CN114494864B - Snow phenological information extraction method based on remote sensing data - Google Patents
Snow phenological information extraction method based on remote sensing data Download PDFInfo
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Abstract
The invention relates to the technical field of remote sensing data, in particular to a method for extracting snow phenological information based on remote sensing data, which comprises the following steps: acquiring an accumulated snow remote sensing image data set of a target area, wherein the accumulated snow remote sensing image data set comprises accumulated snow remote sensing image data of a plurality of days; acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, acquiring an accumulated snow coverage rate corresponding to each point in the accumulated snow coverage rate curve, and acquiring an initial snow day position point and a final snow day position point in the accumulated snow coverage rate curve according to the accumulated snow coverage rate corresponding to each point and a preset error interval; and acquiring snow remote sensing image data in the target time period of the target area according to the initial snow day position point and the final snow day position point, and filling the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set.
Description
Technical Field
The invention relates to the technical field of remote sensing data, in particular to a method, a device, equipment and a storage medium for extracting snow phenological information based on remote sensing data.
Background
The climate of snow is an important mark for global climate system change, and the close relationship between snow and a land ecosystem determines that a series of changes of the climate of snow can have obvious influence on the land ecosystem. For example, due to the advance of snow melting time and the reduction of snow coverage, most of the vegetation exhibits phenomena such as extension of growing season, advance of flowering phase: the change of the accumulated snow causes the reduction of the effective moisture of the soil and the increase of the temperature of the soil, and further causes the obvious change of the vegetation community composition and species diversity in a cold area; as snow cover continues to decrease, vegetation productivity shrinks and carbon absorption capacity also tends to decrease. On the animal side, snow melting times are advanced and temperatures are raised, leading to a change in the life cycle of a large number of invertebrates, such as a reduction in hibernation; the flowering period of plants is advanced and shortened, so that the number of Nippon invertebrate species is reduced; some invertebrates such as spiders have obvious phenotypic variation; vertebrates also respond significantly to snow changes, such as changes in the food chain that result in changes in the biological cycle of some animals and a decrease in the number of species that are present in some animals. Therefore, accurately acquiring the snow climate information is extremely important for researches on global changes, snow hydrology, biodiversity and the like.
The remote sensing technology is a key means for extracting the information of the phenological signs of the accumulated snow. The accumulated snow coverage curve obtained by utilizing the remote sensing data can well identify the initial snow day and the final snow day, and further calculate the accumulated snow duration. However, under the influence of adverse factors such as cloud occlusion, invalid observed values, sensor faults and track deviation, the snow coverage curve obtained by the remote sensing technology is often discontinuous, and abnormal high values and abnormal low values are easy to appear, so that the extraction error of the snow climate information is caused, and the application of the remote sensing data in the extraction of the snow climate information is greatly limited.
Disclosure of Invention
Based on the above, the present invention provides a method, an apparatus, a device, and a storage medium for extracting snow phenology information based on remote sensing data, wherein a high-precision snow remote sensing image dataset is constructed by generating a snow coverage curve and performing filling processing on the snow remote sensing image dataset based on the snow remote sensing image dataset, so as to meet the requirement of snow phenology extraction on the time-space continuity of input snow coverage data and improve the accuracy of snow phenology information extraction.
In a first aspect, an embodiment of the present application provides a method for extracting snow phenological information based on remote sensing data, including the following steps:
acquiring an accumulated snow remote sensing image data set of a target area, wherein the accumulated snow remote sensing image data set comprises accumulated snow remote sensing image data of a plurality of days;
acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve and an ablated snow coverage rate curve;
acquiring an accumulated snow coverage rate corresponding to each point in the accumulated snow coverage rate curve, and acquiring a first snow day position point and a final snow day position point in the accumulated snow coverage rate curve according to the accumulated snow coverage rate corresponding to each point and a preset error interval;
acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point, and filling the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set;
in response to the instruction is drawed to snow phenology information, instruction is drawed to snow phenology information includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according to the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point, as snow phenology information.
