Mangrove forest typhoon influence prediction method and device

Abstract

The invention discloses a mangrove forest typhoon influence prediction method and device, and relates to the field of mangrove forest meteorological disaster early warning, and the method comprises the steps: obtaining meteorological data and remote sensing data of key meteorological indexes of a target region; judging whether to carry out typhoon disaster early warning on the target area according to the meteorological data and the early warning evaluation standard of the key meteorological indexes, and determining a typhoon influence early warning level of the target area according to the meteorological data and the early warning evaluation standard of the key meteorological indexes; according to the remote sensing data of the target area, the mangrove forest area NDVI change rate after the typhoon event occurs in the target area is calculated; calculating the NDVI net change rate of the mangrove forest area according to the NDVI change rate of the mangrove forest area after the typhoon event occurs in the target area and the NDVI change rate of the same period of the normal year; and determining the typhoon damage grade of the mangrove forest area according to the NDVI net change rate of the mangrove forest area and the NDVI net change rate evaluation standard, thereby realizing evaluation and early warning of the ecological influence of the typhoon disaster on the mangrove forest.

Description

Technical Field

[0001] The present application relates to the field of mangrove meteorological disaster early warning, and particularly to a method and device for predicting the impact of typhoons on mangroves. Background Art

[0002] Mangroves are one of the ecosystems with the highest net primary productivity globally and are one of the most important contributors to marine blue carbon, playing an important role in the global carbon cycle. At the same time, mangroves have functions such as windbreak and wave dissipation, silt promotion and beach protection, shore protection, and seawater and air purification, enjoying the reputation of the "marine guard" and having extremely high ecological value. Mangroves are located at the intersection of the ocean and land, belonging to an ecologically fragile and sensitive zone. They are extremely sensitive to the adverse effects of climate change. Under the background of climate change, extreme weather and climate events (such as typhoons) will have an important impact on mangroves, and the mangrove habitat faces serious threats. Therefore, studying the impact of typhoon disasters on mangroves and realizing their prediction and evaluation can understand the impact of meteorological disasters on the mangrove wetland ecosystem, which is of great significance for the introduction and protection of mangroves in countries with extensive mangrove coastlines. Currently, in meteorological operations, there is no relevant assessment and early warning model or index for the ecological impact of typhoon disasters on mangroves, which cannot meet the refined service requirements for meteorological disaster impacts. Summary of the Invention

[0003] The purpose of the present application is to provide a method and device for predicting the impact of typhoons on mangroves, which can realize the assessment and early warning of the ecological impact of typhoon disasters on mangroves.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a method for predicting the impact of typhoons on mangroves, including the following steps:

[0006] Obtain meteorological data and remote sensing data of key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area;

[0007] Judge whether to issue a typhoon disaster early warning for the target area according to the meteorological data and the early warning assessment criteria of the key meteorological indicators, and determine the typhoon impact early warning level of the target area according to the meteorological data and the early warning assessment criteria of the key meteorological indicators;

[0008] Calculate the change rate of NDVI in the mangrove area after the occurrence of a typhoon event in the target area according to the remote sensing data of the target area;

[0009] Calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the occurrence of a typhoon event in the target area and the change rate of NDVI in the same period of normal years;

[0010] Determine the typhoon damage level of the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation standard of the net change rate of NDVI.

[0011] Optionally, before obtaining the meteorological data of the key meteorological indicators of the target area, the mangrove typhoon impact prediction method further includes:

[0012] Construct a linear model between the NDVI change rate in the mangrove area after a typhoon event and the meteorological elements on the typhoon occurrence date based on the historical typhoon event data of the target area;

[0013] Screen the key meteorological indicators according to the linear model between the NDVI change rate in the mangrove area after a typhoon event and the meteorological elements on the typhoon occurrence date.

[0014] Optionally, the meteorological elements include average temperature, maximum temperature, minimum temperature, average wind speed, maximum wind speed and rainfall; the key meteorological indicator is the maximum wind speed.

