System for determining the relationship between sand vegetation coverage and hydrological parameters

Abstract

The application discloses a sand land vegetation coverage degree and hydrological parameter relationship measuring system and belongs to the technical field of ecological hydrological monitoring. The system comprises a vegetation coverage degree measuring module, a soil moisture monitoring module, a rainfall infiltration monitoring module, a surface runoff monitoring module and a relationship analysis module. The vegetation coverage degree measuring module realizes real-time automatic measurement by adopting a multispectral imaging technology. The soil moisture monitoring module realizes multipoint and multi-depth monitoring by adopting a distributed sensor array. The rainfall infiltration monitoring module determines an infiltration rate based on dynamic changes of soil moisture content. The surface runoff monitoring module quantitatively measures runoff. The relationship analysis module integrates data of all modules to establish a vegetation-hydrology mutual feedback model. The application realizes comprehensive and continuous monitoring and quantitative analysis of the sand land vegetation-hydrology process, and provides a scientific basis for ecological restoration effect evaluation and water and soil conservation engineering optimization.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of ecological hydrological monitoring, and relates to a sand land vegetation coverage and hydrological parameter relationship determination system, in particular to a comprehensive determination system integrating vegetation coverage measurement, soil moisture monitoring, rainfall infiltration monitoring and surface runoff monitoring, which can be used for quantitative analysis of the influence law of sand land vegetation restoration on hydrological processes. BACKGROUND

[0002] The sand land ecosystem is a globally important ecologically fragile region, and vegetation coverage, as a key indicator reflecting the ecological condition of the sand land, has an important regulating effect on regional hydrological processes. In recent years, with the implementation of the projects of returning farmland to forest and ecological restoration, the vegetation coverage of the sand land has increased significantly, which has a profound impact on rainfall infiltration, soil moisture distribution, surface runoff and other hydrological elements. Accurate determination of the quantitative relationship between vegetation coverage and hydrological parameters has important theoretical significance and practical value for scientific evaluation of ecological restoration effect, optimization of water and soil conservation measures, and prediction of regional hydrological response.

[0003] Traditional vegetation-hydrological relationship research mainly relies on separate observation and empirical model. Chinese invention patent CN118839621A discloses a variable parameter hydrological simulation method based on Bayesian model and multi-dimensional joint distribution, which constructs a VIC distributed hydrological model considering vegetation dynamics, establishes a Bayesian hierarchical model to estimate the relationship between vegetation parameters and hydrological model parameters, uses Copula function to establish a complex multi-dimensional joint relationship of precipitation, temperature, vegetation and hydrological model parameters, iteratively updates the Copula function parameters through the Bayesian hierarchical model, and realizes the dynamic adaptive adjustment of the hydrological model parameters to the vegetation parameters. However, this method is mainly based on statistical analysis and numerical simulation of historical data, relies on a large number of calibration parameters and prior information, and is mainly suitable for hydrological simulation at the basin scale, which is difficult to realize real-time and continuous monitoring of vegetation-hydrological processes in local areas of the sand land. In addition, the description of vegetation in this method mainly relies on the leaf area index and vegetation coverage inverted by remote sensing, which has limited spatial and temporal resolution, and is difficult to capture the rapid changes of sand land vegetation coverage and its immediate response to hydrological processes, which has limitations in the application of regions such as sand land ecosystem with significant vegetation changes and complex hydrological processes.

[0004] Currently, the measurement methods of vegetation coverage mainly include ground plot investigation and remote sensing inversion. The ground plot investigation has high precision but large workload, poor timeliness, and is difficult to realize large-scale continuous monitoring. Although the remote sensing inversion has wide coverage, it is affected by clouds and atmospheric conditions, and the temporal resolution is usually several days to several weeks, and the spatial resolution is usually several meters to tens of meters, which is difficult to meet the monitoring needs of small scale and high frequency of sandy land. The monitoring of hydrological parameters is usually carried out by observing a single point or a few points, which is not representative in space and cannot reflect the spatial heterogeneity of the hydrological process of sandy land. More importantly, the existing technology lacks a systematic solution to integrate the measurement of vegetation coverage and the monitoring of hydrological parameters. Vegetation observation and hydrological observation are usually independent of each other, and the temporal and spatial scales of data collection do not match, which makes it difficult to establish an accurate quantitative relationship between vegetation and hydrology.

[0005] Under the background of vegetation restoration in sandy land, it is urgent to develop an integrated measurement system that can simultaneously and continuously measure vegetation coverage and multiple hydrological parameters, and quantitatively analyze the correlation between them, in order to accurately monitor and deeply understand the vegetation-hydrological process of sandy land, and provide scientific basis for ecological restoration effect evaluation and soil and water conservation engineering optimization. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art that the measurement of vegetation coverage and the monitoring of hydrological parameters are separated, and it is difficult to establish an accurate quantitative relationship. The present application provides a system for measuring the relationship between vegetation coverage and hydrological parameters in sandy land, which realizes the simultaneous and continuous monitoring of vegetation coverage, soil moisture, rainfall infiltration and surface runoff in sandy land, establishes a vegetation-hydrological feedback model, and provides a systematic technical solution for the study of ecological and hydrological processes in sandy land.

