Comprehensive evaluation method for service function of forest vegetation ecosystem

Abstract

The invention discloses a comprehensive evaluation method for a service function of a forest vegetation ecosystem, and relates to the field of forest ecology, and the method comprises the steps: obtaining and preprocessing multi-source data of an evaluation region, and generating a standardized multi-source data cube; based on the standardized multi-source data cube, calculating the material quality of multiple ecological system service functions of each evaluation unit in the evaluation area, and generating an ecological system service function material quality data set; performing time sequence analysis on the ecosystem service function material quality data set, and calculating to obtain a service function toughness index of each evaluation unit for each ecosystem service function; and extracting each ecological system service function from the standardized multi-source data cube to form resource consumption data. According to the method, the conversion from static evaluation to sustainable comprehensive evaluation covering stability and efficiency is realized, and the problem that the long-term sustainability of an ecological system service function cannot be effectively quantified in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of forest ecology, and in particular to a comprehensive evaluation method for the ecosystem service functions of forest vegetation. Background Technology

[0002] In the field of forest ecology, the quantitative assessment of forest vegetation ecosystem service functions is a crucial foundation for ecological management. Current mainstream technologies rely on the combination of multi-source remote sensing data and ecosystem process models. A typical process involves integrating remote sensing, meteorological, topographic, and soil data, and using models such as CASA, InVEST, and RUSLE to calculate the physical quantities of services such as carbon sequestration, water conservation, and soil retention. Finally, a comprehensive index is synthesized through methods such as linear weighting, enabling a static and comparable evaluation of large-scale ecosystem service functions.

[0003] Existing technologies are mainly limited to assessing the current state of service functions and fail to effectively reveal their inherent sustainability. Existing methods generally lack consideration for the stability of service functions over time and their resource utilization efficiency. They cannot quantify the ecosystem's ability to resist disturbances and recover from them, and they also ignore the huge differences in resource consumption that may exist behind the same service output. This makes it difficult for the evaluation results to support the judgment of the long-term sustainability of the ecosystem and limits its application in guiding precise and efficient forest management. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a comprehensive evaluation method for forest vegetation ecosystem service functions, which solves the problem that existing technologies cannot effectively assess the long-term sustainability of ecosystem service functions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a comprehensive evaluation method for the ecosystem service function of forest vegetation, which includes acquiring multi-source data of the evaluation area and preprocessing it to generate a standardized multi-source data cube. Based on a standardized multi-source data cube, the biomass of multiple ecosystem service functions in each evaluation unit within the evaluation area is calculated to generate an ecosystem service function biomass dataset. Time series analysis was performed on the biomass dataset of ecosystem service functions to calculate the service function resilience index for each ecosystem service function in each evaluation unit. Extract various ecosystem service functions from a standardized multi-source data cube to form resource consumption data; By combining the physical mass dataset of ecosystem service functions with resource consumption data, the energy efficiency ratio of service functions for each ecosystem service function in each evaluation unit is calculated. The ecosystem service function mass data set, service function resilience index, and service function energy efficiency ratio were normalized to obtain the normalized ecosystem service function mass data set, the normalized service function resilience index, and the normalized service function energy efficiency ratio. The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are input into a pre-set comprehensive evaluation matrix model to output a forest vegetation ecosystem service function sustainability level map.

[0007] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method includes: acquiring multi-source data of the evaluation area and preprocessing it to generate a standardized multi-source data cube, comprising the following steps: Remote sensing image data, meteorological data, topographic data and soil data of the evaluation area are acquired to form a multi-source dataset. The remote sensing image data in the multi-source dataset are radiometrically calibrated and atmospherically corrected in sequence to obtain radiometrically calibrated and atmospherically corrected remote sensing image data. Using the radiometrically calibrated and atmospherically corrected remote sensing image data, cloudless remote sensing image data is generated by the maximum value synthesis method. Cloudless remote sensing image data, meteorological data, topographic data and soil data are uniformly converted to the same projection coordinate system and pixel size to obtain multi-source data with a consistent spatial reference. Spatial interpolation is performed on meteorological data from multi-source data with a consistent spatial reference to generate a meteorological data raster layer that matches the spatial resolution of cloudless remote sensing image data. The cloudless remote sensing image data, meteorological data raster layer, topographic data and soil data are registered and overlaid in time and space to generate a standardized multi-source data cube.

[0008] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method includes the following steps: Based on a standardized multi-source data cube, the biomass of multiple ecosystem service functions in each evaluation unit within the evaluation area is calculated to generate an ecosystem service function biomass dataset. Vegetation index data, meteorological data, and soil data are extracted from a standardized multi-source data cube. Using the vegetation index data and meteorological data, the net primary productivity of each evaluation unit is calculated through a light energy utilization algorithm. Using meteorological, soil, and topographic data, the water conservation capacity of each evaluation unit is calculated based on a water balance algorithm. Using rainfall erosivity factor data, soil erodibility factor data, topographic factor data, and vegetation cover factor data, the soil retention of each evaluation unit is calculated using a soil erosion estimation algorithm. The net primary productivity, water conservation, and soil retention of each evaluation unit are summarized to generate an ecosystem service function mass dataset.

