Urban forest vegetation carbon reserve status assessment and prediction method

Abstract

A current situation evaluation and prediction method for urban forest vegetation carbon reserves comprises the steps of obtaining global built-up area data, administrative division data of a target city, remote sensing image data of land coverage over the years, actually measured meteorological data over the years and monthly average prediction data of future meteorology, performing preprocessing, extracting a built-up area range of the target city, and performing prediction on the built-up area range of the target city; generating a forest vegetation coverage distribution map, and calculating total newly-added forest vegetation data Sum (m, j) in a built-up area range; based on the actually measured meteorological data over the years, calculating the biomass Bt of a newly added forest vegetation coverage range in a built-up area range; and constructing a carbon sequestration process model, calculating the total newly-added forest vegetation carbon density Cdensity (m, j) in the built-up area range, and obtaining the current situation and prediction data of the total newly-added forest vegetation carbon reserve Cstore (m, a) in the built-up area range. According to the method, the current situation evaluation and prediction are performed on the carbon reserve of the target city by integrating the processes of land cover change and growth mechanism over the years, and the dynamic evaluation and prediction of the carbon reserve of the newly-added urban forest are realized.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring technology, specifically to a method for assessing and predicting the current status of carbon storage in urban forest vegetation. Background Technology

[0002] Cities, as the most densely populated areas of human activity and energy consumption, are major sources of greenhouse gas emissions, but they also nurture important ecosystems—urban forests. Urban forests, hailed as the "lungs of the city," improve local urban climate, mitigate the heat island effect, purify the air, and are also a potentially huge "urban carbon sink." Compared to natural forests, urban forests are characterized by high spatial heterogeneity, fragmented patch distribution, complex tree species composition, and dense anthropogenic disturbance. This poses a significant challenge to traditional carbon inventory estimation methods based on sample plot surveys. While traditional methods offer high accuracy, they are typically time-consuming, labor-intensive, costly, and somewhat destructive, making it difficult to achieve large-scale, high-frequency continuous monitoring of vast, dynamically changing urban forests. Developing efficient, accurate, and repeatable technologies for monitoring and predicting urban forest carbon inventory has become a critical scientific problem urgently needing to be solved in the interdisciplinary fields of ecology, forestry, and urban science.

[0003] In recent years, remote sensing technology, with its advantages of macroscopic, rapid, and dynamic observation, has become the mainstream technique for estimating forest carbon storage. Domestic and international scholars have conducted extensive research and achieved significant progress in remote sensing assessment and potential prediction of urban forest carbon storage. The core of remote sensing carbon storage estimation lies in establishing a quantitative relationship between remote sensing information and vegetation biomass (which is then converted into carbon storage). Many scholars have used optical remote sensing data, lidar data, and combined multi-source fusion and machine learning methods to calculate forest vegetation carbon storage. Based on the current status assessment, predicting future carbon sink potential is crucial for achieving forward-looking urban planning and management. However, most studies only provide macroscopic predictions of forest carbon sink potential at the national scale. Few studies have integrated long-term high-resolution land cover change data with growth mechanism-based process models to dynamically predict the carbon sink potential of newly added urban forests in specific provinces.

[0004] For example, patent document CN120833559A discloses a method for monitoring and assessing forest carbon storage and carbon sink based on multi-source remote sensing technology, including the following steps: multi-source remote sensing data acquisition: acquiring optical remote sensing data and radar remote sensing data respectively; tree species classification and identification: classifying forests in the study area by tree species to extract spatial distribution information at the tree species scale; calculating vegetation biomass: for different tree species, establishing a tree species-based biomass estimation model based on biomass model parameters obtained from field surveys, with biomass model parameters as input parameters and vegetation biomass as output; calculating carbon storage: using the retrieved vegetation biomass and combining it with the carbon content coefficients of different tree species, calculating vegetation carbon storage; estimating soil carbon storage, and finally obtaining the total carbon storage of the forest ecosystem.

[0005] For example, patent document CN120278404A discloses a method and system for measuring urban green space carbon sink based on multi-source data fusion. The method includes: collecting and preprocessing multi-source data of urban green space; using a dual-channel generative adversarial network to complete the parameters of vegetation in building-shaded blind spots, obtaining complete leaf area index (LAI) distribution data; using a ray tracing algorithm to simulate the reflected light path of building glass curtain walls, obtaining the photosynthetically active radiation correction coefficient received by the vegetation canopy, and identifying urban green space carbon sink distribution data through multi-scale data fusion; obtaining carbon sink error based on the urban green space carbon sink distribution data, and dynamically correcting it using a Bayesian optimization algorithm to generate an urban green space carbon sink measurement report. This invention achieves high-precision spatial continuity reconstruction of LAI in building-shaded blind spots through a dual-channel generative adversarial network and a collaborative training mechanism between a U-Net generator and a PatchGAN discriminator.

