Grassland ecological system monitoring method and system based on remote sensing data, electronic equipment and storage medium

Abstract

The invention discloses a grassland ecological system monitoring method and system based on remote sensing data, electronic equipment and a storage medium, and the method comprises the steps: collecting an original remote sensing image covering a grassland region through a satellite sensor, and carrying out the preprocessing of the original remote sensing image, and obtaining a multispectral image data set; spectral features in the multispectral image data set are extracted, the reflectivity and vegetation index of each wave band are calculated, and a spectral feature distribution diagram of grassland vegetation is determined; identifying the spectral feature distribution map by using a convolutional neural network to obtain vegetation structure features; extracting and analyzing functional characteristic parameters from the vegetation structure characteristics to obtain a quantitative characterization result; on the basis of the spectral feature distribution diagram, the vegetation structure features and the quantitative characterization result, a temporal and spatial variation trend of the grassland vegetation state is obtained through a time sequence analysis method; and comparing the spatio-temporal change trend with a preset ecological health threshold value through a clustering analysis method, and judging a health state partition of the grassland ecosystem to obtain a health state classification map.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment monitoring, and in particular to a grassland ecosystem monitoring method, system, electronic equipment and storage medium based on remote sensing data. Background Art

[0002] The monitoring and management of grassland ecosystems is a key topic in the fields of ecology and environmental science, directly related to the maintenance of biodiversity, the regulation of the carbon cycle, and the sustainable development of human society and economy. As an important component of the Earth's terrestrial ecosystems, the dynamic changes in the health status and service functions of grasslands have a significant impact on regional and even global ecological balances. However, traditional monitoring methods such as ground surveys and plot measurements have significant shortcomings in coverage, data update frequency, and long-term consistency. The limitations of these methods make real-time dynamic monitoring of large-scale grassland ecosystems difficult to achieve, especially in the context of climate change and intensified human activities. More efficient and accurate technical means are urgently needed.

[0003] Existing solutions mostly rely on low-resolution remote sensing data or single indicator analysis, which makes it difficult to fully capture the complex changes in grassland vegetation characteristics and their deep connection with ecosystem functions. Specifically, current methods perform poorly in terms of insufficient resolution, inaccurate spectral feature extraction, and limited spatiotemporal dynamic analysis capabilities, resulting in monitoring results that are difficult to accurately reflect the true state of grassland ecosystems. The core challenges focus on how to achieve a comprehensive analysis of grassland vegetation spectral characteristics, structural characteristics, and functional characteristics through remote sensing data, and how to effectively associate the spatiotemporal changes of these characteristics with the health of the ecosystem. In particular, in the processing and application of high-resolution multispectral images, the accuracy of data interpretation, the comprehensiveness of feature recognition, and the reliability of model construction have become bottlenecks for technological breakthroughs. These unresolved technical factors directly limit the ability to conduct long-term dynamic monitoring of grassland ecosystems, which in turn affects the scientific nature of management decisions.

[0004] Therefore, how to use high-resolution multispectral imaging technology to accurately identify the spatiotemporal variations in the spectral, structural, and functional characteristics of grassland vegetation, and based on this, to construct correlation models between vegetation traits and ecosystem functions, has become a key issue in grassland ecosystem monitoring. Solving this problem will provide solid technical support for the health assessment and sustainable management of grassland ecosystems. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a grassland ecosystem monitoring method based on remote sensing data, the method comprising:

[0006] Using satellite sensors to collect original remote sensing images covering the grassland area, and preprocessing the original remote sensing images to obtain a multispectral image dataset;

[0007] Extracting spectral features from the multispectral image dataset using a spectral analysis method, calculating the reflectance and vegetation index of each band, and determining a spectral feature distribution map of grassland vegetation;

[0008] Constructing a convolutional neural network, and using the convolutional neural network to identify the spectral feature distribution map to obtain vegetation structure features;

[0009] Extracting functional characteristic parameters from the vegetation structural characteristics, and analyzing the functional characteristic parameters using a random forest algorithm to obtain quantitative characterization results;

[0010] Based on the spectral characteristic distribution map, the vegetation structure characteristics and the quantitative characterization results, a temporal and spatial variation trend of grassland vegetation status is obtained using a time series analysis method;

[0011] The temporal and spatial variation trends are compared with the preset ecological health thresholds through cluster analysis to determine the health status zoning of the grassland ecosystem and obtain a health status classification map.

