Vegetation degradation grading evaluation method and system based on hyperspectral remote sensing

Abstract

The invention provides a vegetation degradation grading evaluation method and system based on hyperspectral remote sensing, and relates to the technical field of vegetation monitoring, and the method comprises the steps: integrating a hyperspectral remote sensing imager with an RS485 communication module to construct an unmanned plane collection platform, and carrying out the multi-node collection of a target vegetation region; performing bus transmission analysis on the hyperspectral data set; and performing vegetation degradation analysis based on the vegetation spectral characteristic parameters, converging vegetation degradation indexes through an RS485 communication module, sending the vegetation degradation indexes to a central processing platform for grading evaluation, and constructing a vegetation degradation grading distribution diagram. The technical problem that in the prior art, a vegetation degradation evaluation result is inaccurate due to the fact that a remote sensing technology is difficult to capture key ecological attributes in vegetation degradation evaluation and spectral information is insufficient is solved, and the unmanned aerial vehicle collection platform integrating the hyperspectral remote sensing imager and the RS485 communication module is used for vegetation degradation grading evaluation. And the vegetation degradation evaluation efficiency and accuracy are improved.

Description

Technical Field

[0001] This application relates to the field of vegetation monitoring technology, specifically to a method and system for assessing vegetation degradation based on hyperspectral remote sensing. Background Technology

[0002] Traditional satellite remote sensing for large-scale monitoring relies heavily on multispectral imaging data, composed of several broad bands, which facilitates large-scale coverage and long-term continuous observation. However, it has inherent limitations in spectral resolution. When identifying plant physiological and biochemical states or distinguishing morphologically similar species, spectral energy fusion between broad bands can mask absorption characteristics in narrow bands, thus reducing sensitivity to changes in biochemical indicators. Especially when vegetation exhibits early pathological or stress responses, subtle changes in cell structure and pigment content may have already occurred, initially manifesting as spectral line shifts or deformations in narrow bands such as the red edge. Commonly used multispectral bands have relatively limited responsiveness to these subtle differences. Furthermore, because traditional vegetation degradation grading assessments mostly remain at the macroscopic level, their results are inaccurate in early diagnosis and causal analysis, leading to a lag in the identification of degradation trends.

[0003] In summary, existing technologies suffer from inaccurate vegetation degradation assessment results due to the difficulty of capturing key ecological attributes and insufficient spectral information in remote sensing technology. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for assessing vegetation degradation based on hyperspectral remote sensing, in order to solve the technical problem in the prior art that the remote sensing technology is unable to capture key ecological attributes and has insufficient spectral information in vegetation degradation assessment, resulting in inaccurate vegetation degradation assessment results.

[0005] To achieve the above objectives, this application provides a method and system for assessing vegetation degradation based on hyperspectral remote sensing.

[0006] Firstly, this application provides a method for assessing vegetation degradation grading based on hyperspectral remote sensing. This method is implemented through a system for assessing vegetation degradation grading based on hyperspectral remote sensing. The method includes: constructing a UAV acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager to collect data from multiple nodes in a target vegetation area, obtaining a hyperspectral dataset; transmitting and analyzing the hyperspectral dataset via a bus using the RS485 communication module to extract vegetation spectral characteristic parameters; performing vegetation degradation analysis based on the vegetation spectral characteristic parameters, setting a vegetation degradation index, and aggregating and sending the vegetation degradation index to a central processing platform via the RS485 communication module for grading assessment, thereby constructing a vegetation degradation grading distribution map.

[0007] Optionally, a UAV data acquisition platform is constructed by integrating an RS485 communication module with a hyperspectral remote sensing imager. The UAV data acquisition platform includes multiple hyperspectral imaging nodes. A master-slave communication network is established based on the RS485 bus protocol. The multiple hyperspectral imaging nodes are configured, and their device addresses and communication parameters are determined. A data acquisition sequence is set according to the device addresses and communication parameters of the multiple imaging nodes. The UAV data acquisition platform is activated according to the data acquisition sequence to plan the UAV flight path. Based on the UAV flight path, the multiple hyperspectral imaging nodes are controlled to synchronously acquire data from the target vegetation area to obtain the hyperspectral dataset.

[0008] Optionally, the flight path of the UAV is traversed and matched with the multiple hyperspectral imaging nodes to generate matching results; the target vegetation area is synchronously controlled according to the matching results, and synchronous acquisition parameters are set; the synchronous acquisition parameters are associated with the multiple hyperspectral imaging nodes according to the matching results to generate a global synchronous acquisition command; the multiple hyperspectral imaging nodes are activated according to the global synchronous acquisition command to perform global monitoring and acquisition, and the hyperspectral dataset is obtained.

[0009] Optionally, the hyperspectral dataset is grouped and encapsulated for transmission based on the RS485 communication module to generate multiple data packet transmission results; the multiple data packet transmission results are verified to generate data verification results; error correction is performed on the multiple data packet transmission results based on the data verification results to generate a hyperspectral correction dataset; and feature band analysis is performed on the hyperspectral correction dataset to extract the spectral feature parameters of the vegetation.

[0010] Optionally, the RS485 communication module is used to perform spectral analysis on the multiple hyperspectral imaging nodes to construct a spectral reflectance database; based on the spectral reflectance database, spectral feature analysis is performed on the target vegetation area to plot vegetation spectral feature curves; feature band sensitivity is identified according to the vegetation spectral feature curves to generate feature band sensitivity; the target vegetation area is sorted by sensitivity according to the feature band sensitivity to generate a regional sensitivity sequence; high sensitivity is determined based on the regional sensitivity sequence to identify feature band sensitive areas; the feature bands of the feature band sensitive areas are used as indexes to retrieve the spectral reflectance database to obtain reflectance data for multiple feature bands and calculate vegetation spectral indices; based on the vegetation spectral indices, spectral morphological feature analysis is performed on the target vegetation area to construct the vegetation spectral feature parameters.

[0011] Optionally, the RS485 communication module is connected to the plurality of hyperspectral imaging nodes to establish data communication connection parameters; data acquisition parameters are configured for the plurality of hyperspectral imaging nodes according to the data communication connection parameters; the plurality of hyperspectral imaging nodes are polled according to the data acquisition parameters through the master-slave communication network of the RS485 communication module to generate data acquisition commands; the data acquisition commands are executed to package the responses of the plurality of hyperspectral imaging nodes to obtain multiple spectral reflectance data; the multiple spectral reflectance data are transmitted through the RS485 communication module, and the multiple spectral reflectance data are stored in a structured manner to construct the spectral reflectance database.

