A mangrove non-sudden weather disaster resilience evaluation method

Abstract

This invention relates to a method for assessing the resilience of mangroves against non-sudden meteorological disasters, comprising: acquiring spatial distribution information of mangroves and sea-level height time series in the study area, and determining the start and end times of the disaster event; calculating vegetation indices from remote sensing images, and synthesizing them through a sliding window to obtain a regularized vegetation index time series; determining the time of significant mangrove response to the disaster, and calculating the lag response time; calculating amplitude resistance components and temporal resistance components, and constructing resistance, resilience, and toughness indices; and outputting pixel-scale raster products of the relevant indices. The beneficial effects of this invention are: it can simultaneously quantify the damage magnitude, response lag, and resilience of mangroves, thereby avoiding the limitations of evaluating the ecological state based solely on a single damage outcome.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing monitoring technology, and in particular relates to a method for assessing the resilience of mangroves against non-sudden meteorological disasters. Background Technology

[0002] Located in the transition zone between land and sea, mangroves are typical intertidal blue carbon ecosystems, playing a vital role in carbon sequestration, coastal protection, habitat maintenance, and ecological security. Affected by global change, mangroves face not only sudden disasters such as typhoons and storm surges, but also long-term stresses from non-sudden meteorological disasters such as drought, persistent low temperatures, abnormal sea-level changes, and multi-factor composite anomalies. These disasters are often characterized by slow development, long duration, weak degradation signals, and susceptibility to seasonal variations and tidal background interference, frequently exhibiting a process of "latent accumulation—threshold breach—significant decline," making them more difficult to identify and quantify than sudden disasters. Existing methods for monitoring mangrove ecological status and assessing disasters mostly employ single-temporal remote sensing comparisons, pre- and post-disaster difference analysis, interannual average change analysis, or state characterization methods based on statistics from a specific period. Such methods are applicable to rapid, intense, and short-term sudden disturbances, but they are insufficient for characterizing the delayed response characteristics of mangroves under non-sudden meteorological disasters. They are difficult to simultaneously identify the moment when the disaster begins, the moment when the disaster returns to normal, the moment when the mangroves begin to respond significantly, and the subsequent recovery process. They are also difficult to form a unified quantitative indicator system.

[0003] Furthermore, existing research on ecosystem resilience often emphasizes only one aspect of resistance or resilience, while paying insufficient attention to the process by which mangroves under chronic stress do not immediately decline after a disaster event, but rather respond significantly only after a period of time. Without incorporating this lag time, it is easy to misinterpret "insignificant changes in the short term" as "strong resistance," thus affecting the interpretation of the results.

[0004] Therefore, there is an urgent need for a resilience assessment method for mangroves affected by non-sudden meteorological disasters. This method should be able to identify the start and end times of disaster events at the pixel scale, extract the moments of significant mangrove responses, calculate the lag response time, and further construct a resilience index system that includes amplitude, time, and recovery information. This would provide technical support for mangrove disaster monitoring, identification of priority areas for ecological restoration, and long-term management. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for assessing the resilience of mangroves to non-sudden meteorological disasters.

[0006] Firstly, a method for assessing the resilience of mangroves against non-sudden meteorological disasters is provided, including:

[0007] S1. Obtain spatial distribution information of mangroves in the study area;

[0008] S2. Obtain the time series of sea level height corresponding to the study area, and determine the start and end times of the disaster event;

[0009] S3. Calculate vegetation indices from remote sensing images and synthesize them using a sliding window to obtain a regularized vegetation index time series.

[0010] S4. Calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time.

[0011] S5. Extract the baseline vegetation level and the worst vegetation level, and calculate the amplitude resistance component and the time resistance component to construct a resistance index.

[0012] S6. Extract the average level during the disaster event and the post-disaster vegetation level to construct resilience indicators;

[0013] S7. Conduct quality control on resistance and resilience indicators;

[0014] S8. Calculate the resilience index by weighted summation of the resistance index and the recovery index;

[0015] S9 is a pixel-scale raster product that outputs amplitude resistance component, time resistance component, resistance index, resilience index, and toughness index.

