Ecological environment degradation assessment system, method, storage medium and electronic equipment

Abstract

The present disclosure relates to the technical field of ecological environment assessment, and specifically to an ecological environment degradation assessment system, method, storage medium, and electronic device. The ecological environment degradation assessment system includes: a data acquisition module; a feature extraction module, including a first extraction network and a second extraction network, the first extraction network being used to extract spatial-spectral fusion feature data of hyperspectral remote sensing image time series data, and the second extraction network being used to extract temporal-spatial fusion feature data of hyperspectral remote sensing image time series data; a canopy closure calculation module being used to calculate canopy closure level time series data using a multivariate regression model based on spatial-spectral fusion feature data, temporal-spatial fusion feature data, and environmental monitoring time series data; a degradation assessment module being used to analyze degradation causes based on canopy closure level time series data, hyperspectral remote sensing image time series data, and environmental monitoring time series data; and a report generation module. The present disclosure can improve the accuracy of ecological environment degradation assessment.

Description

Technical Field

[0001] The present disclosure relates to the technical field of ecological environment assessment, and in particular to an ecological environment degradation assessment system, an ecological environment degradation assessment method, a storage medium, and an electronic device. Background Art

[0002] As global ecological and environmental changes intensify, regional ecosystem degradation is becoming increasingly serious. The speed and scope of ecological degradation are continuing to expand, particularly under the dual influence of large-scale human activities and natural factors. Ecosystem degradation not only directly impacts biodiversity, soil quality, and water conservation, but can also trigger serious environmental disasters such as soil erosion, desertification, and forest fires, further threatening the human living environment and sustainable economic development.

[0003] Traditional ecosystem degradation analysis methods mainly rely on manual surveys and ground monitoring. Although these methods can provide certain degradation assessments, they face problems such as poor timeliness, limited coverage, and low data accuracy.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The purpose of the present disclosure is to provide an ecological environment degradation assessment system, an ecological environment degradation assessment method, a storage medium and an electronic device, aiming to solve the problem of poor accuracy in ecological environment degradation assessment.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to one aspect of the present disclosure, an ecological environment degradation assessment system is provided, characterized in that it includes: a data acquisition module for collecting hyperspectral remote sensing image time series data and environmental monitoring time series data; a feature extraction module including a first extraction network and a second extraction network, the first extraction network being used to extract spatial-spectral fusion feature data of the hyperspectral remote sensing image time series data, and the second extraction network being used to extract temporal-spatial fusion feature data of the hyperspectral remote sensing image time series data; a canopy closure calculation module being used to calculate canopy closure level time series data using a multivariate regression model based on the spatial-spectral fusion feature data, the temporal-spatial fusion feature data and the environmental monitoring time series data; a degradation assessment module being used to analyze degradation causes based on the canopy closure level time series data, the hyperspectral remote sensing image time series data and the environmental monitoring time series data; the degradation causes include natural factors and human factors; and a report generation module being used to generate an ecological environment degradation assessment report based on the data analysis process and data analysis results in the degradation assessment module.

[0008] Optionally, the first extraction network includes: an input layer, which extracts a hyperspectral remote sensing image at a target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module, which collaboratively extracts spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer, which fuses the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data at the target time point.

[0009] Optionally, the second extraction network includes: an input layer, which receives the hyperspectral remote sensing image time series data; an LSTM module, which extracts the time series feature data from the hyperspectral remote sensing image time series data through memory units and a gating mechanism; a spatial convolution module, which extracts the spatial feature data from the hyperspectral remote sensing image time series data through a spatial convolution operation; and a fully connected layer, which fuses the time series feature data and the spatial feature data to obtain the time-space fusion feature data.

[0010] Optionally, the degradation cause analysis module includes: a natural factor analysis module, which is used to calculate the correlation between natural factors and canopy levels based on the canopy level time series data and the environmental monitoring time series data; a human factor analysis module, which is used to evaluate the impact of human factors on ecological environment degradation based on the canopy level time series data and the hyperspectral remote sensing image time series data; and a proportion analysis module, which evaluates the proportion of the impact of natural factors and human factors on ecological environment degradation respectively according to the analysis results of the natural factor analysis module and the human factor analysis module.

