Forestry ecological environment monitoring system and method

Abstract

The invention relates to the technical field of environment monitoring systems, in particular to a forestry ecological environment monitoring system and method, and the system comprises an image collection module which is used for obtaining forestry environment image information; the central control module is connected with the image acquisition module and is used for processing and analyzing the forestry environment image information; the data analysis module is connected with the central control module and used for analyzing the processed data and generating environment monitoring parameters; the storage module is connected with the data analysis module and used for storing the environment monitoring parameters; the display module is used for displaying the environment monitoring parameters and the early warning information; and a data early warning module which is connected with the central control module and is used for realizing early warning based on the environment monitoring index. Wherein the central control module comprises an image segmentation module, and the image segmentation module adopts a multi-scale adaptive fusion segmentation algorithm to process forestry environment image information, so that the classification precision is improved, and the adaptability to the dynamic change of the forestry environment is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring systems, and more specifically, to a forestry ecological environment monitoring system and method. Background Art

[0002] With the increasing impact of global climate change and human activities on the natural environment, the monitoring and protection of forestry ecological environment has become an important issue that needs to be solved urgently. Traditional forestry ecological environment monitoring methods mainly rely on manual field surveys and simple remote sensing image analysis, which often seem to be inadequate when faced with large-scale, complex and changeable forestry ecosystems.

[0003] In recent years, with the rapid development of remote sensing technology and computer vision, forestry ecological environment monitoring systems based on satellite and aerial images have gradually become a research hotspot. These systems usually use various image classification algorithms, such as maximum likelihood method, support vector machine and random forest, to identify and classify different elements in the forestry environment. However, these methods still face many challenges in dealing with highly complex and dynamically changing forestry ecological environments.

[0004] First, existing image classification methods often have difficulty in effectively handling multi-scale features in forestry environments. For example, the same image may contain both large areas of forest cover and small rivers or road networks. Traditional single-scale analysis methods have difficulty accurately capturing these different scale features at the same time, resulting in insufficient classification accuracy, especially in complex terrain and diverse vegetation areas.

[0005] Secondly, existing technologies have obvious deficiencies in dealing with the dynamic changes of forestry environments. The forestry ecosystem is a complex system that changes dynamically and is affected by multiple factors such as seasonal changes, climate conditions, and human activities. However, most existing methods use static classification models that are difficult to adapt to such dynamic changes, resulting in unsatisfactory performance in long-term monitoring.

[0006] In addition, the existing forestry ecological environment monitoring system also faces challenges in data processing efficiency and real-time performance. With the rapid increase in the amount of remote sensing data, how to quickly and efficiently process massive image data and promptly detect potential ecological problems has become a technical problem that needs to be solved urgently.

[0007] Finally, there is still much room for improvement in the existing technology in terms of integrating multi-source information and realizing intelligent decision support. Most systems are limited to image classification and simple parameter calculation, lacking comprehensive analysis of multi-dimensional ecological information and intelligent early warning mechanisms. Summary of the invention

[0008] In view of the above problems, the present invention proposes an innovative forestry ecological environment monitoring system and method. The system effectively solves the image segmentation accuracy problem in complex forestry environments through an innovative multi-scale adaptive fusion segmentation algorithm. At the same time, the modular design and intelligent data analysis method of the system realize the full process intelligent monitoring from image acquisition to ecological parameter calculation and early warning analysis.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] A forestry ecological environment monitoring system, comprising:

[0011] Image acquisition module, used to obtain forestry environment image information;

[0012] A central control module, connected to the image acquisition module, for processing and analyzing the forestry environment image information;

[0013] A data analysis module, connected to the central control module, for analyzing the processed data and generating environmental monitoring parameters;

[0014] A storage module, connected to the data analysis module, and used to store the environmental monitoring parameters;

[0015] A display module, connected to the data analysis module, for displaying the environmental monitoring parameters and warning information; and

[0016] A data early warning module, connected to the central control module, for implementing early warning based on environmental monitoring index;

[0017] Wherein, the central control module includes an image segmentation module, and the image segmentation module uses a multi-scale adaptive fusion segmentation algorithm to process the forestry environment image information.

[0018] Preferably, the multi-scale adaptive fusion segmentation algorithm comprises the following steps:

[0019] Topological feature extraction transformation, used to obtain the topological features of the image;

[0020] Dynamic spectral clustering optimization for preliminary segmentation based on the topological features;

[0021] Lie group manifold learning, used for performing manifold learning based on the preliminary segmentation result;

[0022] Nonlinear harmonic analysis for further feature extraction based on the manifold learning results; and

[0023] Adaptive multi-feature fusion decision is used to achieve the final segmentation decision based on the results of the previous steps.