[0012] Optionally, vegetation state matching is performed based on the vegetation spectral feature parameters to determine multiple vegetation state spectral feature parameters; the multiple vegetation state spectral feature parameters are used as evaluation indicators to perform hierarchical analysis on the vegetation spectral feature parameters to determine a weight allocation matrix; multiple feature vectors are obtained by calculating the multiple vegetation state spectral feature parameters based on the weight allocation matrix; an environmental adjustment factor is introduced, and a comprehensive vegetation degradation calculation is performed based on the environmental adjustment factor and the multiple feature vectors to generate an initial vegetation degradation index; the initial vegetation degradation index is processed in multiple stages to set the vegetation degradation index.

[0013] Optionally, parameters are standardized based on the initial vegetation degradation index to generate a two-way index, which includes a positive index and a negative index; degradation level analysis is performed based on the positive index and the initial vegetation degradation index to generate a first degradation level; degradation level analysis is performed based on the negative index and the initial vegetation degradation index to generate a second degradation level; long-term observation of the target vegetation area is conducted based on the first degradation level to construct first observation degradation data; long-term observation of the target vegetation area is conducted based on the second degradation level to construct second observation degradation data; and the vegetation degradation index is calculated and set based on the first observation degradation data and the second observation degradation data.

[0014] Optionally, the vegetation degradation index is prioritized according to the weight allocation matrix, and a data transmission priority is set; the vegetation degradation index is aggregated according to the data transmission priority through the RS485 communication module to obtain multiple data aggregation frames; the multiple data aggregation frames are sent to the central processing platform for spatial autocorrelation analysis to identify degradation clusters; time series analysis is performed based on the degradation clusters, and degradation monitoring is carried out according to the regional time series analysis results to draw a vegetation degradation trend map; based on the vegetation degradation trend map and the environmental regulation factors, a multi-dimensional hierarchical assessment is performed to generate multi-level degradation data; the multi-level degradation data is mapped to the target vegetation area for dynamic coordinate matching to construct the vegetation degradation hierarchical distribution map.

[0015] Secondly, this application also provides a vegetation degradation grading assessment system based on hyperspectral remote sensing, used to execute the vegetation degradation grading assessment method based on hyperspectral remote sensing as described in the first aspect. The vegetation degradation grading assessment system based on hyperspectral remote sensing includes: a multi-node acquisition module, used to construct an unmanned aerial vehicle (UAV) acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager to perform multi-node acquisition of the target vegetation area and obtain a hyperspectral dataset; a bus transmission and analysis module, used to perform bus transmission and analysis of the hyperspectral dataset via the RS485 communication module to extract vegetation spectral feature parameters; and a grading assessment module, used to perform vegetation degradation analysis based on the vegetation spectral feature parameters, set a vegetation degradation index, and send the vegetation degradation index to a central processing platform via the RS485 communication module for grading assessment, thereby constructing a vegetation degradation grading distribution map.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: A drone-based data acquisition platform, integrating a hyperspectral remote sensing imager with an RS485 communication module, was constructed to collect data from multiple nodes in a target vegetated area, obtaining a hyperspectral dataset. This dataset was then transmitted and analyzed via the RS485 communication module to extract vegetation spectral characteristic parameters. Based on these parameters, vegetation degradation analysis was performed, and a vegetation degradation index was established. This index was then aggregated and transmitted to a central processing platform via the RS485 communication module for tiered assessment, resulting in a vegetation degradation tiered distribution map. In other words, by integrating a hyperspectral remote sensing imager with an RS485 communication module onto a drone platform, refined acquisition and rapid transmission of vegetation hyperspectral characteristics were achieved, improving the efficiency and accuracy of vegetation degradation tiered assessment.

[0017] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0018] 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the vegetation degradation grading assessment method based on hyperspectral remote sensing proposed in this application.

[0020] Figure 2 This is a schematic diagram of the vegetation degradation grading assessment system based on hyperspectral remote sensing proposed in this application.

[0021] Figure labeling: 11 Multi-node acquisition module, 12 Bus transmission analysis module, 13 Hierarchical evaluation module. Detailed Implementation

[0022] This application provides a method and system for assessing vegetation degradation based on hyperspectral remote sensing. It addresses the technical problem in existing technologies where remote sensing technology struggles to capture key ecological attributes and lacks sufficient spectral information, leading to inaccurate vegetation degradation assessment results. By integrating a hyperspectral remote sensing imager with an RS485 communication module onto a UAV platform, it achieves refined acquisition and rapid transmission of vegetation hyperspectral features, improving the efficiency and accuracy of vegetation degradation assessment.

[0023] 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 a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1This application provides a method for assessing vegetation degradation grading based on hyperspectral remote sensing. The method is applied to a hyperspectral remote sensing-based vegetation degradation grading assessment system. The method specifically includes the following steps: A drone data acquisition platform was built by integrating an RS485 communication module with a hyperspectral remote sensing imager to collect data from multiple nodes in the target vegetation area, thereby obtaining a hyperspectral dataset.

[0025] Furthermore, this application also includes the following steps: constructing a UAV acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager, the UAV acquisition platform including multiple hyperspectral imaging nodes; establishing a master-slave communication network based on the RS485 bus protocol, configuring the multiple hyperspectral imaging nodes, and determining the device addresses and communication parameters of the multiple imaging nodes; setting the data acquisition sequence according to the device addresses and communication parameters of the multiple imaging nodes; activating the UAV acquisition platform according to the data acquisition sequence to plan the UAV flight path; and controlling the multiple hyperspectral imaging nodes to synchronously acquire data from the target vegetation area based on the UAV flight path to obtain the hyperspectral dataset.

[0026] Furthermore, this application also includes the following steps: traversing the UAV flight path and matching it with the plurality of hyperspectral imaging nodes to generate matching results; performing synchronous control on the target vegetation area according to the matching results and setting synchronous acquisition parameters; associating the synchronous acquisition parameters with the plurality of hyperspectral imaging nodes according to the matching results to generate a global synchronous acquisition command; activating the plurality of hyperspectral imaging nodes according to the global synchronous acquisition command to perform global monitoring and acquisition, and obtaining the hyperspectral dataset.

[0027] Specifically, a UAV data acquisition platform is constructed by integrating a hyperspectral remote sensing imager with an RS485 communication module. This platform comprises multiple hyperspectral imaging nodes. Each hyperspectral imaging node is an imaging unit mounted on the UAV platform, possessing independent data acquisition capabilities. Each node includes at least one hyperspectral camera and an RS485 communication interface. The hyperspectral remote sensing imager is a sensing device capable of acquiring target reflectance spectral information across tens to hundreds of continuous narrow bands. It can not only see visible light but also capture details invisible to the human eye, forming a continuous spectral curve for each pixel. This is used for precise analysis of the biochemical components of vegetation, including chlorophyll, carotenoids, water content, and cell structure. The RS485 communication module is an industrial-grade differential communication interface characterized by strong anti-interference capabilities and long transmission distances. It supports long-distance and multi-device networking, exhibiting strong anti-interference capabilities and is suitable for data synchronization transmission and bus-based online control between multiple devices mounted on a UAV.

