Forest space structure quantification method, device, equipment, storage medium and product

Abstract

The invention discloses a forest space structure quantification method, device and equipment, a storage medium and a product, and relates to the technical field of geographic information. The method comprises the following steps: acquiring remote sensing image data, wherein the remote sensing image data comprises image data acquired by acquiring a target area according to a time sequence; extracting forest space structure quantitative indexes of the target year from the remote sensing image data, wherein the forest space structure quantitative indexes comprise at least one horizontal structure index and at least one vertical structure index; and calculating a forest space structure index of the target year based on the at least one horizontal structure index and the at least one vertical structure index so as to quantify the forest space structure of the target area. The remote sensing data of the forest space structure of the target area is collected to meet the characteristics of large scale range and long-time sequence change, and the indexes of the horizontal dimension and the vertical dimension are constructed to calculate the forest space structure index, so that the overall characteristics of the forest space can be better reflected, and the error with the actual situation is reduced.

Description

Technical Field

[0001] The present application relates to the field of geographic information technology, and specifically to a forest spatial structure quantification method, device, equipment, storage medium and product. Background Art

[0002] Forest ecosystems are the main body of terrestrial ecosystems, and forest spatial structure reflects the functional integrity and complexity of forest ecosystems. FSSI (Forest Spatial Structure Index) is an indicator or method used to quantify the spatial distribution pattern and structural characteristics of forests. Currently, a variety of methods for quantitatively analyzing forest structure have been proposed. However, due to the time-consuming and inefficient acquisition of plant community structural parameters at relatively large scales or inaccessible areas, these traditional methods are often limited to single-dimensional analysis in the actual process of quantifying forest spatial structure. The data results at the single tree scale or sample plot scale are extrapolated to the landscape or regional scale, resulting in potential errors, which makes the quantification less objective and lacks persuasiveness.

[0003] Therefore, the current traditional forest structure analysis method has the problem that the analysis dimension is not comprehensive enough, resulting in a large error between the results obtained after quantification and the actual situation. Summary of the Invention

[0004] In view of this, the present application provides a forest spatial structure quantification method, device, equipment, storage medium and product, the main purpose of which is to improve the current traditional forest structure analysis method, which has the problem that the analysis dimension is not comprehensive enough, resulting in large errors between the results obtained after quantification and the actual situation.

[0005] In the first aspect, the present application provides a method for quantifying forest spatial structure, including: obtaining remote sensing image data; extracting quantitative indicators of forest spatial structure in the target year from the remote sensing image data; and calculating the forest spatial structure index in the target year based on at least one horizontal structure indicator and at least one vertical structure indicator to quantify the forest spatial structure of the target area.

[0006] Optionally, before calculating the forest spatial structure index of the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area, the method also includes: processing the at least one horizontal structure indicator and the at least one vertical structure indicator respectively to obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year.

[0007] Optionally, the processing of the at least one horizontal structure indicator and the at least one vertical structure indicator respectively to obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year includes: normalizing the at least one horizontal structure indicator and the at least one vertical structure indicator respectively; determining the initial weight value of the at least one horizontal structure indicator in the target year and the initial weight value of the at least one vertical structure indicator in the target year by using the entropy weight method; applying the variance inflation factor to perform collinearity detection on the initial weight value of the at least one horizontal structure indicator in the target year and the initial weight value of the at least one vertical structure indicator in the target year respectively to obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year.

[0008] Optionally, the forest spatial structure index of the target year is calculated based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area, including: calculating the forest spatial structure index of the target area in the target year based on the forest spatial structure quantification indicators and the weight values corresponding to each indicator; wherein the forest spatial structure index is the sum of the products of each indicator and the corresponding weight value in the target year, and the sum of the weight values corresponding to each indicator is 1.

[0009] Optionally, the horizontal structure indicators include vegetation aboveground biomass AGB and leaf area index LAI, the vertical structure indicators include coverage FVC, and the forest spatial structure quantitative indicators also include aggregation index CI, which is used to characterize both horizontal spatial structure and vertical spatial structure.

[0010] Optionally, based on the at least one horizontal structure indicator and the at least one vertical structure indicator, the forest spatial structure index of the target year is calculated to quantify the forest spatial structure of the target area, including: calculating the forest spatial structure index of each year; based on the forest spatial structure index of each year and through a trend analysis algorithm, determining the quantitative results of the forest spatial structure of the target area within a preset time period.

