Forestry multi-target monitoring system and monitoring method based on monitoring data

Abstract

The invention discloses a forestry multi-target monitoring system and monitoring method based on monitoring data, relates to the field of forestry monitoring, and aims at solving the problems of single dimension, insufficient precision and early warning lag of traditional monitoring, and realizing three-dimensional monitoring of forest region ecology through double-layer rainfall data acquisition and a multi-level evaluation model. Dividing a forest region into sub-regions, arranging a double-layer rainfall acquisition module, synchronously obtaining rainfall data above a canopy (first rainfall) and on the ground (second rainfall), and calculating a vegetation margin through an interception characteristic difference value; the vegetation uniformity is evaluated based on the ratio of the vegetation margin range to the number of the sub-regions, and the vegetation type succession trend is identified through margin index space-time analysis; historical and real-time data are fused to construct a dynamic early warning model, and multi-target comprehensive evaluation and accurate early warning support are provided for forestry resource management.

Description

Technical Field

[0001] This invention relates to the field of forestry monitoring, specifically to a multi-objective forestry monitoring system and method based on monitoring data. Background Technology

[0002] Given the limitations of existing forestry monitoring technologies, and against the backdrop of both global climate change and ecological environmental protection, the scientific management of forestry resources is crucial for maintaining ecological balance and addressing climate change. Traditional forestry monitoring methods primarily rely on manual patrols and single-point sensor deployments, which have the following significant shortcomings: Existing systems often focus on monitoring single indicators, such as rainfall or vegetation cover, lacking a comprehensive assessment of multiple objectives, including the evenness of vegetation spatial distribution and the dynamic trends of different vegetation types. For example, traditional rainfall monitoring equipment can only obtain ground rainfall and cannot reflect the interception effect of vegetation canopy on rainfall, while canopy interception is a key factor affecting forest water cycle and vegetation growth. Manual monitoring is characterized by long cycles, low frequency, and significant influence from topography and climate, making it difficult to obtain monitoring data with high spatiotemporal resolution. Taking vegetation evenness assessment as an example, traditional methods estimate through sampling surveys, but sample bias leads to an error rate as high as 30%-40% in the assessment results, failing to accurately reflect the actual distribution of vegetation in forest areas. The existing system lacks in-depth integration and analysis of historical data and real-time monitoring data. The early warning model is based on a single indicator threshold, such as triggering an alarm when the vegetation coverage rate decreases by more than 5%. It ignores the synergistic effect of changes in vegetation evenness and vegetation type succession, resulting in an early warning accuracy rate of less than 60% and an inability to identify potential risks to the forest ecosystem in a timely manner. Current technical solutions for forestry rainfall monitoring only achieve single-point measurement of rainfall interception, failing to establish a quantitative relationship between interception characteristics and vegetation status; they also lack methods for assessing vegetation spatial heterogeneity, thus failing to meet the needs of multi-objective monitoring. Specifically, existing technologies have the following shortcomings: Considering only rainfall interception data at a single height, without simultaneously acquiring rainfall data above the canopy and at the ground, makes it impossible to construct a complete rainfall interception gradient feature, resulting in a vegetation margin calculation error exceeding 25%. The lack of a mapping relationship between interception characteristics and vegetation biomass (i.e., vegetation margin), as well as the absence of scientific definitions and calculation methods for vegetation evenness and change trends, makes it difficult to achieve a comprehensive evaluation of the health status of forest ecosystems. The monitoring module and data processing module operate independently, lacking cloud database support and a multi-module collaborative working mechanism. This results in low efficiency in data sharing and analysis, failing to meet the needs of real-time monitoring in large-scale forest areas. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-objective monitoring method for forestry based on monitoring data, comprising the following steps: Step 1: Based on the data collection range of the forest understory rainfall feature collection module, the target monitoring forest area is divided into multiple sub-regions, and forest understory rainfall feature collection modules are deployed in each sub-region. Step 2: The rainfall data monitoring and recording module monitors the rainfall intensity in the target forest area, and the forest understory rainfall characteristic acquisition module collects the first and second rainfall data of the sub-region; based on the first and second rainfall data and the rainfall intensity, the first interception feature and the second interception feature are obtained respectively. Step 3: Based on the first interception feature and the second interception feature, obtain the vegetation margin and vegetation margin index of the corresponding sub-region; Step 4: Obtain the vegetation evenness of the target monitoring forest area based on the vegetation margin of each corresponding sub-region; obtain the vegetation change trend of the target monitoring forest area based on the vegetation margin index of each corresponding sub-region. Step 5: Based on the vegetation evenness and vegetation change trend of the target forest area in the same monitoring period from the historical monitoring data of the target forest area, obtain the current change trend of the target forest area, and issue an early warning based on the current change trend of the target forest area.

[0004] Furthermore, the rainfall data monitoring and recording module monitors the rainfall intensity in the target forest area, and the forest understory rainfall characteristic acquisition module collects the first and second rainfall data of the sub-region, including: The rainfall intensity refers to the amount of rainfall during the rainfall period; the first rainfall data refers to the amount of rainfall at a set height above the ground in the target forest area; and the second rainfall data refers to the amount of rainfall at the ground level in the target forest area.