[0006] Existing technologies do not consider integrating long-term high-resolution land cover change data with growth mechanism-based processes to dynamically predict the carbon sequestration potential of newly added urban forests. Therefore, this application aims to fill this gap by proposing a technical solution for remote sensing assessment and prediction of carbon storage status that integrates long-term high-resolution land cover change data with growth mechanism-based processes. Summary of the Invention

[0007] To address the problem that existing technologies rely on integrating long-term high-resolution land cover change data with growth mechanism-based processes to predict carbon sequestration potential, this invention provides a method for assessing the current status of carbon storage in urban forest vegetation, comprising the following steps: Acquire global built-up area data, administrative division data of the target city, remote sensing imagery data of land cover over the years, and measured meteorological data over the years; the measured meteorological data over the years includes the annual average temperature over the years. and rainfall ; Based on global built-up area data and target city administrative division data, extract the built-up area of ​​the target city; Based on the built-up area, the remote sensing image data of land cover over the years are preprocessed to extract pixel values. Generate a map showing the distribution of forest vegetation cover and calculate the total newly added forest vegetation data within the built-up area. ; wherein, the pixel value This is data on forest vegetation categories; Based on the historical meteorological data, the maximum biomass within the built-up area was calculated. Intrinsic growth rate and the biomass of newly added forest vegetation cover ; Initial carbon sequestration parameters are set, and a carbon sequestration process model is constructed. The initial carbon sequestration parameters include tree age. and initial vegetation biomass The carbon sequestration process model is used to obtain the total carbon density of newly added forest vegetation within the built-up area. Where m represents the name of the built-up area, and j represents the year in which new forest vegetation was added; Combined with the newly added forest vegetation data Calculate the total newly added forest vegetation carbon storage within the built-up area. Based on the current status data, an assessment of the current status of carbon storage in urban forest vegetation is achieved. The calculation expression is as follows: Where a represents the current year, b represents the starting year of the historical data, and b≤j≤a.

[0008] Furthermore, the preprocessing of the remote sensing image data of land cover over the years includes the following: Using the built-up area as the mask area, the remote sensing image data of land cover over the years are masked and cropped according to a specified period. Based on the classification criteria of remote sensing images, extract the pixel values ​​of forest vegetation categories. Record forest vegetation as 1 and background as 0 to generate a forest vegetation distribution map; By performing interpolation on forest vegetation distribution maps of adjacent periods, the pixel values ​​representing the changes in forest vegetation distribution between adjacent periods can be obtained. The expression is: The changes in forest vegetation distribution include newly added forest vegetation and reduced forest vegetation; where i represents the current period number and i-1 represents the previous period number.

[0009] Furthermore, the classification criteria for the remote sensing images include nine categories, specifically: 1 is cultivated land, 2 is forest land, 3 is shrubland, 4 is grassland, 5 is water body, 6 is snow or glacier, 7 is bare land, 8 is impermeable layer, and 9 is wetland.

[0010] Furthermore, the total newly added forest vegetation data within the built-up area. The calculation process includes the following: Based on the pixel values ​​of the forest vegetation distribution changes in adjacent periods Extract the newly added forest vegetation area; By performing the interpolation calculation multiple times at different periods, the pixel values ​​representing the distribution changes of forest vegetation cover at different periods are obtained. The expression is ; Extract the extent of newly added forest vegetation cover that has been present in the built-up area. The expression is = Where e is the total number of mask clipping cycles, 1≤i≤e; The data is processed by rasterization to obtain the count of newly added forest vegetation in each zone within the built-up area. The total number of newly added forest vegetation within the built-up area is then calculated. ; ; in, The area of ​​newly added forest vegetation in year j within the built-up area of ​​city m is represented by ; n is the number of built-up areas within the target city, n≥1; count represents the number of newly added forest vegetation in each zone.

[0011] Furthermore, the maximum biomass and intrinsic growth rate Through the annual average temperature and rainfall Calculation and acquisition; The maximum biomass The calculation expression is: ; The intrinsic growth rate The calculation expression is: ; Where MAT represents the annual average temperature, MAT = MAP represents the average annual rainfall, MAP = .

[0012] Furthermore, the biomass of the newly added forest vegetation cover The calculation expression is: ; in, The vegetation biomass at time t (t*hm) is the biomass of the tree at age t. -2 ); t0 represents the maximum biomass; t0 represents the initial tree age.

[0013] Furthermore, the expression for the carbon fixation process model is as follows: ; in, This represents the biomass of the built-up area of ​​city m in year j.