[0011] According to a second aspect of the present disclosure, an ecological environment degradation assessment method is provided, including: collecting hyperspectral remote sensing image time series data and environmental monitoring time series data; extracting spatial-spectral fusion feature data of the hyperspectral remote sensing image time series data based on a first extraction network, and extracting temporal-spatial fusion feature data of the hyperspectral remote sensing image time series data based on a second extraction network; calculating canopy closure level time series data using a multivariate regression model based on the spatial-spectral fusion feature data, the temporal-spatial fusion feature data and the environmental monitoring time series data; performing degradation cause analysis based on the canopy closure level time series data, the hyperspectral remote sensing image time series data and the environmental monitoring time series data to obtain data analysis results; and generating an ecological environment degradation assessment report based on the data analysis results.

[0012] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the ecological environment degradation assessment method as described in the above embodiment is implemented.

[0013] According to the fourth aspect of the embodiments of the present disclosure, an electronic device is provided, characterized in that it includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the ecological environment degradation assessment method as in the above-mentioned embodiment.

[0014] The exemplary embodiments of the present disclosure may have some or all of the following beneficial effects:

[0015] In the technical solutions provided in some embodiments of the present disclosure, on the one hand, the present disclosure uses hyperspectral remote sensing image time series data and environmental monitoring time series data as the data basis for evaluation, and extracts spatial-spectral fusion feature data and time-space fusion feature data through a deep learning network to perform multi-source data fusion. This is not only applicable to regional ecological environment monitoring and evaluation over a large range and long time series, but also can improve the quality and effectiveness of the data, and improve the accuracy and reliability of ecological environment degradation evaluation; on the other hand, through the results of the deep learning model and the multi-dimensional analysis framework combining hyperspectral remote sensing image time series data and environmental monitoring time series data, it is possible to accurately conduct a comprehensive analysis of natural and human factors in environmental degradation, which is conducive to further formulating corresponding strategies based on the analysis results to alleviate the crisis of ecological environment degradation and provide a scientific basis for ecological protection.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0018] Figure 1 The following is a schematic diagram showing the composition of an ecological environment degradation assessment system in an exemplary embodiment of the present disclosure;

[0019] Figure 2 A schematic diagram schematically illustrates a flow chart of an ecological environment degradation assessment method in an exemplary embodiment of the present disclosure;

[0020] Figure 3 The following schematically shows a structural diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0022] In addition, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present disclosure.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0024] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0025] As global ecological and environmental changes intensify, regional ecosystem degradation is becoming increasingly serious. The speed and scope of ecological degradation are continuing to expand, particularly under the dual influence of large-scale human activities and natural factors. Ecosystem degradation not only directly impacts biodiversity, soil quality, and water conservation, but can also trigger serious environmental disasters such as soil erosion, desertification, and forest fires, further threatening the human living environment and sustainable economic development.

[0026] Traditional ecosystem degradation analysis methods mainly rely on manual surveys and ground monitoring. Although these methods can provide certain degradation assessments, they face problems such as poor timeliness, limited coverage, and low data accuracy.

[0027] At present, although there are some ecological monitoring methods based on deep learning, the following problems still exist: deep learning models are still immature in integrating and processing multi-source data, making it difficult to fully utilize the spatiotemporal correlation between historical data and real-time data; existing methods lack a comprehensive analysis of the impact of natural and human factors, resulting in inaccurate determination of the causes of degradation; and analysis of large areas still relies on a single data source or method, failing to achieve a comprehensive assessment from multiple dimensions and angles.

[0028] Therefore, this paper proposes a new, multi-source data-driven, intelligent regional ecological degradation assessment method based on deep learning and hyperspectral remote sensing. This method, by constructing a deep learning model comprising a multiscale convolutional neural network and a spatiotemporal long-short-term memory network, combines hyperspectral data to conduct large-scale, long-term, and historical degradation analysis across the entire region, inferring the causes of degradation and their impact, thereby enabling intelligent analysis of regional ecosystem degradation. This approach aims to address the limitations of existing methods, improve the accuracy and adaptability of ecological degradation analysis, and provide more reliable data support for ecological protection and resource management.

[0029] The implementation details of the technical solution of the embodiment of the present disclosure are described in detail below.