[0028] A master-slave communication network is established based on the RS485 bus protocol, with an onboard computer as the master and each imaging node as a slave. Each slave node in the network is assigned a unique device address and standardized communication parameters are configured. This ensures that multiple hyperspectral imaging nodes can communicate using the same communication language within the same communication link. The master-slave communication network is a network architecture where one device acts as the master, issuing commands, while other devices, as slaves, can only respond to the master's commands, avoiding communication conflicts caused by multiple nodes speaking simultaneously. The device address is a unique identifier assigned to each device to distinguish each hyperspectral imaging node. The communication parameters are a unified set of rules that all devices must adhere to, including baud rate, data bits, stop bits, and parity bits, to maintain communication consistency and accuracy.

[0029] The data acquisition timing is set according to the device addresses and communication parameters of multiple hyperspectral imaging nodes. This means that the operation sequence of multiple hyperspectral imaging nodes, such as startup, exposure, acquisition, and transmission, is planned, which specifies when each hyperspectral imaging node should start acquiring data, in order to ensure the synchronization of acquisition by multiple nodes.

[0030] Based on the set data acquisition sequence, the UAV acquisition platform is activated, and the UAV flight path is planned. This involves planning the route the UAV will take over the target area according to a predetermined trajectory, including information such as waypoint order, flight altitude, speed, and heading angle, to ensure comprehensive area coverage and coordinated acquisition across multiple nodes. The UAV flight path is traversed, and each waypoint on the path is matched with multiple hyperspectral imaging nodes, generating matching results to determine the acquisition time and operational parameters for each hyperspectral imaging node at each waypoint. The matching results establish the correspondence between each waypoint on the UAV flight path and the acquisition tasks of each hyperspectral imaging node, indicating which hyperspectral imaging node is acquiring data at which waypoint on the UAV flight path.

[0031] Based on the detailed matching results, the target vegetation area is synchronously controlled, and unified synchronous acquisition parameters are set, including trigger time, trigger mode, exposure time, sampling frequency, and band selection. For example, a straight path for the drone with a length of 2km and a flight altitude of 100m is planned. After traversing the drone's flight path, the matching results show that in the 0-500m segment of the path, due to its wider area, nodes 1 and 2 are jointly responsible; in the 500-1500m segment, only node 1 needs to work; and in the 1500-2000m segment, nodes 1 and 3 jointly cover the area. The synchronous acquisition parameters are set to automatic triggering based on GPS location, with acquisition every 25m of flight and an exposure time of 8ms. When the drone reaches the first trigger point, the master device generates and broadcasts a global synchronous acquisition command.

[0032] The synchronous acquisition parameters are associated with each hyperspectral imaging node according to the matching results. That is, multiple hyperspectral imaging nodes are activated simultaneously or in a planned order according to the global synchronous acquisition command, generating a global synchronous acquisition command. The global synchronous acquisition command includes a unified acquisition instruction that is received and executed immediately by all hyperspectral imaging nodes at the same time, and is composed of the synchronous acquisition parameters of all multispectral imaging nodes.

[0033] The system activates multiple hyperspectral imaging nodes via a global synchronization acquisition command to perform global monitoring and acquisition. In other words, when the UAV acquisition platform reaches the predetermined acquisition location, the main device simultaneously activates multiple hyperspectral imaging nodes via the RS485 bus to perform global monitoring and acquisition, completing global hyperspectral monitoring of the target vegetation area and obtaining a hyperspectral dataset that is strictly aligned in time and space. By precisely matching the flight path with the imaging nodes and issuing global synchronization commands, collaborative acquisition by multiple nodes in space and time is achieved, improving the spatiotemporal consistency of hyperspectral data.

[0034] The hyperspectral dataset was analyzed by bus transmission using an RS485 communication module to extract vegetation spectral characteristic parameters.

[0035] Furthermore, this application also includes the following steps: grouping and encapsulating the hyperspectral dataset into multiple data packet transmission results based on the RS485 communication module; performing data verification on the multiple data packet transmission results to generate data verification results; performing error correction on the multiple data packet transmission results based on the data verification results to generate a hyperspectral correction dataset; and performing feature band analysis on the hyperspectral correction dataset to extract the spectral feature parameters of the vegetation.

[0036] Specifically, the hyperspectral dataset is grouped and encapsulated for transmission using an RS485 communication module. This means that the hyperspectral dataset is divided into several fixed-size data packets according to the maximum frame length specified in the RS485 communication protocol, avoiding timeouts or congestion caused by excessively long single data transmissions. Each data packet contains a sequence number and a CRC checksum, thus generating multiple data packet transmission results.

[0037] Data verification is performed on the transmission results of multiple data packets to determine the integrity and correctness of each data packet, such as through CRC check and frame sequence number comparison, to identify potential packet loss, misalignment, or bit flipping errors during transmission. A data verification result containing a list of erroneous packets is generated. Based on the data verification result, error correction is performed on the multiple data packet transmission results. For failed data packets, the sender is requested to retransmit until they are correctly received, ensuring that all data is complete and error-free. Finally, a hyperspectral polarization correction dataset is generated at the receiving end. The hyperspectral polarization correction dataset is formed after all data packets have been successfully received and verified, and is completely consistent with the original data.

[0038] Feature band analysis was performed based on the hyperspectral correction dataset to extract vegetation spectral characteristic parameters, including the spectral and spatial features of the vegetation. For example, a single hyperspectral imaging node generates approximately 40GB of raw data in a single flight. When transmitted via an RS485 module with a baud rate of 115200bps, this data is divided into approximately 50,000 data packets for packet encapsulation and transmission. After transmission, the verification module found that 150 packets failed verification, resulting in CRC errors. A retransmission request was initiated, and the error correction process successfully completed all the lost data, generating a complete hyperspectral correction dataset. In the characteristic band analysis, the reflectance of each pixel at 750nm was calculated, and the red edge position was accurately extracted as a spectral feature using the first-order differential method. For example, the reflectance in the healthy area was 725nm, while the reflectance in the degraded area was blue-shifted to 705nm. At the same time, the contrast index in the texture feature of the image at 550nm was calculated. It was found that the contrast value in the degraded area increased from 25 in the healthy area to 45, indicating that the vegetation canopy was more fragmented and uneven. The red edge position and texture contrast together constitute the spectral feature parameters of vegetation used for degradation assessment.

[0039] By employing a grouping, encapsulation, verification, and retransmission mechanism, the problems of packet loss and bit errors in long-distance, high-volume data transmission are completely resolved, ensuring the absolute reliability of the original data. Based on the hyperspectral correction dataset, through the fusion of spectral and spatial feature band analysis, comprehensive information extraction of vegetation degradation status from internal physiology to external structure is achieved, improving the analytical depth and assessment accuracy of subsequent vegetation degradation index calculations.

