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

Abstract

The invention 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. According to the method, after the historical event database comprising the historical event data, the event time information, the event position information and the event type information is constructed, the first remote sensing image is called through the preset interface based on the event time information, so that the retrieval and downloading efficiency and accuracy of the remote sensing data can be effectively improved; then the first remote sensing image is preprocessed according to the event position information and the event type information to obtain a second remote sensing image, and the vegetation index of the target area is calculated according to the second remote sensing image, so that the accuracy of the vegetation index calculation result of the target area can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of ecological environment data processing, and in particular to a vegetation index extraction method and system based on historical remote sensing images and a storage medium. Background Art

[0002] In the related technologies, long-term remote sensing data plays an important role in disaster risk assessment, ecological environment monitoring, and land use change monitoring. However, the current remote sensing data acquisition process requires manual retrieval and downloading, which is inefficient, time-consuming, labor-intensive, and inaccurate. In addition, it is difficult to match historical events with remote sensing images in the existing technology, resulting in low accuracy of 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 purpose of the embodiments of the present application is to propose a vegetation index extraction method, system and storage medium based on historical remote sensing images, which can improve the efficiency and accuracy of retrieval and download of remote sensing data and improve the accuracy of vegetation index calculation.

[0005] To achieve the above object, one aspect of an embodiment of the present application proposes a vegetation index extraction method based on historical remote sensing images, the method comprising the following steps:

[0006] Constructing a historical event database, wherein the historical event database includes historical event data, event time information, event location information, and event type information;

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

[0008] Preprocessing the first remote sensing image according to the event location information and the event type information to obtain a 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 a historical event database includes:

[0011] Acquire a number of historical event data, as well as 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 space information are saved in 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, calling the first remote sensing image through a preset interface based on the event time information includes:

[0014] Determining an image acquisition interval according to the event time information;

[0015] According to the image acquisition interval, calling a third remote sensing image through the preset interface;

[0016] Calculating a weighted score value based on the event time information, cloud coverage and spatial resolution;

[0017] The third remote sensing image is screened according to the weighted score value to obtain the first remote sensing image.

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

[0019] matching a first remote sensing image corresponding to the event location information as a fourth remote sensing image;

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

[0021] In some embodiments, the step of 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, clipping the fourth remote sensing image by a buffer clipping method to obtain the second remote sensing image;

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

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

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

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

[0027] Calculating a normalized difference vegetation index and an enhanced vegetation index of the target area according to the second remote sensing image;

[0028] According to the distribution characteristics of the normalized difference vegetation index and the enhanced vegetation index in the target area, adjusting the calculation parameters of the vegetation coverage;

[0029] Calculating the vegetation coverage of the target area according to the adjusted calculation parameters and the second remote sensing image;

[0030] A statistical representative value of the vegetation index of the target area is calculated according to the event type information and the second remote sensing image.

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

[0032] Several historical event data are read through the adaptive retry mechanism, and the process data is read through the log file record.

[0033] To achieve the above purpose, another aspect of the embodiment of the present application proposes a vegetation index extraction system based on historical remote sensing images, the system comprising:

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

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

[0036] A third module is used to pre-process the first remote sensing image according to the event location information and the event type information to obtain a second remote sensing image;

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

[0038] To achieve the above purpose, another aspect of the embodiment of the present application proposes a vegetation index extraction system based on historical remote sensing images, including:

[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 implements the above method.

[0042] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0043] The embodiments of the present application include at least the following beneficial effects: the present application provides a vegetation index extraction method, system and storage medium based on historical remote sensing images. The scheme constructs a historical event database including historical event data, event time information, event location information and event type information, and then calls the first remote sensing image based on the event time information and through a preset interface, thereby effectively improving the efficiency and accuracy of retrieval and downloading of remote sensing data; then, the first remote sensing image is preprocessed according to the event location information and event type information to obtain a second remote sensing image, and the vegetation index of the target area is calculated according to the second remote sensing image, thereby effectively improving the accuracy of the vegetation index calculation result of the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a vegetation index extraction method based on historical remote sensing images provided in an embodiment of the present application;

[0045] Figure 2 It is a schematic diagram of the application architecture of the vegetation index extraction method based on historical remote sensing images provided in an embodiment of the present application;

[0046] Figure 3 It is a complete flow chart of the vegetation index extraction method based on historical remote sensing images provided in the embodiment of the present application;

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

[0048] Figure 5 It is a schematic diagram of the hardware structure of the vegetation index extraction system based on historical remote sensing images provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the 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 the embodiments of the present application, they are only examples of systems and methods consistent with some aspects of the embodiments of the present application.

