A new method for water ecological monitoring and evaluation

Abstract

The present application relates to the technical field of water ecological environment monitoring and evaluation, in particular to a novel water ecological monitoring and evaluation method, which comprises a water ecological comprehensive monitoring method, a water ecological health evaluation model establishment and application method, and a spectral knowledge base building and display method; the water ecological comprehensive monitoring method comprises a multi-source data collaborative collection method and a data fusion and standardization method; the water ecological health evaluation model establishment and application method comprises an ecological index correlation mining method and a model construction and self-adaptive optimization method; the spectral knowledge base building and display method comprises a spectral knowledge structured construction method and a dynamic visual display method; through the above fine and interlocking implementation logic, the novel water ecological monitoring and evaluation technology can comprehensively, deeply and dynamically control the water ecological system condition, and achieve the precise monitoring and scientific evaluation goal.

Description

Technical Field

[0001] This invention relates to the field of water ecological environment monitoring and assessment technology, specifically a novel water ecological monitoring and assessment method. Background Technology

[0002] Guided by the concept of ecological civilization, the Ministry of Ecology and Environment has clearly put forward the new stage of water management goals: rivers with water, fish and grass, and harmony between humans and water. This goal marks a qualitative leap in water governance and restoration work, moving from a single-dimensional assessment of physicochemical indicators to a multi-dimensional assessment of water ecological health.

[0003] With the intensification of global climate change and the increasing intensity of human activities, watershed ecosystems are facing unprecedented pressure. Accurately assessing the spatiotemporal dynamics of aquatic ecological products is crucial for achieving sustainable watershed management and ecological civilization construction. Aquatic ecological products, as various benefits provided by watershed ecosystems to humankind, including water quantity regulation, water quality purification, biodiversity maintenance, carbon sequestration and regulation, and cultural services, are vital guarantees for the health of watershed ecosystems and human well-being. Accurately simulating and assessing the spatiotemporal dynamics of these products not only helps to deepen the understanding of the functions and values ​​of watershed ecosystems but also provides important basis for formulating scientific water resource management policies, implementing effective ecological compensation mechanisms, and optimizing land use planning.

[0004] With economic development, water ecology and water environment problems have become increasingly prominent, such as water pollution and river flow interruption. Therefore, protecting water ecology plays a vital role, and water ecology restoration has become a top priority. Water ecology monitoring and assessment of the quality and stability of aquatic ecosystems, accurate identification of water ecology problems, and reflection of the effectiveness of water ecology governance are crucial for supporting water ecology assessment work, guiding local areas to carry out water ecology protection and restoration, and improving the health status of aquatic ecosystems.

[0005] Currently, scholars both domestically and internationally have conducted extensive research on watershed aquatic ecosystem product assessment. Traditional methods are mainly based on static assessment models, which can quantify the spatial distribution of ecosystem services to a certain extent. In recent years, with the development of remote sensing and geographic information system technologies, dynamic assessment methods combined with hydrological models have gradually become a research hotspot. For example, the coupled application of SWAT with ecosystem service assessment models has enabled dynamic simulation of water quantity regulation and water quality purification services. Therefore, how to construct a comprehensive method for evaluating ecosystem status is of great significance for achieving effective management and protection of aquatic ecosystems. Summary of the Invention

[0006] To address the aforementioned problems, this invention provides a novel method for water ecological monitoring and assessment, thereby resolving the issues described in the background section.

[0007] The technical solution adopted by this invention to solve its technical problem is: a novel water ecological monitoring and assessment method, including a comprehensive water ecological monitoring method, a method for establishing and applying a water ecological health assessment model, and a method for building and displaying a spectral knowledge base; the comprehensive water ecological monitoring method includes a multi-source data collaborative acquisition method and a data fusion and standardization method; the method for establishing and applying a water ecological health assessment model includes an ecological indicator correlation mining method and a model construction and adaptive optimization method; the method for building and displaying a spectral knowledge base includes a spectral knowledge structure construction method and a dynamic visualization display method.