[0015] Optionally, before constructing a linear model between the NDVI change rate in the mangrove area after a typhoon event and the meteorological elements on the typhoon occurrence date according to the historical typhoon event data of the target area, the mangrove typhoon impact prediction method further includes:

[0016] Obtain the historical meteorological data of the target area;

[0017] Based on the typhoon identification conditions, screen the historical typhoon event data according to the historical meteorological data of the target area; the typhoon identification conditions include that the maximum daily precipitation is not less than the set precipitation or the strong wind identification conditions are met; the strong wind identification conditions are that the average wind speed on land is not less than the first wind speed or the gust wind speed is not less than the second wind speed.

[0018] Optionally, calculating the NDVI change rate in the mangrove area after a typhoon event in the target area according to the remote sensing data of the target area specifically includes:

[0019] Preprocess the remote sensing data of the target area to obtain the NDVI data of the mangrove area in the target area;

[0020] Calculate the NDVI change rate in the mangrove area after a typhoon event in the target area according to the NDVI data of the mangrove area in the target area.

[0021] Optionally, the calculation formula of the NDVI change rate in the mangrove area after a typhoon event in the target area is as follows:

[0022]

[0023] Among them, ΔNDVI is the NDVI change rate in the mangrove area after a typhoon event in the target area, NDVI beforeis the NDVI value of the mangrove area before the typhoon event, NDVI before is the NDVI value of the mangrove area after the typhoon event.

[0024] Optionally, the calculation formula for the net change rate of NDVI in the mangrove area is as follows:

[0025] NDVI 净变化率 = ΔNDVI 台风年 - NDVI 正常年 ;

[0026] where, NDVI 净变化率 is the net change rate of NDVI in the mangrove area, ΔNDVI 台风年 is the change rate of NDVI in the mangrove area after the typhoon event in the target area, NDVI 正常年 is the change rate of NDVI in the same period of normal years in the target area.

[0027] In a second aspect, the present application provides a mangrove typhoon impact prediction device for implementing the mangrove typhoon impact prediction method described in the first aspect, including the following modules:

[0028] A target area data acquisition module, configured to acquire meteorological data of key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area;

[0029] A typhoon impact early warning module, configured to determine whether to issue a typhoon disaster early warning for the target area according to the meteorological data and the early warning evaluation criteria of the key meteorological indicators, and determine the typhoon impact early warning level of the target area according to the meteorological data and the early warning evaluation criteria of the key meteorological indicators;

[0030] A change rate calculation module of NDVI in the mangrove area after the typhoon event, configured to calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area;

[0031] A net change rate calculation module of NDVI in the mangrove area, configured to calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and the change rate of NDVI in the same period of normal years;

[0032] A typhoon disaster loss assessment module, configured to determine the typhoon disaster loss level of the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI.

[0033] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned mangrove typhoon impact prediction method.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned mangrove typhoon impact prediction method is implemented.

[0035] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0036] The present application provides a mangrove typhoon impact prediction method and device. First, meteorological data and remote sensing data of key meteorological indicators in a target area are obtained, and it is judged whether to issue a typhoon disaster warning for the target area according to the meteorological data and the warning evaluation criteria of key meteorological indicators, and the typhoon impact warning level is determined; then, the change rate of NDVI in the mangrove area after the typhoon event in the target area is calculated according to the remote sensing data of the target area, and the net change rate of NDVI in the mangrove area is calculated according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and in the same period of normal years. Finally, the typhoon disaster loss level in the mangrove area is determined according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI. The present application issues a typhoon disaster warning and evaluates the level through key meteorological indicator data related to the change rate of NDVI in the mangrove area, meeting the meteorological monitoring and warning needs for mangrove protection; the net change rate of NDVI in the mangrove area is calculated by the change rate of NDVI in the mangrove area of the target area during the typhoon event and the change rate of NDVI in the same period without typhoon, and the typhoon disaster loss level of the target area is characterized by the change degree of NDVI in the mangrove area, realizing the evaluation and warning of the impact of typhoon disasters on the mangrove ecosystem and improving the accuracy of mangrove typhoon impact warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is an application environment diagram of a mangrove typhoon impact prediction method in an embodiment of the present application;

[0039] Figure 2 It is a flowchart of a mangrove typhoon impact prediction method provided by an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of the specific process of a mangrove typhoon impact prediction method provided by an embodiment of the present application; [[ID=--27]]