[0024] Preferably, the topological feature extraction transformation step is implemented by the following formula:

[0025] a) First, calculate the discrete gradient vector field of the input image I: b) Then, construct the Morse complex: Where p and q are pixels in the image; c) Next, calculate the continuous coherence: in, is the super level set filter, H * represents the homology group, d) Finally, the final result is obtained by operator composition: Among them, F TFET is the topological feature extraction transformation result, I is the input image, is the discrete Morse operator, is a continuous homology operator, and o represents operator composition.

[0026] Preferably, the dynamic spectral clustering optimization step is implemented by the following formula: a) First, construct a similarity matrix: W ij =exp(-||F TFET (i)-F TFET (j)|| 2 / 2σ 2 ), where F TFET (i) and F TFET (j) represents the topological features of pixels i and j respectively, and σ is the Gaussian kernel parameter; b) Then, the degree matrix is ​​calculated: D = diag (∑ j W ij ), c) Next, calculate the Laplace matrix: L = DW, d) Solve the generalized eigenvalue problem: Lv = λDve, select the eigenvectors corresponding to the smallest k eigenvalues ​​to form a matrix V; f) Finally, perform k-means clustering on the rows of V to obtain the final result: C DSCO =argmin C Tr(V T LV), where C DSCO is the clustering result, L is the graph Laplacian matrix, and Tr represents the trace of the matrix.

[0027] Preferably, the Lie group manifold learning step is implemented by the following formula: a) First, the dynamic spectral clustering optimization result C DSCO Mapping to Lie algebra space: Among them, Y i is the basis of Lie algebra, β i is the mapping coefficient, b) then, optimize the weight coefficient: Among them, X i is the basis of the target Lie algebra, ||·|| Frepresents the Frobenius norm, c) Finally, calculate the manifold: Among them, M LGML is the learned manifold, exp is the exponential mapping, X i is the basis of Lie algebra, α i is the weight coefficient, and n is the dimension of the Lie algebra basis.

[0028] Preferably, the nonlinear harmonic analysis step is implemented by the following formula: a) First, the Lie group manifold learning result M is calculated LGML The Fourier transform of : in, represents the Fourier transform, b) then, define the nonlinear kernel function: Where ω0 is the cutoff frequency, c) Next, calculate the convolution: d) Finally, perform an inverse Fourier transform to get the final result: Among them, H N HA is the result of nonlinear harmonic analysis, is the inverse Fourier transform, and ω is the frequency variable.

[0029] Preferably, the adaptive multi-feature fusion decision step is implemented by the following formula: a) First, nonlinear mapping is performed on each feature: φ i (F i )=tanh(W i F i +b i ), where F i is the output of the first four steps, W i and b i is the mapping parameter, b) then, calculate the adaptive weight: Among them, λ is learned through back propagation. c) Finally, the features are integrated and the decision is made: Among them, D AMFFD is the final decision result, σ is the sigmoid activation function, w i is the adaptive weight, φ i is a nonlinear feature map, F i is the output of the first four steps, and b is the bias term.

[0030] Preferably, the data analysis module further calculates environmental monitoring parameters such as water area, vegetation coverage and soil moisture based on the output results of the image segmentation module.

[0031] Preferably, the data warning module determines whether to trigger a warning by comparing the environmental monitoring index with a preset threshold, and displays corresponding warning information through the display module when the warning is triggered.

[0032] A monitoring method based on the forestry ecological environment monitoring system comprises the following steps:

[0033] Acquiring forestry environment image information through the image acquisition module;

[0034] Using the image segmentation module in the central control module to process the forestry environment image information to obtain a segmentation result;

[0035] Based on the segmentation result, calculating environmental monitoring parameters through the data analysis module;

[0036] Storing the environmental monitoring parameters in the storage module;

[0037] Displaying the environmental monitoring parameters by means of the display module; and

[0038] The data warning module is used to perform warning analysis based on the environmental monitoring parameters, and the warning information is displayed through the display module when necessary.

[0039] Compared with the prior art, the beneficial effects of the present invention are embodied in the following aspects:

[0040] From a macro perspective, the system provides strong technical support for the sustainable management of the forestry ecological environment, helps to timely discover and respond to various ecological problems such as forest degradation, soil erosion, etc., thus making important contributions to the construction of ecological civilization.

[0041] From the perspective of system architecture, the modular design of the present invention not only improves the flexibility and scalability of the system, but also realizes close coordination between various functional modules. For example, the seamless connection between the image acquisition module and the central control module ensures the real-time acquisition and processing of high-quality raw data. The collaborative work of the data analysis module and the data early warning module realizes the intelligent transformation from data to decision-making.

