An ecological monitoring and early warning method and system for a water body and a medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HARBIN UNIV
- Filing Date
- 2026-04-27
- Publication Date
- 2026-07-21
Smart Images
Figure CN122432872A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of environmental science and ecology, and in particular to a method, system and medium for water body ecological monitoring and early warning. Background Technology
[0002] Phytoplankton are primary producers and sensitive indicator organisms in aquatic ecosystems. Their community structure, function, and dynamic changes can sensitively reflect the health status of the aquatic environment. Therefore, accurate monitoring of phytoplankton is an important foundation for water environment quality assessment and ecological risk early warning.
[0003] Traditional phytoplankton monitoring primarily relies on microscopic morphological identification and on-site physicochemical parameter measurements. Morphological identification is time-consuming and labor-intensive, requiring highly skilled personnel, and struggles to distinguish morphologically similar species or detect uncultured groups. Physicochemical parameter measurements only reflect the instantaneous chemical state of the water body, failing to capture the long-term succession process and complex ecological effects of the biological community. In recent years, environmental DNA macrobarcoding technology has been able to unbiasedly reveal phytoplankton species composition and relative abundance; remote sensing technology, especially UAV hyperspectral imaging, can retrieve optical parameters such as chlorophyll a concentration over a wide range; and in-situ multi-parameter sensors can acquire environmental factors such as water temperature, pH, dissolved oxygen, and nutrients in real time. However, each of these technologies has the following problems when used individually: there is a lack of effective means to correlate molecular omics data with macroscopic environmental disturbances and surface phytoscopic characteristics of the water body; remote sensing optical data lacks direct verification information on the biological community response; and sensor data is difficult to characterize the nonlinear response features of the ecosystem. The response of phytoplankton communities to environmental factors is nonlinear, while existing assessment methods are mostly based on linear assumptions, limiting their ability to characterize this nonlinear feature. Summary of the Invention
[0004] The purpose of this invention is to provide a method, system and medium for water body ecological monitoring and early warning, which integrates omics, remote sensing and sensor data and quantifies nonlinear response to achieve early and sensitive early warning of water body ecological stress.
[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for water body ecological monitoring and early warning, comprising the following steps: Sequencing data of phytoplankton communities in the target water area were obtained, and the Shannon-Wiener diversity index was determined based on the sequencing data; Acquire hyperspectral remote sensing images of the target water body, retrieve key optical parameters based on the remote sensing images, and determine the water color change rate based on the degree of change of key optical parameters between two observations at the same sampling point. Acquire in-situ environmental factor data of the target water area, including water temperature, pH, dissolved oxygen, total nitrogen, and total phosphorus; The Shannon-Wiener diversity index and in-situ environmental factor data were standardized. A multiple linear regression model was constructed with the standardized Shannon-Wiener diversity index as the dependent variable and the standardized on-site water physicochemical index data as the independent variable. The residuals of the multiple linear regression model were extracted as environmental response residuals. An ecological stress index was constructed based on the weighted summation of the Shannon-Wiener diversity index, water color change rate, and environmental response residuals. The ecological stress index is used to assess the ecological status of water bodies, and warning information is output when the degree of ecological stress exceeds a preset threshold.
[0006] In some optional embodiments, the phytoplankton community sequencing data is environmental DNA metabarcode sequencing data.
[0007] In some optional embodiments, determining the Shannon-Wiener diversity index based on sequencing data specifically includes: Operational taxonomic units were clustered and species were annotated from the sequencing data, and the Shannon-Wiener diversity index was determined based on the relative abundance of operational taxonomic units.
[0008] In some alternative embodiments, the hyperspectral remote sensing image of the water body is acquired by a hyperspectral imager mounted on a UAV.
[0009] In some alternative embodiments, the key optical parameters include chlorophyll a concentration, the absorption coefficient of colored soluble organic matter, and the overall water color index.
[0010] In some optional embodiments, the weighting coefficients of the Shannon-Wiener diversity index, water color change rate, and environmental response residuals are determined by ridge regression and cross-validation optimization.
[0011] In some optional embodiments, the assessment of the aquatic ecological state based on the ecological stress index specifically includes: When the ecological stress index is greater than or equal to the first threshold, it is determined to be in a healthy state; when the ecological stress index is greater than or equal to the second threshold and less than the first threshold, it is determined to be in a sub-healthy state; when the ecological stress index is less than the second threshold, it is determined to be in a highly stressed state.
[0012] In some alternative embodiments, the target water area is a river, lake, reservoir, or estuary.
[0013] Embodiments of the present invention also provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described water body ecological monitoring and early warning method.
[0014] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when run by a processor, is capable of executing the above-described water body ecological monitoring and early warning method.
