Large-scale forest disturbance detection and disturbance attribution analysis method

Abstract

The invention belongs to the field of forest disturbance monitoring, and provides a large-scale forest disturbance detection and disturbance attribution analysis method. Comprising the steps of acquiring a multi-year forest coverage composite image, optimizing by utilizing a pixel compensation model, generating the coverage composite image, improving a basic CCDC algorithm, extracting disturbance, calculating disturbance data, calculating feature data, fusing data, calculating associated data and determining a target attribution result. According to the method, the multi-year forest coverage composite image is optimized and predicted, so that the quality and the number of images used for analyzing are improved, and the classification accuracy and the classification reliability are improved; through improvement of a basic CCDC algorithm, improvement of a disturbance extraction effect is realized. Through calculation of the feature data and the associated data, visual data is provided for disturbance attribution analysis, and the disturbance attribution analysis efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of forest disturbance monitoring, and in particular to a large-scale forest disturbance detection and disturbance attribution analysis method. Background Art

[0002] With global climate change and increased human activities, the frequency and intensity of forest disturbances have increased significantly, seriously affecting the stability of forest ecosystems. Existing forest disturbance monitoring methods mostly rely on ground observations and traditional remote sensing data, which have problems such as poor data timeliness, limited spatial coverage, and inability to effectively capture large-scale disturbance dynamics.

[0003] Existing technologies mainly include using medium-resolution MODIS and AVHRR data for coarse-grained disturbance monitoring, and using Landsat data for fine-grained change detection. Although these methods have solved the problem of spatial resolution to a certain extent, there is still much room for improvement in data processing capabilities, algorithm accuracy, and monitoring timeliness, and there is also a lack of comprehensive analysis of multiple factors. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a large-scale forest disturbance detection and disturbance attribution analysis method, which improves the quality and quantity of images used for analysis and improves the classification accuracy and reliability by optimizing and predicting multi-year forest cover composite maps; improves the disturbance extraction effect by improving the basic CCDC algorithm; and provides intuitive data for disturbance attribution analysis through feature data and associated data calculation, thereby improving the efficiency of disturbance attribution analysis.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A large-scale forest disturbance detection and disturbance attribution analysis method, comprising:

[0007] Collect and fuse long-term Landsat images and Google Maps historical images to obtain a composite map of forest cover over the years;

[0008] Inputting the multi-year forest cover composite map into a pre-trained pixel compensation model for optimization to obtain a high-precision forest cover composite map;

[0009] Inputting the high-precision forest cover composite map into a pre-trained neural network model for prediction to generate a cover composite map;

[0010] The maximum disturbance amplitude band and ID mapping value are added to the basic CCDC algorithm to obtain the improved CCDC algorithm.

[0011] The improved CCDC algorithm is used to extract the disturbance year, the maximum disturbance amplitude and the number of disturbances from the high-precision forest cover composite map and the generated cover composite map, respectively, to obtain a forest disturbance map; the forest disturbance map includes: a year data map, a maximum amplitude data map and a number data map;

[0012] Extracting the disturbance spatial distribution, disturbance density and change trend of the forest disturbance map respectively to obtain disturbance data;

[0013] The disturbance data are discriminated and analyzed using preset factors respectively to obtain forest fire attribution maps, human attribution maps and other attribution maps; the human attribution maps include: felling attribution maps, farming attribution maps and construction activity attribution maps; the other attribution maps include: climate attribution maps and disaster attribution maps;

[0014] Fusing the forest fire attribution map, the human factor attribution map and the other attribution maps to obtain a fused attribution map;

[0015] The fusion attribution map is analyzed using the global Moran's index, the local Moran's index and the Getis-Ord Gi* index to obtain the target attribution result.

[0016] Preferably, the calculation formula of the change trend includes:

[0017]

[0018] Wherein, θ is the linear change slope of the trend; FDD i is the forest disturbance density in the i-th year; n is the length of time.