[0016] Preferably, in step S2, the sea level height time series is subjected to monthly composite and standardized value calculation. The formula for calculating the standardized value is as follows:

[0017]

[0018] in, For the first The monthly sea level height value, For the first The corresponding month index. The average sea level height over many years for the given monthly category. The standard deviation of sea level height over many years is given for the month category.

[0019] Preferably, when the spatial resolution of sea level height data is coarser than that of the vegetation index, it is reprojected to align with the standard grid of the vegetation index.

[0020] Preferably, in S3, the statistical method used for sliding window synthesis is the maximum value synthesis method, in order to generate a vegetation index time series with a regularized time step.

[0021] As a preferred embodiment, in S4, the formula for calculating the state index is as follows:

[0022]

[0023] in, and These represent the maximum and minimum values ​​of NDVI over multiple years of synchronized time synchronization.

[0024] As a preferred embodiment, in S5, the calculation formulas for the resistance component, time resistance component, and resistance index are as follows:

[0025]

[0026]

[0027]

[0028] in, It is a very small positive number. For time scale parameters;

[0029] In S6, the formula for calculating the resilience index is:

[0030]

[0031] in, for to The average value of the mangrove vegetation index NDVI over the time period. for Post-preset The annual mangrove vegetation index (NDVI) level;

[0032] In S8, the formula for calculating the toughness index is:

[0033]

[0034] in, , , .

[0035] Preferably, S7 further includes: acquiring auxiliary information related to quality control; the auxiliary information includes at least any two of the following: quality control effective pixel mask, observation count, abnormal high value pixel marker, no value reason marker, event coverage validity marker, and threshold trigger status marker.

[0036] Secondly, a mangrove resilience assessment system for non-sudden meteorological disasters is provided for performing any of the methods described in the first aspect, including:

[0037] The first acquisition module is used to acquire spatial distribution information of mangroves in the study area;

[0038] The second acquisition module is used to acquire the sea level height time series corresponding to the study area and determine the start and end times of the disaster event;

[0039] The first calculation module is used to calculate vegetation indices from remote sensing images and synthesize them through a sliding window to obtain a regularized vegetation index time series.

[0040] The second calculation module is used to calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time.

[0041] The first extraction module is used to extract the baseline vegetation level and the worst vegetation level, and to calculate the amplitude resistance component and the time resistance component to construct a resistance index.

[0042] The second extraction module is used to extract the average level during the disaster event and the post-disaster vegetation level to construct resilience indicators.

[0043] The quality control module is used for quality control of resistance and resilience indicators;

[0044] The summation module is used to perform a weighted summation of the resistance and resilience indices to calculate the toughness index;

[0045] The output module is used to output pixel-scale raster products with amplitude resistance component, time resistance component, resistance index, resilience index and toughness index.

[0046] Thirdly, a computer storage medium is provided, wherein a computer program is stored therein; when the computer program is run on a computer, the computer causes the computer to perform any of the methods described in the first aspect.

[0047] Fourthly, an electronic device is provided, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor for executing the computer program to implement the method as described in any of the first aspects.

[0050] The beneficial effects of this invention are:

[0051] 1. This invention proposes a pixel-scale resilience assessment method for mangroves under non-sudden meteorological disaster stress, which can simultaneously quantify the damage extent, response lag, and recovery capacity of mangroves, thereby avoiding the limitations of evaluating the ecological status based on a single damage result.

[0052] 2. This invention couples monthly sea level height anomaly information with the time series of mangrove remote sensing vegetation index to construct an integrated technical chain from disaster event identification, response node extraction, resistance calculation, resilience calculation to comprehensive resilience characterization. The technical process is complete, the logic is clear, and it is easy to implement.

[0053] 3. This invention introduces a quality control mechanism to mask abnormal pixels such as under-observed and abnormally high-value pixels, thereby improving the reliability and traceability of the indicator results.