[0008] As an optimization, the multi-source data collaborative acquisition method includes satellite remote sensing, UAV remote sensing, and ground monitoring; the data fusion and standardization method includes spatiotemporal benchmark framework analysis and spectral data analysis.

[0009] As an optimization, the satellite remote sensing acquisition method involves selecting a satellite constellation or single satellite with specific spectral resolution, spatial resolution, and revisit period suitability based on the latitude and longitude range of the target area and the characteristics of the surrounding geographical environment. Data acquisition is based on the satellite's orbital movement patterns; during its designated orbital transit, sensors scan and image the target area, acquiring images covering multiple spectral bands from visible light to infrared. The imaging process follows optical imaging principles: light passes through the atmosphere, is reflected by water bodies and surface objects, and then enters the satellite sensor to form electrical signals, which are then converted into digital images. The UAV remote sensing acquisition method... To plan the flight path of the UAV based on the preliminary survey of the complexity of the surrounding terrain and the distribution of key ecological sites, the flight altitude and speed are designed to ensure that the onboard multispectral camera can acquire high-resolution images without significant geometric distortion. The ground monitoring data acquisition method is based on the distribution patterns of key elements of the aquatic ecosystem and hydrodynamic characteristics. Set-point monitoring stations are deployed in different river sections, different lake areas, and different functional zones of wetlands. Each monitoring station integrates a multi-parameter water quality analyzer and uses electrochemical, optical and other detection principles to continuously monitor core water quality indicators such as water temperature, pH value, dissolved oxygen, and nutrient concentration in real time.

[0010] As an optimization, the spatiotemporal benchmark framework analysis method is based on the internationally accepted geographic coordinate system. Coordinate transformation and registration are performed on satellite remote sensing, UAV remote sensing, and ground monitoring data to ensure that data from different data sources accurately correspond to their actual geographical locations. The time format of data collected by various monitoring devices is unified according to Greenwich Mean Time (GMT) to achieve consistency in the data time series, laying the foundation for subsequent multi-source data fusion analysis. The spectral data analysis method involves normalizing satellite remote sensing multispectral band data and specific band data from UAV multispectral cameras. Linear or nonlinear transformation algorithms are used to unify the dimensions and numerical ranges of spectral data from various data sources. For non-spectral data such as water quality and biodiversity from ground monitoring, standardized quantitative scoring is performed based on the water ecological assessment index system, enabling various types of data to be fused and calculated within the same mathematical model framework to generate a comprehensive water ecological monitoring dataset.

[0011] As an optimization, the ecological indicator association mining method includes monitoring of changes in reference water area and monitoring using environmental DNA detection technology; the model building and adaptive optimization method adopts a hybrid modeling strategy, integrating mechanistic models and data-driven models, and after the model is built, an adaptive learning module is embedded.

[0012] As an optimization, the monitoring of changes in the reference water area utilizes the interpretation results of long-term satellite remote sensing images, combined with field hydrogeological survey data, to construct a water volume-area dynamic model. This model clarifies how factors such as water level fluctuations, water supply, and human water use drive changes in water area, and the mechanism by which these changes in area affect aquatic habitats and water-heat exchange processes. Based on statistical correlation analysis and geographic information system (GIS) spatial analysis, the model quantifies the relationship between vegetation cover and ecosystem services such as water purification, soil erosion control, and habitat provision, identifying the contribution of vegetation community succession stages and vegetation structure characteristics to the stability of the aquatic ecosystem. The monitoring using environmental DNA detection technology involves extracting fragments of biological genetic material from the water body, identifying species information through high-throughput sequencing technology, and combining this with traditional field survey data to construct a species coexistence network model. This model reveals predation, competition, and symbiotic relationships among different species, uncovers the core roles of key species in maintaining the structure and function of the aquatic ecosystem, and transforms these complex ecological relationships into quantifiable and modelable mathematical correlation expressions, serving as core components of the health assessment model.

