CN116242782B - Permafrost monitoring method and device, storage medium and monitoring equipment - Google Patents
Permafrost monitoring method and device, storage medium and monitoring equipment Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The application provides a permafrost monitoring method, a permafrost monitoring device, a storage medium and monitoring equipment, and relates to the field of environmental monitoring. The monitoring equipment divides the target frozen soil area into a plurality of grids; determining the frozen soil temperature change type of each grid according to the infrared remote sensing data of the frozen soil area; and aggregating grids with the same frozen soil temperature change type and adjacent positions into frozen soil plaques. Therefore, by analyzing the infrared remote sensing data of the target frozen soil area, the large frozen soil area is efficiently monitored.
Description
Technical Field
The application relates to the field of environmental monitoring, in particular to a permafrost monitoring method, a permafrost monitoring device, a storage medium and monitoring equipment.
Background
Frozen soil is soil or rock which is at or below 0 ℃ and contains ice, and frozen state of the frozen soil for two years or more is called permafrost. Frozen soil is the most widely distributed factor in the northern hemisphere frozen circle, accounting for 56% of land area, wherein permafrost accounts for about 24%. Permafrost has important effects on the energy-water balance of the ground surface, hydrology, carbon exchange between ground and air, ecosystem, ground surface landscape and the like, and has non-negligible effects on the local ecological environment and social development.
In the global warming background, the degradation of permafrost brings potential risks to regional ecological environment, infrastructure, socioeconomic performance and the like, so that the dynamic monitoring and early warning of the permafrost are needed to be enhanced, the prevention and control of disastrous events are served, and the loss is reduced to the greatest extent. However, in the prior art, research on monitoring technology of surface disturbance of permafrost areas is mostly focused on the technology of internet of things mainly comprising ground monitoring sensing equipment, but the technology cannot adapt to monitoring of large-range areas, and is high in cost and easy to consume.
Disclosure of Invention
In order to overcome at least one defect in the prior art, the application provides a permafrost monitoring method, a permafrost monitoring device, a storage medium and monitoring equipment, which are used for efficiently monitoring the frozen soil condition of a large-range frozen soil area, and specifically comprise the following steps:
in a first aspect, the present application provides a method of permafrost monitoring, the method comprising:
dividing a target frozen soil area into a plurality of grids;
determining the frozen soil temperature change type of each grid according to the infrared remote sensing data of the frozen soil area;
and aggregating grids with the same frozen soil temperature change type and adjacent positions into frozen soil plaques.
In a second aspect, the present application provides a permafrost monitoring device, the device comprising:
the frozen soil dividing module is used for dividing the target frozen soil area into a plurality of grids;
the frozen soil identification module is used for determining the frozen soil temperature change type of each grid according to the infrared remote sensing data of the frozen soil area;
and the frozen soil aggregation module is used for aggregating grids which are identical in frozen soil temperature change type and adjacent in position into frozen soil plaques.
In a third aspect, the present application provides a storage medium storing a computer program which, when executed by a processor, implements the permafrost monitoring method.
In a fourth aspect, the present application provides a monitoring device comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the permafrost monitoring method.
Compared with the prior art, the application has the following beneficial effects:
according to the permafrost monitoring method, the permafrost monitoring device, the storage medium and the monitoring equipment provided by the application, the monitoring equipment divides a target permafrost area into a plurality of grids; determining the frozen soil temperature change type of each grid according to the infrared remote sensing data of the frozen soil area; and aggregating grids with the same frozen soil temperature change type and adjacent positions into frozen soil plaques. Therefore, by analyzing the infrared remote sensing data of the target frozen soil area, the large frozen soil area is efficiently monitored.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a permafrost monitoring method according to an embodiment of the present application;
fig. 2 shows the practical application effect of the permafrost monitoring method according to the embodiment of the present application;
fig. 3 is a schematic structural diagram of a permafrost monitoring device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a monitoring device according to an embodiment of the present application.