[0040] Furthermore, this application also includes the following steps: performing spectral analysis on the multiple hyperspectral imaging nodes through the RS485 communication module to construct a spectral reflectance database; performing spectral feature analysis on the target vegetation area based on the spectral reflectance database to plot vegetation spectral feature curves; performing feature band sensitivity identification according to the vegetation spectral feature curves to generate feature band sensitivity; sorting the target vegetation area by sensitivity according to the feature band sensitivity to generate a regional sensitivity sequence; determining high sensitivity based on the regional sensitivity sequence to identify feature band sensitive areas; using the feature bands of the feature band sensitive areas as indexes to retrieve the spectral reflectance database to obtain reflectance data for multiple feature bands and calculate vegetation spectral indices; and performing spectral morphological feature analysis on the target vegetation area based on the vegetation spectral indices to construct the vegetation spectral feature parameters.

[0041] Furthermore, this application also includes the following steps: establishing a communication connection between the RS485 communication module and the plurality of hyperspectral imaging nodes, and constructing data communication connection parameters; configuring data acquisition parameters for the plurality of hyperspectral imaging nodes according to the data communication connection parameters; polling the plurality of hyperspectral imaging nodes according to the data acquisition parameters through the master-slave communication network of the RS485 communication module to generate data acquisition commands; executing the data acquisition commands to respond and package the plurality of hyperspectral imaging nodes to obtain multiple spectral reflectance data; transmitting the multiple spectral reflectance data through the RS485 communication module, and storing the multiple spectral reflectance data in a structured manner to construct the spectral reflectance database.

[0042] Specifically, the RS485 communication module is connected to multiple hyperspectral imaging nodes to establish data communication connection parameters. These parameters, used to establish the RS485 communication connection, include baud rate, data bits, stop bits, parity bits, and the device address of each hyperspectral imaging node. Based on these data communication connection parameters, data acquisition parameters are configured for the multiple hyperspectral imaging nodes. These parameters control the acquisition behavior of each hyperspectral imaging node, such as light duration, sampling frequency, band selection, and trigger mode, ensuring that all hyperspectral imaging nodes operate with the same settings.

[0043] Through a master-slave communication network using an RS485 communication module, multiple hyperspectral imaging nodes are polled according to data acquisition parameters. This means data acquisition commands are sent sequentially to each hyperspectral imaging node in a polling manner. Each data acquisition command contains control commands for specific acquisition parameters, triggering one or a series of data acquisition actions. Each hyperspectral imaging node executes the data acquisition command and encapsulates the acquired raw digital signal data into a standard format data packet according to the agreed communication protocol, sending it back to the master device to obtain multiple spectral reflectance data. Spectral reflectance data represents the measured value of light reflected from the target surface by each band output by the hyperspectral imaging node, usually expressed as a proportion or percentage, used to describe the spectral characteristics of vegetation.

[0044] Multiple spectral reflectance data points are received via an RS485 communication module. These data points are then structured and stored according to a specific data model, typically including metadata such as spatial coordinates, spectral dimensions, and timestamps, forming a spectral reflectance database. This database is a structured storage of spectral reflectance data acquired by multiple nodes at different spatial locations and times, containing a collection of multi-node, multi-temporal spectral reflectance data and its metadata.

[0045] By using a spectral reflectance database, spectral feature analysis is performed on each pixel or raster in the target vegetation area, and a unique vegetation spectral feature curve is plotted for each pixel. The vegetation spectral feature curve is a continuous curve plotted with wavelength as the horizontal axis and reflectance as the vertical axis, which intuitively shows the reflection characteristics of vegetation in different electromagnetic bands. Its shape is like a fingerprint of the vegetation's health status.

[0046] Feature band sensitivity identification is performed based on vegetation spectral characteristic curves. This involves identifying specific bands or narrow band intervals from hundreds of bands that show the most significant and dramatic changes in response to vegetation degradation. Feature band sensitivity is then calculated to measure the ability and significance of a particular band in distinguishing between healthy and degraded vegetation. Based on the feature band sensitivity, the target vegetation area is ranked by sensitivity, generating a regional sensitivity sequence from most sensitive to least sensitive. The regional sensitivity sequence is formed by arranging all pixels or sub-regions within the target area according to their sensitivity values ​​to a specific feature band from highest to lowest, and is used to identify degradation hotspots.

[0047] High sensitivity is determined based on the regional sensitivity sequence. This involves setting a threshold and selecting regions with sensitivity higher than the threshold from the sequence as sensitive regions for the characteristic bands. The characteristic bands of these sensitive regions are then used as indexes to search a spectral reflectance database, obtaining reflectance data for multiple characteristic bands. This data is then used to calculate vegetation spectral indices, which are indices constructed through mathematical operations using the reflectance of multiple characteristic bands. These indices are used to enhance or highlight a specific attribute of vegetation; for example, NDVI (Normalized Difference Vegetation Index) is used to enhance greenness, and MSI (Moisture Stress Index) is used to reflect water content.

[0048] Spectral morphological characteristics of the target vegetation area are analyzed based on vegetation spectral indices, and vegetation spectral characteristic parameters are constructed, including both band-based vegetation spectral indices and curve-based spectral morphological characteristics, which are used to describe the state characteristics of vegetation in the target area.

