Lake ecological flow processing method and device based on remote sensing and water balance model

Abstract

The application provides a lake ecological flow processing method and device based on remote sensing and water balance model, which is used for establishing a lake ecological flow standardized processing architecture based on remote sensing and water balance equation, effectively solving the limitation that the traditional method seriously depends on measured hydrological data, and reducing the calculation cost. At the same time, compared with the characteristics that the traditional method needs long-term data accumulation, the timeliness is also significantly enhanced, so as to meet the application demand of low-cost and rapid evaluation, and provide good data support for lake water resource development and utilization control and lake ecological management.

Description

Technical Field

[0001] This application relates to the field of ecological water conservancy, specifically to a method and device for processing lake ecological flow based on remote sensing and water balance models. Background Technology

[0002] Ecological flow refers to the flow rate in rivers and lakes that meets water quality requirements and needs to be maintained to preserve the structure and function of aquatic ecosystems. Clearly, ecological flow is a key element in maintaining the health of lake ecosystems and is of great significance for protecting lake biodiversity and maintaining ecological balance. Therefore, automated and intelligent monitoring of lake ecological flow has data value for hydrological management.

[0003] However, the inventors of this application have discovered that traditional methods for calculating lake ecological flow have the following problems:

[0004] 1) It relies heavily on measured hydrological data, and it is difficult to obtain reliable calculation results for lakes that lack long-term observation stations;

[0005] 2) Most of the development is aimed at river systems, while lakes, as relatively closed water systems, have significantly different hydrological characteristics from rivers, and there are applicability issues when directly applied to lakes;

[0006] 3) Traditional methods are costly, require the construction and maintenance of a large number of monitoring facilities, and have long data acquisition cycles, making it difficult to meet the needs of rapid assessment. Summary of the Invention

[0007] This application provides a method and apparatus for processing lake ecological flow based on remote sensing and a water balance model. This method establishes a standardized processing framework for lake ecological flow based on remote sensing and water balance equations, effectively addressing the limitations of traditional methods that heavily rely on measured hydrological data and reducing computational costs. Furthermore, compared to traditional methods that require long-term data accumulation, this method significantly enhances timeliness, thus meeting the application requirements for low-cost and rapid assessment and providing excellent data support for lake water resource development and utilization management and lake ecological management.

[0008] Firstly, this application provides a method for processing lake ecological flow based on remote sensing and a water balance model, the method comprising:

[0009] For the current lake target, acquire remote sensing image data, meteorological data and digital elevation model of the corresponding area;

[0010] Extracting time series data of lake area from remote sensing image data;

[0011] Precipitation time series and potential evapotranspiration time series are extracted from meteorological data, and the lake area time series, precipitation time series and potential evapotranspiration time series are aligned on a preset time dimension;

[0012] Determining the average runoff coefficient of the watershed based on a digital elevation model;

[0013] Using the time series of lake area, precipitation, potential evapotranspiration, and average runoff coefficient of the watershed as model inputs, the multi-year average lake inflow of the target lake is determined through a preset water balance model.

[0014] The lake ecological flow of the lake target is determined by combining the lake type and the multi-year average lake inflow.

[0015] Secondly, this application provides a lake ecological flow treatment device based on remote sensing and a water balance model, the device comprising:

[0016] The acquisition unit is used to acquire remote sensing image data, meteorological data, and digital elevation models for the corresponding area of ​​the current lake target.

[0017] The first extraction unit is used to extract the time series of lake area from remote sensing image data;

[0018] The second extraction unit is used to extract precipitation time series and potential evapotranspiration time series from meteorological data, wherein the lake area time series, precipitation time series and potential evapotranspiration time series are aligned on a preset time dimension;

[0019] The first determining unit is used to determine the average runoff coefficient of the watershed based on the digital elevation model;

[0020] The second determining unit is used to take the lake area time series, precipitation time series, potential evapotranspiration time series and watershed average runoff coefficient as model inputs, and determine the multi-year average lake inflow of the target lake through a preset water balance model.

[0021] The third determining unit is used to determine the lake ecological flow of the lake target by combining the lake type and the multi-year average lake inflow.

[0022] Thirdly, this application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the method provided in the first aspect of this application when it invokes the computer program in the memory.

[0023] Fourthly, this application provides a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the method provided in the first aspect of this application.

[0024] From the above, it can be concluded that this application has the following beneficial effects:

[0025] To determine the target for lake ecological flow, this application establishes a standardized processing framework for lake ecological flow based on remote sensing and water balance equations. This effectively addresses the limitations of traditional methods that heavily rely on measured hydrological data, reducing computational costs. Furthermore, compared to traditional methods that require long-term data accumulation, it significantly enhances timeliness, thus meeting the application requirements for low-cost and rapid assessment. This provides strong data support for the management and control of lake water resource development and utilization, as well as lake ecological management. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of a lake ecological flow processing method based on remote sensing and water balance model in this application;

[0028] Figure 2 This is a schematic diagram of a lake ecological flow treatment device based on remote sensing and water balance model according to this application.

[0029] Figure 3 This is a schematic diagram of one type of processing equipment used in this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices. The naming or numbering of steps appearing in this application does not imply that the steps in the method flow must be performed in the chronological / logical order indicated by the naming or numbering. The execution order of named or numbered process steps can be changed according to the desired technical purpose, as long as the same or similar technical effect is achieved.