[0013] As an optimization, the spectral knowledge structuring construction method uses spectral bands as the basic dimension, associates spectral features with actual geographical locations, and uses GIS layer overlay technology to visualize the spatial distribution differences of spectral features in different regions, thus constructing an integrated knowledge system from microscopic spectral characteristics to macroscopic ecological and geographical patterns. The dynamic visualization display method is based on WebGL and other three-dimensional graphics rendering technologies to construct a three-dimensional geographical scene that can be interacted with in real time, and sets up spectral knowledge base data on the scene.

[0014] As an optimization, the UAV is equipped with a built-in global positioning system and an inertial navigation system for coordinated positioning.

[0015] As an optimization, the ground monitoring layer uses an acoustic Doppler current profiler to measure hydrodynamic parameters such as water flow velocity and flow rate based on the principle of sound wave reflection, ensuring accurate capture of dynamic changes in the physical and chemical properties of the water body.

[0016] As an optimization, the spectral knowledge structuring method utilizes feature extraction and dimensionality reduction techniques such as principal component analysis and wavelet transform to uncover deeper distinguishing features for ecological objects with similar and easily confused spectral features.

[0017] The beneficial effects of this invention are as follows: This invention provides a novel water ecological monitoring and assessment method. Through targeted monitoring, it can accurately identify key areas, high-risk areas, areas with complex hydrological conditions, ecologically sensitive and diverse environments, and pollution sources within rivers. By classifying and configuring monitoring points, it can optimize the configuration of monitoring equipment and parameters, ensuring the accuracy and reliability of monitoring data. It can also rationally allocate monitoring resources according to the characteristics and needs of different regions, improving resource utilization efficiency. Through comprehensive analysis of topography, geological structure, hydrological characteristics, ecological types, and pollution risks, it achieves refined classification of river areas, facilitating the development of differentiated monitoring and management strategies for different types of areas, thereby improving the efficiency and targeting of water resource management and water environment governance. Through the above-mentioned refined and interconnected implementation logic, this novel water ecological monitoring and assessment technology can comprehensively, deeply, and dynamically control the status of aquatic ecosystems, achieving the goals of precise monitoring and scientific assessment. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0020] This invention discloses a novel method for water ecological monitoring and assessment, including a comprehensive water ecological monitoring method, a method for establishing and applying a water ecological health assessment model, and a method for building and displaying a spectral knowledge base. The comprehensive water ecological monitoring method includes a multi-source data collaborative acquisition method and a data fusion and standardization method. The method for establishing and applying a water ecological health assessment model includes an ecological indicator correlation mining method and a model construction and adaptive optimization method. The method for building and displaying a spectral knowledge base includes a spectral knowledge structure construction method and a dynamic visualization display method.

[0021] I. Integrated Water Ecosystem Monitoring Methods 1. Multi-source data collaborative acquisition method At the satellite remote sensing level: Based on the latitude and longitude range of the target area and the characteristics of the surrounding geographical environment, satellite constellations or individual satellites with specific spectral resolution, spatial resolution, and revisit cycles are selected. For example, for lake groups with large water areas that require frequent monitoring of macro-ecological changes, satellite data with short revisit cycles (e.g., 3-5 days), medium to high resolution (e.g., 10-30 meters), and coverage of rich water color and vegetation-sensitive bands are prioritized. Data acquisition is based on satellite orbital patterns. When the satellite passes over the target area in its designated orbit, sensors scan and image the area, acquiring images covering multiple spectral bands from visible light to infrared. The imaging process follows optical imaging principles: light passes through the atmosphere, is reflected by water bodies and surface objects, enters the satellite sensor to form electrical signals, and is then converted into digital images.

[0022] Simultaneously considering the impact of factors such as satellite attitude, solar altitude angle, and atmospheric conditions on image quality, the satellite's onboard attitude adjustment system is used to maintain a stable imaging attitude as much as possible. After receiving the data, the ground receiving station combines ground meteorological station data and atmospheric models to perform precise atmospheric correction on the images, restoring the true reflectivity information of the land surface. At the same time, high-precision terrain data is used for terrain correction to eliminate the impact of illumination differences caused by terrain undulations, ensuring that the image data can accurately reflect the actual state of the water ecology.

