Vegetation index extraction method and system based on historical remote sensing images and storage medium

Abstract

The application discloses a vegetation index extraction method and system based on historical remote sensing images and a storage medium, and can be widely applied to the technical field of ecological environment data processing. After a historical event database including historical event data, event time information, event location information and event type information is constructed, the event time information is used and a first remote sensing image is called through a preset interface, so that the efficiency and accuracy of the retrieval and download of remote sensing data can be effectively improved. Then, the first remote sensing image is pretreated according to the event location information and the event type information to obtain a second remote sensing image, and the vegetation index of a target region is calculated according to the second remote sensing image, so that the accuracy of the calculation result of the vegetation index of the target region can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of ecological and environmental data processing technology, and in particular to a method, system and storage medium for extracting vegetation indices based on historical remote sensing images. Background Technology

[0002] In related technologies, long-term remote sensing data plays a crucial role in disaster risk assessment, ecological environment monitoring, and land use change monitoring. However, current remote sensing data acquisition processes require manual retrieval and downloading, which is inefficient, time-consuming, labor-intensive, and lacks accuracy. Furthermore, existing technologies struggle to match historical events with remote sensing imagery, resulting in low accuracy in vegetation index analysis.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a method, system, and storage medium for extracting vegetation indices based on historical remote sensing images, which can improve the efficiency and accuracy of remote sensing data retrieval and download, and improve the accuracy of vegetation index calculation.

[0005] To achieve the above objectives, one aspect of this application proposes a method for extracting vegetation indices based on historical remote sensing imagery, the method comprising the following steps:

[0006] Construct a historical event database, which includes historical event data, event time information, event location information, and event type information;

[0007] Based on the event time information, the first remote sensing image is invoked through a preset interface;

[0008] The first remote sensing image is preprocessed according to the event location information and the event type information to obtain the second remote sensing image;

[0009] The vegetation index of the target area is calculated based on the second remote sensing image.

[0010] In some embodiments, constructing the historical event database includes:

[0011] Acquire several historical event data points, as well as the event time information and event space information corresponding to the historical event data;

[0012] The historical event data, the event time information, and the event spatial information are saved to the historical event database. The data in the historical event database is saved through a spatiotemporal index structure and stored in a preset file format.

[0013] In some embodiments, the step of calling the first remote sensing image through a preset interface based on the event time information includes:

[0014] The image acquisition interval is determined based on the event time information;

[0015] Based on the image acquisition range, the third remote sensing image is invoked through the preset interface;

[0016] A weighted score is calculated based on the event time information, cloud coverage, and spatial resolution.

[0017] The third remote sensing image is filtered based on the weighted score to obtain the first remote sensing image.

[0018] In some embodiments, preprocessing the first remote sensing image based on the event location information and the event type information to obtain a second remote sensing image includes:

[0019] The first remote sensing image corresponding to the event location information is used as the fourth remote sensing image;

[0020] The fourth remote sensing image is cropped based on the event type information to obtain the second remote sensing image.

[0021] In some embodiments, cropping the fourth remote sensing image according to the event type information to obtain the second remote sensing image includes:

[0022] When the event type information is a point feature, the fourth remote sensing image is cropped using the buffer cropping method to obtain the second remote sensing image;

[0023] When the event type information is a line feature, the fourth remote sensing image is cropped using the bounding box cropping method to obtain the second remote sensing image;

[0024] When the event type information is a surface feature, the fourth remote sensing image is cropped using a mask cropping method to obtain the second remote sensing image.

[0025] In some embodiments, calculating the vegetation index of the target area based on the second remote sensing image includes:

[0026] Determine the target area corresponding to the second remote sensing image;

[0027] Calculate the normalized differential vegetation index and enhanced vegetation index of the target area based on the second remote sensing image;

[0028] The calculation parameters for vegetation cover are adjusted based on the distribution characteristics of the normalized differential vegetation index and the enhanced vegetation index in the target area.

[0029] The vegetation coverage of the target area is calculated based on the adjusted calculation parameters and the second remote sensing image.