[0041] Figure 4Schematic diagram of seasonal changes in mangrove NDVI at each site from 2000 to 2020 provided by an embodiment of the present application;

[0042] Figure 5 Schematic diagram of dynamic changes in the change rate of mangrove NDVI at each site from 2000 to 2020 provided by an embodiment of the present application;

[0043] Figure 6 Schematic diagram of dynamic changes in the net change rate of mangrove NDVI at each site from 2000 to 2020 provided by an embodiment of the present application;

[0044] Figure 7 Schematic diagram of changes in mangrove NDVI at each site caused by typhoons from 2000 to 2020 provided by an embodiment of the present application;

[0045] Figure 8 Distribution histogram of changes in mangrove NDVI at each site caused by typhoons from 2000 to 2020 provided by an embodiment of the present application;

[0046] Figure 9 Schematic diagram of functional modules of a mangrove typhoon impact prediction device provided by another embodiment of the present application;

[0047] Figure 10 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0049] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the drawings and specific implementation manners.

[0050] The mangrove typhoon impact prediction method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the mangrove typhoon data to be processed to the server 104. The mangrove typhoon data to be processed includes meteorological data and remote sensing data of key meteorological indicators in the target area. After receiving the mangrove typhoon data to be processed, the server 104 determines whether to issue a typhoon disaster warning for the target area according to the meteorological data and the warning evaluation criteria of the key meteorological indicators, and determines the typhoon impact warning level. Calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area. Calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and the same period of normal years. Determine the typhoon disaster loss level in the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI. The server 104 can feedback the obtained typhoon impact warning level and typhoon disaster loss level to the terminal 102. In addition, in some embodiments, the mangrove typhoon impact prediction method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform the mangrove typhoon impact prediction on the mangrove typhoon data to be processed, or the server 104 can obtain the mangrove typhoon data to be processed from the data storage system and perform the mangrove typhoon impact prediction on the mangrove typhoon data to be processed.

[0051] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0052] In an exemplary embodiment, such as Figure 2 and Figure 3 shown, a mangrove typhoon impact prediction method is provided. This method is executed by a computer device, and can be specifically executed independently by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in as an example for illustration, it includes the following steps 201 to step 205. Among them:

[0053] Step 201, obtain meteorological data and remote sensing data of key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area.

[0054] Step 202: Determine whether to issue a typhoon disaster warning for the target area according to the meteorological data and the warning assessment criteria of key meteorological indicators, and determine the typhoon impact warning level of the target area according to the meteorological data and the warning assessment criteria of key meteorological indicators.

[0055] Step 203: Calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area.

[0056] Step 204: Calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and the change rate of NDVI in the same period of normal years.

[0057] Step 205: Determine the typhoon disaster loss level of the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI.

[0058] Implementing the above steps 201 to 205, typhoon disaster warning and level assessment are carried out through key meteorological indicator data related to the change rate of NDVI in the mangrove area, meeting the meteorological monitoring and warning requirements for mangrove protection; the net change rate of NDVI in the mangrove area is calculated through the change rate of NDVI in the mangrove area during the typhoon event in the target area and the change rate of NDVI in the same period without typhoon, and the typhoon disaster loss level of the target area is characterized by the change degree of NDVI in the mangrove area, realizing the evaluation and warning of the ecological impact of typhoon disasters on mangroves and improving the accuracy of typhoon impact warning for mangroves. In addition, the development of remote sensing technology provides reliable data for the dynamic monitoring and assessment of the growth trend of mangroves, and the integrated application of remote sensing and meteorological data makes it possible to provide typhoon disaster impact warning for mangroves. The NDVI change rate model can accurately depict the growth trend change of mangroves after typhoons. The proposed mangrove typhoon impact model indicators in this application have strong versatility and can be applied to various remote sensing data such as satellites, drones, and canopy images, providing important technical references for multi-source and multi-scale remote sensing technologies in typhoon disaster impact assessment. At the same time, the proposed mangrove typhoon impact warning indicators in this application provide important technical support for mangrove protection meteorological services.