[0042] At the algorithm level, the multi-scale adaptive fusion segmentation algorithm of the present invention effectively solves the difficulties of traditional methods in dealing with multi-scale features and nonlinear structures by cleverly integrating advanced technologies such as topological feature extraction, dynamic spectral clustering, and Lie group manifold learning. This innovative algorithm design not only improves the classification accuracy, but also enhances the system's ability to adapt to dynamic changes in the forestry environment.

[0043] From the effect point of view, the present invention is significantly superior to the existing technology in many aspects such as classification accuracy, change detection capability, computing efficiency and environmental parameter estimation. The improvement of these performances is not a simple superposition, but a qualitative leap in overall performance is achieved through the synergy of various modules and algorithms. For example, high-precision image segmentation provides a reliable foundation for subsequent environmental parameter calculations, while fast data processing capabilities support more real-time and frequent monitoring. These factors jointly improve the overall early warning capability of the system.

[0044] It is particularly worth mentioning that the present invention performs well in dealing with complex terrain and diverse vegetation, effectively solving the performance bottleneck of traditional methods in these challenging environments. This advantage enables the present system to adapt to various complex forestry ecological environments, greatly expanding its scope of application.

[0045] In general, the present invention has comprehensively improved the accuracy, efficiency and intelligence level of forestry ecological environment monitoring through innovative technical solutions and system design. This not only provides a powerful tool for the scientific management of forestry resources and ecological environment protection, but also opens up a new direction for technological innovation in related fields. With the continuous optimization and improvement of the system, we have reason to believe that it will play an increasingly important role in the global ecological environment monitoring and protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the overall system of the present invention. DETAILED DESCRIPTION

[0047] The scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. The embodiments in the present invention and all other embodiments obtained by ordinary persons in the art without making creative work are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the present invention provides a forestry ecological environment monitoring system, which includes an image acquisition module 1, a central control module 2, a data analysis module 3, a storage module 4, a display module 5 and a data early warning module 6.

[0049] The image acquisition module 1 is used to obtain forestry environment image information. Preferably, the module may include one or more high-resolution cameras, which may be installed in a fixed position or on a mobile platform, such as a drone or a satellite. In one embodiment of the present invention, the image acquisition module 1 may automatically acquire forestry environment images at regular time intervals (e.g., every hour or every day) to achieve continuous monitoring of the forestry ecological environment.

[0050] The central control module 2 is connected to the image acquisition module 1 and is used to process and analyze the acquired forestry environment image information. This module is the core of the entire system and is responsible for coordinating the work of other modules. The central control module 2 includes an image segmentation module 21, which adopts an important innovation of the present invention: a multi-scale adaptive fusion segmentation algorithm. This algorithm can effectively process complex forestry environment images, thereby providing a reliable basis for subsequent analysis.

[0051] The data analysis module 3 is connected to the central control module 2 to analyze the processed data and generate environmental monitoring parameters. These parameters may include but are not limited to key ecological indicators such as vegetation coverage, water area, soil moisture, etc. In a preferred embodiment of the present invention, the data analysis module 3 can also use machine learning algorithms to perform trend analysis on these parameters to predict future ecological changes.

[0052] The storage module 4 is connected to the data analysis module 3 and is used to store the generated environmental monitoring parameters. This module can be a large-capacity database system that can store historical data for a long time and provide valuable information for future ecological research. Preferably, the storage module 4 can use distributed storage technology to improve data security and access efficiency.

[0053] The display module 5 is connected to the data analysis module 3 and is used to display environmental monitoring parameters and early warning information. This module can be a graphical user interface that can display monitoring results in an intuitive manner such as charts and maps. In one embodiment of the present invention, the display module 5 can also support remote access, so that managers can view monitoring data anytime and anywhere.

[0054] The data warning module 6 is connected to the central control module 2 and is used to implement early warning based on the environmental monitoring index. When certain ecological indicators exceed the preset threshold, the module will trigger an alarm and send early warning information to relevant personnel through the display module 5. Preferably, the early warning threshold can be dynamically adjusted according to different ecological regions and seasons to improve the accuracy of the early warning.

[0055] In an embodiment of the present invention, a multi-scale adaptive fusion segmentation algorithm is one of the core innovations of the present invention, which includes the following five steps: topological feature extraction transformation, dynamic spectral clustering optimization, Lie group manifold learning, nonlinear harmonic analysis and adaptive multi-feature fusion decision.

[0056] The topological feature extraction transformation step first processes the input forestry environment image to extract its topological features. This step can effectively capture the complex structures in the forestry environment, such as the distribution pattern of trees and the network structure of water systems. The dynamic spectral clustering optimization step performs preliminary segmentation based on the extracted topological features, which helps to distinguish different ecological elements, such as woodland, water bodies, and bare land.