[0054] 4. The output of this invention is a pixel-scale raster product, which can directly serve mangrove degradation monitoring, identification of priority areas for ecological restoration, vulnerability diagnosis, and long-term change assessment, and has good value for promotion and application. Attached Figure Description

[0055] Figure 1 A flowchart illustrating a method for calculating indicators to assess the resilience of mangroves under non-sudden meteorological disaster stress;

[0056] Figure 2 In this invention , and A diagram illustrating the extraction of time points;

[0057] Figure 3 The amplitude resistance component in this invention and time resistance components The calculation results are shown in the figure.

[0058] Figure 4 Resistance index in this invention Calculation results graph;

[0059] Figure 5 The restoring force index in this invention Calculation results graph;

[0060] Figure 6 This is a graph showing the quality control results in this invention;

[0061] Figure 7 The comprehensive toughness index in this invention Calculation results graph. Detailed Implementation

[0062] The present invention will be further described below with reference to embodiments. The description of the embodiments below is only for the purpose of helping to understand the present invention. It should be noted that those skilled in the art can make several modifications to the present invention without departing from the principle of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

[0063] Example 1:

[0064] To address the problems of existing technologies, Embodiment 1 of this application provides a method for assessing the resilience of mangroves to non-sudden meteorological disasters, including:

[0065] S1. Obtain spatial distribution information of mangroves in the study area.

[0066] Among them, the spatial distribution information of mangroves is either a mangrove spatial mask or a set of mangrove pixels.

[0067] S2. Obtain the sea level height time series corresponding to the study area and determine the start and end times of the disaster event.

[0068] Specifically, the sea level height time series corresponding to the study area is obtained, and monthly composite and standardized values ​​are calculated for the sea level height time series. The month in which a non-sudden meteorological disaster event begins is determined based on the standardized value being less than a preset threshold three times consecutively, and the first day of that month is taken as the start time of the disaster event. Based on the standardized values ​​regressing to the normal threshold range three times consecutively, the month in which the non-sudden meteorological disaster event ends is determined, and the first day of that month is taken as the end time of the disaster event. and will to The period was identified as a disaster event.

[0069] In S2, the sea level height time series is subjected to monthly composite and standardized value calculation. The formula for calculating the standardized value is as follows:

[0070]

[0071] in, For the first The monthly sea level height value, For the first The corresponding month index. The average sea level height over many years for the given monthly category. The standard deviation of sea level height over many years is given for the month category.

[0072] In addition, when the spatial resolution of sea level height data is coarser than that of the vegetation index, it is reprojected to align with the standard grid of the vegetation index.

[0073] S3. Calculate vegetation indices from remote sensing images and synthesize them using a sliding window to obtain a regularized vegetation index time series.

[0074] Specifically, Landsat image data corresponding to the mangroves in the study area were acquired, the Normalized Difference Vegetation Index (NDVI) was calculated, and a regularized time-step NDVI time series for mangroves was obtained by combining the data through a sliding window method. The statistical method used for the sliding window synthesis was the maximum value synthesis method to generate the vegetation index time series with a regularized time step.

[0075] S4. Calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time.

[0076] Specifically, the Vegetation Condition Index (VCI) is calculated based on the NDVI time series synthesized by a sliding window, resulting in a mangrove condition index time series. The moment when the mangroves begin to significantly respond to the disaster is determined by the time when the VCI time series falls below a preset response threshold for the third consecutive time. The delayed response time is calculated using the following formula:

[0077]

[0078] The formula for calculating the state index is as follows:

[0079]

[0080] in, and These represent the maximum and minimum values ​​of NDVI over multiple years of synchronized time synchronization.

[0081] S5. Extract the baseline vegetation level and the worst vegetation level, and calculate the amplitude resistance component and the time resistance component to construct a resistance index.

[0082] S6. Extract the average level during the disaster event and the post-disaster vegetation level to construct a resilience index.

[0083] S7. Conduct quality control on resistance and recovery indicators.