In a second aspect, an embodiment of the present application provides an apparatus for extracting snow climate information based on remote sensing data, including:
the snow remote sensing image data acquisition module is used for acquiring a snow remote sensing image data set of a target area, wherein the snow remote sensing image data set comprises snow remote sensing image data of a plurality of days;
the curve acquisition module is used for acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve in an accumulation period and an ablated snow coverage rate curve in an ablation period;
the position point acquisition module is used for acquiring the snow cover rate corresponding to each point in the snow cover rate curve and acquiring a first snow day position point and a final snow day position point in the snow cover rate curve according to the snow cover rate corresponding to each point and a preset error interval;
the data set construction module is used for acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point, and filling the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set;
snow phenology information draws module for respond to snow phenology information and draw the instruction, snow phenology information draws the instruction and includes the regional snow remote sensing image data set that awaits measuring, according to the regional snow remote sensing image data set that awaits measuring acquires the regional corresponding target snow remote sensing image data set that awaits measuring, according to the regional corresponding target snow remote sensing image data set that awaits measuring acquires the regional snow remote sensing image data set that awaits measuring corresponds first snow day position point and end snow day position point as snow phenology information.
In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the method for extracting snow climate information based on remote sensing data according to the first aspect.
In a fourth aspect, the present application provides a storage medium, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for extracting snow climate information based on remote sensing data according to the first aspect.
In the embodiment of the application, a method, a device, equipment and a storage medium for extracting snow phenology information based on remote sensing data are provided, based on a snow remote sensing image dataset, a snow coverage rate curve is generated, and the snow remote sensing image dataset is filled to construct a high-precision snow remote sensing image dataset, so that the requirement of snow phenology extraction on time-space continuity of input snow coverage data is met, and the accuracy of snow phenology information extraction is improved.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flowchart of a method for extracting snow climate information based on remote sensing data according to an embodiment of the present application;
fig. 2 is a schematic flowchart of S2 in the method for extracting snow climate information based on remote sensing data according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a method for extracting snow climate information based on remote sensing data according to another embodiment of the present application;
fig. 4 is a schematic flowchart of S4 in the method for extracting snow climate information based on remote sensing data according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an apparatus for extracting snow climate information based on remote sensing data according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if as used herein may be interpreted as" at "8230; \8230when" or "when 8230; \823030, when" or "in response to a determination", depending on the context.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for extracting snow climate information based on remote sensing data according to an embodiment of the present application, where the method includes the following steps:
s1: acquiring an accumulated snow remote sensing image data set of a target area, wherein the accumulated snow remote sensing image data set comprises accumulated snow remote sensing image data of a plurality of days.
The main execution body of the method for extracting the snow phenology information based on the remote sensing data is extraction equipment (hereinafter referred to as extraction equipment for short) of the method for extracting the snow phenology information based on the remote sensing data, and in an optional embodiment, the extraction equipment can be one computer device, a server or a server cluster formed by combining a plurality of computer devices.
In this embodiment, the control device may establish a data connection with a preset network database, and obtain an accumulated snow remote sensing image dataset of the target area from the network database, where the accumulated snow remote sensing image dataset includes accumulated snow remote sensing image data for several days, and the pixel types of the accumulated snow remote sensing image data include an accumulated snow pixel, a non-accumulated snow pixel, and an unoccupied pixel.
S2: and acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve in an accumulation period and an accumulated snow coverage rate curve in an ablation period.
In this embodiment, the extraction device performs time-series arrangement on the snow remote sensing image data in the snow remote sensing image data set according to hydrologic years, and for the northern hemisphere, the hydrologic years refer to 9 months to 8 months of the next year, where 9 months to 2 months of the next year are snow accumulation periods, and 3 months to 8 months are snow ablation periods. Acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the sorted accumulated snow remote sensing image data set, dividing the accumulated snow coverage rate curve according to hydrologic years, and acquiring an accumulated snow coverage rate curve and an ablated snow coverage rate curve.
Referring to fig. 2, fig. 2 is a schematic flow chart of S2 in the method for extracting snow climate information based on remote sensing data according to an embodiment of the present application, including step S201, which is as follows:
s201: the method comprises the steps of extracting an NDSI value of each pixel of snow remote sensing image data in the snow remote sensing image data set, obtaining snow coverage rates corresponding to the pixels according to the NDSI value and a preset snow coverage rate calculation algorithm, and constructing a snow coverage rate curve according to the snow coverage rates corresponding to the pixels.
The NDSI is a normalized snow cover index, and the covered part of the accumulated snow in the remote sensing image is highlighted by utilizing the combination of visible light and short wave infrared wave bands.