[0005] Furthermore, the process of obtaining the first interception feature and the second interception feature based on the first rainfall data and the second rainfall data and the rainfall intensity, respectively, includes: The first interception feature is the difference between the first rainfall data and the rainfall intensity; the second interception feature is the difference between the second rainfall data and the rainfall intensity.

[0006] Furthermore, the process of obtaining the vegetation margin and vegetation margin index of the corresponding sub-region based on the first and second interception features includes: The first vegetation margin is obtained from the cloud database based on the first interception feature; the vegetation margin of the second interception feature is obtained from the cloud database based on the second interception feature; the difference between the vegetation margin of the second interception feature and the first vegetation margin is used to obtain the second vegetation margin; the vegetation margin is the amount of vegetation per unit area. The vegetation margin index of the corresponding sub-region is obtained by the ratio of the first vegetation margin to the second vegetation margin.

[0007] Furthermore, the method of obtaining the vegetation evenness of the target monitoring forest area based on the vegetation margin of each corresponding sub-region includes: The vegetation evenness of the target forest area is determined by the ratio of the difference between the maximum and minimum vegetation margin differences between corresponding sub-regions to the number of sub-regions.

[0008] Furthermore, the process of obtaining the vegetation change trend of the target monitoring forest area based on the vegetation margin index of each corresponding sub-region includes: The vegetation change trend of the target monitoring forest area refers to the change trend of the proportion of different types of plants. If the vegetation margin index of the corresponding sub-regions is consistent, the vegetation change trend of the target monitoring forest area is stable; otherwise, it is unstable.

[0009] Furthermore, the method of obtaining the current trend of vegetation change in the target monitoring forest area based on the vegetation evenness and vegetation change trend in the same monitoring period from historical monitoring data of the target monitoring forest area includes: Based on the vegetation evenness and vegetation change trend of the target forest area in the same monitoring period from the historical monitoring data, if the evenness is uniform and the vegetation change of the target forest area is stable, then the current change trend of the target forest area is stable; otherwise, the current change trend of the target forest area is unstable.

[0010] The forestry multi-objective monitoring system based on monitoring data, which applies the aforementioned forestry multi-objective monitoring method based on monitoring data, includes a forest understory rainfall characteristic acquisition module, a rainfall data monitoring and recording module, a vegetation status assessment module, a communication module, a cloud data server, an early warning module, and a data processing module. The forest understory rainfall characteristic acquisition module, rainfall data recording module, vegetation status assessment module, communication module, and early warning module are respectively connected to the data processing module; the cloud data server is communicatively connected to the communication module. The aforementioned forest understory rainfall feature acquisition module is used to collect forest understory rainfall feature data; The rainfall data monitoring and recording module is used to record rainfall data, including rainfall intensity, rainfall time point, and rainfall duration; The vegetation status assessment module is used to obtain vegetation margin and vegetation evenness based on forest understory rainfall characteristics and rainfall data within the assessment period.

[0011] Preferably, the forest understory rainfall feature acquisition module includes a front-end feature data acquisition module, a back-end feature data acquisition module, and a data processing module; The front-end feature data acquisition module and the back-end feature data acquisition module are respectively connected to the data processing module; The aforementioned front-end feature data acquisition module is used to acquire the first rainfall data; the aforementioned back-end feature data acquisition module is used to acquire the second rainfall data. The data processing module is used to obtain a first interception feature based on the first rainfall data and a second interception feature based on the second rainfall data.

[0012] Preferably, the vegetation status assessment module is used to obtain vegetation margin and vegetation evenness based on forest understory rainfall characteristic data and rainfall data within the assessment period, including: Based on the first interception feature, the second interception feature, and the corresponding rainfall feature, the vegetation margin of the corresponding sub-regions of the target monitoring forest area is obtained; based on the vegetation margin of each corresponding sub-region, the vegetation evenness of the target monitoring forest area is obtained.

[0013] The beneficial effects of this invention are: by constructing a scalable cloud-based vegetation model database, this invention establishes differentiated interception feature-vegetation quantity mapping models for different vegetation types based on their canopy structure and interception characteristics, thereby achieving precise adaptation to complex forestry scenarios. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a multi-objective monitoring method for forestry based on monitoring data. Figure 2 This is a schematic diagram of the principle of a forestry multi-objective monitoring system based on monitoring data. Detailed Implementation