[0084] Specifically, the validity of pixels is determined based on the number of effective observation windows during the baseline period, the disaster period, and the post-disaster pre-set period, and abnormal pixels such as those with insufficient observation or abnormally high values ​​are masked.

[0085] S7 also includes: acquiring auxiliary information related to quality control; the auxiliary information includes at least any two of the following: quality control effective cell mask, observation count, abnormal high value cell marker, no value reason marker, event coverage validity marker, and threshold trigger status marker.

[0086] S8. Calculate the resilience index by weighted summation of the resistance index and the recovery index.

[0087] S9, Output amplitude resistance component Time resistance component Resistance indicators Resilience indicators and resilience indicators Pixel-scale raster products.

[0088] Example 2:

[0089] Building upon Example 1, Example 2 of this application provides a more specific method for assessing the resilience of mangroves against non-sudden meteorological disasters. The method uses a mangrove area in the Bay of Carpentaria, Australia, as the study area for resilience assessment, specifically including:

[0090] S1. Obtain spatial distribution information of mangroves in the study area.

[0091] In this embodiment, the Global Mangrove Distribution (GMW) product is used as the mangrove spatial data source. After reading the mangrove distribution raster, it is cropped according to the study area to generate a mangrove spatial mask.

[0092] S2. Obtain the sea level height time series corresponding to the study area and determine the start and end times of the disaster event.

[0093] In this embodiment, the sea level height time series is obtained from Global Ocean Physics Reanalysis. First, the sea level height time series is synthesized monthly to obtain a monthly sea level height series. Then, the multi-year average and standard deviation are calculated for each month to eliminate the influence of seasonal cycles, and finally, the monthly standardized value is calculated. The calculation of the standardized value includes estimating the mean and standard deviation of the sea level height for each month and calculating...

[0094]

[0095] in, For the first The monthly sea level height value, For the first The corresponding month index. The average sea level height over many years for the given monthly category. The standard deviation of sea level height over many years is given for the month category.

[0096] Subsequently, the start and end times of the disaster event are identified based on a threshold: when the standardized value is lower than the threshold-1 at least 3 times, it is determined to be the month in which a non-sudden meteorological disaster event begins, and the first day of that month is taken as the start time of the disaster event. When the standardized value is greater than -1 at least 3 times, it is determined to be the month in which a non-sudden meteorological disaster event ends, and the first day of that month is taken as the end time of the disaster event. As a preferred implementation, the following is provided: No earlier than . Figure 2 This diagram illustrates how monthly standardized values ​​of sea level height over a time series are used to identify the start and end times of disaster events.

[0097] Furthermore, because the spatial resolution of sea level height data is coarser than that of mangrove NDVI data, this embodiment also requires reprojection and alignment to the standard grid of the vegetation index. In specific implementation, this example first reads the sea level height raster data and aligns it with the Landsat imagery through resampling.

[0098] S3. Calculate vegetation indices from remote sensing images and synthesize them using a sliding window to obtain a regularized vegetation index time series.

[0099] In this embodiment, Collection2Level2 surface reflectance products from Landsat5, Landsat7, Landsat8, and Landsat9 are preferably used. First, fill values, clouds, cloud shadows, cirrus clouds, snow cover, and radiation-saturated pixels are removed according to the quality control bands. Then, the true reflectance is restored according to the surface reflectance scaling parameters. Furthermore, NDVI is calculated based on the red and near-infrared bands of different sensors. After merging the NDVI images obtained from each sensor by time, a regularized NDVI time series is constructed using a sliding window method to improve the continuity and robustness of the time series. Preferably, the sliding window length is set to 16 days, and the step size is set to 8 days. Robust statistical synthesis is performed on the NDVI within each window to obtain a mangrove NDVI time series with a regularized time step. The robust statistical synthesis can use median synthesis, quantile synthesis, or mean synthesis. In this embodiment, the maximum value synthesis method within the window is preferred to enhance the preservation of effective vegetation signals.

[0100] S4. Calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time.