[0015] The water body ecological monitoring and early warning method provided by this invention has at least the following beneficial effects: This invention organically integrates phytoplankton community sequencing data, hyperspectral remote sensing imagery of water bodies, and in-situ environmental factor data, and introduces environmental response residual quantification of nonlinear ecological responses to construct a multi-dimensional ecological stress assessment system. This method overcomes the limitations of single-dimensional technologies in ecological assessment, achieving the synergistic utilization of molecular-scale community structure information, macro-scale water body optical disturbance information, and environmental factor change information, effectively improving the comprehensiveness and accuracy of water body ecological status assessment. Simultaneously, this method is highly sensitive to water body disturbances, capable of capturing abnormal signals and issuing early warnings at the initial stage of water body ecological stress, providing a reliable technical means for watershed water environment management to shift from passive monitoring to proactive early warning. Attached Figure Description
[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a water body ecological monitoring and early warning method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the geographical distribution of 20 phytoplankton sampling points in the Jiamusi section of the Songhua River, according to an embodiment of the present invention. Figure 3 This is a PESI index variation trend diagram of a representative sampling point in the Jiamusi section of the Songhua River in different seasons, provided according to an embodiment of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0018] One embodiment of the present invention relates to a method for monitoring and early warning of aquatic ecosystems. The implementation details of the method for monitoring and early warning of aquatic ecosystems in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.
[0019] The specific process of the water body ecological monitoring and early warning method in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: Obtain phytoplankton community sequencing data in the target water area and determine the Shannon-Wiener diversity index based on the sequencing data; At each sampling point, 2 liters of water were collected from a depth of 0.5 meters using an acrylic water sampler and immediately placed in pre-washed brown polyethylene bottles for low-temperature, light-protected storage. The water samples were first pre-filtered through a 300 μm stainless steel screen to remove large zooplankton and debris. Subsequently, phytoplankton cells were enriched by filtering with a 0.45 μm pore size mixed cellulose ester membrane under negative pressure. The cell-containing membranes were then stored at -80°C until DNA extraction. Total genomic DNA was extracted from the membrane samples using a commercially available DNA extraction kit. A blank control was included to monitor for potential contamination. PCR amplification was performed using universal primers for the V4 variable region of the eukaryotic 18S rRNA gene. After purification and quantification, the amplified products were sequenced using the Illumina MiSeq platform. The raw data from the assay underwent the following bioinformatics processing: adapter sequences were removed and quality-controlled using Cutadapt and FASTP software; paired-end reads were assembled using FLASH software; the assembled sequences were denoised using Usearch software and clustered into operational taxonomic units (OTUs) with 97% similarity; and species annotation was performed on representative OTU sequences using the SILVA database. Based on the final generated OTU table, the Shannon-Wiener diversity index H′ for each sample was calculated using the vegan package in the R language environment to characterize the complexity and stability of the phytoplankton community.
[0020] Step 102: Obtain hyperspectral remote sensing images of the target water body, retrieve key optical parameters based on the remote sensing images, and determine the water color change rate based on the degree of change of key optical parameters between two observations at the same sampling point. Within 30 minutes of each water sample collection, a DJI Matrice 600 Pro drone equipped with a Headwall Photonics miniature hyperspectral imager was used for low-altitude aerial photography. The flight plan ensured coverage of all pre-set sampling points to acquire hyperspectral reflectance data of the water surface. The raw hyperspectral image data was first radiometrically calibrated in ENVI 5.6 software, converting digital quantization values into radiance values. Subsequently, atmospheric correction was performed using the FLAASH model to eliminate the effects of atmospheric scattering and absorption, obtaining the true surface reflectance of the ground features. Based on the corrected reflectance data, key optical parameters, including chlorophyll a concentration (Chl-a, μg / L), absorption coefficient of colored soluble organic matter at 440 nm a(440), and comprehensive water color index, were obtained using semi-analytical or empirical algorithms. The water color change rate was defined. DE The formula for calculating the degree of change of a key optical parameter between two observations at the same sampling point is as follows: ,in and These represent the parameter values from the two tests; or the difference can be used directly. The larger the ΔE value, the more severe the external disturbance (such as algal blooms or pollutant input) the water body at that location is.
[0021] Step 103: Obtain in-situ environmental factor data of the target water area, including water temperature, pH, dissolved oxygen, total nitrogen and total phosphorus. At each sampling point, a YSI EXO2 series multi-parameter water quality monitor was used for simultaneous on-site measurements, continuously recording parameters such as water temperature, pH, dissolved oxygen, oxidation-reduction potential, conductivity, and turbidity for at least 10 minutes. For nutrient indicators such as total nitrogen and total phosphorus, fixatives were added immediately after on-site water sample collection according to standard methods, and the samples were sent back to the laboratory as soon as possible. Total nitrogen was determined using the potassium persulfate oxidation-ultraviolet spectrophotometric method, and total phosphorus was determined using the ammonium molybdate spectrophotometric method. Outliers were removed from the collected time-series data (using the IQR rule), and the average value was calculated to represent the stable environmental state of the sampling point.