[0011] In a second aspect, the present application provides a forest spatial structure quantification device, comprising:

[0012] an acquisition unit configured to acquire remote sensing image data, wherein the remote sensing image data includes image data acquired by collecting the target area in a time series;

[0013] An extraction unit is configured to extract the forest spatial structure quantitative index of the target year from the remote sensing image data, wherein the forest spatial structure quantitative index includes at least one horizontal structure index and at least one vertical structure index;

[0014] The processing unit is configured to calculate the forest spatial structure index of the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area.

[0015] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the forest spatial structure quantification method described in the first aspect.

[0016] In a fourth aspect, the present application provides an electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the forest spatial structure quantification method described in the first aspect when executing the computer program.

[0017] In a fifth aspect, the present application provides a computer program product having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the forest spatial structure quantification method described in the first aspect is implemented.

[0018] By means of the above technical solution, the present application provides a forest spatial structure quantification method, device, equipment, storage medium and product, which first obtains remote sensing image data, and the remote sensing image data includes image data collected from the target area in a time series; extracts forest spatial structure quantification indicators for the target year from the remote sensing image data, and the forest spatial structure quantification indicators include at least one horizontal structure indicator and at least one vertical structure indicator; based on the at least one horizontal structure indicator and at least one vertical structure indicator, calculates the forest spatial structure index for the target year to quantify the forest spatial structure of the target area. By collecting remote sensing data on the forest spatial structure of the target area to meet the characteristics of large-scale range and long time series changes, and at the same time constructing indicators in both horizontal and vertical dimensions to calculate the forest spatial structure index for the target year, it can better reflect the overall characteristics of the forest space and reduce errors from the actual situation.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 A schematic diagram of a process for quantifying forest spatial structure provided by an embodiment of the present application is shown;

[0023] Figure 2 A schematic diagram of a process for quantifying another forest spatial structure provided by an embodiment of the present application is shown;

[0024] Figure 3 A schematic diagram of the structure of a forest spatial structure quantification device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0025] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0026] The forest spatial structure quantification method proposed in this embodiment is applied to a forest spatial structure quantification device or electronic device. The device or electronic device can be installed or integrated into a detection and processing system of some geographic center, or a remote sensing GIS system (Geographic Information System), etc., and can execute any of the forest spatial structure quantification methods mentioned below during operation.

[0027] In order to improve the problem that the current traditional forest structure analysis method has insufficient analysis dimensions, resulting in a large error between the quantified results and the actual situation, this embodiment proposes a forest spatial structure quantification method, such as Figure 1 As shown, the method includes:

[0028] S101, acquiring remote sensing image data;

[0029] Remote sensing imagery data consists of image data collected over a target area in a time series. Remote sensing technology offers a novel approach to understanding vegetation structure over large scales. The use of remote sensing imagery not only addresses data shortages but also expands the geographical scope of research. Multi-source remote sensing technology offers a new perspective for analyzing the complex relationship between forest spatial structure and belowground carbon storage, integrating vertical and horizontal structural characteristics with temporal dynamics.

[0030] S102, extracting quantitative indicators of forest spatial structure in the target year from remote sensing image data;

[0031] In combination with China's actual situation, according to the principles of selecting quantitative indicators such as the greater impact of forest spatial structure, greater variation within the quantified area, relative stability in time series and close correlation with the size of the quantified area, reference is made to national standard documents such as "Forestry Resource Classification and Code Forest Type", "Forest Ecosystem Long-term Positioning Observation Index System" and "Forest Resource Planning, Design and Survey Technical Regulations". Specifically, in this embodiment, the quantitative indicators of forest spatial structure include forest vertical spatial structure indicators and forest horizontal spatial structure indicators, as shown in Table 1. The quantitative indicators of forest spatial structure include at least one horizontal structure indicator and at least one vertical structure indicator, wherein the forest spatial horizontal structure indicators include two indicators: aboveground biomass (AGB) and leaf area index (LAI), and the forest spatial vertical structure indicators include vegetation cover (FVC). The aggregation index (CI) comprehensively reflects the horizontal and vertical structures.

[0032] S103, calculating the forest spatial structure index of the target year based on at least one horizontal structure indicator and at least one vertical structure indicator to quantify the forest spatial structure of the target area.