[0009] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method includes the following steps: performing time series analysis on the ecosystem service function material dataset to calculate the service function resilience index for each evaluation unit for each ecosystem service function. Extract the time series data of individual ecosystem service functions from the ecosystem service function mass quality dataset for each evaluation unit, and smooth the individual ecosystem service function mass quality time series data to obtain smoothed individual ecosystem service function mass quality time series data. The standard deviation of the time series data of the mass of individual ecosystem services is calculated to obtain the standard deviation of the time series data of the mass of individual ecosystem services. Abrupt changes were detected in the smoothed time series data of individual ecosystem service functions, and the time required for recovery from the abrupt changes to the trend level was calculated to obtain the recovery time of the time series data of individual ecosystem service functions. The reciprocal of the standard deviation of the time series data of the mass of individual ecosystem services is taken and normalized to obtain the normalized resistance component that represents resistance. The reciprocal of the recovery time of the mass time series data of individual ecosystem service functions is taken and normalized to obtain the normalized resilience component that characterizes resilience. The normalized resistance component and the normalized resilience component are weighted and combined to obtain the service function resilience index for each evaluation unit for each ecosystem service function.

[0010] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the extraction of various ecosystem service functions from a standardized multi-source data cube to form resource consumption data includes the following steps: Identify parameters related to the formation processes of various ecosystem service functions from standardized multi-source data cubes; Extract actual evapotranspiration data from parameters related to the formation processes of various ecosystem service functions. Data on photosynthetically active radiation were extracted from parameters related to the formation process of various ecosystem service functions; Actual evapotranspiration data and photosynthetically active radiation data are categorized together as resource consumption data related to the formation of various ecosystem service functions.

[0011] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method involves combining ecosystem service function material datasets with resource consumption data to calculate the service function efficiency ratio for each evaluation unit for each ecosystem service function, including the following steps: Extract water conservation data from the ecosystem service function mass dataset, and extract actual evapotranspiration data corresponding to the water conservation data from the resource consumption data; Divide the water conservation data by the actual evapotranspiration data to calculate the water conservation service function energy efficiency ratio for each evaluation unit; Net primary productivity data is extracted from the ecosystem service function mass dataset, and photosynthetically active radiation data corresponding to the net primary productivity data is extracted from the resource consumption data. The carbon sequestration service energy efficiency ratio of each evaluation unit is calculated by dividing the net primary productivity data by the photosynthetically active radiation data. The energy efficiency ratios of water conservation services and carbon sequestration services in each evaluation unit are aggregated to generate the service function energy efficiency ratio for each ecosystem service function in each evaluation unit.

[0012] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method includes the following steps: normalizing the ecosystem service function mass dataset, service function resilience index, and service function energy efficiency ratio to obtain normalized ecosystem service function mass dataset, normalized service function resilience index, and normalized service function energy efficiency ratio. The maximum and minimum values ​​of each indicator in the ecosystem service function mass quality dataset are determined by finding numerical extrema through all time series data points of all evaluation units in the dataset. The ecosystem service function mass data dataset is processed using the maximum and minimum value normalization method to obtain the normalized ecosystem service function mass data dataset. The maximum and minimum values ​​of the service function resilience index are determined by finding the extreme values ​​of the service function resilience index by traversing all evaluation units. The service function resilience index is processed using the maximum and minimum value normalization method to obtain the normalized service function resilience index. The maximum and minimum values ​​of the service function energy efficiency ratio are determined by iterating through all evaluation units to find the extreme values. The service function energy efficiency ratio is processed using the maximum and minimum value normalization method to obtain the normalized service function energy efficiency ratio.

[0013] As a preferred embodiment of the comprehensive evaluation method for forest vegetation ecosystem service functions described in this invention, the method includes the following steps: inputting the normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio into a preset comprehensive evaluation matrix model to output a forest vegetation ecosystem service function sustainability level map. The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are fused to generate a comprehensive evaluation vector for each evaluation unit. The comprehensive evaluation vector of each evaluation unit is input into the preset comprehensive sustainability evaluation matrix model, and the comprehensive evaluation vector of each evaluation unit is judged according to the preset sustainability level classification rules. Based on the assessment results, each evaluation unit is assigned a sustainability level label; The sustainability levels of all evaluation units are spatially rendered to output a sustainability level map of forest vegetation ecosystem service functions.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the comprehensive evaluation method for forest vegetation ecosystem service functions as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the comprehensive evaluation method for forest vegetation ecosystem service functions as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: by acquiring multi-source data and generating a standardized data cube, an ecosystem service function mass dataset is calculated based on this. Then, through time series analysis, a service function resilience index representing functional stability is obtained. Combined with resource consumption data, the service function energy efficiency ratio reflecting resource utilization efficiency is calculated. Finally, the mass, resilience index, and energy efficiency ratio are normalized and input into a comprehensive evaluation matrix model to generate a sustainability level map. This realizes the transformation from static assessment to a comprehensive sustainability evaluation covering stability and efficiency, and solves the problem that existing technologies cannot effectively quantify the long-term sustainability of ecosystem service functions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a comprehensive evaluation method for forest vegetation ecosystem service functions.