[0019] Preferably, the training process of the pixel compensation model includes:

[0020] Collecting a basic forest cover composite map and a fine forest cover composite map; the fine forest cover composite map has a higher accuracy than the basic forest cover composite map;

[0021] Extracting pixel information of the fine forest cover composite image to obtain an original pixel data set;

[0022] Extracting high-precision pixel information of the multi-year forest cover composite map to obtain a target pixel data set;

[0023] Build a knn classification model;

[0024] The original pixel data set is used as input data, the target pixel data set is used as verification data, the knn classification model is trained, and the pixel compensation model is obtained.

[0025] Preferably, the types of maximum amplitude of the disturbance include: low amplitude, medium-low amplitude, medium amplitude, medium-high amplitude and high amplitude; the range of the low amplitude is: 0 to 0.09; the range of the medium-low amplitude is: 0.09 to 0.19; the range of the medium amplitude is: 0.19 to 0.30; the range of the medium-high amplitude is: 0.30 to 0.45; the range of the high amplitude is: greater than 0.45.

[0026] Preferably, the preset factors include: climate factors, human factors and other factors; the other factors include: insect outbreaks and river diversions.

[0027] Preferably, the calculation formula of the global Moran's index is:

[0028]

[0029] Where I is the global Moran index; n is the number of disturbance patches; x i 、x j are the attribute values ​​of the i-th and j-th disturbance patches respectively; w i,j is the spatial weight between the i-th and j-th disturbance patches; is the average of the attribute values.

[0030] Preferably, the calculation formula of the local Moran index includes:

[0031]

[0032] Among them, I i is the local Moran index.

[0033] Preferably, the calculation formula of the Getis-Ord Gi* index includes:

[0034]

[0035] in, is the ith Getis-Ord Gi* index.

[0036] The present invention discloses the following technical effects:

[0037] The present invention provides a large-scale forest disturbance detection and disturbance attribution analysis method. By optimizing and predicting a multi-year forest cover composite map, the problems of low image source accuracy and small number are solved, and the quality and quantity of images used for analysis are improved; by improving the basic CCDC algorithm, the problem of poor disturbance extraction effect of the existing CCDC algorithm is solved, and the disturbance extraction effect is improved; by calculating feature data and associated data, the problem of complex conventional disturbance attribution analysis process is solved, and intuitive feature data is provided for disturbance attribution analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 A schematic diagram of a large-scale forest disturbance detection and disturbance attribution analysis process provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of a pixel compensation model training process provided by an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of the k-nearest neighbor principle provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The purpose of the present invention is to provide a large-scale forest disturbance detection and disturbance attribution analysis method, which improves the quality and quantity of images used for analysis and improves the classification accuracy and reliability by optimizing and predicting multi-year forest cover composite images; improves the disturbance extraction effect by improving the basic CCDC algorithm; and provides intuitive data for disturbance attribution analysis through feature data and associated data calculation, thereby improving the efficiency of disturbance attribution analysis.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] Figure 1 A schematic diagram of a large-scale forest disturbance detection and disturbance attribution analysis process provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a large-scale forest disturbance detection and disturbance attribution analysis method, comprising:

[0046] Step 100: Collect and fuse long-term Landsat images and Google Maps historical images to obtain a composite forest cover map for many years;

[0047] Step 200: inputting the multi-year forest cover composite map into a pre-trained pixel compensation model for optimization to obtain a high-precision forest cover composite map;

[0048] Step 300: inputting the high-precision forest cover composite map into the pre-trained neural network model for prediction to obtain a generated cover composite map;

[0049] Step 400: adding the disturbance maximum amplitude band and ID mapping value to the basic CCDC algorithm to obtain an improved CCDC algorithm;

[0050] Step 500: using the improved CCDC algorithm to extract the disturbance year, the maximum disturbance amplitude and the disturbance frequency from the high-precision forest cover composite map and the generated cover composite map, and obtain a forest disturbance map; the forest disturbance map includes: a year data map, a maximum amplitude data map and a frequency data map;

[0051] Step 600: extracting the disturbance spatial distribution, disturbance density and change trend of the forest disturbance map to obtain disturbance data;

[0052] Step 700: using preset factors to perform discriminant analysis on disturbance data, respectively, to obtain forest fire attribution maps, human attribution maps, and other attribution maps; human attribution maps include: logging attribution maps, farming attribution maps, and construction activity attribution maps; other attribution maps include: climate attribution maps and disaster attribution maps;

[0053] Step 800: Fusing the forest fire attribution map, the human factor attribution map and other attribution maps to obtain a fused attribution map;

[0054] Step 900: Analyze the fused attribution graph using the global Moran's index, the local Moran's index, and the Getis-Ord Gi* index to obtain the target attribution result.