[0033] The Forest Spatial Structure Index (FSSI) is used to represent the current state of forest ecosystems. It quantifies forest spatial structure. The FSSI comprehensively reflects a stand's ability to resist disturbances (e.g., a low CI indicates poor aggregation and susceptibility to wind damage, while abnormal LAI fluctuations indicate pest and disease risk) and can assess the ecological vulnerability of different structural types.

[0034] Table 1 Index system of forest spatial structure

[0035]

[0036] Traditionally, obtaining accurate parameters of plant community structure at a relatively large scale or in an area that is difficult for human beings to reach is a time-consuming and inefficient task. Relying on traditional manual measurement, it is also impossible to perform repeated sampling and precise measurement of long-term series, and it is impossible to obtain geographical information such as spatial heterogeneity and degree of agglomeration. There are potential errors in extrapolating data results at the scale of a single tree or sample plot to the scale of a landscape or region. In this embodiment, remote sensing image data is first obtained, and the remote sensing image data includes image data collected in a time series of the target area; quantitative indicators of forest spatial structure in the target year are extracted from the remote sensing image data, and the quantitative indicators of forest spatial structure include at least one horizontal structure indicator and at least one vertical structure indicator; based on the at least one horizontal structure indicator and the at least one vertical structure indicator, the forest spatial structure index of the target year is calculated to quantify the forest spatial structure of the target area. By collecting remote sensing data on the forest spatial structure of the target area to meet the characteristics of large-scale range and long-term series changes, and constructing indicators in both horizontal and vertical dimensions to calculate the forest spatial structure index of the target year, the overall characteristics of the forest space can be better reflected, and the error with the actual situation can be reduced.

[0037] Optionally, the horizontal structure indicators include vegetation aboveground biomass AGB and leaf area index LAI, the vertical structure indicators include coverage FVC, and the forest spatial structure quantitative indicators also include aggregation index CI, which is used to characterize both horizontal and vertical spatial structures.

[0038] In this example, aboveground biomass (AGB) refers to the total organic matter of vegetation (such as trees, shrubs, and herbs) growing above the ground surface. It is an important indicator for measuring forest productivity, carbon storage, and other ecological functions. The leaf area index (LAI) represents the ratio of total plant leaf area to land area per unit land area. It reflects ecological processes such as vegetation photosynthesis potential and transpiration intensity and is a key parameter for assessing vegetation growth and ecosystem function. Fractional vegetation cover (FVC) describes the vertical coverage of the ground by vegetation, i.e., the proportion of the projected area of vegetation on the ground to the total area. It can intuitively reflect the density and spatial occupancy of vegetation and is of great significance for studying vegetation functions such as soil and water conservation and biodiversity conservation. The aggregation index (CI) is a comprehensive indicator that can be used to describe the horizontal distribution characteristics of a forest, such as the degree of spatial clustering and dispersion of trees, as well as the vertical structural characteristics of a forest, such as the aggregation of vegetation at different height levels. The aggregation index provides a more comprehensive understanding of the spatial structural complexity and heterogeneity of a forest.

[0039] Optionally, before calculating the forest spatial structure index of the target year based on at least one horizontal structure indicator and at least one vertical structure indicator to quantify the forest spatial structure of the target area, the method also includes: processing at least one horizontal structure indicator and at least one vertical structure indicator respectively to obtain the weight value of at least one horizontal structure indicator in the target year and the weight value of at least one vertical structure indicator in the target year.

[0040] In this embodiment, the forest spatial structure index for the target year is calculated based on at least one horizontal structure indicator and at least one vertical structure indicator. The weight of at least one horizontal (or vertical) structure indicator in the target year also needs to be calculated. The weight is used to represent the proportion of a particular horizontal (or vertical) structure indicator among the multiple indicators. Calculating the weight of each indicator makes the forest spatial structure index more accurate.

[0041] Optionally, at least one horizontal structural indicator and at least one vertical structural indicator are processed separately to obtain the weight value of at least one horizontal structural indicator in the target year and the weight value of at least one vertical structural indicator in the target year, including: normalizing at least one horizontal structural indicator and at least one vertical structural indicator respectively; determining the initial weight value of at least one horizontal structural indicator in the target year and the initial weight value of at least one vertical structural indicator in the target year by entropy weight method; applying variance inflation factor to perform collinearity test on the initial weight value of at least one horizontal structural indicator in the target year and the initial weight value of at least one vertical structural indicator in the target year, to obtain the weight value of at least one horizontal structural indicator in the target year and the weight value of at least one vertical structural indicator in the target year.

