Forest cover change carbon income and expenditure estimation method

Abstract

The invention discloses a forest cover change carbon income and expenditure estimation method, and belongs to the technical field of forest cover change region carbon emission. According to the method, medium-high resolution vegetation carbon density spatial distribution data of a target area is obtained through a CASA model and a principle that net primary productivity and forest biomass form a positive linear relationship, and the vegetation and soil carbon density spatial distribution data is calculated by combining the CASA and a Bookkeping model based on the vegetation and soil carbon density spatial distribution data. The carbon flux estimation distribution drawing of the forest coverage change with spatial details in the regional scale is improved and realized, so that a more accurate regional carbon income and expenditure influence result of the forest coverage change is simulated. According to the method, the carbon data with the space details in the CASA model are imported into the Bookkeping model to obtain the carbon flux data with the space details caused by the change of the forest coverage, so that the problem of limitation of the existing model is solved, and scientific adjustment can be carried out on the defects of regional scale forest operation management.

Description

Technical Field

[0001] The invention belongs to the technical field of regional carbon emissions due to forest cover changes, and in particular relates to a method for estimating carbon budgets due to forest cover changes. Background Art

[0002] The study of the impact of forest cover change on carbon balance has received extensive attention. The existing technology has explored the impact of forest cover change on forest carbon storage, but the research scale is mostly global, national, provincial and other scales, and the uncertainty of estimating carbon emissions is very high. Therefore, reducing this uncertainty is a topic of great interest to the scientific community, especially quantifying the carbon balance caused by forest cover change at local and regional scales. Forest cover change affects the carbon cycle of forest ecosystems by changing the structure and function of forest ecosystems, while forest cover change also significantly changes the material production mode of various parts of the ecosystem, jointly affecting the carbon storage and release of forest ecosystems. In terms of forest cover change types, forest cover changes in forest ecosystems are mainly divided into two types: afforestation and deforestation. Deforestation directly causes a reduction in forest carbon storage through activities such as deforestation, and afforestation activities will increase carbon sinks to a certain extent. At the same time, the impact of forest cover change on forest carbon storage may present completely different results with changes in the area of ​​afforestation and deforestation. Therefore, it is necessary to accurately quantify the impact of regional-scale forest cover change on regional forest carbon storage.

[0003] Methods for estimating forest carbon budgets caused by forest cover changes include inventory methods, eddy covariance methods, and ecosystem carbon cycle models. The inventory method relies on vegetation biomass and soil carbon storage data from field sample plot surveys to assess ecosystem carbon pools. However, this method is limited by the density of sample point distribution, data representativeness, and lack of continuous long-term records, making it difficult to accurately reflect spatiotemporal changes. The eddy covariance method (EC) is a method that uses meteorological observation technology to measure the CO2 flux at a specific height by calculating the covariance of the physical quantity pulsation and the vertical wind speed pulsation, thereby assessing ecosystem productivity. However, the eddy covariance flux observation range is limited, usually less than 1 km, resulting in insufficient accuracy in regional scale carbon budget estimates. In addition, the high cost of flux tower construction limits the frequency of deployment of observation stations. Flux towers are usually located in typical areas with good vegetation and flat terrain, which makes it uncertain to expand spatially continuous carbon distribution maps on heterogeneous surfaces and complex terrains. Ecosystem carbon cycle models are divided into three categories according to modeling methods: empirical statistical models, process models, and remote sensing models (light energy utilization models). Empirical models establish statistical relationships by selecting variables related to vegetation growth (such as light and soil) and carbon cycle variables (such as productivity), and then extrapolate to regional scales in combination with spatialized data (such as topography, soil, and meteorological maps) to estimate the spatial distribution of carbon cycles, such as the Miami model developed in 1972. However, due to the complex nonlinear relationships among various environmental factors, it is difficult to establish a complete empirical model. Process models treat the soil-plant-atmosphere continuum as a whole, simulating key ecological processes such as photosynthesis, respiration, evapotranspiration, water and nutrient cycles, and environmental disturbances to estimate the productivity of terrestrial ecosystems. By combining meteorological and environmental parameters, the model can predict terrestrial ecosystem productivity on a larger regional scale and assess the impact of future climate change on the carbon budget of terrestrial ecosystems. Although process models can deeply reveal the mechanism of vegetation productivity, their complexity and difficulty in obtaining the required parameters limit their application. The light utilization efficiency model (LUE) calculates the proportion of light energy effective radiation through the vegetation index or leaf area index inverted by remote sensing, and then multiplies it by the incident photosynthetically active radiation, the maximum light energy utilization rate, and the environmental limiting factor to obtain the total primary productivity. The more mature light utilization efficiency models (LUE) include CASA, MODIS-GPP, EC-LUE, VPM and 3-PGS. This type of model uses a simple empirical relationship between vegetation index and the ratio of effective light radiation to connect process models and remote sensing data to achieve spatial continuous simulation. However, the model usually has a coarse spatial resolution and limited spatial expression of forest cover changes, resulting in significant differences between the simulated carbon storage and the observed value.