In this embodiment, the extraction device extracts an NDSI value of each pixel of the snow remote sensing image data in the snow remote sensing image data set, and obtains an snow coverage rate corresponding to each pixel according to the NDSI value and a preset snow coverage rate calculation algorithm, where the snow coverage rate calculation algorithm is:
FSC＝-0.01+(1.45*NDSI)
wherein FSC is the snow cover ratio, and NDSI is the NDSI value.
And combining according to the snow coverage rate corresponding to each pixel and a time sequence to construct a snow coverage rate curve.
S3: and acquiring the snow cover rate corresponding to each point in the snow cover rate curve, and acquiring an initial snow day position point and a final snow day position point in the snow cover rate curve according to the snow cover rate corresponding to each point and a preset error interval.
In this embodiment, the extraction device obtains the snow coverage corresponding to each point in the snow coverage curve, and obtains the first snow day position point and the final snow day position point in the snow coverage curve according to the snow coverage corresponding to each point and a preset error interval, as follows:
and acquiring a point of the accumulation period snow coverage curve where the snow coverage is greater than a preset snow coverage on an early snow day as a predicted early snow day position point, wherein in an optional embodiment, the snow coverage on the early snow day may be set to 0.
According to the predicted initial snow day position point and the error interval, when the value of the predicted initial snow day position point is positioned in the error interval, taking the predicted initial snow day position point as an initial snow day position point; when the value of the predicted first-snow-day position point is larger than the maximum value of the error interval, acquiring a position point corresponding to the previous day of the predicted first-snow-day position point as a first-snow-day position point;
and acquiring a point, in the ablation stage snow coverage rate curve, where the snow coverage rate is equal to a preset snow coverage rate of the final snow day as a predicted position point of the final snow day, wherein in an alternative embodiment, the snow coverage rate of the final snow day may be set to 0.
Obtaining values of position points corresponding to one day before and after the predicted final snow day position point, and when the value of the position point corresponding to one day before the predicted final snow day position point is larger than the final snow day snow coverage rate and is positioned in the error interval, and the value of the position point corresponding to one day after the predicted final snow day position point is equal to the final snow day snow coverage rate, taking the predicted final snow day position point as the final snow day position point; and when the value of the position point corresponding to the day before the predicted final snow day position point is greater than the final snow day snow accumulation coverage rate and is greater than the maximum value of the error interval, and the value of the position point corresponding to the day after the predicted final snow day position point is equal to the final snow day snow accumulation coverage rate, taking the position point corresponding to the day after the predicted final snow day position point as the final snow day position point.
Referring to fig. 3, fig. 3 is a schematic flow chart of a method for extracting snow climate information based on remote sensing data according to another embodiment of the present application, including step S6, where step S6 is before step S3, specifically as follows:
s6: acquiring MOD10A1F data of the target area and snow coverage rates corresponding to pixels in the MOD10A1F data, constructing a snow coverage rate curve corresponding to the MOD10A1F data according to the snow coverage rates corresponding to the pixels in the MOD10A1F data, acquiring standard deviations and average deviations corresponding to the MOD10A1F data according to the snow coverage rate curve corresponding to the MOD10A1F data, and establishing an error interval according to the standard deviations and the average deviations.
The MOD10A1F data are complete day-to-day NDSI data with 500 m resolution space-time globally during the period from 2001 to 2020.
In this embodiment, an extraction device may obtain MOD10A1F data of a target area from a database, obtain, according to the MOD10A1F data, an accumulated snow coverage rate corresponding to each pixel in the MOD10A1F data, construct, according to the accumulated snow coverage rate corresponding to each pixel in the MOD10A1F data, an accumulated snow coverage rate curve corresponding to the MOD10A1F data, calculate a standard deviation and an average deviation of the accumulated snow coverage rate curve corresponding to the MOD10A1F data, and establish an error interval by taking a value of an origin of the accumulated snow coverage rate curve corresponding to the MOD10A1F data as a center and a standard deviation ± 2 times the standard deviation as an error range according to the standard deviation and the average deviation.
S4: acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point, and filling the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set.
In this embodiment, the extraction device obtains snow remote sensing image data in a target time period of the target area by using the initial snow day position point as a starting point and the final snow day position point as an end point, and constructs preliminary snow remote sensing image data;
in order to improve the space integrity of the preliminary snow remote sensing image data, on the basis of judgment and filling of the prior knowledge, according to the space correlation, filling processing is carried out on vacant pixels in the snow remote sensing image data in the target time period of the target area, and a target snow remote sensing image data set is constructed so as to improve the space integrity of the snow phenological data.