[0042] In this embodiment, normalization is a data preprocessing technique used to scale data to a specific range (typically [0, 1] or [-1, 1]) to eliminate dimensional differences between different features and improve model convergence speed and stability. Collinearity refers to a high degree of linear correlation between independent variables in a regression model. Collinearity exists when two or more independent variables can be approximately represented by a linear combination. In this embodiment, the variance inflation factor (VIF) is used to screen initial weight values, eliminating interference from data with large differences through collinearity detection. The entropy weight method is an objective weighting method based on information entropy theory. It calculates the information entropy of each evaluation indicator to reflect its information content and then determines the indicator weight. The core concept is that the greater the information entropy, the less information the indicator contains (higher uncertainty), and the smaller the weight should be; the smaller the information entropy, the richer the information content (higher certainty), and the larger the weight should be. By quantifying the information content of the indicator by information entropy, the weight is objectively determined, avoiding subjective interference. For example, the weights of the indicators used in this scheme's forest spatial structure index can be shown in Table 2.

[0043] Table 2 Weights of indicators used to construct forest spatial structure index

[0044]

[0045] Optionally, based on at least one horizontal structure indicator and at least one vertical structure indicator, the forest spatial structure index of the target year is calculated to quantify the forest spatial structure of the target area, including: calculating the forest spatial structure index of the target area in the target year based on the forest spatial structure quantification indicators and the weight values corresponding to each indicator; wherein the forest spatial structure index is the sum of the products of each indicator and the corresponding weight value in the target year, and the sum of the weight values corresponding to each indicator is 1.

[0046] In this embodiment, a forest spatial structure index is constructed based on the constructed forest spatial structure quantitative index system combined with the weights determined by the entropy weight method (as shown in Table 2). The sum of the weight values corresponding to each indicator is 1, which means that the sum of the aggregation index weight, the aboveground biomass weight, the leaf area index weight and the vegetation coverage weight is 1, which is 0.27+0.27+0.20+0.26=1 as shown in Table 2. The forest spatial structure index is the sum of the products of each indicator and the corresponding weight value in the target year, which means that the forest spatial structure index is calculated by obtaining the average weight of each indicator by the entropy weight method, and its expression is:

[0047] FSSI=0.27CI+0.27AGB+0.20LAI+0.26FVC (Formula 1)

[0048] Optionally, based on at least one horizontal structure indicator and at least one vertical structure indicator, the forest spatial structure index of the target year is calculated to quantify the forest spatial structure of the target area, including: calculating the forest spatial structure index of each year; based on the forest spatial structure index of each year and through a trend analysis algorithm, determining the quantitative results of the forest spatial structure of the target area within a preset time period.

[0049] In this embodiment, a forest spatial structure index is constructed based on the constructed forest spatial structure quantitative index system combined with the weights determined by the entropy weight method. A trend analysis algorithm is used to obtain the quantitative results of the forest spatial structure index in the target year. Specifically, the trend analysis algorithm uses Sen trend analysis and Mann-Kendall test methods to obtain the changing trend of the forest spatial structure index in the target year and its statistical significance. Specifically, the calculation formula of Sen slope is as follows:

[0050]

[0051] Among them, "median" refers to the median. 1 < i < j < n, where i and j are different times, and FSSI i and FSSI j are the FSSI values of the time series at i and j respectively. When the slope β is greater than 0, it means there is an upward trend. When the slope β is equal to 0, it indicates a trend of no change. When the slope β is less than 0, it represents a downward trend.

[0052] The Mann-Kendall test is used to determine the years of FSSI change. The Mann-Kendall test based on the test statistic S is defined as:

[0053]

[0054] In the formula: sgn() is the sign function, and its calculation formula is:

[0055]

[0056] The calculation formula for the variance Var(S) is:

[0057]

[0058] In the formula: n is the number of data in the series, m is the number of tie groups, and t k is the number of repeated values in the kth tie group. The trend test is performed using the statistic Z, and the calculation method of Z is as follows:

[0059]

[0060] A two-sided trend test is used, and the trend test is performed at a specific significance level ɑ. When the null hypothesis is accepted, the trend is not significant. While when the null hypothesis is rejected, that is, the trend is considered significant.