Icon: 101-a frozen soil dividing module; 102-a frozen soil identification module; 103-a frozen soil polymerization module; 201-a memory; 202-a processor; 203-a communication unit; 204-system bus.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present application, it should be noted that the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the global warming context, the degradation of permafrost will bring potential risks to regional ecology, infrastructure, socioeconomic, etc., making the demands on permafrost cognition increasingly important and urgent.
For example, permafrost degradation can cause thawing of frozen layers, subsidence of the earth's surface, increased risk of hot-thawing disasters, and impact on stability and safe operation of engineering structures in cold regions. The permafrost is melted to form a hot-melt lake and release CO 2 、CH 4 NO isothermal chamber gas, causes exposure of harmful substances, microorganisms, etc. enclosed in the frozen soil layer, and threatens regional biological, ecological and human safety. The permafrost degradation causes the change of the shape and the property of the ground surface, damages to the landform landscape, the ground surface vegetation, the marsh wetland and the frozen soil structure, seriously interferes with the surface hydrothermal dynamic balance, causes the change of the regional ecological environment, and inevitably directly or indirectly affects the production life and the public health safety of people.
However, in the related art, research on monitoring technology of surface disturbance of permafrost areas is mostly focused on the technology of internet of things mainly comprising ground monitoring sensing equipment, but the technology cannot adapt to monitoring of large-scale areas, and is high in cost and easy to consume. For example:
site video measurement, although highly accurate, is fixed in position, limited in scope of observation, and requires periodic maintenance, which in turn results in higher monitoring costs.
The field investigation survey, although the method can adopt a manual or aviation measurement mode to investigate and survey the permafrost region change, the process can acquire the surface change information of the permafrost region in a small range, but consumes a great deal of manpower, material resources and financial resources, is difficult to form long-term observation and timely updated data information, and can cause human interference on the permafrost region.
In addition, since permafrost is a physical state of a stratum, and is distributed in severe cold areas such as Qinghai-Tibet plateau, altaishan mountain, great and great Khingan areas in China; the ground observation sites are sparse and unevenly distributed, so that direct observation from the ground surface is difficult; and, the high cost and the long survey period make many years of frozen soil change research more adopt indirect means, and the monitoring of the earth's surface morphology is concerned.
In recent years, with the progress and development of satellite remote sensing technology, earth surface information with a large area range can be obtained, and a data basis is provided for non-contact, large-range and permafrost dynamic monitoring, so that satellite remote sensing observation capability is rapidly improved, an observation system is gradually improved, the problem is solved to a certain extent, and basic data is provided for researching permafrost change with a large area range.
Research also finds that the most intuitive influence of global warming on permafrost is to change the thermal condition of soil and the surface view, which is mainly reflected on disturbance of a permafrost active layer, and the changes (the thermal condition change and the surface view generated after the change) are all related to the surface temperature, namely, the surface temperature can reflect the thermal condition of permafrost and can also indicate the dynamic change of surface coverage, thereby providing an effective characterization means for monitoring the large-scale permafrost change.
In addition, permafrost is a physical state, and the key characteristic is that the surface temperature changes, and the change forms different surface landscapes or forms, and thermal infrared remote sensing provides a reliable data source for acquiring surface temperature data, so that abnormal changes of the thermal state of the permafrost can be estimated through space-time analysis of the surface temperature, and further the change of the surface form is deduced.
It should be noted that the above prior art solutions have all the drawbacks that the inventors have obtained after practice and careful study, and thus the discovery process of the above problems and the solutions to the problems that the embodiments of the present application hereinafter propose should not be construed as what the inventors have made in the inventive process of the present application, but should not be construed as what is known to those skilled in the art.
Based on the inventive concept in the above embodiments, the present embodiment provides a permafrost monitoring method for efficiently monitoring the frozen soil condition of a large-scale frozen soil area. The device implementing the method may be, but is not limited to, a mobile terminal, a tablet computer, a laptop computer, a desktop computer, a server, etc.
