Remote sensing-based mangrove damage early warning method and system

Abstract

The application provides a mangrove damage early warning method and system based on remote sensing, comprising the following steps: determining a research area and constructing a spatial index database; determining a baseline period and a mangrove distribution area; extracting a vector boundary layer from the mangrove distribution area according to the baseline period, thereby constructing a baseline database; downloading the latest remote sensing image of the research area every month from the baseline period, and calculating NDVI of each latest remote sensing image, thereby forming a time series NDVI dataset; comparing the difference between the monthly NDVI and the NDVI of the same period of the previous year based on each grid unit in the spatial index database; determining the mangrove damage risk according to the difference result; the early warning response is more timely and accurate; and the method is simple and easy to implement automatically.

Description

Technical Field

[0001] This invention relates to the field of marine environmental technology, and in particular to a method and system for early warning of mangrove damage based on remote sensing. Background Technology

[0002] Mangroves are known as "sea forests" and "oceanic lungs," playing an irreplaceable role in wind and wave protection, carbon sequestration, water purification, biodiversity conservation, and coastal ecological security. They are not only important habitats for numerous marine organisms but also provide ecological barriers and economic support for coastal communities.

[0003] Currently, early warning for mangrove ecosystems is mostly carried out through monitoring. This mainly involves selecting indicators such as the area invaded by Spartina alterniflora, the area threatened by harmful vines, the damage rate of plants, and the proportion of coastal erosion to construct an evaluation index system. The evaluation is conducted by organizing monitoring surveys to obtain data.

[0004] The aforementioned monitoring and investigation work is costly and has a long data update cycle, making it difficult to achieve high-frequency updates. In response to emergencies such as extreme weather events and sudden pollution accidents, the early warning response is significantly delayed, which seriously affects the timeliness of ecological early warning and the accuracy of decision support. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a remote sensing-based method and system for early warning of mangrove damage, which provides a more timely and accurate early warning response; the method is simple and easy to automate.

[0006] In a first aspect, embodiments of the present invention provide a method for early warning of mangrove damage based on remote sensing, the method comprising: Define the study area and construct a spatial index database; Determine the baseline period and mangrove distribution area; Based on the baseline period, a vector boundary layer is extracted from the mangrove distribution area to construct a baseline database; Starting from the baseline period, the latest remote sensing images of the study area are downloaded monthly, and NDVI is calculated for each latest remote sensing image to form a time-series NDVI dataset. Based on each grid cell in the spatial index database, compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year; The risk of mangrove damage is determined based on the differences in the results.

[0007] Furthermore, the study area was defined, and a spatial index database was constructed, including: The study area was determined based on the distribution range of mangroves in the study area; The study area is gridded using spatial resolution to obtain multiple grid cells; Each grid cell is assigned a unique number, and the spatial index database is constructed.

[0008] Furthermore, the baseline period and mangrove distribution area were determined, including: The baseline period was selected as a period in which the mangrove ecosystem was relatively stable. Based on the baseline period, high-resolution remote sensing images of the study area are acquired; Based on the red and near-infrared bands of the remote sensing image, the normalized vegetation index is calculated. The distribution area of ​​the mangrove forest was obtained by combining threshold segmentation and visual interpretation.

[0009] Furthermore, based on the red and near-infrared bands of the remote sensing image, the normalized vegetation index is calculated, including: The normalized vegetation index is calculated according to the following formula:

[0010] in, The normalized vegetation index is... The reflectance of the red band of the remotely sensed image is... The reflectance of the near-infrared band of the remote sensing image is denoted as .

[0011] Furthermore, based on the differences in the results, the risk of mangrove damage is classified and determined, including: If the monthly NDVI of any of the grid cells decreases by more than a first set threshold compared to the NDVI of the same period of the previous year, the score is increased by 1. If the monthly NDVI of any of the grid cells is lower than the NDVI of the same period of the previous year by the first set threshold, the score is reduced by 1, and 0 is the lower limit of the score. When the cumulative score within a continuous calculation period is greater than or equal to the second set threshold, it is determined that the mangrove forest is significantly damaged, and an early warning is triggered. Wherein, the first set threshold is less than the second set threshold.