[0061] Exemplarily, in this embodiment, three significance levels of ɑ = 0.01, ɑ = 0.05, and ɑ = 0.1 are given. Therefore, when the absolute value of Z is greater than 1.65, 1.96, and 2.58, it indicates that the trends have passed the significance tests with confidence levels of 90%, 95%, and 99% respectively. The change situations of FSSI are divided into 8 situations as shown in Table 3.

[0062] Table 3 Eight types of change trends of forest spatial structure index

[0063]

[0064] Furthermore, the forest spatial structure quantification method applied in this embodiment is introduced, as Figure 2, which is a flow chart of another forest spatial structure quantification method proposed in this embodiment.

[0065] To determine the changing trends and statistical significance of the forest spatial structure index from 2002 to 2019, data from the 10th China Forest Resources Inventory (2021) showed that China's forest coverage reached 24.02%, with a total forest area of 231 million hectares and a forest stock of 19.493 billion cubic meters. Therefore, this study uses China as the research area and classifies forests into seven types: evergreen coniferous forest (ENF), evergreen broad-leaved forest (EBF), deciduous coniferous forest (DNF), deciduous broad-leaved forest (DBF), mixed forest (MF), woody savanna (WS), and savanna (S). China has a total of 34 provincial-level administrative units, including 23 provinces, 5 autonomous regions, 4 municipalities directly under the Central Government, and 2 special administrative regions. This study divides China into six geographical regions: North China, Northwest China, Northeast China, East China, Central South China, and Southwest China.

[0066] S201, acquiring remote sensing image data.

[0067] Remote sensing image data can be collected using the FVC product, which provides continuous, quantitative land cover maps at a 250-meter pixel resolution and sub-pixel descriptions of percentage cover, referencing three land cover components. Sub-pixel blending of ground cover estimates represents a revolutionary approach to describing vegetation and land cover, enhancing the input for environmental modeling and monitoring applications. After image acquisition, the MRT tool performs batch processing on MODIS data, including format conversion, projection conversion, image stitching, and band extraction. Data on carbon storage in China's forest belowground vegetation is characterized at a spatial resolution of 1 km using regression and machine learning methods, combining multiple remote sensing observations with intensive field measurements. The data to be processed is MODIS remote sensing image data. Preprocessing includes stitching multiple images, extracting individual image bands, and resampling the extracted bands to a common coordinate system and projection. The converted data is then processed to remove outliers and ensure it falls within the valid range, thus obtaining the desired remote sensing image.

[0068] S202: Extract quantitative indicators of forest spatial structure in the target year from remote sensing image data.

[0069] The geospatial data collected from remote sensing image data were used to extract the aggregation index (CI), fractional vegetation cover (FVC), aboveground biomass (AGB), and leaf area index (LAI).

[0070] To improve data accuracy, a sliding window method was used to test the CI, AGB, LAI, FVC, and GLC data. Since the CI and LAI data are monthly, and the peak forest growth season is June, July, and August, the three-month average was used as the data for that year, and the three-year average was used as the final data for the intermediate year. For the GLC data, forest types in the data were marked as 1, and other land types were marked as 0. Data was obtained for one year before and one year after the test year, with a data window length of three years. For the sliding window, when the one-year average is 2 / 3, the data for the test year should be 1. When the three-year average is 1 / 3, the data for the test year should be 0, accurately determining the distribution of forest types. Since the AGB and FVC data are annual, the three-year average was used as the data for the intermediate year.

[0071] However, a large amount of data is lost after the AGB data sliding window test. Therefore, the sliding window test has been further optimized and divided into the following six cases. Ⅰ. If data exists in the middle year but not in the two preceding and following years, the original data for the middle year will be retained as the final data. Ⅱ. If data does not exist in any of the three preceding and following years, the middle year will also not have data. Ⅲ. If data exists in both the middle and the following year, the average of the two years will be used as the final data for the middle year. Ⅳ. If data does not exist in the middle year but data exists in any of the preceding and following years, the data from that year will be used as the final data for the middle year. Ⅴ. If data exists in all three preceding and following years, the average of the three years will be used as the final data for the middle year. Ⅵ. If data does not exist in the middle year but data exists in both the preceding and following years, the average of the two years will be used as the final data for the middle year.

[0072] S203: Normalize at least one horizontal structure indicator and at least one vertical structure indicator respectively.

