Method, device and product for quantifying ecological benefits of artificial rain enhancement

Abstract

The application discloses a method, equipment and product for quantifying ecological benefits of artificial rainmaking, and relates to the field of ecological meteorology.The method comprises the following steps: determining an artificial rainmaking potential influence area based on the flight track of an artificial rainmaking airplane; identifying all weather stations in the artificial rainmaking potential influence area; performing spatial interpolation on the artificial rainmaking quantity observed by the weather stations to form grid data of the artificial rainmaking and determine an artificial rainmaking actual influence area; quantifying the NDVI change rate on a grid scale based on time series NDVI determined by remote sensing multiple times after the artificial rainmaking; detecting the change significance of the time series NDVI corresponding to each grid point; coupling the NDVI change rate and the change significance on the grid scale to quantify an artificial rainmaking ecological benefit significant area; and quantifying the ecological benefits of the artificial rainmaking according to the artificial rainmaking ecological benefit significant area.The application can accurately quantify the ecological benefits of the artificial rainmaking.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ecological meteorology, and also relates to the disciplines of remote sensing, geographic information systems, and the technical fields of weather modification, artificial intelligence, and ecological benefit evaluation, and in particular relates to a method, device and product for quantifying the ecological benefits of artificial rain enhancement. BACKGROUND

[0002] As a typical weather modification technology, artificial rain enhancement mainly implements scientific catalysis on cloud layers with precipitation potential through methods such as airplane seeding, ground rocket / high-voltage gun launching, and ground generator release, to promote the condensation and precipitation of water vapor in the cloud layer. Artificial rain enhancement has various ecological benefits, such as replenishing water resources, improving soil moisture, alleviating drought, accelerating vegetation restoration, slowing down land desertification, suppressing sandstorms, improving ecological quality, enhancing the carbon sequestration capacity of the ecological system, and improving the function of the ecological system. With the widespread application of artificial rain enhancement in the fields of drought relief and disaster reduction and ecological restoration, the demand for evaluation of the ecological benefits of artificial rain enhancement is increasing.

[0003] However, the ecological benefits of artificial rain enhancement are often weak and easily disturbed by subsequent natural precipitation, making it difficult to identify and quantify the ecological benefits. Precise quantification of ecological benefits can help to restore the ecological system and adapt to climate change, and provide technical support. At present, there are relatively few relevant research cases, and a complete and highly operable technical method system for evaluating the ecological benefits of artificial rain enhancement has not yet been established. How to scientifically and accurately quantify the ecological benefits of artificial rain enhancement, especially on a finer grid scale, is still a technical bottleneck that needs to be broken through. SUMMARY

[0004] The purpose of the present application is to provide a method, device and product for quantifying the ecological benefits of artificial rain enhancement, which can accurately quantify the ecological benefits of artificial rain enhancement.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions.

[0006] In a first aspect, the present application provides a method for quantifying the ecological benefits of artificial rain enhancement, comprising the following steps.

[0007] Based on the flight trajectory of the artificial rain enhancement airplane, the potential influence area of artificial rain enhancement is determined.

[0008] All meteorological stations in the potential influence area of artificial rain enhancement are identified.

[0009] The artificial rain enhancement amount observed by the meteorological stations is spatially interpolated to form grid data of artificial rain enhancement, and the actual influence area of artificial rain enhancement is determined according to the grid data.

[0010] quantify the NDVI change rate on the grid scale based on the time series NDVI of the artificial rainfall actual influence area and the non-artificial rainfall area after the artificial rainfall; the natural conditions of the non-artificial rainfall area are the same as the natural conditions of the artificial rainfall actual influence area.

[0011] detect the change significance of the time series NDVI corresponding to each grid point.

[0012] quantify the artificial rainfall ecological benefit significant area by coupling the NDVI change rate and the change significance on the grid scale; the artificial rainfall ecological benefit significant area is the grid with the significant change of the time series NDVI.

[0013] quantify the artificial rainfall ecological benefit according to the artificial rainfall ecological benefit significant area.

[0014] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the artificial rainfall ecological benefit quantification method.