[0057] The Lie group manifold learning step further optimizes the initial segmentation results. By learning the intrinsic structure of the image in the Lie group space, it can better handle nonlinear features in the forestry environment, such as the shape changes of the crown and the boundary curves of the water body. The nonlinear harmonic analysis step further extracts features from the image in the frequency domain, which is particularly effective for identifying periodic structures in the forestry environment (such as the planting pattern of artificial forests).

[0058] Finally, the adaptive multi-feature fusion decision step comprehensively considers the results of all previous steps and makes the final segmentation decision. The adaptive nature of this step enables the algorithm to automatically adjust the weights of each feature according to different forestry environment types (such as tropical rainforest, temperate deciduous forest, etc.), thereby achieving more accurate segmentation results.

[0059] In an embodiment of the present invention, the topological feature extraction transformation step is implemented by a series of mathematical operations. First, the discrete gradient vector field of the input image I is calculated:

[0060]

[0061] in, represents the gradient of the image, and Represent the partial derivatives of the image in the x and y directions respectively. This step can capture the edge information in the image, which is very important for identifying the boundaries of forests and water bodies.

[0062] Next, construct the Morse complex:

[0063]

[0064] in, represents the Morse complex of image I, where p and q are pixels in the image. This step allows the extraction of the topological structure of the image, which helps to identify complex network structures in forestry environments, such as river systems or road networks. Then, the persistence coherence is calculated:

[0065]

[0066] in, represents the persistent homology of image I, is the super level set filter, H* This step can capture the multi-scale features in the image and is very effective for identifying ecological elements of different sizes (such as small shrubs and large trees).

[0067] Finally, the final result is obtained through operator composition:

[0068]

[0069] Among them, F TFET Extract the transformation result for topological features. This step combines the results of all previous steps to obtain a comprehensive topological feature representation of the image.

[0070] In the actual application of forestry ecological environment monitoring, this topological feature extraction method can effectively identify and distinguish different types of vegetation cover (such as dense forest, sparse forest, grassland, etc.), as well as non-vegetated areas such as water bodies and bare land. This provides a reliable basis for subsequent environmental parameter calculation and ecological assessment.

[0071] In an embodiment of the present invention, the dynamic spectral clustering optimization step is implemented through a series of matrix operations. First, a similarity matrix is ​​constructed:

[0072] W ij =exp(-||F TFET (i)-F TFET (j)|| 2 / 2σ 2 )

[0073] Among them, W ij represents the similarity between pixels i and j, F TFET (i) and F TFET (j) represents the topological features of pixels i and j respectively, and σ is the Gaussian kernel parameter. In forestry environmental monitoring, the choice of σ has an important impact on the results. Usually, we can choose the σ value according to the resolution of the image and the scale of the target feature. For example, for high-resolution aerial images, σ can choose a smaller value (such as 0.1 to 0.5) to capture subtle vegetation differences; while for low-resolution satellite images, σ can choose a larger value (such as 1 to 5) to focus on large-scale ecological regional divisions. Then, calculate the degree matrix:

[0074]

[0075] Where D is the degree matrix and deg represents a diagonal matrix. This step can reflect the overall similarity of each pixel to other pixels, which helps to identify key nodes in the forestry environment, such as dense vegetation areas or water sources. Next, calculate the Laplace matrix:

[0076] L=DW

[0077] Where L is the Laplace matrix. The Laplace matrix plays an important role in image segmentation, as it can reflect the overall structure and local connection relationship of the image. Solve the generalized eigenvalue problem:

[0078] Lv=λDv

[0079] Among them, v is the eigenvector and λ is the eigenvalue. This step is the core of spectral clustering. By solving this eigenvalue problem, we can get the spectral embedding representation of the image. Select the eigenvectors corresponding to the smallest k eigenvalues ​​to form the matrix V. In forestry environment monitoring, the choice of k is usually related to the number of ecological types we want to distinguish. For example, if we want to divide the forestry environment into four categories: forest, grassland, water body and bare land, then k can be chosen as 4 or 5.

[0080] Finally, k-means clustering is performed on the rows of V to obtain the final result:

[0081]

[0082] Among them, C DSCO is the clustering result, and Tr represents the trace of the matrix. This step clusters the image pixels into different ecological types, laying the foundation for the subsequent calculation of environmental parameters. In practical applications, the dynamic spectral clustering optimization step can effectively segment forestry environment images into different ecological regions, such as forests, grasslands, water bodies, etc. This segmentation result is more accurate than traditional color or texture-based segmentation methods because it takes into account the global relationship and topological structure between pixels.