[0019] Figure 2 Schematic diagram for calculating the service function resilience index. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figures 1-2 As one embodiment of the present invention, this embodiment provides a comprehensive evaluation method for the ecosystem service functions of forest vegetation, comprising the following steps: S1. Obtain multi-source data of the evaluation area and preprocess it to generate a standardized multi-source data cube.

[0024] S1.1 Acquire remote sensing image data, meteorological data, topographic data, and soil data of the evaluation area to form a multi-source dataset. Perform radiometric calibration and atmospheric correction on the remote sensing image data in the multi-source dataset in sequence to obtain radiometrically calibrated and atmospherically corrected remote sensing image data. Use the radiometrically calibrated and atmospherically corrected remote sensing image data to generate cloudless remote sensing image data through the maximum value synthesis method.

[0025] Furthermore, multispectral remote sensing image data corresponding to the evaluation area within the specified time range is obtained from a designated remote sensing data platform; meteorological data such as precipitation and temperature covering the evaluation area are obtained from a meteorological observation network or reanalysis database; topographic data is obtained from a digital elevation model database; and soil type or texture data is obtained from a soil survey database. These data together constitute a multi-source dataset. The remote sensing image data in the multi-source dataset are then subjected to radiometric calibration processing in sequence, converting the original digital quantization values ​​into surface radiance or apparent reflectance. Atmospheric correction processing is then performed to eliminate the effects of atmospheric scattering and absorption. After obtaining the radiometrically calibrated and atmospherically corrected remote sensing image data, the maximum value synthesis method is used on all cloudless or slightly cloudy pixels within a set time window (e.g., one month) to select the largest vegetation index value for each pixel within the time window. This generates cloudless remote sensing image data that can effectively reflect the peak of vegetation growth and minimize the influence of clouds and their shadows.

[0026] S1.2. Convert cloudless remote sensing image data, meteorological data, topographic data and soil data to the same projection coordinate system and pixel size to obtain multi-source data with a consistent spatial reference.

[0027] Furthermore, cloudless remote sensing image data, acquired meteorological data, topographic data, and soil data are uniformly converted to a preset projection coordinate system and preset pixel size through raster resampling and projection transformation operations, so that all data layers have a completely consistent spatial reference, resulting in multi-source data with a consistent spatial reference.

[0028] S1.3. Perform spatial interpolation on meteorological data from multi-source data with a consistent spatial reference to generate a meteorological data raster layer that matches the spatial resolution of cloudless remote sensing image data.

[0029] Furthermore, for multi-source data with a consistent spatial reference, the meteorological data undergoes spatial interpolation processing, such as Kriging interpolation, based on the spatial distribution of meteorological stations and the variogram model, to generate a meteorological data raster layer that is spatially continuous and perfectly matches the spatial resolution of cloudless remote sensing image data.

[0030] S1.4 Register and overlay cloudless remote sensing image data, meteorological data raster layer, topographic data and soil data in time and space to generate a standardized multi-source data cube.

[0031] Furthermore, the cloudless remote sensing image data, meteorological data raster layer, and topographic and soil data after unified spatial reference processing are precisely spatially registered based on their unified spatial reference information to ensure that all data layers are fully aligned in space. On this basis, the above data are temporally matched according to the same time reference, and the matched cloudless remote sensing image data, meteorological data raster layer, topographic and soil data are overlaid in the spatiotemporal dimension. For each spatial pixel, the corresponding remote sensing spectral information, meteorological element information, topographic feature information and soil attribute information are integrated to generate a standardized multi-source data cube that is fully aligned in the spatiotemporal dimension and contains multi-parameter information.

[0032] S2. Based on a standardized multi-source data cube, calculate the mass of multiple ecosystem service functions in each evaluation unit within the evaluation area, and generate an ecosystem service function mass dataset.

[0033] S2.1 Extract vegetation index data, meteorological data, and soil data from the standardized multi-source data cube. Utilize the vegetation index data and meteorological data to calculate the net primary productivity of each evaluation unit using a light energy utilization algorithm.

[0034] Furthermore, reflectance data in the near-infrared and red bands are extracted from the standardized multi-source data cube, and normalized vegetation index (NDI) data is calculated. Total solar radiation energy (TSE) and temperature data are extracted as meteorological data. The NDI data is transformed using an empirical linear relationship to obtain the photosynthetically active radiation absorptivity (PARA). The temperature stress coefficient is determined by a lookup table method based on the temperature data to correct the maximum light energy utilization rate and obtain the actual light energy utilization rate. The total solar radiation energy, PARA, constant 0.5, and actual light energy utilization rate are substituted into the formula to calculate the net primary productivity of each evaluation unit.

[0035] The expression for net primary productivity is: ; in, For in position time Net primary productivity, For position time The total solar radiation energy received per unit area within the interior. For position time Internal photosynthetically active radiation absorptivity, For position time Internal actual light energy utilization rate For location index, For time indexing.