[0015] In a third aspect, the present application provides a computer program product, comprising a computer program executable by a processor to implement the artificial rainfall ecological benefit quantification method.

[0016] According to the specific embodiments provided by the present application, the present application has the following technical effects: based on the flight trajectory of the artificial rainfall airplane, the present application determines the artificial rainfall potential influence area, and then obtains all the meteorological stations to form the grid data of the artificial rainfall to obtain the artificial rainfall actual influence area. The present application further narrows down the detection range based on the artificial rainfall actual influence area identified, and improves the detection accuracy. At the same time, based on the time series normalized vegetation index (NDVI, Normalized Differential Vegetation Index) of the non-artificial rainfall area with the same natural conditions as the artificial rainfall actual influence area after the artificial rainfall, the present application quantifies the NDVI change rate on the grid scale, detects the change significance of the time series NDVI corresponding to each grid point, and quantifies the artificial rainfall ecological benefit significant area to accurately quantify the artificial rainfall ecological benefit, thereby providing technical support for ecological system restoration and climate adaptation. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of a method for quantifying ecological benefits of artificial rain enhancement is provided for an embodiment of the present application.

[0019] Figure 2 A Mann-Kendall significance test diagram is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0021] In order to make the objects, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0022] As shown in Figure 1 The present application provides a method for quantifying ecological benefits of artificial rain enhancement, which comprises the following steps.

[0023] S1: determining a potential influence area of artificial rain enhancement based on a flight trajectory of an artificial rain enhancement aircraft.

[0024] S2: identifying all meteorological stations in the potential influence area of artificial rain enhancement.

[0025] S3: performing spatial interpolation on artificial rain enhancement amounts observed by the meteorological stations to form grid data of artificial rain enhancement, and determining an actual influence area of artificial rain enhancement according to the grid data.

[0026] S4: quantifying a NDVI change rate on a grid scale based on the actual influence area of artificial rain enhancement and time series NDVI determined by remote sensing multiple times after artificial rain enhancement in a non-artificial rain enhancement area, the natural conditions of the non-artificial rain enhancement area being the same as those of the actual influence area of artificial rain enhancement.

[0027] S5: detecting change significance of time series NDVI corresponding to each grid point.

[0028] S6: coupling the NDVI change rate and change significance on a grid scale to quantify an artificial rain enhancement ecological benefit significant area; the artificial rain enhancement ecological benefit significant area is a grid with significant change of time series NDVI.

[0029] S7: quantifying ecological benefits of artificial rain enhancement according to the artificial rain enhancement ecological benefit significant area.

[0030] In an exemplary embodiment, S1 specifically comprises: generating a flight trajectory according to the artificial precipitation aircraft flight path coordinates.

[0031] A distance is set outwardly from the flight trajectory to determine the artificial precipitation potential impact area.

[0032] In an exemplary embodiment, the artificial precipitation amount observed by the meteorological station is spatially interpolated to form a grid data of artificial precipitation, and the artificial precipitation actual impact area is determined according to the grid data, specifically comprising: using the Kriging interpolation method, the artificial precipitation amount observed by the meteorological station is spatially interpolated to form a grid data of artificial precipitation.

[0033] Based on the grid data, the grid area with artificial precipitation rainfall greater than the set rainfall threshold is determined as the artificial precipitation actual impact area.

[0034] In practical application, the artificial precipitation potential impact area is identified by using the method of establishing a "buffer zone", specifically comprising: generating a flight trajectory (or rocket gun coordinate point) according to the artificial precipitation aircraft flight path coordinates, and then expanding a certain distance outwardly from the flight trajectory (or rocket gun coordinate point) as the "artificial precipitation potential impact area". According to experience, the artificial precipitation influence radius is generally not more than 50km, so the flight trajectory center point is expanded outwardly at least 50km to determine the possible artificial precipitation potential impact area range.

[0035] For the center point (x0, y0), the expanded distance radius r≥50km is defined.

[0036] In practical application, the artificial precipitation actual impact area is identified by using the "spatial matching method", specifically comprising:

[0037] Based on the artificial precipitation potential impact area, all meteorological stations in the space area are searched.