[0004] The bookkeeping model is a carbon accounting method that tracks carbon fluxes generated by land use conversion, deforestation, wildfires, and other types of land use / land cover change. The bookkeeping model provides an effective means to estimate carbon stocks and fluxes generated by land use / land cover change. However, the bookkeeping model is often used to calculate carbon estimates at national and global scales. The minimum modeling unit of the model is an ecoregion, so it is difficult to use it to estimate carbon budgets at a regional scale. At the same time, the bookkeeping model uses the same carbon density for the same land type change, so it is unable to present carbon flux data with spatial details.

[0005] In summary, a single model alone may not be able to fully present forest carbon dynamics with spatial details at the regional scale. Summary of the invention

[0006] In view of the problems existing in the prior art mentioned in the background technology, the present invention proposes a method for estimating the carbon budget of forest cover change, which combines the Bookkeeping model with the CASA model to realize forest carbon density information with complete spatial details to further explore the impact of forest cover change on forest carbon storage.

[0007] Technical solution: In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for estimating the carbon budget of forest cover change uses the CASA model and the principle that net primary productivity and forest biomass are in a positive linear relationship to obtain medium- and high-resolution spatial distribution data of vegetation carbon density in the target area. Combined with CASA and Bookkeeping models, based on the spatial distribution data of vegetation and soil carbon density, the carbon flux estimation distribution mapping of forest cover change with spatial details at the regional scale is improved to simulate more accurate regional carbon budget impact results of forest cover change.

[0009] As a preferred embodiment, the specific implementation steps are:

[0010] S1: Collect and process Landsat and MODIS NDVI remote sensing data, land cover type data, monthly average temperature data, monthly total precipitation data, monthly total solar radiation data and soil spatial distribution data;

[0011] S2: The net primary productivity of the target area is simulated based on the CASA model and land cover type data;

[0012] S3: Collect the average carbon density of each land cover type in the target area, and calculate the forest vegetation carbon density data using the positive linear relationship between net primary productivity and forest biomass;

[0013] S4: Collect average soil carbon density data in the target area, and use the product of litter productivity and litter turnover rate to calculate the change in soil organic carbon stock in the forest cover change area;

[0014] S5: Estimate and analyze the carbon budget caused by forest cover change based on the improved Bookkeeping model.

[0015] Preferably, in S1, the specific contents of collecting and processing Landsat and MODIS NDVI remote sensing data, land cover type data, monthly average temperature data, monthly total precipitation data, monthly total solar radiation data and soil spatial distribution data are as follows:

[0016] The collected NDVI data are synthesized through the high-quality reconstruction algorithm of missing data interpolation of remote sensing time series images to generate monthly NDVI time series data;

[0017] The monthly precipitation dataset with 1 km resolution and the monthly average temperature dataset with 1 km resolution in China were obtained from the National Tibetan Plateau Data Center, and then interpolated using the Kriging method to ensure that the data had a spatial resolution of 30 meters. The monthly total solar radiation data were generated from the long-sequence, high-density, and high-precision surface daily average solar radiation dataset estimated based on the sites, and the monthly total solar radiation data were interpolated using the inverse distance weighted method to ensure that the data had a spatial resolution of 30 meters.