[0055] Specifically, the CCDC algorithm (Change Detection and Classification) is an algorithm used in the field of remote sensing and image processing, mainly used to detect and classify changes in images or time series data. This algorithm is often used to monitor environmental changes, land use changes, urban development, deforestation, etc. The core steps of the CCDC algorithm usually include: Data preprocessing: Correction, denoising, standardization and other processing of the input remote sensing data to ensure the quality and availability of the data. Time series analysis: Use time series remote sensing data to analyze the changes between different time points. By introducing time series information, the change area can be more effectively identified. Change detection: Use statistical methods or machine learning techniques to detect those areas that have changed in the time series. Methods such as threshold method, classification method, regression method, etc. can be used. Change classification: Classify the detected changes, such as classifying the changes into urban construction, farmland changes, forest reduction, etc. Result verification and analysis: By comparing the existing ground truth data or more detailed remote sensing data, the change detection and classification results are verified and analyzed to improve accuracy and reliability. The advantage of CCDC algorithm is that it can handle large-scale remote sensing data sets, and can extract effective information in multi-temporal data, which is suitable for monitoring and evaluation of dynamic changes. It has important application value for analysis and decision support (such as resource management, environmental monitoring, etc.). In the present embodiment, CCDC algorithm can segment the time series data of pixels, and the whole time series is segmented into several sub-time series, and each sub-time series corresponds to a time series model. Based on these time series, the number and length (in years) of line segments, the slope and the corresponding year of vertices can be obtained.

[0056] Furthermore, the source of the data set in this embodiment is shown in Table 1.

[0057] Table 1

[0058]

[0059]

[0060] Preferably, the calculation formula of the change trend includes:

[0061]

[0062] Among them, θ is the slope of the linear change of the trend; FDD i is the forest disturbance density in the i-th year; n is the length of time.

[0063] refer to Figure 2 ,The training process of the pixel compensation model includes:

[0064] Step 201: collecting a basic forest cover composite map and a fine forest cover composite map; the fine forest cover composite map has a higher precision than the basic forest cover composite map;

[0065] Step 202: extracting pixel information of the fine forest cover composite image to obtain an original pixel data set;

[0066] Step 203: extracting pixel information of a high-precision multi-year forest cover composite map to obtain a target pixel data set;

[0067] Step 204: construct a knn classification model;

[0068] Step 205: Use the original pixel data set as input data and the target pixel data set as verification data to train the knn classification model to obtain a pixel compensation model.

[0069] Preferably, the types of maximum disturbance amplitudes include: low amplitude, medium-low amplitude, medium amplitude, medium-high amplitude and high amplitude; the range of low amplitude is: 0 to 0.09; the range of medium-low amplitude is: 0.09 to 0.19; the range of medium amplitude is: 0.19 to 0.30; the range of medium-high amplitude is: 0.30 to 0.45; the range of high amplitude is: greater than 0.45.

[0070] Optionally, the preset factors include: climate factors, human factors and other factors; other factors include: insect outbreaks and river diversion.

[0071] Specifically, the calculation formula of the global Moran index is:

[0072]

[0073] Where I is the global Moran index; n is the number of disturbance patches; x i 、x j are the attribute values ​​of the i-th and j-th disturbance patches respectively; w i,j is the spatial weight between the i-th and j-th disturbance patches; is the average of the attribute values.

