A satellite image-based dynamic monitoring and analysis method for forest ecological sensitive areas

Abstract

The application discloses a kind of forest ecological sensitive area dynamic monitoring analysis method based on satellite image, belong to forest ecological monitoring technical field.It includes the following steps: step 1, generate standardized multidimensional remote sensing dataset;Step 2, build the feature pyramid model including macroscopic, mesoscopic and microscopic three levels, extract ecological sensitivity index on each scale level;Step 3, automatically calculate and adjust the index weight coefficient under different scales, and generate comprehensive ecological sensitivity evaluation results through the lossless fusion of multi-scale features;Step 4, identify ecological sensitive area based on multi-scale feature evaluation results;Step 5, carry out change detection analysis simultaneously in multi-scale level, realize the accurate monitoring of the dynamic change of forest ecological sensitive area, and establish the quantitative evaluation index of change intensity;Step 6, based on change detection information, carry out time series analysis, predict the future change trend of ecological sensitive area, and carry out timely early warning to potential risk.

Description

Technical Field

[0001] This application relates to the field of forest ecological monitoring technology, and more specifically, to a dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery. Background Technology

[0002] With the increasing severity of global climate change and human activities, the impact of ecological and environmental changes on forest ecosystems is becoming increasingly significant. As a major habitat for global biodiversity, forests play a vital role in maintaining ecological balance, regulating climate, and protecting water resources. Therefore, timely monitoring and assessment of the health status of forest ecosystems, especially the dynamic changes in ecologically sensitive areas, has become a key task in ecological protection and forest resource management.

[0003] Currently, traditional methods for monitoring ecologically sensitive forest areas mainly rely on ground surveys and manual sampling. These methods are not only costly and time-consuming, but also have limited spatial coverage, making it difficult to achieve large-scale real-time monitoring and dynamic assessment. Furthermore, ground surveys are susceptible to the subjective judgment of investigators and measurement errors, resulting in poor accuracy and timeliness, and failing to meet the needs of modern forest ecological environment protection and sustainable management.

[0004] In recent years, the rapid development of remote sensing technology has provided new solutions for forest ecological monitoring. Satellite remote sensing imagery, as an efficient and wide-area monitoring method, can provide large-scale, long-term, and continuous forest ecological data. Through the fusion of multi-source remote sensing imagery data and multi-scale feature analysis, the spatial heterogeneity and temporal change trends of the forest ecological environment can be comprehensively reflected. However, existing methods for monitoring forest ecologically sensitive areas based on satellite imagery still have shortcomings in several aspects, such as differences in the spatiotemporal resolution of data, the accuracy of ecological sensitivity feature extraction, and the timeliness of dynamic change monitoring. Although some studies have attempted to use remote sensing imagery for monitoring and assessing forest ecologically sensitive areas, most methods are still insufficient in processing multi-source remote sensing data, cross-scale feature fusion, ecological sensitivity assessment, change detection, and risk prediction, and cannot achieve comprehensive, accurate, and dynamic monitoring of ecologically sensitive areas.

[0005] In summary, how to establish a multi-scale, dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery data, to identify ecologically sensitive areas in real time and accurately, and to monitor and predict dynamic changes, has become an urgent technical problem to be solved. Summary of the Invention

[0006] To overcome a series of shortcomings in existing technologies, the purpose of this application is to provide a dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery, comprising the following steps: Step 1: Collect multi-source satellite imagery data of the forest area and generate a standardized multidimensional remote sensing dataset; Step 2: Based on the multidimensional remote sensing dataset, construct a feature pyramid model with three levels: macroscopic, mesoscopic, and microscopic, and extract ecological sensitivity indicators at each scale level. Step 3: Automatically calculate and adjust the index weight coefficients at different scales, and generate a comprehensive ecological sensitivity assessment result through lossless fusion of multi-scale features; Step 4: Identify ecologically sensitive areas based on multi-scale feature evaluation results, and introduce spatial constraint rules to accurately classify and optimize the boundaries of ecologically sensitive areas; Step 5: Simultaneously conduct change detection and analysis at multiple scale levels to achieve accurate monitoring of dynamic changes in forest ecologically sensitive areas and establish quantitative evaluation indicators for change intensity. Step 6: Perform time series analysis based on change detection information to predict future change trends in ecologically sensitive areas and provide timely warnings of potential risks.

[0007] Furthermore, step 1 includes the following steps: Based on the research objectives and the characteristics of the target forest area, high-quality time-series remote sensing images were acquired to ensure the temporal continuity and spatial representativeness of the data. Radiometric calibration of raw remote sensing images converts digital image values ​​into surface reflectance, effectively eliminating sensor response differences and spectral distortion. The FLAASH atmospheric correction method is applied to remove the spectral interference of atmospheric particulate matter and aerosols on remote sensing images and enhance the expression of surface features in the images. An affine transformation method based on control points is used to accurately correct the geometric deformation of the images, ensuring geometric consistency and spatial registration accuracy between images from different times and different sensors. Based on image matching and band weighting techniques, the spectral features, spatial resolution and temporal characteristics of each source image are comprehensively evaluated to generate a fused image with both high spectral resolution and high spatial resolution, preserving the information features and spatial details of the original image to the greatest extent. Based on research needs, a unified data format, coordinate system, and metadata standard are defined, and a standardized multi-source remote sensing image data storage and management architecture is constructed to organize the preprocessed remote sensing images into a standardized multi-dimensional remote sensing dataset.