[0036] S2.2 Utilizing meteorological data, soil data, and topographic data, calculate the water conservation capacity of each evaluation unit based on the water balance algorithm.

[0037] Furthermore, precipitation data, actual evapotranspiration data, effective soil water holding capacity data, effective root depth data, and rapid runoff coefficient data are extracted from the standardized multi-source data cube; slope length factor is calculated based on digital elevation model data; soil saturated hydraulic conductivity is queried through soil texture data; the product of precipitation, soil saturated hydraulic conductivity, effective soil water holding capacity, and effective root depth is compared with precipitation, the minimum value is taken, multiplied by 1 and the difference of rapid runoff coefficient is subtracted, and then substituted into the formula to calculate the water conservation capacity of each evaluation unit.

[0038] The expression for soil water storage capacity is: ; in, For unit Soil water storage capacity For cell index, For unit Rainfall, For unit The actual evaporation rate For unit Soil effective water holding capacity, For unit The effective root depth of vegetation For unit The rapid runoff coefficient.

[0039] S2.3 Using rainfall erosivity factor data, soil erodibility factor data, topographic factor data and vegetation cover factor data, the soil retention of each evaluation unit is calculated using a soil erosion estimation algorithm.

[0040] Furthermore, rainfall erosivity factor data is generated using Kriging interpolation based on multi-year rainfall data; soil erosibility factor data is calculated based on soil survey data; slope length and slope factors are extracted using digital elevation model data; vegetation cover and management factors are estimated using normalized vegetation index data obtained from remote sensing inversion; soil and water conservation measures factors are determined according to land use type; and the difference between potential soil erosion and actual soil erosion is calculated by substituting each factor into the formula to obtain the soil conservation amount for each evaluation unit.

[0041] The expression for soil retention capacity is: ; in, For soil retention, Potential soil erosion, This represents the actual amount of soil erosion. As the erosivity factor of rainfall, As a soil erodibility factor, For slope length factor, For vegetation cover and management factors, For soil and water conservation measures, This is the slope factor.

[0042] S2.4 Summarize the net primary productivity, water conservation and soil retention of each evaluation unit to generate an ecosystem service function mass dataset.

[0043] Furthermore, the water conservation and soil retention results of the net primary productivity results are summarized according to the spatial location of the evaluation units to generate an ecosystem service function quality dataset containing three service function quality indicators: net primary productivity, water conservation, and soil retention.

[0044] S3. Perform time series analysis on the biomass dataset of ecosystem service functions to calculate the service function resilience index for each evaluation unit for each ecosystem service function.

[0045] S3.1 Extract the time series data of individual ecosystem service functions from the ecosystem service function mass quality dataset for each evaluation unit, and smooth the time series data of individual ecosystem service function mass quality to obtain smoothed time series data of individual ecosystem service function mass quality.

[0046] Furthermore, the quality values ​​of a specific ecosystem service function over several consecutive years are extracted from the ecosystem service function quality dataset according to spatial location for each evaluation unit, forming a time series data of a single ecosystem service function quality. Time series smoothing methods, such as using a three-year moving average with a window size, are then used to process the time series data of a single ecosystem service function quality to eliminate random fluctuations and highlight trends, resulting in smoothed time series data of a single ecosystem service function quality.

[0047] S3.2 Calculate the standard deviation of the time series data of the mass of individual ecosystem services after smoothing to obtain the standard deviation of the time series data of the mass of individual ecosystem services.

[0048] Furthermore, for the smoothed time series data of individual ecosystem service functions, the arithmetic mean of all data points is first calculated; then, the deviation of each data point from the arithmetic mean is calculated, each deviation is squared, the sum of all squared results is divided by the total number of data points, and finally, the square root of the quotient is taken to obtain the standard deviation of the smoothed time series data of individual ecosystem service functions.

[0049] The standard deviation expression is: ; in, The standard deviation of the time series data of individual ecosystem service functions' mass is given by the smoothing process. The length of the time series. For the first The smoothed service function material quality value at each time point This is the arithmetic mean of the smoothed service function quality time series.

[0050] S3.3 Detect abrupt changes in the smoothed time series data of individual ecosystem service functions and calculate the time required for recovery from the abrupt change to the trend level, thus obtaining the recovery time of the time series data of individual ecosystem service functions.

[0051] Furthermore, a mutation point detection algorithm is used to analyze the smoothed time series data of individual ecosystem service functions to identify the statistical mutation point occurrence time. After identifying the mutation point, a linear trend line is established based on the data before the mutation point. The time when the time series data point re-enters and stabilizes within the preset fluctuation range of the trend line after the mutation is determined as the recovery achievement time. The recovery achievement time is subtracted from the mutation point occurrence time, and the recovery duration of the individual ecosystem service function mass time series data is obtained according to the formula Trecovery=treturn-tω.

[0052] The recovery duration expression is: ; in, To restore duration, To restore the time reached, This represents the time when the mutation point occurs.

[0053] S3.4. Take the reciprocal of the standard deviation of the time series data of the mass of individual ecosystem service functions and normalize it to obtain the normalized resistance component that represents resistance.