[0030] Figure 1 The following schematically shows the composition of an ecological environment degradation assessment system in an exemplary embodiment of the present disclosure. Figure 1 As shown, the ecological environment degradation assessment system 100 includes:

[0031] The data acquisition module 101 is used to collect time series data of hyperspectral remote sensing images and time series data of environmental monitoring;

[0032] The feature extraction module 102 includes a first extraction network and a second extraction network, wherein the first extraction network is used to extract spatial-spectral fusion feature data of the hyperspectral remote sensing image time series data, and the second extraction network is used to extract temporal-spatial fusion feature data of the hyperspectral remote sensing image time series data;

[0033] A canopy closure calculation module 103 is configured to calculate canopy closure level time series data using a multivariate regression model based on the spatial-spectral fusion feature data, the temporal-spatial fusion feature data, and the environmental monitoring time series data;

[0034] The degradation assessment module 104 is configured to analyze the causes of degradation based on the canopy density time series data, the hyperspectral remote sensing image time series data, and the environmental monitoring time series data; the causes of degradation include natural factors and human factors;

[0035] The report generation module 105 is used to generate an ecological environment degradation assessment report based on the data analysis process and data analysis results in the degradation assessment module.

[0036] In the technical solutions provided in some embodiments of the present disclosure, on the one hand, the present disclosure uses hyperspectral remote sensing image time series data and environmental monitoring time series data as the data basis for evaluation, and extracts spatial-spectral fusion feature data and time-space fusion feature data through a deep learning network to perform multi-source data fusion. This is not only applicable to regional ecological environment monitoring and evaluation over a large range and long time series, but also can improve the quality and effectiveness of the data, and improve the accuracy and reliability of ecological environment degradation evaluation; on the other hand, through the results of the deep learning model and the multi-dimensional analysis framework combining hyperspectral remote sensing image time series data and environmental monitoring time series data, it is possible to accurately conduct a comprehensive analysis of natural and human factors in environmental degradation, which is conducive to further formulating corresponding strategies based on the analysis results to alleviate the crisis of ecological environment degradation and provide a scientific basis for ecological protection.

[0037] Below, each module of the ecological environment degradation assessment system in this exemplary embodiment will be described in more detail with reference to the accompanying drawings and embodiments.

[0038] In one embodiment of the present disclosure, the data acquisition module 101 is used to acquire hyperspectral remote sensing image time series data and environmental monitoring time series data.

[0039] Specifically, the data acquisition module 101 is used to perform long-term series monitoring of the target geographical area to obtain hyperspectral remote sensing image time series data consisting of hyperspectral remote sensing images that change with time, and environmental monitoring time series data consisting of environmental monitoring data that change with time.

[0040] Among them, hyperspectral remote sensing images can be collected by remote sensing satellites. Each band in the image provides a detailed description of the material properties of the ground objects. And because hyperspectral images contain multiple continuous spectral band information, they can provide sufficient spectral resolution to identify and distinguish the fine differences in surface materials. At the same time, hyperspectral images not only provide spectral information, but also contain spatial distribution information of the ground objects.

[0041] Environmental monitoring data is the environmental information targeted by ecological and environmental degradation assessments. It can be collected through environmental surveys using appropriate data collection instruments. For example, it can include one or more ecological and environmental data on forestry, agriculture, grassland, and wetlands in the target geographic area at a specific point in time, thereby enabling a comprehensive analysis of ecological and environmental degradation and improving the accuracy of data analysis results.

[0042] In one embodiment of the present disclosure, the feature extraction module 102 includes a first extraction network and a second extraction network, which are respectively used to extract spatial-spectral fusion feature data and temporal-spatial fusion feature data of the hyperspectral remote sensing image time series data.

[0043] Among them, the first extraction network is a multi-scale convolutional neural network (MS-CNN) model. The structure of MS-CNN includes: an input layer, which extracts the hyperspectral remote sensing image at the target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module, which collaboratively extracts the spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer, which fuses the spatial feature data and the spectral feature data to obtain the spatial-spectral fusion feature data at the target time point.

[0044] CNN, short for Convolutional Neural Network, is a deep learning model particularly well-suited for processing image data. Its operating principle is based on convolution operations and feature extraction. In the convolution layer, the convolution kernel slides over the input image, extracting local features through weighted summation and bias operations in local regions. These features undergo nonlinear transformations using activation functions, enhancing the model's expressive power. The pooling layer then downsamples the convolutional layer output, reducing the data dimension and computational complexity while retaining important features. Finally, the fully connected layer further processes and classifies the pooling layer output.

