River network water quality evaluation method based on remote sensing sequence and multi-source spatio-temporal data

Abstract

The invention provides a river network water quality evaluation method based on a remote sensing sequence and multi-source spatio-temporal data, which comprises the steps of obtaining river basin data and multi-year noctilucent remote sensing data, and further comprises the following steps: by taking a basin as a unit, calculating the rainfall fluctuation of each basin; calculating the development stages of main cities and towns in the drainage basin by utilizing the change rate of the multi-year luminous remote sensing data; the national economic development level is extracted into the drainage basin; and calculating the water quality level of each river. According to the river network water quality evaluation method based on the remote sensing sequence and the multi-source spatio-temporal data, the urban development stage is evaluated by utilizing the luminous remote sensing data, the national economic development level, the sewage plant, the rainfall characteristics and other data are fused, and large-scale water quality evaluation above the country is carried out.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing target recognition, in particular to a river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data. Background Art

[0002] Rivers are natural systems that humans and other organisms depend on for survival. At the same time, rivers are also the natural systems most severely affected by human activities and climate change. Research on human interference with rivers involves multiple scales such as global, national, regional, urban, and river. The factors of concern include external interference (land use, infrastructure, human settlements, human activities) and internal interference (dams, canalization, embankments). Interference evaluation indicators include: runoff changes, water quality, degree of blockage, geometric characteristics, etc. Among them, changes in river water quality are the most direct manifestation of human impact on rivers. As people's requirements for living standards continue to improve, many countries have carried out large-scale river water quality improvement projects, and the water quality of many rivers has improved. How to quickly and efficiently assess the level of river pollution at the national or even global scale is of great significance to policy formulation and measure arrangements.

[0003] At present, the assessment of river pollution levels mainly relies on various monitoring methods, including monitoring stations and remote sensing. However, these monitoring methods have insufficient coverage and high costs, making it difficult to efficiently obtain pollution levels in a large area of ​​rivers.

[0004] The invention patent application with publication number CN118777222A discloses a water quality monitoring method and system based on remote sensing data, wherein the method includes collecting hyperspectral data and multispectral data of the monitoring area based on a remote sensing platform, forming the collected hyperspectral data and multispectral data into data sets respectively, and performing data correction; calculating the probability distribution of the corrected hyperspectral data set and multispectral data set for registration, and performing data fusion. The disadvantage of this method is that it uses remote sensing data for water quality monitoring without considering other factors, resulting in water quality misjudgment problems, and it is impossible to conduct water quality assessment at a national or global scale.

[0005] The invention patent application with the publication number CN114384015A discloses a water environment monitoring method based on multi-source remote sensing and machine learning, including: pre-processing the historical water quality monitoring site data of the studied water body; processing, parsing and fusing the multi-source historical remote sensing images of the studied area to obtain multi-source historical remote sensing data; spatiotemporally fusing the actual water quality parameters of the monitored water body and the corresponding multi-source historical remote sensing data to form a water quality inversion data set and pre-processing; inverting water quality parameters using machine learning algorithms, and selecting water quality inversion models based on performance evaluation; inverting multi-source real-time satellite remote sensing data based on the selected model to obtain monitoring index values ​​and issue warnings according to relevant standards and specifications. The disadvantage of this method is that it is applicable to specific water quality types and has a limited monitoring range, and is not applicable to large-scale water quality assessments above the national level. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes a river network water quality assessment method based on remote sensing sequences and multi-source spatiotemporal data. It uses night light remote sensing data to assess the stage of urban development, integrates data such as national economic development level, sewage treatment plants, rainfall characteristics, etc., and conducts large-scale water quality evaluation above the national level.

[0007] The purpose of the present invention is to provide a river network water quality assessment method based on remote sensing sequences and multi-source spatiotemporal data, including obtaining river basin data and multi-year night light remote sensing data, and also including the following steps: Step 1: Calculate the precipitation fluctuation rate of each basin based on the basin Variation precipitation ; Step 2: Calculate the development stage of major towns in the basin using the change rate of multi-year night light remote sensing data; Step 3: Extract national economic development levels into watersheds; Step 4: Calculate the water quality level for each river.