[0073] To prevent the impact of different indicator dimensions and trends on the evaluation results during the calculation process, the indicators should first be dimensionless and aligned with the same trend. Positive indicators, also known as benefit indicators, are preferred with larger values. Negative indicators, also known as cost indicators, are preferred with smaller values. Using (Formula 7) and (Formula 8), the raw values of each indicator are normalized to a range between [0, 1].

[0074]

[0075] In the formula: BI i is the pixel value normalized by a certain factor, is the pixel value of a certain factor, and are the maximum and minimum values of the factor respectively.

[0076] S204: Determine initial weight values of at least one horizontal structural indicator and at least one vertical structural indicator in the target year respectively by using an entropy weight method.

[0077] The normalized indicators were used to determine the weights of CI, AGB, LAI, and FVC from 2002 to 2019 using the entropy weight method. The weight values in this embodiment are shown in Table 2.

[0078] S205 , applying a variance inflation factor to process the initial weight value to obtain a weight value of at least one horizontal structure indicator in the target year and a weight value of at least one vertical structure indicator in the target year.

[0079] The variance inflation factor (VIF) was applied to the initial weights to examine the degree of multicollinearity. From 2002 to 2019, the VIF values of the four explanatory variables, CI, AGB, LAI, and FVC, were all less than 10, indicating that there was no multicollinearity in the model.

[0080] S206, calculating the forest spatial structure index of the target area in the target year based on the forest spatial structure quantitative indicators and the weight value corresponding to each indicator.

[0081] The average weight of each indicator is obtained by the entropy weight method, and the forest spatial structure index can be calculated according to (Formula 1).

[0082] The evaluation results of the forest spatial structure index are as follows:

[0083] Since the FSSI was consistently below 0.8 from 2002 to 2019, the FSSI was categorized into four levels: low (0-0.2), middle (0.2-0.4), high (0.4-0.6), and extremely high (0.6-0.8). Regions with high FSSIs accounted for the smallest proportions of China's total forest area from 2002 to 2019, at 0.03% and 0.3% respectively. Regions with low FSSIs accounted for 71.94% and 60.47% of China's total forest area, respectively, indicating a gradual improvement in forest quality. From 2002 to 2019, the average area growth rates of middle, high, and extremely high FSSI regions were 0.02%, 3.05%, and 5.06%, respectively. In contrast, the average area decline rate for low FSSI was 5.43%, representing a 49.42% decrease, representing an area reduction of approximately 4.94 million km². This indicates that China's forests are gradually recovering. Of the four forest spatial structure indices, only the low and high FSSIs showed a decreasing and increasing trend, respectively, year by year. Both the middle and extremely high FSSIs first increased and then decreased. Significant spatial heterogeneity in China's FSSI was observed from 2002 to 2019. From 2002 to 2019, the FSSI showed a significant annual increase of 0.007 (R² = 0.94, p < 0.001). Over the past 18 years, the FSSI has shown an upward trend, with its average value gradually increasing from 0.322 to 0.365, demonstrating an overall steady growth trend (Figure 3.d). From 2002 to 2019, 84.62% of China's regions showed an increasing FSSI, while 15.38% showed a decreasing trend. Regionally, Zhejiang Province, Fujian Province, Hunan Province, Guangxi Zhuang Autonomous Region, Hong Kong Special Administrative Region, and Chongqing Municipality had relatively high FSSIs. The FSSI in South-Central and Northwest China increased significantly faster than in other regions, with a steady and slow rise. Other regions showed no significant change, with increases of 21.47% and 29.62%, respectively. Regarding the average FSSI by forest type, evergreen coniferous forests and evergreen broad-leaved forests had relatively high average FSSIs of 0.39 and 0.40, respectively. Evergreen broad-leaved forests had the highest FSSI, while savannas had the lowest FSSI, at 0.30. With the exception of deciduous coniferous forests, which showed an unusual trend, the average FSSI for all other forest types generally showed an upward trend. The regions in China where FSSI deteriorated extremely significantly, significantly, slightly significantly and not significantly accounted for 1.2%, 1.51%, 0.94% and 6.25% respectively; the regions where FSSI improved extremely significantly, significantly, slightly significantly and not significantly accounted for 39.88%, 15.88%, 6.2% and 18.89% respectively; the regions where FSSI showed no significant change accounted for 9.55%.

