A forest-related ecological health assessment method and system based on multi-source data
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANDONG HUASHI ENG CONSULTING CO LTD
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-09
Smart Images
Figure CN122175141A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of ecological assessment technology, and in particular to a method and system for assessing the ecological health of forests based on multi-source data. Background Technology
[0002] Existing technologies for forest ecological health assessment lack a systematic and standardized process for processing multi-source data. They fail to effectively quantify the inherent error range and spatial representativeness of data from different sources, and have not established a scientific historical accuracy comparison mechanism. This results in poor compatibility among multi-source data, making it difficult to form a unified and reliable basic dataset. This creates hidden dangers for subsequent assessment work and directly affects the accuracy of the assessment results.
[0003] Existing assessment methods have significant shortcomings in the ecological element analysis and index calculation stages. They neglect the statistical correlations and ecological coupling relationships among basic ecological elements, lack dynamic adjustment of data credibility and a dual calibration mechanism for basic ecological element values. This results in insufficient accuracy in calculating the comprehensive ecological health index, failing to accurately reflect the true ecological health status of forested areas. Furthermore, the assessment process is cumbersome and inefficient, making it difficult to meet the needs of rapid and accurate assessment of forested ecological health in real-world scenarios. Therefore, improving the assessment efficiency of forested ecological health has become an urgent problem to be solved. Summary of the Invention
[0004] This invention provides a method and system for assessing the ecological health of forestry based on multi-source data, in order to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, this invention provides a method for assessing the ecological health of forestry based on multi-source data, comprising:
[0006] S1. Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset;
[0007] S2. Based on the inherent error range, historical accuracy, and spatial representativeness of the target forested area of the multi-source dataset, construct the data confidence region of the target forested area;
[0008] S3. Cross-validate the data blocks in the multi-source dataset using the data confidence region to obtain the confidence level of the multi-source dataset, and bind the confidence level to the data block to obtain the fused confidence dataset of the target forested area;
[0009] S4. Perform statistical and ecological correlation analysis on the basic ecological element values of the target forestry area to obtain the ecological health element matrix of the target forestry area;
[0010] S5. Based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix, the basic ecological element values are double-calibrated to obtain the comprehensive ecological health index of the target forestry area.
[0011] S6. Map the comprehensive ecological health index to obtain the ecological health status level of the target forested area.
[0012] In a preferred embodiment, the step of compiling remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset includes:
[0013] Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested areas;
[0014] Radiometric calibration is performed on the remote sensing images to obtain a standardized image layer of the target forested area;
[0015] The time-series monitoring data is smoothed to obtain a regular monitoring data sequence for the target forested area;
[0016] Spatially align the thematic survey with the management vector data to obtain a regularized vector data layer of the target forestry area;
[0017] The standardized image layer, the regularized monitoring data sequence, and the regularized vector data layer are mapped onto the spatiotemporal grid framework of the target forested area to obtain a multi-source dataset of the target forested area.
[0018] In a preferred embodiment, constructing the data confidence region of the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset includes:
[0019] The device specifications and collected metadata of the data sources in the multi-source dataset are analyzed to quantify the systematic error and random error of the multi-source dataset, and the inherent error quantification result of the multi-source dataset is obtained.
[0020] The historical accuracy of the multi-source dataset is obtained by comparing and analyzing the multi-source dataset with the historical data of the target forested area.
[0021] The spatial representativeness of the multi-source dataset is obtained by evaluating the distribution of sampling points and the rationality of spatial interpolation.
[0022] Based on the data type of the multi-source dataset and the typical characteristics of the target forestry area, corresponding weights are assigned to the inherent error quantification result, the historical accuracy, and the spatial representativeness to obtain the weight allocation rule for the target forestry area.
[0023] The data confidence domain of the target forested area is obtained by performing multi-criteria decision fusion on the data types of the multi-source dataset and the weight allocation rules.
[0024] In a preferred embodiment, the step of cross-validating data blocks in the multi-source dataset using the data confidence region to obtain the confidence level of the multi-source dataset, and binding the confidence level with the data block to obtain the fused confidence dataset of the target forested area, includes:
[0025] Identify data values from different sources in the multi-source dataset;
[0026] Call the comprehensive assimilation confidence score corresponding to the data values from different sources in the data confidence domain;
[0027] The differences between the data values from different sources are statistically analyzed, and the comprehensive assimilation confidence score is dynamically adjusted based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset.
[0028] The final confidence level is structured and associated with the multi-source dataset to obtain the fused confidence dataset of the target forested area.
[0029] In a preferred embodiment, the step of dynamically adjusting the comprehensive assimilation confidence score based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset includes:
[0030] The differences are compared and analyzed with the inherent error range of the multi-source dataset;
[0031] When the difference is within the overlapping range of the inherent error range, the comprehensive assimilation confidence score is enhanced to obtain the final confidence of the multi-source dataset;
[0032] When the difference exceeds the overlapping range of the inherent error range, the comprehensive assimilation confidence score is corrected to obtain the final confidence of the multi-source dataset.
[0033] In a preferred embodiment, the step of performing statistical and ecological correlation analysis on the basic ecological element values of the target forested area to obtain the ecological health element matrix of the target forested area includes:
[0034] The vegetation cover index, soil moisture index, land surface temperature index, and leaf area index in the fused confidence dataset are analyzed to obtain the ecological element set of the fused confidence dataset;
[0035] Spatial neighborhood analysis is performed on the basic ecological element values of the ecological element set to obtain the spatial statistical correlation degree of the target forestry area.
[0036] Multi-element coupling analysis is performed on the ecological element set to identify the synergistic change trends and constraints of the ecological element set, and to obtain the ecological coupling intensity matrix of the target forested area.
[0037] By synergistically integrating the spatial statistical correlation degree with the ecological coupling strength matrix, the ecological health element matrix of the target forested area is obtained.
[0038] In a preferred embodiment, the step of performing multi-factor coupling analysis on the ecological element set to identify the synergistic change trends and constraints of the ecological element set, and obtaining the ecological coupling intensity matrix of the target forested area, includes:
[0039] The causal network of the ecological element set is reconstructed to obtain the cooperative change map of the ecological element set;
[0040] Based on the synergistic change map, the differences in the dominant synergistic direction and change phase among the ecological elements in the ecological element cluster are analyzed to obtain the synergistic trend description set of the target forestry area.