Referring to fig. 4, fig. 4 is a schematic flow chart of S4 in the method for extracting snow climate information based on remote sensing data according to an embodiment of the present application, which includes steps S401 to S402, specifically as follows:
s401: and acquiring elevation data and a snow phenological value corresponding to each pixel in each snow remote sensing image data in the target snow remote sensing image data set.
The pixel of snow remote sensing image data has snow phenological value, snow phenological value is used for embodying first snow day position point and final snow day position point in the snow phenological information, the Elevation data does the Elevation data is Digital Elevation Model (DEM), and its Digital Elevation information that represents the ground topography.
In this embodiment, the extraction device acquires elevation image data close to the spatial resolution according to the spatial resolution of each snow remote sensing image data in the target snow remote sensing image data set, resamples the elevation image data to prevent spatial overlapping, and spatially matches the target snow remote sensing image data set. And acquiring elevation data corresponding to each pixel in each accumulated snow remote sensing image data in the target accumulated snow remote sensing image data set.
S402: and filling vacant pixels in the snow remote sensing image data according to the pixel types, the elevation data and the snow phenological values corresponding to the pixels in the snow remote sensing image data in the target snow remote sensing image data set, and acquiring a filled snow remote sensing image data set as a target snow remote sensing image data set.
In this embodiment, the extraction device fills the vacant pixels in each snow remote sensing image data according to the pixel type, the elevation data and the snow phenological value corresponding to each pixel in each snow remote sensing image data in the target snow remote sensing image data set, and obtains the filled snow remote sensing image data set as the target snow remote sensing image data set.
In an optional embodiment, the extracting device obtains neighboring pixels of the vacant pixel and a category of the neighboring pixels;
when all the adjacent pixels of the vacant pixel are the snow covered pixels, acquiring the adjacent pixel with the minimum difference value with the elevation data of the vacant pixel from the adjacent pixels according to the difference value of the elevation data of the vacant pixel and the adjacent pixels, taking the adjacent pixel as a target pixel, modifying the vacant pixel into the snow covered pixel, and filling the snow covered phenological value of the target pixel into the vacant pixel;
when the adjacent pixels of the vacant pixels comprise an accumulated snow pixel and a non-accumulated snow pixel, acquiring the adjacent pixel with the smallest difference value with the elevation data of the vacant pixel from the adjacent pixels as a first target pixel when the elevation data of the vacant pixel is larger than the elevation data of any accumulated snow pixel in the adjacent pixels according to the elevation data values of the vacant pixel and the adjacent pixels, modifying the vacant pixel into the accumulated snow pixel, and filling the accumulated snow phenology value of the first target pixel into the vacant pixel;
when the value of the elevation data of the vacant pixel is smaller than the value of any one of the adjacent pixels which is not covered with snow, modifying the vacant pixel into the pixel which is not covered with snow;
when the value of the elevation data of the vacant pixel is smaller than the value of the elevation data of any one of the adjacent pixels and larger than the value of any one of the adjacent pixels without snow, calculating the mean value of the elevation data corresponding to the snow pixel and the non-snow pixel in the adjacent pixels, comparing the value of the elevation data of the vacant pixel with the mean value of the elevation data of the snow pixel in the adjacent pixels and the mean value of the elevation data corresponding to the non-snow pixel in the adjacent pixels respectively, acquiring a pixel with a value close to the value of the elevation data of the vacant pixel as a second target pixel, modifying the vacant pixel into the non-snow pixel when the second target pixel is the non-snow pixel, modifying the vacant pixel into the snow pixel when the second target pixel is the snow pixel, acquiring the adjacent pixel with the smallest difference value of the elevation data of the vacant pixel from the adjacent pixels as a third target pixel, and filling the snow value of the third target pixel into the paper.
S5: in response to the instruction is drawed to snow phenology information, instruction is drawed to snow phenology information includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according to the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point, as snow phenology information.
The snow climate information is used for embodying an initial snow day, a final snow day and snow duration, the snow climate information extraction instruction is sent by a user, and the extraction equipment receives the snow climate information.