[0049] For example, the target vegetation area is a typical grassland of 10 hectares, divided into three typical sample areas: Area A is a healthy area, dominated by native Leymus chinensis with a coverage of >85%; Area B is a slightly degraded area, dominated by a mixture of Leymus chinensis and drought-resistant Stipa, with a coverage of 60%-70%; and Area C is a severely degraded area, dominated by the degraded indicator plant Clematis chinensis, accompanied by bare ground, with a coverage of <30%. The spectral range is 400-1000 nm, with a total of 256 bands. Data was collected and calibrated from three hyperspectral imaging nodes via an RS485 communication module and stored in a spectral reflectance database. The database includes location ID, latitude and longitude, and spectral vectors. Some example data are shown below: Location ID A01, region A, 550nm green peak reflectance 0.215, 680nm red valley reflectance 0.051, 720nm red edge reflectance 0.418, 800nm ​​near-infrared plateau reflectance 0.556; Location ID A02, region A, 550nm green peak reflectance 0.208, 680nm red valley reflectance 0.04. 9. The reflectance at the 720nm red edge is 0.431, and the reflectance at the 800nm ​​near-infrared plateau is 0.562; for region B with location ID B01, the reflectance at the 550nm green peak is 0.185, the reflectance at the 680nm red valley is 0.068, the reflectance at the 720nm red edge is 0.352, and the reflectance at the 800nm ​​near-infrared plateau is 0.458; for region C with location ID C01, the reflectance at the 550nm green peak is 0.121, the reflectance at the 680nm red valley is 0.105, the reflectance at the 720nm red edge is 0.198, and the reflectance at the 800nm ​​near-infrared plateau is 0.235. Extract the average spectral data for regions A, B, and C from the spectral reflectance database and plot the curves. Region A curve shows a distinct green peak at 550 nm with a reflectance of 0.21, and a deep red valley at 680 nm with a reflectance of 0.05. A steep red edge exists between 680 nm and 750 nm, and the near-infrared plateau reflectance is high (>0.55). In Region C curve, the green peak disappears, and the reflectance at 550 nm drops to 0.12; the red valley fills, and the reflectance at 680 nm rises to 0.10; the red edge slope becomes gentler; and the near-infrared plateau reflectance decreases significantly (<0.25). The shape of Region B curve is between that of Region A and Region C. The separation of reflectance between different degradation levels in each band is calculated, such as the sensitivity value from an ANOVA analysis. A higher sensitivity value indicates that the band is more sensitive to degradation. The sensitivity values ​​are as follows: 125.6 for the red edge position at 720nm; 118.3 for the near-infrared position at 800nm; 98.7 for the short-wave infrared position at 1650nm; 85.4 for the green peak at 550nm; and 80.1 for the red valley at 680nm.Using the reflectance value of the most sensitive 720nm band as an indicator, all pixels in the entire region are ranked. The lower the reflectance, the more severe the degradation, and the higher the sensitivity ranking. A threshold is set to identify the most sensitive region from the sequence. For example, pixels with a 720nm reflectance <0.25 are identified as sensitive regions of the characteristic band. From all 100 sampled pixels, 15 pixels are identified as sensitive regions of the characteristic band, all located in region C and its edges. Using the 720nm and 800nm ​​high-sensitivity bands as indexes, reflectance data for these bands were retrieved from the database, and vegetation spectral indices were calculated. For A01, R680nm was 0.051 and R880nm was 0.556, so the vegetation spectral index was 0.832; for B01, R680nm was 0.068 and R880nm was 0.458, so the vegetation spectral index was 0.741; and for C01, R680nm was 0.105 and R880nm was 0.235, so the vegetation spectral index was 0.382. Based on the vegetation spectral index and the original spectral curve, characteristic parameters representing the curve morphology were further extracted. Area A01 is healthy, with a vegetation spectral index of 0.832, a red edge slope of 4.85, and a green peak-to-red valley ratio of 4.22. Area B01 is slightly affected, with a vegetation spectral index of 0.741, a red edge slope of 3.12, and a green peak-to-red valley ratio of 2.72. Area C01 is severely affected, with a vegetation spectral index of 0.382, a red edge slope of 1.05, and a green peak-to-red valley ratio of 1.15.

[0050] By analyzing spectral features and ranking sensitivity, key information is automatically located from massive spectral data. By constructing comprehensive vegetation spectral feature parameters that integrate vegetation spectral indices and spectral morphological features, a deep characterization of vegetation degradation status is achieved from a single attribute to a comprehensive morphology, improving the accuracy and reliability of degradation level classification.

[0051] Vegetation degradation analysis is performed based on the spectral characteristic parameters of the vegetation. A vegetation degradation index is set, and the vegetation degradation index is aggregated and sent to the central processing platform through the RS485 communication module for hierarchical evaluation, thereby constructing a vegetation degradation hierarchical distribution map.

[0052] Furthermore, this application also includes the following steps: performing vegetation state matching based on the vegetation spectral feature parameters to determine multiple vegetation state spectral feature parameters; using the multiple vegetation state spectral feature parameters as evaluation indicators to perform hierarchical analysis on the vegetation spectral feature parameters to determine a weight allocation matrix; calculating the multiple vegetation state spectral feature parameters based on the weight allocation matrix to obtain multiple feature vectors; introducing an environmental regulation factor, and performing comprehensive vegetation degradation calculation based on the environmental regulation factor and the multiple feature vectors to generate an initial vegetation degradation index; and performing multi-level processing on the initial vegetation degradation index to set the vegetation degradation index.

[0053] Furthermore, this application also includes the following steps: standardizing parameters based on the initial vegetation degradation index to generate a two-way index, the two-way index including a positive index and a negative index; performing degradation level analysis based on the positive index and the initial vegetation degradation index to generate a first degradation level; performing degradation level analysis based on the negative index and the initial vegetation degradation index to generate a second degradation level; conducting long-term observation of the target vegetation area based on the first degradation level to construct first observation degradation data; conducting long-term observation of the target vegetation area based on the second degradation level to construct second observation degradation data; and calculating and setting the vegetation degradation index based on the first observation degradation data and the second observation degradation data.

[0054] Specifically, vegetation status is matched based on vegetation spectral characteristic parameters to determine multiple vegetation status spectral characteristic parameters, extracted from the vegetation's spectral curves, used to describe the vegetation's health status. These parameters include spectral indices, red-edge shift, and spectral reflectance, reflecting the vegetation's physiological characteristics. These multiple vegetation status spectral characteristic parameters are used as evaluation indicators. Hierarchical analysis is performed on these parameters to determine a weighting matrix. The relationship between all characteristic parameters and vegetation degradation is compared at multiple levels to assess the relative importance of each characteristic in the degradation assessment. The weighting matrix quantifies the importance of each evaluation indicator relative to the overall objective; for example, the weight of red-edge slope is 0.5, NDVI is 0.3, and moisture index is 0.2.

[0055] Based on the weighting matrix, multiple spectral characteristic parameters of vegetation state are calculated through weighted summation to obtain multiple eigenvectors, representing the contribution of each vegetation state characteristic parameter to vegetation degradation. Environmental adjustment factors are introduced—correction coefficients introduced to eliminate the influence of abiotic stresses on the assessment results. These are typically calculated based on environmental data such as topography and climate, making the assessment results more purely reflect degradation caused by human activities or problems within the ecosystem. For example, a stress coefficient of 1.2 is applied to pixels on sunny slopes, and a compensation coefficient of 0.8 is applied to pixels on shady slopes. Based on the environmental adjustment factors and multiple eigenvectors, a comprehensive vegetation degradation calculation is performed to generate an initial vegetation degradation index, reflecting the preliminary degradation status under the influence of environmental factors.

[0056] The initial vegetation degradation index was processed in multiple stages to obtain the final vegetation degradation index. First, the initial vegetation degradation index was standardized to a range of 0-1, generating bidirectional indicators with opposite meanings. Positive indicators typically represent healthy or good vegetation conditions, while negative indicators represent degraded or poor conditions. Degradation level analysis was performed based on the positive indicators combined with the initial vegetation degradation index, i.e., from a healthy perspective, determining the first degradation level. Degradation level analysis was also performed based on the negative indicators combined with the initial vegetation degradation index, i.e., from a degraded perspective, determining the second degradation level.

[0057] Based on the first degradation level, long-term observations are conducted in the target area to collect observational data, such as seasonal variations, climate influences, and vegetation change trends. This constructs the first observational degradation data, which is a set of data on the change of vegetation health status over time obtained through long-term observations. Based on the second degradation level, long-term observations are conducted in the target area to record and analyze the degradation trend of the area, constructing the second observational degradation data, which is a set of data on the change of vegetation degradation status over time obtained through long-term observations.