[0041] Based on the collaborative trend description set, the constraint relationships between the ecological elements are identified to obtain the constraint intensity index of the ecological elements;
[0042] By associating and coupling the collaborative trend description set and the constraint intensity index, the initial coupling relationship of the ecological element set is obtained;
[0043] The dimensional differences of the indicators in the initial coupling relationship are eliminated, and the intensity of the processed initial coupling relationship is quantitatively analyzed to obtain the ecological coupling intensity matrix of the target forested area.
[0044] In a preferred embodiment, the step of performing dual calibration on the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix to obtain the comprehensive ecological health index of the target forested area includes:
[0045] Obtain the confidence attribute of the basic ecological element values in the ecological health element matrix and the coupling correlation coefficient of the ecological health element matrix;
[0046] The confidence attribute and the basic ecological element value are weighted and fused to obtain the first calibration value of the target forested area;
[0047] Using the coupling correlation coefficient as the influence weight, the first calibration value is corrected by neighborhood element propagation to obtain the second calibration value of the target forested area.
[0048] The comprehensive ecological health index of the target forested area is calculated based on the second calibration value.
[0049] In a preferred embodiment, the formula for calculating the comprehensive ecological health index is:
[0050] ;
[0051] in, This represents the comprehensive ecological health index. This represents the total number of the aforementioned basic ecological element values. Indicates the first The weighting coefficients of the aforementioned basic ecological element values. Indicates the first The second calibration value for each of the aforementioned basic ecological element values. Indicates the first The confidence level attribute of the aforementioned basic ecological element values. This represents the preset confidence level adjustment factor. Indicates the first The and the first The coupling correlation coefficient between the aforementioned basic ecological element values. This represents the average value of the second calibration value. This represents the preset coupling effect balancing factor. This represents the natural exponential function.
[0052] To address the above problems, the present invention also provides a forestry ecological health assessment system based on multi-source data, the system comprising:
[0053] The data acquisition module is used to collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset;
[0054] The confidence region construction module is used to construct the data confidence region of the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset.
[0055] The data fusion module is used to perform cross-validation on data blocks in the multi-source dataset through the data confidence domain to obtain the confidence of the multi-source dataset, and bind the confidence to the data block to obtain the fused confidence dataset of the target forested area;
[0056] The matrix construction module is used to perform statistical and ecological correlation analysis on the basic ecological element values of the target forested area to obtain the ecological health element matrix of the target forested area.
[0057] The ecological assessment module is used to perform dual calibration of the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix to obtain the comprehensive ecological health index of the target forestry area.
[0058] The ecological level mapping module is used to map the comprehensive ecological health index to obtain the ecological health status level of the target forested area.
[0059] Compared with the prior art, the present invention has the following beneficial effects:
[0060] 1. This invention standardizes the multi-source raw data of the target forestry area, integrates remote sensing images, time-series monitoring data, and thematic survey and management vector data, and then constructs a data confidence domain through systematic assimilation of inherent error range, historical accuracy, and spatial representativeness. Combined with cross-validation of data blocks to dynamically adjust and bind the confidence, this invention significantly improves the regularity and reliability of the assessment data, lays a solid and accurate data foundation for the assessment of forestry ecological health, and greatly improves the quality of the preliminary data for the assessment work.
[0061] 2. This invention extracts basic ecological element values by fusing confidence datasets, deeply analyzes their spatial statistical correlations and ecological coupling relationships to construct an ecological health element matrix, and calculates the comprehensive ecological health index through dual calibration of confidence attributes and coupling correlation coefficients and scientific formulas, making the assessment process more logical and scientific, effectively improving the accuracy of the comprehensive ecological health index, and thus improving the accuracy of ecological health status level determination, achieving simultaneous optimization of the efficiency and quality of forestry ecological health assessment. Attached Figure Description
[0062] Figure 1 This is a flowchart illustrating a method for assessing the ecological health of forestry based on multi-source data, provided in an embodiment of the present invention. Figure 2 This is a functional module diagram of a forestry ecological health assessment system based on multi-source data, provided in an embodiment of the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0063] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
[0064] This application provides a method for assessing the ecological health of forestry based on multi-source data. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for assessing the ecological health of forestry based on multi-source data can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.
[0065] Reference Figure 1 The diagram shown is a flowchart illustrating a forestry ecological health assessment method based on multi-source data according to an embodiment of the present invention. In this embodiment, the forestry ecological health assessment method based on multi-source data includes:
[0066] S1. Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset;
[0067] In this embodiment of the invention, the step of collecting remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset includes:
[0068] Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested areas;
[0069] Radiometric calibration is performed on the remote sensing images to obtain a standardized image layer of the target forested area;
[0070] The time-series monitoring data is smoothed to obtain a regular monitoring data sequence for the target forested area;
[0071] Spatially align the thematic survey with the management vector data to obtain a regularized vector data layer of the target forestry area;
[0072] The standardized image layer, the regularized monitoring data sequence, and the regularized vector data layer are mapped onto the spatiotemporal grid framework of the target forested area to obtain a multi-source dataset of the target forested area.
[0073] The target forested area is fully covered by satellite or aerial remote sensing equipment to obtain remote sensing images containing information such as vegetation distribution, topography, and land cover. At the same time, monitoring sensors are reasonably deployed in the target forested area to collect continuous time-series monitoring data such as meteorological data, soil moisture, and vegetation growth status at fixed time intervals. In addition, professional personnel conduct on-site surveys to record information such as forest land type, management activity trajectory, and forest land boundary range, forming thematic survey data. Combined with forest land management archives, management vector data containing management measures and management time are compiled to ensure that the three types of data fully cover the spatial range of the target forested area and the information dimensions required for assessment.
[0074] The radiometric response coefficient and atmospheric correction parameters of the remote sensing equipment are obtained. The digital grayscale value of each pixel in the remote sensing image is converted into the actual radiance value of the corresponding land surface. During the process, atmospheric scattering, absorption and radiation errors caused by the equipment's own optical system are eliminated, so that the image data can truly reflect the actual radiation characteristics of the target forested area. Finally, a standardized image layer is obtained.