[0014] This invention also provides a method for predicting carbon storage in urban forest vegetation, based on the aforementioned method for assessing the current status of carbon storage in urban forest vegetation, and includes the following: Obtain monthly average weather forecasts and biomass of forest vegetation cover in the built-up area of ​​the target city. Current status data; The monthly average forecast data of future weather conditions are preprocessed to calculate the annual average temperature of future weather conditions. and average annual rainfall ; Set the tree age t corresponding to the predicted year, and combine it with the average annual temperature of the future weather. and average annual rainfall The total newly added forest vegetation carbon storage within the built-up area of ​​the target city was calculated using the aforementioned method for assessing the current status of urban forest vegetation carbon storage. Based on the predicted data, obtain the potential value of carbon storage within the built-up area of ​​the target city; Specifically, the historical measured meteorological data in the current status assessment method for urban forest vegetation carbon storage are replaced with the monthly average forecast data for future meteorological conditions; the initial vegetation biomass is... Replace with the biomass mentioned above Current status data.

[0015] Furthermore, the preprocessing of the monthly average forecast data for future weather includes: standardizing the temperature unit and the annual average rainfall unit; the temperature unit is standardized to degrees Celsius; the rainfall unit is standardized to millimeters per day.

[0016] Furthermore, the annual average temperature The calculation expression is: ; The average annual rainfall The calculation expression is: ; Where y represents the year; w is the number of raster cells; The pixel value for each raster unit; is the pixel value for each raster unit; day is the total number of days in year y.

[0017] The beneficial effects of this invention are as follows: By efficiently integrating multi-source data, including global built-up area data, administrative division data of target cities, remote sensing image data of land cover over the years, measured meteorological data over the years, and monthly average forecast data of future weather, this invention breaks down data barriers and achieves organic integration and collaborative analysis of various types of data. It fully utilizes the advantages of different data to provide a rich and comprehensive information foundation for carbon storage assessment. By fully considering various factors such as meteorological conditions, vegetation growth patterns, soil characteristics, and human activities, a carbon sequestration process model is constructed, making the model closer to the actual ecological process. This significantly improves the accuracy and reliability of the assessment results and effectively avoids assessment bias caused by single factors or simplified models. Attached Figure Description

[0018] Figure 1 This is a flowchart of the current status assessment method for urban forest vegetation carbon storage provided by the present invention; Figure 2 This is a schematic diagram of the extracted built-up area of ​​the target city provided by the present invention; Figure 3 This is a schematic diagram of the mask cutting result provided by the present invention; Figure 4 This is a schematic diagram of the forest vegetation extraction results provided by the present invention; Figure 5 This is a schematic diagram illustrating an example of extracting built-up areas from forest vegetation changes provided by the present invention; Figure 6 This is a schematic diagram of the extraction results of newly added forest vegetation in the built-up area provided by the present invention; Figure 7 This invention provides a statistical calculation process for the built-up area zoning of target cities. Figure 8 This is a schematic diagram of the current status of vegetation carbon density in the built-up area of ​​the target city provided by the present invention; Figure 9 This is a schematic diagram of the current status of carbon storage in the built-up area of ​​the target city provided by the present invention; Figure 10 This is a schematic diagram of the predicted carbon density of urban built-up areas provided by the present invention; Figure 11 This is a schematic diagram of the predicted carbon storage potential of urban built-up areas provided by the present invention. Detailed Implementation

[0019] The technical solution of the present invention is further described below, but the scope of protection is not limited to what is described.

[0020] This invention provides a method for assessing the current status of carbon storage in urban forest vegetation, such as... Figure 1 As shown, it includes the following steps: Step S100: Acquire global built-up area data, administrative division data of the target city, remote sensing image data of land cover over the years, and measured meteorological data over the years; the measured meteorological data over the years includes the annual average temperature over the years. and rainfall ; Specifically: The global built-up area data is the Global Urban Boundary (GUB) dataset, which is mainly based on the Global Artificial Impervious Area (GAIA) data (30 meters high resolution) and developed using the Google Earth Engine platform. The global built-up area data is used to define the scope of urban forests. The administrative division data of the target city includes the provincial, municipal, and county-level administrative division data of the target city, which is used to clarify the administrative boundaries and regional scope. The remote sensing image data of land cover over the years is selected from multi-temporal, high-resolution remote sensing image data. The specified time interval is determined according to the needs, such as one period every five years. If the data is less than five years, the data of the latest year is used. For example, the remote sensing image data of 30 m resolution land cover in China from 1985 to 2023 comes from the annual land cover data of 30 m resolution published by Professors Yang Jie and Huang Xin of Wuhan University.