[0023] At the UAV remote sensing level: Based on preliminary surveys of the complexity of the terrain surrounding the target water body and the distribution of key ecological sites, the UAV flight path is planned to ensure that the flight altitude and speed enable the onboard multispectral camera to acquire high-resolution images without significant geometric distortion. During flight operations, the UAV uses its built-in Global Positioning System (GPS) and Inertial Navigation System (INS) for coordinated positioning and flies stably along a preset route. At the designated shooting points or flight ranges, the camera captures images of the water body and surrounding vegetation and biological communities according to predetermined shooting parameters (such as exposure time and aperture size), obtaining local detail information with centimeter-level or even millimeter-level precision. The focus is on details that are difficult to observe in detail by satellite, such as the structure of riparian vegetation communities, the distribution of aquatic plants in shallow water areas, and changes in local water turbidity.

[0024] The image acquisition process records metadata such as the drone's flight attitude, geographic coordinates, and shooting timestamps in real time, so as to accurately match it with satellite and ground data later. After the data is transmitted back, geometric fine correction is performed using ground control points combined with image feature point matching algorithms to ensure that the images closely match the actual geographic coordinates. The images taken from multiple flights are seamlessly stitched together into a high-resolution mosaic that fully covers the target area through image stitching algorithms, providing a foundation for local fine-grained ecological analysis.

[0025] At the ground monitoring level: Based on the distribution patterns and hydrodynamic characteristics of key elements of the aquatic ecosystem, fixed monitoring stations are deployed in different river sections (such as headwaters, middle reaches, and estuaries), different lake areas (such as central lake areas, shallow water areas, and shorelines), and different functional zones of wetlands (such as core areas, buffer zones, and experimental zones). Each monitoring station integrates a multi-parameter water quality analyzer and uses electrochemical and optical detection principles to continuously monitor key water quality indicators such as water temperature, pH value, dissolved oxygen, and nutrient concentration in real time. Acoustic Doppler current profilers (ADCP) are used to measure hydrodynamic parameters such as flow velocity and flow rate based on the principle of sound wave reflection, ensuring accurate capture of dynamic changes in the physicochemical properties of water bodies.

[0026] For biodiversity monitoring, professionals are regularly deployed to use the quadrat method, setting the area and shape of quadrats according to the characteristics of different ecological regions, and systematically collecting samples of aquatic plants, plankton, and benthic organisms. The species composition is clarified by combining morphological classification and molecular biological identification methods. Underwater acoustic monitoring equipment is used to listen to the acoustic signals of fish activities, and video monitoring is used to help identify fish species, numbers, and schooling behaviors, so as to comprehensively grasp the dynamics of the aquatic ecosystem biological community. This complements remote sensing data and enriches ecological monitoring information at the micro level.

[0027] 2. Data fusion and standardization methods: A unified spatiotemporal reference framework is constructed, based on internationally accepted geographic coordinate systems (such as WGS84), to perform coordinate transformation and registration on satellite remote sensing, UAV remote sensing, and ground monitoring data, ensuring that data from different data sources can accurately correspond to the actual geographical location; based on Greenwich Mean Time, the time format of data collected by various monitoring devices is unified to achieve consistency of data time series, laying the foundation for subsequent multi-source data fusion analysis.

[0028] To address the characteristics of spectral data, satellite remote sensing multispectral band data and UAV multispectral camera specific band data are normalized. Through linear or nonlinear transformation algorithms, the dimensions and numerical ranges of spectral data from various data sources are unified, facilitating comprehensive comparative analysis. For nonspectral data such as water quality and biodiversity from ground monitoring, standardized quantitative scoring is performed based on the water ecological assessment index system. For example, water quality parameters are converted into corresponding water quality category scores according to the surface water environmental quality standard, and biodiversity indicators are converted into comparable values ​​using standardization methods such as the Shannon-Wiener diversity index. This allows various types of data to be integrated and calculated within the same mathematical model framework, generating a comprehensive water ecological monitoring dataset.