[0045] The “MS” in MS-CNN stands for Multi-Scale, which reflects the core characteristics and design goals of the MS-CNN network, which is to detect objects at different scales to improve detection accuracy and robustness.

[0046] Specifically, in order to solve the problem that traditional CNN models cannot fully mine the multi-dimensional features in the data, the MS-CNN model of this application uses multi-scale convolution operations to simultaneously extract image features at multiple scales, while adapting to remote sensing data at different resolutions and scales.

[0047] In addition, the MS-CNN model of this application introduces a collaborative feature extraction module based on the traditional CNN model to achieve joint optimization of spatial information and spectral information, thereby improving the accuracy of ecological environment degradation detection.

[0048] Extract the hyperspectral remote sensing image at the target time point as ,in H is the image height; W is the image width; C is the number of spectral bands. Input it into the trained MS-CNN model to obtain a feature map; that is, ,in F The feature dimension of the model output.

[0049] For each pixel in the hyperspectral remote sensing image, its spatial characteristics are considered simultaneously. and spectral characteristics , can be extracted collaboratively through the following formula:

[0050]

[0051] Then, the spatial features and spectral features are fused by weighted summation to obtain spatial-spectral fusion feature data:

[0052]

[0053] Where α is the weight that controls the contribution of spatial and spectral features.

[0054] During the extraction of spatial features S, multi-scale convolution operations can be employed. Using convolution kernels of varying scales, multi-scale convolutions are performed on the input hyperspectral image. The feature maps extracted at each scale correspond to spatial information within different receptive fields. Small-scale convolutions capture local details such as texture and edges, while large-scale convolutions extract global context such as regional structure and shape. By fusing multi-scale features through feature concatenation, the resulting output spatial feature map (FF) integrates spatial information at different granularities, enhancing the model's adaptability to changes in target scale.

[0055] At every scale s The convolution operation can be expressed as:

[0056]

[0057] Where, For scale s Convolution operation on ; is the convolution weight; is the bias term; is the number of convolution kernels; It is a convolution operation to extract spatial features.

[0058] Then use the feature splicing method to achieve the fusion of multiple convolutional layer output features, specifically:

[0059]

[0060] Where: The convolution results at different scales are concatenated to obtain the final spatial features. S .

[0061] The second extraction network is a spatiotemporal long short-term memory network (ST-LSTM) model. The structure of ST-LSTM includes: an input layer, which receives the time series data of the hyperspectral remote sensing image; an LSTM module, which extracts the time series feature data from the time series data of the hyperspectral remote sensing image through memory units and a gating mechanism; a spatial convolution module, which extracts the spatial feature data from the time series data of the hyperspectral remote sensing image through a spatial convolution operation; and a fully connected layer, which fuses the time series feature data and the spatial feature data to obtain the time-space fusion feature data.

[0062] The "ST" in ST-LSTM stands for "Spatial-Temporal." In ST-LSTM, the "ST" emphasizes the network's ability to process spatiotemporal data. Spatial features refer to the distribution and interrelationships of data in two- or three-dimensional space, while temporal features describe how data changes over time. ST-LSTM combines spatial and temporal features, enabling it to excel at processing data with spatiotemporal dependencies.

[0063] LSTM stands for "Long Short-Term Memory" neural network. It is a special recurrent neural network (RNN) structure designed to address the vanishing and exploding gradient problems encountered by traditional RNNs when processing long sequences of data. By introducing structural units such as input gates, forget gates, and output gates, LSTM enables efficient storage and updating of information, enabling it to capture long-term dependencies in long sequences of data.

[0064] Specifically, the ST-LSTM model combines the time series modeling capability and spatial information capture capability of the LSTM network to monitor changes in the temporal dimension of ecosystem degradation. At the same time, it can also integrate its spatial information and model spatiotemporal relationships simultaneously, making it very suitable for monitoring dynamic changes in ecological degradation.

[0065] Assume that at each time step t, the input hyperspectral remote sensing image time series data , the hyperspectral remote sensing image time series data is converted into a hyperspectral remote sensing image sequence represented as .