[0008] Furthermore, step 2 includes the following steps: At the macro level, using medium- and low-resolution remote sensing images, macro-features of regional ecosystems are extracted, and combined with topographic factors, a set of macro-indicators reflecting the basic characteristics of regional ecosystems is constructed. At the meso-level, medium-resolution remote sensing data are selected to extract ecological sensitivity characteristics of forest communities and landscapes. By identifying the boundaries, connectivity, and spatial distribution patterns of different vegetation types, the composition and structural characteristics of ecosystems at the meso-level are revealed. At the micro level, high-resolution remote sensing images are used to extract detailed features of forest ecosystems, and hyperspectral remote sensing data are combined to achieve accurate characterization of ecosystem features at the micro scale. A multi-scale feature pyramid network architecture is constructed to realize the hierarchical expression of ecological features at the macro, meso, and micro scales, and to form an ecological feature representation with hierarchy and semantic coherence through cross-scale feature association. By using deep convolutional neural networks and attention mechanisms, we can capture the complex nonlinear relationships and spatial dependencies of ecological features, thereby enhancing the semantic richness and discriminativeness of feature representation. We establish semantic mapping relationships between macroscopic, mesoscopic, and microscopic scale features, and employ multi-task learning and regularization techniques to constrain the feature learning process and suppress noise interference and overfitting risks.

[0009] Furthermore, step 3 includes the following steps: By calculating the information gain and discriminative power of each feature, the information value and ecological significance of macroscopic, mesoscopic and microscopic features are evaluated. The information entropy descending order and cumulative information gain method are used to preliminarily screen out key features that have a significant impact on ecological sensitivity. By calculating the coefficient of variation, mutation point detection, and trend analysis of ecological indicators at different time points, and combining them with spatiotemporal sensitivity assessment, the spatiotemporal dynamic change characteristics of each ecological feature are accurately quantified. Based on the importance, sensitivity, and spatiotemporal variation characteristics of ecological features, adaptive weighting coefficients for indicators at different scales are automatically generated. An attention-based feature fusion network is employed to adaptively learn the weights and interrelationships of features at different scales, achieving balanced integration of features at different scales and preserving the ecological significance of the original features to the greatest extent possible. By establishing a nonlinear mapping function and a probability transformation mechanism, the results of multi-scale feature fusion are transformed into a standardized ecological sensitivity index, thereby achieving a precise quantitative assessment of the sensitivity of regional ecosystems. Monte Carlo simulation and bootstrap resampling are used to quantify the confidence interval and uncertainty range of the evaluation results. The spatial distribution, temporal evolution and key areas of ecological sensitivity are displayed intuitively through multi-dimensional and multi-scale thematic maps, heat maps and dynamic charts.

[0010] Furthermore, the information gain of each feature is expressed by the formula: ,in, It is a feature The information gain is used to assess its contribution to the assessment of ecological sensitivity. It is a dataset The total entropy, representing the overall uncertainty of forest ecological characteristics, is expressed by the formula: ,in, It represents the total number of categories in the dataset; It is a category The probability of occurrence in the dataset; The discriminative power of each feature is expressed by the formula: ,in, Features Discrimination; Features The inter-class variance, reflecting the ability to distinguish different ecological sensitivities, is expressed by the formula: ,in, It is the total number of categories in the dataset. It is a category The mean; It is the mean of the entire dataset; It is a category The probability of occurrence in the dataset; It is a feature The intraclass variance reflects the magnitude of changes in ecological sensitivity within the same category.

[0011] Furthermore, the spatiotemporal dynamics of each ecological characteristic are precisely quantified using the following formula: ,in, Spatial location and time The amount of change in ecological characteristics; It is a point in time. The coefficient of variation, which measures the volatility of ecological indicators, is calculated using the following formula: , At a certain point in time The standard deviation of ecological indicators; At a certain point in time The average values ​​of ecological indicators; It is a point in time. The cumulative sum at each point is calculated using the following formula: ,in, It is a point in time. Ecological indicators It is the expected value of ecological indicators; Spatial location and time The spatiotemporal sensitivity index is calculated using the following formula: ,in, It is the rate of change of ecological indicators over time. It is the rate of change of ecological indicators in space.

[0012] Furthermore, the confidence interval for the evaluation results is expressed as follows: ,in, It is the critical value of the standard normal distribution; It is an estimate of the sample standard deviation; Indicates the number of Monte Carlo simulations performed; The formula for estimating the sample mean is: ,in It is the first Results of Monte Carlo simulations.