[0054] Furthermore, the reciprocal of the standard deviation of the time series data of the mass of individual ecosystem service functions is taken to obtain the initial value of resistance; the range normalization method is used to scale the initial value to the range of 0 to 1 to obtain the normalized resistance component that characterizes resistance.

[0055] S3.5. Take the reciprocal of the recovery time of the time series data of the mass of a single ecosystem service function and normalize it to obtain the normalized resilience component that represents resilience.

[0056] Furthermore, the reciprocal of the recovery time of the mass time series data of individual ecosystem service functions is taken to obtain the initial value of resilience; the range normalization method is used to scale the initial value to the range of 0 to 1 to obtain the normalized resilience component characterizing resilience.

[0057] S3.6. The normalized resistance component and the normalized resilience component are weighted and combined to obtain the service function resilience index for each evaluation unit for each ecosystem service function.

[0058] Furthermore, the normalized resistance component and the normalized resilience component are linearly weighted and merged, with the normalized resistance component and the normalized resilience component assigned equal weights. The weighted result is used as the service function resilience index for each evaluation unit for each ecosystem service function.

[0059] S4. Extract various ecosystem service functions from the standardized multi-source data cube to form resource consumption data.

[0060] S4.1 Identify parameters related to the formation processes of various ecosystem service functions from standardized multi-source data cubes.

[0061] Furthermore, by analyzing all the data layers contained in the standardized multi-source data cube, parameters that have a direct driving or consumption relationship with the formation processes of various ecosystem service functions such as water conservation, carbon sequestration and oxygen release are identified. Specifically, these parameters include actual evapotranspiration data used for water balance calculations and photosynthetically active radiation data used for light energy utilization calculations. These parameters together constitute a set of parameters related to the formation processes of various ecosystem service functions.

[0062] S4.2 Extract actual evapotranspiration data from parameters related to the formation process of various ecosystem service functions.

[0063] Furthermore, from the set of parameters identified that are related to the formation process of various ecosystem service functions, data extraction operations are performed to separate the actual evapotranspiration data that characterizes the amount of water consumed by vegetation transpiration and soil evaporation, forming an independent data layer for subsequent energy efficiency ratio calculation.

[0064] S4.3 Extract photosynthetically active radiation data from parameters related to the formation process of various ecosystem service functions.

[0065] Furthermore, from the set of parameters identified that are related to the formation process of various ecosystem service functions, data extraction operations are performed to separate the photosynthetically active radiation data, which characterizes the portion of solar radiation that can be used by plants for photosynthesis, to form another independent data layer for subsequent energy efficiency ratio calculations.

[0066] S4.4. Collect actual evapotranspiration data and photosynthetically active radiation data together as resource consumption data related to the formation of various ecosystem service functions.

[0067] Furthermore, actual evapotranspiration data and photosynthetically active radiation data are logically categorized and merged based on their shared attribute of representing the key natural resources consumed in the process of generating ecosystem service functions, and defined as resource consumption data related to the formation of various ecosystem service functions.

[0068] S5. Combine the data set of ecosystem service function material quantity with the data on resource consumption to calculate the service function energy efficiency ratio for each ecosystem service function in each evaluation unit.

[0069] S5.1 Extract water conservation data from the ecosystem service function mass dataset, and extract actual evapotranspiration data corresponding to the water conservation data from the resource consumption data.

[0070] Furthermore, based on spatial coordinates, water conservation data for each evaluation unit is extracted from the corresponding data fields of the ecosystem service function mass dataset. Based on the same spatial coordinates, actual evapotranspiration data that spatially matches the water conservation data is extracted from the corresponding data fields of the resource consumption dataset.

[0071] S5.2 Divide the water conservation data by the actual evapotranspiration data to calculate the water conservation service function efficiency ratio of each evaluation unit.

[0072] Furthermore, the water conservation data of each evaluation unit is used as the dividend, and the actual evapotranspiration data of the same evaluation unit is used as the divisor. A pixel-by-pixel arithmetic division operation is performed to calculate the water conservation service function efficiency ratio of each evaluation unit, which represents the water conservation volume that can be generated by a unit of evapotranspiration water consumption.

[0073] S5.3 Extract net primary productivity data from the ecosystem service function mass dataset, and extract photosynthetically active radiation data corresponding to the net primary productivity data from the resource consumption data.

[0074] Furthermore, based on spatial location coordinates, net primary productivity data for each evaluation unit is extracted from the corresponding data fields of the ecosystem service function mass dataset. Based on the same spatial location coordinates, photosynthetically active radiation data that spatially matches the net primary productivity data is extracted from the corresponding data fields of the resource consumption dataset.

[0075] S5.4 Divide the net primary productivity data by the photosynthetically active radiation data to calculate the carbon sequestration service energy efficiency ratio for each evaluation unit.

[0076] Furthermore, the net primary productivity data of each evaluation unit is used as the dividend, and the photosynthetically active radiation data of the same evaluation unit is used as the divisor. A pixel-by-pixel arithmetic division operation is performed to calculate the carbon sequestration service energy efficiency ratio of each evaluation unit, which characterizes the amount of carbon that can be fixed per unit of photosynthetically active radiation energy.