[0066] The basic operation of LSTM can be expressed as:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] in: is the input gate; For the Gate of Forgetfulness, is the output gate, For status; is the hidden state at the current moment, which is the time series feature data; is the sigmoid activation function, tanh is the hyperbolic tangent function; W and b are the weight matrix and bias term respectively.

[0073] In addition, in order to enhance the capture of spatial information of the data, the ST-LSTM model adds a spatial convolution module (SCM). After the spatial convolution operation, the spatial feature data of the hyperspectral remote sensing image can be extracted, which is expressed by the following formula:

[0074]

[0075] The fully connected layer of ST-LSTM fuses the temporal feature data and the spatial feature data to obtain the time-space fusion feature data, which is expressed by the following formula:

[0076]

[0077] Where: α is the weight; S t It is the spatial feature data extracted by the spatial convolution module;h t It is the time series feature data output by LSTM.

[0078] In one embodiment of the present disclosure, the canopy calculation module 103 is used to calculate canopy level time series data.

[0079] Specifically, a multivariate regression model was used to calculate the canopy density level of the target geographical area at each period as the canopy density level time series data. The mathematical model is as follows:

[0080]

[0081] Where: C t is the canopy density level at time t; is the independent variable related to the canopy density level; and is the regression coefficient; is the error term.

[0082] Based on the distribution of canopy density levels in different periods, the degradation rate is calculated using differential analysis, and the changing trends in each period are compared. The details are as follows:

[0083]

[0084] Where: For the t The degradation rate of the moment; For the t Canopy density level at the moment; For the t -1 moment of canopy density.

[0085] For the long-term trend of ecological degradation, a time series model is used to fit the overall trend of degradation, specifically:

[0086]

[0087] Where: is the total degradation degree; T is the time span of the data.

[0088] In one embodiment of the present disclosure, the degradation assessment module 104 is used to analyze the causes of degradation. The degradation cause analysis module includes: a natural factor analysis module for calculating the correlation between natural factors and canopy levels based on the canopy level time series data and the environmental monitoring time series data; a human factor analysis module for evaluating the impact of human factors on ecological environment degradation based on the canopy level time series data and the hyperspectral remote sensing image time series data; and a ratio analysis module for evaluating the ratio of the impact of natural factors and human factors on ecological environment degradation based on the analysis results of the natural factor analysis module and the human factor analysis module.

[0089] Specifically, when analyzing the causes of ecological environment degradation, it is necessary to consider both natural and human factors.

[0090] When analyzing natural factors, a deep learning model was used to conduct correlation analysis between various natural factors and changes in canopy density levels, and then the correlation coefficient was used to evaluate the impact of natural factors on changes in canopy density levels.

[0091] The analysis is carried out by taking meteorological data as an example of natural factors. The environmental monitoring time series data includes meteorological data of different periods (referred to as meteorological data for short), which is correlated with the canopy density time series data (referred to as canopy density data for short) to obtain the correlation coefficient between meteorological data and canopy density data. R , the specific calculation process is as follows:

[0092]

[0093] Where: X For meteorological data; Y is the canopy density data; cov ( X , Y ) is the covariance; Var ( X )and Var ( Y ) are their respective variances.

[0094] Similarly, we can also analyze the impact of other natural factors on changes in canopy density levels, such as topography, hydrology, soil, biology, etc., and calculate the correlation coefficients between these environmental content data and canopy density data respectively.

[0095] When analyzing human factors, we compare hyperspectral remote sensing images from different time periods to assess the impact of human factors such as regional development and building additions, and use image difference technology to quantify the changes. Image difference is expressed as:

[0096]

[0097] Where: and They are t and t-1 Hyperspectral remote sensing images at all times.

[0098] By comparing the correlation between natural factors and human factors, the weighted average method is used to comprehensively analyze and calculate the impact ratio of natural and human factors, specifically:

[0099]

[0100]

[0101] Where: The proportion of natural factors; The proportion of human factors; is the weight of each natural factor; is the impact value of each natural factor.

[0102] In one embodiment of the present disclosure, the report generation module 105 is used to generate an ecological environment degradation assessment report.