[0013] Furthermore, step 4 includes the following steps: The identification threshold of ecologically sensitive areas is determined by analyzing the probability distribution, cumulative distribution function, and information gain of the ecological sensitivity assessment results. Based on the intensity of sensitivity, the importance of ecological functions, and the ease of restoration, ecologically sensitive areas are divided into extremely sensitive areas, highly sensitive areas, moderately sensitive areas, and low-sensitive areas. Grading criteria and scoring standards are designed to achieve scientific and refined hierarchical management of ecologically sensitive areas. A spatial association model based on graph theory and geometric constraints is constructed to analyze the shape complexity, patch connectivity and spatial heterogeneity of ecologically sensitive areas. By introducing shape index, edge effect and spatial proximity constraints, the fragmentation and discontinuity of the boundaries of ecologically sensitive areas are effectively suppressed. By constructing a fitness function, taking into account ecosystem integrity, landscape connectivity, and protection costs, the boundaries of ecologically sensitive areas are automatically adjusted and refined to maintain the original spatial structure characteristics of the ecosystem to the greatest extent. Develop a constraint information integration algorithm based on Bayesian networks and fuzzy logic to achieve probabilistic inference and semantic fusion of heterogeneous spatial information.

[0014] Furthermore, step 5 includes the following steps: It integrates multiple advanced change detection algorithms, including object-oriented change detection, time-series change vector analysis, and deep learning-based change recognition models, to achieve accurate capture of multi-dimensional and multi-scale ecological changes; By comparing the spectral features, texture features, and spatial structure of remote sensing images from different periods, a multi-scale change recognition mechanism based on pixels and objects is established to effectively capture the micro-change details of forest ecologically sensitive areas. By comprehensively analyzing indicators such as changes in vegetation coverage, forest area, biodiversity index, and ecological corridor connectivity, an ecological sensitivity index model is constructed to achieve an objective and comprehensive assessment of the intensity of changes. Using spatial autocorrelation analysis, we can quantitatively analyze the spatial distribution characteristics of change intensity. At the same time, we can conduct forward-looking simulations and early warnings of possible future changes in forest ecologically sensitive areas, providing decision support for ecological protection and management.

[0015] Furthermore, step 6 includes the following steps: The collected data on changes in ecologically sensitive areas were systematically organized, including outlier removal, standardization, and time-dimensional alignment. Based on the specific change characteristics and data properties of ecologically sensitive areas, a suitable time series analysis model is selected and its parameters are optimized. Through fine fitting and cross-validation of historical change data, a predictive model that can accurately capture the dynamic changes of the ecosystem is constructed. Using the constructed prediction model, key ecological indicators in ecologically sensitive areas are predicted in multiple dimensions, and possible ecosystem development paths under different environmental conditions and intervention measures are generated through multi-scenario simulation. By quantitatively analyzing the ecological risk levels under different changing trends, we can design refined risk warning indicators to achieve accurate identification, dynamic monitoring, and timely warning of potential risks in ecologically sensitive areas. Through various visualization methods, the changing trends, potential ecological risks, and uncertainties of prediction models in ecologically sensitive areas are displayed intuitively.

[0016] Compared with the prior art, this application has the following beneficial effects: This application achieves accurate identification, quantitative assessment, and change monitoring of ecologically sensitive areas by constructing a multi-source satellite image dataset and combining it with multi-scale feature analysis. It can comprehensively and systematically analyze the dynamic changes of forest ecosystems and predict and warn of future trends, providing scientific basis and decision support for ecological protection and management. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery disclosed in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0019] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] like Figure 1 As shown, a dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery includes the following steps: Step 1: Collect multi-source satellite imagery data of the forest area and generate a standardized multidimensional remote sensing dataset; Step 2: Based on the multidimensional remote sensing dataset, construct a feature pyramid model with three levels: macroscopic, mesoscopic, and microscopic, and extract ecological sensitivity indicators at each scale level. Step 3: Automatically calculate and adjust the index weight coefficients at different scales, and generate a comprehensive ecological sensitivity assessment result through lossless fusion of multi-scale features; Step 4: Identify ecologically sensitive areas based on multi-scale feature evaluation results, and introduce spatial constraint rules to accurately classify and optimize the boundaries of ecologically sensitive areas; Step 5: Simultaneously conduct change detection and analysis at multiple scale levels to achieve accurate monitoring of dynamic changes in forest ecologically sensitive areas and establish quantitative evaluation indicators for change intensity. Step 6: Perform time series analysis based on change detection information to predict future change trends in ecologically sensitive areas and provide timely warnings of potential risks.

[0022] Step 1 is crucial for collecting remote sensing imagery data from different satellite platforms (such as Landsat, Sentinel-2, and MODIS). This data often has varying resolutions, observation bands, and spatial coverage. Standardizing this data eliminates inconsistencies caused by differences in satellite sensors, time signatures, and climate change, ensuring comparability and fusion of various data types in subsequent analyses. The standardization process typically includes radiometric correction, atmospheric correction, and georegistration to ensure consistent geographic coordinate systems and timestamps. The generation of multi-source remote sensing datasets includes not only visualized imagery data but also derived data based on remote sensing images, such as the National Distance Visibility Index (NDVI), soil moisture, temperature, and other environmental variables. These standardized datasets provide a rich source of information for subsequent ecological sensitivity analysis and form the foundation for multi-scale feature extraction and comprehensive evaluation.