[0012] Preferably, the method for obtaining the multispectral image dataset includes:

[0013] Using satellite sensors to collect original remote sensing images covering the grassland area, and using multispectral data extraction to obtain a first image dataset;

[0014] De-noising and cloud removal are performed on the first image using a Gaussian filter method to obtain a second image dataset;

[0015] The second image dataset is filtered using mean filtering, and the spatial distribution characteristics of the multispectral data are determined. The coverage of the grassland area is determined using spectral analysis to obtain the multispectral image dataset.

[0016] Preferably, the method for obtaining the spectral characteristic distribution diagram includes:

[0017] Acquire the multispectral image data set, extract spectral features through spectral analysis method, and obtain initial spectral data;

[0018] Calculating the reflectance of each band in the initial spectral data to obtain a band reflectance value, and calculating a vegetation index based on the band reflectance value to obtain index distribution data;

[0019] The initial spectral data and the exponential distribution data are subjected to feature fusion to obtain a fused feature set, and the fused feature set is smoothed using a mean filtering method to obtain the spectral feature distribution graph.

[0020] Preferably, the method for obtaining the vegetation structure characteristics includes:

[0021] Constructing a convolutional neural network, and using the convolutional neural network to identify the spectral feature distribution map to obtain a preliminary distribution map of vegetation structure;

[0022] Extracting vegetation structure information according to the preliminary vegetation structure distribution map, wherein the vegetation structure information includes: height characteristics, density characteristics and coverage;

[0023] Classifying the height features using a first preset threshold to obtain layered height distribution data;

[0024] The density of the vegetation structure is calculated using the density feature and the coverage. If the density exceeds a second preset threshold, the preliminary distribution map of the vegetation structure is adjusted to obtain a corrected feature matrix to obtain the vegetation structure feature.

[0025] Preferably, the method for obtaining the quantitative characterization result includes:

[0026] Extracting the functional parameters from the vegetation structure characteristics to obtain an initial data set;

[0027] Analyzing and processing the initial data using a random forest algorithm to obtain a changing trend of photosynthetic efficiency;

[0028] According to the changing trend of the photosynthetic efficiency, water utilization characteristics are obtained to obtain photosynthetic efficiency distribution data;

[0029] If the photosynthetic efficiency distribution data exceeds a third preset threshold, optimizing the random forest algorithm by adjusting parameters, and analyzing the vegetation data using the optimized algorithm model to obtain a quantitative value of photosynthetic efficiency;

[0030] The water utilization characteristic and the quantified value of the photosynthetic efficiency are compared to determine the deviation range of the efficiency evaluation, obtain abnormal points within the deviation range, and obtain the quantitative characterization result of the functional characteristic.

[0031] Preferably, the method for obtaining the temporal and spatial variation trend includes:

[0032] Performing dimensionality reduction processing on the quantitative characterization results using principal component analysis to obtain feature data after dimensionality reduction;

[0033] Using a time series analysis method to detect the temporal and spatial changes of the feature data after dimensionality reduction to obtain dynamic change features;

[0034] Based on the dynamic change characteristics, the change rates of the time dimension and the spatial dimension are calculated to obtain a quantitative index of the vegetation state;

[0035] If the quantified vegetation exceeds a fourth preset threshold, the deep spatiotemporal characteristics of the quantified index are extracted, the abnormal trend of the vegetation state is determined, and the correlation between the abnormal trend and the dynamic change characteristics is obtained to obtain the spatiotemporal change trend.

[0036] Preferably, the method for obtaining the health status classification diagram includes:

[0037] Obtaining trend data based on the spatiotemporal change trend, clustering the trend data using a K-means clustering method to obtain a clustering result;

[0038] Extracting each partition feature from the clustering results and comparing it with the preset ecological health threshold to determine the health status;

[0039] If the health status is lower than the ecological health threshold, it is marked as an unhealthy area, and the unhealthy area is processed by a spatial difference method to generate preliminary health status classification data;

[0040] The preliminary health status classification data is visualized using GIS tools to obtain the health status classification map.

