Method and device for monitoring seasonal variation of mangrove patch area and computer equipment

Abstract

The present application relates to the technical field of mangrove ecology analysis, and particularly relates to a mangrove patch area seasonal change monitoring method and device and computer equipment, quality control of satellite remote sensing image data of each time phase is carried out by using observation auxiliary data, a time series of mangrove patch area is constructed, a trend free pre-whitening method is adopted, autocorrelation correction is carried out on the constructed time series of mangrove patch area, and the corrected time series of mangrove patch area is subjected to seasonal processing, so as to carry out area change analysis, and the accuracy and efficiency of the mangrove patch area change monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of mangrove ecological analysis technology, and in particular to a method, device, computer equipment, and storage medium for monitoring seasonal changes in mangrove patch area. Background Technology

[0002] Mangroves are typical coastal blue carbon ecosystems, playing vital roles in carbon sequestration, shoreline protection, water purification, and providing habitat for organisms. However, due to factors such as natural erosion, land reclamation, aquaculture expansion, and extreme weather events, mangrove area and health status exhibit spatial heterogeneity and long-term variability. Current mangrove monitoring methods often employ multi-period classification comparisons or simple linear regression fitting, which suffer from significant time-series noise, insufficient significance discrimination, and inadequate handling of autocorrelation and seasonality, leading to misjudgments and failing to meet long-term monitoring needs. Summary of the Invention

[0003] Based on this, the purpose of this invention is to provide a method, apparatus, computer equipment, and storage medium for monitoring seasonal changes in mangrove patch area. This method utilizes observational auxiliary data to perform quality control on satellite remote sensing image data from various time phases to construct a time series of mangrove patch area. A trend-free pre-whitening method is then used to perform autocorrelation correction on the constructed mangrove patch area time series. The corrected mangrove patch area time series is then subjected to seasonal processing for area change analysis, thereby improving the accuracy and efficiency of mangrove patch area change monitoring.

[0004] In a first aspect, embodiments of this application provide a method for monitoring seasonal changes in mangrove patch area, comprising the following steps: The satellite remote sensing image data and observation auxiliary data of the target mangrove area at several time phases are obtained, wherein the target mangrove area includes several spatial units; Based on the aforementioned observational auxiliary data, quality control is performed on the satellite remote sensing image data for each time phase to obtain processed satellite remote sensing image data for each time phase. Based on the processed satellite remote sensing image data of each time phase, a time series of mangrove patch area is constructed to obtain the mangrove patch area time series, wherein the mangrove patch area time series includes the mangrove patch area of ​​several spatial units at several time points; The trend-free pre-whitening method was used to perform autocorrelation correction on the time series of mangrove patch area to obtain the corrected time series of mangrove patch area. The corrected mangrove patch area time series is divided and sorted according to the season to obtain the mangrove patch area observation series for several seasons in each spatial unit; the area change analysis is performed based on the mangrove patch area observation series for each season in each spatial unit to obtain the monitoring results of mangrove patch area change in each spatial unit.

[0005] Secondly, embodiments of this application provide a device for monitoring seasonal changes in mangrove patch area, comprising: The data acquisition module is used to acquire satellite remote sensing image data and observation auxiliary data of the target mangrove area at several time phases, wherein the target mangrove area includes several spatial units; The quality control module is used to perform quality control on the satellite remote sensing image data of each time phase based on the observation auxiliary data, and obtain the processed satellite remote sensing image data of each time phase. The sequence construction module is used to construct a time series of mangrove patch area based on the processed satellite remote sensing image data of each time phase, and obtain a time series of mangrove patch area, wherein the time series of mangrove patch area includes the mangrove patch area of ​​several spatial units at several time points; The sequence correction module is used to perform autocorrelation correction on the mangrove patch area time series using a trend-free pre-whitening method to obtain the corrected mangrove patch area time series. The change monitoring module is used to divide and sort the mangrove patch area time series according to the season, and obtain the mangrove patch area observation series for several seasons in each spatial unit; and to perform area change analysis based on the mangrove patch area observation series for each season in each spatial unit, and obtain the mangrove patch area change monitoring results for each spatial unit.

[0006] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the method for monitoring seasonal changes in mangrove patch area as described in the first aspect.

[0007] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the method for monitoring seasonal changes in mangrove patch area as described in the first aspect.

