Dish lake state evaluation method based on real-time remote sensing and monitoring

Abstract

The invention belongs to the technical field of natural resource evaluation, and particularly relates to a dish lake state evaluation method based on real-time remote sensing and monitoring. The evaluation indexes of the state of the dished lake include the flooding condition, the vegetation condition, the water quality condition and the human activity interference condition. The indexes are respectively obtained through radar remote sensing data, optical remote sensing data, on-line water quality monitoring equipment and an on-line video monitoring or unmanned aerial vehicle patrol mode; and performing ratio method operation on the indexes and historical monitoring data to obtain corresponding scores, and performing weighting operation to obtain a habitat comprehensive score. According to the method, the field sampling survey work is greatly reduced, the state of the dished lake habitat can be rapidly, continuously and repeatedly evaluated, and data support is provided for a wetland management department to timely adjust water resource distribution and utilization policies and promote sustainable utilization of water resources.

Description

Technical Field

[0001] The invention belongs to the field of natural resource evaluation, and in particular relates to a method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring. Background Art

[0002] Traditional lake or river status evaluation usually has a stable water body boundary, selects indicators such as water quantity, water quality, and aquatic biological communities, and uses model simulation, national standards, or expert knowledge to determine the score of the indicators to achieve evaluation.

[0003] A saucer-shaped lake refers to a seasonal sub-lake that appears on the beach in the lake basin during the dry season. The appearance of a saucer-shaped lake is mainly caused by the seasonal changes in the lake water level. During the flood season, the lake is vast and the saucer-shaped lake merges into the main lake body, and the lake fully shows the characteristics of a large lake. When the lake water level drops to, the saucer-shaped lakes appear one by one. When the water level drops to a certain level, they become isolated waters with no direct water flow connection with the main lake area, forming a unique landscape of a lake in a lake. If there is no human activity, the water level of the saucer-shaped lake depends on precipitation, evaporation and infiltration; there is little rain in autumn and winter, and the water level of the saucer-shaped lake is relatively stable, maintaining the characteristics of a shallow lake. This habitat provides excellent environmental conditions for the development of wetland ecosystems, and is particularly suitable for the habitat of zooplankton, floating plants, aquatic plants, benthic organisms, fish, and water birds.

[0004] For disc-shaped lakes, the water level is affected by natural and human activities and changes periodically, resulting in large changes in the water boundary within the year. Traditional evaluation indicators and methods are difficult to apply, and thus there is a lack of corresponding evaluation methods. In addition, the indicator data of the traditional evaluation system may rely on field surveys and laboratory analysis, which requires a long period and high analysis and testing and labor costs, and cannot be evaluated quickly and continuously. Summary of the invention

[0005] In view of the above problems, the present invention proposes a method for evaluating the state of a saucer-shaped lake based on non-contact means such as remote sensing satellites, online monitoring or drones to directly obtain data without conducting field surveys and laboratory analysis of data. The specific technical solution is as follows:

[0006] A method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring, the steps of which are as follows:

[0007] Step S1: Determine the vector boundary range of the saucer-shaped lake to be evaluated, and the range is obtained by collecting existing historical data, documents published by the competent authorities, or based on visual interpretation of remote sensing images;

[0008] Step S2: Calculate the flooding condition index, represented by F1,

[0009] Step S21: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, that is, including the flooded and non-flooded parts, the radar remote sensing data of the corresponding area (such as Sentienl-1 SAR) is called by the Google Earth Engine platform, and the data is subjected to radiation correction, filtering, spatial coordinate correction and clipping preprocessing. The dual-polarized water index SDWI (Sentinel-1 Dual-Polarized Water Index, SDWI) method is used to calculate the bands to obtain a water body binary image map, and then the Otsu algorithm OTSU is combined to determine the optimal threshold for water body extraction to obtain the water body range of the saucer-shaped lake.

[0010] SDWI formula: SDWI=ln(10×VV×VH)-8

[0011] Wherein, VV is the SAR backscatter coefficient of vertical transmission and vertical reception; VH is the SAR backscatter coefficient of vertical transmission and horizontal reception.