The server may be a single server or a server group. The server farm may be centralized or distributed (e.g., the servers may be distributed systems). In some embodiments, the server may be local or remote to the user terminal. In some embodiments, the server may be implemented on a cloud platform; by way of example only, the Cloud platform may include a private Cloud, public Cloud, hybrid Cloud, community Cloud (Community Cloud), distributed Cloud, cross-Cloud (Inter-Cloud), multi-Cloud (Multi-Cloud), or the like, or any combination thereof. In some embodiments, the server may be implemented on an electronic device having one or more components.
In order to make the solution easier to implement, the steps of the method are described in detail below in connection with fig. 1. It should be understood that the operations of the flow diagrams may be performed out of order and that steps that have no logical context may be performed in reverse order or concurrently. Moreover, one or more other operations may be added to or removed from the flow diagrams by those skilled in the art under the direction of the present disclosure. As shown in fig. 1, the method includes:
s101, dividing a target frozen soil area into a plurality of grids.
It should be understood here that the dimension of the location of the grid has a large influence on the surface temperature, so that when the target frozen soil area is divided into a plurality of grids in this embodiment, the target frozen soil area is divided along the longitude and latitude directions.
With continued reference to fig. 1, after dividing the target frozen soil area into a plurality of grids, the method further includes:
s102, determining the frozen soil temperature change type of each grid according to the infrared remote sensing data of the frozen soil area.
Research finds that when a local area in a target frozen soil area is abnormal, compared with other areas in the target frozen soil area, the long-term evolution characteristic of the surface temperature of the local area along with time can be obviously different from other areas; the local area is also significantly different in surface temperature from other areas at the same latitude as compared with other areas at the same latitude. In view of this, the present example provides the following implementation for determining the type of frozen soil temperature change for each grid from the dimensions of time and space:
s102-1, obtaining the temperature sequences of each grid at different time points according to the infrared remote sensing data.
In a specific embodiment, the monitoring device acquires infrared remote sensing data of a target frozen soil area, and inverts to obtain an original temperature sequence consisting of original surface temperature based on a corresponding relation between infrared signals and temperature, wherein the infrared remote sensing data is acquired through a thermal infrared satellite.
Since the surface temperature varies periodically on a annual basis, for ease of comparative analysis, each raw surface temperature in the raw temperature sequence of each grid is acquired from the same time of year for each grid. The original temperature sequence of each grid is illustratively represented as T i ＝[t 1 ,t 2 ,…t n ]Wherein T is i Representing the original temperature sequence, t, of the ith grid n Representing the original surface temperature of the nth observation year in the sequence, wherein n is more than or equal to 3; and each of the raw surface temperatures in the raw temperature sequence is arranged in chronological order.
In addition, in consideration of the tendency of global warming, the temperature raised by global warming is included in the temperature difference between adjacent two of the original surface temperatures in the original temperature series, and therefore, in order to eliminate the influence of global warming, the monitoring apparatus also obtains the original temperature series of each grid at different time points based on the infrared remote sensing data, and then subtracts the linear interference temperature from the original temperature series of each grid, respectively, to obtain the target temperature series of each grid, wherein the linear interference temperature represents the temperature raised by global warming.
Illustratively, the corresponding computational expression is:
t n ′＝t n -β×n；
wherein t is n ' to eliminate the surface temperature after the linear interference temperature, beta is the original temperature sequence T i And t n And if the linear regression coefficients between the corresponding observation years n have no obvious linear relation, the beta is 0. In this way, the temperature component of the original surface temperature due to the linear trend of global warming is eliminated.
S102-2, determining the time sequence change type of each grid according to target temperature sequences of the grids at different time points.
Wherein the time sequence change type characterizes the change characteristic of the earth surface temperature of the grid along with time. In this regard, the timing change types provided in the present embodiment include a time enhancement type and a time decay type; in a specific embodiment, a monitoring device obtains a first mean value and a first standard deviation of a grid according to a target temperature sequence of the grid; and obtaining a first enhancement threshold and a first reduction threshold according to the first mean and the first standard deviation.