[0032] The module division described in this application is a logical division. In practical applications, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual coupling, direct coupling, or communication connections may be through interfaces, and the indirect coupling or communication connections between modules may be electrical or other similar forms, none of which are limited in this application. Moreover, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed across multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this application.

[0033] Before introducing the lake ecological flow processing method based on remote sensing and water balance model provided in this application, we will first introduce the background content involved in this application.

[0034] The lake ecological flow processing method, apparatus, and computer-readable storage medium based on remote sensing and water balance models provided in this application can be applied to processing equipment. This application establishes a standardized processing architecture for lake ecological flow based on remote sensing and water balance equations, effectively addressing the limitations of traditional methods that heavily rely on measured hydrological data and reducing computational costs. Furthermore, compared to traditional methods that require long-term data accumulation, it significantly enhances timeliness, thus meeting the application requirements for low-cost and rapid assessment, and providing excellent data support for lake water resource development and utilization management and lake ecological management.

[0035] The lake ecological flow processing method based on remote sensing and water balance models mentioned in this application can be implemented by a lake ecological flow processing device based on remote sensing and water balance models, or by different types of processing devices such as servers, physical hosts, or user equipment (UE) that integrate such a device. The lake ecological flow processing device based on remote sensing and water balance models can be implemented in hardware or software. The UE can be a terminal device such as a smartphone, tablet, laptop, desktop computer, or personal digital assistant (PDA). The processing devices can be configured in a cluster.

[0036] It is understandable that the proposed solution is usually based on existing data or data that has already been collected. Therefore, the processing equipment that implements the lake ecological flow processing method based on the remote sensing and water balance model of this application, or that carries the corresponding application service of the lake ecological flow processing method based on the remote sensing and water balance model of this application, usually only needs to meet the required data processing capabilities. The specific equipment type and equipment deployment form are quite flexible.

[0037] If the direct collection of existing data mentioned above is also involved, then further hardware and software adaptation configurations are obviously required for the processing equipment to enable it to have the corresponding data collection capabilities. For example, if it is necessary to collect the corresponding scheme input data in real time, the data collection devices corresponding to these scheme input data can be included in the equipment cluster of the processing equipment, or the processing equipment itself can be the control part of these data collection devices, or the data collection devices outside the processing equipment can be triggered by a third party to perform the actual data collection operation in real time.

[0038] In addition, if there is a need to display the processing progress (including the processing results), the processing device itself can be configured with the required display screen (including touch screen) to display the specific content. Of course, the processing device can also display the specific content through an external display device or other devices with a display screen.

[0039] The following section introduces the lake ecological flow processing method based on remote sensing and water balance model provided in this application.

[0040] First, refer to Figure 1 , Figure 1This paper illustrates a flowchart of a lake ecological flow processing method based on remote sensing and a water balance model, as described in this application. The lake ecological flow processing method based on remote sensing and a water balance model provided in this application may specifically include the following steps S101 to S106:

[0041] Step S101: For the current lake target, acquire remote sensing image data, meteorological data, and digital elevation model of the corresponding area;

[0042] Understandably, for the specific ecological flow processing object of lakes, the input data of the scheme involved in this application specifically involve (satellite) remote sensing image data, meteorological data and digital elevation model (DEM).

[0043] For ease of explanation, the lake that needs to be processed by the scheme is referred to as the lake target. In practical applications, the scheme of this application is usually initiated in the form of a task to determine the ecological flow of the relevant lake (i.e., a work task).

[0044] As an example, in practical applications, users can manually select the lake target that needs to be processed from different lake targets within the scope of the lake ecological flow monitoring, either through manual operation or under the corresponding autonomous triggering strategy of the system, to promote the corresponding lake ecological flow determination and processing.

[0045] In practice, the input data for the above three aspects can be collected in real time, retrieved from local storage, retrieved from the storage space of other devices or online, or entered manually. All of these are possible in practice and can be flexibly configured according to actual needs.

[0046] In the case of the aforementioned task of determining the ecological flow of lakes, the input data for the above three aspects can be either directly included in the task information or indicated by direct or indirect instructions regarding the acquisition method.

[0047] As an example, the input data for the above three aspects of the solution can be readily available data products, which can be either open-source or paid. Compared to the solution implementer collecting data independently, using readily available data products is easier to understand, requires no on-site monitoring, helps reduce the cost of solution application, and can also take into account the characteristics of stable and high-precision data.

[0048] Furthermore, considering that remote sensing image data and meteorological data themselves involve a time dimension, that is, both are time-series data, this corresponds to the processing of time-related situations in the scheme of this application, and the time span of both can be set to at least 5 years.

[0049] Digital elevation models (DEMs) take into account the low probability of changes in terrain and landforms. In practical applications, if there are no significant changes in terrain and landforms, a fixed DEM can be used. However, if significant changes in terrain and landforms are involved or detected, then appropriate DEMs can be configured for different stages of terrain and landforms.

[0050] Step S102: Extract the time series of lake area from the remote sensing image data;

[0051] Understandably, for remote sensing image data, this application can start from the time dimension to identify lakes and determine the corresponding lake areas. After directly or indirectly determining the lake area at each time point, a time series of lake areas can be formed for subsequent processing.

[0052] As an example, the time series of lake area here can be denoted as A.

[0053] In addition, to obtain high-precision time series data on lake area, this application can also perform corresponding preprocessing on the previously obtained remote sensing image data to improve data quality.