[0023] Step 2 is crucial in utilizing the constructed feature pyramid model to perform stratified processing of data at different spatial scales. The macro-scale focuses on the ecological background and climate characteristics within a region, extracting long-term data related to global change (such as temperature and precipitation) to obtain basic information affecting the stability of forest ecosystems. The meso-scale involves local forest types, vegetation cover, soil moisture, and other environmental factors; this level of analysis helps identify the sensitivity of specific ecological sub-regions. The micro-scale focuses on the microclimate, species diversity, and ecological processes within the forest, such as tree growth cycles and groundwater levels. By extracting ecological sensitivity indicators from these multidimensional remote sensing data at different scales, a comprehensive analysis of ecological sensitivity can be conducted from multiple dimensions and scales. The information at each scale level complements each other, enabling a comprehensive capture of ecological changes and their potential impacts from macro to micro levels.

[0024] In step 3, a crucial technical task is quantifying the importance of the extracted feature indicators at each scale. Traditional methods may rely on expert experience to set weighting coefficients, but automated weighting methods can improve the system's adaptability and accuracy. This step utilizes machine learning algorithms (such as weighted regression and support vector machines) to analyze the influence of various ecological features at different scales and adaptively adjust the weights through the model. Lossless fusion of multi-scale features refers to integrating data from various scale levels while ensuring that no important information is lost during the fusion process. Commonly used lossless fusion methods include wavelet transform and principal component analysis (PCA), which can effectively combine features from different scales, avoid information loss, and ensure the accuracy of the integrated result. Finally, through multi-scale feature fusion, a comprehensive ecological sensitivity assessment result is generated, which accurately reflects the sensitivity status of the forest ecosystem at different levels.

[0025] Step 4 is crucial for spatial distribution analysis of the comprehensive ecological sensitivity assessment results, identifying sensitive areas within the forest. Spatial constraint rules play a vital role in this process, typically using factors such as forest geographic information, ecological environment conditions, and human impacts, combined with data on topography, land use, and vegetation cover, to accurately delineate the boundaries of ecologically sensitive areas. To improve the accuracy of sensitive area delineation, spatial constraint algorithms, such as shortest path algorithms and cluster analysis, are often introduced to avoid unreasonable sensitive area delineation. Furthermore, boundary optimization techniques can reduce false positives or false negatives, improve spatial resolution, and ensure that the delineation of each ecologically sensitive area more closely reflects actual environmental characteristics.

[0026] Step 5 aims to monitor the dynamic changes in ecologically sensitive areas through multi-scale hierarchical change detection and analysis. This process involves comparing remote sensing image data from different time points to detect changes in the ecological environment, such as forest cover changes and land use changes. Multi-scale analysis means that change detection is not limited to overall changes at the macro level but can also identify detailed changes at the micro level. Quantitative change intensity evaluation indicators can be used to quantify the magnitude and trend of changes, providing a basis for subsequent decision-making. Change intensity quantification indicators are typically based on the dissimilarity index of remote sensing images, such as NDVI change and vegetation index change rate. These indicators help assess the stability of ecologically sensitive areas and further understand the impact of environmental changes on ecosystems.

[0027] Step 6 aims to model historical change data using time series analysis techniques to predict future trends in ecologically sensitive areas. Commonly used time series analysis methods include ARIMA models and LSTM (Long Short-Term Memory) networks. These methods can identify periodic and nonlinear trends in the data and predict future change trajectories. Simultaneously, by assessing potential risks, timely warnings can be issued of potential ecological disasters, such as forest fires and pests. Utilizing satellite imagery data for dynamic monitoring and forecasting not only allows for the early identification of potential risks in forest ecosystems but also provides a scientific basis for forest management, helping decision-makers take effective countermeasures.

[0028] In summary, the dynamic monitoring and analysis method for forest ecologically sensitive areas based on satellite imagery, through techniques such as multi-source remote sensing data acquisition and processing, feature pyramid model construction, automated weight adjustment, and multi-scale feature fusion, can comprehensively and accurately assess the current status and changing trends of forest ecological sensitivity. The core advantage of this method lies in its high-precision, multi-dimensional analytical capabilities and its ability to monitor the dynamic changes of forest ecosystems in real time, providing scientific decision support. By introducing advanced technologies such as spatial constraint rules, change detection, and time series analysis, this method can effectively identify potential ecological risks and provide early warnings of future trends, thus providing strong technical support for forest resource protection and ecological environment monitoring.

[0029] Furthermore, step 1 includes the following steps: Based on the research objectives and the characteristics of the target forest area, high-quality time-series remote sensing images were acquired to ensure the temporal continuity and spatial representativeness of the data. Radiometric calibration of raw remote sensing images converts digital image values ​​into surface reflectance, effectively eliminating sensor response differences and spectral distortion. The FLAASH atmospheric correction method is applied to remove the spectral interference of atmospheric particulate matter and aerosols on remote sensing images and enhance the expression of surface features in the images. An affine transformation method based on control points is used to accurately correct the geometric deformation of the images, ensuring geometric consistency and spatial registration accuracy between images from different times and different sensors. Based on image matching and band weighting techniques, the spectral features, spatial resolution and temporal characteristics of each source image are comprehensively evaluated to generate a fused image with both high spectral resolution and high spatial resolution, preserving the information features and spatial details of the original image to the greatest extent. Based on research needs, a unified data format, coordinate system, and metadata standard are defined, and a standardized multi-source remote sensing image data storage and management architecture is constructed to organize the preprocessed remote sensing images into a standardized multi-dimensional remote sensing dataset.