[0012] Furthermore, based on the differences in the results, the risk of mangrove damage is classified and determined, including: If the monthly NDVI of any of the grid cells decreases by more than the threshold for determining abnormal changes compared to the NDVI of the same period of the previous year, the verification process is triggered. When the verification process indicates the presence of sudden damage, the mangrove forest is deemed to be in an early warning state.

[0013] Secondly, embodiments of the present invention provide a remote sensing-based mangrove damage early warning system, the system comprising: The first determination module is used to determine the study area and construct a spatial index database; The second determination module is used to determine the baseline period and mangrove distribution area; The extraction module is used to extract vector boundary layers from the mangrove distribution area based on the baseline period, thereby constructing a baseline database; The calculation module is used to download the latest remote sensing images of the study area month by month starting from the baseline period, and to perform NDVI calculation on the latest remote sensing images in each period, thereby forming a time series NDVI dataset; The comparison module is used to compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year, based on each grid cell in the spatial index database. The assessment module is used to classify and determine the risk of mangrove damage based on the differences in the results.

[0014] Furthermore, the first determining module is specifically used for: The study area was determined based on the distribution range of mangroves in the study area; The study area is gridded using spatial resolution to obtain multiple grid cells; Each grid cell is assigned a unique number, and the spatial index database is constructed.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described above.

[0016] Fourthly, embodiments of the present invention provide a computer-readable medium having processor-executable non-volatile program code that causes the processor to perform the method described above.

[0017] This invention provides a remote sensing-based method and system for early warning of mangrove damage, comprising: determining the study area and constructing a spatial index database; determining the baseline period and mangrove distribution area; extracting vector boundary layers from the mangrove distribution area based on the baseline period to construct a baseline database; downloading the latest remote sensing images of the study area monthly starting from the baseline period, and calculating NDVI for each latest remote sensing image to form a time-series NDVI dataset; comparing the difference between the NDVI of the current month and the NDVI of the same period of the previous year based on each grid cell in the spatial index database; classifying and determining the risk of mangrove damage based on the difference results; providing more timely and accurate early warning responses; and being simple and easy to automate.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a remote sensing-based mangrove damage early warning method provided in Embodiment 1 of the present invention; Figure 2 The overall flowchart of the remote sensing-based mangrove damage early warning method provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the mangrove damage early warning assessment results from July to December 2021, provided in Embodiment 1 of the present invention. Figure 4 This is a schematic diagram of mangrove images of the study area from July to December 2021, provided in Embodiment 1 of the present invention. Figure 5 This is a schematic diagram of a remote sensing-based mangrove damage early warning system provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] To facilitate understanding of this embodiment, the embodiments of the present invention will be described in detail below.

[0024] Example 1: Figure 1 The flowchart is a remote sensing-based mangrove damage early warning method provided in Embodiment 1 of the present invention.

[0025] Reference Figure 1 The method includes the following steps: Step S101: Determine the study area and construct a spatial index database; Step S102: Determine the baseline period and mangrove distribution area; Step S103: Extract vector boundary layers from the mangrove distribution area based on the baseline period to construct a baseline database; Step S104: Starting from the baseline period, download the latest remote sensing images of the study area month by month, and calculate NDVI for the latest remote sensing images in each period to form a time series NDVI dataset. Step S105: Based on each grid cell in the spatial index database, compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year; Step S106: Determine the risk of mangrove damage based on the differential results.

[0026] Furthermore, step S101 includes the following steps: Step S201: Determine the study area based on the distribution range of mangroves in the study area; Step S202: The study area is meshed using spatial resolution to obtain multiple mesh cells; Step S203: Assign a unique number to each grid cell and build a spatial index database.

[0027] Specifically, the delineation of the region and the setting of the resolution are based on the research objectives and data accuracy requirements, using the distribution range of mangroves in the study area (e.g., nature reserves, coastal mudflats, provinces with typical mangrove distribution) as a basis to determine the research scope.