[0050] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0051] Vegetation index: Based on the spectral characteristics of vegetation, various vegetation indices are formed by combining satellite visible light and near-infrared bands. Vegetation index is a simple, effective and empirical measure of the surface vegetation status.

[0052] The Enhanced Vegetation Index (EVI) algorithm is a major algorithm in the biophysical parameter products of remote sensing thematic data products. It can reduce the impact of atmospheric and soil noise at the same time and stably reflect the vegetation conditions in the measured area. The range of infrared and near-infrared detection bands is set narrower, which not only improves the ability to detect sparse vegetation, but also reduces the impact of water vapor. At the same time, the blue light band is introduced to correct the scattering of atmospheric aerosols and soil background.

[0053] The Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (strongly reflected by vegetation) and red light (absorbed by vegetation). NDVI is always between -1 and +1. When the NDVI is negative, it means that the detected area is likely to be water; when the NDVI is close to 1, it means that the detected area is likely to be dense green foliage.

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

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

[0056] Cloud Cover Percentage (CCP) refers to the percentage of cloud coverage area in a remote sensing image to the total area of ​​the entire image. It is a key indicator for quantifying the degree of cloud obstruction in an image. The calculation method is to divide the number of pixels identified as clouds in the image by the total number of pixels in the image, and then multiply by 100%. The presence of clouds will obstruct ground object information, affecting data quality and application. Cloud coverage is an important parameter for evaluating image availability, and different applications have different requirements for cloud coverage. High-precision ground object information extraction requires low cloud coverage, while macro monitoring can tolerate higher cloud coverage. The accuracy of cloud coverage is affected by the cloud detection algorithm. Different algorithms have different recognition of clouds, resulting in different calculation results. When using cloud coverage data, it is necessary to understand the cloud detection algorithm used and its potential uncertainties.

[0057] In the related technologies, long-term remote sensing image data plays an important role in disaster risk assessment, ecological environment monitoring, land use change and other processes. However, the existing remote sensing image acquisition process requires manual retrieval and downloading, which is inefficient, time-consuming, labor-intensive and inaccurate. In addition, it is difficult to match historical events with remote sensing images in the existing technology, resulting in low accuracy of vegetation index analysis.

[0058] In view of this, the embodiments of the present application provide a vegetation index extraction method, system and storage medium based on historical remote sensing images. After constructing a historical event database including historical event data, event time information, event location information and event type information, the present application calls the first remote sensing image based on the event time information and through a preset interface, thereby effectively improving the efficiency and accuracy of retrieval and downloading of remote sensing data; then, the first remote sensing image is preprocessed according to the event location information and event type information to obtain a second remote sensing image, and the vegetation index of the target area is calculated according to the second remote sensing image, thereby effectively improving the accuracy of the vegetation index calculation result of the target area.

[0059] The vegetation index extraction method based on historical remote sensing images provided in the embodiment of the present application relates to the technical field of ecological environment data processing. The vegetation index extraction method based on historical remote sensing images provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in a blockchain network; the software can be an application that implements a vegetation index extraction method based on historical remote sensing images, etc., but is not limited to the above forms.

[0060] The present application can be used in many general or special computer system environments or configurations. For example: 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, distributed computing environments including any of the above systems or devices, etc. The present 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. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0061] Figure 1 is an optional flow chart of the vegetation index extraction method based on historical remote sensing images provided in the embodiment of the present 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, calling the first remote sensing image through a preset interface;

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

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

[0066] In an embodiment of the present application, the process of constructing a historical event database may be to first obtain a number of historical event data and the event time information and event space information corresponding to the historical event data, and then save the historical event data, event time information and event space information into the historical event database. Specifically, historical events may include but are not limited to natural disaster events, mining events, etc. The data in the historical event database may be stored through a spatiotemporal index structure, such as using an R-tree or a quadtree to organize the subordinate event data, thereby optimizing the retrieval efficiency of the data in the database and achieving a time complexity of O(log n). This embodiment integrates the time dimension and the space dimension into the same index structure, so that the retrieval process can efficiently perform range queries and nearest neighbor searches, laying the foundation for subsequent image matching and analysis. It is understandable that the data in the historical event database can be classified and stored according to the event type, such as storing data according to geometric types such as points, lines, and surfaces in the event type. For example, if a natural disaster occurs in a certain area, the geometric type of the natural disaster event in the vegetation monitoring process belongs to a point; if a road needs to be built in a certain area, the geometric type of the road building event in the vegetation monitoring process belongs to a line; if mining is needed in a certain area, the geometric type of the mining event in the vegetation monitoring process belongs to a surface. In this embodiment, after determining the event type, the data can be saved in a standardized CSV format in the database to ensure data portability and compatibility.