[0083] In an embodiment of the present invention, the Lie group manifold learning step further optimizes the segmentation result by learning the intrinsic structure of the image in the Lie group space. DSCO Mapping to Lie algebra space:

[0084]

[0085] Among them, Y i is the basis of Lie algebra, β i is the mapping coefficient, and n is the dimension of the Lie algebra. In forestry environmental monitoring, the dimension n of the Lie algebra is usually related to the complexity of the environmental characteristics we want to capture. For example, for simple plain forest areas, n can choose a smaller value (such as 3 to 5); while for complex mountain forest ecosystems, n can choose a larger value (such as 8 to 12) to capture more nonlinear characteristics. Then, optimize the weight coefficient:

[0086]

[0087] Among them, X iis the basis of the target Lie algebra, ||·|| F represents the Frobenius norm, α i is the optimized weight coefficient. The purpose of this step is to find the best Lie algebra representation to better describe the nonlinear characteristics of the forestry environment.

[0088] Finally, compute the manifold:

[0089]

[0090] Among them, M LGML is the learned manifold, and exp is the exponential mapping. This manifold represents the intrinsic structure of forestry environment images and can better handle complex ecosystem features, such as the irregular shape of tree crowns and the winding direction of rivers.

[0091] In practical applications, the Lie group manifold learning step can effectively handle nonlinear features in forestry environments. For example, it can more accurately describe the complex shape of forest edges or capture the natural transition area between water bodies and land. This method is particularly suitable for dealing with forestry environments with complex terrain and diverse ecology, such as mountain forests or wetland ecosystems.

[0092] In the embodiment of the present invention, the nonlinear harmonic analysis step is implemented by further extracting features from the image in the frequency domain. This step is of great significance for identifying periodic structures in forestry environments, such as the planting pattern of artificial forests or the spatial distribution of natural vegetation.

[0093] First, calculate the Lie group manifold learning result M LGML The Fourier transform of :

[0094]

[0095] in, represents the Fourier transform, M LGML In the frequency domain, ω is a frequency variable. This step converts the information in the spatial domain to the frequency domain, allowing us to analyze the characteristics of the forestry environment from another perspective. Then, the nonlinear kernel function is defined:

[0096]

[0097] Among them, ω0 is the cutoff frequency, which is a parameter that needs to be adjusted according to the specific application scenario. In forestry ecological environment monitoring, the choice of ω0 is closely related to the scale of ecological characteristics we are concerned about. For example, if we are mainly concerned with large-scale forest distribution patterns, we can choose a smaller ω0 (such as 0.1 to 0.5); if we are more concerned about small-scale vegetation structures, such as the texture of the crown, we can choose a larger ω0 (such as 1 to 5). Preferably, we can select the ω0 value that can best distinguish different ecological types by analyzing historical monitoring data.

[0098] Next, calculate the convolution:

[0099]

[0100] This step implements filtering in the frequency domain, which can effectively suppress noise and highlight the key features in the forestry environment. Finally, an inverse Fourier transform is performed to obtain the final result:

[0101]

[0102] Among them, H NHA is the result of nonlinear harmonic analysis, is the inverse Fourier transform. This step converts the information in the frequency domain back to the spatial domain to obtain an image processed by nonlinear harmonic analysis.

[0103] In practical applications, the nonlinear harmonic analysis step can effectively enhance certain specific features in forestry environment images. For example, it can help us more clearly identify clearings or forest paths in the forest, which is very helpful for assessing the degree of forest fragmentation or the impact of human disturbance. At the same time, this step can also reduce the noise in the image and improve the accuracy of subsequent analysis.

[0104] In the embodiment of the present invention, the adaptive multi-feature fusion decision step is the last step of the entire algorithm and the most critical step. This step comprehensively considers the results of all previous steps and makes the final segmentation decision. Its adaptive characteristics enable the algorithm to automatically adjust the weights of each feature according to different forestry environment types, thereby achieving more accurate segmentation results.

[0105] First, a nonlinear mapping is performed on each feature:

[0106] φ i (F i )=tanh(W i F i +b i )

[0107] Among them, F i is the output of the first four steps, namely, F TFET,C DSCO ,M LGML ,H NHA ,W i and b i is the mapping parameter, and tanh is the hyperbolic tangent function. The purpose of this step is to map features of different scales and ranges into the same space to facilitate subsequent fusion. In forestry ecological environment monitoring, this step can help us better integrate different types of information such as terrain features, vegetation density, moisture content, etc.

[0108] Then, the adaptive weights are calculated:

[0109]

[0110] Among them, λ i Through back propagation learning, w i The weight of each feature. The adaptability of this step allows the algorithm to automatically adjust the importance of each feature according to different forestry environment types. For example, in forestry monitoring in arid areas, features related to moisture may receive higher weights; while in tropical rainforests with rich biodiversity, features related to vegetation structure may be more important.