[0075] The collected time-series monitoring data are arranged sequentially according to time, and the data change trend is analyzed segment by segment. For abrupt changes in the data, the normal data change patterns of adjacent time points are referenced, and the abrupt changes are corrected by trend extension. Meaningless abnormal fluctuations are eliminated so that the data can smoothly present the natural change trend of the monitoring indicators over time, thereby obtaining a regular monitoring data sequence.
[0076] Using the nationally unified geodetic coordinate system as a benchmark, the spatial coordinates of the thematic survey data and management vector data are calibrated one by one, and the geographical location information of each element in the two types of data is adjusted to ensure that the spatial elements such as forest boundaries, management areas, and survey points are completely corresponding, eliminating spatial deviations caused by different collection methods, ensuring that the two types of data are accurately matched in spatial location, and forming a regular vector data layer.
[0077] Based on the size of the target forested area and the required assessment accuracy, a uniformly sized spatial grid is divided, and a spatiotemporal grid framework is constructed by setting a unified time scale. Each pixel information in the standardized image layer is mapped to a spatial grid unit in the framework, and the regularized monitoring data sequence is filled into the corresponding time node according to the time scale. At the same time, the attribute information of the regularized vector data layer is associated with the corresponding spatial grid, so as to achieve seamless integration of the three types of data in the spatiotemporal dimension and finally obtain a multi-source dataset.
[0078] The beneficial effects are that comprehensive raw data is obtained through a systematic collection method, and targeted standardization processing is carried out for the characteristics of different types of data. This effectively eliminates problems such as equipment errors, spatial deviations and temporal fluctuations in the data collection process, and realizes the unified integration of multi-source data in the spatiotemporal dimension. The resulting multi-source dataset has the characteristics of complete information, unified format and reliable accuracy, providing high-quality data support for subsequent assessment stages such as data assimilation and confidence region construction, and ensuring the accuracy and efficiency of forestry ecological health assessment.
[0079] S2. Based on the inherent error range, historical accuracy, and spatial representativeness of the target forested area of the multi-source dataset, construct the data confidence region of the target forested area;
[0080] In this embodiment of the invention, constructing the data confidence region of the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset includes:
[0081] The device specifications and collected metadata of the data sources in the multi-source dataset are analyzed to quantify the systematic error and random error of the multi-source dataset, and the inherent error quantification result of the multi-source dataset is obtained.
[0082] The historical accuracy of the multi-source dataset is obtained by comparing and analyzing the multi-source dataset with the historical data of the target forested area.
[0083] The spatial representativeness of the multi-source dataset is obtained by evaluating the distribution of sampling points and the rationality of spatial interpolation.
[0084] Based on the data type of the multi-source dataset and the typical characteristics of the target forestry area, corresponding weights are assigned to the inherent error quantification result, the historical accuracy, and the spatial representativeness to obtain the weight allocation rule for the target forestry area.
[0085] The data confidence domain of the target forested area is obtained by performing multi-criteria decision fusion on the data types of the multi-source dataset and the weight allocation rules.
[0086] Carefully review the equipment specifications for each data point in the multi-source dataset to clarify the device's sensor accuracy, measurement range, resolution, and other equipment specifications. Simultaneously, extract metadata related to the data acquisition process, such as acquisition time, environmental conditions, and operator information. Analyze the equipment specifications to determine the fixed deviations caused by the equipment's own performance during data acquisition; these deviations are the systematic errors, such as the slight measurement offsets present in sensors at the factory. Then, compare the results of multiple acquisitions of the same object using the same equipment under the same environment to statistically analyze the dispersion of the results; this dispersion is the random error, such as the reading differences caused by instantaneous environmental fluctuations during acquisition. Present the systematic and random errors separately in numerical form, and compile them into a quantitative result of the inherent error of the multi-source dataset.
[0087] Collect historical data such as remote sensing imagery, time-series monitoring records, and thematic survey archives from the same period in the target forested area. Select data points with the same spatiotemporal coordinates and the same monitoring indicators from the multi-source dataset and historical data for one-to-one comparison. For example, select vegetation cover index data at the same location and time period, and count the total number of data points whose data values in the multi-source dataset are completely consistent with the corresponding data values in the historical data or are within the reasonable fluctuation range of the historical data. Divide this total number by the total number of all data points participating in the comparison, and the resulting ratio is the historical accuracy rate of the multi-source dataset.
[0088] A comprehensive review of the geographic location information of all sampling points in the multi-source dataset was conducted. The specific location of each sampling point was marked on the map of the target forested area. It was checked whether the sampling points covered all key landforms and vegetation distribution areas in the region, such as mountains, valleys, forest edges, and core forest areas. At the same time, it was confirmed whether the distance distribution between sampling points was uniform and whether there were any large, continuous uncovered blank areas. Subsequently, when spatial interpolation was performed based on the existing sampling point data, it was analyzed whether the interpolation results conformed to the natural ecological laws of the forested area. For example, whether the interpolated soil moisture data matched the terrain slope and water source distribution of the region. No abnormal interpolation results that contradicted the actual situation were found. Based on the comprehensive analysis of sampling point coverage and interpolation rationality, a spatial representativeness assessment result of the multi-source dataset was formed.
[0089] The different data types included in the multi-source dataset are clearly defined, such as remote sensing image data, time-series monitoring data, and thematic survey and management vector data. At the same time, the core characteristics of the target forested area are sorted out, such as whether the area is a mountainous forest area or a plain forest area, and whether there are typical characteristics such as rare vegetation communities and special hydrological environments. Based on the accuracy requirements of the data type and the application scenario, combined with the degree of influence of the typical characteristics of the area on the data reliability, specific weight values are assigned to the inherent error quantification result, historical accuracy and spatial representativeness. For example, for ecologically sensitive mountainous forest areas, and the data type is time-series monitoring data with high accuracy requirements, the weight allocation of the inherent error quantification result is 40%, the weight allocation of historical accuracy is 30%, and the weight allocation of spatial representativeness is 30%. These weight allocation information are organized into clear weight allocation rules.