[0101] In this embodiment, the mangrove status index is preferably the Vegetation Status Index (VCI). Specifically, the regularized time series within a year is divided into multiple window positions with a fixed step size. For each window position, the minimum and maximum values ​​of the NDVI over many years are calculated, and the mangrove status index is calculated according to the following formula:

[0102]

[0103] in and These represent the maximum and minimum values ​​of NDVI over multiple years of synchronized time synchronization.

[0104] Preferably, when the VCI is below the threshold of 0.6 for at least three consecutive times, the starting point of the window that first falls below the threshold is taken as the threshold. . Figure 2 The diagram illustrates the regularized NDVI time series, VCI construction, and extraction of significant response times in mangroves.

[0105] S5. Extract the baseline vegetation level and the worst vegetation level, and calculate the amplitude resistance component and the time resistance component to construct a resistance index.

[0106] In this embodiment, The average NDVI of mangroves over the previous two years is defined as the baseline period, and the average NDVI of mangroves over all valid rule windows within that time period is defined as the baseline level. .Will The minimum NDVI value of mangroves within all valid rule windows within a preset evaluation period of 2 years is defined as the worst vegetation level. At the same time, the moment when mangroves begin to respond significantly to disasters will be determined. At the start of the disaster event The difference is converted into lag response time. ,Right now:

[0107]

[0108] In specific implementation, it can be The number of days is converted according to the window step size in the rules.

[0109] In addition, calculation The average NDVI of mangroves over the previous two years is used as the baseline level for mangroves. ,calculate The minimum NDVI of mangroves within a preset time period is used as the worst-case mangrove vegetation level. From the above and Calculate the amplitude resistance component According to the above Calculate the time resistance component , will the and Building resistance indicators The calculation formula is:

[0110]

[0111]

[0112]

[0113] in, These are extremely small positive numbers, used to prevent the denominator from being 0; for example, they can be set to... . This is a time scale parameter, preferably set to 120 days. The time scale parameter is used to specify the response lag time. Set an interpretable timescale, giving the metric an upper limit, and addressing abnormally large values. It won't be magnified indefinitely. It needs to be clarified that... It is a "half-saturated timescale", when equal hour, The time resistance has reached half of the "maximum possible value". In the experiment, it was found that the lag time of vegetation was mostly around 8 months, so 120 days (4 months) was chosen as half of the maximum time resistance.

[0114] Figure 3 This illustrates the amplitude resistance component in the present invention. and time resistance components The calculation results are shown in the figure. Figure 4 The resistance index in this invention is shown. Calculation results graph.

[0115] S6. Extract the average level during the disaster event and the post-disaster vegetation level to construct a resilience index.

[0116] Specifically, calculation to The average NDVI of mangroves over a period of time is used as the average level during mangrove disaster events. ;Pick The next preset The NDVI level of mangroves in 2018 was [missing information]. ;in The value is a preset positive integer; preferably, Take the average NDVI of the rule window over the three months following two years after the disaster begins; based on , and Constructing resilience indicators The calculation formula is:

[0117]

[0118] in, for to The average value of the mangrove vegetation index NDVI over the time period. for Post-preset The annual mangrove vegetation index (NDVI) level. Figure 5 The diagram shows the calculation results of the restoring force index in this invention.

[0119] In actual calculations, if the vegetation state in year k after the disaster... Fully restored to baseline state ,Right now Approximately equal to At this point, the denominator will approach 0, leading to a decrease in the resilience index. The value tends to be abnormally high. In S7's quality control, abnormally small denominators are extracted (this can be traced back to its source). It should also be noted that resilience calculations are only performed when it is confirmed that the mangroves have been affected by damage; therefore, the denominator is small. A large size indicates a good recovery.

[0120] S7. Conduct quality control on resistance and recovery indicators.

[0121] In this embodiment, the resistance index and resilience indicators Quality control is implemented. Specifically, the validity of pixels is determined based on the number of effective observation windows during the baseline period, the disaster period, and the post-disaster pre-set period; abnormal pixels, such as abnormally high-value pixels, are masked.