[0030] In summary, the various processing techniques in step 1 complement each other, jointly ensuring the quality and accuracy of remote sensing image data. From acquiring high-quality time-series images to radiometric calibration, atmospheric correction, geometric correction, and multi-source data fusion, each step lays a solid foundation for subsequent monitoring and analysis of forest ecologically sensitive areas. The integrated application of these processing methods not only improves the spatial and spectral accuracy of remote sensing images but also ensures the comparability of data from different times and different sensors, providing reliable data support for accurate ecological monitoring, feature extraction, change detection, and risk early warning. In practical applications, these technologies can provide important decision-making basis for forest resource management, ecological protection, and environmental monitoring, promoting the widespread application of remote sensing technology in ecological research.

[0031] Furthermore, step 2 includes the following steps: At the macro level, using medium- and low-resolution remote sensing images, macro-features of regional ecosystems are extracted, and combined with topographic factors, a set of macro-indicators reflecting the basic characteristics of regional ecosystems is constructed. At the meso-level, medium-resolution remote sensing data are selected to extract ecological sensitivity characteristics of forest communities and landscapes. By identifying the boundaries, connectivity, and spatial distribution patterns of different vegetation types, the composition and structural characteristics of ecosystems at the meso-level are revealed. At the micro level, high-resolution remote sensing images are used to extract detailed features of forest ecosystems, and hyperspectral remote sensing data are combined to achieve accurate characterization of ecosystem features at the micro scale. A multi-scale feature pyramid network architecture is constructed to realize the hierarchical expression of ecological features at the macro, meso, and micro scales, and to form an ecological feature representation with hierarchy and semantic coherence through cross-scale feature association. By using deep convolutional neural networks and attention mechanisms, we can capture the complex nonlinear relationships and spatial dependencies of ecological features, thereby enhancing the semantic richness and discriminativeness of feature representation. We establish semantic mapping relationships between macroscopic, mesoscopic, and microscopic scale features, and employ multi-task learning and regularization techniques to constrain the feature learning process and suppress noise interference and overfitting risks.

[0032] In summary, step 2, by constructing a multi-scale feature extraction framework at the macro, meso, and micro levels, achieved a comprehensive and in-depth analysis of ecologically sensitive forest areas. The feature extraction methods at each scale level, from a broad perspective at the regional scale to precise monitoring at the detailed scale, ensured comprehensive perception and accurate identification of ecologically sensitive areas. Combining deep learning techniques, especially deep convolutional neural networks and attention mechanisms, further enhanced the understanding and representation of complex ecological features. Furthermore, through multi-task learning and regularization techniques, overfitting was effectively controlled during multi-scale feature fusion, improving the model's stability and reliability. Overall, the combination of these technologies provides strong technical support for the dynamic monitoring and management of ecologically sensitive forest areas and offers a scientific basis for ecological protection decision-making.

[0033] Furthermore, step 3 includes the following steps: By calculating the information gain and discriminative power of each feature, the information value and ecological significance of macroscopic, mesoscopic and microscopic features are evaluated. The information entropy descending order and cumulative information gain method are used to preliminarily screen out key features that have a significant impact on ecological sensitivity. By calculating the coefficient of variation, mutation point detection, and trend analysis of ecological indicators at different time points, and combining them with spatiotemporal sensitivity assessment, the spatiotemporal dynamic change characteristics of each ecological feature are accurately quantified. Based on the importance, sensitivity, and spatiotemporal variation characteristics of ecological features, adaptive weighting coefficients for indicators at different scales are automatically generated. An attention-based feature fusion network is employed to adaptively learn the weights and interrelationships of features at different scales, achieving balanced integration of features at different scales and preserving the ecological significance of the original features to the greatest extent possible. By establishing a nonlinear mapping function and a probability transformation mechanism, the results of multi-scale feature fusion are transformed into a standardized ecological sensitivity index, thereby achieving a precise quantitative assessment of the sensitivity of regional ecosystems. Monte Carlo simulation and bootstrap resampling are used to quantify the confidence interval and uncertainty range of the evaluation results. The spatial distribution, temporal evolution and key areas of ecological sensitivity are displayed intuitively through multi-dimensional and multi-scale thematic maps, heat maps and dynamic charts.

[0034] In summary, step 3, through multi-dimensional feature selection, weight optimization, feature fusion, standardized assessment, and uncertainty analysis, forms a comprehensive, accurate, and dynamic ecological sensitivity assessment framework. By calculating information gain and discriminative power, key features are selected, and combined with spatiotemporal dynamic analysis, the changing trends and risk points of the ecosystem can be captured. Adaptive weighting coefficients and attention-based feature fusion networks further improve the accuracy and reliability of the assessment. Nonlinear mapping and probability transformation mechanisms realize the transformation of complex features into standardized indicators, while Monte Carlo simulation and bootstrap resampling provide uncertainty quantification and risk assessment. The combination of these technologies can provide scientific support for dynamic monitoring and protection decisions in ecologically sensitive areas, ensuring the long-term health and sustainable development of ecosystems.