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0016] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0017] like Figure 1 As shown, Step 1: Forest area sub-region division and data collection module deployment Data collection scope determined The effective data acquisition range of the forest understory rainfall feature acquisition module is affected by the sensor type, installation height, and environmental factors (such as vegetation density and terrain undulation). For the pre-stage feature data acquisition module (used to collect the first rainfall data, i.e., rainfall at a set height above the ground) and the post-stage feature data acquisition module (used to collect the second rainfall data, i.e., rainfall at the ground level) used in this invention, their acquisition range mainly depends on the sensor's monitoring accuracy and signal coverage. Typically, the pre-stage sensor uses a high-precision tipping bucket rain gauge or a weighing rain gauge, installed at a height of 2-5 meters above the ground (determined based on the common canopy height range of shrubs and trees), and its horizontal monitoring range can cover an area with a radius of 5-10 meters centered on the sensor. The post-stage sensor is installed on a flat surface, using the same type of rain gauge, and its monitoring range is consistent with that of the pre-stage sensor. Subregion partitioning method A Geographic Information System (GIS) was used to digitally model the target forest area. Taking into account factors such as topography (e.g., slope, aspect, altitude), vegetation type (e.g., coniferous forest, broadleaf forest, mixed forest), and vegetation density, the forest area was divided into several sub-regions with relatively uniform geographical and vegetation characteristics. The specific division steps are as follows: Collect basic geographic data (such as contour maps and land use type maps) and vegetation data (such as vegetation distribution maps and vegetation height data) of the forest area, and integrate and preprocess the data using GIS software. Clustering analysis algorithms (such as K-means algorithm) are used to divide the forest area into regions. Topographic parameters (slope, aspect, altitude) and vegetation parameters (vegetation type, vegetation coverage, vegetation height) are used as clustering indicators to divide regions with similar characteristics into the same sub-region. Boundary corrections are performed on the divided sub-regions to ensure that the area of ​​each sub-region is moderate (generally 0.1-1 square kilometers) to facilitate sensor deployment and data collection and management. Data Acquisition Module Deployment Principles Within each sub-region, forest understory rainfall characteristic acquisition modules are deployed according to the following principles: Sensor location selection: The front-end sensor should be installed above a representative vegetation canopy in the sub-area to avoid obstruction by tall trees or buildings, ensuring accurate collection of rainfall data above the canopy; the rear-end sensor should be installed in an open area within 5-10 meters of the front-end sensor to avoid the influence of surface water, vegetation cover, and other factors on ground rainfall collection. Sensor quantity configuration: The number of sensors should be configured reasonably according to the area and vegetation uniformity of the sub-region. For sub-regions with small areas (e.g., 0.1-0.5 square kilometers) and uniform vegetation, 1-2 sets of data acquisition modules should be deployed in each sub-region; for sub-regions with large areas (e.g., 0.5-1 square kilometers) or uneven vegetation, the number of sensors should be increased appropriately to ensure the representativeness and reliability of the data. Sensor Calibration and Initialization: Before deploying the sensors, all upstream and downstream sensors are calibrated. By comparing with a standard rain gauge, the sensor's measurement parameters are adjusted to ensure the accuracy of the collected data. Simultaneously, initial information such as the installation location coordinates, altitude, and vegetation type of each sensor is recorded for subsequent data processing and analysis. Step 2: Rainfall and precipitation data collection and interception feature calculation Rainfall data monitoring and recording The rainfall data monitoring and recording module employs a tipping bucket rain gauge or an ultrasonic rain gauge to monitor the rainfall intensity, timing, and duration in the target forest area in real time. Rainfall intensity is defined as the amount of rainfall per unit time (unit: mm / h). By acquiring and processing the pulse or electrical signals output by the sensors in real time, the rainfall intensity within each time interval (e.g., 1 minute) is calculated. Simultaneously, the start time, end time, and total rainfall for each rainfall event are recorded, providing fundamental data for subsequent interception feature calculations. First and second rainfall data collection Pre-stage feature data acquisition module (first rainfall data acquisition): A pre-stage sensor installed at a set height (e.g., 3 meters) above the ground collects rainfall data above the canopy in real time. Because the vegetation canopy has a interception effect on rainfall, there is a certain difference between the rainfall amount above the canopy and the rainfall intensity. The pre-stage sensor can accurately acquire this data, reflecting the initial interception of rainfall by the vegetation canopy. Post-stage feature data acquisition module (secondary rainfall data acquisition): A post-stage sensor installed on the ground collects the amount of rainfall that reaches the ground after being intercepted by the vegetation canopy. This data is affected by factors such as vegetation canopy interception, tree stem flow, and ground cover, and can directly reflect the final interception effect of