[0007] To achieve the above purpose, the present application provides a system for measuring the relationship between vegetation coverage and hydrological parameters in sandy land, which includes a vegetation coverage measurement module, a soil moisture monitoring module, a rainfall infiltration monitoring module, a surface runoff monitoring module and a relationship analysis module.

[0008] The vegetation coverage measurement module is used for real-time measurement of the vegetation coverage of the sandy area, which includes a multispectral imaging unit and an image processing unit. The multispectral imaging unit acquires multispectral image data of the sandy area, and the image processing unit calculates the vegetation coverage based on the multispectral image data. By using computer vision technology, the automatic and high-frequency measurement of the vegetation coverage of the sandy land is realized, which avoids the tediousness of traditional manual investigation and the time lag problem of remote sensing inversion.

[0009] The soil moisture monitoring module and the vegetation coverage measurement module correspond in space, and are used for monitoring the change of the water content of the sandy soil, and comprise a distributed sensor array and a data acquisition unit.

[0010] The rainfall infiltration monitoring module is used for monitoring the rainfall infiltration process, and comprises a rainfall measurement unit and an infiltration rate calculation unit.

[0011] The surface runoff monitoring module is used for monitoring the surface runoff, and comprises a runoff collection unit and a flow measurement unit.

[0012] The relationship analysis module is connected with the vegetation coverage measurement module, the soil moisture monitoring module, the rainfall infiltration monitoring module and the surface runoff monitoring module respectively, and is used for establishing the correlation between the vegetation coverage and the hydrological parameters.

[0013] The sand vegetation coverage and hydrological parameter relationship measuring system provided by the present application has the following beneficial effects:

[0014] Firstly, the present application uses multispectral imaging technology to realize real-time automatic measurement of vegetation coverage, breaking through the limitations of low efficiency of traditional manual investigation and poor time resolution of remote sensing inversion.

[0015] Secondly, the application constructs a multi-point and multi-depth soil moisture monitoring network, which comprehensively reflects the spatial heterogeneity and vertical profile characteristics of sandy soil moisture, and can more accurately represent the spatial distribution of soil moisture compared with traditional single-point or a few-point monitoring, thereby providing a reliable data basis for analyzing the influence of vegetation coverage on soil moisture.

[0016] Thirdly, the application realizes quantitative monitoring of the whole process of rainfall-infiltration-runoff through the rainfall infiltration monitoring module and the surface runoff monitoring module, can directly measure the infiltration rate and runoff under different vegetation coverage conditions, avoids the uncertainty caused by the dependence of traditional methods on empirical formula and model simulation, and greatly improves the accuracy of hydrological parameter determination.

[0017] Finally, the application realizes data fusion and correlation analysis of vegetation coverage and various hydrological parameters through the relationship analysis module, establishes a vegetation-hydrology mutual feedback model, quantitatively reveals the influence mechanism of vegetation coverage change on infiltration, soil moisture and runoff, and provides a scientific basis for ecological restoration effect evaluation, soil and water conservation measure optimization and regional hydrological response prediction, which has important theoretical value and practical significance for promoting sandy land ecological hydrology research and promoting ecological civilization construction. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall structure of the system of the application.

[0019] Figure 2 It is a schematic diagram of the structure of the vegetation coverage measurement module.

[0020] Figure 3 It is a schematic diagram of the working principle of the rainfall infiltration monitoring module.

[0021] Figure 4 It is a schematic diagram of the data processing flow of the relationship analysis module. DETAILED DESCRIPTION

[0022] Please refer to the accompanying Figures 1-4 The application will be further described in detail below in combination with the drawings and specific embodiments.

[0023] Referring to Figure 1 The sandy land vegetation coverage and hydrological parameter relationship determination system of the application comprises a vegetation coverage measurement module 1, a soil moisture monitoring module 2, a rainfall infiltration monitoring module 3, a surface runoff monitoring module 4 and a relationship analysis module 5. Each module cooperates with each other to realize comprehensive monitoring and quantitative analysis of the sandy land vegetation-hydrology process.

[0024] Referring to Figure 2The vegetation coverage measurement module 1 comprises a multispectral imaging unit and an image processing unit. The multispectral imaging unit is used to collect multispectral image data of the sand region, including a near-infrared band collection unit and a visible light band collection unit. The near-infrared band collection unit collects near-infrared spectral data with a wavelength of 760-900 nm, and the visible light band collection unit collects red light band spectral data with a wavelength of 620-750 nm. In an embodiment of the present application, the multispectral imaging unit uses a CCD or CMOS image sensor equipped with narrowband filters to collect images in the near-infrared and red light bands, respectively. The multispectral imaging unit is installed on a support with a height of 2-5 m, monitors the region from above, has a field of view angle of 45-90°, and can cover a sand region with an area of 10-100 .

