Shallow lake water ecological environment quality fluctuation analysis system

Abstract

The invention relates to the technical field of water quality analysis, and particularly discloses a shallow lake water ecological environment quality fluctuation analysis system, which comprises a data acquisition module used for acquiring remote sensing images of shallow lake water ecological environments in different periods; the data processing module is used for preprocessing the acquired remote sensing images of the water ecological environment of the shallow lake in different periods and calculating the total phosphorus concentration of the shallow lake in different periods; the data analysis module is used for analyzing influence factors of increase of the total phosphorus concentration in the shallow lake based on the calculated change rule of the total phosphorus concentration of the shallow lake in different periods; according to the invention, through remote sensing image acquisition, preprocessing and data analysis, dynamic monitoring of the total phosphorus concentration of the shallow lake and deep analysis of influence factors are realized.

Description

Technical Field

[0001] This invention relates to the field of water quality analysis technology, and more specifically to a system for analyzing fluctuations in the aquatic ecological environment quality of shallow lakes. Background Technology

[0002] As important freshwater ecosystems, the water quality of shallow lakes, especially the concentration of nutrients (such as total phosphorus), directly affects the health and ecological function of the lakes. However, shallow lakes face the following challenges, which make their water quality, especially the concentration of total phosphorus, prone to fluctuations or even deterioration.

[0003] 1. Shallow lakes have short water exchange cycles, and the water quality of the lake is easily affected by the rivers flowing into the lake. Although the total phosphorus concentration of the rivers flowing into the lake basically meets the standards, it is difficult to degrade to the Class III water quality standard of the lake in a short period of time.

[0004] 2. Shallow lakes typically have an average depth of 3-4 meters, making the bottom sediment susceptible to disturbances from various physical, chemical, and biological processes. When disturbed by physical disturbances such as wind, waves, or ship traffic, or when sediment environmental conditions (such as pH, redox potential, temperature, and microbial activity) change, dissolved pollutants accumulated in the surface sediment, particularly nutrients like phosphorus, nitrogen, and organic carbon, are released back into the water. This "endogenous release" phenomenon is a significant cause of water quality deterioration and "secondary pollution" in shallow lakes, especially under conditions of frequent water disturbance or deteriorating sediment environment, which can significantly increase the total phosphorus concentration in the water.

[0005] 3. Remote sensing monitoring practices targeting specific shallow lakes (such as Chenghu Lake, Kuncheng Lake, Yuandang Lake, and Dianshan Lake) show that the total phosphorus concentration in these lakes is generally higher during the flood season than during the non-flood season. Specifically, this is manifested in an increase in the average total phosphorus concentration in the lake area during the flood season, and an increase in the area where the total phosphorus concentration exceeds the Class III water standard.

[0006] Preliminary analysis suggests that the main reasons for the increased total phosphorus concentration during the flood season may include:

[0007] a. Increased input of non-point source pollution: The flood season is usually accompanied by increased rainfall. Heavy rainfall not only directly washes away the surface, carrying phosphorus-containing pollutants (such as agricultural runoff and urban surface runoff) into lakes, but may also activate agricultural non-point source pollution, causing a large amount of phosphorus to flow into the lake area with the runoff.

[0008] b. Increased river inflow load: During the flood season, the river runoff increases significantly, and the total amount of pollutants carried (total phosphorus load) also increases accordingly. More phosphorus-containing water flows into the lake, directly pushing up the total phosphorus concentration in the lake area.

[0009] c. Increased Urban Sewage Overflow: Increased rainfall during the flood season puts greater pressure on urban drainage systems. In some older urban areas or areas with inadequate drainage systems, incomplete separation of rainwater and sewage or insufficient sewage treatment capacity may lead to large amounts of rainwater mixing into the sewage pipe network, exceeding the treatment capacity of sewage treatment plants and causing urban sewage overflow. This overflowing sewage often contains high concentrations of phosphorus, which is directly discharged into surrounding water bodies and eventually flows into lakes, exacerbating total phosphorus pollution in the lake area.