[0054] Therefore, prior to extracting the lake area time series from remote sensing image data, the method of this application may also include:

[0055] Preprocessing operations are performed on remote sensing image data to enhance data quality. These operations include radiometric correction, atmospheric correction, geometric correction, and cloud removal.

[0056] Understandably, given that preprocessing remote sensing image data is a common operation in satellite remote sensing work, this application focuses on highlighting the four main preprocessing methods that can be applied: radiometric correction, atmospheric correction, geometric correction, and cloud removal. Therefore, these specific preprocessing operations are not described in detail.

[0057] For example, cloud removal can be performed using the QA band to remove cloud-covered pixels. This example also shows that the preprocessing of remote sensing image data and lake area extraction can be carried out at the pixel level / granularity.

[0058] Of course, preprocessing can be included in the acquisition of remote sensing image data. Similarly, the other two types of data can also involve corresponding preprocessing operations.

[0059] Regarding how to extract the time series of lake area from remote sensing image data, this application also proposes an optimization scheme.

[0060] Specifically, extracting lake area time series data from remote sensing imagery can, in practical applications, include:

[0061] 2.1) Calculate the normalized difference water index and the improved normalized difference water index for different pixels involved in the remote sensing image data;

[0062] Among them, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) are included.

[0063] Understandably, the Normalized Difference Water Index and its improved version, the Improved Normalized Difference Water Index, are fundamental indicators in hydrological work. The focus of this application is to introduce these two indicators and integrate them into the lake water identification framework specially designed in this application.

[0064] The normalized difference water index is expressed by the following quantitative formula:

[0065] NDWI=(Green-NIR) / (Green+NIR),

[0066] The corresponding quantitative formula for the improved normalized difference water index is as follows:

[0067] MNDWI=(Green-SWIR1) / (Green+SWIR1),

[0068] Wherein, NDWI represents the Normalized Difference Water Index, Green represents the green band reflectance, NIR represents the near-infrared band reflectance, MNDWI represents the Modified Normalized Difference Water Index, and SWIR1 represents the shortwave infrared band 1 reflectance (this involves the specific band range corresponding to the MNDWI index).

[0069] 2.2) Based on the normalized differential water index and the improved normalized differential water index, water pixels are selected by combining the corresponding thresholds to obtain preliminary water pixel extraction results.

[0070] Understandably, this involves a fusion processing architecture that combines corresponding index thresholds. In this way, given the normalized differential water index and the improved normalized differential water index for each pixel, water pixels can be identified or selected, resulting in water pixel extraction results.

[0071] It should be noted that the water pixel extraction results obtained here are not the final determined water pixel divisions. The subsequent step 2.3) will remove some pixels, thus it is called the preliminary water pixel extraction results.

[0072] As a more specific application, the fusion processing architecture combining exponential thresholds can be represented as follows:

[0073] Watermask=(MNDWI>0.1)AND(NDWI>-0.1)

[0074] Wherein, Watermask is the water pixel mask, and 0.1 and -0.1 are the thresholds for MNDWI and NDWI, respectively.

[0075] In other words, this application identifies pixels with a normalized difference water index greater than -0.1 and an improved normalized difference water index greater than 0.1 as the lake water pixels targeted by the scheme of this application.

[0076] 2.3) Morphological filtering is performed on the preliminary water pixel extraction results to form lake units, and the corresponding areas are extracted to obtain the lake area time series.

[0077] Understandably, the water body pixels identified above are the smallest granular water body identification results. In order to more accurately determine the lake area, morphological filtering can be performed on the water body pixels identified above to remove possible noise and small patches from the perspective of lake morphology, forming a smoother and more realistic lake water body or lake unit.

[0078] At this point, the area of ​​the lake unit in the current time period can be extracted, and then, based on the time series characteristics of the lake area time series required by this application scheme, a complete lake area time series can be configured based on the area of ​​the lake unit in different or all time periods.

[0079] Step S103: Extract the precipitation time series and potential evapotranspiration time series from the meteorological data, wherein the lake area time series, precipitation time series and potential evapotranspiration time series are aligned on a preset time dimension;

[0080] It is understandable that meteorological data can directly contain information on both precipitation and evapotranspiration, or it can indirectly contain information on both precipitation and evapotranspiration, which requires secondary processing.

[0081] In this way, we can obtain precipitation time series and potential evapotranspiration time series related to changes in lake water volume.

[0082] It is easy to understand that the lake area time series obtained earlier, and the precipitation time series and potential evapotranspiration time series obtained here, can be aligned with a preset time dimension / granularity when processing the time series, or the time series can be extracted first and then aligned. For the latter, in specific operations, if the time dimension of the data source itself is different, interpolation, inverse interpolation, pooling, fusion and other types of processing may be involved.

[0083] As an example, the precipitation time series here can be denoted as P, and the potential evapotranspiration time series here can be denoted as ET.

[0084] Step S104: Determine the average runoff coefficient of the watershed based on the digital elevation model;

[0085] It is understandable that the runoff coefficient is an indicator that characterizes the relationship between surface runoff and rainfall. This application specifically starts from topography and geomorphology to quantify the runoff coefficient at different locations in the target area of ​​the lake, and then determines the corresponding watershed average runoff coefficient.

[0086] In this regard, the optimization scheme designed for practical application of this application determines the average runoff coefficient of the watershed based on the digital elevation model, which may specifically include the following:

[0087] 4.1) After filling depressions in the digital elevation model, calculate the flow direction matrix to determine the water flow direction of each pixel in the model;

[0088] As is easily understood, filling depressions helps eliminate depressions in the model caused by data errors, thereby avoiding interruptions or errors in water flow paths caused by water accumulation in depressions, and providing a reliable basis for subsequent flow direction analysis, confluence accumulation calculation, etc.