[0090] Based on the multi-criteria decision fusion approach, the inherent error quantification results, historical accuracy, and spatial representativeness corresponding to each data type are first weighted and summed according to the weight allocation rules to obtain the comprehensive confidence assessment score for each data type. Then, the comprehensive confidence assessment scores of all data types are summarized to divide the data into three confidence level intervals: high, medium, and low. Each interval has a clearly defined score range, and each confidence interval is assigned a corresponding confidence level description to clarify the applicable scope and usage priority of data with different confidence levels in subsequent assessment work. Finally, a data confidence domain for the target forestry area is formed, which includes information such as confidence interval range, confidence level, applicable data types, and usage requirements.
[0091] The beneficial effects are as follows: by analyzing equipment specifications and collecting metadata to quantify inherent errors, and comparing with historical data to determine historical accuracy, the spatial representativeness is obtained by comprehensively evaluating the distribution of sampling points and the rationality of spatial interpolation. Then, weights are scientifically allocated according to data type and typical regional characteristics, and a data confidence domain is constructed through multi-criteria decision fusion. The whole process is progressive and logically rigorous, ensuring that the data confidence domain can comprehensively and accurately reflect the reliability and applicability of multi-source data. This provides a solid and accurate foundation for subsequent data cross-validation and adjustment of data credibility through the data confidence domain, effectively ensuring the reliability of data use in the process of forestry ecological health assessment.
[0092] S3. Cross-validate the data blocks in the multi-source dataset using the data confidence region to obtain the confidence level of the multi-source dataset, and bind the confidence level to the data block to obtain the fused confidence dataset of the target forested area;
[0093] In this embodiment of the invention, the step of cross-validating data blocks in the multi-source dataset through the data confidence region to obtain the confidence level of the multi-source dataset, and binding the confidence level with the data block to obtain the fused confidence dataset of the target forested area, includes:
[0094] Identify data values from different sources in the multi-source dataset;
[0095] Call the comprehensive assimilation confidence score corresponding to the data values from different sources in the data confidence domain;
[0096] The differences between the data values from different sources are statistically analyzed, and the comprehensive assimilation confidence score is dynamically adjusted based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset.
[0097] The final confidence level is structured and associated with the multi-source dataset to obtain the fused confidence dataset of the target forested area.
[0098] The step of dynamically adjusting the comprehensive assimilation confidence score based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset includes:
[0099] The differences are compared and analyzed with the inherent error range of the multi-source dataset;
[0100] When the difference is within the overlapping range of the inherent error range, the comprehensive assimilation confidence score is enhanced to obtain the final confidence of the multi-source dataset;
[0101] When the difference exceeds the overlapping range of the inherent error range, the comprehensive assimilation confidence score is corrected to obtain the final confidence of the multi-source dataset.
[0102] Each data point in the multi-source dataset is checked for identification of the acquisition equipment, data record source, and acquisition method. Data values derived from remote sensing images, data values collected by time-series monitoring sensors, data values recorded on-site in thematic surveys, and values corresponding to operational vector data are clearly distinguished. The specific source of each data value is clearly defined to ensure that data values from different sources are classified without omission or confusion.
[0103] Based on the clearly defined attribution type of data values from different sources, targeted retrieval is performed within the constructed data confidence domain. By accurately matching the data source with the preset source identifier in the confidence domain, the comprehensive assimilation confidence score corresponding to each source data value, which is obtained through systematic assimilation calculation, is extracted to ensure that the score completely corresponds to the source of the data value.
[0104] For data values from different sources under the same evaluation indicator and at the same time and space location, calculate the numerical differences between them one by one, fully statistically analyze the differences of all corresponding data values, clarify the specific numerical magnitude of each difference, ensure that the difference statistics are comprehensive and accurate, and provide an accurate basis for subsequent credibility adjustments.
[0105] Extract the inherent error range of the quantized multi-source dataset within the data confidence region, and directly compare the specific numerical value of the difference of each data value obtained from the previous statistics with this inherent error range to clearly determine whether each difference value is within the overlapping interval of the inherent error range and clarify the attribution of the difference.
[0106] When the comparison results show that the difference in data values is within the overlapping range of the inherent error range, it indicates that the difference is within the reasonable error range of data collection and the data consistency is good. At this time, the corresponding comprehensive assimilation confidence score is increased by a fixed ratio to enhance the confidence score and ultimately form the final credibility of the multi-source dataset corresponding to the data.
[0107] When the comparison results show that the difference in data values exceeds the overlap range of the inherent error range, it indicates that the difference in data exceeds the reasonable error range and there is a deviation in the consistency of the data. At this time, according to the specific extent that the difference exceeds the overlap range, the corresponding comprehensive assimilation confidence score is reduced by an appropriate proportion to complete the correction of the confidence score, and thus obtain the final confidence of the multi-source dataset corresponding to the data.
[0108] According to the data block division rules of the multi-source dataset, the final confidence of each data block is matched one-to-one with the core information such as the original data content, data source information, and collection time contained in the data block. The data is integrated and encapsulated using a unified structured data format to ensure that each data block is closely related to its own final confidence, forming a fused confidence dataset of the target forestry area with a complete structure and clear association.
[0109] The beneficial effects are that by accurately identifying data sources, targeting confidence scores, comprehensively statistically analyzing differences, and dynamically adjusting the confidence level in conjunction with the inherent error range, the final confidence level can truly reflect the actual reliability of the data. Furthermore, through structured association and encapsulation, each data block is bound with clear confidence information. The resulting fused confidence dataset retains the complete original data content and has clear reliability indicators, providing data support with both completeness and reliability for subsequent ecological element extraction, matrix construction, and other steps, further ensuring the accuracy of forestry ecological health assessment.
[0110] S4. Perform statistical and ecological correlation analysis on the basic ecological element values of the target forestry area to obtain the ecological health element matrix of the target forestry area;
[0111] In this embodiment of the invention, the step of performing statistical and ecological correlation analysis on the basic ecological element values of the target forested area to obtain the ecological health element matrix of the target forested area includes:
[0112] The vegetation cover index, soil moisture index, land surface temperature index, and leaf area index in the fused confidence dataset are analyzed to obtain the ecological element set of the fused confidence dataset;
[0113] Spatial neighborhood analysis is performed on the basic ecological element values of the ecological element set to obtain the spatial statistical correlation degree of the target forestry area.