[0122] To improve the stability of the restoring force results, this embodiment can further set quality control conditions: only when , and Sufficient number of effective observation windows are available for calculation, and If the value is not less than a preset threshold, the calculated resilience index result is retained; otherwise, the pixel is masked. Preferably, the quality control condition can be set as follows: The number of effective windows for calculation is no less than 2. The number of effective windows should be no less than 10. The number of effective windows should be no less than 2.

[0123] Simultaneously, it outputs raster files such as effective pixel masks for quality control, observation counts, and abnormal high-value pixels for result traceability. Figure 6 A graph showing the quality control results in this invention is provided.

[0124] S8. Calculate the resilience index by weighted summation of the resistance index and the recovery index.

[0125] Specifically, based on the aforementioned resistance index and resilience indicators Calculate toughness index The calculation formula is:

[0126]

[0127] in, , , .

[0128] Preferably, Take 0.7, Take 0.3, that is:

[0129]

[0130] Figure 7 The diagram shows the calculation results of the comprehensive toughness index in this invention.

[0131] S9 is a pixel-scale raster product that outputs amplitude resistance component, time resistance component, resistance index, resilience index, and toughness index.

[0132] In this embodiment, the output includes an amplitude resistance component. Time resistance component Resistance indicators Resilience indicators and resilience indicators The output is a pixel-scale raster product. The preferred output format is a single-band GeoTIFF raster file. If necessary, a quality control-related auxiliary information layer can also be output simultaneously for subsequent cartographic display, regional statistics, and result verification.

[0133] In its specific implementation, this embodiment can complete sea level height event identification, Landsat regularized NDVI sequence construction, VCI calculation, significant response time extraction, resistance index and resilience index calculation based on the Google Earth Engine cloud platform, and export the results as a raster product; furthermore, Python is used for... and The grid is weighted and the results are plotted to obtain a comprehensive resilience index grid, its spatial distribution map, and a frequency histogram. This implementation method has a clear calculation process, facilitates engineering deployment, and can retain a complete quality control chain and result traceability information.

[0134] It should be noted that the parts in this embodiment that are the same as or similar to those in Embodiment 1 can be referred to each other, and will not be repeated in this application.

[0135] Example 3:

[0136] Based on Example 2, Example 3 of this application provides a mangrove resilience assessment system for non-sudden meteorological disasters, including:

[0137] The first acquisition module is used to acquire spatial distribution information of mangroves in the study area;

[0138] The second acquisition module is used to acquire the sea level height time series corresponding to the study area and determine the start and end times of the disaster event;

[0139] The first calculation module is used to calculate vegetation indices from remote sensing images and synthesize them through a sliding window to obtain a regularized vegetation index time series.

[0140] The second calculation module is used to calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time.

[0141] The first extraction module is used to extract the baseline vegetation level and the worst vegetation level, and to calculate the amplitude resistance component and the time resistance component to construct a resistance index.

[0142] The second extraction module is used to extract the average level during the disaster event and the post-disaster vegetation level to construct resilience indicators.

[0143] The quality control module is used for quality control of resistance and resilience indicators;

[0144] The summation module is used to perform a weighted summation of the resistance and resilience indices to calculate the toughness index;

[0145] The output module is used to output pixel-scale raster products with amplitude resistance component, time resistance component, resistance index, resilience index and toughness index.

[0146] It should be noted that the system provided in this embodiment is the corresponding system of the method provided in embodiment 2. Therefore, the parts that are the same as or similar to those in embodiment 2 in this embodiment can be referred to each other, and will not be described again in this application.

[0147] In summary, this application, based on mangrove distribution data, sea level height time series, and Landsat regularized NDVI time series, utilizes a complete technical chain—"disaster event identification—regularized NDVI construction—mangrove state index standardization—significant response time extraction—resistance calculation—resilience calculation—resilience weighting—quality control and result output"—to achieve pixel-scale assessment of mangrove resilience under non-sudden meteorological disaster stress. This method simultaneously incorporates pre-disaster baseline, worst-case state during the disaster event, delayed response time, average state during the disaster event, and post-disaster state information, enabling a more comprehensive characterization of mangrove resistance, recovery, and overall resilience to chronic meteorological disaster stress.