[0021] The remote sensing image data includes optical remote sensing data and hyperspectral remote sensing data. The optical remote sensing data provides surface spectral information and spatial distribution characteristics, such as data from satellites like Landsat and Sentinel-2. The hyperspectral remote sensing data provides richer spectral information, which helps to more accurately identify forest types and vegetation status.

[0022] Preprocessing remote sensing images, including radiometric correction, geometric correction, and atmospheric correction, eliminates noise and errors, improves image quality, and provides a reliable data foundation for subsequent forest vegetation extraction.

[0023] The historical meteorological data includes key meteorological elements such as temperature and precipitation, providing environmental background information for studying the relationship between forest vegetation growth and carbon storage. Data sources include meteorological station observation data, statistical yearbooks, or existing meteorological databases. For forecasting future meteorological data, internationally recognized climate model data, such as the climate model datasets recommended by the IPCC, are selected. The meteorological data is interpolated to unify it to the same spatial resolution as the geospatial data, and indicators such as annual average temperature and annual average precipitation are calculated to provide necessary meteorological parameters for calculating forest vegetation biomass.

[0024] If we take the urban area of ​​Guizhou Province as the target city, we can obtain the annual average temperature for each year before 2025 based on meteorological station observation data, statistical yearbooks, or existing meteorological databases. and rainfall Meteorological data after 2025 comes from the IPCC climate model data CMIP6 BCC-CSM2-MR_ssp245 monthly average forecast data, including monthly average temperature and precipitation data. The file includes simulated meteorological data from 2015 to 2100, totaling 1032 months of monthly average data.

[0025] Step S200: Extract the built-up area of ​​the target city based on global built-up area data and target city administrative division data; Specifically: Before extraction, the global built-up area data and the target city administrative division data are preprocessed, including format conversion, coordinate unification, and error correction, to ensure data consistency and accuracy.

[0026] By utilizing global built-up area data and target city administrative division data, spatial analysis tools are used to accurately extract the urban built-up area and administrative boundaries of the target city. For complex geographical areas, multi-level buffer analysis or overlay analysis methods can be employed to ensure the accuracy of the study area definition.

[0027] By linking the target city's administrative name, code, area, and other attribute information to the corresponding geospatial data, geographic location and attribute identification are provided for subsequent data analysis and result display.

[0028] For example, extracting the urban built-up area of ​​Guizhou Province: Based on the prefecture-level cities established in Guizhou Province, the built-up areas of Guiyang, Liupanshui, Zunyi, Anshun, Bijie, Tongren, Qianxinan, Qiandongnan, and Qiannan prefectures are extracted. Given the administrative divisions of Guizhou Province, the maximum and minimum values ​​of the inflection points of the administrative boundaries are obtained. Based on the coordinate range, the vectors of all built-up areas within this range are extracted. Then, the positional relationship between the built-up area and the prefecture-level administrative divisions of Guizhou Province is determined. If both the inflection points and edges of the built-up area are contained within the range of a certain administrative division, then the attribute of that administrative division is attached. The calculation process is as follows: 1) Data preparation: Built-up area polygon: composed of inflection point coordinate series P= Let z be the inflection point number.

[0029] Set of administrative division polygons: Each administrative division consists of polygons express.

[0030] 2) Determine if the target polygon P is contained within the administrative division. The condition is: all vertices are in Interior; edges of P and The edges have no intersection points.

[0031] 3) Determine the key calculation steps: Use the ray casting method to determine if a point is inside the polygon, including: For each inflection point of P, determine whether it is in internal: from Starting from here, emit a ray L in a horizontal direction to the right: y = ;for Each edge Calculate the intersection point with ray L, first parameterizing the edge as follows: , [0,1]. If the edge is perpendicular ( Skip this step; otherwise, solve the equation y= Obtain the intersection parameters If t [0,1] and the x-coordinate of the intersection point is x= + ≥ If the number of intersections is odd, then the count is incremented by 1; if the number of intersections is odd, then... exist internal.

[0032] 4) Perform edge intersection detection: Check the edges of P and of Do the edges intersect? Parameterize two line segments: (1) , Solve the equation a(s)=b(t) to obtain s and t.

[0033] like , If the two lines intersect, then they will meet.

[0034] (2) 5) Determine the complete inclusion condition: If the following two conditions are met, P : Including all vertices: Unintersecting boundaries: In summary, by linking the built-up areas of each city in Guizhou Province with their administrative division attributes, the extracted results are as follows: Figure 2 As shown.