[0030] Calculate a statistical representative value of the vegetation index of the target area based on the event type information and the second remote sensing image.

[0031] In some embodiments, acquiring a plurality of historical event data includes:

[0032] A number of historical event data are read through an adaptive retry mechanism, and the reading process data is recorded in a log file.

[0033] To achieve the above objectives, another aspect of this application proposes a vegetation index extraction system based on historical remote sensing imagery, the system comprising:

[0034] The first module is used to construct a historical event database, which includes historical event data, event time information, event location information, and event type information.

[0035] The second module is used to call the first remote sensing image through a preset interface based on the event time information;

[0036] The third module is used to preprocess the first remote sensing image based on the event location information and the event type information to obtain the second remote sensing image.

[0037] The fourth module is used to calculate the vegetation index of the target area based on the second remote sensing image.

[0038] To achieve the above objectives, another aspect of this application proposes a vegetation index extraction system based on historical remote sensing imagery, comprising:

[0039] At least one processor;

[0040] At least one memory for storing at least one program;

[0041] When the at least one program is executed by the at least one processor, the at least one processor performs the method described above.

[0042] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0043] The embodiments of this application include at least the following beneficial effects: This application provides a method, system, and storage medium for extracting vegetation indices based on historical remote sensing images. This scheme constructs a historical event database including historical event data, event time information, event location information, and event type information. Based on the event time information, it calls a first remote sensing image through a preset interface, thereby effectively improving the efficiency and accuracy of remote sensing data retrieval and download. Then, it preprocesses the first remote sensing image according to the event location information and event type information to obtain a second remote sensing image. Based on the second remote sensing image, it calculates the vegetation index of the target area, thereby effectively improving the accuracy of the vegetation index calculation results of the target area. Attached Figure Description

[0044] Figure 1 This is a flowchart of the vegetation index extraction method based on historical remote sensing images provided in the embodiments of this application;

[0045] Figure 2 This is a schematic diagram of the application architecture of the vegetation index extraction method based on historical remote sensing images provided in the embodiments of this application;

[0046] Figure 3 This is a complete flowchart of the vegetation index extraction method based on historical remote sensing images provided in the embodiments of this application;

[0047] Figure 4 This is a schematic diagram of the vegetation index extraction system based on historical remote sensing images provided in an embodiment of this application;

[0048] Figure 5 This is a schematic diagram of the hardware structure of the vegetation index extraction system based on historical remote sensing images provided in the embodiments of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application.

[0050] Before providing a detailed description of the embodiments of this application, some of the nouns and terms used in the embodiments of this application will be explained first. The nouns and terms used in the embodiments of this application shall be interpreted as follows:

[0051] Vegetation indices are formed by combining visible and near-infrared satellite wavelengths based on the spectral characteristics of vegetation. They are simple, effective, and empirical measures of the condition of land surface vegetation.

[0052] The Enhanced Vegetation Index (EVI) algorithm is a key algorithm in biophysical parameter products within remote sensing thematic data. It can simultaneously reduce the impact of atmospheric and soil noise, stably reflecting the vegetation status of the measured area. Narrower infrared and near-infrared detection band settings not only improve the ability to detect sparse vegetation but also reduce the influence of water vapor. Furthermore, the introduction of the blue light band corrects for atmospheric aerosol scattering and soil background.

[0053] The Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared light (strong reflection from vegetation) and red light (absorption by vegetation). NDVI always ranges from -1 to +1. A negative NDVI indicates that the detected area is likely to be water; a NDVI close to 1 indicates that the detected area is likely to be densely covered with green leaves.

[0054] Vegetation cover (FVC) refers to the percentage of the total area of ​​a statistical area whose vertical projection of vegetation (including leaves, stems, and branches) is on the ground. Vegetation cover measurement can be divided into two methods: ground measurement and remote sensing estimation. Ground measurement is commonly used at the field scale, while remote sensing estimation is commonly used at the regional scale.