In this embodiment, the extraction device obtains the snow climate information extraction instruction sent by the user, and in response, obtains a snow remote sensing image dataset of the area to be measured. The method comprises the steps that extraction equipment acquires a target snow remote sensing image data set corresponding to an area to be detected according to the snow remote sensing image data set of the area to be detected, acquires a first snow day position point and a final snow day position point corresponding to the snow remote sensing image data set of the area to be detected according to the target snow remote sensing image data set corresponding to the area to be detected, and returns to a display interface of the extraction equipment to be displayed and labeled as snow phenology information.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an apparatus for extracting snow climate information based on remote sensing data according to an embodiment of the present application, where the apparatus may implement all or a part of the apparatus for extracting snow climate information based on remote sensing data through software, hardware, or a combination of the two, and the apparatus 5 includes:
a data set obtaining module 51, configured to obtain an accumulated snow remote sensing image data set of a target area, where the accumulated snow remote sensing image data set includes accumulated snow remote sensing image data for a plurality of days;
a curve obtaining module 52, configured to obtain an accumulated snow coverage curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, where the accumulated snow coverage curve includes an accumulated snow coverage curve in an accumulation period and an ablated snow coverage curve in an ablation period;
a position point obtaining module 53, configured to obtain an accumulated snow coverage rate corresponding to each point in the accumulated snow coverage rate curve, and obtain an initial snow day position point and a final snow day position point in the accumulated snow coverage rate curve according to the accumulated snow coverage rate corresponding to each point and a preset error interval;
the data set construction module 54 is configured to obtain snow remote sensing image data in the target time period of the target area according to the first snow day position point and the final snow day position point, and perform filling processing on the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set;
snow phenology information draws module 55 for respond to snow phenology information and draw the instruction, snow phenology information draws the instruction and includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according to the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point, as snow phenology information.
In the embodiment of the application, an accumulated snow remote sensing image data set of a target area is obtained through a data set obtaining module, wherein the accumulated snow remote sensing image data set comprises accumulated snow remote sensing image data of a plurality of days; acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set through a curve acquisition module according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve in an accumulation period and an accumulated snow coverage rate curve in an ablation period; acquiring an accumulated snow coverage rate corresponding to each point in the accumulated snow coverage rate curve through a position point acquisition module, and acquiring an initial snow day position point and a final snow day position point in the accumulated snow coverage rate curve according to the accumulated snow coverage rate corresponding to each point and a preset error interval; acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point through a data set construction module, and filling the snow remote sensing image data in the target time period of the target area to construct a target snow remote sensing image data set; through snow phenological information extraction module, respond to snow phenological information and draw the instruction, snow phenological information draws the instruction and includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according to the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point as snow phenological information. Based on the accumulated snow remote sensing image data set, the accumulated snow coverage rate curve is generated, the accumulated snow remote sensing image data set is filled, so that the high-precision accumulated snow remote sensing image data set is constructed, the requirement of accumulated snow phenology extraction on time-space continuity of the input accumulated snow coverage data is met, and the accuracy of accumulated snow phenology information extraction is improved.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application, where the computer device 6 includes: a processor 61, a memory 62 and a computer program 63 stored on the memory 62 and executable on the processor 61; the computer device may store a plurality of instructions, where the instructions are suitable for being loaded by the processor 61 and executing the method steps in the embodiments shown in fig. 1 to fig. 4, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to fig. 4, which is not described herein again.
The Memory 62 may include a Random Access Memory (RAM) 62, and may also include a Read-Only Memory (Read-Only Memory) 62. Optionally, the memory 62 includes a non-transitory computer-readable medium. The memory 62 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 62 may optionally be at least one memory device located remotely from the aforementioned processor 61.
An embodiment of the present application further provides a storage medium, where the storage medium may store multiple instructions, and the instructions are suitable for being loaded by a processor and being executed by the method steps in the embodiments shown in fig. 1 to fig. 4, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to fig. 4, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are only illustrative, and for example, the division of the modules or units is only one type of logical function division, and other division manners may be available in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included within the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.
Claims (6)
1. A snow phenology information extraction method based on remote sensing data is characterized by comprising the following steps:
acquiring an accumulated snow remote sensing image data set of a target area, wherein the accumulated snow remote sensing image data set comprises accumulated snow remote sensing image data of a plurality of days;
acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve in an accumulation period and an ablated snow coverage rate curve in an ablation period;
acquiring the snow cover rate corresponding to each point in the snow cover rate curve, and acquiring an initial snow day position point and a final snow day position point in the snow cover rate curve according to the snow cover rate corresponding to each point and a preset error interval;
acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point, and constructing a preliminary snow remote sensing image data set; acquiring elevation data and snow phenological values corresponding to all pixels in all snow remote sensing image data in the preliminary snow remote sensing image data set; filling vacant pixels in the snow remote sensing image data according to pixel types, elevation data and snow phenological values corresponding to the pixels in the snow remote sensing image data in the preliminary snow remote sensing image data set to obtain a filled snow remote sensing image data set serving as a target snow remote sensing image data set, wherein the pixel types of the snow remote sensing image data comprise snow pixels, non-snow pixels and vacant pixels;
in response to the instruction is drawed to snow phenology information, instruction is drawed to snow phenology information includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according to the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point as snow phenology information.