[0035] Step S300: Based on the built-up area, preprocess the remote sensing image data of land cover over the years to extract pixel values. Generate a map showing the distribution of forest vegetation cover and calculate the total newly added forest vegetation data within the built-up area. ; wherein, the pixel value This is data on forest vegetation categories; Preprocessing of the remote sensing image data of land cover over the years includes the following: Using the built-up area as the mask area, the remote sensing image data of land cover over the years for a specified period are masked and cropped; the specified period is 5 years. Based on the classification criteria of remote sensing images, extract the pixel values ​​of forest vegetation categories. Record forest vegetation as 1 and background as 0 to generate a forest vegetation distribution map; By performing interpolation on forest vegetation distribution maps of adjacent periods, the pixel values ​​representing the changes in forest vegetation distribution between adjacent periods can be obtained. The expression is: (3) Where i represents the current cycle number, and i-1 represents the previous cycle number; The changes in forest vegetation distribution include both newly added and reduced forest vegetation.

[0036] The classification criteria for the remote sensing images include nine categories: 1. Farmland, 2. Forestland, 3. Shrubland, 4. Grassland, 5. Water bodies, 6. Snow or glaciers, 7. Bare land, 8. Impermeable layer, and 9. Wetland.

[0037] For example: Step 1: Data cropping; Using the built-up areas of each city in Guizhou Province extracted in step S200 as the mask area, mask clipping was performed on nine scenes of data from 1985 to 2023, with each scene representing a five-year period. The clipping results are as follows. Figure 3 As shown, the mask cutting steps are as follows: Coordinate unification. Ensure that vector polygons and raster data have the same spatial reference frame.

[0038] (4) in,( , ) represents the coordinates in the target coordinate system, and a, b, c, d, e, f are the affine transformation parameters.

[0039] Vector polygon rasterization. Convert the vector polygon into a binary raster mask, where the pixel value inside the polygon is 1 and the pixel value outside is 0.

[0040] Determine the clipping region. Based on the minimum bounding rectangle of the vector polygon, determine the range of row and column numbers for the raster data: Line range: (5) Column range: (6) in,( , () represents the coordinates of the top-left corner of the raster, and cellsize is the cell size, which is equal to 30. , , , The minimum and maximum values ​​of the minimum bounding rectangle of the vector polygon.

[0041] Raster data cropping. For each pixel within the cropping area, check if it lies within the vector polygon (via the raster mask): Output pixel value = (7) Step 2: Extracting forest vegetation; The remote sensing images are classified into nine categories: 1-arable land, 2-forest land, 3-shrubland, 4-grassland, 5-water body, 6-snow / glacier, 7-bare land, 8-impermeable layer, and 9-wetland. Based on cropping, the forest vegetation area is extracted, with a pixel value of 2 recorded as 1 and a background value recorded as 0. The extraction results are as follows: Figure 4 As shown.

[0042] (8) Where Value is the pixel value, and i is the data year, i=[1985, 1990, ..., 2020, 2023].

[0043] Step 3: Extract changes in forest vegetation; The forest vegetation change results are obtained by subtracting the previous period's results from the forest vegetation results extracted in the second step. 1 represents newly added forest vegetation (green), 0 represents background value (gray), and -1 represents reduced forest vegetation (red). An example result of extracting forest vegetation changes from built-up areas is shown below. Figure 5 As shown.

[0044] The newly added forest vegetation area in each 5-year period was extracted separately. A pixel value of 1 indicates newly added forest vegetation, and 0 indicates background value; the area of ​​forest vegetation reduction is extracted separately every 5 years. Pixel value This indicates a reduction in forest vegetation; 0 represents a background value.

[0045] The total newly added forest vegetation data within the built-up area. The calculation process includes the following: Based on the pixel values ​​of the forest vegetation distribution changes in adjacent periods Extract the newly added forest vegetation area; By performing the interpolation calculation multiple times at different periods, the pixel values ​​representing the distribution changes of forest vegetation cover at different periods are obtained. The expression is （9） Extracting pixel values ​​of newly added forest vegetation cover that has been present in the built-up area. The expression is: = (10) Where e is the total number of mask clipping cycles, 1≤i≤e; The data is processed by rasterization to obtain the count of newly added forest vegetation in each zone within the built-up area. The total number of newly added forest vegetation within the built-up area is then calculated. ; (11) in, The area of ​​newly added forest vegetation in year j within the built-up area of ​​city m is represented by ; n is the number of built-up areas within the target city, n≥1; count represents the number of newly added forest vegetation in each zone.

[0046] The extraction of continuously existing newly added forest vegetation coverage within the built-up area is achieved, for example, by overlaying newly added forest vegetation from a certain period with forest vegetation distribution maps from subsequent periods. If a pixel value is identified as newly added forest vegetation in that period and remains forest vegetation in all subsequent periods' classification products, it is determined to be continuously existing newly added forest vegetation; conversely, if it changes during a certain period, it is excluded. This method is used to calculate continuously existing newly added forest vegetation for each period.