[0067] In an embodiment of the present application, when calling a remote sensing image, the image acquisition interval can be determined based on the event time information, and the remote sensing image can be called as the third remote sensing image through a preset interface. Among them, the preset interface is the Machine-to-Machine API interface of USGSEarth Exp l orer. Within the image acquisition interval, by dynamically adjusting the time window and iteratively expanding the search range, the data availability can be maximized while ensuring the time correlation of the data. In the acquisition process, the time complexity is O(w), w represents the maximum window size, which shows excellent efficiency and adaptability in practical applications.

[0068] Then, the third remote sensing image is screened by comprehensively considering factors such as cloud coverage and time correlation to achieve an image screening strategy with multi-parameter joint optimization. It can be understood that first, according to the event time information (T i), cloud coverage (C i ) and spatial resolution (S i ) calculates the weighted score value, and the calculation formula is as follows:

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

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

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

[0072] In an embodiment of the present application, the first remote sensing image can be downloaded through a multi-threaded parallel download mechanism to improve data acquisition efficiency. Specifically, the remote sensing image can be read and geographically aligned through the raster io library to improve spatial accuracy. Among them, the raster io library provides efficient raster data processing functions. By maintaining the geographic reference information (transform and crs) of the original image, the accuracy of spatial alignment is ensured, sub-pixel accuracy is guaranteed, and complex spatial analysis operations are also supported.

[0073] It can be understood that 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 by the buffer cropping method to obtain the second remote sensing image; when the event type information is a line feature, the fourth remote sensing image is cropped by the bounding box cropping method to obtain the second remote sensing image; when the event type information is a surface feature, the fourth remote sensing image is cropped by the mask cropping method to obtain the second remote sensing image. This embodiment can effectively improve the flexibility and efficiency of data processing by adaptively cropping based on event geometry type information.

[0074] In an embodiment of the present application, the parallel calculation of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Vegetation Cover (FVC) can be realized by utilizing Python's concurrent.futures module. This method takes advantage of multi-core processing and can effectively improve computing efficiency. It can be understood that this embodiment first determines the target area corresponding to the second remote sensing image, calculates the Normalized Difference Vegetation Index and Enhanced Vegetation Index of the target area according to the second remote sensing image, and then analyzes the distribution characteristics of the Normalized Difference Vegetation Index and Enhanced Vegetation Index in the target area, dynamically adjusts the calculation parameters of the vegetation coverage, and then calculates the vegetation coverage of the target area according to the adjusted calculation parameters and the second remote sensing image. Specifically, this embodiment uses percentiles to determine the maximum and minimum values ​​of the Enhanced Vegetation Index, which can effectively avoid the influence of outliers, thereby improving the accuracy and adaptability of vegetation coverage calculation.

[0075] It is understandable that the present embodiment can also calculate the representative value of the vegetation index of the target area according to the event type information. Specifically, for event type information of line elements or surface elements, the present embodiment can calculate a series of statistical indicators such as the mean, median, standard deviation, maximum value and minimum value of several vegetation indices in the corresponding target area as statistical representative values, so that a comprehensive description of the overall vegetation status can be made through these data, thereby supporting more in-depth spatial analysis and comparative research.

[0076] In the embodiments of the present application, the method of the embodiments of the present application can be applied to Figure 2 In the interactive architecture shown in Figure 1, Figure 2 The interactive architecture includes a user layer, a data layer, an application layer, and a service layer. The method of the embodiment of the present application can be applied to Figure 2 In the application layer, by executing the method of the present application, the corresponding data can be stored in the remote sensing image library, historical event library and vegetation index result database of the data layer. A visual interface is set in the user layer, so that the index change trend can be intuitively displayed. This embodiment uses a layered design to customize the processing logic for different levels of abnormalities, and can save the operation when encountering problems, thereby improving the overall robustness and reliability.

[0077] It is understandable that in Figure 2 In the architecture shown, several historical event data can also be read through an adaptive retry mechanism to effectively ensure the reliability of data acquisition when temporary errors such as network fluctuations occur, and to avoid excessive requests for external services. At the same time, this embodiment also records the reading process data through log files, which can facilitate 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 the present application, Figure 3 As shown, the complete process of the method provided in the embodiment of the present application includes but is not limited to the following steps:

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

[0080] Step 2: Determine whether there is any unprocessed historical event data, for example, whether there is any data that has been acquired but not processed due to a network interruption; if so, execute step 4; otherwise, execute step 3;

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

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

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

[0084] Step 6: Call the remote sensing image API and search for remote sensing images based on event location information;

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

[0086] Step 8. Download appropriate remote sensing images;

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

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

[0089] It can be 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 the process of ecological restoration, using the time series changes of NDVI and EVI to accurately evaluate the vegetation restoration effects in mines, contaminated sites and other areas; it can be applied to urban expansion research to analyze the impact of urbanization on surrounding vegetation coverage; it can also be applied to the long-term impact of climate change on regional vegetation growth; it can also be applied to agricultural production monitoring to evaluate crop growth conditions and yield predictions.