[0012] Step S22: Combined with the GEE platform, call the high-resolution optical remote sensing image data (such as Sentienl-2 image) or drone aerial image of the corresponding area for verification, and compare the water area calculated in step S21 and the water surface area obtained by remote sensing or image in step S22. When the difference between the water area processed in step S21 and the area processed in step S22 is less than 5%, proceed to the next step; otherwise, repeat step S21 and adjust the threshold with reference to the optical image result in step S22 until the accuracy meets the requirements of step S22;

[0013] Step S23: Repeat steps S21 and S22 to calculate the radar remote sensing data that can be extracted by the GEE platform within ≥5 years of the disc-shaped lake to be evaluated, obtain the data set of the water surface of the disc-shaped lake in the same period of history under different seasons and water level conditions, and calculate the average value of the historical data of the same period within ≥5 years; based on the average value of the historical data of the data set, assign points to the flooding conditions at the evaluation time node, and the scoring is based on the following table;

[0014] Table 1 Flooding index scoring table

[0015] The percentage difference between the flooded area and the historical average for the same period >60 （40,60] （30,40] （20,30] （10,20] [0,10] Index scoring 0 [0，30） [30，60） [60，80） [80，90） [90,100]

[0016] The F1 score calculation method is:

[0017] S1 is the flooded area during the evaluation period, and Sn is the average flooded area during the same period in the multi-year history;

[0018] ;

[0019] Rmax is the maximum value within the percentage difference between the flooded area corresponding to F1 and the historical average value for the same period, Rmin is the minimum value within the percentage difference between the flooded area corresponding to F1 and the historical average value for the same period, Tmax is the maximum value within the index scoring range corresponding to the percentage difference, and Tmin is the minimum value within the index scoring range corresponding to the percentage difference.

[0020] Step S3: Calculate the vegetation condition index, represented by F2,

[0021] Step S31: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, the optical remote sensing data or low-altitude aerial photography data of the corresponding area (such as Sentienl-2 or Landsat series images) are called up using the Google Earth Engine platform. The data are subjected to radiation correction, filtering, spatial coordinate correction, and clipping preprocessing. The Normalized Difference Vegetation Index (NDVI) is calculated through band operations. If there are multiple images during the evaluation period, the maximum value of the pixel is synthesized to reflect the optimal growth state; or the existing NDVI remote sensing product data product is directly used for clipping.

[0022] Step S32: Repeat step S31, and obtain the historical data set of vegetation conditions of the disc-shaped lake under different seasons and water level conditions by calculating the radar remote sensing data that can be extracted by the GEE platform within ≥5 years of the disc-shaped lake to be evaluated; and calculate the data of the same period in history within ≥5 years; finally, synthesize the maximum NDVI value of each pixel in 5 years to reflect the best state of vegetation in the past period; and assign points to the vegetation conditions at the evaluation time node, and the points are assigned using the following table;

[0023] Table 2 Vegetation status index scoring table

[0024] The percentage difference between the NDVI value and the historical maximum value during the same period >50 （40,50] （20,40] （10,20] （5,10] [0,5] Index scoring 0 [0，30） [30，60） [60，80） [80，90） [90,100]

[0025] The F2 score calculation method is: ;

[0026] N1 is the maximum NDVI value during the evaluation period, and Nn is the maximum NDVI value during the same period in history;

[0027] ;

[0028] Qmax is the maximum value within the percentage difference between the NDVI value corresponding to F2 and the historical maximum value during the same period, Qmin is the minimum value within the percentage difference between the NDVI value corresponding to F2 and the historical maximum value during the same period, Pmax is the maximum value within the index scoring range corresponding to the percentage difference, and Pmin is the minimum value within the index scoring range corresponding to the percentage difference.