[0008] In this application embodiment, a method, apparatus, computer equipment, and storage medium for monitoring seasonal changes in mangrove patch area are provided. The method utilizes observational auxiliary data to perform quality control on satellite remote sensing image data of various time phases to construct a time series of mangrove patch area. A trend-free pre-whitening method is used to perform autocorrelation correction on the constructed mangrove patch area time series. The corrected mangrove patch area time series is then subjected to seasonal processing for area change analysis, thereby improving the accuracy and efficiency of mangrove patch area change monitoring.

[0009] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application; Figure 2 This is a flowchart illustrating step S2 of the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application. Figure 3 This is a flowchart illustrating step S4 of the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application. Figure 4 This is a flowchart illustrating step S5 of the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application. Figure 5 This is a flowchart illustrating step S5 of the method for monitoring seasonal changes in mangrove patch area provided in the second embodiment of this application. Figure 6 This is a flowchart illustrating step S5 of the method for monitoring seasonal changes in mangrove patch area provided in the third embodiment of this application. Figure 7 A flowchart illustrating the method for monitoring seasonal changes in mangrove patch area provided in the fourth embodiment of this application; Figure 8 This is a schematic diagram of the structure of the mangrove patch area seasonal change monitoring device provided in the fifth embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in the sixth embodiment of this application. Detailed Implementation

[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0012] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0013] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0014] Please see Figure 1 , Figure 1 The flowchart of the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application is shown. The method includes the following steps: S1: Obtain satellite remote sensing image data and observation auxiliary data for the target mangrove area at several time points.

[0015] The implementing entity of the method for monitoring seasonal changes in mangrove patch area is the monitoring equipment for monitoring seasonal changes in mangrove patch area (hereinafter referred to as the monitoring equipment). In an optional embodiment, the monitoring equipment may be a computer device, a server, or a server cluster composed of multiple computer devices.

[0016] In this embodiment, the monitoring equipment obtains satellite remote sensing image data and observation auxiliary data for several time phases of the target mangrove area. The target mangrove area includes several spatial units. The satellite remote sensing image data for each time phase includes satellite remote sensing images at several times. The observation auxiliary data includes cloud / cloud shadow ratio data and tide data. The cloud cover data includes cloud / cloud shadow ratio at several times of each time phase.

[0017] S2: Based on the aforementioned observation auxiliary data, perform quality control on the satellite remote sensing image data for each time phase to obtain processed satellite remote sensing image data for each time phase.

[0018] In this embodiment, the monitoring equipment performs quality control on the satellite remote sensing image data of each time phase based on the observation auxiliary data, and obtains the processed satellite remote sensing image data of each time phase.

[0019] Please see Figure 2 , Figure 2 The flowchart of step S2 in the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application is as follows: S21: Perform radiometric calibration, atmospheric correction, geometric correction, and water mask preprocessing on satellite remote sensing image data for each time phase to obtain preprocessed satellite remote sensing image data for each time phase.

[0020] In this embodiment, the monitoring equipment performs radiometric calibration, atmospheric correction, geometric correction, and water mask preprocessing on satellite remote sensing image data of each time phase to obtain preprocessed satellite remote sensing image data of each time phase.

[0021] S22: Determine the time of the annual lowest tide based on the tide level data; obtain the tide level matching degree data for each time phase based on the annual lowest tide time and the preset tide level matching degree calculation algorithm.

[0022] In this embodiment, the monitoring device determines the time of the annual lowest tide based on the tide level data.

[0023] The monitoring equipment obtains tide level matching degree data for each time phase based on the annual lowest tide occurrence time and a preset tide level matching degree calculation algorithm. The tide level matching degree data includes tide level matching degrees for several time periods. The tide level matching degree calculation algorithm is as follows:

[0024] In the formula, The first phase of the current time t The degree of tidal level matching at a given moment This is the time when the lowest tide of the year occurs. The number of days in a year The width is measured on a daily scale.

[0025] S23: Based on the tide level matching degree, cloud / cloud shadow ratio data, and the preset quality score calculation algorithm, obtain the quality score data for each time phase.