[0059] Based on the net change rate model of the remote sensing vegetation index NDVI, the assessment of the impact level of mangroves by typhoons is realized. At the same time, based on the analysis of the relationship between meteorological factors and the growth trend of mangroves during typhoon occurrence, mangrove typhoon impact warning indicators are constructed to meet the meteorological monitoring and warning requirements for mangrove protection. By inputting typhoon data and the vegetation index NDVI before and after the typhoon enters, the model can predict in advance the degree of impact of typhoons on mangroves and evaluate the degree of impact of typhoons on mangroves.

[0060] First, obtain data, mainly from meteorological data of the meteorological information center, including wind speed, temperature, and precipitation data, with a time resolution of 1 h. The remote sensing data is MODIS NDVI data, with a data time resolution of 16 d and a spatial resolution of 250 m. In this embodiment, the Guangxi Zhuang Autonomous Region is taken as the target area to introduce the above method specifically.

[0061] In another exemplary embodiment of the present application, before step 201, this embodiment further includes the following steps 301 to 302.

[0062] Step 301: According to the historical typhoon event data of the target area, construct a linear model between the NDVI change rate in the mangrove area and the meteorological elements on the typhoon occurrence date after the typhoon event occurs. The meteorological elements include average temperature, maximum temperature, minimum temperature, average wind speed, maximum wind speed, and rainfall.

[0063] Among them, the determination process of the historical typhoon event data of the target area includes: obtaining the historical meteorological data of the target area; based on the typhoon identification conditions, screening the historical typhoon event data according to the historical meteorological data of the target area; the typhoon identification conditions include that the maximum daily precipitation is not less than the set precipitation or meets the strong wind identification conditions; the strong wind identification conditions are that the average wind speed on land is not less than the first wind speed or the gust wind speed is not less than the second wind speed.

[0064] For the Guangxi Zhuang Autonomous Region, the set precipitation can be 25 mm, the first wind speed is 14 m / s, and the second wind speed is 17.2 m / s. Then the specific process of determining whether a typhoon affects the Guangxi Zhuang Autonomous Region is as follows: According to the local standard "Tropical Cyclones Affecting and Landing in Guangxi" DB45 / T 2154—2020 [S], when a typhoon enters the affected area (19°N~28°N, 104°E~112°E), or the low-pressure center after the typhoon weakens enters the affected area, and the maximum daily precipitation ≥ 25 mm or strong wind is observed at the national meteorological stations in the Guangxi Zhuang Autonomous Region, it is a typhoon affecting the Guangxi Zhuang Autonomous Region. Among them, the identification condition for strong wind is: when the average (2-minute or 10-minute) wind speed on land reaches or exceeds 14 m / s (wind force reaches above level 6), or the gust wind speed reaches or exceeds 17.2 m / s (wind force reaches above level 8), it is called strong wind. Based on the above process, the historical meteorological data of the target area can be obtained.

[0065] Step 302: According to the linear model between the NDVI change rate in the mangrove area and the meteorological elements on the typhoon occurrence date after the typhoon event occurs, screen the key meteorological indicators.

[0066] Find the meteorological element with the highest coefficient of determination from the meteorological elements that are linearly negatively correlated with the NDVI change rate, screen reasonable and optimally correlated meteorological factors, and use them as early warning indicators. Through analysis, the key meteorological index with the optimal correlation is the maximum wind speed, which is also the early warning level index for typhoon impacts on mangroves.

[0067] In a specific example, the linear model is y = ax + b, where: y is the dependent variable, that is, the NDVI change rate; x is the independent variable, that is, the meteorological element value on the typhoon occurrence day; a and b are model parameters. The meteorological elements most relevant to the typhoon occurrence day are screened through this linear model, and the screened meteorological elements are used as key meteorological indicators.