vegetation on rainfall. Method for calculating interception features First interception feature calculation: The first interception feature is the difference between the first rainfall data and the rainfall intensity within the same time interval. The specific calculation formula is as follows: C 1= Ph - I × t in, C 1 indicates the first interception feature (unit: mm).Ph This represents the first rainfall data (unit: mm) collected by the preceding sensor. I Indicates rainfall intensity (unit: mm / h). t This indicates the duration of rainfall (in hours). The first interception characteristic reflects the amount of rainwater intercepted by the vegetation canopy during rainfall; the higher the value, the stronger the canopy's interception capacity. The second interception feature is calculated as the difference between the second rainfall data and the rainfall intensity over the same time interval. The specific calculation formula is as follows: C 2= Pg - I × t in, C 2 indicates the second retention feature (unit: mm). Pg This represents the second rainfall data (unit: mm) collected by the subsequent sensor. The second interception feature reflects the reduction in rainfall reaching the ground after being intercepted by the vegetation canopy and surface vegetation, comprehensively demonstrating the vegetation system's ability to intercept rainfall. When calculating interception characteristics, the temporal distribution of rainfall needs to be considered. For prolonged, continuous rainfall, canopy interception may reach saturation, at which point the trend of interception characteristics tends to stabilize. However, for short-duration, heavy rainfall, canopy interception may not reach saturation, and the interception characteristics change more significantly with rainfall intensity. Therefore, in data processing, it is necessary to classify and analyze different types of rainfall events to improve the accuracy of interception characteristic calculations. Step 3: Calculation of Vegetation Margin and Vegetation Margin Index Cloud database establishment The cloud database stores a large amount of data on the correlation between interception characteristics and vegetation cover (plant cover per unit area, unit: kg / m²) under different vegetation types (such as pine, cypress, shrubs, etc.), different growth stages (seedling stage, growth stage, maturity stage), and different environmental conditions (such as different rainfall, temperature, and soil types). This data was obtained through the following methods: Field survey and experiment: Select typical forest area plots, set up standard monitoring equipment, collect interception characteristic data of different vegetation types under different rainfall conditions, and obtain corresponding vegetation volume data through manual measurement or remote sensing technology. Data preprocessing and modeling: The collected data is cleaned, filtered, and normalized. Data modeling methods such as regression analysis and neural networks are used to establish a mathematical model between the cutoff features and vegetation cover. For example, for a specific vegetation type, the following regression model can be established: M = a × C 1+b × C 2+ c in, M Indicates vegetation cover. a , b , c These are the model parameters, obtained through training on historical data. First vegetation margin calculation The first retention characteristic calculated in step two C 1. Query the corresponding vegetation data in the cloud database. Since different vegetation types respond differently to the first cutoff feature, it is necessary to first determine the vegetation type within the sub-region, and then obtain the corresponding vegetation data based on the model or data table for that vegetation type. C The first vegetation margin corresponding to 1 M 1 (Unit: kg / m²). For example, if the main vegetation in a sub-region is pine trees, by querying the pine tree interception characteristics-vegetation quantity correspondence table, when... C When 1 is 5mm, the corresponding M 1 is 10 kg / m². Second vegetation margin calculation Similarly, based on the second interception feature C 2. Obtain the corresponding second vegetation margin from the cloud database. M 2′. Then, calculate the second vegetation margin. M 2 is M 2′− M 1, which is the difference between the vegetation margin corresponding to the second interception feature and the first vegetation margin. It is important to note that the second vegetation margin reflects the amount of vegetation intercepted by surface vegetation (such as herbaceous plants and ground cover plants) for rainfall, while the first vegetation margin mainly reflects the amount of vegetation intercepted by canopy vegetation (such as trees and shrubs). Vegetation margin index calculation Vegetation margin index R The ratio of the first vegetation margin to the second vegetation margin is calculated using the following formula: R = M 2 M 1 The vegetation margin index reflects the relative contribution of canopy vegetation and ground vegetation to rainfall interception. This index can be used to analyze the growth status and rainfall interception capacity changes of different vegetation layers. For example, when... R A larger size indicates a denser canopy vegetation and stronger interception capacity; when R A smaller value may indicate that the surface vegetation is growing well or that the canopy vegetation is sparse. Step 4: Calculation of vegetation evenness and vegetation change trend Calculation of vegetation evenness Vegetation evenness is used to measure the degree of difference in vegetation margin among different sub-regions within a target monitored forest area. The specific calculation steps are as follows: Collect vegetation cover data for all sub-regions Mi ( i =1,2,⋯, n , n (Number of sub-regions). Calculate the maximum vegetation margin. Mmax and minimum value Mmin and their differences D = Mmax - Mmin .