[0026] In this embodiment, the monitoring device obtains quality score data for each time phase based on the tide level matching degree, cloud / cloud shadow ratio data, and a preset quality score calculation algorithm. The quality score data includes quality scores for several time periods, and the quality score calculation algorithm is as follows:

[0027] In the formula, The first phase of the current time t Quality score at any given moment The first phase of the current time t Cloud / cloud shadow ratio at any given moment; S24: Based on the quality score data of each time phase and the preset quality threshold, the satellite remote sensing images of each time phase of the preprocessed satellite remote sensing image data are filtered to obtain the processed satellite remote sensing image data of each time phase.

[0028] In this embodiment, the monitoring device filters the satellite remote sensing images at each moment of the preprocessed satellite remote sensing image data for each time phase based on the quality score data of each time phase and a preset quality threshold, thereby obtaining the processed satellite remote sensing image data for each time phase. Specifically, if the quality score at a certain moment in the current time phase is less than the quality threshold, the monitoring device removes the satellite remote sensing image corresponding to that moment, thus obtaining the processed satellite remote sensing image data for each time phase.

[0029] S3: Construct a time series of mangrove patch area based on the processed satellite remote sensing image data of each time phase to obtain the time series of mangrove patch area.

[0030] In this embodiment, the monitoring device constructs a time series of mangrove patch area based on the processed satellite remote sensing image data of each time phase, and obtains a time series of mangrove patch area, wherein the time series of mangrove patch area includes the mangrove patch area of ​​several spatial units at several time points.

[0031] Specifically, the monitoring equipment identifies mangrove patches in satellite remote sensing images at various times in each phase and calculates the area of ​​the mangrove patches to obtain the area of ​​the mangrove patches at each time in each phase, thus constructing a time series of mangrove patch areas.

[0032] S4: The trend-free pre-whitening method is used to perform autocorrelation correction on the time series of mangrove patch area to obtain the corrected time series of mangrove patch area.

[0033] To address the issue of artificially high MK significance due to autocorrelation in the time series, in this embodiment, the monitoring device employs a trend-free pre-whitening method to correct the autocorrelation of the mangrove patch area time series, thereby obtaining the corrected mangrove patch area time series.

[0034] Please see Figure 3 , Figure 3 The flowchart of step S4 in the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application includes steps S41 to S43, as follows: S41: Based on the time series of mangrove patch area and the preset change rate calculation algorithm, obtain the change rate of each spatial unit.

[0035] In this embodiment, the monitoring device obtains the rate of change of each spatial unit based on the time series of mangrove patch area and a preset rate of change calculation algorithm, wherein the rate of change calculation algorithm is as follows:

[0036] In the formula, For the rate of change, It is a median function. , These are the times in the time series of mangrove patch areas. j The moment and the i The area of ​​mangrove patches at any given moment. , They represent the first j The moment and the i That moment.

[0037] S42: Based on the mangrove patch area time series, the rate of change of each spatial unit, and the slope estimation detrending algorithm, obtain the detrended mangrove patch area time series.

[0038] In this embodiment, the monitoring device obtains the detrended mangrove patch area time series based on the mangrove patch area time series, the rate of change of each spatial unit, and a slope estimation detrending algorithm. The slope estimation detrending algorithm is as follows:

[0039] In the formula, The first time series of mangrove patch area after detrending t The area of ​​mangrove patches at any given moment. The first time series of mangrove patch area t The area of ​​mangrove patches at any given moment. , These are the times in the time series of mangrove patch areas. j The moment and the i The area of ​​mangrove patches at any given moment. , They represent the first j The moment and the i That moment.

[0040] S43: Estimate the first-order autocorrelation coefficient based on the detrended mangrove patch area time series to obtain the first-order autocorrelation coefficient of each spatial unit at each time; obtain the corrected mangrove patch area time series based on the detrended mangrove patch area time series, the first-order autocorrelation coefficient of each spatial unit at each time, and the preset sequence correction algorithm.

[0041] In this embodiment, the monitoring device estimates the first-order autocorrelation coefficient based on the detrended mangrove patch area time series to obtain the first-order autocorrelation coefficient of each spatial unit at each time point. The first-order autocorrelation coefficient... >0 indicates a positive correlation between adjacent time points. =0 indicates that adjacent time points are approximately independent. <0 indicates a negative correlation between adjacent time points.

[0042] The monitoring equipment obtains the corrected mangrove patch area time series based on the detrended mangrove patch area time series, the first-order autocorrelation coefficient of each spatial unit at each time point, and a preset sequence correction algorithm. The sequence correction algorithm is as follows:

[0043] In the formula, For the corrected mangrove patch area time series, the first... t The area of ​​mangrove patches at a given moment.