[0008] Preferably, the river basin data includes the monthly average precipitation of each river from January to December.

[0009] In any of the above schemes, preferably, the precipitation fluctuation rate of each basin is Variation precipitation The calculation formula is

[0010] in, is the average rainfall from January to December in each basin, Precipitation m For this basin m Monthly rainfall.

[0011] In any of the above schemes, preferably, step 2 comprises the following sub-steps: Step 21: Summarize the night light remote sensing brightness values ​​of each basin year by year according to the basin boundaries; Step 22: Calculate the brightness ratio of the light brightness values ​​in the last five years to the light brightness values ​​in the previous five years; Step 23: Use the brightness ratio to calculate the development stage of towns in the basin.

[0012] In any of the above solutions, preferably, the calculation formula of the brightness ratio is:

[0013] in, NTL i For this basini The night light remote sensing brightness value of the year, y is the current year, y -5 means the year is pushed back 5 years. y -6 means the year is pushed back 6 years. y -10 means the year is pushed back 10 years. Gradient ntl is the ratio of the night light remote sensing brightness values ​​of the current year.

[0014] In any of the above schemes, it is preferred that the evaluation method of the development stage is: the larger the brightness ratio, the faster the town development, and the smaller the brightness ratio, the more mature the town development.

[0015] In any of the above schemes, preferably, step 3 includes the following sub-steps: Step 31: Divide countries into three categories: developed countries, developing countries, and underdeveloped countries, and assign an economic development level value to each country; Step 32: assign the economic development level value corresponding to the country where each river basin unit is located to each river basin.

[0016] In any of the above schemes, it is preferred that the economic development level values ​​corresponding to the developed countries, developing countries and underdeveloped countries are 0.5, 1.5 and 1 respectively, indicating the impact of the overall economic development model and pollution control strategy at the national level on river water quality.

[0017] In any of the above schemes, preferably, step 4 includes the following sub-steps: Step 41: Calculate the amount of pollution generated in each watershed Pollution ; Step 42: Calculate water quality levels for each watershed Waterindex catchment .

[0018] In any of the above schemes, preferably, the pollution generation amount of each watershed is Pollution The formula is Pollution = NTL catchment × Gradient ntl × Variation precipitation × Coe develop in, NTL catchment is the night light remote sensing brightness value of the basin, Gradient ntl is the ratio of the night light remote sensing brightness of the basin, Variation precipitation is the rainfall fluctuation rate,Coe develop is the economic development level of the basin.

[0019] In any of the above schemes, it is preferred that the water quality level of each watershed is Waterindex catchment The calculation formula is

[0020] in, Runoff river is the flow of rivers in the basin, WaterIndex catchment is the water quality level of the river contained in the basin.

[0021] The present invention proposes a river network water quality assessment method based on remote sensing sequences and multi-source spatiotemporal data. By taking advantage of the high consistency of night light remote sensing data in different regions of the world and integrating data such as human activities and socio-economic levels, a global-scale river network water quality assessment can be carried out. The assessment results are highly consistent between regions and countries, thereby enabling water quality assessment between countries and regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a preferred embodiment of a method for evaluating river network water quality based on remote sensing sequences and multi-source spatiotemporal data.

[0023] Figure 2 It is a flow chart of another preferred embodiment of the river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data according to the present invention. DETAILED DESCRIPTION

[0024] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0025] Example 1 like Figure 1 As shown, a river network water quality assessment method based on remote sensing sequences and multi-source spatiotemporal data executes step 100 to obtain river basin data and multi-year night light remote sensing data, wherein the river basin data includes the monthly average precipitation of each river from January to December.

[0026] Execute step 110 to calculate the precipitation fluctuation rate of each basin based on the basin. Variation precipitation , the precipitation fluctuation rate of each basin Variation precipitation The calculation formula is

[0027] in, is the average rainfall from January to December in each basin, Precipitation m For this basin m Monthly rainfall.

[0028] Execute step 120 to calculate the development stages of major towns in the basin using the multi-year night light remote sensing data change rate, including the following sub-steps: Execute step 121 to summarize the night light remote sensing brightness values ​​of each watershed year by year according to the watershed boundaries.