If the earth surface temperature with the latest grid acquisition time is greater than a first enhancement threshold value, the monitoring equipment determines the grid as a time enhancement type; if the surface temperature at the latest grid acquisition time is smaller than the first reduction threshold value, the monitoring equipment determines the grid as a time reduction type.
The first enhancement threshold and the first reduction threshold are obtained according to the first mean and the first standard deviation, and the expression is:
p 1 ＝(μ 1 +th 1 ×σ 1 )；
q 1 ＝(μ 1 -th 1 ×σ 1 )；
wherein p is 1 Represents a first enhancement threshold, q 1 Represents a first reduction threshold, mu 1 Represents the first mean value, th 1 Representing a preset time sequence variation threshold value sigma 1 Representing a first standard deviation. Wherein, is 1.ltoreq.th 1 And 3. Ltoreq.3, and one skilled in the art may make an adaptive selection when practicing the present embodiment.
In the above embodiment, if the surface temperature with the latest acquisition time in the target temperature sequence of any grid is greater than the first enhancement threshold or less than the first attenuation threshold, the grid is regarded as having an abnormality, and the grid is distinguished from a grid with normal surface temperature change. Further, for the abnormal mesh determined from the time dimension, the degree of abnormality of the mesh in which abnormality exists is also calculated by the following expression:
wherein S is 1 Indicating the degree of abnormality, t n ' represents the surface temperature of the nth observation year (last observation year) of the grid, t max Representing the highest temperature, t, in the corresponding target temperature sequence of the grid min Representing the lowest temperature in the sequence of target temperatures corresponding to the grid.
S102-3, comparing the temperature difference of the grids with the temperature difference of the related grids to determine the space change type of the grids.
The method comprises the steps of determining a grid corresponding to a target temperature sequence, wherein the spatial variation type represents the difference between the temperature difference of the grid and the temperature difference of an associated grid, the temperature difference of the grid is the difference between two adjacent surface temperatures with the latest acquisition time in the target temperature sequence corresponding to the grid, and the temperature difference of the associated grid is the difference between two adjacent surface temperatures with the latest acquisition time in the target temperature sequence corresponding to the associated grid.
Exemplary, assume that a target temperature sequence of the grid is representedIs T i ′＝[t 1 ′,t 2 ′,…t n ′]The temperature difference Δt=t of the grid n ′-t n ′ -1 。
The spatial variation types provided in this embodiment include a spatial enhancement type and a spatial reduction type, and in a specific embodiment, the monitoring device obtains a second mean value and a second standard deviation between the grids and the associated grid according to the temperature difference of the grids and the temperature difference of the associated grid; and obtaining a second enhancement threshold and a second reduction threshold according to the second mean and the second standard deviation.
If the temperature difference of the grid is greater than the second enhancement threshold, the monitoring device determines the grid as a spatial enhancement type.
If the temperature difference of the grid is less than the second reduction threshold, the monitoring device determines the grid as a spatial reduction type.
Wherein, according to the second mean and the second standard deviation, a second enhancement threshold and a second reduction threshold expression are obtained:
p 2 ＝(μ 2 +th 2 ×σ 2 )；
q 2 ＝(μ 2 -th 2 ×σ 2 )；
wherein p is 2 Represents a second enhancement threshold, q 2 Represents a second reduction threshold, μ 2 Represent the second mean, th 2 Representing a preset spatial variation threshold, sigma 2 Representing a second standard deviation. Wherein, is 1.ltoreq.th 2 And 3. Ltoreq.3, and one skilled in the art may make an adaptive selection when practicing the present embodiment.
With the above embodiment, for any grid, if the temperature difference of the grid acquisition time is greater than the second enhancement threshold or less than the second attenuation threshold, this means that there is a great difference between the temperature change of the grid and the temperature difference of the associated grid, and thus is also regarded as an abnormal grid in the present embodiment.
Further, for an abnormal mesh determined from the spatial dimension, the degree of abnormality of the mesh in which abnormality exists is also calculated by the following expression:
wherein S is 2 Represents the degree of abnormality, Δt represents the temperature difference of the grid, Δt max Representing the maximum temperature difference, Δt, of the temperature differences of the grid and the associated grid min Representing the minimum temperature difference among the temperature differences of the grid and the associated grid.