[0090] In summary, the method of the embodiment of the present application realizes the efficient and accurate extraction of vegetation index data such as NDVI, EVI, FVC before and after the corresponding time of historical event points, lines, and surface elements, and improves the accuracy of vegetation index calculation and data processing efficiency, providing strong technical support for ecological environment monitoring and evaluation.

[0091] Reference Figure 4 The embodiment of the present application provides a vegetation index extraction system based on historical remote sensing images, the system comprising:

[0092] The first module 410 is used to construct a historical event database, wherein the historical event database 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 the event time information;

[0094] The third module 430 is used to pre-process the first remote sensing image according to the event location information and the event type information to obtain a second remote sensing image;

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

[0096] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments 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.

[0097] The embodiment of the present application also provides a vegetation index extraction system based on historical remote sensing images, the electronic system includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned vegetation index extraction method based on historical remote sensing images when executing the computer program. The system can be any intelligent terminal including a tablet computer, a car computer, etc.

[0098] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments 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] See also Figure 5 , Figure 5 The hardware structure of a vegetation index extraction system based on historical remote sensing images of another embodiment is illustrated. The system includes:

[0100] The processor 510 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 the present application;

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

[0102] Input / output interface 530, used to implement information input and output;

[0103] Communication interface 540, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);

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

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

[0106] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned vegetation index extraction method based on historical remote sensing images is implemented.

[0107] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments 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.

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

[0109] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

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

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

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

[0113] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0114] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0115] In the several embodiments provided in the present 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 only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

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

[0117] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0118] If the integrated unit is implemented in the form of 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 the present application is essentially 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0119] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A vegetation index extraction method based on historical remote sensing images, characterized in that: The method comprises the following steps: Constructing a historical event database, wherein the historical event database includes historical event data, event time information, event location information, and event type information; Based on the event time information, calling the 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; The vegetation index of the target area is calculated based on the second remote sensing image.

2. The method according to claim 1, characterized in that The step of constructing a historical event database includes: Acquire a number of historical event data, as well as event time information and event space information corresponding to the historical event data; The historical event data, the event time information and the event space information are saved in 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.

3. The method according to claim 1, characterized in that: The calling the first remote sensing image through a preset interface based on the event time information includes: Determining an image acquisition interval according to the event time information; According to the image acquisition interval, calling a third remote sensing image through the preset interface; Calculating a weighted score value based on the event time information, cloud coverage and spatial resolution; The third remote sensing image is screened according to the weighted score value to obtain the first remote sensing image.

4. The method according to claim 1, characterized in that The preprocessing of the first remote sensing image according to the event location information and the event type information to obtain the second remote sensing image includes: matching a first remote sensing image corresponding to the event location information as a fourth remote sensing image; The fourth remote sensing image is cropped according to the event type information to obtain the second remote sensing image.

5. The method according to claim 4, characterized in that The step of clipping the fourth remote sensing image according to the event type information to obtain the second remote sensing image includes: When the event type information is a point feature, clipping the fourth remote sensing image by a buffer clipping method to obtain the second remote sensing image; When the event type information is a line feature, clipping the fourth remote sensing image by a bounding box clipping method to obtain the second remote sensing image; When the event type information is a surface element, the fourth remote sensing image is cropped by a mask cropping method to obtain the second remote sensing image.

6. The method according to claim 1, characterized in that The step of calculating the vegetation index of the target area according to the second remote sensing image includes: determining a target area corresponding to the second remote sensing image; Calculating a normalized difference vegetation index and an enhanced vegetation index of the target area according to the second remote sensing image; According to the distribution characteristics of the normalized difference vegetation index and the enhanced vegetation index in the target area, adjusting the calculation parameters of the vegetation coverage; Calculating the vegetation coverage of the target area according to the adjusted calculation parameters and the second remote sensing image; A statistical representative value of the vegetation index of the target area is calculated according to the event type information and the second remote sensing image.

7. The method according to claim 1, characterized in that The obtaining of several historical event data includes: Several historical event data are read through the adaptive retry mechanism, and the process data is read through the log file record.

8. A vegetation index extraction system based on historical remote sensing images, characterized in that: The system comprises: 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; The second module is used to call the first remote sensing image through a preset interface based on the event time information; A third module is used to pre-process 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 used to calculate the vegetation index of the target area according to the second remote sensing image.

9. A vegetation index extraction system based on historical remote sensing images, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images