[0103] The software automatically generates an analysis report, including output in text, images (e.g., PDF and JPG formats), and relevant mathematical formulas. The images are automatically generated by a computer based on the output of the degradation assessment module, showcasing the ecological environment degradation process and analysis of its causes. Examples of ecological environment degradation processes include changes in canopy density, meteorological data comparisons, and satellite imagery. This disclosure is provided for illustrative purposes only.

[0104] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0105] The present disclosure also provides an ecological environment degradation assessment method, which is completed using the above-mentioned ecological environment degradation assessment system. Figure 2 The following is a schematic diagram showing a flow chart of an ecological environment degradation assessment method in an exemplary embodiment of the present disclosure. Figure 2 The specific steps of the ecological environment degradation assessment method are as follows:

[0106] Start to conduct intelligent analysis of ecological environment degradation assessment;

[0107] Multivariate data retrieval and acquisition, including hyperspectral remote sensing data and environmental monitoring time series data (such as tree species, soil type, vegetation cover, NDVI value, etc.);

[0108] Data spatial feature extraction: using the MS-CNN model to extract spatial features at multiple scales from images, and to achieve joint optimization of spatial and spectral information to improve data accuracy;

[0109] Extracting temporal features from data, using the ST-LSTM model to monitor temporal changes in ecosystem degradation, while also building a spatiotemporal relationship model to monitor dynamic changes in ecological degradation;

[0110] The canopy density distribution was determined based on the results of the above feature extraction and combined with multi-source data using a multivariate regression model to determine the canopy density distribution in each period.

[0111] Analysis of canopy density changes and degradation, using differentials to calculate degradation rates and comparing trends over time;

[0112] Analysis and inference of degradation causes: Based on environmental monitoring time series data, the causes of ecological degradation are analyzed and inferred;

[0113] Analysis of the proportion of degradation causes, using the weighted average method to comprehensively analyze and calculate the proportion of the impact of natural and human factors;

[0114] End, automatic generation of reports.

[0115] The above-mentioned ecological environment degradation assessment method has been described in detail when introducing the functions of each module in the ecological environment degradation assessment system, so it will not be elaborated here.

[0116] Based on this approach, a deep learning model combining a multi-scale convolutional neural network (MS-CNN) and a spatiotemporal long short-term memory network (ST-LSTM) is used to extract spatial features and temporal variation from hyperspectral remote sensing imagery. Furthermore, by constructing a multidimensional analysis framework for multiple sets of environmental and remote sensing data, a comprehensive analysis of natural and human factors is conducted. Using multivariate regression and time series analysis techniques, the types and causes of ecological degradation within the region are accurately identified, providing a scientific basis for ecological protection.

[0117] By combining deep learning with remote sensing technology, efficient and accurate monitoring of ecosystem degradation is possible. This approach, particularly in complex ecological environments, can automatically extract and analyze large-scale, long-term data series, significantly improving analysis accuracy and efficiency and enabling accurate assessments of regional ecological degradation. Intelligent data processing techniques, through model optimization and multi-source data fusion, enhance the accuracy and reliability of degradation analysis results. Automated report output not only improves analysis efficiency but also provides a scientific basis for applications in ecological protection, environmental monitoring, and other fields.

[0118] In an exemplary embodiment of the present disclosure, a storage medium capable of implementing the above method is also provided. This storage medium may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a mobile phone. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0119] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. Figure 3 The following schematically shows a structural diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure.

[0120] It should be noted that Figure 3 The computer system 300 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0121] like Figure 3 As shown, computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage 308 into random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0122] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, allowing computer programs read from the media to be installed in the storage section 308 as needed.

[0123] In particular, according to embodiments of the present disclosure, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the system of the present disclosure.

[0124] It should be noted that the computer-readable medium described in the embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, and the units described may also be provided in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0127] As another aspect, the present disclosure further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the methods described in the above embodiments.