[0089] In practice, this can involve smoothing and leveling. The former uses mathematical methods to smooth the terrain and is suitable for shallow and small depressions; the latter increases the elevation inside the depression to the elevation of its outlet.

[0090] For a digital elevation model, it is understandable that it is composed of different pixels. In this case, the slope between the current pixel and its neighboring pixels (e.g., 8 neighboring grids) can be calculated to determine the direction of water flow. This can then form a flow direction matrix at the overall level and at the pixel level, which records the direction of water flow for each pixel.

[0091] 4.2) Based on the flow direction matrix, calculate the cumulative flow to identify the river network structure;

[0092] Based on the previously processed flow direction matrix, we can scan the flow accumulation to determine specific flow directions where the flow accumulation exceeds the corresponding flow accumulation threshold. This serves as a result of determining the river network structure, thus ultimately forming the river network structure. In the river network structure obtained through this process, there are corresponding inlets (flow start points) and outlets (flow end points), the latter being the target for the next step.

[0093] 4.3) Using the lake object as the outlet, the watershed algorithm is used to extract the watershed boundary corresponding to the river network structure;

[0094] Understandably, extracting the watershed boundary essentially involves tracing the water flow path in reverse to delineate the catchment area boundary corresponding to the outlet, i.e., the lake object.

[0095] 4.4) Using the watershed boundary as the boundary constraint, calculate the watershed area and analyze the corresponding topographic features at the pixel granularity.

[0096] It is easy to understand that after obtaining the specific watershed boundary, the specific pixels contained in the watershed area can be determined. At this point, the corresponding watershed area can be calculated, and the specific terrain features within the watershed area can be analyzed.

[0097] Specifically, the corresponding ones are:

[0098] S=∑S P ,

[0099] Where S is the total area of ​​the watershed, in m². 2 ∑ represents the summation over all pixels in the watershed, S P The area of ​​a single pixel, in meters. 2 .

[0100] 4.5) Based on the corresponding topographic features at the pixel granularity, and combined with different runoff coefficients adapted to different topographic features, determine the average runoff coefficient of the watershed.

[0101] It is understandable that in the design of this application, different terrain features or landforms have different runoff coefficients. In this case, the average runoff coefficient of the watershed at the overall level can be determined based on this characteristic.

[0102] Specifically, the quantitative formula for calculating the average runoff coefficient of a watershed can be expressed as:

[0103] C = ∑(C i ×Area i ) / S

[0104] Where C represents the average runoff coefficient of the watershed, C i Area represents the runoff coefficient of the i-th type of terrain feature. i This represents the area of ​​the i-th type of terrain feature, in meters. 2 S represents the total area of ​​the watershed, in meters. 2 .

[0105] As an example, different runoff coefficients can be applied to different terrain features:

[0106] For cities, the corresponding runoff coefficient C c1 Take 0.75;

[0107] For forests, the corresponding runoff coefficient C s1 Take 0.15;

[0108] For grassland, the corresponding runoff coefficient C c2 Take 0.25;

[0109] For farmland, the corresponding runoff coefficient C n Take 0.35;

[0110] For water bodies, the corresponding runoff coefficient C s2 Take 1.0.

[0111] Furthermore, it is understood that for the above steps S102, S103 and S104, there is no need to configure a specific execution order among them, which can be adjusted according to actual needs.

[0112] Step S105: Using the time series of lake area, precipitation, potential evapotranspiration, and average runoff coefficient of the watershed as model inputs, the multi-year average lake inflow of the target lake is determined through a preset water balance model.

[0113] As can be easily seen, the water balance model designed in this application specifically determines the multi-year average lake inflow of the target lake based on the data obtained from the three aspects mentioned above in this processing stage.

[0114] Furthermore, it is understandable that in practical applications, data analysis of lakes is usually conducted using years as the primary time unit. If there is a need for processing data at other time granularities, it can of course be adjusted to other time spans.

[0115] In practice, compared to machine learning methods / models that can also be introduced, this application also introduces a quantization formula to obtain a simpler multi-year average lake inflow processing scheme.

[0116] Specifically, this water balance model can be configured with the following settings for daily lake inflow:

[0117] Qin(t)=(P(t)×C×S-ET(t)×A(t)) / 86400,

[0118] Wherein, Qin(t) represents the lake inflow on day t, P(t) represents the precipitation on day t, C represents the average runoff coefficient of the basin, S represents the total area of ​​the basin, ET(t) represents the potential evapotranspiration on day t, A(t) represents the lake area on day t, and 86400 represents the unit conversion factor.

[0119] As can be seen, this application presents a practical solution for calculating the daily inflow of a lake using a very concise quantitative formula.

[0120] Once the daily lake inflow rate is determined, the corresponding multi-year average lake inflow rate can obviously be calculated based on the specific multi-year span for subsequent use.

[0121] Step S106: Determine the lake ecological flow of the lake target by combining the lake type and the multi-year average lake inflow.

[0122] Understandably, lake ecological flow is included in lake inflow. Under this condition, this application can determine the specific lake ecological flow based on the average lake inflow over the previous years and in combination with the lake type of the target lake.