[0018] vegetation evenness U Defined as difference D Number of sub-regions n The ratio, that is: U = nD In addition, to more comprehensively reflect the distribution of vegetation margin, the standard deviation (SDO) can also be used. σ ), coefficient of variation ( CV Statistical indicators such as ( ) are used for auxiliary calculations. The formula for calculating the standard deviation is: σ = n 1 i =1∑ n ( Mi - M )2 in, M This represents the average vegetation margin. The coefficient of variation is calculated using the following formula: CV = Mσ The smaller the vegetation evenness (or the smaller the standard deviation or coefficient of variation), the more uniform the vegetation margin distribution in the forest area and the more consistent the vegetation growth. Conversely, it indicates that there are significant differences in vegetation distribution, and there may be problems of local vegetation being too dense or too sparse. Calculation of vegetation change trends The vegetation change trend analysis mainly examines the changes in the proportion of different plant types within the target monitored forest area. The specific methods are as follows: For each sub-region, based on the vegetation margin index R Historical data (such as data from multiple past monitoring periods) are used to analyze its changing trends over time. The vegetation margin index reflects the relative proportion of canopy vegetation to ground vegetation, so its changes can indirectly reflect changes in the proportion of different types of plants (such as trees, shrubs, and herbaceous plants). Compare the vegetation margin index of each sub-region during the current monitoring period with historical data for the same period (data under the same season and rainfall conditions). If the vegetation margin index of most sub-regions remains stable during the monitoring period (i.e., the change range is within the set threshold, such as ±5%), and the difference in vegetation margin index between sub-regions is small, it indicates that the vegetation change trend of the target monitoring forest area is stable. Conversely, if the vegetation margin index of multiple sub-regions changes significantly (exceeding the threshold), or the difference in index between sub-regions increases significantly, it indicates that the vegetation change trend is unstable, and there may be vegetation type succession, pest and disease impacts, or human intervention. To more accurately predict vegetation change trends, time series analysis methods (such as ARIMA model and exponential smoothing) can be used to model the vegetation margin index, analyze its long-term trends, seasonal changes and periodic fluctuations, and provide a more scientific basis for vegetation management. Step 5: Trend Analysis and Early Warning Historical data processing Collect historical monitoring data for the target forest area for at least 3-5 years, including vegetation evenness for each monitoring period (e.g., monthly, quarterly). Uhist and vegetation change trends Thist Historical data is preprocessed, including data cleaning (removing outliers and handling missing values) and normalization, to ensure data accuracy and usability. Current trend judgment The vegetation evenness calculated in the current monitoring period Ucurr and vegetation change trends Tcurr Compare and analyze the data with historical data from the same period (i.e., historical data from the same monitoring period): If the current vegetation uniformity Ucurr Within the normal fluctuation range of the historical average for the same period (i.e.) Ucurr exist Uhist (within the range of mean ± standard deviation), and the vegetation change trend Tcurr If the vegetation margin index of each sub-region changes little and the difference is not significant, then the current trend of change in the target monitoring forest area is judged to be stable. If the current vegetation uniformity Ucurr Significant deviation from historical levels for the same period (e.g., greater than the historical average + 2 standard deviations or less than the historical average - 2 standard deviations), or vegetation change trends. Tcurr If the vegetation margin index of multiple sub-regions changes significantly and the differences increase, then the current trend of change is judged to be unstable. Early warning mechanism established Based on the assessment of current trends, establish early warning mechanisms at different levels: Level 1 Warning (Stable State): When the vegetation change trend is stable, the system sends a normal state notification, which requires no special handling, but real-time monitoring continues. Level 2 Warning (Mild Instability): When vegetation evenness deviates to a certain extent or the vegetation change trend begins to show signs of instability (such as an increase in the coefficient of variation of 5%-10%), the system issues a warning signal to remind forestry managers to pay attention to the vegetation status of the relevant sub-areas and conduct on-site verification and preliminary analysis. Level 3 Warning (Severe Instability): When vegetation evenness deviates significantly from historical levels or vegetation change trends show obvious instability (such as a coefficient of variation increasing by more than 10%, or abnormal changes in vegetation margin index in multiple sub-regions), the system issues an emergency warning signal and automatically generates a detailed analysis report, including the location of the abnormal sub-regions, changes in vegetation margin, possible influencing factors (such as pests and diseases, drought, human deforestation, etc.), and recommends corresponding control measures (such as vegetation replanting, pest and disease control, restriction of human activities, etc.). During the early warning process, the system can also combine other environmental data (such as temperature, humidity, soil moisture, wind speed, etc.) for comprehensive analysis to improve the accuracy and reliability of the warning. For example, when unstable vegetation change trends are accompanied by prolonged drought or abnormally high temperatures, it may indicate the impact of drought stress on vegetation growth, requiring timely irrigation or drought relief measures. like Figure 2 As shown, a forestry multi-objective monitoring system based on monitoring data. The forestry multi-objective monitoring system based on monitoring data of the present invention includes a forest understory rainfall characteristic acquisition module, a rainfall data monitoring and recording module, a vegetation status assessment module, a communication module, a cloud data server, an early warning module, and a data processing module. The connection relationships between the modules are as follows: The forest understory rainfall characteristic acquisition module, rainfall data recording module, vegetation status assessment module, communication module, and early warning module are connected to the data processing module to realize data acquisition, processing, transmission, and early warning command sending. The cloud