S102-4, determining the frozen soil temperature change type of the grid according to the time sequence change type and the space change type.
Illustratively, the present example provides 9 frozen soil temperature change types in the following table:
here, it should be noted that "enhancement" in the time-series change column in the above table refers to "first enhancement type" and "attenuation" refers to "first attenuation type", and if the grid does not belong to both types, it is determined as "no change" type. Similarly, "enhancement" in spatially varying rot refers to "second enhancement type" and "attenuation" refers to "second attenuation type", and if the grid is not of both types, it is determined to be "no change" type.
The above embodiments describe the frozen soil temperature change type of each grid, with continued reference to fig. 1, the method further comprising:
s103, the grids which are the same in frozen soil temperature change type and adjacent in position are aggregated into frozen soil plaques.
As described in the above embodiments, the present embodiment provides 9 frozen soil temperature change types, which are classified into one of the above 9 types for each grid within the target frozen soil region; then, the grids which are adjacent in space and have the same type are aggregated into a frozen soil patch. And, the monitoring device can also display the frozen soil plaques of different frozen soil temperature change types, wherein the plaques of different frozen soil temperature change types in the display interface are marked by different marks (for example, different colors).
By way of example, the technical scheme provided by the embodiment is actually applied to a permafrost region of Qinghai-Tibet plateau, and a monitoring result shown in fig. 2 is obtained, wherein data used in the actual application are derived from MODIS thermal infrared remote sensing data of a Terra satellite of an earth observation system in the united states.
Therefore, the infrared remote sensing data of the target frozen soil area is analyzed through the embodiment, and the large frozen soil area is efficiently monitored.
The above embodiments describe a permafrost monitoring method, and based on the same inventive concept, the present embodiment further provides a permafrost monitoring device. Wherein the permafrost monitoring device comprises at least one software functional module which can be stored in a memory in the form of software or solidified in the monitoring device. The processor in the monitoring device is configured to execute executable modules stored in the memory, such as software functional modules and computer programs included in the permafrost monitoring device. Referring to fig. 3, functionally divided, the permafrost monitoring device may include:
the frozen soil dividing module 101 is configured to divide a target frozen soil area into a plurality of grids.
In this embodiment, the frozen soil dividing module 101 is used to implement step S101 in fig. 1, and a detailed description of the frozen soil dividing module 101 may be referred to in the detailed description of step S101.
The frozen soil identification module 102 is configured to determine a frozen soil temperature change type of each grid according to infrared remote sensing data of the frozen soil area.
In this embodiment, the frozen soil identification module 102 is used to implement step S102 in fig. 1, and a detailed description of the frozen soil identification module 102 may be referred to in the detailed description of step S102.
And the frozen soil aggregation module 103 is used for aggregating grids which are identical in frozen soil temperature change type and adjacent in position into frozen soil plaques.
In this embodiment, the frozen soil aggregation module 103 is used to implement step S103 in fig. 1, and a detailed description of the frozen soil deactivation module may be referred to in the detailed description of step S103.
It should be noted that, since the permafrost monitoring device and the permafrost monitoring method have the same inventive concept, the above permafrost dividing module 101, the permafrost identifying module 102, and the permafrost aggregation module 103 may also be used to implement other steps or sub-steps of the method, which is not limited in detail in this embodiment.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
It should also be appreciated that the above embodiments, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application.
Accordingly, the present embodiment also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the permafrost monitoring method provided by the present embodiment. The computer readable storage medium may be any of various media capable of storing a program code, such as a usb (universal serial bus), a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk.
Fig. 4 shows a schematic hardware structure of the monitoring device according to the present embodiment, which includes a processor 202 and a memory 201. The memory 201 stores a computer program, and the processor reads and executes the computer program corresponding to the above embodiment in the memory 201 to realize the permafrost monitoring method provided in the present embodiment.