[0058] Since misjudgments exist for individual indicators, the final vegetation degradation index is calculated based on the first and second observational degradation data. This involves a weighted sum of the first and second observational degradation data. For example, suppose the initial vegetation degradation index of a grassland reserve is standardized to generate a two-way index: a positive index of 0.167 and a negative index of 0.833. Based on the positive index, the first degradation level is determined to be severe degradation; based on the negative index, the second degradation level is also determined to be severe degradation. After three years of observation in this grassland reserve, in all areas where the first degradation level was determined to be severe degradation, the vegetation cover and dominant species biomass decreased. The cover decreased from 20% to 15%, and the biomass of *Leymus chinensis* decreased from 50 g / m². 2 Reduced to 30g / m 2The first observed degradation data is 0.167. For all areas classified as severely degraded at the second degradation level, the soil exposure rate increased from 40% to 60%, and the proportion of poisonous weeds increased from 10% to 25%, resulting in a second observed degradation data of 0.833. To balance short-term fluctuations and long-term trends, the weight of the first observed degradation data was set at 0.3, and the weight of the second observed degradation data was set at 0.7. The final calculated vegetation degradation index is: This indicates that the target area is in a state of severe degradation.

[0059] By standardizing the initial vegetation degradation index and generating both positive and negative bidirectional indicators, it is ensured that different characteristic parameters can be compared on the same scale, effectively avoiding errors caused by differences in indicators. Degradation level analysis combining positive and negative indicators helps identify healthy areas, while negative indicators help reveal degraded areas. Long-term monitoring of areas at different degradation levels dynamically tracks vegetation degradation trends, allowing for timely adjustments to management strategies and avoiding delays.

[0060] Furthermore, this application also includes the following steps: prioritizing the vegetation degradation index according to the weight allocation matrix and setting data transmission priority; aggregating the vegetation degradation index according to the data transmission priority through the RS485 communication module to obtain multiple data aggregation frames; sending the multiple data aggregation frames to the central processing platform for spatial autocorrelation analysis to identify degradation clusters; performing time series analysis based on the degradation clusters, monitoring degradation based on the regional time series analysis results, and drawing a vegetation degradation trend map; performing multi-dimensional hierarchical assessment based on the vegetation degradation trend map and the environmental regulation factors to generate multi-level degradation data; mapping the multi-level degradation data to the target vegetation area for dynamic coordinate matching to construct the vegetation degradation hierarchical distribution map.

[0061] Specifically, vegetation degradation indices are prioritized according to a weighted distribution matrix, and data transmission priorities are set, with higher-priority data being transmitted and processed first. Through an RS485 communication module, the data from each vegetation degradation index are aggregated according to the set transmission priorities, generating multiple data aggregation frames sorted by priority. These data aggregation frames integrate data scattered across different nodes.

[0062] Multiple data frames are aggregated and sent to a central processing platform for spatial autocorrelation analysis. This analysis determines whether a geographical phenomenon exhibits a clustering pattern in its spatial distribution, identifying significant spatially concentrated areas of degradation. The Moran index is used to quantify the degree of spatial clustering. For example, a Moran index of 0.52 indicates significant spatial clustering of degradation. Three distinct areas of degradation were identified.

[0063] Time series analysis was conducted on degraded areas to analyze the changing trends of vegetation degradation by combining data from different periods. Based on the analysis results, a vegetation degradation trend map was drawn, displaying the vegetation degradation index at different time points and showing the direction and rate of vegetation degradation change in each area during the monitoring period. Based on the vegetation degradation trend map and combined with environmental regulating factors, a multi-dimensional hierarchical assessment was conducted. That is, by combining information from multiple dimensions such as space, time, and environment, a thorough assessment of the degradation level was carried out to obtain multi-level degradation data, including the degree of degradation, the type of degradation space, and the degradation trend.

[0064] Multi-level degradation data is mapped to target vegetation areas for dynamic coordinate matching. The multi-level degradation data is accurately mapped to the corresponding locations on the map according to its geographic coordinates, thereby obtaining a vegetation degradation classification distribution map. The map details the degree of vegetation degradation, spatial pattern and temporal evolution trend at each location, thus determining which area has a more severe degree of degradation.

[0065] Data is transmitted according to priority via an RS485 communication module, ensuring that data from severely degraded areas is prioritized for collection and processing, avoiding redundant and inefficient data transmission. Spatial autocorrelation analysis accurately identifies degraded clusters, and time series analysis effectively captures the dynamic changes in vegetation degradation. A multi-dimensional hierarchical assessment, combined with environmental regulating factors and vegetation degradation trend maps, visually presents the location and severity of degraded areas, facilitating ecological protection and restoration by relevant departments.

[0066] In summary, the vegetation degradation grading assessment method based on hyperspectral remote sensing provided in this application has the following technical advantages: A drone-based data acquisition platform, integrating a hyperspectral remote sensing imager with an RS485 communication module, was constructed to collect data from multiple nodes in a target vegetated area, obtaining a hyperspectral dataset. This dataset was then transmitted and analyzed via the RS485 communication module to extract vegetation spectral characteristic parameters. Based on these parameters, vegetation degradation analysis was performed, and a vegetation degradation index was established. This index was then aggregated and transmitted to a central processing platform via the RS485 communication module for tiered assessment, resulting in a vegetation degradation tiered distribution map. In other words, by integrating a hyperspectral remote sensing imager with an RS485 communication module onto a drone platform, refined acquisition and rapid transmission of vegetation hyperspectral characteristics were achieved, improving the efficiency and accuracy of vegetation degradation tiered assessment.

[0067] Example 2: Based on the same inventive concept as the vegetation degradation grading assessment method based on hyperspectral remote sensing in Example 1, this application also provides a vegetation degradation grading assessment system based on hyperspectral remote sensing. Please refer to the appendix. Figure 2 The vegetation degradation grading assessment system based on hyperspectral remote sensing includes: The multi-node acquisition module 11 is used to construct a UAV acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager to acquire data from multiple nodes in the target vegetation area and obtain a hyperspectral dataset. The bus transmission and analysis module 12 is used to perform bus transmission and analysis of the hyperspectral dataset through the RS485 communication module to extract vegetation spectral feature parameters. The hierarchical evaluation module 13 is used to perform vegetation degradation analysis based on the vegetation spectral feature parameters, set a vegetation degradation index, and send the vegetation degradation index to the central processing platform through the RS485 communication module for hierarchical evaluation and to construct a vegetation degradation hierarchical distribution map.