[0025] The image processing unit is connected to the multispectral imaging unit and is used to calculate the vegetation coverage based on the collected multispectral image data. In a preferred embodiment of the present application, the image processing unit uses a method based on the normalized difference vegetation index (NDVI) to calculate the vegetation coverage. The calculation formula of NDVI is:

[0026] ,

[0027] wherein, is the near-infrared band reflectance, is the red light band reflectance. Healthy vegetation has high reflectance in the near-infrared band and low reflectance in the red light band, so the NDVI value is relatively high; the NDVI value of bare soil or sparse vegetation is relatively low.

[0028] The formula for calculating the vegetation coverage based on NDVI is:

[0029] ,

[0030] wherein, is the NDVI value of bare soil, usually taken as 0.05-0.15, is the NDVI value of complete vegetation coverage, usually taken as 0.70-0.90. In a sand region, preferably, is taken as 0.10, is taken as 0.75. This formula maps NDVI to the vegetation coverage interval of 0-1 through linear normalization, with 0 indicating complete bare soil and 1 indicating complete vegetation coverage.

[0031] ​The image processing unit also includes an image preprocessing function to perform radiation correction, geometric correction and atmospheric correction on the collected multispectral images, eliminate the effects of light changes, lens distortion and atmospheric scattering, and ensure the accuracy of the vegetation coverage calculation. In an embodiment of the present application, the radiation correction uses the dark pixel method to estimate the effect of atmospheric scattering on each waveband by identifying shadow areas or dark objects in the image and performing subtraction correction. The geometric correction uses perspective transformation based on ground control points to project the image onto the ground coordinate system and eliminate the perspective distortion.

[0032] The calculation process of the image processing unit is as follows: first, pre-process the collected near-infrared and red light waveband images, then calculate the NDVI pixel by pixel, then calculate the vegetation coverage based on the NDVI, and finally count the vegetation coverage of the entire monitoring area to obtain the average vegetation coverage and its spatial distribution. The sampling frequency of the image processing unit can be set to 10 minutes to 2 hours, which can be flexibly adjusted according to the monitoring requirements to achieve continuous dynamic monitoring of the vegetation coverage.

[0033] In another embodiment, the image processing unit also uses a deep learning method to perform pixel-level classification of vegetation, trains a convolutional neural network model to identify vegetation pixels and non-vegetation pixels in the image, and directly obtains the vegetation coverage by counting the proportion of vegetation pixels to total pixels. This method has stronger recognition ability for complex ground background and mixed pixels, further improving the accuracy of vegetation coverage measurement.

[0034] The soil moisture monitoring module 2 includes a distributed sensor array and a data acquisition unit. The distributed sensor array includes a plurality of soil moisture sensors, each of which is deployed at a different depth layer. In a preferred embodiment of the present application, the soil moisture sensor uses frequency domain reflectance (FDR) or time domain reflectance (TDR) technology to measure the dielectric constant of the soil and then calculate the soil volume water content.

[0035] The spatial arrangement of the distributed sensor array uses a grid scheme to uniformly arrange a plurality of monitoring points in the monitoring area, each point including soil moisture sensors at three depth layers: a surface soil moisture sensor deployed at a depth of 0-20 cm for monitoring surface soil moisture; a middle soil moisture sensor deployed at a depth of 20-60 cm for monitoring middle soil moisture; and a deep soil moisture sensor deployed at a depth of 60-120 cm for monitoring deep soil moisture. In a monitoring area with an area of 100 m2, preferably 9 monitoring points are arranged in a 3x3 grid, with a point spacing of 5 m, and a total of 27 soil moisture sensors.

[0036] The data acquisition unit is connected with the distributed sensor array and is used for collecting the soil moisture data output by each sensor. The data acquisition unit comprises an analog-digital converter, a microprocessor and a data storage. The analog-digital converter converts the analog signal output by the sensor into a digital signal, the microprocessor processes and stores the digital signal, and the data storage records the time series of the soil moisture data. The sampling frequency of the data acquisition unit is set to 10-30 minutes, which can be dynamically adjusted according to the rainfall event, and the sampling frequency is increased to 5 minutes during the rainfall to capture the rapid change of the soil moisture.

[0037] The data acquisition unit determines the soil moisture profile distribution characteristics based on the soil moisture data of different depth layers. In an embodiment of the present application, the soil moisture profile is constructed by using a cubic spline interpolation method, and the measured soil moisture of the three depth layers of 0-20 cm, 20-60 cm and 60-120 cm is used as the node to obtain a continuous soil moisture profile curve by interpolation. The soil moisture profile curve reflects the change rule of the soil moisture with the depth, and can be used to analyze the rainfall infiltration depth, evaporation layer thickness and root water absorption layer position.

[0038] In another embodiment, the soil moisture monitoring module 2 further comprises a temperature compensation function. Each soil moisture sensor is equipped with a temperature sensor to measure the soil temperature, and the data acquisition unit compensates and corrects the dielectric constant according to the soil temperature to eliminate the influence of temperature change on the measurement of the soil moisture, and the measurement accuracy can reach ±0.02 .