[0028] A regular grid is established based on latitude and longitude, and a method is adopted. The spatial resolution was used to grid the study area, with each grid covering an area of ​​approximately 0.0484 hectares.

[0029] Grid numbering and spatial index construction: Each grid cell is assigned a unique number, and a spatial index database is built to ensure that subsequent NDVI change analysis, time series calculation, and early warning information can be spatially located and traced.

[0030] Furthermore, refer to Figure 2 Step S102 includes the following steps: Step S301: Select a period when the mangrove ecosystem is relatively stable as the baseline period; Step S302: Based on the baseline period, acquire high-resolution remote sensing images of the study area; Step S303: Calculate the normalized vegetation index based on the red band and near-infrared band of the remote sensing image. Step S304: Obtain the mangrove distribution area by combining threshold segmentation and visual interpretation.

[0031] Furthermore, step S303 includes: Calculate the normalized vegetation index according to formula (1): (1) in, Normalized Difference Vegetation Index (NDVI) The reflectance of the red band in the remote sensing image. This represents the reflectance in the near-infrared band of the remote sensing image.

[0032] Specifically, the baseline period is set as follows: the period when the mangrove ecosystem is relatively stable is selected as the baseline year and month, and this point in time represents the reference state of the mangrove health status.

[0033] Remote sensing image acquisition: Based on the baseline period, acquire high-resolution remote sensing images of the study area (such as Sentinel-2 MSI, Landsat 8 OLI, GF-6 PMS, etc.). The images need to undergo atmospheric correction, geometric correction, and cloud masking to ensure data consistency and comparability.

[0034] NDVI calculation and mangrove distribution identification: The normalized vegetation index was calculated using formula (1). By combining threshold segmentation and visual interpretation, mangroves were distinguished from other land features (such as bare beaches, water bodies, and salt marshes), and the vector boundary layer of the mangrove distribution area at the baseline period was extracted to form a baseline database.

[0035] During the dynamic monitoring of mangroves, time-series imagery was constructed as follows: starting from the baseline period, the latest remote sensing images of the study area were downloaded monthly, maintaining the same spatial resolution and projection coordinates. The same NDVI calculation was performed on each image to form a time-series NDVI dataset.

[0036] Change detection: For each grid cell, compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year.

[0037] Furthermore, step S106 includes the following steps: Step S401: If the monthly NDVI of any grid cell decreases by more than a first set threshold compared to the NDVI of the same period of the previous year, the score is increased by 1. Step S402: If the monthly NDVI of any grid cell is lower than the NDVI of the same period of the previous year by a first set threshold, the score is reduced by 1, and 0 is the lower limit of the score. Step S403: When the cumulative score within a continuous calculation period is greater than or equal to the second set threshold, it is determined that the mangrove forest is significantly damaged, and an early warning is triggered. The first set threshold is less than the second set threshold.

[0038] Furthermore, step S106 includes the following steps: Step S501: If the monthly NDVI of any grid cell decreases more than the threshold for judging abnormal mutations compared with the NDVI of the same period of the previous year, the verification process is triggered. Step S502: When the verification process results in the existence of sudden damage, the mangrove forest is determined to enter an early warning state.

[0039] Specifically, the mangrove damage classification and early warning system establishes a quantitative scoring system based on dynamic changes in NDVI, comprehensively considering short-term trends and annual variation magnitudes to classify and determine the risk of mangrove damage.

[0040] Standard change scoring mechanism: If the monthly NDVI value of a grid cell decreases by more than 0.1 compared to the same period of the previous year, the score is +1; if the NDVI decreases by less than 0.1 compared to the previous month, the score is -1; the minimum score is 0, and there is no negative value accumulation; when the cumulative score within a continuous calculation period is ≥5, it is determined that the mangrove forest is significantly damaged, and an early warning is triggered.