[0114] Multi-element coupling analysis is performed on the ecological element set to identify the synergistic change trends and constraints of the ecological element set, and to obtain the ecological coupling intensity matrix of the target forested area.
[0115] By synergistically integrating the spatial statistical correlation degree with the ecological coupling strength matrix, the ecological health element matrix of the target forested area is obtained.
[0116] The multi-element coupling analysis of the ecological element set identifies the synergistic change trends and constraints of the ecological element set, resulting in the ecological coupling intensity matrix of the target forested area, including:
[0117] The causal network of the ecological element set is reconstructed to obtain the cooperative change map of the ecological element set;
[0118] Based on the synergistic change map, the differences in the dominant synergistic direction and change phase among the ecological elements in the ecological element cluster are analyzed to obtain the synergistic trend description set of the target forestry area.
[0119] Based on the collaborative trend description set, the constraint relationships between the ecological elements are identified to obtain the constraint intensity index of the ecological elements;
[0120] By associating and coupling the collaborative trend description set and the constraint intensity index, the initial coupling relationship of the ecological element set is obtained;
[0121] The dimensional differences of the indicators in the initial coupling relationship are eliminated, and the intensity of the processed initial coupling relationship is quantitatively analyzed to obtain the ecological coupling intensity matrix of the target forested area.
[0122] Each data block in the fused confidence dataset is analyzed one by one to accurately extract the specific values of vegetation cover index, soil moisture index, surface temperature index, and leaf area index. At the same time, the confidence information corresponding to each index is associated to ensure that the four extracted indices fully cover the core ecological characteristics of the target forested area. These indices are then classified and organized according to data type and spatial location to form the ecological element set of the fused confidence dataset.
[0123] Taking the spatiotemporal grid unit corresponding to each basic ecological element value in the ecological element set as the core, a fixed range of spatial neighborhood is delineated. The numerical overlap and trend consistency of the same basic ecological element value in the core grid unit and all grid units in the neighborhood are statistically analyzed. At the same time, the distribution matching of different basic ecological element values in the same spatial neighborhood is analyzed. The spatial statistical correlation of the target forestry area is obtained by combining these statistical results.
[0124] By sorting out the complete change time series of each basic ecological element in the ecological element set, analyzing whether the numerical fluctuation of each element will affect other elements, clarifying the triggering conditions and action paths of the influence between elements, and taking each ecological element as a network node and the influence relationship between elements as the connection between nodes, a collaborative change map of the ecological element set that can intuitively reflect the interaction between elements is constructed.
[0125] By comparing the synergistic change map, we traced the directional relationship between the lines connecting each ecological element to determine which ecological elements would change first and drive changes in other elements, clarifying the dominant synergistic direction. At the same time, we recorded the time sequence of the peak and trough values of different ecological elements, analyzed the differences in the rhythm of change of each element, and formed a synergistic trend description set of the target forestry area that includes the dominant synergistic direction and the differences in the phase of change.
[0126] By deeply analyzing the synergistic trend description set, we can observe whether the change of one ecological element according to a specific trend will lead to a limitation on the magnitude of change, a change in the direction of change, or a slowdown in the rate of change of another ecological element. In this way, we can identify the type of constraint relationship between ecological elements, and then classify the specific intensity level according to the degree of limitation of the change of the constrained element, and transform it into a specific constraint intensity index of ecological element.
[0127] Each collaborative trend in the collaborative trend description set is matched with its corresponding constraint strength index to clarify the constraint strength accompanying each collaborative change process. According to the correspondence of "collaborative direction-phase difference-constraint strength", all related information is organized into a unified structure to form the initial coupling relationship of the ecological element set.
[0128] By adopting a normalization process, the constraint strength indices of different dimensions in the initial coupling relationship are uniformly converted to the numerical range of [0,1] to eliminate the influence of the difference in dimensions. Then, the correlation strength of each item in the processed initial coupling relationship is evaluated and scored one by one. A matrix is constructed based on the scoring results. The value of each position in the matrix corresponds to the coupling strength between a pair of ecological elements, thus obtaining the ecological coupling strength matrix of the target forested area.
[0129] The specific values of spatial statistical correlation are integrated into the corresponding cells of the ecological coupling strength matrix according to the pairing relationship of the corresponding ecological elements. This makes the matrix contain both the coupling strength information between ecological elements and the statistical correlation characteristics of the elements in space. Through structured integration, an ecological health element matrix of the target forestry area is formed that simultaneously carries information on spatial correlation and coupling.
[0130] The beneficial effects are that by systematically extracting core basic ecological elements, comprehensively analyzing the spatial statistical correlation and multi-dimensional ecological coupling relationship between elements, and constructing an ecological health element matrix that fully integrates key information such as element values, credibility, spatial correlation characteristics and coupling strength, a structured and high-precision data carrier is formed. This provides solid and comprehensive data support for the subsequent dual calibration of basic ecological element values and the accurate calculation of the comprehensive ecological health index, effectively improving the scientificity and reliability of forestry ecological health assessment.
[0131] S5. Based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix, the basic ecological element values are double-calibrated to obtain the comprehensive ecological health index of the target forestry area.
[0132] In this embodiment of the invention, the step of performing dual calibration on the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix to obtain the comprehensive ecological health index of the target forested area includes:
[0133] Obtain the confidence attribute of the basic ecological element values in the ecological health element matrix and the coupling correlation coefficient of the ecological health element matrix;
[0134] The confidence attribute and the basic ecological element value are weighted and fused to obtain the first calibration value of the target forested area;
[0135] Using the coupling correlation coefficient as the influence weight, the first calibration value is corrected by neighborhood element propagation to obtain the second calibration value of the target forested area.
[0136] The comprehensive ecological health index of the target forested area is calculated based on the second calibration value.
[0137] The formula for calculating the comprehensive ecological health index is as follows:
[0138] ;
[0139] in, This represents the comprehensive ecological health index. This represents the total number of the aforementioned basic ecological element values. Indicates the first The weighting coefficients of the aforementioned basic ecological element values. Indicates the first The second calibration value for each of the aforementioned basic ecological element values. Indicates the first The confidence level attribute of the aforementioned basic ecological element values. This represents the preset confidence level adjustment factor. Indicates the first The and the first The coupling correlation coefficient between the aforementioned basic ecological element values. This represents the average value of the second calibration value. This represents the preset coupling effect balancing factor. This represents the natural exponential function.