[0018] As a preferred embodiment, in S2, the specific content of the net primary productivity of the target area simulated based on the CASA model and land cover type data is:

[0019] Firstly, the land cover type data were used to detect the forest cover changes in the target area and obtain the spatiotemporal distribution data of forest cover changes.

[0020] Then, based on the maximum light energy utilization parameters, the model parameters were adjusted to match the forests in the target area. The land cover type data, monthly NDVI time series data, monthly average temperature, monthly total precipitation and monthly total solar radiation data were imported into the CASA model to obtain the spatial distribution data of net primary productivity in the target area over the years.

[0021] Preferably, in S3, the average carbon density of each land cover type in the target area is collected and processed, and the positive linear relationship between net primary productivity and forest biomass is used to calculate the specific content of forest vegetation carbon density data:

[0022] The formula for calculating forest vegetation carbon density is as follows:

[0023]

[0024] Among them, i represents a pixel; C t represents the average carbon density of forest vegetation; N i represents the net primary productivity of pixel i; C i represents the forest vegetation carbon density of pixel i; n t Indicates the total number of pixels occupied by forests in the target area; ∑ i∈t N i Represents the total net primary productivity of forest pixels.

[0025] Preferably, in S4, the average carbon density data of soil types in the target area are collected, and the specific content of calculating the change of soil organic carbon storage in the forest cover change area by multiplying litter productivity and litter turnover rate is:

[0026] Through the litter decomposition process, a fixed first-order decomposition rate is used to multiply the litter productivity and litter turnover rate to calculate the change in soil organic carbon storage in the forest cover change area; the calculation formula for litter productivity and soil organic carbon storage change is as follows:

[0027]

[0028] ΔSOCD=ΔLitter n ×TR L ,

[0029] Where n represents the nth year of each cycle, ΔLitter n represents litter productivity in the nth year; NPP Ai and NPP Bi Refers to above-ground and below-ground net primary productivity, respectively; TR A and TR B are the turnover rates of aboveground and underground biomass, respectively; ΔSOCD is the change in soil organic carbon stock, TR L is the turnover rate of litter biomass.

[0030] As a preferred method, field data from the target area in past years are used to verify the reliability of forest vegetation carbon density simulated by the CASA model. The specific contents are as follows:

[0031] In order to verify the accuracy of the vegetation carbon density estimated from the CASA model, a statistical analysis was conducted on the correlation between the forest vegetation carbon density of past years estimated by the CASA model and the forest vegetation carbon density data measured in the small-scale survey in the same year. The sample points of the forest small-scale data in the target area were selected by random sampling, and the forest aboveground biomass at the sample points was extracted and converted into forest vegetation carbon density data by multiplying it by the forest vegetation carbon content coefficient of 0.5.

[0032] As a preferred embodiment, in S5, the specific contents of estimating and analyzing the carbon budget caused by forest cover change based on the improved Bookkeeping model are as follows:

[0033] The calculation formula for the carbon budget caused by forest cover change is as follows:

[0034]

[0035] Where M represents the carbon budget caused by forest cover change; m represents the total number of years studied; ΔSOC n represents the change in soil carbon storage in the nth year; ΔVC n represents the change in vegetation carbon storage in year n;

[0036] The calculation formulas for vegetation carbon storage changes and soil carbon storage changes caused by forest cover changes are as follows:

[0037]

[0038] Among them, i and j represent the forest cover change of land cover type i and another land type j respectively, and the conversion between the two represents the change of forest cover; A i，j，n is the area of ​​land cover type i transformed into land cover type j in year n; ΔSOC i，j,n Represents the change in soil organic carbon density when land cover type i changes to land cover type j in year n.