[0044] S5: Divide and sort the mangrove patch area time series according to the seasons to obtain the mangrove patch area observation series for several seasons in each spatial unit; perform area change analysis based on the mangrove patch area observation series for each season in each spatial unit to obtain the monitoring results of mangrove patch area change in each spatial unit.

[0045] In this embodiment, the monitoring equipment divides and sorts the mangrove patch area according to the season based on the corrected mangrove patch area time series, and obtains the mangrove patch area observation sequence for several seasons in each spatial unit.

[0046] Specifically, the monitoring equipment, based on each spatial unit, divides the corrected mangrove patch area time series according to the season, obtaining several seasonal mangrove patch area observation groups for each spatial unit. Each mangrove patch area observation group includes mangrove patch area observation values ​​at several time points. The monitoring equipment arranges the mangrove patch area observation values ​​from each seasonal mangrove patch area observation group for each spatial unit in chronological order, obtaining several seasonal mangrove patch area observation sequences for each spatial unit.

[0047] The monitoring equipment analyzes mangrove patch area changes based on seasonal observation sequences for each spatial unit, obtaining monitoring results for mangrove patch area changes in each spatial unit. Quality control is performed on satellite remote sensing imagery data from various time phases using auxiliary observation data to construct a time series of mangrove patch areas. A trend-free pre-whitening method is employed to perform autocorrelation correction on the constructed mangrove patch area time series. Seasonal processing is then applied to the corrected mangrove patch area time series for area change analysis, improving the accuracy and efficiency of mangrove patch area change monitoring.

[0048] The monitoring results of mangrove patch area changes include trend direction monitoring results. Please refer to... Figure 4 , Figure 4 The flowchart of step S5 in the method for monitoring seasonal changes in mangrove patch area provided in the first embodiment of this application includes steps S51 to S53, as follows: S51: Based on the observation sequence of mangrove patch area in each season of each spatial unit and the preset trend statistics calculation algorithm, obtain the trend statistics of mangrove patch area in each season of each spatial unit; accumulate the trend statistics of mangrove patch area in each season of the same spatial unit to obtain the overall trend statistics of mangrove patch area of ​​each spatial unit.

[0049] In this embodiment, the monitoring equipment obtains the trend statistics of mangrove patch area for each spatial unit in each season based on the observation sequence of mangrove patch area for each season and a preset trend statistics calculation algorithm. The trend statistics calculation algorithm is as follows:

[0050] In the formula, For the first k Statistics on the trend of mangrove patch area in each season For the first k The number of time points in the observation sequence of mangrove patch area for each season. For symbolic functions, , These are the corrected mangrove patch area time series. j The moment and the i The area of ​​mangrove patches at a given moment.

[0051] The monitoring equipment accumulates the trend statistics of mangrove patch area in different seasons for the same spatial unit to obtain the overall trend statistics of mangrove patch area for each spatial unit.

[0052] S52: Obtain the number of times when several identical mangrove patch area observation values ​​are obtained in the mangrove patch area observation sequence for each season in each spatial unit; based on the number of times when several identical mangrove patch area observation values ​​are obtained in the mangrove patch area observation sequence for each season in each spatial unit and the preset variance calculation algorithm, obtain the variance of mangrove patch area for each season in each spatial unit; sum up the variances of mangrove patch area for each season in the same spatial unit to obtain the overall variance of mangrove patch area for each spatial unit.

[0053] In this embodiment, the monitoring device obtains the number of times at which several identical mangrove patch area observation values ​​are recorded in the mangrove patch area observation sequence for each spatial unit in each season. Based on the number of times at which several identical mangrove patch area observation values ​​are recorded in the mangrove patch area observation sequence for each spatial unit in each season, and a preset variance calculation algorithm, the monitoring device obtains the variance of the mangrove patch area for each spatial unit in each season. The variance calculation algorithm is as follows:

[0054] In the formula, For the first k Variance of mangrove patch area in each season For the first k The number of identical mangrove patch area observations within a seasonal mangrove patch area observation sequence. For the first k The first of the seasonal mangrove patch area observation sequences The number of observed areas of identical mangrove patches.