[0010] However, existing technologies for monitoring and analyzing total phosphorus concentrations in shallow lakes often rely on limited field sampling and laboratory analysis, which has limitations in terms of spatiotemporal coverage. Summary of the Invention

[0011] The purpose of this invention is to provide a system for analyzing fluctuations in the aquatic ecological environment quality of shallow lakes. Through remote sensing image acquisition, preprocessing, and data analysis, it enables dynamic monitoring of total phosphorus concentration in shallow lakes and in-depth analysis of influencing factors.

[0012] To achieve the above objectives, the present invention provides the following technical solution:

[0013] A system for analyzing fluctuations in the aquatic ecological environment quality of shallow lakes includes:

[0014] The data acquisition module is used to collect remote sensing images of the aquatic ecological environment of shallow lakes at different times;

[0015] The data processing module is used to preprocess remote sensing images of shallow lake water ecological environment acquired at different times and to calculate the total phosphorus concentration of shallow lakes at different times.

[0016] The data analysis module analyzes the factors influencing the increase in total phosphorus concentration in shallow lakes based on the calculated variation patterns of total phosphorus concentration in different periods.

[0017] Furthermore, the preprocessing includes: radiometric correction, geometric correction, and atmospheric correction.

[0018] Furthermore, the analysis of the influencing factors of the increase in total phosphorus concentration in shallow lakes, based on the calculated variation patterns of total phosphorus concentration at different periods, includes the following steps:

[0019] S1. Based on long-term series analysis, determine the trend of total phosphorus concentration;

[0020] S2. Based on the instantaneous phase synchronization algorithm, determine whether there is a similar trend in the total phosphorus concentration between two time series.

[0021] S3. Utilize the changing trend of total phosphorus concentration and, based on partial correlation coefficient analysis, quantify the correlation between total phosphorus concentration and different indicators;

[0022] S4. Based on spatial interpolation methods, identify the spatial distribution characteristics of total phosphorus concentration and key areas of interest;

[0023] S5. Utilize the correlation between total phosphorus concentration and different indicators, and based on the random forest regression model, analyze the main factors affecting the increase in total phosphorus concentration.

[0024] Furthermore, in S3, the different indicators include: transparency, redox potential, dissolved oxygen, turbidity, total phosphorus, chlorophyll a, suspended solids, water temperature, and total phosphorus flux into the lake.

[0025] Furthermore, in S3, based on partial correlation coefficient analysis, the correlation between total phosphorus concentration and different indicators is quantified, specifically as follows:

[0026] Given three variables: turbidity, total phosphorus flux into the lake, and total phosphorus concentration, and controlling for total phosphorus concentration, calculate the partial correlation coefficient between turbidity and total phosphorus flux into the lake. The expression is as follows:

[0027]

[0028] Where: x is turbidity, y is total phosphorus flux into the lake, z is total phosphorus concentration; r XY It is the Pearson correlation coefficient between the variable turbidity and the total phosphorus flux into the lake; r XZ It is the Pearson correlation coefficient between the variable turbidity and total phosphorus concentration; r YZ It is the Pearson correlation coefficient between total phosphorus flux into the lake and total phosphorus concentration.

[0029] Furthermore, in step S4, based on spatial interpolation methods, the spatial distribution characteristics of total phosphorus concentration and key areas of interest are identified, specifically as follows:

[0030] Spatial interpolation is performed using the inverse distance weighting method. The interpolated raster data is then statistically analyzed to identify the regions with the largest variations. The calculation formula is as follows:

[0031]

[0032] Where, Δ i c is the cumulative change of the i-th grid cell. i，t c is the concentration value of the i-th grid at time t. i，0 It is the concentration value of the i-th grid at the initial moment.

[0033] Furthermore, in S5, based on the random forest regression model, the expression for the main factors affecting the increase in total phosphorus concentration is as follows:

[0034] The importance of permutations is calculated based on an increase in mean squared error or a decrease in accuracy:

[0035]

[0036] Where T is the total tree; It is the error after shuffling the OOB value of variable j; VI is the original error of the t-th tree on the OOB sample; j This is the importance value of variable j.