[0084] In the above embodiment, a forest spatial structure index is constructed based on the constructed forest spatial structure quantitative index system combined with the weights determined by the entropy weight method. Sen trend analysis and Mann-Kendall test methods are used to obtain the changing trend of the forest spatial structure index in the target year and its statistical significance. Specifically, the entropy weight method quantifies FFSI by integrating aboveground biomass (AGB) to reflect vertical structural characteristics, forest cover (FVC) to capture horizontal structural characteristics, and using leaf area index (LAI) and aggregation index (CI) to comprehensively analyze the combined effects of vertical and horizontal structures. The variance inflation factor (VIF) is used to check the degree of multicollinearity, avoiding the deviation caused by human factors and improving the objectivity and accuracy of the quantitative results. Sen trend analysis estimates the amplitude of the trend by calculating the median slope of all possible data point pairs; the Mann-Kendall test is used to determine whether there is a monotonic trend (increasing or decreasing) in the time series without assuming linearity. Sen trend analysis and Mann-Kendall test are two non-parametric statistical methods that are widely used in trend analysis of remote sensing time series. They are insensitive to outliers and do not require data to follow a specific distribution. They are particularly suitable for processing remote sensing data with non-normal distribution, noise or missing values, and make up for the defects of traditional linear methods that are easily interfered by outlier data or have large measurement errors.

[0085] Further, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides a forest spatial structure quantification device, such as Figure 3 As shown, the device includes: an acquisition unit 301, an extraction unit 302 and a processing unit 303.

[0086] An acquisition unit 301 is configured to acquire remote sensing image data, wherein the remote sensing image data includes image data acquired from a target area in a time series;

[0087] An extraction unit 302 is configured to extract the forest spatial structure quantitative index of the target year from the remote sensing image data, wherein the forest spatial structure quantitative index includes at least one horizontal structure index and at least one vertical structure index;

[0088] The processing unit 303 is configured to calculate the forest spatial structure index of the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area.

[0089] In a specific application scenario, the processing unit 303 is specifically configured to process the at least one horizontal structure indicator and the at least one vertical structure indicator respectively to obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year.

[0090] In a specific application scenario, the processing unit 303 is further configured to perform normalization processing on the at least one horizontal structure indicator and the at least one vertical structure indicator respectively; determine the initial weight value of the at least one horizontal structure indicator in the target year and the initial weight value of the at least one vertical structure indicator in the target year by using the entropy weight method; apply the variance inflation factor to perform collinearity detection on the initial weight value of the at least one horizontal structure indicator in the target year and the initial weight value of the at least one vertical structure indicator in the target year, and obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year.

[0091] In a specific application scenario, the processing unit 303 is further configured to calculate the forest spatial structure index of the target area in the target year based on the forest spatial structure quantitative indicators and the weight values corresponding to each indicator; wherein the forest spatial structure index is the sum of the products of each indicator and the corresponding weight value in the target year, and the sum of the weight values corresponding to each indicator is 1.

[0092] In a specific application scenario, the extraction unit 302 is specifically configured to include vegetation aboveground biomass AGB and leaf area index LAI. The vertical structure index includes coverage FVC, and the forest spatial structure quantitative index also includes aggregation index CI. The aggregation index CI is used to characterize both horizontal spatial structure and vertical spatial structure.

[0093] In a specific application scenario, the processing unit 303 is further configured to calculate the forest spatial structure index of each year; based on the forest spatial structure index of each year and through a trend analysis algorithm, determine the quantitative results of the forest spatial structure of the target area within a preset time period.

[0094] It should be noted that for other corresponding descriptions of the functional units involved in the forest spatial structure quantification device provided in this embodiment, please refer to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0095] Based on the above Figure 1 and Figure 2The method shown in FIG. 1 is a method for performing the above-mentioned steps. Accordingly, this embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program can realize the above-mentioned steps. Figure 1 and Figure 2 The method shown.

[0096] Based on the above Figure 1 and Figure 2 The method shown in FIG. 1 is a method for performing the above-mentioned operations. Accordingly, this embodiment further provides a computer program product having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The method shown.

[0097] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0098] Based on the above Figure 1 and Figure 2 The method shown, and Figure 3 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides an electronic device that can be configured on a computer terminal, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The method shown.