[0055] Spatial resolution refers to the size of the ground area represented by a pixel, that is, the instantaneous field of view of a scanner, or the smallest unit that a ground object can be distinguished. Spatial resolution refers to the minimum distance between two adjacent ground objects that can be identified on a remote sensing image. For photographic images, it is usually expressed as the number of resolvable black-and-white "line pairs" per unit length (line pairs / mm); for scanned images, it is usually expressed as the instantaneous field of view angle. Spatial resolution is one of the important indicators for evaluating sensor performance and remote sensing information, and it is also an important basis for identifying the shape and size of ground objects. Intuitively, spatial resolution is the critical geometric size of an object that can be identified by the instrument.

[0056] Cloud Cover Percentage (CCP) refers to the percentage of cloud cover area in a remote sensing image, and is a key indicator for quantifying the degree of cloud obstruction. It is calculated by dividing the number of pixels identified as clouds in the image by the total number of pixels, and then multiplying by 100%. The presence of clouds obscures ground feature information, affecting data quality and applications. Cloud cover percentage is an important parameter for evaluating image usability, and different applications have different requirements for cloud cover percentage. High-precision ground feature information extraction requires low cloud cover percentage, while macroscopic monitoring can tolerate higher cloud cover percentages. The accuracy of cloud cover percentage is affected by cloud detection algorithms; different algorithms identify clouds differently, leading to different calculation results. When using cloud cover percentage data, it is necessary to understand the cloud detection algorithm used and its potential uncertainties.

[0057] In related technologies, long-term remote sensing imagery data plays a crucial role in processes such as disaster risk assessment, ecological environment monitoring, and land use change. However, current remote sensing image acquisition processes require manual retrieval and downloading, which is inefficient, time-consuming, labor-intensive, and lacks accuracy. Furthermore, existing technologies struggle to match historical events with remote sensing images, resulting in low accuracy in vegetation index analysis.

[0058] In view of this, this application provides a method, system, and storage medium for extracting vegetation indices based on historical remote sensing images. This application constructs a historical event database including historical event data, event time information, event location information, and event type information. Based on the event time information, it calls a first remote sensing image through a preset interface, thereby effectively improving the efficiency and accuracy of remote sensing data retrieval and download. Then, it preprocesses the first remote sensing image according to the event location information and event type information to obtain a second remote sensing image. Based on the second remote sensing image, it calculates the vegetation index of the target area, thereby effectively improving the accuracy of the vegetation index calculation results for the target area.

[0059] The vegetation index extraction method based on historical remote sensing imagery provided in this application relates to the field of ecological environment data processing technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vegetation index extraction method based on historical remote sensing imagery, but is not limited to the above forms.

[0060] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0061] Figure 1 This is an optional flowchart of the vegetation index extraction method based on historical remote sensing imagery provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S106.

[0062] Step S110: Construct a historical event database, wherein the historical event database includes historical event data, event time information, event location information, and event type information;

[0063] Step S120: Based on the event time information, call the first remote sensing image through a preset interface;

[0064] Step S130: Preprocess the first remote sensing image according to the event location information and event type information to obtain the second remote sensing image;

[0065] Step S140: Calculate the vegetation index of the target area based on the second remote sensing image.

[0066] In this embodiment, the process of constructing a historical event database can involve first acquiring several historical event data points, along with their corresponding event time and spatial information, and then saving the historical event data, event time information, and event spatial information into the historical event database. Specifically, historical events can include, but are not limited to, natural disaster events and mining events. The data in the historical event database can be stored using a spatiotemporal index structure, such as an R-tree or quadtree, to organize the subordinate event data, thereby optimizing the retrieval efficiency of the database and achieving a time complexity of O(log n). This embodiment integrates the time and spatial dimensions into the same index structure, enabling the retrieval process to efficiently perform range queries and nearest neighbor searches, laying the foundation for subsequent image matching and analysis. It is understood that the data in the historical event database can be categorized and stored according to event type, such as storing data based on geometric types like points, lines, and surfaces within the event type. For example, if a natural disaster occurs in a region, the geometric type of this natural disaster event in the vegetation monitoring process is a point; if a road needs to be built in a region, the road construction event in the vegetation monitoring process is a line; if a mining operation needs to be carried out in a region, the mining event in the vegetation monitoring process is an area. In this embodiment, after determining the event type, the data can be saved in a database using a standardized CSV format to ensure data portability and compatibility.