[0041] Abnormal mutation scoring mechanism: Under normal circumstances, the interannual NDVI variation of healthy mangroves is small. Affected by natural factors such as seasons, tides or sunlight, its year-on-year fluctuation generally does not exceed 0.1. When NDVI decreases by more than 0.2 year-on-year, it often corresponds to significant canopy damage, massive leaf drop, or large-scale dieback, exhibiting characteristics of a sudden event. Therefore, 0.2 is used as the threshold for judging abnormal mutations.

[0042] When the NDVI value in a given month decreases by more than 0.2 compared to the same period of the previous year, it is considered that a drastic and discontinuous abnormal change may have occurred in the area, and the system immediately triggers a manual verification process. The verification process requires combining high-resolution UAV aerial photographs, recent remote sensing imagery, and necessary field investigations to assess whether there is any sudden and significant vegetation loss. If the verification confirms sudden damage, there is no need to wait for subsequent monthly data or accumulated scores; the area is directly placed into an early warning state, enabling rapid response and accurate identification of drastic change events.

[0043] The main advantages of this application include: 1) More timely and accurate early warning response. Based on monthly changes, this method employs a dual-mechanism simultaneous threshold triggering mechanism of "routine change judgment + abnormal mutation identification," enabling rapid early warning when a significant monthly decline in mangrove forests occurs. Combined with manual verification, this effectively reduces the false alarm rate, thus achieving both rapid response and high reliability. 2) The method is simple and easy to automate. Utilizing publicly available remote sensing imagery and standardized NDVI indicators, automated calculations and batch regional monitoring can be achieved within GIS or remote sensing platforms. This is suitable for large-scale, long-term dynamic tracking of mangrove forests, significantly reducing monitoring costs.

[0044] Based on this invention, an early warning assessment was conducted on the damage to mangrove forests in the Jinpai Port area of ​​Lingao, Hainan. The assessment results ( Figure 3 The study clearly shows the damage to mangroves in the study area from July to December 2021, indicating that an early warning was issued in the southeast in August 2021, and the warning area gradually expanded from August to November.

[0045] Reference Figure 4 Extracting remote sensing images of the region reveals that in July 2021, the mangroves in the study area were growing well. However, starting in August 2021, sparse patches began to appear in the southeast and gradually expanded, consistent with the early warning results.

[0046] Example 2: Figure 5 This is a schematic diagram of a remote sensing-based mangrove damage early warning system provided in Embodiment 2 of the present invention. (Refer to...) Figure 5 The system includes: The first determination module is used to determine the study area and construct a spatial index database; The second determination module is used to determine the baseline period and mangrove distribution area; The extraction module is used to extract vector boundary layers from the mangrove distribution area based on the baseline period, thereby constructing a baseline database; The calculation module is used to download the latest remote sensing images of the study area month by month starting from the baseline period, and to perform NDVI calculation on the latest remote sensing images in each period to form a time series NDVI dataset; The comparison module is used to compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year, based on each grid cell in the spatial index database. The assessment module is used to classify and determine the risk of mangrove damage based on the differences in the results.

[0047] Furthermore, the first determining module is specifically used for: The study area was determined based on the distribution range of mangroves in the study area; The study area is gridded using spatial resolution to obtain multiple grid cells; Each grid cell is assigned a unique number, and a spatial index database is built.

[0048] This application constructs a rapid early warning model for NDVI using a dual-path approach of "conventional scoring + anomalous mutation". This model can capture the continuity and gradual degradation trend of mangroves, and can also respond instantly to significant losses caused by a single sudden event, greatly improving the response speed to sudden events and reducing the risk of missed reports.

[0049] This application makes full use of publicly released remote sensing image products to construct an automated mangrove grid-based early warning system, which is characterized by low cost, replicability, and high timeliness, significantly reducing the technical application threshold and providing an efficient and convenient solution for large-scale, multi-time mangrove early warning monitoring.

[0050] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the remote sensing-based mangrove damage early warning method provided in the above embodiments.

[0051] This invention also provides a computer-readable medium having processor-executable non-volatile program code, on which a computer program is stored, and which, when run by a processor, executes the steps of the remote sensing-based mangrove damage early warning method described above.