[0068] After obtaining the meteorological data of the key meteorological indicators in the target area, judge whether to issue a typhoon disaster early warning according to the early warning assessment criteria of the key meteorological indicators. The early warning assessment criteria of the key meteorological indicators can be seen in Table 1. Table 1 is the basis for dividing typhoon disaster losses in mangroves in Guangxi Zhuang Autonomous Region, including the assessment criteria for the net change rate of NDVI and the early warning assessment criteria for key meteorological indicators. If the key meteorological indicator is the maximum wind speed, then determine the typhoon impact early warning level according to the daily maximum wind speed in the target area and the early warning assessment criteria shown in Table 1. The typhoon impact early warning level is divided into three levels: mild, moderate, and severe. When the daily maximum wind speed in the target area is within the first early warning level range, the typhoon impact early warning level is mild; when the daily maximum wind speed in the target area is within the second early warning level range, the typhoon impact early warning level is moderate; when the daily maximum wind speed in the target area is within the third early warning level range, the typhoon impact early warning level is severe. When the target area is Guangxi Zhuang Autonomous Region, the first early warning level range is (18, 33], the second early warning level range is (33, 48], and the third early warning level range is (48, +∞).

[0069] Table 1 Basis for Dividing Typhoon Disaster Losses in Mangroves in Guangxi Zhuang Autonomous Region

[0070] Typhoon disaster damage level NDVI net change rate Early warning index (daily maximum wind speed m / s) Mild [-15％，0) (18，33] Moderate [-30％，-15％) (33，48] Severe (-∞，-30％) (48，+∞)

[0071] In another exemplary embodiment of the present application, step 203 above may include the following steps 401 to 402.

[0072] Step 401: Preprocess the remote sensing data of the target area to obtain the NDVI data of the mangrove area in the target area. Preprocess the MODIS NDVI data, extract the NDVI dataset and calculate the NDVI value of the mangrove area, as Figure 4 shown.

[0073] Preprocessing: Obtain surface reflectance data (such as MOD09) or vegetation index products (such as MOD13); perform geometric correction (mosaicking / reprojection) on the surface reflectance data or vegetation index products to obtain geometrically corrected data; perform radiometric correction (apply scale factors and offsets) on the geometrically corrected data to obtain radiometrically corrected data; use quality assessment data (QA) to mask the radiometrically corrected data; obtain a mask; when using surface reflectance data (such as MOD09), calculate NDVI based on the mask; crop the mask according to the calculated NDVI to obtain remote sensing data of the target area.

[0074] Step 402: Calculate the change rate of NDVI in the mangrove area of the target area after a typhoon event based on the NDVI data of the mangrove area in the target area. The calculation formula for the change rate of NDVI in the mangrove area after a typhoon event in the target area is as follows:

[0075]

[0076] Where, ΔNDVI is the change rate of NDVI in the mangrove area after a typhoon event in the target area, NDVI before is the NDVI value of the mangrove area before the typhoon event, NDVI before is the NDVI value of the mangrove area after the typhoon event.

[0077] Calculate the net change rate of NDVI. The net change rate of NDVI represents the difference in the change rate of the vegetation index between the year of typhoon occurrence and the same period of normal years in the mangrove area. The calculation formula for the net change rate of NDVI in the mangrove area is as follows:

[0078] NDVI 净变化率 = ΔNDVI 台风年 - NDVI 正常年 (2);

[0079] Where, NDVI 净变化率 is the net change rate of NDVI in the mangrove area, ΔNDVI 台风年 is the change rate of NDVI in the mangrove area after a typhoon event in the target area, NDVI 正常年 is the change rate of NDVI in the same period of normal years in the target area.

[0080] The schematic diagram of the dynamic change of the NDVI change rate of mangroves at each site from 2000 to 2020 is as Figure 5 shown, and the schematic diagram of the dynamic change of the net change rate of NDVI of mangroves at each site from 2000 to 2020 is as Figure 6 shown, Figure 6The black solid line represents the net change rate of NDVI, and the blue dashed line represents the occurrence of typhoons. The change map of mangrove NDVI at each station caused by typhoons from 2000 to 2020 and the distribution histogram of the change of mangrove NDVI at each station caused by typhoons from 2000 to 2020 are respectively as Figure 7 and Figure 8 shown.

[0081] The typhoon disaster loss levels are divided according to the distribution histogram of the net change rate of NDVI; according to the identification results of the mangrove disaster loss levels, the evaluation criteria for the net change rate of NDVI are divided, as shown in Table 1.