[0067] In this embodiment, when retrieving remote sensing images, the image acquisition interval can be determined based on event time information, and the remote sensing image can be retrieved as a third remote sensing image through a preset interface. The preset interface is the Machin-to-Machin API interface of the USGSEarth Explorer. Within this image acquisition interval, the search range is iteratively expanded by dynamically adjusting the time window, thereby maximizing data availability while ensuring data temporal relevance. During the acquisition process, the time complexity is O(w), where w represents the maximum window size, demonstrating excellent efficiency and adaptability in practical applications.

[0068] Then, factors such as cloud cover and temporal correlation are comprehensively considered to filter third-party remote sensing images, thereby achieving a multi-parameter joint optimization image selection strategy. This can be understood as first selecting images based on event time information (T...). i), cloud coverage (C i ) and spatial resolution (S i The weighted score is calculated using the following formula:

[0069] Score = w1 * (1 - C i )+w2*T i +w3*S i ;

[0070] In the formula, w1, w2, and w3 represent weighting coefficients.

[0071] By adjusting these weights, the importance of different parameters can be flexibly balanced, enabling the selection of the optimal remote sensing image as the first remote sensing image.

[0072] In this embodiment, the first remote sensing image can be downloaded using a multi-threaded parallel download mechanism to improve data acquisition efficiency. Specifically, the remote sensing image can be read and georeferenced using the raster io library to improve spatial accuracy. The raster io library provides efficient raster data processing capabilities. By preserving the georeferenced information (transform and CRS) of the original image, it ensures the accuracy of spatial registration, guarantees sub-pixel level precision, and supports complex spatial analysis operations.

[0073] Understandably, after matching the remote sensing image (fourth remote sensing image) corresponding to the event location information, the fourth remote sensing image is cropped according to the event type information to obtain the second remote sensing image. Specifically, when the event type information is a point feature, the fourth remote sensing image is cropped using a buffer clipping method to obtain the second remote sensing image; when the event type information is a line feature, the fourth remote sensing image is cropped using a bounding box clipping method to obtain the second remote sensing image; and when the event type information is a polygon feature, the fourth remote sensing image is cropped using a mask clipping method to obtain the second remote sensing image. This embodiment, by performing adaptive clipping based on the event geometry type information, can effectively improve the flexibility and efficiency of data processing.

[0074] In this embodiment, the parallel calculation of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Free Vegetation Cover (FVC) can be achieved using Python's `concurrent.futures` module. This approach leverages the advantages of multi-core processing, effectively improving computational efficiency. Understandably, this embodiment first determines the target area corresponding to the second remote sensing image, then calculates the NDVI and EVC for the target area based on the second remote sensing image. Next, it analyzes the distribution characteristics of the NDVI and EVC in the target area, dynamically adjusts the calculation parameters for FVC, and finally calculates the FVC for the target area based on the adjusted parameters and the second remote sensing image. Specifically, this embodiment uses percentiles to determine the maximum and minimum values ​​of the EVC, effectively avoiding the influence of outliers and thus improving the accuracy and adaptability of FVC calculation.

[0075] It is understood that this embodiment can also calculate representative values ​​of vegetation indices for the target area based on event type information. Specifically, for event type information that is a linear or isal feature, this embodiment can calculate a series of statistical indicators such as the mean, median, standard deviation, maximum, and minimum values ​​of several vegetation indices in the corresponding target area as statistical representative values. This allows for a comprehensive description of the overall vegetation condition through these data, thereby supporting more in-depth spatial analysis and comparative studies.