[0055] Since the changes in mangrove patch area between different seasonal groups are affected by different environmental factors, each group can be approximated as independent of the others. The monitoring equipment accumulates the variance of mangrove patch area in each season for the same spatial unit to obtain the overall variance of mangrove patch area for each spatial unit.

[0056] S53: Based on the overall mangrove patch area trend statistics, overall mangrove patch area variance, and preset standard normal distribution statistics calculation algorithm of each spatial unit, obtain the standard normal distribution statistics of each spatial unit; determine the trend direction based on the standard normal distribution statistics of each spatial unit, and obtain the trend direction monitoring results of each spatial unit.

[0057] In this embodiment, the monitoring device obtains the standard normal distribution statistic for each spatial unit based on the overall mangrove patch area trend statistic, the overall mangrove patch area variance, and a preset standard normal distribution statistic calculation algorithm. The standard normal distribution statistic calculation algorithm is as follows:

[0058] In the formula, This is the standard normal distribution statistic. This represents the overall trend of mangrove patch area. The variance represents the overall mangrove patch area.

[0059] The monitoring equipment determines the trend direction based on the standard normal distribution statistics of each spatial unit, thereby obtaining the trend direction monitoring results for each spatial unit. Specifically, the monitoring equipment determines the trend direction based on the standard normal distribution statistics if... Z A value >0 indicates that the area of ​​mangrove patches showed a net increase (expansion or recovery) during the study period. The monitoring equipment was set to monitor the trend direction of the spatial unit as the expansion direction. Z <0 indicates that the area of ​​mangrove patches showed a net decrease (degradation or loss) during the study period. The monitoring equipment was set to monitor the trend direction of the spatial unit as the degradation direction, and the standard normal distribution statistics of each spatial unit were obtained.

[0060] The monitoring results of mangrove patch area changes also include significance monitoring results. Please refer to [link / reference needed]. Figure 5 , Figure 5 The flowchart of step S5 in the method for monitoring seasonal changes in mangrove patch area provided in the second embodiment of this application is shown below, including step S54: S54: Based on the standard normal distribution statistic of each spatial unit and the preset two-sided test probability calculation algorithm, obtain the two-sided test probability value of each spatial unit; based on the two-sided test probability value of each spatial unit and the preset two-sided test probability threshold, perform significance discrimination to obtain the significance monitoring result of each spatial unit.

[0061] In this embodiment, the monitoring device obtains the two-sided test probability value of each spatial unit based on the standard normal distribution statistic of each spatial unit and a preset two-sided test probability calculation algorithm, wherein the two-sided test probability calculation algorithm is as follows:

[0062] In the formula, This represents the probability value for a two-tailed test. This is the cumulative distribution function of the standard normal distribution.

[0063] The monitoring equipment performs saliency discrimination based on the two-sided test probability value of each spatial unit and a preset two-sided test probability threshold, thereby obtaining the saliency monitoring result of each spatial unit. Specifically, the two-sided test probability threshold includes a first two-sided test probability threshold and a second two-sided test probability threshold, wherein the first two-sided test probability threshold is set to 0.05 and the second two-sided test probability threshold is set to 0.01.

[0064] Based on the two-sided test probability value, if the two-sided test probability value is less than the first two-sided test probability threshold, it indicates that the mangrove patch area change trend is significant, and the monitoring device sets the significance monitoring result of the spatial unit to significant change trend; if the two-sided test probability value is less than the second two-sided test probability threshold, it indicates that the mangrove patch area change trend is extremely significant, and the monitoring device sets the significance monitoring result of the spatial unit to extremely significant change trend; if the two-sided test probability value is greater than or equal to the first two-sided test probability threshold, it indicates that the mangrove patch area change trend is not significant, that is, the area fluctuation is within the normal interannual variability range, and the monitoring device sets the significance monitoring result of the spatial unit to not significant change trend, thus obtaining the significance monitoring results of each spatial unit.

[0065] The monitoring results of mangrove patch area changes also include monitoring results of the rate of change. Please refer to [link / reference]. Figure 6 , Figure 6 The flowchart of step S5 in the method for monitoring seasonal changes in mangrove patch area provided in the third embodiment of this application is as follows: S55: Based on the rate of change of each spatial unit and the preset rate of change threshold, the rate of change is increased or decreased to determine the rate of change and obtain the rate of change monitoring results of each spatial unit.