[0029] Step S4: Calculate the water quality index, represented by F3,

[0030] Based on the online water quality monitoring equipment, chlorophyll a (Chla), total nitrogen (TN), total phosphorus (TP), transparency (SD) and permanganate index (COD) are obtained in real time. Mn ) Calculate the comprehensive nutritional status index, and the comprehensive nutritional status index score is based on Table 4;

[0031] Table 4 Comprehensive nutritional status index scoring table

[0032] Comprehensive nutritional status index >100 （70,100] （60,70] （50,60] （30,50] [0,30] Index scoring 0 [0，20） [20，50） [50，80） [80，90） [90,100]

[0033] The calculation formula of comprehensive nutritional status index is:

[0034] ;

[0035] Where: TLI—comprehensive nutritional status index; Wj—the relevant weight of the nutritional status index of the jth parameter;

[0036] TLI (j) - represents the nutritional status index of the jth parameter; taking chla as the benchmark parameter, the normalized correlation weight calculation formula of the jth parameter is:

[0037] ;

[0038] Where: rij is the correlation coefficient between the jth parameter and the benchmark parameter chla; m is the number of evaluation parameters;

[0039] The correlation between Chla and other parameters rij and rij 2 As shown in Table 5:

[0040] Table 5 Correlation between Chla and other parameters ij and r ij 2

[0041] parameter Chla TP TN SD CODM <![CDATA[r ij ]]> 1 0.84 0.82 -0.83 0.83 <![CDATA[r ij 2 ]]> 1 0.7056 0.6724 0.6889 0.6889

[0042] The nutritional status calculation formula for a single indicator is:

[0043] TLI(COD Mn )=10*(0.109+2.661*Ln(COD Mn ));

[0044] TLI(TN)=10*(5.453+1.694*Ln(TN));

[0045] TLI(TP)=10*(9.436+1.624*Ln(TP));

[0046] TLI(Chla)=10*(2.5+1.086*Ln(Chla));

[0047] TLI(SD)=10*(5.118-1.94*Ln(SD));

[0048] Wherein: the unit of chlorophyll a chl is mg / m3, the unit of transparency SD is m; the units of other indicators are all mg / L.

[0049] Step S5: Calculate the human activity interference status index, represented by F4,

[0050] For activities that can recover naturally without human intervention within the year, the ratio of the area affected by human activities to the total area of ​​the saucer-shaped lake is calculated based on online video monitoring and regular drone patrol video data. The scoring table is as follows. For those that cannot recover naturally within the year, the score is 0.

[0051] Table 6 Human activity interference status index scoring table

[0052] Ratio of the disturbance affected area to the total area of ​​the saucer-shaped lake Unable to recover naturally within one year （40,100] （20,40] （10,20] （5,10] [0,5] Index scoring 0 [0，20） [20，50） [50，80） [80，90） [90,100]

[0053] Among them, typical human activity interference includes changing the morphology and structure of the saucer-shaped lake basin, obtaining the natural resources of the saucer-shaped lake, etc.

[0054] Step S6: The above-mentioned indexes are weighted and summed to obtain the disc-shaped lake condition score and the warning level; the score is continuously updated according to the season or water level changes;

[0055] Calculation formula: W = Fi × Wi

[0056] Where W is the comprehensive score, Fi is the index, i ranges from 1 to 4, and Wi is the corresponding weight;

[0057] Warning level state Score range Red Alert inferior [0，20） Orange warning Difference [20，40） Yellow Warning middle [40，60） No warning good [60，80） No warning excellent [80,100] .