[0076] In this application embodiment, the method can be applied to, for example... Figure 2 In the interactive architecture shown, [the following is included] Figure 2 The interaction architecture includes a user layer, a data layer, an application layer, and a service layer. The method described in this application embodiment can be applied to... Figure 2 In the application layer, by executing the method of this application, the corresponding data can be stored in the remote sensing image database, historical event database, and vegetation index result database of the data layer, respectively. A visualization interface is set up in the user layer to intuitively display the index change trend. This embodiment, through its layered design, allows for customized processing logic for different levels of anomalies, and enables saving and resuming operations when problems occur, improving overall robustness and reliability.

[0077] Understandably, in Figure 2 In the architecture shown, an adaptive retry mechanism can also be used to read several historical event data points. This effectively ensures the reliability of data acquisition in the event of temporary errors such as network fluctuations, while also avoiding excessive requests to external services. Furthermore, this embodiment records the reading process data in a log file, which facilitates subsequent problem diagnosis. Specifically, Figure 2The architecture shown adopts a parallel computing architecture and reserves a distributed computing interface, thereby enabling seamless migration to a cloud computing platform.

[0078] In the embodiments of this application, such as Figure 3 As shown, the complete process of the method provided in this application embodiment includes, but is not limited to, the following steps:

[0079] Step 1: Read historical event data from an external data source;

[0080] Step 2: Determine if there is any unprocessed historical event data, such as data that was acquired but not processed due to network interruption; if so, proceed to Step 4; otherwise, proceed to Step 3.

[0081] Step 3: Generate the corresponding historical event data processing report;

[0082] Step 4: Obtain event time and event location information;

[0083] Step 5: Calculate the adaptive time window based on the event time information;

[0084] Step 6: Call up the remote sensing image API and locate the remote sensing image based on the event location information;

[0085] Step 7: Determine if the remote sensing image API has found a suitable remote sensing image. If not, expand the time window and return to step 5; otherwise, proceed to step 8.

[0086] Step 8: Download suitable remote sensing imagery;

[0087] Step 9: Preprocess suitable remote sensing images;

[0088] Step 10: Calculate the vegetation index of the target area based on the preprocessed remote sensing image, save the corresponding calculation results, and return to step 2.

[0089] It is understood that the method provided in this embodiment can be applied to disaster impact assessment to analyze the short-term and long-term impacts of natural disasters on vegetation; it can be applied to ecological restoration processes to accurately assess the vegetation restoration effect in areas such as mines and contaminated sites by utilizing the time series changes of NDVI and EVI; it can be applied to urban expansion studies to analyze the impact of urbanization on surrounding vegetation cover; it can also be applied to the long-term impact of climate change on regional vegetation growth; and it can also be applied to agricultural production monitoring to assess crop growth status and yield prediction.

[0090] In summary, the method of this application embodiment achieves efficient and accurate extraction of vegetation index data such as NDVI, EVI, and FVC before and after the corresponding time of historical event point, line, and area elements, and improves the accuracy of vegetation index calculation and data processing efficiency, providing strong technical support for ecological environment monitoring and assessment.

[0091] Reference Figure 4 This application provides a vegetation index extraction system based on historical remote sensing imagery, the system comprising:

[0092] The first module 410 is used to construct a historical event database, which includes historical event data, event time information, event location information, and event type information.

[0093] The second module 420 is used to call the first remote sensing image through a preset interface based on event time information;

[0094] The third module 430 is used to preprocess the first remote sensing image based on the event location information and event type information to obtain the second remote sensing image;

[0095] The fourth module 440 is used to calculate the vegetation index of the target area based on the second remote sensing image.

[0096] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0097] This application also provides a vegetation index extraction system based on historical remote sensing imagery. The electronic system includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described vegetation index extraction method based on historical remote sensing imagery. This system can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0098] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0099] Please see Figure 5 , Figure 5 The hardware structure of a vegetation index extraction system based on historical remote sensing imagery, according to another embodiment, is illustrated. The system includes:

[0100] The processor 510 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0101] The memory 520 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 520 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 520 and is called and executed by the processor 510 to execute the vegetation index extraction method based on historical remote sensing images of this application embodiment.