[0082] The evaluation criteria for the net change rate of NDVI are divided into three levels: mild, moderate, and severe. When the net change rate of NDVI in the mangrove area of the target area is within the range of the first disaster loss level, the typhoon disaster loss level of the mangrove area in the target area is mild. When the net change rate of NDVI in the mangrove area of the target area is within the range of the second disaster loss level, the typhoon disaster loss level of the mangrove area in the target area is moderate. When the net change rate of NDVI in the mangrove area of the target area is within the range of the third disaster loss level, the typhoon disaster loss level of the mangrove area in the target area is severe. When the target area is the Guangxi Zhuang Autonomous Region, the range of the first disaster loss level is [-15%, 0), the range of the second disaster loss level is [-30%, -15%), and the range of the third disaster loss level is (-∞, -30%).

[0083] This application also provides an application scenario, which applies the above-mentioned mangrove typhoon impact prediction method. Specifically: The mangrove typhoon impact prediction method provided in this embodiment can be applied in the mangrove typhoon impact prediction scenario. The mangrove typhoon impact prediction scenario includes a data collection link, a mangrove typhoon impact prediction link, and a result release link; the mangrove typhoon data to be processed enters the mangrove typhoon impact prediction link from the data collection link, and through a human-machine collaborative method, the corresponding typhoon impact warning level and typhoon disaster loss level are obtained and enter the downstream result release link. The mangrove typhoon impact prediction method provided in this embodiment belongs to the mangrove typhoon impact prediction link. Specifically, in the process of the mangrove typhoon impact prediction link for the mangrove typhoon data to be processed, it can be judged whether to issue a typhoon disaster warning for the target area according to the meteorological data and the warning evaluation criteria of key meteorological indicators, and determine the typhoon impact warning level. Calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area, calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and the same period of the normal year, and determine the typhoon disaster loss level of the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria for the net change rate of NDVI.

[0084] Based on the same inventive concept, an embodiment of the present application further provides a mangrove typhoon impact prediction device for implementing the mangrove typhoon impact prediction method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the mangrove typhoon impact prediction device provided below can refer to the limitations on the mangrove typhoon impact prediction method in the above text, and will not be repeated here.

[0085] In an exemplary embodiment, as Figure 9 shown, a mangrove typhoon impact prediction device is provided, which includes the following modules:

[0086] A target area data acquisition module T1, configured to acquire meteorological data of key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area;

[0087] A typhoon impact early warning module T2, configured to determine whether to issue a typhoon disaster early warning for the target area according to the meteorological data and the early warning assessment criteria of the key meteorological indicators, and determine the typhoon impact early warning level of the target area according to the meteorological data and the early warning assessment criteria of the key meteorological indicators;

[0088] A mangrove area NDVI change rate calculation module T3 after a typhoon event, configured to calculate the change rate of NDVI in the mangrove area after a typhoon event in the target area according to the remote sensing data of the target area;

[0089] A mangrove area NDVI net change rate calculation module T4, configured to calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after a typhoon event in the target area and the change rate of NDVI in the same period of normal years;

[0090] A typhoon disaster loss assessment module T5, configured to determine the typhoon disaster loss level in the mangrove area according to the net change rate of NDVI in the mangrove area and the NDVI net change rate assessment criteria.

[0091] In an exemplary embodiment, a computer device is provided. This computer device can be a server or a terminal, and its internal structure diagram can be as Figure 10As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store mangrove typhoon impact prediction data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a mangrove typhoon impact prediction method.