[0039] Referring to Figure 3 , the rainfall infiltration monitoring module 3 comprises a rainfall measurement unit and an infiltration rate calculation unit. The rainfall measurement unit is used for measuring the rainfall, and a tipping bucket rain gauge or a weighing rain gauge is used. The tipping bucket rain gauge or the weighing rain gauge is installed in an open area near the monitoring area, and the measurement height is 1.5 m to avoid the influence of ground splashing and vegetation shielding. The resolution of the tipping bucket rain gauge is 0.1 mm or 0.2 mm, and the starting and ending time of the rainfall, the rainfall intensity and the cumulative rainfall can be accurately recorded.

[0040] The infiltration rate calculation unit is connected with the rainfall measurement unit and the soil moisture monitoring module 2, and is used for determining the infiltration rate based on the dynamic change of the soil moisture data. The calculation of the infiltration rate is based on the principle of water balance. During the rainfall event, part of the rainfall is converted into the infiltration amount, part of the rainfall is converted into the surface runoff, and part of the rainfall is intercepted by the vegetation. The infiltration amount can be obtained by monitoring the increment of the soil moisture.

[0041] In an embodiment of the present application, the infiltration rate calculation unit comprises a moisture content change rate calculation unit and an infiltration depth determination unit. The moisture content change rate calculation unit calculates the change rate of the soil moisture data with time, and the formula is:

[0042] ,

[0043] in, for Soil volumetric water content at any given time The sampling time interval is preferably 5 to 10 minutes. The rate of change in soil moisture content reflects the dynamic process of rainfall infiltration. After the onset of rainfall, the surface soil moisture content increases rapidly, and the rate of change is relatively large. As the infiltration wavefront moves downward, the surface moisture content tends to stabilize, and the rate of change decreases.

[0044] The infiltration depth determination unit determines the infiltration wavefront advancement depth based on the temporal sequence of water content changes at different depth layers. When the soil water content at a certain depth layer begins to increase significantly, it indicates that the infiltration wavefront has reached that depth. In a preferred embodiment of the invention, a 5% increase in soil water content relative to its initial value is used as the criterion for the arrival of the infiltration wavefront. By monitoring the temporal sequence of changes in soil water content in the surface, middle, and deep layers, the advancement depth of the infiltration wavefront over time can be determined.

[0045] Infiltration rate Based on the rate of change of water content and the infiltration depth, the calculation formula is as follows:

[0046] ,

[0047] in, This represents the number of soil layers. For the first Soil moisture content of the first layer For the first Soil layer thickness, For the first The rate of change of soil moisture content at each depth is calculated by summing the products of the rate of change of moisture content at each depth and the layer thickness. The infiltration rate per unit area is expressed in mm / h or cm / h.

[0048] In another embodiment, the infiltration rate calculation unit also uses the Philip infiltration model or the Green-Ampt model to fit the measured infiltration data, determine infiltration parameters such as saturated hydraulic conductivity, matrix suction and initial water content, and establish an infiltration model suitable for sandy land to predict the infiltration process under different vegetation cover and initial water content conditions.

[0049] Surface runoff monitoring module 4 includes a runoff acquisition unit and a flow metering unit. The runoff acquisition unit collects surface runoff and includes a runoff flume unit and a flow diversion unit. The runoff flume unit is located at the boundary of the monitoring area, arranged along the low-lying terrain, with a V-shaped or rectangular cross-section and a bottom slope of 2%–5% to ensure smooth runoff flow. The runoff flume unit is made of stainless steel or PVC with a smooth surface to reduce water flow resistance. It is installed in a 100… The monitoring area has a runoff channel unit that is 10m long, 0.2m wide, and 0.3m deep.

[0050] The flow guiding unit connects to the runoff channel unit and directs the collected runoff to the flow metering unit. The flow guiding unit includes a guide pipe and a collection tank. The guide pipe connects the outlet of the runoff channel unit to the collection tank, which has a volume of 50–200L, set according to the expected maximum runoff volume. A water level sensor is installed inside the collection tank to monitor the collected water volume in real time.

[0051] A flow metering unit is used to measure runoff, including a volumetric metering device or a flow velocity measuring device. In one embodiment of the invention, the flow metering unit employs a volumetric metering method, measuring water level changes through a water level sensor within a collection tank, and calculating the cumulative runoff based on the cross-sectional area of ​​the collection tank. The formula for calculating the cumulative runoff is:

[0052] ,

[0053] in, To accumulate runoff, The cross-sectional area of ​​the water collection tank is... This represents the change in water level. The volume of water in the collection tank after a rainfall event is the total runoff for that event. The runoff coefficient can be calculated as the ratio of total runoff to total rainfall.

[0054] ,

[0055] in, The runoff coefficient, This represents the total rainfall. The area to be monitored is [area]. The runoff coefficient reflects the proportion of rainfall that is converted into surface runoff and is affected by factors such as vegetation cover, soil moisture content, and rainfall intensity.