[0029] Execute step 122 to calculate the brightness ratio of the light brightness value in the last five years to the light brightness value in the previous five years. The calculation formula of the brightness ratio is:

[0030] in, NTL i For this basin i The night light remote sensing brightness value of the year, y is the current year, y -5 means 5 years before the current year. y -6 means 6 years before the current year. y -10 means the year is pushed forward 10 years. Gradient ntl is the ratio of the night light remote sensing brightness values ​​of the current year.

[0031] Execute step 123, use the brightness ratio to calculate the development stage of the towns in the basin, and the evaluation method of the development stage is: the larger the brightness ratio, the faster the town develops, and the smaller the brightness ratio, the more mature the town development.

[0032] Execute step 130 to extract the national economic development level into the watershed, including the following sub-steps: Execute step 131 to divide the countries into three categories: developed countries, developing countries, and underdeveloped countries, and assign an economic development level value to each country. The economic development level values ​​corresponding to the developed countries, developing countries, and underdeveloped countries are 0.5, 1.5, and 1, respectively, indicating the impact of the overall economic development model and pollution control strategy at the national level on river water quality.

[0033] Execute step 132 to assign the economic development level value corresponding to the country where each river basin unit is located to each river basin.

[0034] Execute step 140 to calculate the water quality level of each river, including the following sub-steps: Execute step 141 to calculate the pollution generation amount of each watershed Pollution , the amount of pollution generated in each river basin PollutionThe formula is Pollution = NTL catchment × Gradient ntl × Variation precipitation × Coe develop in, NTL catchment is the night light remote sensing brightness value of the basin, Gradient ntl is the ratio of the night light remote sensing brightness of the basin, Variation precipitation is the rainfall fluctuation rate, Coe develop is the economic development level of the basin.

[0035] Execute step 142 to calculate the water quality level of each watershed Waterindex catchment , the water quality level of each basin Waterindex catchment The calculation formula is

[0036] in, Runoff river is the flow of rivers in the basin, WaterIndex catchment is the water quality level of the river contained in the basin.

[0037] Example 2 The present invention proposes a water quality evaluation method that integrates multi-source spatiotemporal data such as remote sensing, uses night light remote sensing data to evaluate the stage of urban development, integrates data such as the national economic development level, sewage treatment plants, and rainfall characteristics, and conducts large-scale water quality evaluation at national scales and above.

[0038] The present invention uses night light remote sensing sequence data to evaluate the level of urban development as the basis for the amount of pollutant emissions; the present invention integrates rainfall data, national economic development level and other data to carry out a comprehensive evaluation of river water quality pressure over a large range.

[0039] The present invention proposes a method for evaluating river water quality over a large area by integrating remote sensing and other multi-source data. Figure 2 As shown, the specific process is as follows: The first step is to calculate the rainfall fluctuation rate in each basin Taking the watershed as a unit, the average rainfall from January to December is collected, and the rainfall fluctuation rate of each watershed is calculated based on this.

[0040] In the formula, is the average rainfall from January to December in each basin, Precipitation m For this basin m Monthly rainfall, Variation precipitation is the volatility of the basin.

[0041] The second step is to use the change rate of multi-year night light remote sensing data to calculate the development stage of major towns in the basin. Towns, especially cities, are the main sources of pollutant emissions in the basin. First, the night light remote sensing brightness values ​​of each basin are summarized year by year according to the basin boundary, and then the ratio of the light brightness values ​​in the last five years to the light brightness values ​​in the previous five years is calculated. This ratio is used to calculate the development stage of the towns in the basin. The larger the ratio, the faster the town develops, and the smaller the ratio, the more mature the town development. The calculation method for a basin is as follows:

[0042] In the formula, NTL i For this basin i The night light remote sensing brightness value of the year, y is the current year, y -5 means the year is pushed back 5 years. If this year is 2024, then y-5 means 2000. Gradient ntl is the ratio of the night light remote sensing brightness values ​​of the current year.

[0043] The third step is to extract the national economic development level into the watershed.

[0044] Countries are divided into three categories: developed, developing, and underdeveloped, and assigned values ​​of 0.5, 1.5, and 1, indicating the impact of the overall economic development model and pollution control strategy at the national level on river water quality. This value is assigned to each river basin according to the country where each river basin unit is located.