[0074] Alternatively, Global Moran's I is a statistic used for spatial data analysis, especially for detecting spatial autocorrelation. It was proposed by statistician Patrick A. Moran in 1950. Global Moran's I is used to evaluate the spatial distribution pattern of a variable to determine whether the variable exhibits characteristics of clustering, discreteness or random distribution.

[0075] Preferably, the calculation formula of the local Moran index includes:

[0076]

[0077] Among them, I i is the local Moran index.

[0078] Alternatively, Local Moran's I is a statistic used for spatial data analysis, which is an extension of the global Moran's I and is used to detect spatial autocorrelation at a specific geographic location. Local Moran's I can help identify clustering or distribution characteristics at specific locations in spatial data, and is particularly suitable for identifying hot spots (high-value clustering areas) and cold spots (low-value clustering areas).

[0079] Specifically, the calculation formula of the Getis-Ord Gi* index includes:

[0080]

[0081] in, is the i-th Getis-Ord Gi* index.

[0082] Alternatively, the Getis-Ord Gi* index (also known as Getis-Ord hot spot analysis) is a statistic used in spatial data analysis to detect local clustering patterns in spatial data. The index can identify "hot spots" (high-value clustering areas) and "cold spots" (low-value clustering areas) and assess whether these clustering phenomena are significant.

[0083] Preferably, the obtained multi-year forest cover composite image is input into the pixel compensation model, each subdivided pixel point corresponds to a group of color weights, and the first four color weights with the highest weights are selected and combined as the optimized color of the subdivided pixel point.

[0084] Specifically, kNN (k-nearest neighbor algorithm) is a basic and simple classification algorithm. As a supervised learning, the KNN model needs labeled training data. The category of a new sample is determined by the k training sample points closest to the new sample according to the classification decision rule. The k-nearest neighbor method (kNN) is a basic classification and regression method; a model based on labeled training data; and a supervised learning algorithm. The three key points of the basic approach are: first, determining the distance metric; second, the selection of the k value (finding the k instance points closest to the estimated point in the training set); and third, the classification decision rule. In the classification task, the "voting method" can be used, that is, selecting the most frequently appearing labeled category in these k instances as the prediction result; in the regression task, the "average method" can be used, that is, the average value of the real-valued output labels of these k instances is used as the prediction result; and weighted averaging or weighted voting can also be performed based on the distance, and the closer the distance, the greater the weight of the instance. The k-nearest neighbor method does not have an explicit learning process. It is a well-known representative of lazy learning. This type of learning technology simply saves samples during the training phase, with zero training time overhead, and processes them after receiving test samples.

[0085] refer to Figure 3 The main method of k-nearest neighbor clustering analysis is: initially randomly given K (hyperparameter) cluster centers, these K cluster centers are randomly selected from the sample points. The sample points to be classified are divided into clusters according to the nearest neighbor principle. After the division is completed, the centroid of each cluster is recalculated by the average method to determine the new cluster center. Iterate until the moving distance of the cluster center is less than a given value. The evaluation criterion of the algorithm is: the error square sum criterion. When the error square sum reaches the optimal (small) value, the class can be as compact as possible and the clusters can be separated as much as possible. The loss function is a non-convex function, so there are many minimum values, which may not always fall to the global optimum, but may fall to the local optimum. The loss function is used for global optimization during iteration. The remaining data is clustered according to the silhouette coefficient; the calculation formula of the silhouette coefficient is:

[0086]

[0087] Among them, s is the silhouette coefficient; b is the sum of the distances between the data to be assigned and all the points in the next nearest cluster; a is the sum of the distances between the data to be assigned and other points in the same cluster; max() is the maximum value function. The range of the silhouette coefficient is (-1, 1). The closer the value is to 1, the more similar the sample is to the sample in its own cluster, and the less similar it is to the samples in other clusters. When the sample point is more similar to the sample points outside the cluster, the silhouette coefficient is negative. When the silhouette coefficient is 0, it means that the similarity between the two clusters is the same, and the two clusters should be one cluster.