[0077] The energy efficiency ratio expression for carbon sequestration services is: ; in, Energy efficiency ratio for carbon sequestration functions For net primary productivity, It is photosynthetically active radiation.

[0078] S5.5 Summarize the energy efficiency ratios of water conservation service function and carbon sequestration service function of each evaluation unit to generate the service function energy efficiency ratio for each ecosystem service function of each evaluation unit.

[0079] Furthermore, the energy efficiency ratio results of water conservation service function of all evaluation units and the energy efficiency ratio results of carbon sequestration service function of all evaluation units are arranged and integrated according to the spatial location of the evaluation units to generate a service function energy efficiency ratio dataset for each ecosystem service function of each evaluation unit, which includes two indicators: energy efficiency ratio of water conservation service function and energy efficiency ratio of carbon sequestration service function.

[0080] S6. Normalize the ecosystem service function mass dataset, service function resilience index, and service function energy efficiency ratio to obtain the normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio.

[0081] S6.1. Use the method of traversing all time series data points of all evaluation units in the ecosystem service function mass quality dataset to find numerical extrema, and determine the maximum and minimum values ​​of each indicator in the ecosystem service function mass quality dataset.

[0082] Furthermore, a global scan of the ecosystem service function mass dataset is performed, reading the net primary productivity, water conservation, and soil retention values ​​of each evaluation unit in the dataset over the entire time series. By comparing these values, the maximum and minimum values ​​of all net primary productivity, water conservation, and soil retention values ​​are identified, thereby determining the maximum and minimum values ​​of each indicator in the ecosystem service function mass dataset.

[0083] S6.2. Use the maximum and minimum value normalization method to process the ecosystem service function mass dataset to obtain the normalized ecosystem service function mass dataset.

[0084] Furthermore, for each value in the ecosystem service function mass dataset, the maximum and minimum value normalization formula is applied for processing. The formula is (current value - minimum value of the indicator) / (maximum value of the indicator - minimum value of the indicator). The net primary productivity value, water conservation value, and soil retention value are converted to the value range of 0 to 1 respectively, and the normalized ecosystem service function mass dataset is obtained.

[0085] S6.3. Use the method of traversing all evaluation units to find the extreme values ​​of the service function resilience index to determine the maximum and minimum values ​​of the service function resilience index.

[0086] Furthermore, a global scan is performed on the service function resilience index values ​​of all evaluation units, and the magnitudes of the service function resilience index values ​​of all evaluation units are compared one by one to find the maximum and minimum values ​​among all service function resilience index values, thereby determining the maximum and minimum values ​​of the service function resilience index.

[0087] S6.4. Use the maximum and minimum value normalization method to process the service function resilience index to obtain the normalized service function resilience index.

[0088] Furthermore, the service function resilience index value of each evaluation unit is processed using the maximum and minimum value normalization formula, which is (current service function resilience index value - minimum service function resilience index value) / (maximum service function resilience index value - minimum service function resilience index value). This transforms all service function resilience index values ​​into a range of 0 to 1, resulting in the normalized service function resilience index.

[0089] S6.5. Use the method of traversing all evaluation units to find the extreme values ​​of the service function energy efficiency ratio to determine the maximum and minimum values ​​of the service function energy efficiency ratio.

[0090] Furthermore, a global scan is performed on the energy efficiency ratio values ​​of all evaluation units, and the values ​​of the energy efficiency ratio values ​​of water conservation service function and carbon sequestration service function of all evaluation units are compared one by one. The maximum and minimum values ​​of the energy efficiency ratio values ​​of all water conservation service function and carbon sequestration service function are found, thereby determining the maximum and minimum values ​​of the energy efficiency ratio of service functions.

[0091] S6.6. Use the maximum and minimum value normalization method to process the service function energy efficiency ratio and obtain the normalized service function energy efficiency ratio.

[0092] Furthermore, for each value in the service function energy efficiency ratio dataset, including the energy efficiency ratio values ​​of water conservation service function and carbon sequestration service function, the maximum and minimum value normalization formula is applied respectively. The formula is (current service function energy efficiency ratio value - minimum service function energy efficiency ratio) / (maximum service function energy efficiency ratio - minimum service function energy efficiency ratio). This transforms the energy efficiency ratio values ​​of water conservation service function and carbon sequestration service function to the range of 0 to 1, respectively, to obtain the normalized service function energy efficiency ratio dataset.

[0093] S7. Input the normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio into the preset comprehensive evaluation matrix model, and output the forest vegetation ecosystem service function sustainability level map.

[0094] S7.1. The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are fused to generate a comprehensive evaluation vector for each evaluation unit.

[0095] Furthermore, the net primary productivity, water conservation, and soil retention values ​​of each evaluation unit in the normalized ecosystem service function mass dataset are combined with the normalized service function resilience index, the normalized water conservation service function energy efficiency ratio, and the normalized carbon sequestration service function energy efficiency ratio of the same evaluation unit in a preset order to generate a comprehensive evaluation vector containing multiple dimensional feature values. Each evaluation unit corresponds to a unique comprehensive evaluation vector.