data server communicates with the data processing module through the communication module to realize the storage, management and sharing of data. Forest Rainfall Feature Collection Module Module composition: It includes a front-end feature data acquisition module, a back-end feature data acquisition module, and a data processing sub-module. Pre-amplitude feature data acquisition module: Employs a high-precision tipping bucket rain gauge or weighing rain gauge, installed at a height of 2-5 meters above the ground, to collect initial rainfall data (rainfall above the canopy). The sensor is waterproof, dustproof, and interference-resistant, enabling it to operate stably in harsh outdoor environments. The subsequent feature data acquisition module uses the same type of rain sensor as the previous stage, installed on a flat surface, to collect secondary rainfall data (rainfall at the ground level). Protective devices are installed around the sensor to prevent surface debris, animal activity, and other factors from affecting data acquisition. Data processing submodule: It adopts an embedded microprocessor (such as an ARM processor), connects to the front-end and back-end sensors, collects the signals output by the sensors in real time, performs analog-to-digital conversion, data filtering and preliminary processing (such as calculating the rainfall within a certain time interval), and transmits the processed data to the data processing module. Rainfall data monitoring and recording module Module function: Real-time monitoring of rainfall intensity, rainfall time and duration in the target forest area, and recording the data to provide basic data for subsequent interception feature calculation. Sensor selection: Use either a tipping bucket rain gauge or an ultrasonic rain gauge. The tipping bucket rain gauge measures rainfall by the number of times the bucket tipps, and has the advantages of simple structure and low cost. The ultrasonic rain gauge calculates rainfall by measuring the reflection signal of ultrasonic waves by raindrops, and has the characteristics of no mechanical parts, high accuracy and fast response speed. Data recording method: Rainfall data is recorded in the form of time series, with time intervals set to 1 minute or 5 minutes to ensure accurate reflection of the dynamic changes in rainfall. Vegetation status assessment module Module Functions: Within the assessment period (e.g., weekly, monthly), based on forest understory rainfall characteristics and precipitation data, calculate vegetation margin, vegetation evenness, and vegetation change trends to assess the growth status and ecological function of vegetation. Hardware composition: It adopts a high-performance digital signal processor (DSP) or field-programmable gate array (FPGA), which has powerful data processing capabilities and can quickly process a large amount of monitoring data. Software Algorithms: Integrating algorithms for calculating intercepted features, vegetation margin and vegetation margin index, vegetation evenness and vegetation change trends, etc., the system achieves accurate assessment of vegetation status by calling vegetation models and historical data in the cloud data server. Communication module Communication methods: Supports both wired communication (such as Ethernet, RS-485) and wireless communication (such as 4G, LoRa, WiFi), selecting the appropriate communication method based on the actual environment of the forest area. In areas with good signal coverage, 4G or WiFi is used for wireless communication to achieve real-time data transmission; in remote or weak signal areas, LoRa technology is used for long-distance, low-power communication, or a wired connection is made to a nearby data aggregation node. Communication Protocol: Standard communication protocols (such as TCP / IP and Modbus) are used to ensure reliable data transmission and compatibility. Data is encrypted during transmission to prevent leakage and tampering. cloud data server Hardware architecture: It adopts a distributed server cluster, which has a large capacity for data storage and high-concurrency data processing, and can support the data access and storage of a large number of monitoring nodes. Early warning module Early warning methods: Supports multiple early warning methods such as sound alarm, SMS notification, and email reminder to ensure that forestry management personnel can obtain early warning information in a timely manner. Warning level settings: Based on the severity of vegetation change trends, three warning levels (Level 1, Level 2, and Level 3) are set, with different warning signals and handling measures corresponding to different levels. Early warning report generation: Automatically generates detailed early warning reports, including early warning time, early warning area, early warning level, changes in vegetation status, possible influencing factors, and recommended control measures, providing a basis for decision-making. Data processing module Module Function: As the core processing unit of the system, it is responsible for receiving data from various modules, performing comprehensive processing and analysis, including data fusion, data calibration, algorithm invocation, and result output. Hardware platform: It adopts industrial-grade computers or servers, which have stable operating performance and powerful computing capabilities, and can support real-time data processing and the operation of complex algorithms. Software system: Based on Linux or Windows operating systems, develop customized data processing software to achieve control and management of the entire monitoring system. System Workflow Data acquisition phase: The forest rainfall characteristic acquisition module and the rainfall data monitoring and recording module collect rainfall data and rainfall characteristic data in the forest area in real time. After preliminary processing by the data processing submodule, the data is transmitted to the data processing module. Data processing and analysis phase: After receiving the data, the data processing module calls the algorithm in the vegetation status assessment module to calculate the cutoff characteristics, vegetation margin, vegetation margin index, vegetation evenness, and vegetation change trend. Simultaneously, it retrieves vegetation model data and historical data from the cloud data server through the communication module for comparative analysis and modeling. Early warning and decision-making phase: Based on the calculated vegetation evenness and vegetation change trends, combined with historical monitoring data, the current trend of change in the target monitoring forest area is determined. If an anomaly occurs, the early warning module is triggered, sending corresponding early warning signals and reports. Data storage and sharing stage: The processed data and analysis results are stored on the cloud data server through the communication module. At the same time, external users can access and retrieve the data through the API interface to realize long-term data storage and shared application.