[0041] The present invention also provides a grassland ecosystem monitoring system based on remote sensing data, wherein the monitoring system applies any of the above-mentioned monitoring methods and comprises: a spectral data acquisition module, a feature distribution calculation module, a structural feature extraction module, a quantitative characterization analysis module, a spatiotemporal trend analysis module, and a health status monitoring module;

[0042] The spectral data acquisition module uses satellite sensors to collect original remote sensing images covering the grassland area, and preprocesses the original remote sensing images to obtain a multispectral image dataset;

[0043] The characteristic distribution calculation module uses a spectral analysis method to extract spectral characteristics from the multispectral image data set, and calculates the reflectance and vegetation index of each band to determine the spectral characteristic distribution map of grassland vegetation;

[0044] The structural feature extraction module is used to construct a convolutional neural network, and use the convolutional neural network to identify the spectral feature distribution map to obtain vegetation structural features;

[0045] The quantitative characterization analysis module is used to extract functional characteristic parameters from the vegetation structure characteristics, and analyze the functional characteristic parameters using a random forest algorithm to obtain quantitative characterization results;

[0046] The spatiotemporal trend analysis module obtains the spatiotemporal variation trend of grassland vegetation status using a time series analysis method based on the spectral characteristic distribution map, the vegetation structure characteristics and the quantitative characterization results;

[0047] The health status monitoring module compares the temporal and spatial variation trends with preset ecological health thresholds through cluster analysis, determines the health status zones of the grassland ecosystem, and obtains a health status classification map.

[0048] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the grassland ecosystem monitoring method based on remote sensing data as described in any one of claims 1 to 7 is implemented.

[0049] The present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed, the grassland ecosystem monitoring method based on remote sensing data as described in any one of claims 1 to 7 is implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention obtains high-resolution multispectral satellite images, uses spectral analysis algorithms to extract vegetation spectral characteristics, and uses convolutional neural networks to identify vegetation structural characteristics. Subsequently, the random forest algorithm is applied to analyze vegetation functional characteristics, such as photosynthetic efficiency and water use efficiency. The present invention innovatively combines the characteristics of the three dimensions of spectrum, structure, and function, detects their dynamic changes through time series analysis methods, and finally uses cluster analysis to compare the change data with preset ecological health thresholds to achieve a zoning assessment of the health status of the grassland ecosystem. This multi-dimensional, dynamic assessment method provides a scientific basis for the monitoring and management of grassland ecosystems, helps to timely discover ecological problems and formulate corresponding protection measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0055] Description of reference numerals:

[0056] 1010 , processor; 1020 , memory; 1030 , input / output interface; 1040 , communication interface; 1050 , bus. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Example 1

[0061] In this embodiment, if Figure 1 As shown, a grassland ecosystem monitoring method based on remote sensing data is characterized in that the method includes:

[0062] S1. Use satellite sensors to collect original remote sensing images covering the grassland area and preprocess the original remote sensing images to obtain a multispectral image dataset.

[0063] The method for obtaining a multispectral image dataset includes: using satellite sensors to collect original remote sensing images covering a grassland area, and using multispectral data extraction to obtain a first image dataset; using Gaussian filtering to denoise and eliminate clouds on the first image to obtain a second image dataset; using mean filtering to filter the second image dataset, and determining the spatial distribution characteristics of the multispectral data, using spectral analysis to determine the coverage of the grassland area, and obtaining a multispectral image dataset.

[0064] In this embodiment, satellite sensors are used to capture spectral information of multiple bands, such as visible light and near-infrared, covering the grassland area to be monitored, generating original remote sensing images with a resolution of 30 meters, and extracting multispectral data to obtain a first image dataset; data of different bands in the first image dataset are stored in layers, for example, the red light band, the green light band, and the near-infrared band are extracted as independent layers to facilitate subsequent analysis; Gaussian filtering is used to remove noise from the first image dataset; in this embodiment, radiation correction technology can also be used to eliminate sensor bias, or spatial smoothing methods can be used to weaken the influence of random noise. , obtaining the second image dataset; using mean filtering to filter the second image dataset, the mean filter calculates the average value of each pixel using a 3×3 sliding window. For example, if the original value of a pixel is 150 and the values of the eight surrounding pixels fluctuate between 130 and 160, the filtered value approaches 145, with a smooth transition; then determine the spatial distribution characteristics of the multispectral data, use spectral analysis to determine the coverage of the grassland area, and combine the spectral reflectance curve to identify the typical characteristics of grassland vegetation, such as high reflectivity in the near-infrared band. If the reflectivity of a certain area is above 0.4, it can be determined as a grassland-covered area, thus obtaining the multispectral image dataset.