[0111] Finally, the features are integrated and a decision is made:

[0112]

[0113] Among them, D AMFFD is the final decision result, σ is the sigmoid activation function, and b is the bias term. This step combines the information of all features to obtain the final segmentation result.

[0114] In practical applications, the adaptive multi-feature fusion decision step can automatically adjust the strategy according to different forestry environments. For example, in areas where coniferous and broad-leaved forests are mixed, it can automatically increase the weight of features that can distinguish between the two types of forests; in forests threatened by pests and diseases, it can pay more attention to features related to the health of vegetation. This adaptability enables the forestry ecological environment monitoring system of the present invention to adapt to various complex forest ecosystems and provide more accurate and reliable monitoring results.

[0115] In the embodiment of the present invention, the data analysis module 3 of the present invention further calculates environmental monitoring parameters such as water area, vegetation coverage and soil moisture based on the output results of the image segmentation module 21. These parameters are key indicators for evaluating the status of forestry ecological environment.

[0116] Preferably, the calculation of water body area can be achieved by counting the number of pixels identified as water bodies and combining the geographic information of the image (such as scale). Vegetation coverage can be obtained by calculating the proportion of pixels identified as vegetation to the total pixels. The estimation of soil moisture may require the combination of multiple factors, such as soil reflectivity, surrounding vegetation conditions, etc., which can be achieved by establishing a regression model.

[0117] In one embodiment of the present invention, the data analysis module 3 can also calculate more ecological indicators, such as forest stand density, tree species diversity index, etc. The calculation of these indicators may require the combination of field survey data and remote sensing image analysis results. For example, forest stand density can be estimated by analyzing the size and distribution of tree crowns; the tree species diversity index may need to be realized by combining hyperspectral remote sensing data and machine learning algorithms.

[0118] In the embodiment of the present invention, the data warning module 6 of the present invention determines whether to trigger a warning by comparing the environmental monitoring index with a preset threshold, and displays the corresponding warning information through the display module 5 when the warning is triggered. This function is crucial for timely discovery and response to forestry ecological environment problems.

[0119] In practical applications, the setting of warning thresholds needs to consider multiple factors, such as forest type, geographical location, seasonal changes, etc. For example, for water conservation forests, we may be more sensitive to the reduction of water area, so we can set a lower threshold for water area reduction (such as 10%); while for windbreak and sand fixation forests, we may pay more attention to the changes in vegetation coverage, and we can set a higher threshold for vegetation coverage reduction (such as 20%).

[0120] Preferably, the data early warning module 6 of the present invention can set a multi-level early warning mechanism. For example, three levels of early warnings, namely "attention", "warning" and "serious", can be set, corresponding to different degrees of ecological environment changes. This multi-level early warning mechanism can help managers better assess the severity of the problem and take corresponding measures.

[0121] In an embodiment of the present invention, the present invention also provides a monitoring method based on the above-mentioned forestry ecological environment monitoring system. The method comprises the following steps:

[0122] First, forestry environment image information is obtained through the image acquisition module 1. This step can be achieved through fixed monitoring stations, mobile drones or satellite remote sensing, etc. Preferably, a suitable acquisition frequency can be set according to monitoring requirements, such as once a day or once a week.

[0123] Then, the image segmentation module 21 in the central control module 2 is used to process the forestry environment image information to obtain a segmentation result. This step uses the multi-scale adaptive fusion segmentation algorithm described in detail above, which can effectively distinguish different ecological elements, such as forests, grasslands, water bodies, etc.

[0124] Next, based on the segmentation results, the environmental monitoring parameters are calculated by the data analysis module 3. These parameters may include but are not limited to water area, vegetation coverage, soil moisture, etc. In this step, these parameters can be analyzed in time series in combination with historical data and ecological models to discover potential ecological change trends.

[0125] The calculated environmental monitoring parameters will be stored in the storage module 4. This step is very important for long-term ecological monitoring and research because it can provide continuous historical data and help analyze the long-term change trend of the ecological environment.

[0126] At the same time, the environmental monitoring parameters will be displayed through the display module 5. Preferably, the display module 5 can provide a variety of data visualization methods, such as trend charts, heat maps, GIS maps, etc., so that managers can intuitively understand the status of the forestry ecological environment.

[0127] Finally, the data warning module 6 is used to perform warning analysis based on the environmental monitoring parameters, and the warning information is displayed through the display module 5 when necessary. This step can help to timely discover potential ecological problems, such as forest degradation, soil erosion, etc., so as to support timely intervention and management decisions.

[0128] Through the above steps, the forestry ecological environment monitoring method of the present invention can realize comprehensive, continuous and intelligent monitoring of the forestry ecological environment, and provide important decision-making support for the sustainable management of forestry resources.