[0128] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0129] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented through software or through a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0130] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0131] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An ecological environment degradation assessment system, characterized in that: include: Data acquisition module, used to collect hyperspectral remote sensing image time series data and environmental monitoring time series data; A feature extraction module includes a first extraction network and a second extraction network, wherein the first extraction network is used to collaboratively extract spatial feature data and spectral feature data of the hyperspectral remote sensing image time series data, and fuse the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data, and the second extraction network is used to extract the time-space fusion feature data of the hyperspectral remote sensing image time series data through a memory unit, a gating mechanism, and a spatial convolution operation; The first extraction network is a multi-scale convolutional neural network (MS-CNN) model, comprising: an input layer for extracting a hyperspectral remote sensing image at a target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module for collaboratively extracting spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer for fusing the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data at the target time point. The second extraction network is a spatiotemporal long short-term memory network ST-LSTM model, including: an input layer, which extracts a hyperspectral remote sensing image at a target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module, which collaboratively extracts spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer, which fuses the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data at the target time point. The canopy closure calculation module is used to calculate the canopy closure level time series data using a multivariate regression model based on the spatial-spectral fusion feature data, the temporal-spatial fusion feature data, and the environmental monitoring time series data; the details are as follows: ； Where: C t For in time Canopy density level at the moment; is the independent variable related to the canopy density level; and is the regression coefficient; is the error term; A degradation assessment module, configured to analyze degradation causes based on the canopy density level time series data, the hyperspectral remote sensing image time series data, and the environmental monitoring time series data; the degradation causes include natural factors and human factors; The report generation module is used to generate an ecological environment degradation assessment report based on the data analysis process and data analysis results in the degradation assessment module.

2. The ecological environment degradation assessment system according to claim 1, characterized in that: The degradation cause analysis module includes: A natural factor analysis module is used to calculate the correlation between natural factors and canopy density levels based on the canopy density level time series data and the environmental monitoring time series data; A human factor analysis module, configured to evaluate the impact of human factors on ecological environment degradation based on the canopy density level time series data and the hyperspectral remote sensing image time series data; The proportion analysis module evaluates the impact proportions of natural factors and human factors on ecological environment degradation respectively according to the analysis results of the natural factor analysis module and the human factor analysis module.

3. A method for assessing ecological environment degradation, characterized in that: include: Collect hyperspectral remote sensing image time series data and environmental monitoring time series data; Collaboratively extracting spatial feature data and spectral feature data of the hyperspectral remote sensing image time series data based on the first extraction network, and fusing the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data, and extracting temporal-spatial fusion feature data of the hyperspectral remote sensing image time series data based on the second extraction network through memory units, gating mechanisms, and spatial convolution operations; The first extraction network is a multi-scale convolutional neural network (MS-CNN) model, comprising: an input layer for extracting a hyperspectral remote sensing image at a target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module for collaboratively extracting spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer for fusing the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data at the target time point. The second extraction network is a spatiotemporal long short-term memory network ST-LSTM model, including: an input layer, which extracts a hyperspectral remote sensing image at a target time point based on the hyperspectral remote sensing image time series data; a collaborative feature extraction module, which collaboratively extracts spatial feature data and spectral feature data of the hyperspectral remote sensing image; and a fully connected layer, which fuses the spatial feature data and the spectral feature data to obtain spatial-spectral fusion feature data at the target time point. Calculating canopy density level time series data using a multivariate regression model based on the spatial-spectral fusion feature data, the temporal-spatial fusion feature data, and the environmental monitoring time series data; The degradation cause analysis is performed based on the canopy density level time series data, the hyperspectral remote sensing image time series data, and the environmental monitoring time series data to obtain data analysis results; specifically, the following: ； Where: C t For in time Canopy density level at the moment; is the independent variable related to the canopy density level; and is the regression coefficient; is the error term; An ecological environment degradation assessment report is generated based on the data analysis results.

4. The ecological environment degradation assessment method according to claim 3, characterized in that: The data analysis results obtained by performing degradation cause analysis based on the canopy density level time series data, the hyperspectral remote sensing image time series data, and the environmental monitoring time series data include: Calculating the correlation between natural factors and canopy levels based on the canopy level time series data and the environmental monitoring time series data; and Assessing the impact of human factors on ecological environment degradation based on the canopy density level time series data and the hyperspectral remote sensing image time series data; Based on the correlation and the impact value, the impact ratios of natural factors and human factors on ecological environment degradation are evaluated to obtain the data analysis results.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ecological environment degradation assessment method according to any one of claims 3 to 4 is implemented.

6. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more computer programs, enables the one or more processors to implement the ecological environment degradation assessment method as described in any one of claims 3 to 4.

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