multispectral image data. The construction module is used to construct a digital surface model of the target monitoring slope based on the laser point cloud data, and compare the digital surface model with a preset benchmark digital surface model to obtain topographic change point cloud data. The analysis module is used to perform spatial overlay analysis on the first vegetation index change map and the topographic change point cloud data to obtain the spatially overlapping area, and extract the vegetation change patches and topographic deformation patches that meet the first preset conditions within the spatially overlapping area to obtain the potential erosion area. The second calculation module is used to segment active erosion areas from the topographic change point cloud data based on the three-dimensional point cloud features corresponding to the potential erosion areas, and to calculate soil loss based on the three-dimensional point cloud data of the active erosion areas. The generation module is used to generate soil and water conservation monitoring results based on the spatial distribution information of the active erosion area and the amount of soil loss.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a soil and water conservation monitoring method based on UAV remote sensing as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for monitoring soil and water conservation based on unmanned aerial vehicle (UAV) remote sensing as described in any one of claims 1 to 6.

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