[0123] In this process, lake type plays a role in introducing a regulating coefficient. Therefore, this section combines the lake type of the target lake with the multi-year average lake inflow to determine the target lake's ecological flow, which may specifically include:

[0124] Based on the lake type and average annual lake inflow of the target lake, the lake ecological flow of the target lake is determined by the following formula:

[0125] ,

[0126] in, This indicates the results of determining the ecological flow of the lake. Indicates the lake type adjustment coefficient. This represents the average annual inflow into a lake.

[0127] As for the different lake type adjustment coefficients involved, it is understandable that they need to be determined comprehensively by combining the ecological protection goals of the lake, the status of aquatic biodiversity, and the sensitivity of the ecosystem.

[0128] In this regard, this application may also be configured with the following:

[0129] For shallow lakes (with an average water depth of less than 3m), the lake type adjustment coefficient ranges from [0.15 to 0.40].

[0130] For deep lakes (with an average water depth greater than or equal to 3m), the corresponding lake type adjustment coefficient ranges from [0.10, 0.35].

[0131] For lake types with important ecological functions, the corresponding lake type adjustment coefficient ranges from [0.30, 0.50].

[0132] For general lake types, the corresponding lake type adjustment coefficient ranges from [0.15, 0.30].

[0133] It is easy to see that there is overlap between the first two lake types and the last two lake types. In specific applications, the user can choose between the first lake type adjustment coefficient setting scheme for the first two lake types or the second lake type adjustment coefficient setting scheme for the last two lake types, depending on the user settings or the system's own settings (which involve corresponding automatic switching strategies).

[0134] In addition, this application also takes into account the seasonal regulation design of the lake ecosystem, that is, for different seasons, further seasonal regulation is carried out on the previously determined basic lake ecological flow, so as to conduct a more detailed and accurate assessment of the lake ecological flow in terms of detailed operation, so as to meet the water demand of the lake ecosystem at different times.

[0135] Specifically, after determining the lake ecological flow of the target lake by combining the lake type and the average annual lake inflow, the method of this application may further include:

[0136] Continuing to consider seasonal adjustment needs, the ecological flow of the lake is seasonally adjusted using the following formula:

[0137] ,

[0138] in, This indicates the results of determining seasonal lake ecological flow. This represents the seasonal adjustment coefficient.

[0139] In addition to configuring different seasonal adjustment coefficients for different seasons, this application may also include the following:

[0140] For spring (March-May), the seasonal adjustment coefficient ranges from [1.2, 1.5].

[0141] For summer (June-August), the seasonal adjustment coefficient ranges from [1.0, 1.2].

[0142] For autumn (September-November), the seasonal adjustment coefficient ranges from [0.8, 1.0].

[0143] For winter (December to February), the seasonal adjustment coefficient ranges from [0.6, 0.8].

[0144] As an application example of the scheme, for a typical shallow lake (the target experimental lake), the annual ecological flow is 3.03 m³. 3 / s, the minimum ecological flow threshold in spring is 3.94m³. 3 / s, the minimum ecological flow threshold in summer is 3.33m³. 3 / s, the minimum ecological flow threshold in autumn is 2.73m. 3 / s, the minimum ecological flow threshold in winter is 2.12m. 3 / s.

[0145] It is easy to understand that, for the lake ecological flow determined by the current lake target, the processing of the proposed solution may also involve the corresponding result output stage.

[0146] For example, lake ecological flow can be stored locally or remotely, results can be displayed, results can be forwarded, a completion message can be output, or further data analysis can be performed. The specific result output strategy can obviously be flexibly configured according to actual needs.

[0147] Regarding further data analysis, this application specifically considers a more detailed and data-applicable uncertainty analysis of the determination results of lake ecological flow.

[0148] Understandably, uncertainty analysis of the processing results is specifically used to assess the reliability of the processing results. This allows users or systems viewing the processing results to have a more comprehensive understanding of the reliability of the current processing results, thereby enabling more precise response measures.

[0149] In this regard, after determining the lake ecological flow of the target lake by combining the lake type and the annual average lake inflow, the method of this application may further include:

[0150] Uncertainty analysis was performed on the determination results of lake ecological flow / seasonal lake ecological flow using the Monte Carlo method;

[0151] Output a comprehensive analysis report and visualization results. The comprehensive analysis report includes lake area change trend analysis, water balance calculation results, multi-year average inflow statistics, baseline ecological flow value, monthly seasonal ecological flow standard and uncertainty analysis results. The visualization results include lake area time series map, water body extraction effect map, watershed boundary vector data, water balance process line and seasonal ecological flow process curve.

[0152] In short, uncertainty analysis based on the Monte Carlo method can include:

[0153] The Monte Carlo method was used to perform perturbation analysis on key parameters, calculate the confidence interval of ecological flow to quantify the uncertainty of the results, conduct parameter sensitivity analysis to identify the parameters that have the greatest impact on the calculation results, and propose suggestions for improving data quality.

[0154] In practical operation, the following can also be included:

[0155] The key parameters (runoff coefficient C, lake type regulation coefficient α, etc.) are perturbed according to a normal distribution;

[0156] The probability distribution of lake ecological flow was obtained by repeating the calculation 1000 times.

[0157] Calculate the 95% confidence interval: Q ± σ, where Q is the mean and σ is the standard deviation;

[0158] Perform sensitivity analysis:

[0159] ,

[0160] in, The sensitivity coefficient, This refers to changes in ecological flow. This represents the change in parameters.

[0161] In this way, it can be achieved through To identify the parameters that have the greatest impact on the ecological flow calculation results.