[0140] The confidence attribute corresponding to each basic ecological element value is extracted one by one from the ecological health element matrix. The corresponding confidence attribute information is clearly marked under each entry of basic ecological element value in the matrix, ensuring that each basic ecological element value can be accurately matched with its exclusive confidence attribute without omission or mismatch. At the same time, according to the pairing relationship of basic ecological element values in the matrix, the coupling correlation coefficients corresponding to all different basic ecological element values are extracted. Each pair of basic ecological element values has a unique corresponding coupling correlation coefficient entry in the matrix. After complete extraction, the coefficients are organized and archived according to the pairing relationship to ensure that the coefficients and element values are accurately matched.
[0141] Based on the ecological characteristics and assessment standards of the target forestry area, a fixed weight ratio is assigned to each confidence attribute. This weight ratio is determined based on the degree of influence of basic ecological elements on the ecological health of the forestry area, and the sum of the weight ratios of all confidence attributes is 1. Each basic ecological element value is multiplied by its corresponding confidence attribute to obtain the confidence weighted result of that element value. This weighted result is then added to the original basic ecological element value. Through this calculation method, the first calibration value corresponding to each basic ecological element value is obtained, ensuring that each first calibration value can reflect the influence of the confidence attribute.
[0142] Based on the spatiotemporal grid framework of the target forestry area, the spatial neighborhood range corresponding to each basic ecological element value is determined. The neighborhood range is a fixed number of grid cells adjacent to the grid cell where the element value is located. The first calibration value in these neighboring grid cells is extracted as the neighborhood element. The previously extracted coupling correlation coefficient is used as the influence weight of the corresponding neighborhood element. The product of the first calibration value of each neighborhood element and the corresponding coupling correlation coefficient is calculated. Then, all the product results are summed to obtain the total influence of the neighborhood elements. Finally, this total is added to the first calibration value of the current basic ecological element value to complete the neighborhood element propagation correction and obtain the second calibration value corresponding to each basic ecological element value.
[0143] First, count the total number of second calibration values corresponding to all basic ecological element values, and simultaneously obtain the weight coefficient corresponding to each second calibration value. This weight coefficient is consistent with the weight allocation rule set when constructing the data confidence region. Calculate the product of each second calibration value and its corresponding weight coefficient, and sum all the product results to obtain the total product sum. Then, sum all the weight coefficients to obtain the weight sum. Divide the total product sum by the weight sum to obtain the first part of the calculation result. Next, calculate the average of all second calibration values, that is, sum all the second calibration values and divide by the total number. Then, for each pair of different basic ecological element values, calculate the difference between their respective second calibration values and the average value. Multiply the two differences and then multiply them by the coupling correlation coefficient corresponding to the pair of element values. Sum all such product results to obtain the total coupling correlation impact value. Count the total number of all different element value pairs, divide the total coupling correlation impact value by the total number of pairs, and then multiply by the preset coupling influence balance factor to obtain the second part of the calculation result. Finally, add the first part of the calculation result and the second part of the calculation result to obtain the comprehensive ecological health index of the target forested area.
[0144] The total number of basic ecological element values comes from the actual number of basic ecological element values extracted from the fused confidence dataset.
[0145] The weighting coefficient is a coefficient corresponding to the value of each basic ecological element, which is determined simultaneously based on the data type of the multi-source dataset and the typical characteristics of the target forestry area, when assigning corresponding weights to the inherent error quantification results, historical accuracy and spatial representativeness.
[0146] The process of obtaining the second calibration value involves first performing a first calibration on the basic ecological element values based on the confidence attribute in the ecological health element matrix to obtain the first calibration value, and then performing a second calibration on the first calibration value based on the coupling correlation coefficient in the ecological health element matrix to obtain the final value.
[0147] The confidence attribute is a related attribute that is determined synchronously after parsing the basic ecological element values from the fused confidence dataset when constructing the ecological health element matrix of the target forested area.
[0148] The confidence adjustment factor is a fixed value set in advance before calculating the comprehensive ecological health index, used to adjust the degree of influence of the confidence attribute on the calculation results.
[0149] The coupling correlation coefficient is obtained by performing spatial neighborhood analysis on the basic ecological element values in the fused confidence dataset to obtain the spatial statistical correlation, performing multi-element coupling analysis on the ecological element set to obtain the ecological coupling strength matrix, and then extracting the value reflecting the degree of correlation between the two basic ecological element values after coordinating and integrating the spatial statistical correlation and the ecological coupling strength matrix to construct the ecological health element matrix.
[0150] The average value of the second calibration value is obtained by summing all the second calibration values and then dividing the sum by the total number of second calibration values.
[0151] The coupling effect balancing factor is a fixed value set in advance before calculating the comprehensive ecological health index. It is used to balance the influence of the coupling correlation coefficient on the calculation results.
[0152] The core of this calculation process is to comprehensively integrate key factors affecting the ecological health of forestry areas. A comprehensive ecological health index is constructed through two parts of calculation. The first part uses weighted coefficients to sum the second calibration values after adjusting for confidence attributes, and then divides the sum of all weighted coefficients to accurately measure the health contribution of individual basic ecological elements. The second part uses coupling correlation coefficients to measure the interaction between different basic ecological elements, and calculates the index by combining the deviation between the second calibration value and the average value. The impact of this part on the overall index is then adjusted by coupling influence balancing factors, ultimately achieving a comprehensive and accurate quantitative reflection of the ecological health status of the target forestry area.
[0153] As the value of the second calibration value increases, the comprehensive ecological health index will show an upward trend.
[0154] As the confidence level attribute value increases, the calculation result of the natural exponential function part will increase accordingly, which in turn will increase the calculation result of the first part, thus driving the overall increase of the comprehensive ecological health index.
[0155] When the coupling correlation coefficient shows a positive correlation and the value increases, the calculation result of the second part will increase accordingly, and the comprehensive ecological health index will also rise.
[0156] The larger the weight coefficient of a basic ecological element, the more significant its corresponding second calibration value has on the comprehensive ecological health index, and the higher the contribution of the health status of that basic ecological element to the overall forestry ecological health.