[0029] II. Methods for Establishing and Applying Aquatic Ecological Health Assessment Models 1. Ecological Indicator Correlation Mining Method: This method deeply analyzes the intrinsic relationship between changes in water area and other elements of the aquatic ecosystem. Utilizing the interpretation results of long-term satellite remote sensing images and combining them with field hydrogeological survey data, a dynamic water-area model is constructed to clarify how factors such as water level fluctuations, water supply, and human water use drive changes in water area, and the mechanism by which these changes in area affect aquatic habitats and water-heat exchange processes. Based on statistical correlation analysis and geographic information system (GIS) spatial analysis, the relationship between vegetation cover and ecological service functions such as water purification, soil erosion control, and habitat provision is quantified. The method also identifies the contribution of vegetation community succession stages and vegetation structure characteristics to the stability of the aquatic ecosystem.

[0030] By using environmental DNA (eDNA) detection technology to assist in biodiversity monitoring, fragments of biological genetic material are extracted from water bodies, species information is identified through high-throughput sequencing technology, and combined with traditional field survey data, a species coexistence network model is constructed to reveal the predation, competition, and symbiotic relationships among different biological species. The core role of key species in maintaining the structure and function of aquatic ecosystems is explored, and these complex ecological relationships are transformed into quantifiable and modelable mathematical correlation expressions as core components of the health assessment model.

[0031] 2. Model Construction and Adaptive Optimization Methods: Based on the previously identified correlations among ecological indicators, a hybrid modeling strategy was adopted, combining the advantages of mechanistic models and data-driven models. For example, for clearly defined physicochemical processes such as hydrodynamics and water quality diffusion, mechanistic models based on fluid mechanics and the law of conservation of mass were used; for complex nonlinear processes such as biological community response and integrated ecosystem evolution, neural networks and deep learning algorithms were introduced to construct data-driven models. The model was trained using multi-source monitoring data accumulated over many years and on-site surveys and assessments of aquatic ecosystem health during corresponding periods. The training process employed optimization algorithms such as cross-validation and stochastic gradient descent to continuously adjust the model's internal parameters, ensuring that the model's predicted output closely matches the actual health status.

[0032] After the model is established, an adaptive learning module is embedded. As new monitoring data continues to flow in, the model's predicted values ​​are compared with the actual monitoring and evaluation values ​​in real time. Once the deviation exceeds the preset threshold, the model is automatically triggered to recalibrate and optimize the process. The model structure or parameters are dynamically adjusted based on the latest data characteristics to ensure that the model can accurately track the dynamic changes of the aquatic ecosystem and adapt to the new trends in aquatic ecosystem evolution caused by external factors such as climate change and human activity disturbance.

[0033] III. Methods for Building and Displaying a Spectral Knowledge Base 1. Spectral Knowledge Structure Construction Method: A multi-dimensional spectral knowledge storage architecture is designed, based on spectral bands as the fundamental dimension, further subdivided into visible light (red, green, blue, etc.), near-infrared, and mid-infrared sub-bands. This sub-band stores the reflectance, absorptivity, and transmittance spectral characteristics and statistical distribution parameters (such as mean, standard deviation, kurtosis, skewness, etc.) of different water body types (clear freshwater, eutrophic water, seawater, etc.), vegetation types (emergent plants, submerged plants, phytoplankton, etc.), and soil types (clay, sandy soil, loam, and other common wetland soil textures) in each band. A time dimension is introduced, archiving the spectral characteristic change sequences of the same object at different times according to seasonal and annual cycles, recording the dynamic patterns of the spectrum caused by factors such as ecological succession and climate fluctuations. Combined with the geographic information dimension, spectral characteristics are associated with actual geographical locations. Using GIS layer overlay technology, the spatial distribution differences of spectral characteristics in different regions are visualized, constructing an integrated knowledge system from microscopic spectral characteristics to macroscopic ecological and geographical patterns.

[0034] For ecological objects with similar spectral features that are easily confused, feature extraction and dimensionality reduction techniques such as principal component analysis (PCA) and wavelet transform are used to mine deep distinguishing features. For example, PCA transform is used to extract unique combinations of spectral principal component scores during different algal blooms, which are stored in the knowledge base as key identifiers to distinguish algal species and abundance. This helps to accurately identify complex aquatic ecological phenomena and improves the accuracy and practicality of spectral knowledge.