[0056] In another embodiment, the flow metering unit employs a velocity measurement method. An ultrasonic or electromagnetic flow meter is installed inside the guide pipe to measure the runoff velocity in real time. The instantaneous flow rate is calculated by combining this with the cross-sectional area of ​​the guide pipe. The cumulative runoff flow rate is obtained by integrating the instantaneous flow rate over time. This velocity measurement method is suitable for situations with large runoff volumes and high velocities, and can obtain detailed time-history curves of the runoff process.

[0057] Reference Figure 4 The relationship analysis module 5 includes a data fusion unit and a feedback model construction unit. The data fusion unit is connected to the vegetation cover measurement module 1, soil moisture monitoring module 2, rainfall infiltration monitoring module 3, and surface runoff monitoring module 4, respectively, and is used to integrate vegetation cover, soil moisture content, infiltration rate, and runoff. The data fusion unit first performs spatiotemporal matching on the data collected by each module to ensure time synchronization and spatial correspondence of different data sources.

[0058] In the preferred embodiment of the present application, the relationship analysis module 5 further comprises a spatio-temporal matching unit. The spatio-temporal matching unit matches the vegetation coverage, soil moisture data, infiltration rate and runoff at the spatio-temporal scale. In terms of time matching, the sampling times of each module are unified to the same time reference. For data with different sampling frequencies, time interpolation or resampling methods are used to generate data sequences with uniform time steps. Preferably, the uniform time step is 10 minutes or 30 minutes. In terms of spatial matching, the pixel value of vegetation coverage is associated with the soil moisture, infiltration rate and runoff at the corresponding position to ensure the spatial correspondence of the data.

[0059] The data fusion unit also includes a data quality control function, which detects and removes outliers from the collected raw data, identifies outliers using a sliding window method or a statistical distribution-based method, and interpolates missing data. Common interpolation methods include linear interpolation, cubic spline interpolation or spatial interpolation based on neighboring points. Data quality control ensures the reliability of subsequent analysis.

[0060] The relationship analysis module 5 further comprises a correlation analysis unit connected to the spatio-temporal matching unit, for calculating the correlation coefficient between vegetation coverage and each hydrological parameter. The correlation analysis unit uses the Pearson correlation coefficient or the Spearman rank correlation coefficient to quantify the linear or monotonic relationship strength between vegetation coverage and infiltration rate, soil moisture, and runoff. The formula for calculating the correlation coefficient is:

[0061] ,

[0062] wherein, is the correlation coefficient, is the number of samples, is the th vegetation coverage observation value, is the th hydrological parameter observation value, is the average vegetation coverage, is the average hydrological parameter. The correlation coefficient has a value range of -1 to 1, and the greater the absolute value, the stronger the correlation. A positive value indicates a positive correlation, and a negative value indicates a negative correlation.

[0063] The mutual feedback model construction unit is connected to the correlation analysis unit and is used to establish a quantitative model of the impact of vegetation coverage changes on hydrological processes based on the integrated data. In the preferred embodiment of the present application, the mutual feedback model construction unit includes a regression analysis unit and a mechanism coupling unit.

[0064] The regression analysis unit is used to establish the regression relationship between vegetation cover and infiltration rate. Based on measured data, linear regression, multinomial regression, or nonlinear regression methods are used to fit the vegetation cover. With infiltration rate The functional relationship between vegetation cover and infiltration rate is as follows: In sandy soil ecosystems, vegetation cover has a significant positive impact on infiltration rate. Vegetation roots loosen the soil, increase macropores, and improve infiltration capacity. The general form of the regression model is:

[0065] ,

[0066] in, , , The regression coefficients are determined using the least squares method or a nonlinear optimization algorithm. In one embodiment of the invention, they are obtained by fitting 100 sets of measured data. , , Coefficient of determination This indicates that the model can effectively describe the impact of vegetation cover on infiltration rate.

[0067] The mechanism coupling unit is used to couple the regression relationship with the hydrophysical model. The vegetation-hydrological feedback model not only includes statistical regression relationships but also incorporates hydrophysical mechanisms. In a preferred embodiment of the invention, the vegetation-hydrological feedback model includes the enhancing effect of vegetation cover on infiltration capacity and the promoting effect on evapotranspiration.

[0068] The core mechanism of the vegetation-hydrology feedback model is as follows: increased vegetation cover → root growth → increased soil porosity → increased infiltration rate → increased soil moisture content → promoted vegetation growth → enhanced evapotranspiration → decreased soil moisture content → decreased surface runoff. This feedback process forms a dynamic coupling relationship between vegetation and hydrological processes.

[0069] The mechanism coupling unit uses the water balance equation to describe the vegetation-hydrological feedback process. The basic equation is:

[0070] ,

[0071] in, This refers to the soil volumetric water content. For infiltration rate, Evaporation rate For deep leakage rate, Let be the runoff rate. All terms on the right-hand side of the equation are affected by vegetation cover: infiltration rate. The evapotranspiration rate increases with increasing vegetation cover. Runoff rate increases with increasing vegetation cover. Decreases with the increase of vegetation coverage.