[0035] Furthermore, the information gain of each feature is expressed by the formula: ,in, It is a feature The information gain is used to assess its contribution to the assessment of ecological sensitivity. It is a dataset The total entropy, representing the overall uncertainty of forest ecological characteristics, is expressed by the formula: ,in, It represents the total number of categories in the dataset; It is a category The probability of occurrence in the dataset; The discriminative power of each feature is expressed by the formula: ,in, Features Discrimination; Features The inter-class variance, reflecting the ability to distinguish different ecological sensitivities, is expressed by the formula: ,in, It is the total number of categories in the dataset. It is a category The mean; It is the mean of the entire dataset; It is a category The probability of occurrence in the dataset; It is a feature The intraclass variance reflects the magnitude of changes in ecological sensitivity within the same category.

[0036] Furthermore, the spatiotemporal dynamics of each ecological characteristic are precisely quantified using the following formula: ,in, Spatial location and time The amount of change in ecological characteristics; It is a point in time. The coefficient of variation, which measures the volatility of ecological indicators, is calculated using the following formula: , At a certain point in time The standard deviation of ecological indicators; At a certain point in time The average values ​​of ecological indicators; It is a point in time. The cumulative sum at each point is calculated using the following formula: ,in, It is a point in time. Ecological indicators It is the expected value of ecological indicators; Spatial location and time The spatiotemporal sensitivity index is calculated using the following formula: ,in, It is the rate of change of ecological indicators over time. It is the rate of change of ecological indicators in space.

[0037] Furthermore, the confidence interval for the evaluation results is expressed as follows: ,in, It is the critical value of the standard normal distribution; It is an estimate of the sample standard deviation; Indicates the number of Monte Carlo simulations performed; The formula for estimating the sample mean is: ,in It is the first Results of Monte Carlo simulations.

[0038] Furthermore, step 4 includes the following steps: The identification threshold of ecologically sensitive areas is determined by analyzing the probability distribution, cumulative distribution function, and information gain of the ecological sensitivity assessment results. Based on the intensity of sensitivity, the importance of ecological functions, and the ease of restoration, ecologically sensitive areas are divided into extremely sensitive areas, highly sensitive areas, moderately sensitive areas, and low-sensitive areas. Grading criteria and scoring standards are designed to achieve scientific and refined hierarchical management of ecologically sensitive areas. A spatial association model based on graph theory and geometric constraints is constructed to analyze the shape complexity, patch connectivity and spatial heterogeneity of ecologically sensitive areas. By introducing shape index, edge effect and spatial proximity constraints, the fragmentation and discontinuity of the boundaries of ecologically sensitive areas are effectively suppressed. By constructing a fitness function, taking into account ecosystem integrity, landscape connectivity, and protection costs, the boundaries of ecologically sensitive areas are automatically adjusted and refined to maintain the original spatial structure characteristics of the ecosystem to the greatest extent. Develop a constraint information integration algorithm based on Bayesian networks and fuzzy logic to achieve probabilistic inference and semantic fusion of heterogeneous spatial information.

[0039] In summary, step 4, through a combination of analytical methods, achieves refined and scientific management in the delineation of ecologically sensitive areas. First, probability distribution and information gain analysis determine the identification thresholds for ecologically sensitive areas, providing a basis for accurate delineation. Then, based on sensitivity intensity, ecological function, and ease of restoration, sensitive areas are divided into different levels for differentiated management. Furthermore, spatial correlation models are used to analyze boundary shape, connectivity, and heterogeneity, optimizing the spatial layout of ecologically sensitive areas. Finally, the application of fitness functions and information integration algorithms provides multi-dimensional support for boundary refinement and ecological information fusion. By integrating these technologies, step 4 provides a comprehensive, refined, and efficient scheme for the classification and optimization management of ecologically sensitive areas, which has significant practical implications and application value for ecological protection and environmental management.

[0040] Furthermore, step 5 includes the following steps: It integrates multiple advanced change detection algorithms, including object-oriented change detection, time-series change vector analysis, and deep learning-based change recognition models, to achieve accurate capture of multi-dimensional and multi-scale ecological changes; By comparing the spectral features, texture features, and spatial structure of remote sensing images from different periods, a multi-scale change recognition mechanism based on pixels and objects is established to effectively capture the micro-change details of forest ecologically sensitive areas. By comprehensively analyzing indicators such as changes in vegetation coverage, forest area, biodiversity index, and ecological corridor connectivity, an ecological sensitivity index model is constructed to achieve an objective and comprehensive assessment of the intensity of changes. Using spatial autocorrelation analysis, we can quantitatively analyze the spatial distribution characteristics of change intensity. At the same time, we can conduct forward-looking simulations and early warnings of possible future changes in forest ecologically sensitive areas, providing decision support for ecological protection and management.

[0041] In summary, step 5 provides comprehensive and accurate technical support for the dynamic monitoring of ecologically sensitive forest areas by integrating multiple advanced change detection algorithms, establishing a multi-scale change identification mechanism, constructing an ecological sensitivity index model, and applying spatial autocorrelation analysis and forward-looking simulation. The combination of multiple change detection algorithms enables precise capture of ecological changes at different scales and dimensions, improving the accuracy and reliability of detection results. The construction of the ecological sensitivity index model allows for an objective assessment of the intensity of ecological changes, further providing a scientific basis for ecological protection. The combination of spatial autocorrelation analysis and forward-looking simulation provides forward-looking predictions and early warnings for future changes in ecologically sensitive forest areas. Overall, step 5 provides strong decision support for ecological protection and management, helping relevant departments to respond promptly to potential ecological risks and promote sustainable ecological environment management.