[0019] Example: Monitoring Application in Temperate Deciduous Broadleaf Forests A temperate deciduous broad-leaved forest area (approximately 5 square kilometers) was selected, with the main vegetation consisting of shrubs (60%), locust trees (30%), and herbaceous plants. Local vegetation degradation exists in this forest area, necessitating monitoring of vegetation evenness and the changing trends in the tree-to-herbaceous vegetation ratio.

[0020] Specific implementation steps Step 1: Forest Area Sub-region Division and Sensor Deployment The data acquisition range is determined by selecting a tipping bucket rain gauge (accuracy ±0.2mm) as the front-end sensor, with an installation height of 3 meters and a horizontal monitoring radius of 8 meters; the rear-end sensor is of the same model and is installed in an open area on the ground 8 meters away from the front-end sensor, avoiding coverage by herbaceous plants (coverage <10%).

[0021] Subregion division utilizes GIS to import forest area DEM data (1m resolution) and vegetation distribution maps (based on UAV remote sensing interpretation). Slope (0-25°), aspect (north-southeast, east-west), vegetation height (5-15 meters), and vegetation coverage (30%-80%) are extracted as clustering indicators. The K-means algorithm is used to divide the area into 10 subregions (0.3-0.7 square kilometers), including 6 sunny slopes (slope <10°) and 4 shady slopes (slope 10-25°).

[0022] Two sets of data acquisition modules were deployed in each sub-region (one set in the sunny slope with relatively uniform vegetation, and two sets in the shady slope with different vegetation), for a total of 20 sets. Sensor calibration: The sensors were compared with a standard rain gauge (accuracy ±0.1mm) in the laboratory, and the tipping bucket tilting threshold was adjusted to 0.5mm / time to ensure data consistency.

[0023] Step 2: Rainfall Data Acquisition and Interception Feature Calculation Rainfall data monitoring showed that on July 15, 2024, a short-term heavy rainfall occurred (14:00-14:30). The rainfall data monitoring module recorded a peak rainfall intensity of 60 mm / h, a total rainfall of 25 mm, and a rainfall duration of 0.5 hours.

[0024] Rainfall data collection: In sunny slope area A, the first-stage sensor collected 18mm of rainfall data (7mm intercepted by the canopy), and the second-stage sensor collected 12mm of rainfall data (13mm intercepted by the ground surface); in shady slope area B, the first-stage sensor collected 15mm of rainfall data (10mm intercepted by the canopy), and the second-stage sensor collected 10mm of rainfall data (15mm intercepted by the ground surface).

[0025] Calculation of interception features Sub-region A, first interception characteristic: C_1 = 18 - 60Ã - 0.5 = -12mm (Note: A negative value indicates that the canopy is not saturated. The actual canopy interception amount = rainfall intensity × time - first rainfall data = 30 - 18 = 12mm. The formula is corrected to C_1 = IÃ - t - P_h to avoid negative values).

[0026] Sub-region A, second interception characteristic: C_2 = 60⁻⁵ - 0.5 - 12 = 18 mm (total rainfall 30 mm, ground reception 12 mm, total interception 18 mm).

[0027] Step 3: Calculation of Vegetation Margin and Index The interception feature of shrubs in the cloud database query - the vegetation volume model is: M = 0.8Ã-C_1 + 0.6Ã-C_2 +2 (trained with 3 years of measured data in this forest area, R²=0.92).

[0028] Subregion A: M_1 = 0.8⁻¹² + 0.6⁻¹⁸ + 2 = 23.6 kg / m² (canopy vegetation).

[0029] The surface vegetation coverage of sub-region A is M_2' = 0.5Ã-C_2 + 1 = 0.5Ã-18 + 1 = 10kg / m² (herbaceous plant model).

[0030] The second vegetation margin M_2 = 10kg / m² (calculated directly using the surface model; M_2' - M_1 in the original step two was corrected to an independent model).

[0031] Vegetation margin index R = 23.6 / 10 = 2.36 (ratio of canopy to surface vegetation).

[0032] Step 4: Calculation of Uniformity and Trend Vegetation evenness data were collected from 10 sub-regions: average 20 kg / m², maximum 25 kg / m², minimum 15 kg / m², difference D=10, evenness U=10 / 10=1 kg / m²; standard deviation σ=3.2, coefficient of variation CV=16% (historical CV=12% for the same period, indicating a decrease in evenness).

[0033] Compared with the vegetation margin index in the same period of 2023 (average R=2.5, current R=2.36), the change is -5.6% (exceeding the threshold ±5%), and the R value of 3 sub-regions has decreased by more than 8%, indicating that the vegetation change trend is unstable (the proportion of trees may decrease).

[0034] Step 5: Early Warning and Handling The system triggered a level-two warning and generated a report indicating that vegetation margin in the shady slope area B decreased by 10% and R value decreased by 12%. It recommended to investigate pests and diseases (in fact, poplar longhorn beetle damage was found) and to plan for replanting shrub seedlings in the fall.

Claims

1. A forestry multi-objective monitoring method based on monitoring data, characterized in that, Includes the following steps: Step 1: Based on the data collection range of the forest understory rainfall feature collection module, the target monitoring forest area is divided into multiple sub-regions, and forest understory rainfall feature collection modules are deployed in each sub-region. Step 2: The rainfall data monitoring and recording module monitors the rainfall intensity in the target forest area, and the forest understory rainfall characteristic acquisition module collects the first and second rainfall data of the sub-region; based on the first and second rainfall data and the rainfall intensity, the first interception feature and the second interception feature are obtained respectively. Step 3: Based on the first interception feature and the second interception feature, obtain the vegetation margin and vegetation margin index of the corresponding sub-region; Step 4: Obtain the vegetation evenness of the target monitoring forest area based on the vegetation margin of each corresponding sub-region; obtain the vegetation change trend of the target monitoring forest area based on the vegetation margin index of each corresponding sub-region. Step 5: Based on the vegetation evenness and vegetation change trend of the target forest area in the same monitoring period from the historical monitoring data of the target forest area, obtain the current change trend of the target forest area, and issue an early warning based on the current change trend of the target forest area.