[0072] Evapotranspiration rate The Penman-Monteith formula is used for calculation, combined with the influence of vegetation coverage on canopy resistance:

[0073]

[0074] wherein, is the slope of the saturated water vapor pressure curve, is the net radiation, is the soil heat flux, is the air density, is the specific heat capacity of air, is the saturated water vapor pressure, is the actual water vapor pressure, is the aerodynamic resistance, is the latent heat of vaporization, is the constant of the wet and dry surface, is the canopy resistance. The canopy resistance is negatively correlated with the vegetation coverage, the higher the vegetation coverage, the smaller the canopy resistance, and the greater the evapotranspiration rate.

[0075] The mutual feedback model construction unit is based on the above equation group, uses a numerical solution method, simulates the temporal and spatial variation of soil water content, infiltration, evapotranspiration and runoff under different vegetation coverage conditions, and quantitatively evaluates the influence of vegetation restoration on hydrological processes. The model parameters are calibrated and verified by measured data to ensure the accuracy and applicability of the model.

[0076] In an embodiment of the present application, the system further comprises a wireless communication module connected with the vegetation coverage measurement module 1, the soil moisture monitoring module 2, the rainfall infiltration monitoring module 3 and the surface runoff monitoring module 4 respectively, for transmitting the data collected by each module to the relationship analysis module 5. The wireless communication module adopts LoRa or ZigBee communication protocol, has the characteristics of low power consumption, long distance and strong penetration, and is suitable for data transmission in sandy field outdoor environment. The transmission distance of LoRa communication protocol can reach 2-5km, the data rate is 0.3-50kbps, and it is suitable for large area monitoring area; the transmission distance of ZigBee communication protocol is 10-100m, the data rate is 20-250kbps, and it is suitable for small area high density monitoring area.

[0077] The data of each monitoring module is uploaded to the relationship analysis module 5 through the wireless communication module, the relationship analysis module 5 is usually deployed in the monitoring station house, equipped with industrial computer or server, for data storage, processing and analysis. The wireless communication module adopts star or mesh topology structure, each monitoring node communicates with the relationship analysis module 5 directly or through relay node, to ensure the reliability and real-time performance of data transmission.​

[0078] In another embodiment, the system further comprises a weather monitoring module for monitoring rainfall, air temperature, humidity, and wind speed, which is connected to the relationship analysis module 5. The weather monitoring module includes a rain gauge, a temperature sensor, a humidity sensor, and a wind speed and direction instrument, integrated in a weather station installed in an open area near the monitoring area. Weather data provides important driving factors and boundary conditions for vegetation-hydrological relationship analysis.

[0079] The relationship analysis module 5 classifies and analyzes the relationship between vegetation coverage and hydrological parameters based on weather conditions. For example, under different rainfall intensity conditions, the reduction effect of vegetation coverage on runoff is different: under light rain conditions, the interception and infiltration enhancement effect of vegetation is significant, and the runoff reduction rate is higher; under heavy rain or rainstorm conditions, the soil tends to be saturated, and the infiltration capacity is limited, and the runoff reduction rate decreases. Through classification analysis, the environmental dependence of vegetation-hydrological relationship can be revealed, providing a basis for ecological restoration strategies under different climate conditions.

[0080] The relationship analysis module 5 also includes a data storage unit and a trend prediction unit. The data storage unit is used to store historical monitoring data, using a relational database or a time series database to record long-term vegetation coverage, soil moisture content, infiltration rate, runoff, and weather data. The data storage unit supports data query, export, and visualization functions, facilitating user data analysis and report generation.

[0081] The trend prediction unit is connected to the data storage unit and is used to predict the trend of vegetation coverage changes and its impact on hydrological processes based on historical monitoring data. The trend prediction unit uses time series analysis methods or machine learning algorithms. Time series analysis methods include ARIMA models, exponential smoothing methods, etc., which are suitable for short-term prediction; machine learning algorithms include random forests, support vector machines, neural networks, etc., which can mine the complex nonlinear relationship between vegetation coverage and hydrological parameters, and are suitable for medium and long-term prediction.

[0082] In one embodiment of the present application, the trend prediction unit uses a long short-term memory network (LSTM) to predict vegetation coverage and soil moisture content. The LSTM network can learn the long-term dependence of time series data, and through historical data training, it can predict the vegetation coverage and soil moisture content in the future period. The prediction results can be used to evaluate the hydrological response under future vegetation restoration scenarios and provide decision support for ecological restoration planning.

[0083] The sand vegetation coverage and hydrological parameter relationship determination system of the present application is suitable for a variety of application scenarios:

[0084] In the evaluation of ecological restoration effect, through long-term continuous monitoring, the growth trend of vegetation coverage after the implementation of ecological engineering such as returning farmland to forest and vegetation restoration is quantitatively evaluated, and the effects of vegetation restoration on soil water retention, rainfall infiltration enhancement and surface runoff reduction are evaluated, which provides scientific basis for acceptance and subsequent management of ecological restoration project.

[0085] In the optimization of soil and water conservation measures, by comparing the hydrological response under different vegetation types and coverage conditions, the optimal vegetation configuration scheme is determined to balance the water and soil conservation benefit and vegetation water consumption, and technical parameters are provided for sand soil and water conservation engineering design.