[0129] In general, the forestry ecological environment monitoring system and method provided by the present invention can achieve high-precision monitoring and early warning of complex forestry environments through innovative image processing algorithms and intelligent data analysis methods. This not only helps to improve the efficiency of forestry resource management, but also provides an important scientific basis for ecological environment protection and sustainable development.

[0130] In order to verify the superiority of the forestry ecological environment monitoring system of the present invention, we selected a mixed forest area in southwest China as a test site and conducted a one-year monitoring experiment. The area has rich vegetation types and complex terrain, which is an ideal place to verify the performance of this system.

[0131] Example 1: We deployed the forestry ecological environment monitoring system of the present invention, including an image acquisition module combining high-resolution satellite remote sensing and drone aerial photography, and a central control module using a multi-scale adaptive fusion segmentation algorithm. The system collects and analyzes data once a week for one year.

[0132] Comparative Example 1: As a control, we used a traditional remote sensing image classification system based on maximum likelihood method with the same data collection frequency and duration.

[0133] Testing standards and methods:

[0134] 1. Classification accuracy: The confusion matrix is ​​used to calculate the overall accuracy, Kappa coefficient, and user accuracy of each category.

[0135] 2. Change detection capability: The accuracy and recall of change detection are calculated through manually marked change areas.

[0136] 3. Computational efficiency: record the average time required to process each image.

[0137] 4. Accuracy of environmental parameter estimation: Compare the vegetation coverage, water area, etc. estimated by the system with the results of field surveys.

[0138] The test results are shown in the following table:

[0139]

[0140]

[0141] From the above results, it can be seen that the forestry ecological environment monitoring system of the present invention is significantly superior to traditional methods in multiple key indicators. First, in terms of classification accuracy, the overall accuracy of the system reached 92.8%, 7.5 percentage points higher than the traditional method. This high-precision classification capability is particularly reflected in complex forest environments, with forest user accuracy reaching 94.5%, which is crucial for accurately assessing forest resources and ecological conditions.

[0142] Secondly, the invention performs well in change detection, with an accuracy and recall rate of 89.7% and 87.3% respectively, far exceeding traditional methods. This advantage comes from the fact that our multi-scale adaptive fusion segmentation algorithm can effectively capture subtle ecological changes, such as early signs of forest degradation or small-scale human disturbance. This highly sensitive change detection capability makes it possible to discover and respond to ecological problems in a timely manner.

[0143] In terms of computing efficiency, the average processing time of this system is only 3.5 minutes per frame, which is more than twice as fast as traditional methods. This high efficiency can not only save computing resources, but more importantly, it can support more frequent monitoring and improve the system's ability to respond to emergencies.

[0144] Finally, in terms of environmental parameter estimation, the system also demonstrated superiority. The estimation errors of vegetation coverage and water area were ±2.1% and ±1.8%, respectively, with high accuracy. This precise parameter estimation provides reliable data support for ecosystem health assessment and resource management decisions.

[0145] It is worth noting that our system performs particularly well in dealing with complex terrain and diverse vegetation. For example, in a steep mountain mixed forest in the test area, traditional methods often misclassify shadow areas as water bodies, but our system is able to accurately distinguish them. This is due to our algorithm's ability to simultaneously consider terrain, texture, and spectral information, thereby better understanding complex ecological environments.

[0146] In addition, during the one-year monitoring period, the system successfully captured several small-scale deforestation activities and a local forest fire, while traditional methods failed to detect these changes in time. This highlights the practical application value of the system in ecological environment protection and resource management.

[0147] In summary, the forestry ecological environment monitoring system of the present invention is significantly superior to traditional methods in classification accuracy, change detection capability, computational efficiency, parameter estimation accuracy, etc. This comprehensive performance improvement can not only provide more accurate and timely ecological environment information, but also provide strong technical support for the sustainable management of forestry resources and ecosystem protection.

[0148] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A forestry ecological environment monitoring system, characterized in that: include: Image acquisition module, used to obtain forestry environment image information; A central control module, connected to the image acquisition module, for processing and analyzing the forestry environment image information; A data analysis module, connected to the central control module, for analyzing the processed data and generating environmental monitoring parameters; A storage module, connected to the data analysis module, and used to store the environmental monitoring parameters; A display module, connected to the data analysis module, for displaying the environmental monitoring parameters and warning information; as well as A data early warning module, connected to the central control module, for implementing early warning based on environmental monitoring index; Wherein, the central control module includes an image segmentation module, and the image segmentation module uses a multi-scale adaptive fusion segmentation algorithm to process the forestry environment image information.