[0039] Preferably, the change in vegetation carbon stock caused by land cover change includes two main processes: the increase in vegetation organic carbon caused by restoration or artificial planting after the removal of native vegetation; the removed vegetation biomass is used for construction, furniture manufacturing or burned as carbon fuel, and is eventually released into the atmosphere as carbon dioxide at different oxidation rates; its calculation formula is as follows:

[0040] ΔVC n =ΔVC RS -ΔVC RM ,

[0041]

[0042] Among them, ΔVC n is the change in vegetation carbon stock after conversion to other land cover types in year n; ΔVC RS and ΔVC RM are the changes in vegetation carbon stock due to restoration and removal of biomass in year n, respectively; A i，j,n is the area of ​​land cover type i transformed into land cover type j in year n; VCD i is the vegetation carbon density change rate of land cover type i, α kis the carbon storage of vegetation and the carbon storage at x k The ratio of total carbon storage under different oxidation rates; x k represents the oxidation rate of removed vegetation in k forms; k represents the oxidation form of the removed vegetation.

[0043] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0044] (1) The present invention obtains medium- and high-resolution spatial distribution data (30 meters) of forest vegetation carbon density in the target area over the years through the CASA model and the principle that net primary productivity and forest biomass are in a positive linear relationship. The 30-meter resolution forest vegetation carbon density spatial data provided by the present invention has a higher spatiotemporal resolution than the plot-scale vegetation carbon density data or discontinuous spatial distribution data obtained by previous methods. It not only provides quantitative information on forest vegetation carbon density, but also more comprehensively reflects the spatial details and time series characteristics of the distribution of forest vegetation carbon density in the target area. At the same time, the forest vegetation carbon density data with a spatial resolution of 30 meters in the target area over the years are imported into the vegetation carbon pool of the Bookkeeping model to improve the accuracy and timeliness of the model in estimating the carbon balance dynamics of forest cover changes. The medium- and high-resolution spatial distribution data (30 meters) of forest vegetation carbon density obtained by the present invention effectively solves the problem that previous methods cannot obtain vegetation carbon density data with high resolution and regional scale spatial details.

[0045] (2) Different from the method of using static carbon density data to calculate carbon balance in the Bookkeeping model, the method of the present invention combines CASA and the Bookkeeping model, based on the spatial distribution data of vegetation and soil carbon density, to improve the carbon flux estimation distribution mapping of forest cover changes with spatial details at the regional scale, so as to simulate more accurate regional carbon balance impact results of forest cover changes. Compared with the traditional Bookkeeping model, the present invention has the following advantages:

[0046] 1. Improve data accuracy: The CASA model can use remote sensing data and meteorological data to synthesize the net primary productivity (NPP) of the target area over the years. The vegetation carbon density with spatial details is obtained through the positive linear relationship between NPP data and forest biomass, thereby more accurately reflecting the spatiotemporal changes in forest vegetation carbon density. At the same time, the NPP data is used to calculate the litter decomposition process to obtain the changes and spatial distribution of soil organic carbon storage in the forest cover change area. The carbon data obtained by introducing the CASA model can effectively improve the accuracy of the Bookkeeping model in estimating the carbon budget caused by forest cover changes.

[0047] 2. Solve the model limitation. The previous Bookkeeping model only emphasized the comparison of carbon density between different land use types and its static spatial variability, and it was impossible to obtain carbon flux data with spatial details. The present invention imports the carbon data with spatial details in the CASA model into the Bookkeeping model to obtain carbon flux data caused by forest cover changes with spatial details. This limitation is solved, and carbon flux data with spatial details are generated, which can make scientific adjustments to the deficiencies in forest management at the regional scale, and provide strong technical support for achieving the "dual carbon" goal. This greatly makes up for the shortcomings of the Bookkeeping model in regional scale application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a technical flow chart of the present invention;

[0049] Figure 2 It is a spatial distribution map of forest cover change generated by the CLCD data of the present invention, wherein (a) is a distribution map of forest cover change from 2000 to 2010; (b) is a distribution map of forest cover change in the target area from 2010 to 2022; (c) is a distribution map of forest cover change in the target area from 2000 to 2022;