[0052] The computer program product provided in this embodiment of the invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0054] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0055] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0056] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0057] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote sensing-based method for early warning of mangrove damage, characterized in that, The method includes: Define the study area and construct a spatial index database; Determine the baseline period and mangrove distribution area; Based on the baseline period, a vector boundary layer is extracted from the mangrove distribution area to construct a baseline database; Starting from the baseline period, the latest remote sensing images of the study area are downloaded monthly, and NDVI is calculated for each latest remote sensing image to form a time-series NDVI dataset. Based on each grid cell in the spatial index database, compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year; The risk of mangrove damage is determined based on the differences in the results.

2. The remote sensing-based mangrove damage early warning method according to claim 1, characterized in that, The study area was defined, and a spatial index database was constructed, including: The study area was determined based on the distribution range of mangroves in the study area; The study area is gridded using spatial resolution to obtain multiple grid cells; Each grid cell is assigned a unique number, and the spatial index database is constructed.

3. The remote sensing-based mangrove damage early warning method according to claim 1, characterized in that, Determine the baseline period and mangrove distribution area, including: The baseline period was selected as a period in which the mangrove ecosystem was relatively stable. Based on the baseline period, high-resolution remote sensing images of the study area are acquired; Based on the red and near-infrared bands of the remote sensing image, the normalized vegetation index is calculated. The distribution area of ​​the mangrove forest was obtained by combining threshold segmentation and visual interpretation.

4. The remote sensing-based mangrove damage early warning method according to claim 3, characterized in that, Based on the red and near-infrared bands of the remote sensing imagery, the normalized vegetation index is calculated, including: The normalized vegetation index is calculated according to the following formula: in, The normalized vegetation index is... The reflectance of the red band of the remotely sensed image is... The reflectance of the near-infrared band of the remote sensing image is denoted as .

5. The remote sensing-based mangrove damage early warning method according to claim 1, characterized in that, The risk of mangrove damage is determined based on the differential results, including: If the monthly NDVI of any of the grid cells decreases by more than a first set threshold compared to the NDVI of the same period of the previous year, the score is increased by 1. If the monthly NDVI of any of the grid cells is lower than the NDVI of the same period of the previous year by the first set threshold, the score is reduced by 1, and 0 is the lower limit of the score. When the cumulative score within a continuous calculation period is greater than or equal to the second set threshold, it is determined that the mangrove forest is significantly damaged, and an early warning is triggered. Wherein, the first set threshold is less than the second set threshold.

6. The remote sensing-based mangrove damage early warning method according to claim 1, characterized in that, The risk of mangrove damage is determined based on the differential results, including: If the monthly NDVI of any of the grid cells decreases by more than the threshold for determining abnormal changes compared to the NDVI of the same period of the previous year, the verification process is triggered. When the verification process indicates the presence of sudden damage, the mangrove forest is deemed to be in an early warning state.

7. A remote sensing-based early warning system for mangrove damage, characterized in that, The system includes: The first determination module is used to determine the study area and construct a spatial index database; The second determination module is used to determine the baseline period and mangrove distribution area; The extraction module is used to extract vector boundary layers from the mangrove distribution area based on the baseline period, thereby constructing a baseline database; The calculation module is used to download the latest remote sensing images of the study area month by month starting from the baseline period, and to perform NDVI calculation on the latest remote sensing images in each period, thereby forming a time series NDVI dataset; The comparison module is used to compare the difference between the NDVI of the current month and the NDVI of the same period of the previous year, based on each grid cell in the spatial index database. The assessment module is used to classify and determine the risk of mangrove damage based on the differences in the results.

8. The remote sensing-based mangrove damage early warning system according to claim 7, characterized in that, The first determining module is specifically used for: The study area was determined based on the distribution range of mangroves in the study area; The study area is gridded using spatial resolution to obtain multiple grid cells; Each grid cell is assigned a unique number, and the spatial index database is constructed.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the method described in any one of claims 1 to 6.

10. A computer-readable medium having processor-executable non-volatile program code, characterized in that, The program code causes the processor to execute the method described in any one of claims 1 to 6.

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