[0066] In this embodiment, the monitoring device determines the rate of change (increase or decrease) of each spatial unit based on its rate of change and a preset rate of change threshold, thereby obtaining the rate of change monitoring results for each spatial unit. Specifically, the rate of change threshold is set to 0. If the rate of change is greater than the rate of change threshold, it is considered an increasing rate, and the monitoring device sets the rate of change monitoring result for the spatial unit to an increasing rate of change. If the rate of change is greater than the rate of change threshold, it is considered a decreasing rate, and the monitoring device sets the rate of change monitoring result for the spatial unit to a decreasing rate of change, thus obtaining the rate of change monitoring results for each spatial unit.

[0067] In an optional embodiment, please refer to Figure 7 , Figure 7 The flowchart of the method for monitoring seasonal changes in mangrove patch area provided in the fourth embodiment of this application also includes step S6, as follows: S6: Based on the trend direction monitoring results, significance monitoring results, and change rate monitoring results of each spatial unit, identify change hotspot areas to obtain the change hotspot areas of the target mangrove area.

[0068] In this embodiment, the monitoring equipment identifies change hotspot areas based on the trend direction monitoring results, significance monitoring results, and change rate monitoring results of each spatial unit, thereby obtaining the change hotspot areas of the target mangrove area.

[0069] Specifically, if the trend direction monitoring result of the spatial unit is an expansion direction, the significance monitoring result is a significant or extremely significant change trend, and the change rate monitoring result is an increasing change rate, the monitoring device sets the spatial unit as a hotspot region of significant expansion; if the trend direction monitoring result of the spatial unit is a degradation direction, the significance monitoring result is a significant or extremely significant change trend, and the change rate monitoring result is a decreasing change rate, the monitoring device sets the spatial unit as a hotspot region of significant degradation, thereby obtaining the change hotspot region of the target mangrove area.

[0070] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a mangrove patch area seasonal change monitoring device provided in the fifth embodiment of this application. This device can be implemented entirely or partially through software, hardware, or a combination of both. The device 8 includes: The data acquisition module 81 is used to acquire satellite remote sensing image data and observation auxiliary data of the target mangrove area at several time phases, wherein the target mangrove area includes several spatial units; The quality control module 82 is used to perform quality control on the satellite remote sensing image data of each time phase based on the observation auxiliary data, and obtain the processed satellite remote sensing image data of each time phase. The sequence construction module 83 is used to construct a time series of mangrove patch area based on the processed satellite remote sensing image data of each time phase, and obtain a time series of mangrove patch area, wherein the time series of mangrove patch area includes the mangrove patch area of ​​several spatial units at several time points. The sequence correction module 84 is used to perform autocorrelation correction on the mangrove patch area time series using a trend-free pre-whitening method to obtain the corrected mangrove patch area time series. The change monitoring module 85 is used to divide and sort the mangrove patch area time series according to the season, and obtain the mangrove patch area observation series for several seasons of each spatial unit; and to perform area change analysis based on the mangrove patch area observation series for each season of each spatial unit, and obtain the mangrove patch area change monitoring results for each spatial unit.

[0071] In this embodiment, a data acquisition module obtains satellite remote sensing image data and observational auxiliary data for several time phases of a target mangrove area, wherein the target mangrove area comprises several spatial units; a quality control module performs quality control on the satellite remote sensing image data for each time phase based on the observational auxiliary data to obtain processed satellite remote sensing image data for each time phase; a sequence construction module constructs a time series of mangrove patch area based on the processed satellite remote sensing image data for each time phase to obtain a mangrove patch area time series, wherein the mangrove patch area time series includes... The method includes mangrove patch areas in several spatial units at several time points; using a sequence correction module, the time series of mangrove patch areas is autocorrelation-corrected using a trend-free pre-whitening method to obtain a corrected mangrove patch area time series; using a change monitoring module, the corrected mangrove patch area time series is divided and sorted according to the season to obtain the mangrove patch area observation sequence for several seasons in each spatial unit; based on the mangrove patch area observation sequence for each season in each spatial unit, area change analysis is performed to obtain the mangrove patch area change monitoring results for each spatial unit.

[0072] Please refer to Figure 9 , Figure 9This is a schematic diagram of the structure of a computer device provided in the sixth embodiment of this application. The computer device 9 includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91. The computer device can store multiple instructions, which are applicable to the method steps of the first to fourth embodiments shown above being loaded and executed by the processor 91. For the specific execution process, please refer to the specific description of the first to fourth embodiments shown, which will not be repeated here.