[0050] Figure 3 is the spatial distribution map of net primary productivity estimated by the CASA model of the present invention, wherein A is the net primary productivity distribution map in 2000; B is the net primary productivity distribution map in 2010; C is the net primary productivity distribution map in 2022;

[0051] Figure 4 It is a statistical map of the area changes of the main land cover types in Shaoguan City, Guangdong Province of the present invention;

[0052] Figure 5 This is a comparative analysis diagram of the correlation between the measured vegetation carbon density of the small class of the present invention and the vegetation carbon density simulated by the CASA model;

[0053] Figure 6 This is the carbon flux distribution map caused by forest cover changes in Shaoguan City, Guangdong Province from 2000 to 2022 of the present invention;

[0054] Figure 7 This is a carbon budget statistical chart caused by different forest cover change types in Shaoguan City, Guangdong Province from 2000 to 2022 of the present invention. DETAILED DESCRIPTION

[0055] The present invention is further illustrated below in conjunction with specific examples. The examples are implemented based on the technical solutions of the present invention. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0056] like Figure 1 As shown, the method for estimating the carbon budget of forest cover change provided in this embodiment includes the following steps:

[0057] S1: Collect and process Landsat and MODIS NDVI remote sensing data, land cover type data, monthly average temperature data, monthly total precipitation data, monthly total solar radiation data and soil spatial distribution data. The specific contents include:

[0058] Take the forest cover change in Shaoguan City, Guangdong Province as an example:

[0059] NDVI remote sensing data: Based on the Google Earth Engine (GEE) platform, the NDVI (normalized difference vegetation index) remote sensing data of Landsat and moderate-resolution imaging spectroradiometer (MODIS) from 2000 to 2022 were obtained, and the above NDVI data were synthesized through the remote sensing time series image missing data interpolation high-quality reconstruction algorithm (GF-SG) to generate monthly scale NDVI time series data (30 meters).

[0060] Land cover type data: The data comes from the annual China Land Cover Dataset (CLCD) in 2000, 2010, and 2022 in the paper "China's 30-meter annual land cover dataset and its dynamic changes (1985-2022)" published by Yang Jie of Wuhan University. The data comes from the National Center for Glacier, Frozen Soil and Desert Science Data (http: / / www.ncdc.ac.cn). The forest cover change area in Shaoguan from 2000 to 2022 was obtained through the land use transfer matrix and the area was counted.

[0061] Table 1 CLCD land cover data classification system

[0062] CLCD Type Name Code farmland 1 forest 2 shrub 3 grassland 4 Water 5 Icefield 6 Bare Land 7 Impermeable layer 8 Wetlands 9

[0063] Table 2 Area of ​​forest cover change

[0064] 2000-2010 Area / hectare 2010-2022 Area / hectare 2000-2022 Area / hectare Forest Farmland 43639 Forest-farmland 58410 Forest-farmland 102049 Forest shrubs 293.4 Forest-Shrub 352.5 Forest shrubs 645.9 Forest meadow 144.3 Forest-Grassland 42.8 Forest meadow 187.1 Forest impermeable layer 1259.1 Forest-impermeable layer 1532.4 Forest impermeable layer 2791.5 Farmland-Forest 18223 Farmland-Forest 26618 Farmland-Forest 44841 Shrub-Forest 837 Shrub-Forest 535.2 Shrub-Forest 1372.2 Grassland Forest 141.8 Grassland-Forest 265.5 Grassland-Forest 407.3 Impervious layer - forest 1.1 Impervious layer - forest 2 Impervious layer - forest 3.1

[0065] Monthly average temperature data, monthly total precipitation data, and monthly total solar radiation data: Obtain the 1-km resolution monthly precipitation dataset (1901-2023) and the 1-km resolution monthly average temperature dataset (1901-2023) from the National Tibetan Plateau Data Center (https: / / data.tpdc.ac.cn), and generate monthly total solar radiation data based on the long sequence, high-density, and high-precision surface daily average solar radiation dataset (1960-2021) estimated by the site (the missing data in 2022 will be filled with the solar radiation data in 2021).