[0086] In the prediction of regional hydrological response, based on the vegetation-hydrological feedback model, the change of vegetation coverage under the future climate change and human activity scenario is simulated, and the response of regional hydrological process is predicted, which provides support for water resources management and flood control and drought resistance decision-making.

[0087] In the research of ecological hydrology, the system provides high-precision, multi-element and long-time series observation data of vegetation-hydrological process, which provides important data support for revealing the water cycle mechanism of sand ecosystem and the coupling relationship between vegetation, soil and hydrology, and promotes the development of ecological hydrology theory.

[0088] Example 1: In a test area of Mu Us sandy land, the sand vegetation coverage and hydrological parameter relationship determination system of the application is deployed, and the monitoring area is 100 m2. The vegetation type is a mixed community of Artemisia ordosica and Hedysarum mongolicum. The system has been running continuously for 2 years, and 45 rainfall events have been recorded. The monitoring data show that the vegetation coverage increases from 0.32 at the beginning of the year to 0.58 at the end of the year, with an increase of 81%. With the increase of vegetation coverage, the average infiltration rate increases from 3.2 mm / h to 5.8 mm / h, with an increase of 81%, the average runoff coefficient decreases from 0.28 to 0.15, with a decrease of 46%, the average soil moisture content increases from 0.08 to 0.12 , with an increase of 50%. The vegetation-hydrological feedback model established based on the measured data has a determination coefficient of 0.88, and the average relative error of the predicted runoff volume and the measured value is 12%, which verifies the accuracy of the model. The system provides reliable data support for evaluating the hydrological effect of vegetation restoration in Mu Us sandy land.

[0089] Example 2: In a certain ecological restoration area of Horqin sandy land, the system of the application is used to monitor the hydrological response under different vegetation coverage gradients. Five monitoring sample plots are selected, and the vegetation coverage is 0.10, 0.25, 0.40, 0.55 and 0.70 respectively, and the area of each sample plot is 50 . The monitoring results show that the vegetation coverage is significantly positively correlated with the infiltration rate, , , and the runoff coefficient is significantly negatively correlated with the vegetation coverage, , When the vegetation coverage increases from 0.10 to 0.70, the infiltration rate increases from 2.1 mm / h to 7.3 mm / h, increasing by 247%, and the runoff coefficient decreases from 0.35 to 0.08, decreasing by 77%. The study shows that when the vegetation coverage reaches more than 0.40, the water and soil conservation benefit is significantly enhanced, thereby providing a basis for determining the vegetation coverage threshold for ecological restoration of the Horqin Sandy Land.

[0090] In Example 3, the system is used to study the influence of seasonal change on the vegetation-hydrological relationship at a monitoring station in the Hunshandake Sandy Land. The monitoring time is from May to September in the whole growing season, for a total of 5 months. The results show that the vegetation coverage presents a seasonal change law of first increasing and then decreasing, and the vegetation coverage reaches a peak of 0.65 in the growing season from June to July, and gradually decreases to 0.45 from August to September. The infiltration rate and the soil moisture content are consistent with the seasonal change trend of the vegetation coverage, while the runoff coefficient changes inversely with the vegetation coverage. The trend prediction unit predicts the vegetation coverage and the soil moisture content in the fifth month based on the monitoring data of the first four months, and the average absolute error of the predicted value and the measured value is 0.03 and 0.01 , respectively, which verifies the prediction ability of the system.

[0091] The sandy land vegetation coverage and hydrological parameter relationship determination system of the present application integrates multispectral vegetation coverage measurement, distributed soil moisture monitoring, rainfall infiltration monitoring and surface runoff monitoring, realizes comprehensive, continuous and quantitative monitoring of the sandy land vegetation-hydrological process, and breaks through the technical bottleneck that the vegetation observation and the hydrological observation are separated in the traditional method and it is difficult to establish a quantitative relationship. The system uses computer vision technology to realize automatic measurement of the vegetation coverage, has high spatial and temporal resolution and low data acquisition cost; uses a distributed sensor network to realize spatialized monitoring of the soil moisture, fully reflects the heterogeneity of the soil moisture; uses the water balance principle to realize quantitative determination of the rainfall infiltration and the surface runoff, avoids the uncertainty of the empirical formula; uses data fusion and model construction methods to establish a vegetation-hydrological feedback model, and reveals the influence mechanism of the vegetation coverage change on the hydrological process. The system provides an advanced technical means for sandy land ecological restoration effect evaluation, soil and water conservation measure optimization, regional hydrological response prediction and ecological hydrology research, and has significant application value and popularization prospect.