[0037] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] This invention integrates remote sensing data acquisition, image processing, and data analysis to achieve effective monitoring and in-depth analysis of the aquatic ecological environment quality of shallow lakes, providing crucial technical support for the protection and restoration of shallow lake water environments. Its technical benefits are reflected in improved monitoring efficiency, accuracy, and reliability; a deeper understanding of pollution mechanisms; and support for scientific decision-making. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only implementation examples of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] The following description, in conjunction with the accompanying drawings, further illustrates the shallow lake aquatic ecological environment quality fluctuation analysis system of the present invention.

[0041] Figure 1 This is the overall flowchart of the shallow lake water ecological environment quality fluctuation analysis system provided by the present invention;

[0042] Figure 2 This is a trend diagram of wind speed, wind direction, and turbidity changes in Embodiment 1 provided by the present invention;

[0043] Figure 3 This is a graph showing the data of each indicator in Embodiment 1 provided by the present invention;

[0044] Figure 4 This is a comparison chart of total phosphorus concentration and various indicators in Example 1 of this invention; wherein... Figure 4 (a) in the figure is a comparison of total phosphorus concentration and total phosphorus flux in Nanxiaojing. Figure 4 (b) in the figure is a comparison of total phosphorus concentration and chlorophyll a. Figure 4 (c) in the figure is a comparison chart of total phosphorus concentration and turbidity;

[0045] Figure 5 This is a partial correlation coefficient diagram of each indicator in Embodiment 1 provided by the present invention;

[0046] Figure 6 This refers to the variation range of pollutants within a certain time period in Embodiment 1 provided by the present invention. Detailed Implementation

[0047] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0048] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0049] like Figure 1 The present invention provides a system for analyzing fluctuations in the aquatic ecological environment quality of shallow lakes, comprising:

[0050] The data acquisition module is used to collect remote sensing images of the aquatic ecological environment of shallow lakes at different times;

[0051] The data processing module is used to preprocess remote sensing images of shallow lake water ecological environment acquired at different times and to calculate the total phosphorus concentration of shallow lakes at different times.

[0052] The data analysis module analyzes the factors influencing the increase in total phosphorus concentration in shallow lakes based on the calculated variation patterns of total phosphorus concentration at different times.

[0053] The preprocessing includes: radiometric correction, geometric correction, and atmospheric correction.

[0054] The analysis of the factors influencing the increase in total phosphorus concentration in shallow lakes, based on the calculated variation patterns of total phosphorus concentration at different periods, includes the following steps:

[0055] S1. Based on long-term series analysis, determine the trend of total phosphorus concentration;

[0056] S2. Based on the instantaneous phase synchronization algorithm, determine whether there is a similar trend in the total phosphorus concentration between two time series.

[0057] S3. Utilize the changing trend of total phosphorus concentration and, based on partial correlation coefficient analysis, quantify the correlation between total phosphorus concentration and different indicators; among which, different indicators include: transparency, redox potential, dissolved oxygen, turbidity, total phosphorus, chlorophyll a, suspended solids, water temperature, wind speed, and total phosphorus inflow flux into the lake.

[0058] In this embodiment, as Figure 2 and Figure 3As shown, in addition to the increase in total phosphorus concentration, turbidity and dissolved oxygen also fluctuated in some periods, indicating that endogenous factors contributed to the increase in total phosphorus concentration. From December 12, 2023 to January 2, 2024, the total phosphorus concentration showed two significant increases: (1) From December 12 to 16, the total phosphorus concentration increased from 0.046 mg / L to 0.062 mg / L. During this period, the wind speed increased significantly, and the turbidity increased from 12 ntu to 21 ntu. During this period, the water temperature increased, and the dissolved oxygen dropped to the lowest value of 8 mg / L in nearly a month. (2) From December 29, 2023 to January 1, 2024, the total phosphorus concentration increased from 0.059 mg / L to 0.062 mg / L. During this period, the turbidity reached 11-13 ntu. Wind and wave disturbances accelerated the release of phosphorus from the bottom sediment, causing an increase in water turbidity and total phosphorus concentration. The increase in water temperature caused a decrease in dissolved oxygen in the water, which accelerated the release of phosphorus from the bottom sediment. However, the data shows that turbidity and dissolved oxygen do not fluctuate continuously, suggesting that endogenous factors have a short-term impact on the total phosphorus concentration in the center of Yangcheng Lake, but are not the main reason for the persistently high total phosphorus concentration in the center of Yangcheng Lake.