[0035] 2. Dynamic Visualization Method: Interactive visualization components are developed on the front-end interface of the self-developed platform. Based on 3D graphics rendering technologies such as WebGL, a 3D geographic scene that can be interacted with in real time is constructed. Satellite remote sensing imagery and UAV orthophotos are used as the underlying terrain texture maps to intuitively display the real landforms and water body distribution of the monitoring area. On top of the scene, based on spectral knowledge base data, the spectral characteristics of various ecological objects are rendered in real time using different colors, transparency, and texture mapping rules. For example, gradient color bands are used to intuitively present the gradient change of spectral reflectance of water bodies from shallow to deep water areas, and flashing bright spots are used to identify the unique spectral response of biological aggregation areas. Users can use mouse clicks, zooming, panning, and other operations to query detailed spectral values, time series curves, and surrounding ecological correlation information for any point, realizing an immersive spectral knowledge exploration experience.

[0036] To facilitate the comparative display of spectral images from different periods, a time slider control and image overlay switching function were developed. Users can drag the slider to dynamically play the fusion and change process of spectral images of the same area in different seasons and years. The image difference algorithm is used to highlight areas with significant changes in spectral features in real time. Combined with animation transition effects, the spectral trajectory of the aquatic ecosystem over time is clearly presented, which helps researchers quickly understand the dynamic trends of the ecosystem, uncover clues to potential ecological problems, and provide intuitive and efficient data support for water ecological protection decisions.

[0037] The above-described specific embodiments are merely specific examples of the present invention. The patent protection scope of the present invention includes, but is not limited to, the product form and style of the above-described specific embodiments. Any appropriate changes or modifications made by a person skilled in the art that conform to the claims of the present invention should fall within the patent protection scope of the present invention.

Claims

1. A novel method for monitoring and assessing aquatic ecosystems, characterized in that, This includes methods for comprehensive water ecological monitoring, methods for establishing and applying water ecological health assessment models, and methods for building and displaying spectral knowledge bases; The comprehensive water ecology monitoring method includes a multi-source data collaborative acquisition method and a data fusion and standardization method; The method for establishing and applying the water ecological health assessment model includes ecological indicator correlation mining method and model construction and adaptive optimization method; The method for building and displaying the spectral knowledge base includes a structuring method for spectral knowledge and a dynamic visualization method.

2. The novel water ecological monitoring and assessment method according to claim 1, characterized in that, The multi-source data collaborative acquisition method includes satellite remote sensing, UAV remote sensing, and ground monitoring. The data fusion and standardization methods include spatiotemporal benchmark framework analysis and spectral data analysis.

3. The novel water ecological monitoring and assessment method according to claim 2, characterized in that, The data acquisition method at the satellite remote sensing level is based on the latitude and longitude range of the monitoring target area and the characteristics of the surrounding geographical environment. A satellite constellation or a single satellite with specific spectral resolution, spatial resolution and revisit period is selected. The data acquisition is based on the satellite orbit operation law. When the satellite passes over the target area in a predetermined orbit, the sensor is used to scan and image the target area to obtain images covering multiple spectral bands from visible light to infrared. The imaging process follows the optical imaging principle. Light passes through the atmosphere and is reflected by water bodies and surface objects before entering the satellite sensor to form an electrical signal, which is then converted into a digital image. The data acquisition method at the UAV remote sensing level is based on preliminary surveys of the complexity of the terrain around the target water body and the distribution of key ecological sites. The flight path of the UAV is planned to ensure that the flight altitude and speed enable the onboard multispectral camera to acquire high-resolution images without significant geometric distortion. The ground-level monitoring data collection method is based on the distribution patterns of key elements of the aquatic ecosystem and hydrodynamic characteristics. Set-point monitoring stations are deployed in different river sections, different lake areas, and different functional zones of wetlands. Each monitoring station integrates a multi-parameter water quality analyzer and uses electrochemical, optical and other detection principles to monitor core water quality indicators such as water temperature, pH value, dissolved oxygen, and nutrient concentration in real time and continuously.