[0042] Furthermore, step 6 includes the following steps: The collected data on changes in ecologically sensitive areas were systematically organized, including outlier removal, standardization, and time-dimensional alignment. Based on the specific change characteristics and data properties of ecologically sensitive areas, a suitable time series analysis model is selected and its parameters are optimized. Through fine fitting and cross-validation of historical change data, a predictive model that can accurately capture the dynamic changes of the ecosystem is constructed. Using the constructed prediction model, key ecological indicators in ecologically sensitive areas are predicted in multiple dimensions, and possible ecosystem development paths under different environmental conditions and intervention measures are generated through multi-scenario simulation. By quantitatively analyzing the ecological risk levels under different changing trends, we can design refined risk warning indicators to achieve accurate identification, dynamic monitoring, and timely warning of potential risks in ecologically sensitive areas. Through various visualization methods, the changing trends, potential ecological risks, and uncertainties of prediction models in ecologically sensitive areas are displayed intuitively.

[0043] In summary, step 6 provides comprehensive support for the dynamic monitoring and management of ecologically sensitive areas through data processing, predictive model construction, multi-scenario simulation, risk assessment and early warning, and visualization. Refined data processing and parameter optimization ensure high accuracy of the predictive model; multi-dimensional prediction and scenario simulation provide multiple ecological development paths, offering decision-makers diverse response strategies; ecological risk assessment and early warning ensure timely identification and effective response to ecological risks; and visualization allows for the intuitive presentation of complex ecological changes and prediction results, further enhancing the transparency and scientific rigor of decision-making. Overall, step 6 provides scientific tools and methods for monitoring changes, assessing risks, and issuing early warnings in forest ecologically sensitive areas, contributing to the sustainable management of ecosystems.

[0044] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic monitoring and analysis of ecologically sensitive forest areas based on satellite imagery, characterized in that, Includes the following steps: Step 1: Collect multi-source satellite imagery data of the forest area and generate a standardized multidimensional remote sensing dataset; Step 2: Based on the multidimensional remote sensing dataset, construct a feature pyramid model with three levels: macroscopic, mesoscopic, and microscopic, and extract ecological sensitivity indicators at each scale level. Step 3: Automatically calculate and adjust the index weight coefficients at different scales, and generate a comprehensive ecological sensitivity assessment result through lossless fusion of multi-scale features; Step 4: Identify ecologically sensitive areas based on multi-scale feature evaluation results, and introduce spatial constraint rules to accurately classify and optimize the boundaries of ecologically sensitive areas; Step 5: Simultaneously conduct change detection and analysis at multiple scale levels to achieve accurate monitoring of dynamic changes in forest ecologically sensitive areas and establish quantitative evaluation indicators for change intensity. Step 6: Perform time series analysis based on change detection information to predict future change trends in ecologically sensitive areas and provide timely warnings of potential risks.

2. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 1 includes the following steps: Based on the research objectives and the characteristics of the target forest area, high-quality time-series remote sensing images were acquired to ensure the temporal continuity and spatial representativeness of the data. Radiometric calibration of raw remote sensing images converts digital image values ​​into surface reflectance, effectively eliminating sensor response differences and spectral distortion. The FLAASH atmospheric correction method is applied to remove the spectral interference of atmospheric particulate matter and aerosols on remote sensing images and enhance the expression of surface features in the images. An affine transformation method based on control points is used to accurately correct the geometric deformation of the images, ensuring geometric consistency and spatial registration accuracy between images from different times and different sensors. Based on image matching and band weighting techniques, the spectral features, spatial resolution and temporal characteristics of each source image are comprehensively evaluated to generate a fused image with both high spectral resolution and high spatial resolution, preserving the information features and spatial details of the original image to the greatest extent. Based on research needs, a unified data format, coordinate system, and metadata standard are defined, and a standardized multi-source remote sensing image data storage and management architecture is constructed to organize the preprocessed remote sensing images into a standardized multi-dimensional remote sensing dataset.

3. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 2 includes the following steps: At the macro level, using medium- and low-resolution remote sensing images, macro-features of regional ecosystems are extracted, and combined with topographic factors, a set of macro-indicators reflecting the basic characteristics of regional ecosystems is constructed. At the meso-level, medium-resolution remote sensing data are selected to extract ecological sensitivity characteristics of forest communities and landscapes. By identifying the boundaries, connectivity, and spatial distribution patterns of different vegetation types, the composition and structural characteristics of ecosystems at the meso-level are revealed. At the micro level, high-resolution remote sensing images are used to extract detailed features of forest ecosystems, and hyperspectral remote sensing data are combined to achieve accurate characterization of ecosystem features at the micro scale. A multi-scale feature pyramid network architecture is constructed to realize the hierarchical expression of ecological features at the macro, meso, and micro scales, and to form an ecological feature representation with hierarchy and semantic coherence through cross-scale feature association. By using deep convolutional neural networks and attention mechanisms, we can capture the complex nonlinear relationships and spatial dependencies of ecological features, thereby enhancing the semantic richness and discriminativeness of feature representation. We establish semantic mapping relationships between macroscopic, mesoscopic, and microscopic scale features, and employ multi-task learning and regularization techniques to constrain the feature learning process and suppress noise interference and overfitting risks.

4. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 3 includes the following steps: By calculating the information gain and discriminative power of each feature, the information value and ecological significance of macroscopic, mesoscopic and microscopic features are evaluated. The information entropy descending order and cumulative information gain method are used to preliminarily screen out key features that have a significant impact on ecological sensitivity. By calculating the coefficient of variation, mutation point detection, and trend analysis of ecological indicators at different time points, and combining them with spatiotemporal sensitivity assessment, the spatiotemporal dynamic change characteristics of each ecological feature are accurately quantified. Based on the importance, sensitivity, and spatiotemporal variation characteristics of ecological features, adaptive weighting coefficients for indicators at different scales are automatically generated. An attention-based feature fusion network is employed to adaptively learn the weights and interrelationships of features at different scales, achieving balanced integration of features at different scales and preserving the ecological significance of the original features to the greatest extent possible. By establishing a nonlinear mapping function and a probability transformation mechanism, the results of multi-scale feature fusion are transformed into a standardized ecological sensitivity index, thereby achieving a precise quantitative assessment of the sensitivity of regional ecosystems.

5. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 4, characterized in that, Step 3 also includes the following steps: Monte Carlo simulation and bootstrap resampling are used to quantify the confidence interval and uncertainty range of the evaluation results. The spatial distribution, temporal evolution and key areas of ecological sensitivity are displayed intuitively through multi-dimensional and multi-scale thematic maps, heat maps and dynamic charts.

6. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 4, characterized in that, The following formula is used to accurately quantify the spatiotemporal dynamics of each ecological characteristic and measure the volatility of ecological indicators.

7. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 4, characterized in that, Step 4 includes the following steps: The identification threshold of ecologically sensitive areas is determined by analyzing the probability distribution, cumulative distribution function, and information gain of the ecological sensitivity assessment results. Based on the intensity of sensitivity, the importance of ecological functions, and the ease of restoration, ecologically sensitive areas are divided into extremely sensitive areas, highly sensitive areas, moderately sensitive areas, and low-sensitive areas. Grading criteria and scoring standards are designed to achieve scientific and refined hierarchical management of ecologically sensitive areas. A spatial association model based on graph theory and geometric constraints is constructed to analyze the shape complexity, patch connectivity and spatial heterogeneity of ecologically sensitive areas. By introducing shape index, edge effect and spatial proximity constraints, the fragmentation and discontinuity of the boundaries of ecologically sensitive areas are effectively suppressed. By constructing a fitness function that comprehensively considers ecosystem integrity, landscape connectivity, and protection costs, the boundaries of ecologically sensitive areas are automatically adjusted and refined to maintain the original spatial structure characteristics of the ecosystem to the greatest extent.

8. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 4 includes the following steps: Develop a constraint information integration algorithm based on Bayesian networks and fuzzy logic to achieve probabilistic inference and semantic fusion of heterogeneous spatial information.

9. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 5 includes the following steps: It integrates multiple advanced change detection algorithms, including object-oriented change detection, time-series change vector analysis, and deep learning-based change recognition models, to achieve accurate capture of multi-dimensional and multi-scale ecological changes; By comparing the spectral features, texture features, and spatial structure of remote sensing images from different periods, a multi-scale change recognition mechanism based on pixels and objects is established to effectively capture the micro-change details of forest ecologically sensitive areas. By comprehensively analyzing indicators such as changes in vegetation coverage, forest area, biodiversity index, and ecological corridor connectivity, an ecological sensitivity index model is constructed to achieve an objective and comprehensive assessment of the intensity of changes. Using spatial autocorrelation analysis, we can quantitatively analyze the spatial distribution characteristics of change intensity. At the same time, we can conduct forward-looking simulations and early warnings of possible future changes in forest ecologically sensitive areas, providing decision support for ecological protection and management.

10. The method for dynamic monitoring and analysis of forest ecologically sensitive areas based on satellite imagery according to claim 1, characterized in that, Step 6 includes the following steps: The collected data on changes in ecologically sensitive areas were systematically organized, including outlier removal, standardization, and time-dimensional alignment. Based on the specific change characteristics and data properties of ecologically sensitive areas, a suitable time series analysis model is selected and its parameters are optimized. Through fine fitting and cross-validation of historical change data, a predictive model that can accurately capture the dynamic changes of the ecosystem is constructed. Using the constructed prediction model, key ecological indicators in ecologically sensitive areas are predicted in multiple dimensions, and possible ecosystem development paths under different environmental conditions and intervention measures are generated through multi-scenario simulation. By quantitatively analyzing the ecological risk levels under different changing trends, we can design refined risk warning indicators to achieve accurate identification, dynamic monitoring, and timely warning of potential risks in ecologically sensitive areas. Through various visualization methods, the changing trends, potential ecological risks, and uncertainties of prediction models in ecologically sensitive areas are displayed intuitively.

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