[0065] S2. Use spectral analysis to extract spectral features from multispectral image datasets, calculate the reflectance and vegetation index of each band, and determine the spectral characteristic distribution map of grassland vegetation.

[0066] The method for obtaining a spectral feature distribution map includes: obtaining a multispectral image data set, extracting spectral features through a spectral analysis method, and obtaining initial spectral data; calculating the reflectivity of each band in the initial spectral data to obtain a band reflectance value, and calculating a vegetation index based on the band reflectance value to obtain exponential distribution data; performing feature fusion on the initial spectral data and the exponential distribution data to obtain a fused feature set, and smoothing the fused feature set using a mean filtering method to obtain a spectral feature distribution map.

[0067] In this embodiment, for images of grassland areas, common near-infrared and red bands are selected, their spectral reflectance characteristics are analyzed, and initial spectral data is generated. For the initial spectral data, the reflectance of each band is calculated. The reflectance of the near-infrared band is 0.4, and that of the red band is 0.15. Based on the above reflectance values, the difference between the near-infrared and red bands is divided by their sum to obtain a vegetation index between 0 and 1, i.e., index distribution data. The spatial distribution of the vegetation index distribution data is superimposed with the multi-band characteristics of the initial spectral data to form a fused feature set. The fused feature set is then smoothed using a mean filtering method to obtain a spectral feature distribution map.

[0068] S3. Construct a convolutional neural network and use it to identify the spectral feature distribution map to obtain vegetation structure characteristics.

[0069] The method for obtaining vegetation structure characteristics includes: constructing a convolutional neural network, using the convolutional neural network to identify the spectral feature distribution map, and obtaining a preliminary vegetation structure distribution map; extracting vegetation structure information based on the preliminary vegetation structure distribution map, the vegetation structure information including: height characteristics, density characteristics and coverage; using a first preset threshold to classify the height characteristics to obtain layered height distribution data; calculating the density of the vegetation structure through the density characteristics and coverage, if the density exceeds a second preset threshold, adjusting the preliminary vegetation structure distribution map, obtaining a corrected feature matrix, and obtaining vegetation structure characteristics.

[0070] In this embodiment, the constructed convolutional neural network can adopt a three-layer convolution structure, with a 3×3 convolution kernel in each layer, gradually extracting the spatial and spectral features of the image, and finally outputting a preliminary distribution map; when extracting vegetation structure information from the preliminary distribution map, the vegetation boundary can be identified by edge detection technology. Specifically, for a 1000m 2 In the grassland area, the detected vegetation height characteristics show that the average height of some areas is 0.8m and that of some areas is 0.3m. The density characteristics are obtained through pixel statistics. The vegetation coverage pixel ratio per square meter is 60%. The coverage can be calculated based on the proportion of green areas in the image. For example, the coverage of a certain area is 75%. When the preset threshold is used to classify the height characteristics, 0.5m can be set as the dividing point. Areas above this value are classified as high vegetation areas, and areas below this value are classified as low vegetation areas. When calculating the density through density characteristics and coverage, a simple ratio relationship is introduced. When the ratio of density ratio to coverage is greater than 1.2, it is judged as a high-density area. When the density exceeds the preset range, for example, the ratio reaches 1.5, it indicates that the image processing may be interfered with by light or shadow. At this time, the feature matrix is corrected by adjusting the contrast or brightness parameters to obtain the vegetation structure characteristics.

[0071] S4. Extract functional characteristic parameters from vegetation structural characteristics and use random forest algorithm to analyze the functional characteristic parameters to obtain quantitative characterization results.

[0072] The method for obtaining quantitative characterization results includes: extracting functional parameters from vegetation structural characteristics to obtain an initial data set; using a random forest algorithm to analyze and process the initial data to obtain a changing trend of photosynthetic efficiency; based on the changing trend of photosynthetic efficiency, obtaining water utilization characteristics to obtain photosynthetic efficiency distribution data; if the photosynthetic efficiency distribution data exceeds a third preset threshold, optimizing the random forest algorithm by adjusting parameters, and analyzing vegetation data through the optimized algorithm model to obtain a quantitative value of photosynthetic efficiency; comparing the water utilization characteristics and the quantitative value of photosynthetic efficiency to determine the deviation range of the efficiency evaluation, obtaining abnormal points within the deviation range, and obtaining a quantitative characterization result of the functional characteristics.