[0047] Taking 1990 as an example, we first subtract the newly added forest vegetation results from 1985 from 1990 and then sum the forest vegetation results from 1995, 2000, 2005, 2010, 2015, 2020, and 2023. We then extract the cells with a pixel value of 8, which represent newly added forest vegetation that has existed since 1990. We continue to calculate the newly added forest vegetation that has existed in 1995, 2000, 2005, 2010, 2015, 2020, and 2023.

[0048] Taking newly added forest vegetation after 2015 as an example, we first need to obtain the newly added forest vegetation from 2015 to 2020, and then add the data of the current status of forest vegetation in 2023. We then calculate 0 - background value (gray), 1 - forest vegetation that disappeared midway (red), and 2 - newly added forest vegetation that has always existed (green). An example of the results for extracting newly added forest vegetation from built-up areas is shown below. Figure 6 As shown.

[0049] After calculating the extent of newly added forest vegetation, it is necessary to... Maximum value of the calculation result Extracting the continuously existing newly added vegetation range yields the results. ; =[2, 3, 4, 5, 6, 7, 8].

[0050] Using the urban built-up area of ​​Guizhou Province as the statistical unit, the number of newly added forest vegetation raster units since 1990 was counted for each period, and then converted to hectares. First, the urban built-up area of ​​Guizhou Province was divided into raster units of 30m size according to remote sensing classification products, with the same upper left corner (…). , The data is rasterized, and the values ​​of the raster cells within the raster area are summed to obtain the newly added forest vegetation data within the built-up area. The statistical calculation process for urban built-up area zoning is as follows: Figure 7 As shown.

[0051] After the calculation is completed, the total amount of newly added forest vegetation in the built-up areas of each city needs to be summarized according to formula (11).

[0052] Step S400: Based on the historical measured meteorological data, calculate the maximum biomass within the built-up area. Intrinsic growth rate and the biomass of newly added forest vegetation cover ; The maximum biomass and intrinsic growth rate Through the annual average temperature and rainfall Calculation and acquisition; The maximum biomass The calculation expression is: (12) The intrinsic growth rate The calculation expression is: (13) Where MAT represents the annual average temperature, MAT = MAP represents the average annual rainfall, MAP = .

[0053] Taking 2005 data as an example, the calculation results are shown in Table 1: Table 1

[0054] The biomass of the newly added forest vegetation cover The calculation expression is: (14) in, The vegetation biomass at time t (t×hm) is the biomass of the trees at age t. -2 ); t is the maximum biomass; t0 is the initial tree age; when t= hour, = .

[0055] Step S500: Set initial carbon sequestration parameters and construct a carbon sequestration process model, wherein the initial carbon sequestration parameters include tree age. and initial vegetation biomass The carbon sequestration process model is used to obtain the total carbon density of newly added forest vegetation within the built-up area. ; Where m represents the name of the built-up area, and j represents the year in which the forest vegetation was newly added. This represents the carbon density of the built-up area of ​​city m in year j. Setting the tree age t=5, initial vegetation biomass =5t×hm -2 The total newly added forest vegetation carbon density within the built-up area The calculation expression is: (15) in, Let represent the biomass of the built-up area of ​​city m in year j; 'a' represents the current year, and 'b' represents the starting year of historical data, where b ≤ j ≤ a.

[0056] For example, to calculate the carbon density of urban built-up areas in 2005, it is the average of the carbon density of newly added vegetation in 2005 plus the cumulative carbon density of newly added forest vegetation from 1990, 1995, and 2000 up to 2005. The current urban built-up area density calculated using cities in Guizhou Province as the target urban area is as follows. Figure 8 As shown, the horizontal axis represents the administrative region code of each city, and the vertical axis represents the carbon density.

[0057] Step S600, combining the newly added forest vegetation data Calculate the total newly added forest vegetation carbon storage within the built-up area. Based on the current status data, an assessment of the current status of carbon storage in urban forest vegetation is achieved. The calculation expression is as follows: (16) For example, to calculate the carbon storage of urban built-up areas in 2005, it is the sum of the carbon storage of newly added vegetation in 2005 and the cumulative carbon storage of newly added forest vegetation in 1990, 1995, and 2000 up to 2005. The current status of urban built-up area carbon storage calculated using cities in Guizhou Province as the target urban area is as follows: Figure 9 As shown, the horizontal axis represents the administrative region code of each city, and the vertical axis represents carbon storage.