[0088] First, the official CCDC code was used for disturbance monitoring, and there were some problems in extracting the year of maximum change. For example, the NDVI of a certain pixel changed three times during the entire study period, in 1995, 2005 and 2018, with change amplitudes of -0.0833, -0.3828 and -0.3237, respectively. The year of maximum change determined by the absolute value of the rate of change was 2005, but the year extracted by the CCDC algorithm was 2018. After checking the underlying source code of CCDC, it was found that the algorithm did not use the "magnitude" band when calculating the year of the maximum change breakpoint, and did not use the ID mapping value, but determined tBreak based on the year of the latest breakpoint. Therefore, this study modified the CCDC code, added the disturbance maximum amplitude band and ID mapping value to the basic CCDC algorithm, and obtained the improved CCDC algorithm.

[0089] Furthermore, the CCDC algorithm requires adjustment of several parameters. The range of the parameters is determined by visually inspecting known disturbance sample points (Table 2 is the range setting value of this embodiment). The consistency of the trajectory under different parameter combinations with reality is intuitively evaluated to determine the optimal parameter combination across samples in each forest area. The forest disturbance map is output based on the CCDC algorithm. The disturbance map includes the year of maximum disturbance, the number of disturbances, and the maximum amplitude of disturbance: the year with the largest disturbance amplitude during the detection period, that is, for a given date range and spectral band, the time when the maximum change amplitude detected visually occurs; the number of disturbances, that is, the total number of disturbances detected within a specified time period; the maximum amplitude of disturbance, that is, the maximum change value detected within a specified time period and spectral band, is measured by the difference between the end point and the starting point of adjacent time periods (the slope of the fitting curve, the larger the slope, the more severe the disturbance).

[0090] Table 2

[0091]

[0092]

[0093] Specifically, the maximum disturbance amplitude detected by the CCDC algorithm is the slope of the fitting curve. The larger the slope of the maximum disturbance amplitude (mag), the more severe the disturbance. According to the natural discontinuity point, mag is divided into five categories: low (0 < mag ≤ 0.09), medium-low (0.09 < mag ≤ 0.19), medium (0.19 < mag ≤ 0.30), medium-high (0.30 < mag ≤ 0.45) and high (mag > 0.45).

[0094] Specifically, the main drivers of forest disturbance include logging, agriculture (deforestation caused by shifting agriculture and expansion of commercial agriculture), forest fires, urbanization, and other disturbance factors (such as insect outbreaks, wind and ice disasters, floods or river diversion).

[0095] Preferably, forest fire disturbance discrimination: GABAM products are superimposed with forest disturbance areas, and condition discrimination is performed pixel by pixel and year by year to extract forest fire disturbance. The specific discrimination method is as follows: if the disturbance year of a certain pixel is consistent with the fire year, it is judged as a forest fire disturbance area, otherwise it is other disturbances.

[0096] Furthermore, human disturbance is identified as follows: assuming that a certain pixel is disturbed in year t, it is forest in year t-1 in CLCD, but: if it is shrub, grassland or bare land in year t+1 and t+2, it is determined to be logging; if it is impermeable in year t+1 and t+2, it is determined to be construction; if it is cultivated land in year t+1 and t+2, it is determined to be farming.

[0097] Specifically, the I value range is [-1,1], I>0 indicates positive correlation, I=0 indicates no correlation, and I<0 indicates negative correlation. This tool evaluates expression clustering, discreteness, or randomness. Like z-scores or p-values, statistical significance is indicated. If I>0, it indicates clustering, otherwise it indicates discreteness.

[0098] Furthermore, the Getis-Ord Gi* index of different periods is visualized using the natural break method. The Gi_Bin field identifies statistically significant hot and cold spots. Pixels with confidence intervals of +3 to -3, +2 to -2, and +1 to -1 are statistically significant at 99%, 95%, and 90% confidence levels, respectively; while a confidence interval of 0 is not statistically significant.