[0157] The magnitude of the confidence adjustment factor directly determines the degree of influence of the confidence attribute on the calculation results of the first part. The larger the value, the more gradual the impact of the change in the confidence attribute on the comprehensive ecological health index; the smaller the value, the more drastic the impact of the change in the confidence attribute on the comprehensive ecological health index.
[0158] The magnitude of the coupling effect equilibrium factor directly determines the degree of influence of the coupling correlation coefficient on the results of the second part. The larger the value, the more prominent the influence of the coupling relationship between different basic ecological elements on the comprehensive ecological health index; the smaller the value, the weaker the influence of this part on the comprehensive ecological health index.
[0159] The beneficial effects are that by accurately extracting key attributes and coefficients from the ecological health element matrix and adopting a dual calibration method that combines weighted fusion and neighborhood propagation correction, the reliability of the basic ecological element values is fully considered, while also taking into account the coupling and correlation effects between different ecological elements. This effectively corrects the limitations of single-element value assessment, making the second calibration value more consistent with the actual situation of the forestry ecosystem. Based on this, the comprehensive ecological health index obtained through step-by-step calculation can comprehensively and accurately quantify the ecological health level of the target forestry area, providing solid and reliable data support for the subsequent scientific determination of the ecological health status level, and further improving the accuracy and rationality of forestry ecological health assessment.
[0160] In this embodiment of the invention, S6, the comprehensive ecological health index is mapped to obtain the ecological health status level of the target forested area.
[0161] Based on the core characteristics of forest ecosystems, such as structural stability, functional integrity, and sustainable development potential, and with reference to relevant ecological assessment industry standards, the specific number of ecological health status levels is determined. At the same time, a comprehensive ecological health index value range is set for each level. The range is continuous and non-overlapping, and the upper and lower limits of each range are clearly defined and fixed. Each level is given a unique name, such as excellent ecological health, good ecological health, medium ecological health, and poor ecological health, to ensure that the level definition standards are unified and have clear direction.
[0162] The comprehensive ecological health index of the target forested area, calculated through double calibration, is precisely compared with the index intervals corresponding to each preset ecological health status level. The relationship between the index value and the upper and lower limits of each interval is checked one by one to clarify the unique index interval corresponding to the comprehensive ecological health index and avoid the ambiguity of interval classification.
[0163] Based on the index range corresponding to the comprehensive ecological health index, the corresponding ecological health status level name is directly matched, and the ecosystem characteristics represented by the level are associated with it. For example, the ecological health superior level corresponds to characteristics such as stable ecosystem structure, rich biodiversity, and complete ecological functions, and finally forms a clear and specific ecological health status level for the target forested area.
[0164] The beneficial effects are that by establishing clear and standardized grading criteria and a precise index interval matching mechanism, the comprehensive ecological health index has been effectively transformed into a concrete ecological health status level. This makes the assessment results more intuitive and easier to understand, rather than just abstract numerical forms. It not only accurately presents the ecological health level of the target forestry area, but also provides a clear and specific basis for subsequent ecological protection measures, restoration plans, and dynamic monitoring and management, significantly improving the practicality and application value of the forestry ecological health assessment results.
[0165] like Figure 2 The diagram shown is a functional module diagram of a forestry ecological health assessment system based on multi-source data provided in an embodiment of the present invention.
[0166] The forestry ecological health assessment system 100 based on multi-source data described in this invention can be installed in an electronic device. Depending on the functions implemented, the forestry ecological health assessment system 100 based on multi-source data may include a data acquisition module 101, a confidence region construction module 102, a data fusion module 103, a matrix construction module 104, an ecological assessment module 105, and an ecological level mapping module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.
[0167] In this embodiment, the functions of each module / unit are as follows:
[0168] The data acquisition module 101 is used to collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset;
[0169] The confidence region construction module 102 is used to construct the data confidence region of the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset.
[0170] The data fusion module 103 is used to perform cross-validation on data blocks in the multi-source dataset through the data confidence domain to obtain the confidence of the multi-source dataset, and bind the confidence to the data block to obtain the fused confidence dataset of the target forested area.
[0171] The matrix construction module 104 is used to perform statistical and ecological correlation analysis on the basic ecological element values of the target forestry area to obtain the ecological health element matrix of the target forestry area.
[0172] The ecological assessment module 105 is used to perform dual calibration on the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix to obtain the comprehensive ecological health index of the target forestry area.
[0173] The ecological level mapping module 106 is used to perform level mapping on the comprehensive ecological health index to obtain the ecological health status level of the target forested area.
[0174] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.
[0175] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0176] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.
[0177] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.
[0178] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.
[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims
1. A method for assessing the ecological health of forestry based on multi-source data, characterized in that, The method includes: S1. Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset; S2. Based on the inherent error range, historical accuracy, and spatial representativeness of the target forested area of the multi-source dataset, construct the data confidence region of the target forested area; S3. Cross-validate the data blocks in the multi-source dataset using the data confidence region to obtain the confidence level of the multi-source dataset, and bind the confidence level to the data block to obtain the fused confidence dataset of the target forested area; S4. Perform statistical and ecological correlation analysis on the basic ecological element values of the target forestry area to obtain the ecological health element matrix of the target forestry area; S5. Based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix, the basic ecological element values are double-calibrated to obtain the comprehensive ecological health index of the target forestry area. S6. Map the comprehensive ecological health index to obtain the ecological health status level of the target forested area.
2. The forestry ecological health assessment method based on multi-source data as described in claim 1, characterized in that, The process involves compiling remote sensing images, time-series monitoring data, thematic surveys, and management vector data of the target forested area into a multi-source dataset, including: Collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested areas; Radiometric calibration is performed on the remote sensing images to obtain a standardized image layer of the target forested area; The time-series monitoring data is smoothed to obtain a regular monitoring data sequence for the target forested area; Spatially align the thematic survey with the management vector data to obtain a regularized vector data layer of the target forestry area; The standardized image layer, the regularized monitoring data sequence, and the regularized vector data layer are mapped onto the spatiotemporal grid framework of the target forested area to obtain a multi-source dataset of the target forested area.