Claims

1. A method for assessing the resilience of mangrove forests against non- sudden weather disasters, characterized in that, include: S1. Obtain spatial distribution information of mangroves in the study area; S2. Obtain the time series of sea level height corresponding to the study area, and determine the start and end times of the disaster event; S3. Calculate vegetation indices from remote sensing images and synthesize them using a sliding window to obtain a regularized vegetation index time series. S4. Calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time. S5. Extract the baseline vegetation level and the worst vegetation level, and calculate the amplitude resistance component and the time resistance component to construct a resistance index. S6. Extract the average level during the disaster event and the post-disaster vegetation level to construct resilience indicators; S7. Conduct quality control on resistance and resilience indicators; S8. Calculate the resilience index by weighted summation of the resistance index and the recovery index; S9 is a pixel-scale raster product that outputs amplitude resistance component, time resistance component, resistance index, resilience index, and toughness index.

2. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 1, characterized in that, In S2, the sea level height time series is subjected to monthly composite and standardized value calculation. The formula for calculating the standardized value is as follows: ； in, For the first The monthly sea level height value, For the first The corresponding month index. The average sea level height over many years for the given monthly category. The standard deviation of sea level height over many years is given for the month category.

3. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 2, characterized in that, In S2, when the spatial resolution of sea level height data is coarser than that of the vegetation index, it is reprojected to align with the standard grid of the vegetation index.

4. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 3, characterized in that, The sliding window synthesis described in S3 uses the maximum value synthesis method to generate a vegetation index time series with a regularized time step.

5. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 4, characterized in that, In S4, the formula for calculating the state index is as follows: ； in, and These represent the maximum and minimum values ​​of NDVI over multiple years of synchronized time synchronization.

6. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 5, characterized in that, In S5, the formulas for calculating the resistance component, time resistance component, and resistance index are as follows: ； ； ； in, It is a very small positive number. For time scale parameters; In S6, the formula for calculating the resilience index is: ； in, for to The average value of the mangrove vegetation index NDVI over the time period. for Post-preset The annual mangrove vegetation index (NDVI) level; In S8, the formula for calculating the toughness index is: ； in, , , .

7. The method for assessing the resilience of mangroves to non-sudden meteorological disasters according to claim 6, characterized in that, S7 also includes: acquiring auxiliary information related to quality control; the auxiliary information includes at least any two of the following: quality control effective cell mask, observation count, abnormal high value cell marker, no value reason marker, event coverage validity marker, and threshold trigger status marker.

8. A mangrove resilience assessment system for non-sudden meteorological disasters, characterized in that, For performing the method according to any one of claims 1 to 7, comprising: The first acquisition module is used to acquire spatial distribution information of mangroves in the study area; The second acquisition module is used to acquire the sea level height time series corresponding to the study area and determine the start and end times of the disaster event; The first calculation module is used to calculate vegetation indices from remote sensing images and synthesize them through a sliding window to obtain a regularized vegetation index time series. The second calculation module is used to calculate the state index based on the vegetation index time series, determine the time of significant response of mangroves to disasters based on the state index, and calculate the lag response time. The first extraction module is used to extract the baseline vegetation level and the worst vegetation level, and to calculate the amplitude resistance component and the time resistance component to construct a resistance index. The second extraction module is used to extract the average level during the disaster event and the post-disaster vegetation level to construct resilience indicators. The quality control module is used for quality control of resistance and resilience indicators; The summation module is used to perform a weighted summation of the resistance and resilience indices to calculate the toughness index; The output module is used to output pixel-scale raster products with amplitude resistance component, time resistance component, resistance index, resilience index and toughness index.

9. A computer storage medium, characterized in that, The computer storage medium stores a computer program; when the computer program is run on the computer, it causes the computer to perform the method described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method as described in any one of claims 1 to 7.

Images