[0068] Furthermore, the multi-node acquisition module 11 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: constructing a UAV acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager, the UAV acquisition platform including multiple hyperspectral imaging nodes; establishing a master-slave communication network based on the RS485 bus protocol, configuring the multiple hyperspectral imaging nodes, and determining the device addresses and communication parameters of the multiple imaging nodes; setting the data acquisition sequence according to the device addresses and communication parameters of the multiple imaging nodes; activating the UAV acquisition platform according to the data acquisition sequence to plan the UAV flight path; and controlling the multiple hyperspectral imaging nodes to synchronously acquire data from the target vegetation area based on the UAV flight path to obtain the hyperspectral dataset.

[0069] Furthermore, the multi-node acquisition module 11 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: traversing the UAV flight path and matching it with the multiple hyperspectral imaging nodes to generate matching results; synchronously controlling the target vegetation area according to the matching results and setting synchronous acquisition parameters; associating the synchronous acquisition parameters with the multiple hyperspectral imaging nodes according to the matching results to generate a global synchronous acquisition command; and activating the multiple hyperspectral imaging nodes according to the global synchronous acquisition command to perform global monitoring and acquisition to obtain the hyperspectral dataset.

[0070] Furthermore, the bus transmission analysis module 12 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: grouping and encapsulating the hyperspectral dataset for transmission based on the RS485 communication module to generate multiple data packet transmission results; performing data verification on the multiple data packet transmission results to generate data verification results; performing error correction on the multiple data packet transmission results based on the data verification results to generate a hyperspectral correction dataset; and performing feature band analysis on the hyperspectral correction dataset to extract the vegetation spectral feature parameters.

[0071] Furthermore, the bus transmission analysis module 12 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: performing spectral analysis on the multiple hyperspectral imaging nodes through the RS485 communication module to construct a spectral reflectance database; performing spectral feature analysis on the target vegetation area based on the spectral reflectance database to draw vegetation spectral feature curves; performing feature band sensitivity identification according to the vegetation spectral feature curves to generate feature band sensitivity; sorting the target vegetation area by sensitivity according to the feature band sensitivity to generate a regional sensitivity sequence; determining high sensitivity based on the regional sensitivity sequence to identify feature band sensitive areas; using the feature bands of the feature band sensitive areas as indexes to retrieve the spectral reflectance database to obtain reflectance data for multiple feature bands and calculate vegetation spectral indices; and performing spectral morphological feature analysis on the target vegetation area based on the vegetation spectral indices to construct the vegetation spectral feature parameters.

[0072] Furthermore, the bus transmission analysis module 12 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: establishing a communication connection between the RS485 communication module and the multiple hyperspectral imaging nodes, and constructing data communication connection parameters; configuring data acquisition parameters for the multiple hyperspectral imaging nodes according to the data communication connection parameters; polling the multiple hyperspectral imaging nodes according to the data acquisition parameters through the master-slave communication network of the RS485 communication module to generate data acquisition instructions; executing the data acquisition instructions to respond and package the multiple hyperspectral imaging nodes to obtain multiple spectral reflectance data; transmitting the multiple spectral reflectance data through the RS485 communication module, and storing the multiple spectral reflectance data in a structured manner to construct the spectral reflectance database.

[0073] Furthermore, the classification assessment module 13 in the vegetation degradation classification assessment system based on hyperspectral remote sensing is also used for: matching vegetation status based on the vegetation spectral feature parameters to determine multiple vegetation status spectral feature parameters; performing hierarchical analysis on the vegetation spectral feature parameters as assessment indicators to determine a weight allocation matrix; calculating the multiple vegetation status spectral feature parameters based on the weight allocation matrix to obtain multiple feature vectors; introducing an environmental adjustment factor, and performing comprehensive vegetation degradation calculation based on the environmental adjustment factor and the multiple feature vectors to generate an initial vegetation degradation index; and performing multi-level processing on the initial vegetation degradation index to set the vegetation degradation index.

[0074] Furthermore, the grading assessment module 13 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: standardizing parameters based on the initial vegetation degradation index to generate a two-way index, the two-way index including a positive index and a negative index; performing degradation level analysis based on the positive index combined with the initial vegetation degradation index to generate a first degradation level; performing degradation level analysis based on the negative index combined with the initial vegetation degradation index to generate a second degradation level; conducting long-term observation of the target vegetation area based on the first degradation level to construct first observation degradation data; conducting long-term observation of the target vegetation area based on the second degradation level to construct second observation degradation data; and calculating and setting the vegetation degradation index based on the first observation degradation data and the second observation degradation data.

[0075] Furthermore, the grading assessment module 13 in the vegetation degradation grading assessment system based on hyperspectral remote sensing is also used for: prioritizing the vegetation degradation index according to the weight allocation matrix and setting data transmission priority; aggregating the vegetation degradation index according to the data transmission priority through the RS485 communication module to obtain multiple data aggregation frames; sending the multiple data aggregation frames to the central processing platform for spatial autocorrelation analysis to identify degradation clusters; performing time series analysis based on the degradation clusters, monitoring degradation based on the regional time series analysis results, and drawing a vegetation degradation trend map; performing multi-dimensional grading assessment based on the vegetation degradation trend map and the environmental regulation factors to generate multi-level degradation data; and mapping the multi-level degradation data to the target vegetation area for dynamic coordinate matching to construct the vegetation degradation grading distribution map.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The vegetation degradation grading assessment method and specific examples based on hyperspectral remote sensing in the aforementioned Embodiment 1 are also applicable to the vegetation degradation grading assessment system based on hyperspectral remote sensing in this embodiment. Through the foregoing detailed description of the vegetation degradation grading assessment method based on hyperspectral remote sensing, those skilled in the art can clearly understand the vegetation degradation grading assessment system based on hyperspectral remote sensing in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for assessing vegetation degradation based on hyperspectral remote sensing, characterized in that, include: A drone data acquisition platform was built by integrating an RS485 communication module with a hyperspectral remote sensing imager to collect data from multiple nodes in the target vegetation area and obtain a hyperspectral dataset. The hyperspectral dataset was analyzed by bus transmission using an RS485 communication module to extract vegetation spectral characteristic parameters. Vegetation degradation analysis is performed based on the spectral characteristic parameters of the vegetation. A vegetation degradation index is set, and the vegetation degradation index is aggregated and sent to the central processing platform through the RS485 communication module for hierarchical evaluation, thereby constructing a vegetation degradation hierarchical distribution map.

2. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 1, characterized in that, A drone data acquisition platform was constructed by integrating a hyperspectral remote sensing imager with an RS485 communication module to perform multi-node data acquisition on target vegetated areas, obtaining a hyperspectral dataset. The methods included: A drone data acquisition platform is constructed by integrating an RS485 communication module with a hyperspectral remote sensing imager. The drone data acquisition platform includes multiple hyperspectral imaging nodes. A master-slave communication network is established based on the RS485 bus protocol to configure the multiple hyperspectral imaging nodes and determine the device addresses and communication parameters of the multiple imaging nodes. The data acquisition timing is set according to the device addresses and communication parameters of the multiple imaging nodes; Activate the UAV acquisition platform and plan the UAV flight path according to the data acquisition sequence; Based on the UAV's flight path, the multiple hyperspectral imaging nodes are controlled to simultaneously acquire data in the target vegetation area, thereby obtaining the hyperspectral dataset.

3. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 2, characterized in that, Based on the UAV flight path, multiple hyperspectral imaging nodes are controlled to simultaneously acquire data from the target vegetation area to obtain the hyperspectral dataset. The method includes: The flight path of the UAV is traversed and matched with the multiple hyperspectral imaging nodes to generate matching results; Based on the matching results, synchronous control is performed on the target vegetation area, and synchronous acquisition parameters are set. The synchronous acquisition parameters are associated with the multiple hyperspectral imaging nodes according to the matching results to generate a global synchronous acquisition command; The multiple hyperspectral imaging nodes are activated according to the global synchronization acquisition command to perform global monitoring and acquisition, thereby obtaining the hyperspectral dataset.

4. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 2, characterized in that, The hyperspectral dataset is transmitted and analyzed via an RS485 communication module to extract vegetation spectral characteristic parameters. The method includes: The hyperspectral dataset is grouped and encapsulated for transmission using an RS485 communication module, generating multiple data packet transmission results. The multiple data packet transmission results are verified to generate a data verification result. Based on the data verification results, error correction is performed on the transmission results of the multiple data packets to generate a hyperspectral correction dataset. Based on the hyperspectral correction dataset, feature band analysis is performed to extract the spectral feature parameters of the vegetation.

5. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 4, characterized in that, Based on the hyperspectral correction dataset, feature band analysis is performed to extract the spectral feature parameters of the vegetation. The method includes: The RS485 communication module is used to perform spectral analysis on the multiple hyperspectral imaging nodes to construct a spectral reflectance database. Based on the spectral reflectance database, spectral feature analysis is performed on the target vegetation area, and vegetation spectral feature curves are plotted. Based on the vegetation spectral characteristic curve, characteristic band sensitivity is identified, and characteristic band sensitivity is generated. Based on the sensitivity of the characteristic bands, the target vegetation areas are sorted by sensitivity to generate a regional sensitivity sequence. High-sensitivity determination is performed based on the aforementioned regional sensitivity sequence to identify sensitive regions in characteristic bands; The characteristic bands of the sensitive area are used as indexes to search the spectral reflectance database, obtain reflectance data of multiple characteristic bands, and calculate the vegetation spectral index. Based on the vegetation spectral index, the spectral morphological characteristics of the target vegetation area are analyzed, and the vegetation spectral characteristic parameters are constructed.

6. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 4, characterized in that, The method for performing spectral analysis on the multiple hyperspectral imaging nodes and constructing a spectral reflectance database via the RS485 communication module includes: The RS485 communication module is connected to the multiple hyperspectral imaging nodes to establish data communication connection parameters; Configure data acquisition parameters for the plurality of hyperspectral imaging nodes according to the data communication connection parameters; The master-slave communication network of the RS485 communication module cycles through the multiple hyperspectral imaging nodes according to the data acquisition parameters to generate data acquisition commands. The data acquisition command is executed to package the responses of the multiple hyperspectral imaging nodes, thereby obtaining multiple spectral reflectance data. The RS485 communication module transmits the multiple spectral reflectance data, stores the multiple spectral reflectance data in a structured manner, and constructs the spectral reflectance database.

7. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 1, characterized in that, Vegetation degradation analysis is performed based on the aforementioned vegetation spectral characteristic parameters, and a vegetation degradation index is set. The method includes: Based on the vegetation spectral feature parameters, vegetation status matching is performed to determine multiple vegetation status spectral feature parameters. The multiple vegetation state spectral characteristic parameters are used as evaluation indicators to perform hierarchical analysis on the vegetation spectral characteristic parameters and determine the weight allocation matrix. Based on the weight allocation matrix, the multiple vegetation state spectral feature parameters are calculated to obtain multiple feature vectors; An environmental regulation factor is introduced, and a comprehensive vegetation degradation calculation is performed based on the environmental regulation factor and the multiple feature vectors to generate an initial vegetation degradation index. The initial vegetation degradation index is processed in multiple stages to set the vegetation degradation index.

8. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 7, characterized in that, The initial vegetation degradation index is processed through multiple stages to set the vegetation degradation index. The method includes: Based on the initial vegetation degradation index, parameters are standardized to generate a two-way index, which includes positive and negative indicators. Based on the positive indicators and the initial vegetation degradation index, a degradation level analysis is performed to generate a first degradation level. Based on the negative indicators and the initial vegetation degradation index, a degradation level analysis is performed to generate a second degradation level. Based on the first degradation level, long-term observation of the target vegetation area is carried out to construct the first observation degradation data; Based on the second degradation level, long-term observations of the target vegetation area are conducted to construct second observation degradation data; The vegetation degradation index is calculated based on the first and second observed degradation data.

9. The vegetation degradation grading assessment method based on hyperspectral remote sensing as described in claim 7, characterized in that, The vegetation degradation index is aggregated and sent to a central processing platform via an RS485 communication module for graded assessment, and a vegetation degradation graded distribution map is constructed. The method includes: The vegetation degradation index is prioritized according to the weight allocation matrix, and the data transmission priority is set. The vegetation degradation index is aggregated according to the data transmission priority through the RS485 communication module to obtain multiple data aggregation frames; The multiple data aggregation frames are sent to the central processing platform for spatial autocorrelation analysis to identify degraded clustering regions. Time series analysis was performed on the degraded clustered areas, and degradation monitoring was carried out based on the regional time series analysis results to draw a vegetation degradation trend map. Based on the vegetation degradation trend map and the environmental regulation factors, a multi-dimensional hierarchical assessment is conducted to generate multi-level degradation data. The multi-level degradation data is mapped to the target vegetation area for dynamic coordinate matching to construct the vegetation degradation grading distribution map.

10. A vegetation degradation grading assessment system based on hyperspectral remote sensing, characterized in that, The steps for implementing the vegetation degradation grading assessment method based on hyperspectral remote sensing according to any one of claims 1 to 9, wherein the vegetation degradation grading assessment system based on hyperspectral remote sensing comprises: The multi-node acquisition module is used to build a drone acquisition platform by integrating an RS485 communication module with a hyperspectral remote sensing imager to acquire hyperspectral datasets from target vegetation areas through multi-node acquisition. The bus transmission analysis module is used to perform bus transmission analysis of the hyperspectral dataset according to the RS485 communication module to extract vegetation spectral characteristic parameters. The grading assessment module is used to perform vegetation degradation analysis based on the vegetation spectral characteristic parameters, set a vegetation degradation index, and send the vegetation degradation index to the central processing platform for grading assessment through the RS485 communication module to construct a vegetation degradation grading distribution map.

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