[0058] The present invention is a method for evaluating the condition of a saucer-shaped lake, which directly obtains data through non-contact means such as remote sensing satellites, online monitoring or drones, and does not require field investigations and laboratory analysis of data. This evaluation uses remote sensing radar data, optical images, online water quality monitoring, online video monitoring and drone cruise multimedia data as data sources, and selects comprehensive, non-contact, and traceable evaluation indicators to evaluate the condition of the saucer-shaped lake habitat. The evaluation indicators of this evaluation method cover the physical, chemical and biological elements of the saucer-shaped lake wetland, and use the historical state of the saucer-shaped lake as a reference standard, which can reflect the deviation of the saucer-shaped lake habitat from the same period in history, thereby providing scientific and technological support for management departments to grasp the changing state of the wetland and initiate early warning and prevention measures when necessary. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Technical roadmap of the method of the present invention;

[0060] Figure 2 The flooded area status map in Example 1;

[0061] Figure 3 Vegetation status map (NDVI value) of the non-flooded area in Example 1. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] Poyang Lake, a typical disc-shaped lake, was selected as the evaluation object, and July 2022 was selected as the evaluation time node. The flooding conditions, vegetation conditions, water quality conditions and human activity interference conditions were calculated using the following methods.

[0065] Step S1: Determine the vector boundary range of the disc-shaped lake to be evaluated. The range is obtained by collecting existing historical data, documents published by the competent authorities, or based on visual interpretation of remote sensing images.

[0066] Step S2: Calculate the flooding condition index, represented by F1,

[0067] Step S21: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, that is, including the flooded and non-flooded parts, the Sentienl-1 SAR radar remote sensing data of the corresponding area is called by using the Google Earth Engine platform, and the data is subjected to radiation correction, filtering processing, spatial coordinate correction and clipping preprocessing. The dual-polarization water index SDWI method is used to calculate the bands to obtain a water body binary image map, and then the Otsu algorithm OTSU is combined to determine the optimal threshold for water body extraction to obtain the water body range of the saucer-shaped lake;

[0068] Step S22: Combined with the GEE platform, call the high-resolution Sentienl-2 optical remote sensing image data or drone aerial images of the corresponding area for verification. By comparing the water area calculated in step S21 and the water surface area obtained by remote sensing or image in step S22, when the difference between the water area processed in step S21 and the area processed in step S22 is less than 5%, proceed to the next step; otherwise, repeat step S21 and adjust the threshold with reference to the optical image result in step S22 until the accuracy meets the requirements of step S22;

[0069] Step S23: Repeat steps S21 and S22 to calculate the radar remote sensing data that can be extracted by the GEE platform within ≥5 years of the disc-shaped lake to be evaluated, obtain the data set of the water surface of the disc-shaped lake in the same period of history under different seasons and water level conditions, and calculate the average value of the historical data for the same period within ≥5 years;

[0070] In this embodiment, the boundary of the evaluation time period and the boundary of the historical average value for the same period are as follows: Figure 2 As shown, the difference between the two is 7.92%. According to the flood condition index scoring table, the index score is calculated to be 92.08 points.

[0071] Step S3: Calculate the vegetation condition index, represented by F2,

[0072] Step S31: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, the optical remote sensing data or low-altitude aerial photography data of the corresponding area (such as Sentienl-2 or Landsat series images) are called up using the Google Earth Engine platform. The data are subjected to radiation correction, filtering, spatial coordinate correction, and clipping preprocessing. The Normalized Difference Vegetation Index (NDVI) is calculated through band operations. If there are multiple images during the evaluation period, the maximum value of the pixel is synthesized to reflect the optimal growth state; or the existing NDVI remote sensing product data product is directly used for clipping.

[0073] Step S32: Repeat step S31, and obtain the historical vegetation data set of the disc-shaped lake under different seasons and water level conditions by calculating the radar remote sensing data that can be extracted by the GEE platform within ≥5 years; and calculate the data of the same period in history within ≥5 years; finally, synthesize the maximum NDVI value of each pixel in 5 years to reflect the best state of vegetation in the past period;

[0074] In this embodiment, the vegetation conditions in the non-flooded area during the evaluation period are as follows: Figure 3 As shown, the preparation status during the evaluation period in this embodiment differs by 11.80% from the historical maximum value during the same period. According to the vegetation status index scoring table, the index score is calculated to be 63.6 points.