[0099] Based on the above Figure 1 and Figure 2 The method shown, and Figure 3 In order to achieve the above-mentioned purpose, the embodiment of the virtual device shown in the embodiment of the present application further provides a chip, including one or more interface circuits and one or more processors; the interface circuit is used to receive a signal from the memory of the electronic device and send the signal to the processor, the signal including the computer instruction stored in the memory; when the processor executes the computer instruction, the electronic device executes the above-mentioned Figure 1 and Figure 2 The method shown.

[0100] Optionally, the physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and the like. The user interface may include a display, an input unit such as a keyboard, and the like. The optional user interface may also include a USB interface, a card reader interface, and the like. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), and the like.

[0101] Those skilled in the art will understand that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0102] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical device, supporting the execution of information processing programs and other software and / or programs. The network communication module is used to enable communication between components within the storage medium, as well as with other hardware and software within the physical information processing device.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that this application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the solution of this embodiment, compared with related technologies, by collecting remote sensing data of the forest spatial structure of the target area to meet the characteristics of large-scale range and long time series changes, and at the same time constructing indicators in the horizontal and vertical dimensions to calculate the forest spatial structure index of the target year, it can better reflect the overall characteristics of the forest space and reduce the error with the actual situation.

[0104] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a..." do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0105] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A forest spatial structure quantification method, characterized in that: include: Acquiring remote sensing image data, wherein the remote sensing image data includes image data acquired by collecting the target area in a time series; Extracting the forest spatial structure quantitative index of the target year from the remote sensing image data, wherein the forest spatial structure quantitative index includes at least one horizontal structure index and at least one vertical structure index; Based on the at least one horizontal structure indicator and the at least one vertical structure indicator, a forest spatial structure index for the target year is calculated to quantify the forest spatial structure of the target area.

2. The method according to claim 1, characterized in that Before calculating the forest spatial structure index for the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area, the method further includes: The at least one horizontal structure indicator and the at least one vertical structure indicator are processed separately to obtain a weight value of the at least one horizontal structure indicator in the target year and a weight value of the at least one vertical structure indicator in the target year.

3. The method according to claim 2, characterized in that The processing of the at least one horizontal structure indicator and the at least one vertical structure indicator respectively to obtain a weight value of the at least one horizontal structure indicator in the target year and a weight value of the at least one vertical structure indicator in the target year includes: performing normalization processing on the at least one horizontal structure indicator and the at least one vertical structure indicator respectively; Determining an initial weight value of the at least one horizontal structural indicator in the target year and an initial weight value of the at least one vertical structural indicator in the target year by an entropy weight method; The variance inflation factor is applied to perform collinearity detection on the initial weight value of the at least one horizontal structure indicator in the target year and the initial weight value of the at least one vertical structure indicator in the target year, so as to obtain the weight value of the at least one horizontal structure indicator in the target year and the weight value of the at least one vertical structure indicator in the target year.

4. The method according to claim 3, characterized in that The calculating of the forest spatial structure index for the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area includes: Calculate the forest spatial structure index of the target area in the target year based on the forest spatial structure quantitative indicators and the weight value corresponding to each indicator; The forest spatial structure index is the sum of the products of each indicator in the target year and the corresponding weight value, and the sum of the weight values corresponding to each indicator is 1.

5. The method according to any one of claims 1 to 4, characterized in that The horizontal structure indicators include vegetation aboveground biomass AGB and leaf area index LAI, the vertical structure indicators include coverage FVC, and the forest spatial structure quantitative indicators also include aggregation index CI, which is used to characterize both horizontal and vertical spatial structures.

6. The method according to claim 1, characterized in that Calculating a forest spatial structure index for the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area includes: Calculate the forest spatial structure index for each year; Based on the forest spatial structure index of each year and through a trend analysis algorithm, a quantitative result of the forest spatial structure of the target area within a preset time period is determined.

7. A forest spatial structure quantification device, characterized in that: include: an acquisition unit configured to acquire remote sensing image data, wherein the remote sensing image data includes image data acquired by collecting the target area in a time series; An extraction unit is configured to extract the forest spatial structure quantitative index of the target year from the remote sensing image data, wherein the forest spatial structure quantitative index includes at least one horizontal structure index and at least one vertical structure index; The processing unit is configured to calculate the forest spatial structure index of the target year based on the at least one horizontal structure indicator and the at least one vertical structure indicator to quantify the forest spatial structure of the target area.

8. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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