[0102] The input / output interface 530 is used to implement information input and output;

[0103] The communication interface 540 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0104] Bus 550 transmits information between various components of the device (e.g., processor 510, memory 520, input / output interface 530, and communication interface 540);

[0105] The processor 510, memory 520, input / output interface 530 and communication interface 540 are connected to each other within the device via bus 550.

[0106] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described vegetation index extraction method based on historical remote sensing images.

[0107] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0108] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0109] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0110] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0111] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0113] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0114] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0115] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0116] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part 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 multiple 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 of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for extracting a vegetation index based on historical remote sensing images, characterized in that, The method comprises the following steps: constructing a historical event database, the historical event database comprising historical event data, event time information, event location information and event type information; based on the event time information, calling a first remote sensing image through a preset interface; preprocessing the first remote sensing image according to the event location information and the event type information to obtain a second remote sensing image; calculating a vegetation index of a target region according to the second remote sensing image; wherein, based on the event time information, calling a first remote sensing image through a preset interface, comprising: determining an image acquisition interval according to the event time information; calling a third remote sensing image through the preset interface according to the image acquisition interval; calculating a weighted score value according to the event time information, cloud coverage and spatial resolution; screening the third remote sensing image according to the weighted score value to obtain the first remote sensing image.

2. The method of claim 1, wherein, The construction of the historical event database comprises: obtaining a plurality of historical event data, and event time information and event location information corresponding to the historical event data; saving the historical event data, the event time information and the event location information into the historical event database, and saving the data in the historical event database through a space-time index structure and storing in a preset file format.

3. The method of claim 1, wherein, The preprocessing of the first remote sensing image according to the event location information and the event type information to obtain a second remote sensing image comprises: matching a first remote sensing image corresponding to the event location information as a fourth remote sensing image; cropping the fourth remote sensing image according to the event type information to obtain the second remote sensing image.

4. The method of claim 3, wherein, The cropping of the fourth remote sensing image according to the event type information to obtain the second remote sensing image comprises: when the event type information is a point element, cropping the fourth remote sensing image through a buffer zone cropping method to obtain the second remote sensing image; when the event type information is a line element, cropping the fourth remote sensing image through a bounding box cropping method to obtain the second remote sensing image; when the event type information is a surface element, cropping the fourth remote sensing image through a mask cropping method to obtain the second remote sensing image.

5. The method of claim 1, wherein, The calculation of a vegetation index of a target region according to the second remote sensing image comprises: determining a target region corresponding to the second remote sensing image; calculating a normalized difference vegetation index and an enhanced vegetation index of the target region according to the second remote sensing image; adjusting a calculation parameter of vegetation coverage according to the distribution characteristics of the normalized difference vegetation index and the enhanced vegetation index on the target region; calculating the vegetation coverage of the target region according to the adjusted calculation parameter and the second remote sensing image; calculating a statistical representative value of the vegetation index of the target region according to the event type information and the second remote sensing image.

6. The method of claim 2, wherein, The obtaining of a plurality of historical event data comprises: reading a plurality of historical event data through an adaptive retry mechanism, and recording reading process data through a log file.

7. A vegetation index extraction system based on historical remote sensing imagery, characterized in that, The system comprises: The first module is configured to construct a historical event database, wherein the historical event database comprises historical event data, event time information, event location information, and event type information. The second module is configured to call a first remote sensing image through a preset interface based on the event time information. The third module is configured to preprocess the first remote sensing image according to the event location information and the event type information to obtain a second remote sensing image. The fourth module is configured to calculate a vegetation index of a target region according to the second remote sensing image. The calling of the first remote sensing image through the preset interface based on the event time information comprises: determining an image acquisition interval according to the event time information; calling a third remote sensing image through the preset interface according to the image acquisition interval; calculating a weighted score value according to the event time information, a cloud coverage rate, and a spatial resolution; screening the third remote sensing image according to the weighted score value to obtain the first remote sensing image.

8. A vegetation index extraction system based on historical remote sensing imagery, characterized in that, The device comprises: at least one processor; at least one memory configured to store at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the method according to any one of claims 1 to 6.

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