[0075] Step S4: Calculate the water quality index, represented by F3, based on the real-time acquisition of chlorophyll a (Chla), total nitrogen (TN), total phosphorus (TP), transparency (SD) and permanganate index (COD) by the water quality online monitoring equipment. Mn ) Calculate the comprehensive nutritional status index;

[0076] In this embodiment, based on the real-time monitoring data of the water quality online monitoring equipment, the average concentrations of the relevant parameters are 12.892 mg / m 3 , 1.38mg / L, 0.065mg / L, 0.450m, 3.55mg / L, the calculated comprehensive nutritional index of water quality is 52.78. According to the calculation formula and scoring criteria, the water quality score is 57.38.

[0077] Step S5: Calculate the human activity interference status index, represented by F4,

[0078] The human activity interference during the evaluation period is 0 and the score is 100.

[0079] Step S6: The above-mentioned indexes after scoring are weighted and summed to calculate the condition score of the saucer-shaped lake and obtain the warning level;

[0080] time Flooding conditions Vegetation status Water quality Human activity disturbance Overall Rating July 2022 92.08 63.6 57.38 100 78.27

[0081] According to the above evaluation score of 78.27, the warning level is no warning and the grade is good.

[0082] The above describes in detail the preferred implementation of this patent, but this patent is not limited to the above implementation. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of this patent.

Claims

1. A method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring, characterized in that: The steps include: Step S1: Determine the vector boundary range of the saucer-shaped lake to be evaluated, and the range is obtained by collecting existing historical data, documents published by the competent authorities, or based on visual interpretation of remote sensing images; Step S2: Calculate the flooding condition index, represented by F1, Step S21: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, that is, including the flooded and non-flooded parts, the radar remote sensing data of the corresponding area is called by using the Google Earth Engine platform, and the data is subjected to radiation correction, filtering, spatial coordinate correction and clipping preprocessing. The dual-polarization water index SDWI method is used to calculate the bands to obtain a water body binary image map, and then the Otsu algorithm OTSU is combined to determine the optimal threshold for water body extraction to obtain the water body range of the saucer-shaped lake. SDWI formula: SDWI=ln(10×VV×VH)-8 Wherein, VV is the SAR backscatter coefficient of vertical transmission and vertical reception; VH is the SAR backscatter coefficient of vertical transmission and horizontal reception; Step S22: Combined with the GEE platform, call the high-resolution optical remote sensing image data or drone aerial image of the corresponding area for verification, and compare the water area calculated in step S21 and the water surface area obtained by remote sensing or image in step S22. When the difference between the water area processed in step S21 and the area processed in step S22 is less than 5%, proceed to the next step; otherwise, repeat step S21 and adjust the threshold with reference to the optical image result of step S22 until the accuracy meets the requirements of step S22; Step S23: Repeat steps S21 and S22 to calculate the radar remote sensing data that can be extracted by the GEE platform within ≥5 years of the disc-shaped lake to be evaluated, obtain the data set of the water surface of the disc-shaped lake in the same period of history under different seasons and water level conditions, and calculate the average value of the historical data for the same period within ≥5 years; Based on the average value of historical data of the same period in the data set, the flooding conditions at the assessment time node are scored, and the scoring is based on the following table: Flooding index scoring table Step S3: Calculate the vegetation condition index, represented by F2, Step S31: Based on the maximum boundary range of the saucer-shaped lake to be evaluated, the optical remote sensing data or low-altitude aerial photography data of the corresponding area is called by using the Google Earth Engine platform, and the data is subjected to radiation correction, filtering, spatial coordinate correction, and clipping preprocessing, and the normalized vegetation index NDVI is calculated by band operation; if there are multiple images during the evaluation period, the maximum value of the pixel is synthesized to reflect the optimal growth state; or the existing NDVI remote sensing product data product is directly used for clipping; Step S32: Repeat step S31, and obtain the historical data set of vegetation conditions of the disc-shaped lake under different seasons and water level conditions by calculating the radar remote sensing data that can be extracted by the GEE platform within ≥5 years of the disc-shaped lake to be evaluated; and calculate the data of the same period in history within ≥5 years; finally, synthesize the maximum NDVI value of each pixel in 5 years to reflect the best state of vegetation in the past period; and assign points to the vegetation conditions at the evaluation time node, and the points are assigned using the following table; Vegetation status index scoring table Step S4: Calculate the water quality index, represented by F3, Based on the online water quality monitoring equipment, real-time acquisition of chlorophyll a Chla, total nitrogen TN, total phosphorus TP, transparency SD and permanganate index COD Mn Calculate the comprehensive nutritional status index, and use the comprehensive nutritional status index scoring table to assign points to the comprehensive nutritional status index; Comprehensive nutritional status index scoring table Step S5: Calculate the human activity interference status index, represented by F4; For activities that can recover naturally without human intervention within the year, the ratio of the area affected by human activities to the total area of ​​the saucer-shaped lake is calculated based on online video monitoring and drone regular patrol video data. The scoring table is as follows. For those that cannot recover naturally within the year, the score is 0; Human activity interference status index scoring table Typical human activity interference includes changing the shape and structure of the saucer-shaped lake basin and obtaining the natural resources of the saucer-shaped lake; Step S6: The above-mentioned indexes are weighted and summed to obtain the disc-shaped lake condition score and the warning level; the score is continuously updated according to the season or water level changes; Calculation formula: W = Fi × Wi Where W is the comprehensive score, Fi is the index, i ranges from 1 to 4, and Wi is the corresponding weight; 。 2. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: The radar remote sensing data in step S21 adopts SAR data of the Sentienl-1 satellite.

3. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: The optical remote sensing image data in step S22 is a Sentien1-2 image.

4. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: The F1 score calculation method is: ; S1 is the flooded area during the evaluation period, and Sn is the average flooded area during the same period in the multi-year history; ； Rmax is the maximum value within the percentage difference between the flooded area corresponding to F1 and the historical average value for the same period, Rmin is the minimum value within the percentage difference between the flooded area corresponding to F1 and the historical average value for the same period, Tmax is the maximum value within the index scoring range corresponding to the percentage difference, and Tmin is the minimum value within the index scoring range corresponding to the percentage difference.

5. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: The optical remote sensing data in step S31 is Sentien1-2 or Landsat series image data.

6. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: The F2 score calculation method is: ; N1 is the maximum NDVI value during the evaluation period, and Nn is the maximum NDVI value during the same period in history; ； Qmax is the maximum value within the percentage difference between the NDVI value corresponding to F2 and the historical maximum value during the same period, Qmin is the minimum value within the percentage difference between the NDVI value corresponding to F2 and the historical maximum value during the same period, Pmax is the maximum value within the index scoring range corresponding to the percentage difference, and Pmin is the minimum value within the index scoring range corresponding to the percentage difference.

7. The method for evaluating the state of a saucer-shaped lake based on real-time remote sensing and monitoring according to claim 1, characterized in that: In step S4, the comprehensive nutritional status index calculation formula is: ； Where: TLI—comprehensive nutritional status index; Wj—the relevant weight of the nutritional status index of the jth parameter; TLI (j) – nutritional status index representing the jth parameter; Taking chla as the benchmark parameter, the normalized correlation weight calculation formula of the jth parameter is: ； Where: rij is the correlation coefficient between the jth parameter and the benchmark parameter chla; m is the number of evaluation parameters; The correlation between Chla and other parameters rij and rij 2 As shown in the following table: The correlation between Chla and other parameters rij and rij 2 The nutritional status calculation formula for a single indicator is: TLI(CODE Mn )=10*(0.109+2.661*Ln(COD Mn )); TLI(TN)=10*(5.453+1.694*Ln(TN)); TLI(TP)=10*(9.436+1.624*Ln(TP)); TLI(Chla)=10*(2.5+1.086*Ln(Chla)); TLI(SD)=10*(5.118-1.94*Ln(SD)); Where: Chlorophyll a chl unit is mg / m 3 , transparency SD unit is m; other indicators units are mg / L.

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