[0038]

[0039] Wherein, is the search result of the meteorological station, is the i-th meteorological station, S is the search range, R is the search radius, and PointInRegion is a spatial inclusion determination function.

[0040] According to the artificial precipitation actual case, the precipitation duration after a single artificial precipitation operation is generally not more than 6 hours. Therefore, the precipitation amount observed within 6 hours after the start of the artificial precipitation operation is defined as the artificial precipitation amount.

[0041] ,

[0042] wherein, P is the artificial rainfall amount (mm), is the hourly precipitation amount, and D is the precipitation duration.

[0043] The ordinary Kriging interpolation method is selected to spatially interpolate the artificial rainfall amount observed by each meteorological station to obtain grid data. Based on the grid data, the grid area of the artificial rainfall precipitation amount is determined as the actual artificial rainfall influence area.

[0044] In an exemplary embodiment, the ecological benefit of artificial rainfall is quantified on a grid scale, specifically including:

[0045] Following the principles of scientificity and operability, the normalized vegetation index, which is sensitive to precipitation and easy to obtain by remote sensing, is selected as the quantitative index of the ecological benefit of artificial rainfall. Based on the time series NDVI values determined by remote sensing after artificial rainfall in the artificial rainfall actual influence area and the non-rainfall area with similar natural conditions nearby, the trend method of the one-dimensional regression equation is used to quantify the NDVI change rate on a grid scale. By comparing the NDVI change rates of the time series in the artificial rainfall actual influence area and the non-rainfall area, the ecological benefit of artificial rainfall is identified.

[0046] The NDVI change rate is: 。

[0047] wherein, n is the total number of rainy days, is the NDVI variable value on the i day.

[0048] In an exemplary embodiment, S5 specifically includes: based on the time series NDVI corresponding to each grid point, calculating the NDVI rank sequence of the sequential time series and the NDVI rank sequence of the reverse time series.

[0049] According to the sequential time series NDVI rank sequence and the reverse time series NDVI rank sequence, the order statistics and the reverse order statistics are calculated.

[0050] According to the sequence curve formed by the order statistics, the sequence curve formed by the reverse order statistics, and two critical straight lines, the change significance of the time series NDVI corresponding to each grid point is determined.

[0051] In practical application, the "change rate" and "change significance" are coupled on the grid scale to quantify the artificial precipitation ecological benefit significant area, which specifically includes: based on the time series NDVI values measured by remote sensing after artificial precipitation, the change rate of NDVI of each grid point is calculated; at the same time, Mann-Kendall test method is selected to detect the change significance of time series NDVI of each grid point.

[0052] Mann-Kendall is a non-parametric statistical test method, which is suitable for detecting the change significance of sequence, and has the advantages of not requiring the sample to comply with a certain distribution and not being disturbed by a few abnormal values.

[0053] Firstly, the NDVI rank sequence of the sequential time series is calculated S k , and the order statistics are calculated according to the equation UF k .

[0054] For a time series X with m NDVI sample quantities, a rank sequence is constructed.

[0055]

[0056]

[0057] wherein the rank sequence is the cumulative number of the i-th moment value greater than the j-th moment value ; k is the sample quantity; is the NDVI value of the i-th sample. Under the assumption that the time series is random and independent, the order statistics are defined.

[0058]

[0059] wherein UF 1=0, , are the mean and variance of , respectively.

[0060] When the time series order x1, x2, …, x m are independent of each other and have the same continuous distribution, they can be calculated by the following formula, wherein x m is the NDVI value of the m-th sample.

[0061]

[0062] is the standard normal distribution, which is calculated according to the time series order x1, x2, …, x mThe calculated statistic sequence. Given a significance level a, consult the normal distribution table, if | U α then it indicates that there is a significant trend change in the sequence.

[0063] Then, the rank sequence of the inverse time sequence is calculated S k , and the above method is used to calculate UB k . That is, according to the inverse time sequence x m , x m-1 ,…, x1, the above process is repeated, so that UB k =– UF k , k= , -1,…,1), UB 1=0.