[0059] S4. Based on spatial interpolation methods, identify the spatial distribution characteristics of total phosphorus concentration and key areas of interest;

[0060] S5. Utilize the correlation between total phosphorus concentration and different indicators, and based on the random forest regression model, analyze the main factors affecting the increase in total phosphorus concentration.

[0061] In this embodiment, as Figure 4 (a) Figure 4 (b) and Figure 4 As shown in (c), quantitative analysis using a random forest regression model compared the total phosphorus concentration with the total phosphorus flux, chlorophyll a, and turbidity of the Nanxiaojing lake. The analysis revealed that the total phosphorus inflow flux and turbidity were the main influencing factors on the increase in total phosphorus concentration. Starting from December 10th, the inflow gradually increased, reaching a maximum of 58.83 m³ on December 14th. 3 The total phosphorus flux reached 4.71 g / s, a 3.25-fold increase compared to December 9th, and the total phosphorus flux also reached 4.71 g / s, a 3.86-fold increase compared to December 9th. It is estimated that it takes approximately four days for the total phosphorus load entering the lake to be transported from the inlet to the central section of Yangcheng Lake. Data shows that the increase in total phosphorus concentration and the increase in total phosphorus flux at the section are basically synchronous in time. The total phosphorus concentration in the diverted water was 0.07 mg / L-0.09 mg / L. The total phosphorus brought in by the diverted water continuously mixed and diluted with the total phosphorus in the lake, maintaining the total phosphorus concentration at the section at around 0.06 mg / L for a long period. Although the diverted water volume decreased starting on December 19th, diverting water continued, and the cumulative effect of the large amount of water diverted in the early stages persisted, thus the total phosphorus concentration at the section remained high for some time.

[0062] In S3, based on partial correlation coefficient analysis, the correlation between total phosphorus concentration and different indicators is quantified, such as... Figure 5 As shown, specifically:

[0063] Given three variables: turbidity, total phosphorus flux into the lake, and total phosphorus concentration, and controlling for total phosphorus concentration, calculate the partial correlation coefficient between turbidity and total phosphorus flux into the lake. The expression is as follows:

[0064]

[0065] Where: x is turbidity, y is total phosphorus flux into the lake, z is total phosphorus concentration; r XY It is the Pearson correlation coefficient between the variable turbidity and the total phosphorus flux into the lake; r XZ It is the Pearson correlation coefficient between the variables turbidity and total phosphorus; r YZ It is the Pearson correlation coefficient between total phosphorus flux into the lake and total phosphorus concentration.

[0066] In step S4, the spatial distribution characteristics of total phosphorus concentration and key areas of interest are identified based on spatial interpolation methods, specifically as follows:

[0067] like Figure 6 As shown, spatial interpolation is performed using the inverse distance weighting method. The interpolated raster data is then statistically analyzed to identify the areas with the largest variations. The calculation formula is as follows:

[0068]

[0069] Where, Δ i c is the cumulative change of the i-th grid cell. i，t c is the concentration value of the i-th grid at time t. i，0 It is the concentration value of the i-th grid at the initial moment.

[0070] In S5, based on the random forest regression model, the expression for the main factors affecting the increase in total phosphorus concentration is as follows:

[0071] Calculate permutation importance based on either an increase in mean squared error (MSE) or a decrease in accuracy:

[0072]

[0073] Where T is the total tree; It is the error after shuffling the OOB value of variable j; VI is the original error of the t-th tree on the OOB sample; j This is the importance value of variable j.

[0074] Example 2

[0075] This invention also provides an analytical process for determining the causes of total phosphorus fluctuations:

[0076] I. Temporal Variation Pattern of Water Quality in Yangcheng Lake

[0077] The fluctuations in total phosphorus concentration in the center of Yangcheng Lake can be roughly divided into three stages: a rapid increase in total phosphorus concentration (December 10-16, 2023), a persistently high level of total phosphorus concentration (December 17, 2023 - January 2, 2024), and a gradual decrease in total phosphorus concentration (January 3-28, 2024). When analyzing external influences, based on the convection-diffusion equation, it is estimated that it takes approximately 3-4 days for total phosphorus from the Qiputang inflow to the center of the lake to be transported. Therefore, the data on the inflow rate and total phosphorus flux from the Qiputang inflow were delayed by 4 days. The relevant indicators are as follows:

[0078] (1) The stage of rising total phosphorus concentration: From December 10th to December 16th, 2023, the total phosphorus concentration in the center of Yangcheng Lake increased from 0.037 mg / L to 0.066 mg / L. During this period, indicators such as wind speed, turbidity, and total phosphorus flux into the lake from Qiputang Pond fluctuated significantly. The wind speed increased from 2.4 m / s to 4.9 m / s, and the turbidity increased from 7.0 NTU to 21 NTU. The inflow of water into the lake from Qiputang Pond and the total phosphorus concentration both increased significantly. Among them, the inflow of water into the lake from Qiputang Pond increased from 29 m³ / s to 29 m³ / s. 3 / s increased to 67m 3 / s, the total phosphorus concentration at the inflow section of Nanxiaojing increased from 0.061 mg / L to 0.082 mg / L, and the total phosphorus concentration at Jieqiao increased from 0.070 mg / L to 0.071 mg / L. Correlation analysis showed that the flow rate, total phosphorus concentration, turbidity, and water temperature of Nanxiaojing were significantly correlated with the total phosphorus concentration in the center of Yangcheng Lake, with correlation coefficients of 0.76, 0.85, 0.92, and -0.78, respectively.

[0079] (2) Period of persistently high total phosphorus concentration: From December 17, 2023 to January 2, 2024, the total phosphorus concentration in the center of Yangcheng Lake remained between 0.056 and 0.062 mg / L. During this period, the total phosphorus concentration at the Qiputang inflow section of the Nanxiaojing River fluctuated between 0.066 and 0.089 mg / L, with the highest value occurring on December 24 and the lowest on December 18. The total phosphorus concentration at the Jieqiao section fluctuated between 0.056 and 0.077 mg / L, with the highest value occurring on December 14 and the lowest on December 23. The inflow rate from the Qiputang River showed a trend of first decreasing and then increasing, with the highest inflow rate reaching 69.3 m³. 3 / s, occurred on December 13th, with a minimum value of 16.7m. 3The / s value appeared on December 21st. Related analysis data shows that the correlation between total phosphorus in Yangcheng Lake and various indicators was not significant during this period, indicating that the fluctuations in total phosphorus in Yangcheng Lake during this period were influenced by complex factors and were not directly related to any single indicator.

[0080] (3) Total phosphorus concentration decline phase: From January 3rd to 28th, 2024, the total phosphorus concentration in the center of Yangcheng Lake decreased from 0.056 mg / L to 0.041 mg / L. During this period, both the inflow rate and the total phosphorus concentration at the Qiputang inflow point decreased significantly. Specifically, the total phosphorus concentration at the Nanxiaojing section decreased from 0.077 mg / L to 0.069 mg / L, and the total phosphorus concentration at the Jieqiao section decreased from 0.066 mg / L to 0.044 mg / L. The inflow rate decreased from 42.3 m³ / L. 3 / s decreased to 35.7m 3 / s, with a minimum value of 19.4m during the period. 3 / s (January 18th). Meanwhile, the water temperature in the center of Yangcheng Lake decreased from 7.9℃ to 2.8℃, and the turbidity decreased from 8.5 NTU to 4.6 NTU. Correlation analysis results showed that total phosphorus in the center of Yangcheng Lake exhibited significant correlations with most indicators during this period. The highest correlation coefficient was found between the total phosphorus concentration in Nanxiaojing and the total phosphorus concentration in the center of Yangcheng Lake, reaching 0.8, followed by the total phosphorus concentration in Jieqiao, with a correlation coefficient of 0.79. The correlation coefficients between endogenous indicators and the total phosphorus concentration in the center of Yangcheng Lake were also relatively high, with water temperature and turbidity showing correlation coefficients of 0.72 and 0.67, respectively.