[0162] Corresponding to the output operation, based on the lake ecological flow determination results / seasonal lake ecological flow determination results and the corresponding uncertainty analysis results, this application can specifically involve the specific configuration of the two output result types mentioned above: comprehensive analysis report and visualization results. In this way, through the output content of multiple aspects involved, a high-performance data tool can be provided for lake management work, which can conveniently and intuitively control the current lake ecological flow situation of the target lake, thus promoting a better user experience.

[0163] In conclusion, regarding the above-mentioned solutions, this application establishes a standardized processing framework for lake ecological flow based on remote sensing and water balance equations to determine the target. This effectively addresses the limitations of traditional methods that heavily rely on measured hydrological data and reduces computational costs. Furthermore, compared to traditional methods that require long-term data accumulation, it significantly enhances timeliness, thus meeting the application requirements for low-cost and rapid assessment and providing strong data support for lake water resource development and utilization management and lake ecological management.

[0164] More specifically, the effects of the detailed solutions include:

[0165] 1. The data is readily available and can be based entirely on publicly available satellite remote sensing data and reanalysis data, without the need for on-site monitoring;

[0166] 2. It is cost-effective, avoiding the expensive construction and maintenance costs of monitoring facilities and reducing labor costs;

[0167] 3. Comprehensive spatiotemporal coverage, enabling the acquisition of continuous data over long time series, supporting historical backtracking and trend analysis;

[0168] 4. It has a high degree of standardization, and has established a standardized calculation process, which facilitates its promotion and application in different regions;

[0169] 5. It has strong ecological adaptability, taking into account the seasonal changes and functional differences of the lake ecosystem;

[0170] 6. The results are highly reliable. Uncertainty analysis quantifies the calculation accuracy and enhances the credibility of the results.

[0171] 7. This provides a feasible technical solution for assessing the ecological flow of lakes in data-scarce regions, which has important scientific value and practical significance for promoting the development, utilization, management, and ecological protection of lake ecological water resources.

[0172] The above is an introduction to the lake ecological flow processing method based on remote sensing and water balance model provided in this application. In order to facilitate the better implementation of the lake ecological flow processing method based on remote sensing and water balance model provided in this application, this application also provides a lake ecological flow processing device based on remote sensing and water balance model from the perspective of functional modules.

[0173] See Figure 2 , Figure 2 This is a schematic diagram of a lake ecological flow treatment device based on a remote sensing and water balance model, as described in this application. Specifically, the lake ecological flow treatment device 200 based on the remote sensing and water balance model may include the following structure:

[0174] The acquisition unit 201 is used to acquire remote sensing image data, meteorological data and digital elevation model of the corresponding area for the current lake target;

[0175] The first extraction unit 202 is used to extract the time series of lake area from remote sensing image data;

[0176] The second extraction unit 203 is used to extract precipitation time series and potential evapotranspiration time series from meteorological data, wherein the lake area time series, precipitation time series and potential evapotranspiration time series are aligned on a preset time dimension;

[0177] The first determining unit 204 is used to determine the average runoff coefficient of the watershed based on the digital elevation model;

[0178] The second determining unit 205 is used to take the lake area time series, precipitation time series, potential evapotranspiration time series and watershed average runoff coefficient as model inputs, and determine the multi-year average lake inflow of the lake target through a preset water balance model.

[0179] The third determining unit 206 is used to determine the lake ecological flow of the lake target by combining the lake type and the multi-year average lake inflow.

[0180] In yet another exemplary embodiment, the apparatus further includes a preprocessing unit 207, for:

[0181] Preprocessing operations are performed on remote sensing image data to enhance data quality. These preprocessing operations include radiometric correction, atmospheric correction, geometric correction, and cloud removal.

[0182] In yet another exemplary embodiment, the first extraction unit 202 is specifically used for:

[0183] For different pixels involved in remote sensing image data, the normalized differential water index and the improved normalized differential water index are calculated respectively.

[0184] Based on the normalized differential water body index and the improved normalized differential water body index, water body pixels are selected by combining the corresponding thresholds to obtain preliminary water body pixel extraction results.

[0185] Morphological filtering was applied to the initial water pixel extraction results to form lake units, and the corresponding areas were extracted to obtain the lake area time series.

[0186] In yet another exemplary embodiment, the first determining unit 204 is specifically used for:

[0187] After filling depressions in the digital elevation model, the flow direction matrix is ​​calculated to determine the water flow direction of each pixel in the model.

[0188] Based on the flow direction matrix, the cumulative flow is calculated to identify the river network structure;

[0189] Using lake objects as water outlets, the watershed algorithm is used to extract the watershed boundaries corresponding to the river network structure;

[0190] Using the watershed boundary as the boundary constraint, the watershed area is calculated, and the corresponding topographic features at the pixel granularity are analyzed.

[0191] Based on the corresponding topographic features at the pixel level, and combined with different runoff coefficients adapted to different topographic features, the average runoff coefficient of the watershed is determined.

[0192] In yet another exemplary embodiment, the water balance model for a single day's lake inflow has:

[0193] Qin(t)=(P(t)×C×S-ET(t)×A(t)) / 86400,

[0194] Wherein, Qin(t) represents the lake inflow on day t, P(t) represents the precipitation on day t, C represents the average runoff coefficient of the basin, S represents the total area of ​​the basin, ET(t) represents the potential evapotranspiration on day t, A(t) represents the lake area on day t, and 86400 represents the unit conversion factor.