[0064] Finally, given a significance level α (e.g. α =0.05, the critical value U 0.05 = 1.96). The sequence curve formed by UF k and UB k and the two critical straight lines (U 1.96) are plotted on the same graph, as shown in Figure 2 . When the intersection of the two curves exceeds the critical straight line, that is, outside the two straight lines, it indicates that the NDVI of the time sequence has a significant upward or downward trend.

[0065] Then, the open source tool Raster Calculator or the Python library Rasterio is used to couple the "change rate" and "change significance" to identify the significant change area of NDVI.

[0066] Among them, the method of coupling "change rate" and "change significance" is as follows.

[0067] Using the open source tool Raster Calculator or the Python library Rasterio, the "change rate" and "change significance" are coupled by combining mathematical and logical operations, that is, .

[0068] In an exemplary embodiment, the artificial rainmaking ecological benefit significant area is as follows.

[0069]

[0070] wherein, is a preset threshold value; is a significance level, for characterizing the change significance.

[0071] In an example embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program. The computer device can be a server or a terminal. The computer device comprises a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store to-be-processed data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement an artificial rain ecological benefit quantification method.

[0072] In an example embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0073] In an example embodiment, a computer program product is provided, comprising a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0074] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by computer program instructions related to hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of the method. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0075] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processor, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0076] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0077] The principles and implementation modes of the present application are described by using specific examples in the present application. The above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. An artificial rain enhancement ecological benefit quantification method, characterized in that, The method comprises the following steps: Based on the flight trajectory of the artificial precipitation aircraft, the potential influence area of artificial precipitation is determined, specifically including: Generating the flight trajectory according to the flight trajectory coordinates of the artificial precipitation aircraft; Expanding outward by a certain distance from the flight trajectory to determine the potential influence area of artificial precipitation; Identifying all weather stations within the potential influence area of artificial precipitation; Spatially interpolating the artificial precipitation observed by the weather stations to form grid data of artificial precipitation, and determining the actual influence area of artificial precipitation according to the grid data, specifically including: Using the Kriging interpolation method to spatially interpolate the artificial precipitation observed by the weather stations to form grid data of artificial precipitation; Based on the grid data, the grid area with artificial precipitation rainfall greater than a certain rainfall threshold is determined as the actual influence area of artificial precipitation; Based on the actual influence area of artificial precipitation and the time series NDVI of the non-artificial precipitation area determined by remote sensing multiple times after artificial precipitation, the NDVI change rate is quantified at the grid scale; the natural conditions of the non-artificial precipitation area are the same as those of the actual influence area of artificial precipitation; Detecting the change significance of the time series NDVI corresponding to each grid point, specifically including: Based on the time series NDVI corresponding to each grid point, the NDVI rank sequence of the sequential time series and the NDVI rank sequence of the reverse time series are calculated; According to the NDVI rank sequence of the sequential time series and the NDVI rank sequence of the reverse time series, the sequential statistics and the reverse statistics are calculated; According to the sequence curve formed by the sequential statistics, the sequence curve formed by the reverse statistics, and the two critical straight lines, the change significance of the time series NDVI corresponding to each grid point is determined; Coupling the NDVI change rate and the change significance at the grid scale to quantify the artificial precipitation ecological benefit significant area; the artificial precipitation ecological benefit significant area is the grid with significant change of time series NDVI; Quantifying the ecological benefit of artificial precipitation according to the artificial precipitation ecological benefit significant area.

2. The method for quantifying ecological benefits of artificial rain enhancement according to claim 1, characterized in that, The NDVI rate of change is: ； wherein, n is the total number of rainy days, is the variable value for the i day.

3. The method for quantifying ecological benefits of artificial rain enhancement according to claim 1, characterized in that, The artificial rainmaking ecological benefit remarkable area Is: ； wherein, is a rate of change of NDVI; is a preset threshold value; is a significance level, for characterizing change significance.

4. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the artificial precipitation ecological benefit quantification method of any one of claims 1-3.

5. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the artificial precipitation ecological benefit quantification method of any one of claims 1-3.

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