[0022] Step 104: Standardize the Shannon-Wiener diversity index and in-situ environmental factor data respectively; construct a multiple linear regression model with the standardized Shannon-Wiener diversity index as the dependent variable and the standardized on-site water physicochemical index data as the independent variable, and extract the residual of the multiple linear regression model as the environmental response residual. To achieve multi-source data fusion, a unified database with "sampling point number - sampling time" as the primary key was established. All variables (H′, ΔE, water temperature, pH, dissolved oxygen, total nitrogen, total phosphorus, Chl-a, CDOM, etc.) were Z-score standardized to eliminate the influence of different dimensions. The standardization formula is as follows: Z=(X-μ) / σ ,in X The original value, m The mean, s The standard deviation is given. For the small amount of missing remote sensing data due to weather or other reasons, the mean of the spatially and temporally nearest samples is used for interpolation. The standardized Shannon-Wiener diversity index is used. H′ Using standardized environmental factors such as water temperature, pH, dissolved oxygen, total nitrogen, and total phosphorus as the dependent variable, a multiple linear regression model was constructed. Y=b 0 ＋b 1 X 1 ＋…＋b p X p +e The residual ε of the model is extracted as the environmental response residual Cres. The environmental response residual reflects the portion of the observed community diversity that cannot be linearly explained by environmental factors. This portion usually includes information such as interspecies interactions, the interaction effects of environmental factors, and potential stressors that have not yet been measured. It is a key indicator for quantifying the nonlinear response of phytoplankton communities to environmental factors.
[0023] Step 105: Construct an ecological stress index based on the weighted summation of the Shannon-Wiener diversity index, water color change rate, and environmental response residuals; The phytoplankton ecological stress index (PESI) was constructed using a linear weighted model. PESOS i =α· H i ′ +β· D E i +γ· Cres i ,in PESOS i For the first i Phytoplankton ecological stress index at each sampling point H i ′ For the first i Shannon-Wiener diversity index at each sampling point DE i For the first i The rate of change in water color at each sampling point Cres i For the firsti Environmental response residuals at each sampling point α , β , c These are the weighting coefficients. The determination of these weighting coefficients employs a combination of data-driven and ecological principles: firstly, initial weights are assigned based on common ecological knowledge (…). α=0.5, β=0.3, γ=0.2 Then, using ridge regression combined with 10-fold cross-validation, the weights are optimized on the training dataset to minimize model prediction error and prevent overfitting. The optimized weight coefficients can better reflect the actual contribution of each component under the local data characteristics.
[0024] Step 106: Assess the ecological status of the water body based on the ecological stress index, and output early warning information when the ecological stress level exceeds the preset threshold.
[0025] Based on the actual distribution of PESI values at all sample points (e.g., quartiles) and calibrated using known ecological disturbance events from historical monitoring (e.g., algal blooms, dominant species succession), the grading criteria are determined as follows: A PESI ≥ 0.6 indicates a healthy state, signifying a complex and stable phytoplankton community structure, minimal external disturbances, and a well-functioning ecosystem; a PESI ≤ 0.4 < 0.6 indicates a sub-healthy state, suggesting the community structure is beginning to show signs of instability, with increased frequency or intensity of external disturbances, requiring attention and enhanced monitoring; a PESI < 0.4 indicates a highly stressed state, signifying a significant decline in community diversity, severe ecosystem disturbances, and the need for immediate on-site verification and intervention measures. When the degree of ecological stress exceeds the preset threshold (i.e., PESI < 0.4), an early warning message is output.
[0026] This embodiment uses the Jiamusi section of the Songhua River as the study area, establishing 20 fixed sampling points (S1-S20) along the river from upstream to downstream. These points cover the upstream rural area (S1-S3), the core area of urban and tributary influence in the midstream (S4-S14, including key locations such as the Songhua River Bridge, the Port Authority, and the Lingdangmai River estuary), and the downstream open water area (S15-S20). Each sampling point was positioned using high-precision GPS to ensure spatial consistency between sampling and remote sensing data acquisition. Sampling was conducted in spring (April, snowmelt period), summer (August, high water level and high pollution load period), and autumn (October, stable period), all during periods of clear weather and low wind speeds. A geographical distribution diagram of the 20 phytoplankton sampling points in the Jiamusi section of the Songhua River is shown below. Figure 2 As shown, the system covers three functional areas: upstream (S1–S3), midstream (S4–S14), and downstream (S15–S20), systematically reflecting the changes in the composition and diversity of phytoplankton communities under different hydrological backgrounds and land use types.