[0045] The fourth step is to calculate the water quality level of each river.

[0046] First, calculate the pollution generation in each watershed: Pollution = NTL catchment × Gradient ntl × Variation precipitation × Coe develop In the formula, NTL catchment is the night light remote sensing brightness value of the basin (obtained by summarizing the brightness values ​​of each night light remote sensing grid within the basin boundary), Gradientntl is the ratio of the night light remote sensing brightness of the basin (calculated in the second step), Variation precipitation is the rainfall fluctuation rate obtained in the first step, Coe develop It is the economic development level of the basin calculated in the third step.

[0047] Then, the water quality level for each watershed was calculated

[0048] In the formula, Runoff river is the flow of rivers in the basin, WaterIndex catchment That is, the water quality level of the river included in the basin.

[0049] In order to better understand the present invention, the above is described in detail in conjunction with the specific embodiments of the present invention, but it is not intended to limit the present invention. Any simple modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention. Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referenced to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A river network water quality assessment method based on remote sensing sequences and multi-source spatiotemporal data, comprising obtaining river basin data and multi-year night light remote sensing data, characterized in that: The following steps are also included: Step 1: Calculate the precipitation fluctuation rate of each basin based on the basin Variation precipitation ; Step 2: Calculate the development stage of major towns in the basin using the change rate of multi-year night light remote sensing data. Step 2 includes the following sub-steps: Step 21: Summarize the night light remote sensing brightness values ​​of each basin year by year according to the basin boundaries; Step 22: Calculate the brightness ratio of the light brightness values ​​in the last five years to the light brightness values ​​in the previous five years. The calculation formula for the brightness ratio is: ， in, NTL i For this basin i The night light remote sensing brightness value of the year, y is the current year, y -5 means 5 years before the current year. y -6 means 6 years before the current year. y -10 means the year is pushed forward 10 years. Gradient ntl is the ratio of the night light remote sensing brightness values ​​of the current year; Step 23: Calculate the development stage of towns in the watershed using the brightness ratio; Step 3: Extract national economic development levels into watersheds; Step 4: Calculate the water quality level for each river.

2. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data according to claim 1, characterized in that: The river basin data include the monthly average precipitation from January to December for each river.

3. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data as claimed in claim 2, characterized in that: The precipitation fluctuation rate of each basin Variation precipitation The calculation formula is ， in, is the average rainfall from January to December in each basin, Precipitation m For this basin m Monthly rainfall.

4. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data as claimed in claim 3, characterized in that: The evaluation method of the development stage is: the larger the brightness ratio is, the faster the town is developing; the smaller the brightness ratio is, the more mature the town is developing.

5. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data as claimed in claim 4, characterized in that: The step 3 includes the following sub-steps: Step 31: Divide countries into three categories: developed countries, developing countries, and underdeveloped countries, and assign an economic development level value to each country; Step 32: assign the economic development level value corresponding to the country where each river basin unit is located to each river basin.

6. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data according to claim 5, characterized in that: The economic development level values ​​corresponding to the developed countries, developing countries and underdeveloped countries are 0.5, 1.5 and 1 respectively, indicating the impact of the overall economic development model and pollution control strategy at the national level on river water quality.

7. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data according to claim 6, characterized in that: The step 4 includes the following sub-steps: Step 41: Calculate the amount of pollution generated in each watershed Pollution ; Step 42: Calculate water quality levels for each watershed Waterindex catchment .

8. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data according to claim 7, characterized in that: The amount of pollution generated in each river basin Pollution The formula is Pollution=NTL catchment × Gradient ntl × Variation precipitation × Coe develop in, NTL catchment is the night light remote sensing brightness value of the basin, Gradient ntl is the ratio of the night light remote sensing brightness of the basin, Variation precipitation is the rainfall fluctuation rate, Coe develop is the economic development level of the basin.

9. The river network water quality assessment method based on remote sensing sequence and multi-source spatiotemporal data as claimed in claim 8, characterized in that: The water quality levels for each watershed Waterindex catchment The calculation formula is ， in, Runoff river is the flow of rivers in the basin, WaterIndex catchment is the water quality level of the river contained in the basin.

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