[0195] In yet another exemplary embodiment, the second determining unit 205 is specifically used for:

[0196] Based on the lake type and multi-year average lake inflow of the target lake, the lake ecological flow of the target lake is determined by the following formula:

[0197] ,

[0198] in, This indicates the results of determining the ecological flow of the lake. Indicates the lake type adjustment coefficient. This represents the average annual inflow into a lake.

[0199] In yet another exemplary embodiment, the apparatus further includes:

[0200] Adjustment unit 208 is used to further adjust the lake's ecological flow seasonally based on seasonal regulation needs, using the following formula:

[0201] ,

[0202] in, This indicates the results of determining seasonal lake ecological flow. This represents the seasonal adjustment coefficient;

[0203] Analysis unit 209 is used to perform uncertainty analysis on the determination results of seasonal lake ecological flow using the Monte Carlo method;

[0204] Output unit 210 is used to output a comprehensive analysis report and visualization results. The comprehensive analysis report includes lake area change trend analysis, water balance calculation results, multi-year average inflow statistics, baseline ecological flow value, monthly seasonal ecological flow standard and uncertainty analysis results. The visualization results include lake area time series plot, water body extraction effect plot, watershed boundary vector data, water balance process line and seasonal ecological flow process curve.

[0205] This application also provides a processing device from a hardware architecture perspective. As mentioned earlier, in practice, a processing device may exist as a device cluster. In this case, each device in the device cluster can also be referred to as a processing device. See [reference needed]. Figure 3 , Figure 3 This diagram illustrates a structural schematic of the processing device of this application. Specifically, the processing device may include a processor 301, a memory 302, and an input / output device 303. The processor 301 executes the computer program stored in the memory 302 to implement, for example... Figure 1 The corresponding embodiments describe the steps of the lake ecological flow processing method based on remote sensing and water balance models; or, when processor 301 executes the computer program stored in memory 302, it implements the following... Figure 2 Corresponding to the functions of each unit in the embodiment, the memory 302 is used to store the functions executed by the processor 301 as described above. Figure 1 The computer program required for the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiment.

[0206] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 302 and executed by processor 301 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a computer device.

[0207] The processing device may include, but is not limited to, processor 301, memory 302, and input / output device 303. Those skilled in the art will understand that the illustrations are merely examples of the processing device and do not constitute a limitation on the processing device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the processing device may also include network access devices, buses, etc., and processor 301, memory 302, input / output device 303, etc., are connected via a bus.

[0208] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the processing device, connecting various parts of the device through various interfaces and lines.

[0209] The memory 302 can be used to store computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and by calling data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the processing device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0210] When processor 301 executes a computer program stored in memory 302, it can specifically perform the following functions:

[0211] For the current lake target, acquire remote sensing image data, meteorological data and digital elevation model of the corresponding area;

[0212] Extracting time series data of lake area from remote sensing image data;

[0213] Precipitation time series and potential evapotranspiration time series are extracted from meteorological data, and the lake area time series, precipitation time series and potential evapotranspiration time series are aligned on a preset time dimension;

[0214] Determining the average runoff coefficient of the watershed based on a digital elevation model;

[0215] Using the time series of lake area, precipitation, potential evapotranspiration, and average runoff coefficient of the watershed as model inputs, the multi-year average lake inflow of the target lake is determined through a preset water balance model.

[0216] The lake ecological flow of the lake target is determined by combining the lake type and the multi-year average lake inflow.

[0217] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the lake ecological flow treatment device, treatment equipment, and its corresponding units based on remote sensing and water balance models described above can be found in the following reference: Figure 1 The description of the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiment will not be repeated here.

[0218] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0219] Therefore, this application provides a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute the present application. Figure 1 The steps of the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiment can be referred to as follows for specific operations. Figure 1 The description of the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiment will not be repeated here.

[0220] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0221] Because of the instructions stored in the computer-readable storage medium, the present application can be executed as described above. Figure 1 The steps of the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiment can therefore achieve the results of this application. Figure 1 The beneficial effects of the lake ecological flow processing method based on remote sensing and water balance model in the corresponding embodiments are detailed in the preceding description and will not be repeated here.

[0222] The foregoing has provided a detailed description of the lake ecological flow processing method, apparatus, processing equipment, and computer-readable storage medium based on remote sensing and water balance models provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the core ideas of this application; furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A lake ecological flow processing method based on remote sensing and water balance model, characterized in that, The method comprises: For the current lake target, acquire remote sensing image data, meteorological data and a digital elevation model of the corresponding region; extract a lake area time series from the remote sensing image data; extract a precipitation time series and a potential evapotranspiration time series from the meteorological data, wherein the lake area time series, the precipitation time series and the potential evapotranspiration time series are aligned in a preset time dimension; determine a basin average runoff coefficient based on the digital elevation model; determine a multi-year average lake inflow of the lake target by a preset water balance model, taking the lake area time series, the precipitation time series, the potential evapotranspiration time series and the basin average runoff coefficient as model inputs; determine a lake ecological flow of the lake target in combination with a lake type of the lake target and the multi-year average lake inflow; The method further comprises: after the digital elevation model is subjected to depression filling processing, calculate a flow direction matrix to determine a water flow direction of each pixel in the model; on the basis of the flow direction matrix, calculate a flow accumulation to identify a river network structure; extract a basin boundary corresponding to the river network structure by using a watershed algorithm, taking the lake object as a water outlet; take the basin boundary as a boundary constraint, calculate a basin area, and analyze corresponding terrain features of a pixel granularity; on the basis of the corresponding terrain features of the pixel granularity, determine the basin average runoff coefficient in combination with different runoff coefficients adapted to different terrain features; The water balance model has, for a single-day lake inflow: Qin(t) = (P(t) × C × S - ET(t) × A(t)) / 86400, where Qin(t) represents a lake inflow on the tth day, P(t) represents a precipitation on the tth day, C represents the basin average runoff coefficient, S represents a total basin area, ET(t) represents a potential evapotranspiration on the tth day, A(t) represents a lake area on the tth day, and 86400 represents a unit conversion coefficient, The single-day lake inflow is calculated in combination with a specific set multi-year span to calculate a corresponding multi-year average lake inflow; The method further comprises: determine a lake ecological flow of the lake target by the following formula in combination with a lake type of the lake target and the multi-year average lake inflow: ， wherein, represents the lake ecological flow determination result, represents the lake type adjustment coefficient, represents the multi-year average lake inflow.