[0096] S7.2 Input the comprehensive evaluation vector of each evaluation unit into the preset comprehensive sustainability evaluation matrix model, and judge the comprehensive evaluation vector of each evaluation unit according to the preset sustainability level classification rules.

[0097] Furthermore, the comprehensive evaluation vector of each evaluation unit is used as input and fed into a pre-defined comprehensive sustainability evaluation matrix model. In the comprehensive sustainability evaluation matrix model, a two-dimensional decision matrix is ​​pre-defined based on the normalized service function resilience index and the normalized service function energy efficiency ratio, and is divided into four regions: high resilience and high efficiency, high resilience and low efficiency, low resilience and high efficiency, and low resilience and low efficiency. Based on the landing region of the comprehensive evaluation vector in the two-dimensional decision matrix, fine-tuning is performed in combination with the numerical level of the normalized ecosystem service function mass dataset, and the comprehensive evaluation vector of each evaluation unit is judged according to the pre-defined sustainability level classification rules.

[0098] S7.3. Based on the judgment results, assign a sustainability level label to each evaluation unit.

[0099] Furthermore, based on the discrimination results, evaluation units that fall in high-resilience and high-efficiency areas and have higher values ​​in the normalized ecosystem service function mass data set are assigned a sustainability level label, while evaluation units that fall in low-resilience and low-efficiency areas and have lower values ​​in the normalized ecosystem service function mass data set are assigned a sustainability level label, and so on, assigning a clear sustainability level label to each evaluation unit.

[0100] S7.4. Render the sustainability level of all evaluation units spatially and output a sustainability level map of forest vegetation ecosystem service functions.

[0101] Furthermore, the spatial coordinates of each evaluation unit are associated with its assigned sustainability level identifier. Different colors are assigned to different sustainability level identifiers within the geographic information unit. All evaluation units are then color-rendered according to their sustainability level identifiers to generate a spatial distribution map, namely, a sustainability level map of forest vegetation ecosystem service functions, which intuitively displays the sustainability status of forest ecosystems in different locations within the evaluation area.

[0102] This embodiment also provides a computer device applicable to the comprehensive evaluation method of forest vegetation ecosystem service functions, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the comprehensive evaluation method of forest vegetation ecosystem service functions as proposed in the above embodiment.

[0103] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0104] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the comprehensive evaluation method for realizing the ecosystem service functions of forest vegetation as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0105] In summary, this invention achieves the following:

[0106] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A comprehensive evaluation method for the ecosystem service functions of forest vegetation, characterized in that: This includes acquiring multi-source data from the evaluation area and preprocessing it to generate a standardized multi-source data cube; Based on a standardized multi-source data cube, the biomass of multiple ecosystem service functions in each evaluation unit within the evaluation area is calculated to generate an ecosystem service function biomass dataset. Time series analysis was performed on the biomass dataset of ecosystem service functions to calculate the service function resilience index for each ecosystem service function in each evaluation unit. Extract various ecosystem service functions from a standardized multi-source data cube to form resource consumption data; By combining the physical mass dataset of ecosystem service functions with resource consumption data, the energy efficiency ratio of each ecosystem service function for each evaluation unit is calculated. The ecosystem service function mass data set, service function resilience index, and service function energy efficiency ratio were normalized to obtain the normalized ecosystem service function mass data set, the normalized service function resilience index, and the normalized service function energy efficiency ratio. The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are input into a pre-set comprehensive evaluation matrix model to output a forest vegetation ecosystem service function sustainability level map.

2. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 1, characterized in that: Acquire multi-source data for the evaluation region and preprocess it to generate a standardized multi-source data cube, including the following steps: Remote sensing image data, meteorological data, topographic data and soil data of the evaluation area are acquired to form a multi-source dataset. The remote sensing image data in the multi-source dataset are radiometrically calibrated and atmospherically corrected in sequence to obtain radiometrically calibrated and atmospherically corrected remote sensing image data. Using the radiometrically calibrated and atmospherically corrected remote sensing image data, cloudless remote sensing image data is generated by the maximum value synthesis method. Cloudless remote sensing image data, meteorological data, topographic data and soil data are uniformly converted to the same projection coordinate system and pixel size to obtain multi-source data with a consistent spatial reference. Spatial interpolation is performed on meteorological data from multi-source data with a consistent spatial reference to generate a meteorological data raster layer that matches the spatial resolution of cloudless remote sensing image data. The cloudless remote sensing image data, meteorological data raster layer, topographic data and soil data are registered and overlaid in time and space to generate a standardized multi-source data cube.

3. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 2, characterized in that: Based on a standardized multi-source data cube, the biomass of multiple ecosystem service functions in each evaluation unit within the evaluation area is calculated to generate an ecosystem service function biomass dataset, including the following steps: Vegetation index data, meteorological data, and soil data are extracted from a standardized multi-source data cube. Using the vegetation index data and meteorological data, the net primary productivity of each evaluation unit is calculated through a light energy utilization algorithm. Using meteorological, soil, and topographic data, the water conservation capacity of each evaluation unit is calculated based on a water balance algorithm. Using rainfall erosivity factor data, soil erodibility factor data, topographic factor data, and vegetation cover factor data, the soil retention of each evaluation unit is calculated using a soil erosion estimation algorithm. The net primary productivity, water conservation, and soil retention of each evaluation unit are summarized to generate an ecosystem service function mass dataset.

4. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 3, characterized in that: Time series analysis was performed on the biomass dataset of ecosystem services to calculate the service function resilience index for each ecosystem service function in each evaluation unit, including the following steps: Extract the time series data of individual ecosystem service functions from the ecosystem service function mass quality dataset for each evaluation unit, and smooth the individual ecosystem service function mass quality time series data to obtain smoothed individual ecosystem service function mass quality time series data. The standard deviation of the time series data of the mass of individual ecosystem services is calculated to obtain the standard deviation of the time series data of the mass of individual ecosystem services. Abrupt changes were detected in the smoothed time series data of individual ecosystem service functions' mass content, and the time required for recovery from the abrupt changes to the trend level was calculated to obtain the recovery time of the time series data of individual ecosystem service functions' mass content. The reciprocal of the standard deviation of the time series data of the mass of individual ecosystem services is taken and normalized to obtain the normalized resistance component that represents resistance. The reciprocal of the recovery time of the mass time series data of individual ecosystem service functions is taken and normalized to obtain the normalized resilience component that characterizes resilience. The normalized resistance component and the normalized resilience component are weighted and combined to obtain the service function resilience index for each evaluation unit for each ecosystem service function.

5. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 4, characterized in that: Extracting various ecosystem service functions from a standardized multi-source data cube to form resource consumption data includes the following steps: Identify parameters related to the formation processes of various ecosystem service functions from standardized multi-source data cubes; Extract actual evapotranspiration data from parameters related to the formation processes of various ecosystem service functions. Data on photosynthetically active radiation were extracted from parameters related to the formation process of various ecosystem service functions; Actual evapotranspiration data and photosynthetically active radiation data are categorized together as resource consumption data related to the formation of various ecosystem service functions.

6. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 5, characterized in that: By combining the biomass dataset of ecosystem services with resource consumption data, the energy efficiency ratio of each ecosystem service function for each evaluation unit is calculated, including the following steps: Extract water conservation data from the ecosystem service function mass dataset, and extract actual evapotranspiration data corresponding to the water conservation data from the resource consumption data; Divide the water conservation data by the actual evapotranspiration data to calculate the water conservation service function energy efficiency ratio for each evaluation unit; Net primary productivity data is extracted from the ecosystem service function mass dataset, and photosynthetically active radiation data corresponding to the net primary productivity data is extracted from the resource consumption data. The carbon sequestration service energy efficiency ratio of each evaluation unit is calculated by dividing the net primary productivity data by the photosynthetically active radiation data. The energy efficiency ratios of water conservation services and carbon sequestration services in each evaluation unit are aggregated to generate the service function energy efficiency ratio for each ecosystem service function in each evaluation unit.

7. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 6, characterized in that: The ecosystem service function mass data set, service function resilience index, and service function energy efficiency ratio are normalized to obtain the normalized ecosystem service function mass data set, normalized service function resilience index, and normalized service function energy efficiency ratio. This process includes the following steps: The maximum and minimum values ​​of each indicator in the ecosystem service function mass quality dataset are determined by finding numerical extrema through all time series data points of all evaluation units in the dataset. The ecosystem service function mass data dataset is processed using the maximum and minimum value normalization method to obtain the normalized ecosystem service function mass data dataset. The maximum and minimum values ​​of the service function resilience index are determined by finding the extreme values ​​of the service function resilience index by traversing all evaluation units. The service function resilience index is processed using the maximum and minimum value normalization method to obtain the normalized service function resilience index. The maximum and minimum values ​​of the service function energy efficiency ratio are determined by iterating through all evaluation units to find the extreme values. The service function energy efficiency ratio is processed using the maximum and minimum value normalization method to obtain the normalized service function energy efficiency ratio.

8. The comprehensive evaluation method for forest vegetation ecosystem service functions as described in claim 7, characterized in that, The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are input into a pre-set comprehensive evaluation matrix model to output a forest vegetation ecosystem service function sustainability level map, including the following steps: The normalized ecosystem service function mass dataset, the normalized service function resilience index, and the normalized service function energy efficiency ratio are fused to generate a comprehensive evaluation vector for each evaluation unit. The comprehensive evaluation vector of each evaluation unit is input into the preset comprehensive sustainability evaluation matrix model, and the comprehensive evaluation vector of each evaluation unit is judged according to the preset sustainability level classification rules. Based on the assessment results, each evaluation unit is assigned a sustainability level label; The sustainability levels of all evaluation units are spatially rendered to output a sustainability level map of forest vegetation ecosystem service functions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the comprehensive evaluation method for forest vegetation ecosystem service functions as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the comprehensive evaluation method for forest vegetation ecosystem service functions as described in any one of claims 1 to 8.

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