[0073] The processor 91 may include one or more processing cores. The processor 91 connects to various parts of the server using various interfaces and lines, and executes various functions and processes data of the mangrove patch area seasonal change monitoring device 8 by running or executing instructions, programs, code sets, or instruction sets stored in the memory 92, and by calling data stored in the memory 92. Optionally, the processor 91 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 91 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required to be displayed on the touch screen; and the modem is used for wireless communication. It is understood that the modem may also not be integrated into the processor 91 and may be implemented as a separate chip.

[0074] The memory 92 may include random access memory (RAM) or read-only memory. Optionally, the memory 92 may include a non-transitory computer-readable storage medium. The memory 92 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 92 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), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 92 may also be at least one storage device located remotely from the aforementioned processor 91.

[0075] This application also provides a storage medium that can store multiple instructions. These instructions are applicable to being loaded by a processor and executed by the method steps of the first to fourth embodiments shown above. For details of the execution process, please refer to the specific descriptions of the first to fourth embodiments shown above, which will not be repeated here.

[0076] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0077] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0078] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0079] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0080] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0081] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0082] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0083] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for monitoring seasonal changes in mangrove patch area, characterized in that, Includes the following steps: The satellite remote sensing image data and observation auxiliary data of the target mangrove area at several time phases are obtained, wherein the target mangrove area includes several spatial units; Based on the aforementioned observational auxiliary data, quality control is performed on the satellite remote sensing image data for each time phase to obtain processed satellite remote sensing image data for each time phase. Based on the processed satellite remote sensing image data of each time phase, a time series of mangrove patch area is constructed to obtain the mangrove patch area time series, wherein the mangrove patch area time series includes the mangrove patch area of ​​several spatial units at several time points; The trend-free pre-whitening method was used to perform autocorrelation correction on the time series of mangrove patch area to obtain the corrected time series of mangrove patch area. The corrected mangrove patch area time series is divided and sorted according to the season to obtain the mangrove patch area observation series for several seasons in each spatial unit; the area change analysis is performed based on the mangrove patch area observation series for each season in each spatial unit to obtain the monitoring results of mangrove patch area change in each spatial unit.

2. The method for monitoring seasonal changes in mangrove patch area according to claim 1, characterized in that: The observational auxiliary data includes cloud / cloud shadow ratio data and tide data, wherein the cloud cover data includes cloud / cloud shadow ratio at several times in each time phase; The step of performing quality control on satellite remote sensing image data for each time phase based on the observation auxiliary data to obtain processed satellite remote sensing image data for each time phase includes the following steps: Radiometric calibration, atmospheric correction, geometric correction, and water mask preprocessing are performed on satellite remote sensing image data of each time phase to obtain preprocessed satellite remote sensing image data of each time phase. Based on the tide level data, the time of the annual lowest tide is determined; based on the time of the annual lowest tide and a preset tide level matching degree calculation algorithm, tide level matching degree data for each time phase is obtained, wherein the tide level matching degree data includes tide level matching degree for several times. Based on the tide level matching degree, cloud / cloud shadow ratio data and preset quality score calculation algorithm for each time phase, the quality score data for each time phase is obtained, wherein the quality score data includes the quality score for several time moments. Based on the quality score data of each time phase and the preset quality threshold, the satellite remote sensing images of each time phase of the preprocessed satellite remote sensing image data are filtered to obtain the processed satellite remote sensing image data of each time phase.

3. The method for monitoring seasonal changes in mangrove patch area according to claim 2, characterized in that, The method employing a trend-free pre-whitening technique to perform autocorrelation correction on the mangrove patch area time series, thereby obtaining a corrected mangrove patch area time series, includes the following steps: The rate of change of each spatial unit is obtained based on the time series of mangrove patch area and the preset rate of change calculation algorithm; Based on the time series of mangrove patch area, the rate of change of each spatial unit and the slope estimation detrending algorithm, the detrended mangrove patch area time series is obtained. Based on the detrended mangrove patch area time series, the first-order autocorrelation coefficient is estimated to obtain the first-order autocorrelation coefficient of each spatial unit at each time point. Based on the detrended mangrove patch area time series, the first-order autocorrelation coefficient of each spatial unit at each time point, and the preset sequence correction algorithm, the corrected mangrove patch area time series is obtained.