[0027] The following is a graph showing the PESI index trends at representative sampling points along the Jiamusi section of the Songhua River in different seasons. Figure 3 As shown in the figure, the dashed line indicates that PESI=0.4 is the empirical ecological stress threshold; values below this threshold are considered a potential risk state. Figure 3 The data showed that the PESI values at most sampling points were above 0.6 in spring and autumn, indicating that the phytoplankton community was in a relatively stable ecological state under suitable water temperature and minimal disturbance. However, in summer, due to factors such as increased input from tributaries, local point source pollution, and reduced flow velocity, the PESI indices at several sampling points (such as S5 and S6) dropped sharply to 0.35 and 0.30, respectively, significantly below the warning threshold, suggesting that the system may be impacted by local pollution or undergo drastic changes in phytoplankton structure.
[0028] Source tracing analysis of the warning sites revealed a significant increase in the water color change rate ΔE retrieved from remote sensing, indicating a dramatic change in the optical properties of the water body. In-situ sensor data confirmed peaks in total phosphorus and total nitrogen concentrations, and intensified diurnal fluctuations in dissolved oxygen. Omics data showed a significant decrease in H′, a sharp increase in the relative abundance of pollution-tolerant Cryptophytes from approximately 8% to over 24%, and a deterioration in community evenness. The environmental response residual Cres exhibited a large negative value, indicating that the degree of community degradation exceeded the linear prediction range. The low PESI value successfully integrated and amplified the anomalous signals from the three dimensions, achieving early, sensitive, and comprehensive early warning of local pollution events.
[0029] The model reliability verification results include: in all samples where PESI indicated high stress, significant species replacement, key species disappearance, or pollution-tolerant species outbreaks were observed through independent omics data, indicating a high degree of consistency between PESI and ecological processes; when the spatial distribution map of PESI was overlaid with the high-value areas of chlorophyll a concentration retrieved from remote sensing during the same period on the ArcGIS platform, the spatial overlap reached more than 78%; the importance ranking of random forest model variables was ΔE (0.43) > Cres (0.32) > H′ (0.25), indicating that ΔE, which reflects the intensity of short-term external disturbances, and Cres, which reflects the nonlinear intrinsic response, have stronger predictive power than H′, which reflects the community structure itself, confirming the importance of introducing remote sensing and nonlinear response indicators for early warning.
[0030] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.
[0031] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.
[0032] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0033] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.
Claims
1. A method for water body ecological monitoring and early warning, characterized in that, The method includes: Sequencing data of phytoplankton communities in the target water area were obtained, and the Shannon-Wiener diversity index was determined based on the sequencing data; Acquire hyperspectral remote sensing images of the target water body, retrieve key optical parameters based on the remote sensing images, and determine the water color change rate based on the degree of change of key optical parameters between two observations at the same sampling point. Acquire in-situ environmental factor data of the target water area, including water temperature, pH, dissolved oxygen, total nitrogen, and total phosphorus; The Shannon-Wiener diversity index and in-situ environmental factor data were standardized. A multiple linear regression model was constructed with the standardized Shannon-Wiener diversity index as the dependent variable and the standardized on-site water physicochemical index data as the independent variable. The residuals of the multiple linear regression model were extracted as environmental response residuals. An ecological stress index was constructed based on the weighted summation of the Shannon-Wiener diversity index, water color change rate, and environmental response residuals. The ecological stress index is used to assess the ecological status of water bodies, and warning information is output when the degree of ecological stress exceeds a preset threshold.
2. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The phytoplankton community sequencing data were environmental DNA macrobarcode sequencing data.
3. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The determination of the Shannon-Wiener diversity index based on sequencing data specifically includes: Operational taxonomic units were clustered and species were annotated from the sequencing data, and the Shannon-Wiener diversity index was determined based on the relative abundance of operational taxonomic units.
4. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The hyperspectral remote sensing images of the water bodies were acquired by a hyperspectral imager mounted on a drone.
5. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The key optical parameters include chlorophyll a concentration, the absorption coefficient of colored soluble organic matter, and the comprehensive water color index.
6. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The weighting coefficients of the Shannon-Wiener diversity index, water color change rate, and environmental response residuals were determined through ridge regression and cross-validation optimization.
7. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The assessment of the aquatic ecological status based on the ecological stress index specifically includes: When the ecological stress index is greater than or equal to the first threshold, it is determined to be in a healthy state; when the ecological stress index is greater than or equal to the second threshold and less than the first threshold, it is determined to be in a sub-healthy state; when the ecological stress index is less than the second threshold, it is determined to be in a highly stressed state.
8. The water body ecological monitoring and early warning method as described in claim 1, characterized in that, The target water area is a river, lake, reservoir, or estuary.
9. A computer system, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the water body ecological monitoring and early warning method as described in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, is capable of performing the water body ecological monitoring and early warning method as defined in any one of claims 1 to 8.