2. The forestry multi-objective monitoring method based on monitoring data according to claim 1, characterized in that, The rainfall data monitoring and recording module monitors the rainfall intensity in the target forest area, while the forest understory rainfall characteristic acquisition module collects the first and second rainfall data for its sub-region, including: The rainfall intensity refers to the amount of rainfall during the rainfall period; the first rainfall data refers to the amount of rainfall at a set height above the ground in the target forest area; and the second rainfall data refers to the amount of rainfall at the ground level in the target forest area.

3. The forestry multi-objective monitoring method based on monitoring data according to claim 2, characterized in that, The process of obtaining the first interception feature and the second interception feature based on the first rainfall data, the second rainfall data, and the rainfall intensity, respectively, includes: The first interception feature is the difference between the first rainfall data and the rainfall intensity; the second interception feature is the difference between the second rainfall data and the rainfall intensity.

4. The forestry multi-objective monitoring method based on monitoring data according to claim 3, characterized in that, The process of obtaining the vegetation margin and vegetation margin index of the corresponding sub-region based on the first and second interception features includes: The first vegetation margin is obtained from the cloud database based on the first interception feature; the vegetation margin of the second interception feature is obtained from the cloud database based on the second interception feature; the difference between the vegetation margin of the second interception feature and the first vegetation margin is used to obtain the second vegetation margin; the vegetation margin is the amount of vegetation per unit area. The vegetation margin index of the corresponding sub-region is obtained by the ratio of the first vegetation margin to the second vegetation margin.

5. The forestry multi-objective monitoring method based on monitoring data according to claim 4, characterized in that, The method of obtaining the vegetation evenness of the target monitoring forest area based on the vegetation margin of each corresponding sub-region includes: The vegetation evenness of the target forest area is determined by the ratio of the difference between the maximum and minimum vegetation margin differences between corresponding sub-regions to the number of sub-regions.

6. The forestry multi-objective monitoring method based on monitoring data according to claim 5, characterized in that, The method of obtaining the vegetation change trend of the target monitoring forest area based on the vegetation margin index of each corresponding sub-region includes: The vegetation change trend of the target monitoring forest area refers to the change trend of the proportion of different types of plants. If the vegetation margin index of the corresponding sub-regions is consistent, the vegetation change trend of the target monitoring forest area is stable; otherwise, it is unstable.

7. The forestry multi-objective monitoring method based on monitoring data according to claim 6, characterized in that, The method of obtaining the current trend of vegetation change in the target monitoring forest area based on the vegetation evenness and vegetation change trend in the same monitoring period from historical monitoring data of the target monitoring forest area includes: Based on the vegetation evenness and vegetation change trend of the target forest area in the same monitoring period from the historical monitoring data, if the evenness is uniform and the vegetation change of the target forest area is stable, then the current change trend of the target forest area is stable; otherwise, the current change trend of the target forest area is unstable.

8. A forestry multi-objective monitoring system based on monitoring data, characterized in that, The forestry multi-target monitoring method based on monitoring data according to any one of claims 1-7 includes a forest understory rainfall characteristic acquisition module, a rainfall data monitoring and recording module, a vegetation status assessment module, a communication module, a cloud data server, an early warning module, and a data processing module; The forest understory rainfall characteristic acquisition module, rainfall data recording module, vegetation status assessment module, communication module, and early warning module are respectively connected to the data processing module; the cloud data server is communicatively connected to the communication module. The aforementioned forest understory rainfall feature acquisition module is used to collect forest understory rainfall feature data; The rainfall data monitoring and recording module is used to record rainfall data, including rainfall intensity, rainfall time point, and rainfall duration; The vegetation status assessment module is used to obtain vegetation margin and vegetation evenness based on forest understory rainfall characteristics and rainfall data within the assessment period.

9. The forestry multi-objective monitoring system based on monitoring data according to claim 8, characterized in that, The forest understory rainfall feature acquisition module includes a front-end feature data acquisition module, a back-end feature data acquisition module, and a data processing module; The front-end feature data acquisition module and the back-end feature data acquisition module are respectively connected to the data processing module; The aforementioned front-end feature data acquisition module is used to acquire the first rainfall data; the aforementioned back-end feature data acquisition module is used to acquire the second rainfall data. The data processing module is used to obtain a first interception feature based on the first rainfall data and a second interception feature based on the second rainfall data.

10. The forestry multi-objective monitoring system based on monitoring data according to claim 9, characterized in that, The vegetation status assessment module is used to obtain vegetation margin and vegetation evenness based on forest understory rainfall characteristics and rainfall data within the assessment period, including: Based on the first interception feature, the second interception feature, and the corresponding rainfall feature, the vegetation margin of the corresponding sub-regions of the target monitoring forest area is obtained; based on the vegetation margin of each corresponding sub-region, the vegetation evenness of the target monitoring forest area is obtained.

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