4. The method for monitoring seasonal changes in mangrove patch area according to claim 3, characterized in that: The monitoring results of changes in mangrove patch area include trend direction monitoring results; The method of analyzing the area change of mangrove patches based on observation groups in each season to obtain the monitoring results of mangrove patch area change in the target mangrove area includes the following steps: Based on the observation sequence of mangrove patch area in each season of each spatial unit and the preset trend statistics calculation algorithm, the trend statistics of mangrove patch area in each season of each spatial unit are obtained. The trend statistics of mangrove patch area in each season of the same spatial unit are accumulated to obtain the overall trend statistics of mangrove patch area in each spatial unit. The number of times when several identical mangrove patch area observations are obtained in the mangrove patch area observation sequence for each season in each spatial unit is obtained; based on the number of times when several identical mangrove patch area observations are obtained in the mangrove patch area observation sequence for each season in each spatial unit and the preset variance calculation algorithm, the variance of mangrove patch area for each season in each spatial unit is obtained; the variances of mangrove patch area for each season in the same spatial unit are accumulated to obtain the overall variance of mangrove patch area for each spatial unit. Based on the overall mangrove patch area trend statistics, overall mangrove patch area variance, and the preset standard normal distribution statistics calculation algorithm of each spatial unit, the standard normal distribution statistics of each spatial unit are obtained; the trend direction is determined based on the standard normal distribution statistics of each spatial unit, and the trend direction monitoring results of each spatial unit are obtained.

5. The method for monitoring seasonal changes in mangrove patch area according to claim 4, characterized in that: The monitoring results of changes in mangrove patch area also include significant monitoring results; The method of analyzing the area change of mangrove patches based on observation groups in each season to obtain the monitoring results of mangrove patch area change in the target mangrove area includes the following steps: Based on the standard normal distribution statistic of each spatial unit and the preset two-sided test probability calculation algorithm, the two-sided test probability value of each spatial unit is obtained. Significance is determined based on the two-sided test probability value of each spatial unit and the preset two-sided test probability threshold, and the significance monitoring results of each spatial unit are obtained.

6. The method for monitoring seasonal changes in mangrove patch area according to claim 5, characterized in that: The monitoring results of changes in mangrove patch area also include monitoring results of the rate of change; The method of analyzing the area change of mangrove patches based on observation groups in each season to obtain the monitoring results of mangrove patch area change in the target mangrove area includes the following steps: The rate of change of each spatial unit is determined by the rate of change of each spatial unit and the preset rate of change threshold, and the rate of change monitoring results of each spatial unit are obtained.

7. The method for monitoring seasonal changes in mangrove patch area according to claim 6, characterized in that, It also includes the following steps: Based on the trend direction monitoring results, significance monitoring results, and change rate monitoring results of each spatial unit, change hotspot areas are identified to obtain the change hotspot areas of the target mangrove area.

8. A device for monitoring the seasonal changes in mangrove patch area, characterized in that, include: The data acquisition module is used to acquire satellite remote sensing image data and observation auxiliary data of the target mangrove area at several time phases, wherein the target mangrove area includes several spatial units; The quality control module is used to perform quality control on the satellite remote sensing image data of each time phase based on the observation auxiliary data, and obtain the processed satellite remote sensing image data of each time phase. The sequence construction module is used to construct a time series of mangrove patch area based on the processed satellite remote sensing image data of each time phase, and obtain a time series of mangrove patch area, wherein the time series of mangrove patch area includes the mangrove patch area of ​​several spatial units at several time points; The sequence correction module is used to perform autocorrelation correction on the mangrove patch area time series using a trend-free pre-whitening method to obtain the corrected mangrove patch area time series. The change monitoring module is used to divide and sort the mangrove patch area time series according to the season, and obtain the mangrove patch area observation series for several seasons in each spatial unit; and to perform area change analysis based on the mangrove patch area observation series for each season in each spatial unit, and obtain the mangrove patch area change monitoring results for each spatial unit.

9. A computer device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor; the computer program, when executed by the processor, implements the steps of the method for monitoring seasonal changes in mangrove patch area as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program that, when executed by a processor, implements the steps of the method for monitoring seasonal changes in mangrove patch area as described in any one of claims 1 to 7.

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