2. The forestry ecological environment monitoring system according to claim 1 is characterized in that: The multi-scale adaptive fusion segmentation algorithm comprises the following steps: Topological feature extraction transformation, used to obtain the topological features of the image; Dynamic spectral clustering optimization for preliminary segmentation based on the topological features; Lie group manifold learning, used for performing manifold learning based on the preliminary segmentation result; Nonlinear harmonic analysis for further feature extraction based on the manifold learning results; and Adaptive multi-feature fusion decision is used to achieve the final segmentation decision based on the results of the previous steps.

3. The forestry ecological environment monitoring system according to claim 2 is characterized in that: The topological feature extraction transformation step is implemented by the following formula: a) First, calculate the discrete gradient vector field of the input image I: b) Then, construct the Morse complex: Where p and q are pixels in the image; c) Next, calculate the continuous coherence: in, is the super level set filter, H * represents the homology group, d) Finally, the final result is obtained by operator composition: Among them, F TFET is the topological feature extraction transformation result, I is the input image, M is the discrete Morse operator, is a continuous homology operator, and o represents operator composition.

4. The forestry ecological environment monitoring system according to claim 3 is characterized in that: The dynamic spectral clustering optimization step is implemented by the following formula: a) First, construct a similarity matrix: W ij =exp(-||F TFET (i)-F TFET (j)|| 2 / 2σ 2 ), where F TFET (i) and F TFET (j) represents the topological features of pixels i and j respectively, and σ is the Gaussian kernel parameter; b) Then, the degree matrix is ​​calculated: D = diag (∑ j W ij ), c) Next, calculate the Laplace matrix: L = DW, d) solve the generalized eigenvalue problem: Lv = λDv e , select the eigenvectors corresponding to the smallest k eigenvalues ​​to form a matrix V; i) Finally, perform k-means clustering on the rows of V to obtain the final result: C DSCO =argmin C Tr(V T LV), where C DSCO is the clustering result, L is the graph Laplacian matrix, and Tr represents the trace of the matrix.

5. The forestry ecological environment monitoring system according to claim 4 is characterized in that: The Lie group manifold learning step is implemented by the following formula: a) First, the dynamic spectral clustering optimization result C DSCO Mapping to Lie algebra space: Among them, Y i is the basis of Lie algebra, β i is the mapping coefficient, b) then, optimize the weight coefficient: Among them, X i is the basis of the target Lie algebra, ||·|| F represents the Frobenius norm, c) Finally, calculate the manifold: Among them, M LGML is the learned manifold, exp is the exponential mapping, X i is the basis of Lie algebra, α i is the weight coefficient, and n is the dimension of the Lie algebra basis.

6. The forestry ecological environment monitoring system according to claim 5, characterized in that: The nonlinear harmonic analysis step is implemented by the following formula: a) First, calculate the Lie group manifold learning result M LGML The Fourier transform of : in, represents the Fourier transform, b) then, define the nonlinear kernel function: Where ω0 is the cutoff frequency, c) Next, calculate the convolution: d) Finally, perform an inverse Fourier transform to get the final result: Among them, H N HA is the result of nonlinear harmonic analysis, is the inverse Fourier transform, and ω is the frequency variable.

7. The forestry ecological environment monitoring system according to claim 6 is characterized in that: The adaptive multi-feature fusion decision step is implemented by the following formula: a) First, nonlinear mapping is performed on each feature: φ i (F i )=tanh(W i F i +b i ), where F i is the output of the first four steps, W i and b i is the mapping parameter, b) then, calculate the adaptive weight: Among them, λ i Learned through back propagation, c) Finally, the features are integrated and decisions are made: Among them, D AMFFD is the final decision result, σ is the sigmoid activation function, w i is the adaptive weight, φ i is a nonlinear feature map, F i is the output of the first four steps, and b is the bias term.

8. The forestry ecological environment monitoring system according to claim 1 is characterized in that: The data analysis module further calculates water area, vegetation coverage and soil moisture environmental monitoring parameters based on the output results of the image segmentation module.

9. The forestry ecological environment monitoring system according to claim 1, characterized in that: The data warning module determines whether to trigger a warning by comparing the environmental monitoring index with a preset threshold, and displays corresponding warning information through the display module when a warning is triggered.

10. A monitoring method based on the forestry ecological environment monitoring system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Acquiring forestry environment image information through the image acquisition module; Using the image segmentation module in the central control module to process the forestry environment image information to obtain a segmentation result; Based on the segmentation result, calculating environmental monitoring parameters through the data analysis module; Storing the environmental monitoring parameters in the storage module; Displaying the environmental monitoring parameters through the display module; as well as The data warning module is used to perform warning analysis based on the environmental monitoring parameters, and the warning information is displayed through the display module when necessary.

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