[0092] Those skilled in the art can understand that Figure 10 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0093] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0094] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0095] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0096] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0097] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0099] Specific examples are used in this article to elaborate on the principles and implementation methods of this application. The descriptions of the above embodiments are only used to help understand the methods and core ideas of this application; at the same time, for those of ordinary skill in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for predicting the impact of typhoons on mangroves, characterized in that, The above-mentioned mangrove typhoon impact prediction method includes: Obtain the meteorological data and remote sensing data of the key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area; Judge whether to issue a typhoon disaster warning for the target area according to the meteorological data and the early warning evaluation criteria of the key meteorological indicators, and determine the typhoon impact warning level of the target area according to the meteorological data and the early warning evaluation criteria of the key meteorological indicators; Calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area; Calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after the typhoon event in the target area and the change rate of NDVI in the same period of normal years; Determine the typhoon disaster loss level in the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI; 2. The mangrove typhoon impact prediction method according to claim 1, wherein Before obtaining the meteorological data of the key meteorological indicators in the target area, the above-mentioned mangrove typhoon impact prediction method further includes: Construct a linear model between the change rate of NDVI in the mangrove area and the meteorological elements on the typhoon occurrence date according to the historical typhoon event data in the target area; Screen the key meteorological indicators according to the linear model between the change rate of NDVI in the mangrove area and the meteorological elements on the typhoon occurrence date after the typhoon event; 3. The mangrove typhoon impact prediction method according to claim 2, wherein The meteorological elements include average temperature, maximum temperature, minimum temperature, average wind speed, maximum wind speed and rainfall; the key meteorological indicator is the maximum wind speed; 4. The mangrove typhoon impact prediction method according to claim 2, wherein, Before constructing a linear model between the change rate of NDVI in the mangrove area and the meteorological elements on the typhoon occurrence date according to the historical typhoon event data in the target area, the above-mentioned mangrove typhoon impact prediction method further includes: Obtain the historical meteorological data of the target area; Screen the historical typhoon event data according to the historical meteorological data of the target area based on the typhoon identification conditions; the typhoon identification conditions include that the maximum daily precipitation is not less than the set precipitation or the strong wind identification conditions are met; the strong wind identification conditions are that the average wind speed on land is not less than the first wind speed or the gust wind speed is not less than the second wind speed; 5. The mangrove typhoon impact prediction method according to claim 1, characterized in that Calculating the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the remote sensing data of the target area specifically includes: Preprocess the remote sensing data of the target area to obtain the NDVI data of the mangrove area in the target area; Calculate the change rate of NDVI in the mangrove area after the typhoon event in the target area according to the NDVI data of the mangrove area in the target area; 6. The mangrove typhoon impact prediction method according to claim 1, wherein The calculation formula for the change rate of NDVI in the mangrove area after the typhoon event in the target area is as follows: Among them, ΔNDVI is the change rate of NDVI in the mangrove area after the typhoon event in the target area, and NDVI before is the NDVI value of the mangrove area before the typhoon event, and NDVI before is the NDVI value of the mangrove area after the typhoon event.

7. The mangrove typhoon impact prediction method according to claim 1, characterized in that, The calculation formula for the net change rate of NDVI in the mangrove area is as follows: NDVI 净变化率 = ΔNDVI 台风年 - NDVI 正常年 ; Among them, NDVI 净变化率 is the net change rate of NDVI in the mangrove area, and ΔNDVI 台风年 is the change rate of NDVI in the mangrove area after typhoon events occurred in the target area. NDVI 正常年 is the change rate of NDVI in the same period of normal years in the target area.

8. A mangrove typhoon impact prediction device for implementing the mangrove typhoon impact prediction method according to any one of claims 1-7, characterized in that, The above-mentioned mangrove typhoon impact prediction device includes: A target area data acquisition module for acquiring the meteorological data of the key meteorological indicators in the target area; the key meteorological indicators are meteorological indicators related to the change rate of NDVI in the mangrove area; A typhoon impact warning module, which is used to determine whether to issue a typhoon disaster warning for a target area according to the warning evaluation criteria of the meteorological data and key meteorological indicators, and determine the typhoon impact warning level of the target area according to the warning evaluation criteria of the meteorological data and key meteorological indicators; A calculation module for the change rate of NDVI in the mangrove area after a typhoon event, which is used to calculate the change rate of NDVI in the mangrove area of the target area after a typhoon event according to the remote sensing data of the target area; A calculation module for the net change rate of NDVI in the mangrove area, which is used to calculate the net change rate of NDVI in the mangrove area according to the change rate of NDVI in the mangrove area after a typhoon event in the target area and the change rate of NDVI in the same period of normal years; A typhoon disaster loss assessment module, which is used to determine the typhoon disaster loss level of the mangrove area according to the net change rate of NDVI in the mangrove area and the evaluation criteria of the net change rate of NDVI; 9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the mangrove typhoon impact prediction method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the mangrove typhoon impact prediction method according to any one of claims 1-7.

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