[0081] II. Analysis of the Reasons for Fluctuations in Total Phosphorus in Yangcheng Lake

[0082] Based on the analysis of data variation patterns, and by comprehensively utilizing geospatial analysis and partial correlation statistical analysis, the reasons for the fluctuations in total phosphorus in Yangcheng Lake at different stages were analyzed. The results are as follows:

[0083] 1. Rapid Rise in Total Phosphorus Concentration: A spatial distribution heat map of total phosphorus concentration was created based on monitoring data from 8 monitoring points in the Yangcheng Lake area and 22 river monitoring points within a 500m radius of the lake. From December 10th to 16th, 2023, high levels of total phosphorus concentration in Yangcheng Lake were concentrated at the Qiputang inlet, the northern part of the central lake area, and the southwestern part of the lake area. Based on the seven-day spatial distribution changes in total phosphorus concentration, there was a pollutant migration characteristic from the Qiputang inlet to the center of Yangcheng Lake in the central lake area. From December 14, 2023, the total phosphorus concentration near the northern enclosure aquaculture area of ​​Yangcheng Lake rapidly increased, reaching a peak of 0.077 mg / L, lasting for seven days (December 14-21, 2023). During this period, Yangcheng Lake was dominated by westerly and northwesterly winds, with wind speeds reaching a maximum of 4.9 m / s. The westerly winds likely caused water to migrate towards the center of Yangcheng Lake, while the high wind speeds increased turbidity, accelerating the release of pollutants from the bottom sediment into the lake. Partial correlation analysis showed a significant partial correlation between the total phosphorus concentration in the center of Yangcheng Lake and the total phosphorus concentration of the Nanxiaojing River, the inflow of the Nanxiaojing River into the lake, and the turbidity of the center of Yangcheng Lake during this period, with partial correlation coefficients reaching 0.85, 0.76, and 0.92, respectively. This indicates that the increase in total phosphorus in the center of Yangcheng Lake during this period was mainly due to the influence of endogenous turbidity and the exogenous Nanxiaojing River, with the influence of the total phosphorus concentration in the Nanxiaojing River being greater than that of the inflow of the Nanxiaojing River into the lake.

[0084] 2. High Total Phosphorus Concentration Stage: Based on the spatial distribution of total phosphorus concentration, the overall total phosphorus concentration in Yangcheng Lake was relatively high during this stage, with the concentration entering the lake from Qiputang remaining at 0.06-0.07 mg / L. Simultaneously, some areas in the western lake region also showed high total phosphorus concentrations. Combined with endogenous data, dissolved oxygen remained at a relatively high level during this stage, averaging 10.04 mg / L, while turbidity was relatively low, averaging 9.2 NTU. Therefore, it is determined that endogenous sources were not the main cause of the persistently high total phosphorus concentration during this stage. Partial correlation analysis also indicates that the factors leading to the change in total phosphorus concentration in the central part of Yangcheng Lake during this stage were complex. The influence of any single indicator and inflow channel was not significant, indicating that it was not influenced by a single factor but by the combined effects of multiple factors. Given the high background level in the central part of Yangcheng Lake, the inflow from Nanxiaojing and Jieqiao brought a continuous high concentration of exogenous total phosphorus. Coupled with the ongoing cumulative effects of continuous water diversion and the large amount of water diverted in the early stages, the total phosphorus concentration in the central section of Yangcheng Lake remained high for a period of time.

[0085] 3. Total Phosphorus Concentration Decline Phase: The range of high total phosphorus values ​​along the Qiputang waterway showed a significant shrinking trend, especially after January 15th, when the high-phosphorus area near Qiputang almost disappeared. Time-series data reveals a substantial decrease in both the volume and concentration of water flowing into Qiputang after January 15th, from a peak of 0.067 mg / L to a minimum of 0.042 mg / L, a decrease of 40%. Simultaneously, the volume of water also decreased by over 66% during this phase. From an endogenous perspective, turbidity decreased significantly during this phase, by 46%, with an average turbidity of 8.35 NTU, which is at a low level. Numerous studies have shown a significant correlation between total phosphorus and turbidity in water; higher turbidity significantly increases the measured concentration of total phosphorus. Therefore, the decrease in turbidity during this phase had a significant impact on the decrease in total phosphorus concentration. Furthermore, the average dissolved oxygen level during this period was 9.67 mg / L, indicating an oxygen-rich state in the water, which is conducive to the adsorption of phosphorus from the overlying water by the sediment. Meanwhile, the lower water temperature during this stage was unfavorable for the release of phosphorus from the sediment. Partial correlation analysis showed a significant partial correlation between the total phosphorus concentration in Yangcheng Lake and that in Nanxiaojing Lake during this stage, with a partial correlation coefficient reaching 0.57. Simultaneously, the partial correlation coefficient between the total phosphorus concentration in Yangcheng Lake and turbidity also reached 0.42 (p < 0.1). In summary, the reduction in pollution load and the improvement of the endogenous environment in Qiputang Lake led to a continuous decrease in total phosphorus concentration during this stage.