[0099] The beneficial effects of the present invention are as follows:

[0100] The present invention optimizes and predicts the composite forest cover map for many years, improves the quality and quantity of images used for analysis, and improves the classification accuracy and reliability; improves the disturbance extraction effect by improving the basic CCDC algorithm; provides intuitive data for disturbance attribution analysis through feature data and associated data calculation, and improves the efficiency of disturbance attribution analysis

[0101] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0102] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A large-scale forest disturbance detection and disturbance attribution analysis method, characterized in that: include: Collect and fuse long-term Landsat images and Google Maps historical images to obtain a composite map of forest cover over the years; Inputting the multi-year forest cover composite map into a pre-trained pixel compensation model for optimization to obtain a high-precision forest cover composite map; Inputting the high-precision forest cover composite map into a pre-trained neural network model for prediction to generate a cover composite map; The maximum disturbance amplitude band and ID mapping value are added to the basic CCDC algorithm to obtain the improved CCDC algorithm. The improved CCDC algorithm is used to extract the disturbance year, the maximum disturbance amplitude and the number of disturbances from the high-precision forest cover composite map and the generated cover composite map, respectively, to obtain a forest disturbance map; the forest disturbance map includes: a year data map, a maximum amplitude data map and a number data map; Extracting the disturbance spatial distribution, disturbance density and change trend of the forest disturbance map respectively to obtain disturbance data; The disturbance data are discriminated and analyzed using preset factors respectively to obtain forest fire attribution maps, human attribution maps and other attribution maps; the human attribution maps include: felling attribution maps, farming attribution maps and construction activity attribution maps; the other attribution maps include: climate attribution maps and disaster attribution maps; Fusing the forest fire attribution map, the human factor attribution map and the other attribution maps to obtain a fused attribution map; The fusion attribution map is analyzed using the global Moran's index, the local Moran's index and the Getis-Ord Gi* index to obtain the target attribution result.

2. A large-scale forest disturbance detection and disturbance attribution analysis method according to claim 1, characterized in that: The calculation formula of the change trend includes: Wherein, θ is the linear change slope of the trend; FDD i is the forest disturbance density in the i-th year; n is the length of time.

3. The method for large-scale forest disturbance detection and disturbance attribution analysis according to claim 1, characterized in that: The training process of the pixel compensation model includes: Collecting a basic forest cover composite map and a fine forest cover composite map; the fine forest cover composite map has a higher accuracy than the basic forest cover composite map; Extracting pixel information of the fine forest cover composite image to obtain an original pixel data set; Extracting high-precision pixel information of the multi-year forest cover composite map to obtain a target pixel data set; Build a knn classification model; The original pixel data set is used as input data, the target pixel data set is used as verification data, the knn classification model is trained, and the pixel compensation model is obtained.

4. The method for large-scale forest disturbance detection and disturbance attribution analysis according to claim 1, characterized in that: The types of maximum disturbance amplitudes include: low amplitude, medium-low amplitude, medium amplitude, medium-high amplitude and high amplitude; the low amplitude ranges from 0 to 0.09; the medium-low amplitude ranges from 0.09 to 0.19; the medium amplitude ranges from 0.19 to 0.30; the medium-high amplitude ranges from 0.30 to 0.45; the high amplitude ranges from greater than 0.

45.

5. The method for large-scale forest disturbance detection and disturbance attribution analysis according to claim 1, characterized in that: The preset factors include: climate factors, human factors and other factors; the other factors include: insect outbreaks and river diversion.

6. A large-scale forest disturbance detection and disturbance attribution analysis method according to claim 2, characterized in that: The calculation formula of the global Moran index is: Where I is the global Moran index; n is the number of disturbance patches; x i 、x j are the attribute values ​​of the i-th and j-th disturbance patches respectively; w i,j is the spatial weight between the i-th and j-th disturbance patches; is the average of the attribute values.

7. A large-scale forest disturbance detection and disturbance attribution analysis method according to claim 6, characterized in that: The calculation formula of the local Moran index includes: Among them, I i is the local Moran index.

8. A large-scale forest disturbance detection and disturbance attribution analysis method according to claim 7, characterized in that: The calculation formula of the Getis-Ord Gi* index includes: in, is the ith Getis-Ord Gi* index.

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