4. The novel water ecological monitoring and assessment method according to claim 3, characterized in that, The spatiotemporal reference framework analysis method is based on the internationally accepted geographic coordinate system. It performs coordinate transformation and registration on satellite remote sensing, UAV remote sensing and ground monitoring data to ensure that data from different data sources can accurately correspond to the actual geographical location. According to Greenwich Mean Time, the time format of data collected by each monitoring device is unified to achieve the consistency of data time series and lay the foundation for subsequent multi-source data fusion analysis. The method for spectral data analysis involves normalizing satellite remote sensing multispectral band data and UAV multispectral camera specific band data, and unifying the dimensions and numerical ranges of spectral data from various data sources through linear or nonlinear transformation algorithms. For nonspectral data such as water quality and biodiversity monitored on the ground, standardized quantitative scoring is performed based on the water ecological assessment index system, enabling various types of data to be integrated and calculated under the same mathematical model framework to generate a comprehensive water ecological monitoring dataset.

5. The novel water ecological monitoring and assessment method according to claim 1, characterized in that, The method for identifying ecological indicators includes monitoring changes in the area of ​​reference water bodies and monitoring using environmental DNA detection technology. The model building and adaptive optimization method adopts a hybrid modeling strategy, which integrates mechanistic models and data-driven models. After the model is built, an adaptive learning module is embedded.

6. The novel water ecological monitoring and assessment method according to claim 5, characterized in that, The monitoring of changes in the reference water area utilizes the interpretation results of long-term satellite remote sensing images, combined with field hydrogeological survey data, to construct a dynamic water-area model. This model clarifies how factors such as water level fluctuations, water supply, and human water use drive changes in water area, and the mechanisms by which these changes, in turn, affect aquatic habitats and water-heat exchange processes. Based on statistical correlation analysis and geographic information system spatial analysis, the model quantifies the relationship between vegetation cover and ecosystem services such as water purification, soil erosion control, and habitat provision, and identifies the contribution of vegetation community succession stages and vegetation structure characteristics to the stability of the aquatic ecosystem. The monitoring using environmental DNA detection technology involves extracting fragments of biological genetic material from water bodies, identifying species information through high-throughput sequencing technology, and combining this with traditional field survey data to construct a species coexistence network model. This model reveals the predation, competition, and symbiotic relationships among different biological species, explores the core role of key species in maintaining the structure and function of aquatic ecosystems, and transforms these complex ecological relationships into quantifiable and modelable mathematical correlation expressions, serving as the core component of the health assessment model.

7. The novel water ecological monitoring and assessment method according to claim 1, characterized in that, The proposed spectral knowledge structuring method uses spectral bands as the basic dimension, associates spectral features with actual geographical locations, and uses GIS layer overlay technology to visualize the spatial distribution differences of spectral features in different regions, thus constructing an integrated knowledge system from microscopic spectral characteristics to macroscopic ecological and geographical patterns. The dynamic visualization display method is based on 3D graphics rendering technology such as WebGL to construct a 3D geographic scene that can be interacted with in real time, and spectral knowledge base data is set on the scene.

8. The novel water ecological monitoring and assessment method according to claim 3, characterized in that, The UAV uses a built-in global positioning system and an inertial navigation system for coordinated positioning.

9. The novel water ecological monitoring and assessment method according to claim 3, characterized in that, The ground-based monitoring method uses an acoustic Doppler current profiler to measure hydrodynamic parameters such as water flow velocity and flow rate based on the principle of sound wave reflection, ensuring accurate capture of dynamic changes in the physical and chemical properties of the water body.

10. The novel water ecological monitoring and assessment method according to claim 7, characterized in that, The proposed spectral knowledge structuring method targets ecological objects with similar and easily confused spectral features, and utilizes feature extraction and dimensionality reduction techniques such as principal component analysis and wavelet transform to uncover deep distinguishing features.