2. The method for extracting snow phenology information based on remote sensing data of claim 1, wherein the step of obtaining a snow coverage curve corresponding to the snow remote sensing image dataset according to the snow remote sensing image dataset comprises the steps of:
the method comprises the steps of extracting an NDSI value of each pixel of snow remote sensing image data in the snow remote sensing image data set, obtaining snow coverage rates corresponding to the pixels according to the NDSI value and a preset snow coverage rate calculation algorithm, and constructing a snow coverage rate curve according to the snow coverage rates corresponding to the pixels, wherein the snow coverage rate calculation algorithm is as follows:
FSC＝-0.01+(1.45*NDSI)
wherein FSC is the coverage rate of the accumulated snow, and NDSI is the value of NDSI.
3. The method for extracting snow climate information based on remote sensing data according to claim 1, wherein before acquiring an initial snow day location point and a final snow day location point in the snow coverage curve according to the snow coverage corresponding to each point and a preset error interval, the method comprises:
obtaining MOD10A1F data of the target area and the snow coverage rate corresponding to each pixel in the MOD10A1F data, constructing a snow coverage rate curve corresponding to the MOD10A1F data according to the snow coverage rate corresponding to each pixel in the MOD10A1F data, obtaining a standard deviation and an average difference corresponding to the MOD10A1F data according to the snow coverage rate curve corresponding to the MOD10A1F data, and establishing an error interval according to the standard deviation and the average difference.
4. The utility model provides an extraction element of snow phenology information based on remote sensing data which characterized in that includes:
the snow remote sensing image data acquisition module is used for acquiring a snow remote sensing image data set of a target area, wherein the snow remote sensing image data set comprises snow remote sensing image data of a plurality of days;
the curve acquisition module is used for acquiring an accumulated snow coverage rate curve corresponding to the accumulated snow remote sensing image data set according to the accumulated snow remote sensing image data set, wherein the accumulated snow coverage rate curve comprises an accumulated snow coverage rate curve in an accumulation period and an ablated snow coverage rate curve;
the position point acquisition module is used for acquiring the snow cover rate corresponding to each point in the snow cover rate curve and acquiring a first snow day position point and a final snow day position point in the snow cover rate curve according to the snow cover rate corresponding to each point and a preset error interval;
the data set construction module is used for acquiring snow remote sensing image data in a target time period of the target area according to the initial snow day position point and the final snow day position point and constructing a preliminary snow remote sensing image data set; acquiring elevation data and snow phenological values corresponding to all pixels in all snow remote sensing image data in the preliminary snow remote sensing image data set; filling vacant pixels in the snow remote sensing image data according to pixel types, elevation data and snow phenological values corresponding to the pixels in the snow remote sensing image data in the preliminary snow remote sensing image data set to obtain a filled snow remote sensing image data set serving as a target snow remote sensing image data set, wherein the pixel types of the snow remote sensing image data comprise snow pixels, non-snow pixels and vacant pixels;
snow phenology information draws module for in response to snow phenology information draws the instruction, snow phenology information draws the instruction and includes the regional snow remote sensing image dataset that awaits measuring, according to the regional snow remote sensing image dataset that awaits measuring acquires the regional corresponding target snow remote sensing image dataset that awaits measuring, according the regional corresponding target snow remote sensing image dataset that awaits measuring acquires the regional snow remote sensing image dataset that awaits measuring corresponds first snow day position point and end snow day position point, as snow phenology information.
5. A computer device, comprising: a processor, a memory, and a computer program stored on the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the method for extracting snow climate information based on remote sensing data according to any of claims 1 to 3.
6. A storage medium, characterized by: the storage medium stores a computer program which, when executed by a processor, implements the steps of the method for remote sensing data-based extraction of snow climate information according to any of claims 1 to 3.
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