2. The method of claim 1, wherein, The method further comprises, before the lake area time series is extracted from the remote sensing image data: perform a preprocessing operation on the remote sensing image data to enhance data quality, wherein the preprocessing operation comprises a radiation correction operation, an atmospheric correction operation, a geometric correction operation and a cloud removal operation.

3. The method of claim 2, wherein, The method further comprises: for different pixels involved in the remote sensing image data, respectively calculate a normalized difference water index and an improved normalized difference water index; On the basis of the normalized difference water index and the improved normalized difference water index, water body pixels are screened out by combining a corresponding threshold to obtain a preliminary water body pixel extraction result; The preliminary water body pixel extraction result is subjected to morphological filtering processing to form a lake unit, and a corresponding area is extracted to obtain a lake area time series.

4. The method of claim 1, wherein, After the lake type of the lake target and the multi-year average lake inflow are combined to determine the lake ecological flow of the lake target, the method further comprises: The lake ecological flow is further subjected to seasonal adjustment processing by combining seasonal regulation requirements according to the following formula: ， wherein, denotes the seasonal lake ecological flow determination result, denotes the seasonal regulation coefficient; The seasonal lake ecological flow determination result is subjected to uncertainty analysis by using a Monte Carlo method; An integrated analysis report and a visualized result are output, wherein the integrated analysis report comprises lake area change trend analysis, water balance calculation results, multi-year average inflow statistics, benchmark ecological flow values, monthly seasonal ecological flow standards and uncertainty analysis results, and the visualized result comprises a lake area time series graph, a water body extraction effect graph, a drainage basin boundary vector data, a water balance process line and a seasonal ecological flow process curve.

5. A lake ecological flow processing device based on remote sensing and water balance model, characterized in that, The device comprises: An acquisition unit configured to acquire remote sensing image data, meteorological data and a digital elevation model of a corresponding region for a current lake target; A first extraction unit configured to extract a lake area time series from the remote sensing image data; A second extraction unit configured to extract a precipitation time series and a potential evapotranspiration time series from the meteorological data, wherein the lake area time series, the precipitation time series and the potential evapotranspiration time series are aligned in a preset time dimension; A first determination unit configured to determine a drainage basin average runoff coefficient based on the digital elevation model; A second determination unit configured to take the lake area time series, the precipitation time series, the potential evapotranspiration time series and the drainage basin average runoff coefficient as model inputs, and determine a multi-year average lake inflow of the lake target by using a preset water balance model; A third determination unit configured to determine a lake ecological flow of the lake target by combining the lake type of the lake target and the multi-year average lake inflow; The first determination unit is specifically configured to: After the digital elevation model is subjected to depression filling processing, a flow direction matrix is calculated to determine a water flow direction of each pixel in the model; On the basis of the flow direction matrix, a flow accumulation amount is calculated to identify a river network structure; The lake object is taken as a water outlet, and a drainage basin boundary corresponding to the river network structure is extracted by using a watershed algorithm; The drainage basin boundary is taken as a boundary constraint, and a drainage basin area is calculated, and a corresponding topographic feature of a pixel granularity is analyzed; On the basis of the corresponding topographic feature of the pixel granularity, the drainage basin average runoff coefficient is determined by combining different runoff coefficients adapted to different topographic features; The water balance model has the following formula for a single-day lake inflow: Qin(t) = (P(t) × C × S - ET(t) × A(t)) / 86400, Wherein, Qin(t) represents the lake inflow on the tth day, P(t) represents the precipitation on the tth day, C represents the average runoff coefficient of the basin, S represents the total area of the basin, ET(t) represents the potential evapotranspiration on the tth day, A(t) represents the lake area on the tth day, and 86400 represents a unit conversion coefficient, The single-day lake inflow is calculated according to a specific set of multi-year spans to obtain corresponding multi-year average lake inflows; The second determining unit is specifically configured to: The lake ecological flow of the lake target is determined according to the following formula by combining the lake type of the lake target and the multi-year average lake inflow: ， wherein, represents the lake ecological flow determination result, represents the lake type adjustment coefficient, represents the multi-year average lake inflow.

6. A processing device, characterized by The computer readable storage medium stores a plurality of instructions, which are adapted to be loaded by the processor to execute the method of any one of claims 1 to 4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a plurality of instructions, which are adapted to be loaded by the processor to execute the method of any one of claims 1 to 4.

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