[0073] In this embodiment, parameters such as height and density are extracted from the vegetation structure characteristics, and combined with spectral data to preliminarily form an initial data set containing multidimensional features; then, the initial data set is subjected to dimensionality reduction processing based on statistical analysis, and feature items that are strongly correlated with functional parameters are screened out. The initial data set is processed using a random forest algorithm to determine the changing trend of photosynthetic efficiency; based on the changing trend of photosynthetic efficiency, relevant features of water utilization are obtained to obtain efficiency distribution data. If the efficiency distribution data exceeds a preset threshold, the random forest model is optimized by adjusting parameters to obtain an updated model; in this embodiment, the preset threshold can be set to the efficiency fluctuation range ±1 0%; if the efficiency distribution data of a certain area reaches 90%, exceeding the upper threshold, the tree depth or feature weight of the random forest is adjusted, the model is re-optimized, and the vegetation data is analyzed by the optimized algorithm model to obtain the quantitative value of photosynthetic efficiency; the water use characteristics and the quantitative values of photosynthetic efficiency are compared to determine the deviation range of the efficiency evaluation. The deviation range can be determined by the difference between the quantitative value and the measured water use efficiency; if the abnormal points within the deviation range are concentrated in a certain area, such as the deviation value is continuously higher than 0.1, it may be due to differences in vegetation types or external environmental interference. The abnormal points within the deviation range are obtained to obtain the quantitative characterization results of the functional characteristics.

[0074] S5. Based on the spectral characteristic distribution map, vegetation structure characteristics and quantitative characterization results, the temporal and spatial variation trends of grassland vegetation status were obtained using the time series analysis method.

[0075] The method for obtaining the temporal and spatial change trend includes: using the principal component analysis method to reduce the dimension of the quantitative characterization results to obtain the feature data after dimensionality reduction; using the time series analysis method to detect the temporal and spatial changes of the feature data after dimensionality reduction to obtain dynamic change characteristics; based on the dynamic change characteristics, calculating the change rate of the time dimension and the spatial dimension to obtain the quantitative index of the vegetation status; if the quantified vegetation exceeds the fourth preset threshold, extracting the deep temporal and spatial characteristics of the quantitative index, judging the abnormal trend of the vegetation status, and obtaining the correlation between the abnormal trend and the dynamic change characteristics to obtain the temporal and spatial change trend.

[0076] In this embodiment, the principal component analysis method is used to reduce the dimensionality of the quantitative characterization results. If the infrared band of the spectral feature is highly correlated with the photosynthetic rate of the functional feature, the principal component analysis will highlight this correlation and obtain the feature data after dimensionality reduction; for the feature data after dimensionality reduction, the time series analysis method is applied to detect temporal changes and spatial changes, and determine the dynamic change characteristics; based on the dynamic change characteristics, the change rates of the time dimension and the spatial dimension are calculated to obtain the quantitative indicators of the vegetation status. Specifically, the change rate of the time dimension can be defined as the relative change percentage of the photosynthetic rate in a certain time period, and the change rate of the spatial dimension can be defined as the relative change percentage of the spatial change; if the quantitative indicator of the vegetation status exceeds the fourth preset threshold, the deep spatiotemporal characteristics are extracted to determine the abnormal trend of the vegetation status; the correlation between the abnormal trend of the vegetation status and the dynamic change characteristics is obtained, and the spatiotemporal change trend is output.

[0077] S6. Use cluster analysis to compare spatiotemporal trends with pre-set ecological health thresholds, determine the health status of grassland ecosystems, and obtain a health status classification map.

[0078] The method for obtaining a health status classification map includes: obtaining trend data based on spatiotemporal change trends, clustering the trend data using the K-means clustering method to obtain clustering results; extracting the characteristics of each partition from the clustering results, and comparing them with a preset ecological health threshold to determine the health status; if the health status is lower than the ecological health threshold, it is marked as an unhealthy area, and the unhealthy area is processed using the spatial difference method to generate preliminary health status classification data; and using GIS tools to visualize the preliminary health status classification data to obtain a health status classification map.