[0058] This invention also provides a method for predicting carbon storage in urban forest vegetation, based on the aforementioned method for assessing the current status of carbon storage in urban forest vegetation. The prediction method includes the following: Obtain monthly average weather forecasts and biomass of forest vegetation cover in the built-up area of ​​the target city. Current status data; The monthly average forecast data of future weather conditions are preprocessed to calculate the annual average temperature of future weather conditions. and average annual rainfall ; Set the tree age t corresponding to the predicted year, and combine it with the average annual temperature of the future weather. and average annual rainfall The total newly added forest vegetation carbon storage within the built-up area of ​​the target city was calculated using the aforementioned method for assessing the current status of urban forest vegetation carbon storage. Based on the predicted data, obtain the potential value of carbon storage within the built-up area of ​​the target city; Specifically, the historical measured meteorological data in the current status assessment method for urban forest vegetation carbon storage is replaced with the monthly average forecast data of future meteorological conditions, and the initial vegetation biomass is adjusted accordingly. Replace with the biomass mentioned above Current status data.

[0059] In this embodiment, the preprocessing of the monthly average forecast data of future weather includes: unifying the temperature unit and the annual average rainfall unit; the temperature unit is unified to degrees Celsius; the rainfall unit is unified to millimeters per day.

[0060] For example, future meteorological data after 2025 comes from the monthly average forecast data of IPCC climate model CMIP6 BCC-CSM2-MR_ssp245. Since the temperature data is in Kelvin and the rainfall data is in kg / m² / s, the temperature unit is mainly unified to degrees Celsius and the annual average rainfall unit is unified to millimeters / day.

[0061] The temperature conversion relationship is as follows: (17) The conversion relationship of rainfall is as follows: (18) in, Let be the Kelvin temperature in year i.

[0062] The average annual temperature The calculation expression is: (19) The average annual rainfall The calculation expression is: (20) Where y represents the year; n is the number of raster cells; The pixel value for each raster unit; This represents the pixel value for each raster unit; day represents the total number of days in year y. The monthly average forecast data for future weather are derived from meteorological product data, which is sourced from the Earth System Grid Consortium. MRI-ESM2-0, as a climate model within CMIP6, has participated in the study of multiple SSP scenarios, including SSP119, SSP126, SSP245, SSP370, and SSP585. SSP119 is a combination of SSP1 (Sustainable Development Pathway) and RCP1.9 (Low Radiation Forcing Scenario), representing the lowest radiation emission scenario currently available in CMIP6, with a radiation forcing of approximately 1.9 W / m² by 2100. SSP126 is a combination of SSP1 (Sustainable Development Pathway) and RCP2.6 (Low Emission Scenario), with a radiation forcing of 2.6 W / m² by 2100. SSP245 is a combination of SSP2 (Intermediate Pathway) and RCP4.5 (Medium Emission Scenario), with a radiation forcing of 4.5 W / m² by 2100. SSP370 is a combination of SSP3 (regional competition pathway) and the newly added RCP7.0 emission pathway, with radiative forcing reaching 7.0 W / m² by 2100. SSP585 is a combination of SSP5 (conventional fossil fuel pathway) and RCP8.5 (high emission scenario), with radiative forcing reaching 8.5 W / m² by 2100. Multiple prediction models were set up, incorporating different meteorological product study scenarios. Initial vegetation biomass parameters for 2023 were assessed based on the current situation. Annual mean temperature and rainfall were calculated using different scenarios, and tree age was considered to predict biomass, carbon density, and carbon sequestration. Using SSP245 as an example, the predicted carbon density for urban built-up areas is shown below. Figure 10 As shown, the predicted results of carbon storage potential in urban built-up areas are as follows: Figure 11 As shown.

[0063] The above-disclosed embodiments are merely specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A method for assessing the current status of carbon storage in urban forest vegetation, characterized in that, Includes the following steps: Acquire global built-up area data, administrative division data of the target city, remote sensing imagery data of land cover over the years, and measured meteorological data over the years; the measured meteorological data over the years includes the annual average temperature (Avg) over the years. t(y) and rainfall Avg r(y) ; Based on global built-up area data and target city administrative division data, extract the built-up area of ​​the target city; Based on the built-up area, the remote sensing image data of land cover over the years are preprocessed to extract pixel values. i Generate a forest vegetation cover distribution map and calculate the total newly added forest vegetation data (Sum) within the built-up area. （m,j） ; wherein, the cell value Value i This is data on forest vegetation categories; Based on the historical meteorological data, the maximum biomass B within the built-up area was calculated. max The intrinsic growth rate V0 and the biomass of newly added forest vegetation cover B t ; Initial carbon sequestration parameters are set, and a carbon sequestration process model is constructed. The initial carbon sequestration parameters include tree age. and initial vegetation biomass B t0 The carbon sequestration process model is used to obtain the total carbon density C of newly added forest vegetation within the built-up area. density(m,j) Where m represents the name of the built-up area, and j represents the year in which new forest vegetation was added; Combined with the newly added forest vegetation data Sum （m,j） Calculate the total newly added forest vegetation carbon storage C within the built-up area. storage(m,a) Based on the current status data, an assessment of the current status of carbon storage in urban forest vegetation is achieved. The calculation expression is as follows: ; Where a represents the current year, b represents the starting year of the historical data, and b≤j≤a.

2. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 1, characterized in that, Preprocessing of the remote sensing image data of land cover over the years includes the following: Using the built-up area as the mask area, the remote sensing image data of land cover over the years are masked and cropped according to a specified period. Based on the classification criteria of remote sensing images, extract the pixel values ​​of forest vegetation categories. Record forest vegetation as 1 and background as 0 to generate a forest vegetation distribution map; By performing interpolation on forest vegetation distribution maps of adjacent periods, the pixel values ​​representing the changes in forest vegetation distribution between adjacent periods can be obtained. The expression is: The changes in forest vegetation distribution include newly added forest vegetation and reduced forest vegetation; where i represents the current period number and i-1 represents the previous period number.

3. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 2, characterized in that, The classification criteria for the remote sensing images include nine categories:

1. Farmland, 2. Forestland, 3. Shrubland, 4. Grassland, 5. Water bodies, 6. Snow or glaciers, 7. Bare land, 8. Impermeable layer, and 9. Wetland.

4. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 3, characterized in that, The total newly added forest vegetation data within the built-up area. The calculation process includes the following: Based on the pixel values ​​of the forest vegetation distribution changes in adjacent periods Extract the newly added forest vegetation area; By performing the interpolation calculation multiple times at different periods, the pixel values ​​representing the distribution changes of forest vegetation cover at different periods are obtained. The expression is Where e is the total number of mask clipping cycles, 1≤i≤e; Extracting pixel values ​​of newly added forest vegetation cover that has been present in the built-up area. The expression is = ; The data is processed by rasterization to obtain the count of newly added forest vegetation in each zone within the built-up area. The total number of newly added forest vegetation within the built-up area is then calculated. ; ； in, The area of ​​newly added forest vegetation in year j within the built-up area of ​​city m is represented by ; n is the number of built-up areas within the target city, n≥1; count represents the number of newly added forest vegetation in each zone.

5. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 1, characterized in that, The maximum biomass and intrinsic growth rate Through the annual average temperature and rainfall Calculation and acquisition; The maximum biomass The calculation expression is: ; The intrinsic growth rate The calculation expression is: ； Where MAT represents the annual average temperature, MAT = MAP represents the average annual rainfall, MAP = .

6. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 5, characterized in that, The biomass of the newly added forest vegetation cover The calculation expression is: ； in, The vegetation biomass at time t (t×hm) is the biomass of the trees at age t. -2 ); t0 represents the maximum biomass; t0 represents the initial tree age.

7. The method for assessing the current status of urban forest vegetation carbon storage as described in claim 6, characterized in that, The expression for the carbon fixation process model is as follows: ； in, This represents the biomass of the built-up area of ​​city m in year j.

8. A method for predicting carbon storage in urban forest vegetation, characterized in that, The method for assessing the current status of urban forest vegetation carbon storage as described in any one of claims 1-7 includes the following: Obtain monthly average weather forecasts and biomass of forest vegetation cover in the built-up area of ​​the target city. Current status data; The monthly average forecast data of future weather conditions are preprocessed to calculate the annual average temperature of future weather conditions. and average annual rainfall ; Set the tree age t corresponding to the predicted year, and combine it with the average annual temperature of the future weather. and average annual rainfall The total newly added forest vegetation carbon storage within the built-up area of ​​the target city was calculated using the aforementioned method for assessing the current status of urban forest vegetation carbon storage. Based on the predicted data, obtain the potential value of carbon storage within the built-up area of ​​the target city; Specifically, the historical measured meteorological data in the current status assessment method for urban forest vegetation carbon storage is replaced with the monthly average forecast data of future meteorological conditions, and the initial vegetation biomass is adjusted accordingly. Replace with the biomass mentioned above Current status data.

9. The method for predicting carbon storage in urban forest vegetation as described in claim 8, characterized in that, The preprocessing of the monthly average forecast data for future weather includes: standardizing the temperature unit and the annual average rainfall unit; the temperature unit is standardized to degrees Celsius; the rainfall unit is standardized to millimeters per day.

10. The method for predicting carbon storage in urban forest vegetation as described in claim 9, characterized in that, The average annual temperature The calculation expression is: ； The average annual rainfall The calculation expression is: ； Where y represents the year; w is the number of raster cells; The pixel value for each raster unit; is the pixel value for each raster unit; day is the total number of days in year y.

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