[0092] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A system for determining the relationship between sand dune vegetation cover and hydrologic parameters, the system comprising , comprising: a vegetation coverage measurement module for measuring vegetation coverage of sandy land in real time, including a multispectral imaging unit for collecting multispectral image data and an image processing unit for calculating vegetation coverage based on the multispectral image data; a soil moisture monitoring module corresponding in space to the vegetation coverage measurement module for monitoring changes in soil water content, including a distributed sensor array and a data acquisition unit, the distributed sensor array including a plurality of soil moisture sensors respectively deployed at different depth layers, and the data acquisition unit for collecting water content data output by the distributed sensor array; a rainfall infiltration monitoring module for monitoring rainfall infiltration process, including a rainfall measurement unit for measuring rainfall and an infiltration rate calculation unit for determining infiltration rate based on dynamic changes in the water content data; a surface runoff monitoring module for monitoring surface runoff, including a runoff collection unit for collecting surface runoff and a flow metering unit for measuring runoff; a relationship analysis module connected to the vegetation coverage measurement module, the soil moisture monitoring module, the rainfall infiltration monitoring module and the surface runoff monitoring module for establishing a correlation between vegetation coverage and hydrological parameters, including a data fusion unit for integrating vegetation coverage, water content data, infiltration rate and runoff, and a mutual feedback model construction unit for establishing a quantitative model of the impact of vegetation coverage changes on hydrological processes based on integrated data, wherein the vegetation coverage measurement module, the soil moisture monitoring module, the rainfall infiltration monitoring module and the surface runoff monitoring module form a four-in-one synchronous monitoring system to achieve spatio-temporal synchronous observation of sandy land vegetation-hydrological processes; the relationship analysis module matches the data collected by each module to the same spatio-temporal scale through the data fusion unit, ensuring a one-to-one correspondence between vegetation coverage and each hydrological parameter; the quantitative model established by the mutual feedback model construction unit includes an enhancement effect model of vegetation coverage on infiltration capacity and a promotion effect model of vegetation coverage on evapotranspiration, quantitatively describing how vegetation coverage changes affect infiltration rate by changing soil pore structure and how it affects evapotranspiration rate by changing surface roughness and canopy resistance.

2. The system of claim 1, wherein The vegetation coverage measurement module further includes: a near-infrared band acquisition unit for collecting near-infrared band reflectance spectrum data; a visible light band acquisition unit for collecting visible light band reflectance spectrum data; The image processing unit determines vegetation coverage by calculating a normalized vegetation index based on the ratio of the difference to the sum of near-infrared band reflectance and visible light band reflectance.

3. The system of claim 1, wherein The distributed sensor array includes: a surface soil moisture sensor deployed at a depth of 0-20 cm for monitoring surface soil water content; The middle layer soil moisture sensor is arranged at a depth of 20-60 cm and is used for monitoring middle layer soil water content; The deep layer soil moisture sensor is arranged at a depth of 60-120 cm and is used for monitoring deep layer soil water content; The data acquisition unit determines soil moisture profile distribution characteristics based on soil water content data of different depth layers.

4. The system of claim 3, wherein The infiltration rate calculation unit includes: A water content change rate calculation unit for calculating a rate of change of the soil water content data over time; An infiltration depth determination unit for determining an infiltration wave front propagation depth based on a time sequence of water content changes of different depth layers; The infiltration rate is determined based on the water content change rate and the infiltration depth.

5. The system of claim 1, wherein The surface runoff monitoring module further includes: A runoff groove unit arranged at a boundary of the monitoring area and used for collecting surface runoff; A flow guide unit connected with the runoff groove unit and used for guiding the collected runoff to the flow metering unit; The flow metering unit includes a volumetric metering device or a flow rate measuring device.

6. The system of claim 4, wherein The relationship analysis module further includes: A space-time matching unit for matching the vegetation coverage, the soil water content data, the infiltration rate and the runoff on a space-time scale; A correlation analysis unit connected with the space-time matching unit and used for calculating correlation coefficients between the vegetation coverage and the hydrological parameters; The mutual feedback model construction unit establishes a vegetation-hydrology mutual feedback model based on the correlation coefficients.

7. The system of claim 6, wherein The mutual feedback model construction unit includes: A regression analysis unit for establishing a regression relationship between the vegetation coverage and the infiltration rate; A mechanism coupling unit for coupling the regression relationship with a hydrological physics model; The vegetation-hydrology mutual feedback model includes an enhancement effect of the vegetation coverage on the infiltration capacity and a promotion effect on evapotranspiration.

8. The system of claim 1, wherein Further including: A weather monitoring module for monitoring rainfall, air temperature, humidity and wind speed, the weather monitoring module being connected with the relationship analysis module; The relationship analysis module performs classified analysis on the relationship between the vegetation coverage and the hydrological parameters based on weather conditions.

9. The system of claim 1, wherein Further including: A wireless communication module connected with the vegetation coverage measurement module, the soil moisture monitoring module, the rainfall infiltration monitoring module and the surface runoff monitoring module respectively, and used for transmitting data collected by each module to the relationship analysis module; The wireless communication module adopts LoRa or ZigBee communication protocol.

10. The system of claim 1, wherein The relationship analysis module further includes: A data storage unit for storing historical monitoring data; A trend prediction unit connected with the data storage unit and used for predicting a vegetation coverage change trend and its influence on hydrological processes based on historical monitoring data; The trend prediction unit adopts a time series analysis method or a machine learning algorithm.

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