With continued reference to fig. 3, the monitoring device further comprises a communication unit 203. The memory 201, the processor 202, and the communication unit 203 are electrically connected to each other directly or indirectly through a system bus 204 to achieve data transmission or interaction.
The memory 201 may be an information recording device based on any electronic, magnetic, optical or other physical principle for recording execution instructions, data, etc. In some embodiments, the memory 201 may be, but is not limited to, volatile memory, non-volatile memory, storage drives, and the like.
In some embodiments, the volatile memory may be random access memory (Random Access Memory, RAM); in some embodiments, the non-volatile Memory may be Read Only Memory (ROM), programmable ROM (Programmable Read-Only Memory, PROM), erasable ROM (Erasable Programmable Read-Only Memory, EPROM), electrically erasable ROM (Electric Erasable Programmable Read-Only Memory, EEPROM), flash Memory, or the like; in some embodiments, the storage drive may be a magnetic disk drive, a solid state disk, any type of storage disk (e.g., optical disk, DVD, etc.), or a similar storage medium, or a combination thereof, etc.
The communication unit 203 is used for transmitting and receiving data through a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a public switched telephone network (Public Switched Telephone Network, PSTN), a bluetooth network, a ZigBee network, a near field communication (Near Field Communication, NFC) network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the service request processing system may connect to the network to exchange data and/or information.
The processor 202 may be an integrated circuit chip with signal processing capabilities and may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processors may include a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a special instruction set Processor (Application Specific Instruction-set Processor, ASIP), a graphics processing unit (Graphics Processing Unit, GPU), a physical processing unit (Physics Processing Unit, PPU), a digital signal Processor (Digital Signal Processor, DSP), a field programmable gate array (Field Programmable Gate Array, FPGA), a programmable logic device (Programmable Logic Device, PLD), a controller, a microcontroller unit, a reduced instruction set computer (Reduced Instruction Set Computing, RISC), a microprocessor, or the like, or any combination thereof.
It should be understood that the apparatus and method disclosed in the above embodiments may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is merely illustrative of various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the scope of the present application, and the application is intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A method of permafrost monitoring, the method comprising:
dividing a target frozen soil area into a plurality of grids;
acquiring target temperature sequences of each grid at different time points according to the infrared remote sensing data of the target frozen soil area;
determining a time sequence change type of the grids according to target temperature sequences of the grids at different time points, wherein the time sequence change type comprises a time enhancement type and a time weakening type; the method specifically comprises the following steps:
obtaining a first mean value and a first standard deviation of the grid according to the target temperature sequence of the grid;
obtaining a first enhancement threshold and a first reduction threshold according to the first mean value and the first standard deviation;
if the latest surface temperature between the grid acquisitions is greater than the first enhancement threshold, determining the grid as the time enhancement type;
if the earth surface temperature with the latest grid acquisition time is smaller than the first reduction threshold value, determining the grid as the time reduction type;
comparing the temperature difference of the grid with the temperature difference of an associated grid to determine a spatial variation type of the grid, wherein the spatial variation type represents the difference between the temperature difference of the grid and the temperature difference of the associated grid, the temperature difference of the grid is the difference between two surface temperatures with the latest acquisition time and adjacent surface temperatures in a target temperature sequence of the grid, and the temperature difference of the associated grid is the difference between the two surface temperatures with the latest acquisition time and adjacent surface temperatures in the target temperature sequence of the associated grid;
the spatial variation type comprises a spatial enhancement type and a spatial reduction type, the comparing the temperature difference of the grid with the temperature difference of the associated grid, and determining the spatial variation type of the grid comprises the following steps:
obtaining a second mean value and a second standard deviation between the grid and the associated grid according to the temperature difference of the grid and the temperature difference of the associated grid;
obtaining a second enhancement threshold and a second reduction threshold according to the second mean and the second standard deviation;
if the temperature difference of the grid is larger than the second enhancement threshold value, determining the grid as the space enhancement type;
if the temperature difference of the grid is smaller than the second reduction threshold value, determining the grid as the space weakening type;
determining the frozen soil temperature change type of the grid according to the time sequence change type and the space change type;
and aggregating grids with the same frozen soil temperature change type and adjacent positions into frozen soil plaques.