3. The forestry ecological health assessment method based on multi-source data as described in claim 1, characterized in that, The step of constructing a data confidence region for the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset includes: The device specifications and collected metadata of the data sources in the multi-source dataset are analyzed to quantify the systematic error and random error of the multi-source dataset, and the inherent error quantification result of the multi-source dataset is obtained. The historical accuracy of the multi-source dataset is obtained by comparing and analyzing the multi-source dataset with the historical data of the target forested area. The spatial representativeness of the multi-source dataset is obtained by analyzing the distribution of sampling points and the rationality of spatial interpolation. Based on the data type of the multi-source dataset and the typical characteristics of the target forestry area, corresponding weights are assigned to the inherent error quantification result, the historical accuracy, and the spatial representativeness to obtain the weight allocation rule for the target forestry area. The data confidence domain of the target forested area is obtained by performing multi-criteria decision fusion on the data types of the multi-source dataset and the weight allocation rules.
4. The forestry ecological health assessment method based on multi-source data as described in claim 1, characterized in that, The process involves cross-validating data blocks in the multi-source dataset using the data confidence region to obtain the confidence level of the multi-source dataset, and then binding the confidence level with the data block to obtain the fused confidence dataset of the target forested area, including: Identify data values from different sources in the multi-source dataset; Call the comprehensive assimilation confidence score corresponding to the data values from different sources in the data confidence domain; The differences between the data values from different sources are statistically analyzed, and the comprehensive assimilation confidence score is dynamically adjusted based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset. The final confidence level is structured and associated with the multi-source dataset to obtain the fused confidence dataset of the target forested area.
5. The forestry ecological health assessment method based on multi-source data as described in claim 4, characterized in that, The step of dynamically adjusting the comprehensive assimilation confidence score based on the differences and the data confidence region to obtain the final confidence of the multi-source dataset includes: The differences are compared and analyzed with the inherent error range of the multi-source dataset; When the difference is within the overlapping range of the inherent error range, the comprehensive assimilation confidence score is enhanced to obtain the final confidence of the multi-source dataset; When the difference exceeds the overlapping range of the inherent error range, the comprehensive assimilation confidence score is corrected to obtain the final confidence of the multi-source dataset.
6. The forestry ecological health assessment method based on multi-source data as described in claim 1, characterized in that, The basic ecological element values of the target forested area are analyzed based on statistical and ecological correlation to obtain the ecological health element matrix of the target forested area, including: The vegetation cover index, soil moisture index, land surface temperature index, and leaf area index in the fused confidence dataset are analyzed to obtain the ecological element set of the fused confidence dataset; Spatial neighborhood analysis is performed on the basic ecological element values of the ecological element set to obtain the spatial statistical correlation degree of the target forestry area. Multi-element coupling analysis is performed on the ecological element set to identify the synergistic change trends and constraints of the ecological element set, and to obtain the ecological coupling intensity matrix of the target forested area. By synergistically integrating the spatial statistical correlation degree with the ecological coupling strength matrix, the ecological health element matrix of the target forested area is obtained.
7. The forestry ecological health assessment method based on multi-source data as described in claim 6, characterized in that, The multi-element coupling analysis of the ecological element set identifies the synergistic change trends and constraints of the ecological element set, resulting in the ecological coupling intensity matrix of the target forested area, including: The causal network of the ecological element set is reconstructed to obtain the cooperative change map of the ecological element set; Based on the synergistic change map, the differences in the dominant synergistic direction and change phase among the ecological elements in the ecological element cluster are analyzed to obtain the synergistic trend description set of the target forestry area. Based on the collaborative trend description set, the constraint relationships between the ecological elements are identified to obtain the constraint intensity index of the ecological elements; By associating and coupling the collaborative trend description set and the constraint intensity index, the initial coupling relationship of the ecological element set is obtained; The dimensional differences of the indicators in the initial coupling relationship are eliminated, and the intensity of the processed initial coupling relationship is quantitatively analyzed to obtain the ecological coupling intensity matrix of the target forested area.
8. The forestry ecological health assessment method based on multi-source data as described in claim 1, characterized in that, The comprehensive ecological health index of the target forested area is obtained by double-calibrating the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix, including: Obtain the confidence attribute of the basic ecological element values in the ecological health element matrix and the coupling correlation coefficient of the ecological health element matrix; The confidence attribute and the basic ecological element value are weighted and fused to obtain the first calibration value of the target forested area; Using the coupling correlation coefficient as the influence weight, the first calibration value is corrected by neighborhood element propagation to obtain the second calibration value of the target forested area. The comprehensive ecological health index of the target forested area is calculated based on the second calibration value.
9. The forestry ecological health assessment method based on multi-source data as described in claim 8, characterized in that, The formula for calculating the comprehensive ecological health index is as follows: ； in, This represents the comprehensive ecological health index. This represents the total number of the aforementioned basic ecological element values. Indicates the first The weighting coefficients of the aforementioned basic ecological element values. Indicates the first The second calibration value for each of the aforementioned basic ecological element values. Indicates the first The confidence level attribute of the aforementioned basic ecological element values. This represents the preset confidence level adjustment factor. Indicates the first The and the first The coupling correlation coefficient between the aforementioned basic ecological element values. This represents the average value of the second calibration value. This represents the preset coupling effect balancing factor. This represents the natural exponential function.
10. A forestry ecological health assessment system based on multi-source data, characterized in that, The system for implementing the forestry ecological health assessment method based on multi-source data as described in claim 1 includes: The data acquisition module is used to collect remote sensing images, time-series monitoring data, thematic survey and management vector data of the target forested area into a multi-source dataset; The confidence region construction module is used to construct the data confidence region of the target forested area based on the inherent error range, historical accuracy, and spatial representativeness of the multi-source dataset. The data fusion module is used to perform cross-validation on data blocks in the multi-source dataset through the data confidence domain to obtain the confidence of the multi-source dataset, and bind the confidence to the data block to obtain the fused confidence dataset of the target forested area; The matrix construction module is used to perform statistical and ecological correlation analysis on the basic ecological element values of the target forested area to obtain the ecological health element matrix of the target forested area. The ecological assessment module is used to perform dual calibration of the basic ecological element values based on the confidence attribute and coupling correlation coefficient in the ecological health element matrix to obtain the comprehensive ecological health index of the target forestry area. The ecological level mapping module is used to map the comprehensive ecological health index to obtain the ecological health status level of the target forested area.