[0079] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0080] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0081] Example 2

[0082] In this embodiment, a grassland ecosystem monitoring system based on remote sensing data includes: a spectral data acquisition module, a feature distribution calculation module, a structural feature extraction module, a quantitative characterization analysis module, a spatiotemporal trend analysis module, and a health status monitoring module.

[0083] The spectral data acquisition module uses satellite sensors to collect original remote sensing images covering the grassland area and preprocesses the original remote sensing images to obtain a multispectral image dataset.

[0084] The feature distribution calculation module uses spectral analysis to extract spectral features from multispectral image datasets, calculates the reflectance and vegetation index of each band, and determines the spectral feature distribution map of grassland vegetation.

[0085] The structural feature extraction module is used to construct a convolutional neural network, which is used to identify the spectral feature distribution map and obtain the vegetation structure characteristics.

[0086] The quantitative characterization analysis module is used to extract functional characteristic parameters from vegetation structural characteristics and use the random forest algorithm to analyze the functional characteristic parameters to obtain quantitative characterization results.

[0087] The spatiotemporal trend analysis module uses the time series analysis method to obtain the spatiotemporal change trend of grassland vegetation status based on the spectral characteristic distribution map, vegetation structure characteristics and quantitative characterization results.

[0088] The health status monitoring module compares the spatiotemporal change trends with the preset ecological health thresholds through cluster analysis, determines the health status zoning of the grassland ecosystem, and obtains a health status classification map.

[0089] The system of the above embodiment is used to implement the corresponding grassland ecosystem monitoring method based on remote sensing data in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0090] It should be noted that the above-mentioned grassland ecosystem monitoring system based on remote sensing data is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0091] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the aforementioned functionality. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0092] Example 3

[0093] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the grassland ecosystem monitoring method based on remote sensing data described in any of the above embodiments.

[0094] Figure 2 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0095] The processor 1010 can be implemented using 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 this specification.

[0096] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an 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 1020 and is called and executed by the processor 1010.

[0097] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0098] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (e.g., USB (Universal Serial Bus), network cable, etc.) or a wireless method (e.g., mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0099] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0100] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0101] The system of the above embodiment is used to implement the corresponding grassland ecosystem monitoring method based on remote sensing data in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0102] Example 4

[0103] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the grassland ecosystem monitoring method based on remote sensing data as described in any of the above embodiments.

[0104] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0105] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the grassland ecosystem monitoring method based on remote sensing data as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0106] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0107] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0108] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0109] Therefore, the units of each example described in the embodiments of this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A grassland ecosystem monitoring method based on remote sensing data, characterized in that: The method comprises: Using satellite sensors to collect original remote sensing images covering the grassland area, and preprocessing the original remote sensing images to obtain a multispectral image dataset; Extracting spectral features from the multispectral image dataset using a spectral analysis method, calculating the reflectance and vegetation index of each band, and determining a spectral feature distribution map of grassland vegetation; Constructing a convolutional neural network, and using the convolutional neural network to identify the spectral feature distribution map to obtain vegetation structure features; Extracting functional characteristic parameters from the vegetation structural characteristics, and analyzing the functional characteristic parameters using a random forest algorithm to obtain quantitative characterization results; Based on the spectral characteristic distribution map, the vegetation structure characteristics and the quantitative characterization results, a temporal and spatial variation trend of grassland vegetation status is obtained using a time series analysis method; The temporal and spatial variation trends are compared with the preset ecological health thresholds through cluster analysis to determine the health status zoning of the grassland ecosystem and obtain a health status classification map.

2. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the multispectral image dataset includes: Using satellite sensors to collect original remote sensing images covering the grassland area, and using multispectral data extraction to obtain a first image dataset; De-noising and cloud removal are performed on the first image using a Gaussian filter method to obtain a second image dataset; The second image dataset is filtered using mean filtering, and the spatial distribution characteristics of the multispectral data are determined. The coverage of the grassland area is determined using spectral analysis to obtain the multispectral image dataset.

3. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the spectral characteristic distribution diagram includes: Acquire the multispectral image data set, extract spectral features through spectral analysis method, and obtain initial spectral data; Calculating the reflectance of each band in the initial spectral data to obtain a band reflectance value, and calculating a vegetation index based on the band reflectance value to obtain index distribution data; The initial spectral data and the exponential distribution data are subjected to feature fusion to obtain a fused feature set, and the fused feature set is smoothed using a mean filtering method to obtain the spectral feature distribution graph.

4. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the vegetation structure characteristics includes: Constructing a convolutional neural network, and using the convolutional neural network to identify the spectral feature distribution map to obtain a preliminary distribution map of vegetation structure; Extracting vegetation structure information according to the preliminary vegetation structure distribution map, wherein the vegetation structure information includes: height characteristics, density characteristics and coverage; Classifying the height features using a first preset threshold to obtain layered height distribution data; The density of the vegetation structure is calculated using the density feature and the coverage. If the density exceeds a second preset threshold, the preliminary distribution map of the vegetation structure is adjusted to obtain a corrected feature matrix to obtain the vegetation structure feature.

5. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the quantitative characterization result includes: Extracting the functional parameters from the vegetation structure characteristics to obtain an initial data set; Analyzing and processing the initial data using a random forest algorithm to obtain a changing trend of photosynthetic efficiency; According to the changing trend of the photosynthetic efficiency, water utilization characteristics are obtained to obtain photosynthetic efficiency distribution data; If the photosynthetic efficiency distribution data exceeds a third preset threshold, optimizing the random forest algorithm by adjusting parameters, and analyzing the vegetation data using the optimized algorithm model to obtain a quantitative value of photosynthetic efficiency; The water utilization characteristic and the quantified value of the photosynthetic efficiency are compared to determine the deviation range of the efficiency evaluation, obtain abnormal points within the deviation range, and obtain the quantitative characterization result of the functional characteristic.

6. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the temporal and spatial variation trend includes: Performing dimensionality reduction processing on the quantitative characterization results using principal component analysis to obtain feature data after dimensionality reduction; Using a time series analysis method to detect the temporal and spatial changes of the feature data after dimensionality reduction to obtain dynamic change features; Based on the dynamic change characteristics, the change rates of the time dimension and the spatial dimension are calculated to obtain a quantitative index of the vegetation state; If the quantified vegetation exceeds a fourth preset threshold, the deep spatiotemporal characteristics of the quantified index are extracted, the abnormal trend of the vegetation state is determined, and the correlation between the abnormal trend and the dynamic change characteristics is obtained to obtain the spatiotemporal change trend.

7. The grassland ecosystem monitoring method based on remote sensing data according to claim 1, characterized in that: The method for obtaining the health status classification diagram includes: Obtaining trend data based on the spatiotemporal change trend, clustering the trend data using a K-means clustering method to obtain a clustering result; Extracting each partition feature from the clustering results and comparing it with the preset ecological health threshold to determine the health status; If the health status is lower than the ecological health threshold, it is marked as an unhealthy area, and the unhealthy area is processed by a spatial difference method to generate preliminary health status classification data; The preliminary health status classification data is visualized using GIS tools to obtain the health status classification map.

8. A grassland ecosystem monitoring system based on remote sensing data, wherein the monitoring system applies the monitoring method according to any one of claims 1 to 7, characterized in that: include: Spectral data acquisition module, feature distribution calculation module, structural feature extraction module, quantitative characterization analysis module, spatiotemporal trend analysis module and health status monitoring module; The spectral data acquisition module uses satellite sensors to collect original remote sensing images covering the grassland area, and preprocesses the original remote sensing images to obtain a multispectral image dataset; The characteristic distribution calculation module uses a spectral analysis method to extract spectral characteristics from the multispectral image data set, and calculates the reflectance and vegetation index of each band to determine the spectral characteristic distribution map of grassland vegetation; The structural feature extraction module is used to construct a convolutional neural network, and use the convolutional neural network to identify the spectral feature distribution map to obtain vegetation structural features; The quantitative characterization analysis module is used to extract functional characteristic parameters from the vegetation structure characteristics, and analyze the functional characteristic parameters using a random forest algorithm to obtain quantitative characterization results; The spatiotemporal trend analysis module obtains the spatiotemporal variation trend of grassland vegetation status using a time series analysis method based on the spectral characteristic distribution map, the vegetation structure characteristics and the quantitative characterization results; The health status monitoring module compares the temporal and spatial variation trends with preset ecological health thresholds through cluster analysis, determines the health status zones of the grassland ecosystem, and obtains a health status classification map.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for monitoring grassland ecosystems based on remote sensing data as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the grassland ecosystem monitoring method based on remote sensing data according to any one of claims 1 to 7 is implemented.

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