2. The permafrost monitoring method of claim 1, wherein the obtaining a first enhancement threshold and a first reduction threshold from the first mean and the first standard deviation is expressed as:
p 1 ＝(μ 1 +th 1 ×σ 1 )；
q 1 ＝(μ 1 -th 1 ×σ 1 )；
wherein p is 1 Represents the first enhancement threshold, q 1 Represents the first reduction threshold, mu 1 Representation houseThe first mean value, th 1 Representing a preset time sequence variation threshold value sigma 1 Representing the first standard deviation.
3. The permafrost monitoring method of claim 1, wherein a second enhancement threshold and a second reduction threshold expression are obtained from the second mean and the second standard deviation:
p 2 ＝(μ 2 +th 2 ×σ 2 )；
q 2 ＝(μ 2 -th 2 ×σ 2 )；
wherein p is 2 Represents the second enhancement threshold, q 2 Represents the second reduction threshold, mu 2 Representing the second mean value, th 2 Representing a preset spatial variation threshold, sigma 2 Representing the second standard deviation.
4. The permafrost monitoring method of claim 1, wherein said obtaining a target temperature sequence for each of said grids at different time points from said infrared remote sensing data comprises:
obtaining an original temperature sequence of each grid at different time points according to the infrared remote sensing data;
subtracting the linear interference temperature from the original temperature sequence of each grid to obtain a target temperature sequence of each grid, wherein the linear interference temperature represents the temperature increased due to global warming.
5. A permafrost monitoring device, the device comprising:
the frozen soil dividing module is used for dividing the target frozen soil area into a plurality of grids;
the frozen soil identification module is used for obtaining target temperature sequences of each grid at different time points according to the infrared remote sensing data of the target frozen soil area;
determining a time sequence change type of the grids according to target temperature sequences of the grids at different time points, wherein the time sequence change type comprises a time enhancement type and a time weakening type; the frozen soil identification module is also specifically used for:
obtaining a first mean value and a first standard deviation of the grid according to the target temperature sequence of the grid;
obtaining a first enhancement threshold and a first reduction threshold according to the first mean value and the first standard deviation;
if the latest surface temperature between the grid acquisitions is greater than the first enhancement threshold, determining the grid as the time enhancement type;
if the earth surface temperature with the latest grid acquisition time is smaller than the first reduction threshold value, determining the grid as the time reduction type;
comparing the temperature difference of the grid with the temperature difference of an associated grid to determine a spatial variation type of the grid, wherein the spatial variation type represents the difference between the temperature difference of the grid and the temperature difference of the associated grid, the temperature difference of the grid is the difference between two surface temperatures with the latest acquisition time and adjacent surface temperatures in a target temperature sequence of the grid, and the temperature difference of the associated grid is the difference between the two surface temperatures with the latest acquisition time and adjacent surface temperatures in the target temperature sequence of the associated grid;
the spatial variation type comprises a spatial enhancement type and a spatial weakening type, and the frozen soil identification module is further specifically used for:
obtaining a second mean value and a second standard deviation between the grid and the associated grid according to the temperature difference of the grid and the temperature difference of the associated grid;
obtaining a second enhancement threshold and a second reduction threshold according to the second mean and the second standard deviation;
if the temperature difference of the grid is larger than the second enhancement threshold value, determining the grid as the space enhancement type;
if the temperature difference of the grid is smaller than the second reduction threshold value, determining the grid as the space weakening type;
determining the frozen soil temperature change type of the grid according to the time sequence change type and the space change type;
and the frozen soil aggregation module is used for aggregating grids which are identical in frozen soil temperature change type and adjacent in position into frozen soil plaques.
6. A storage medium storing a computer program which, when executed by a processor, implements a permafrost monitoring method as claimed in any one of claims 1 to 4.
7. A monitoring device comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the permafrost monitoring method of any of claims 1-4.
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