[0086] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A shallow lake water ecological environment quality fluctuation analysis system, characterized in that, The utility model relates to a shallow lake total phosphorus concentration change rule analysis method and device, including: Data acquisition module is used to gather remote sensing image of shallow lake water ecological environment in different period; Data processing module is used to preprocess the remote sensing image of shallow lake water ecological environment in different period and calculate the total phosphorus concentration of shallow lake in different period; Data analysis module is based on the change rule of the calculated total phosphorus concentration of shallow lake in different period, and the influence factor of the increase of total phosphorus concentration in shallow lake is analyzed.

2. The shallow lake water ecological environment quality fluctuation analysis system according to claim 1, characterized in that, The preprocessing includes radiation correction, geometric correction and atmospheric correction.

3. The shallow lake water ecological environment quality fluctuation analysis system according to claim 1, characterized in that, The influence factor of the increase of total phosphorus concentration in shallow lake based on the change rule of the calculated total phosphorus concentration of shallow lake in different period includes the following steps: S1, based on long time sequence analysis, judge the change trend of total phosphorus concentration; S2, based on instantaneous phase synchronization algorithm, judge whether there is same change trend between the total phosphorus concentration of two time series; S3, utilize the change trend of total phosphorus concentration, and based on partial correlation coefficient analysis, quantify the correlation between total phosphorus concentration and different indexes; S4, based on spatial interpolation method, identify the spatial distribution characteristics and key attention area of total phosphorus concentration; S5, utilize the correlation between total phosphorus concentration and different indexes, and based on random forest regression model, analyze the main factors affecting the increase of total phosphorus concentration.

4. The shallow lake water ecological environment quality fluctuation analysis system according to claim 2, characterized in that, In S3, different indexes include transparency, oxidation-reduction potential, dissolved oxygen, turbidity, total phosphorus, chlorophyll a, suspended solids, water temperature and total phosphorus inflow.

5. The shallow lake water ecological environment quality fluctuation analysis system according to claim 2, characterized in that, In S3, based on partial correlation coefficient analysis, the correlation between total phosphorus concentration and different indexes is quantified, specifically: There are three variables, turbidity, total phosphorus inflow and total phosphorus concentration, after controlling the variable total phosphorus concentration, the partial correlation coefficient of turbidity and total phosphorus inflow is calculated, the expression is: wherein: x is turbidity, y is total phosphorus influx into the lake, and z is total phosphorus concentration; r XY is the Pearson correlation coefficient of the variable turbidity and total phosphorus influx into the lake; r XZ is the Pearson correlation coefficient of the variable turbidity and total phosphorus concentration; r YZ is the Pearson correlation coefficient of total phosphorus influx into the lake and total phosphorus concentration.

6. The shallow lake water eutrophication analysis system according to claim 2, characterized in that, In S4, based on spatial interpolation method, the spatial distribution characteristics and key attention area of total phosphorus concentration are identified, specifically: Adopt inverse distance weighting method to carry out spatial interpolation, carry out statistics to the interpolated raster data, find out the area with the largest change range, the calculation formula is as follows: where Δ i is the cumulative change amount of the i-th grid, c i，t is the concentration value of the i-th grid at time t, c i，0 is the concentration value of the i-th grid at the start time.

7. The shallow lake water ecological environment quality fluctuation analysis system according to claim 2, characterized in that, In S5, based on random forest regression model, the expression for analyzing the main factors affecting the increase of total phosphorus concentration is as follows: Based on mean square error increase or accuracy reduction calculation replacement importance: where T is the total tree; is the error after shuffling the OOB values of variable j; is the raw error of the tth tree on the OOB samples; VI j is the importance value of variable j.

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