[0066] Then, the Kriging method is used to interpolate the monthly precipitation dataset of China with a resolution of 1 km (1901-2023) and the monthly average temperature dataset of China with a resolution of 1 km (1901-2023), and the inverse distance weighted method (IDW) is used to interpolate the monthly total solar radiation data to ensure that the data have a spatial resolution of 30 meters.

[0067] Soil spatial distribution data: Soil spatial distribution data (1:1,000,000) from the Second National Soil Survey of the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https: / / www.resdc.cn / ) were converted to a spatial resolution of 30 meters by converting vectors to raster.

[0068] S2: The spatial distribution data of net primary productivity (NPP) in the target area are obtained based on the CASA model and land cover type data;

[0069] First, the annual China Land Cover Dataset (CLCD) was used to detect forest cover changes in the target area to obtain the spatiotemporal distribution data of forest cover changes. Secondly, the land cover type data (CLCD), monthly NDVI time series data (30 meters), monthly average temperature (30 meters), monthly total precipitation (30 meters) and monthly total solar radiation (30 meters) were imported into the CASA model to obtain the spatial distribution data of net primary productivity (NPP) in the target area (Shaoguan City from 2000 to 2022).

[0070] Based on the sorted and processed data, the optimal light energy utilization rate adjustment model parameters proposed by Zhu Wenquan's team were used to make it most suitable for the forests in the target area, and then imported into the improved CASA model of Zhu Wenquan's team to obtain the spatial distribution data of the net primary productivity (NPP) of the target area over the years. The optimal light energy utilization rate adjustment model parameters and the improved CASA model are derived from the paper "Remote Sensing Estimation of Net Primary Productivity of Terrestrial Vegetation in China" published by Zhu Wenquan's team in the Acta Ecologica Sinica. The paper contains data for the optimal light energy utilization rate model parameters and the improved CASA model.

[0071] Table 3 Maximum light energy utilization parameters of CASA model

[0072] Land cover type Maximum light energy utilization farmland 0.415 forest 0.985 shrub 0.429 grassland 0.389 Water 0 Bare Land 0.389 Impermeable layer 0.389

[0073] S3: Collect the average carbon density of each land cover type in the target area, and calculate the forest vegetation carbon density data using the positive linear relationship between net primary productivity and forest biomass;

[0074] Based on the collected literature data, the average carbon density of each land cover type in the target area (Shaoguan) was processed, including soil carbon density and vegetation carbon density data. Vegetation carbon density comes from the paper entitled "Carbon Emissions from Land-Use Change and Management in China between 1990 and 2010" published by Professor Huang Xianjin of Nanjing University and the research paper entitled "Spatial and Temporal Changes of Carbon Storage in the Yellow River Basin Based on InVEST and CA-Markov Model" published by Yang Jie.

[0075] Table 4 Average vegetation carbon density of each land cover type

[0076]

[0077] Based on the principle that forest NPP and forest biomass are in a positive linear relationship, as proposed in the paper “A Global Forest Growing Stock, Biomass and Carbon Map Based on FAO Statistics” published by Kindermann et al., the carbon density distribution of Shaoguan forest vegetation was calculated using the average carbon density data of Shaoguan forests. The carbon density distribution map of Shaoguan vegetation was obtained by combining it with the average carbon density of other land types.

[0078] The formula for calculating forest vegetation carbon density is as follows:

[0079]

[0080] Among them, i represents a pixel; C t represents the average carbon density of forest vegetation; N i represents the net primary productivity (NPP) of pixel i; C i represents the forest vegetation carbon density of pixel i; n t Indicates the total number of pixels occupied by forests in the target area; Σ i∈t N i Represents the total net primary productivity of forest pixels.

[0081] S4: Collect the average carbon density data of soil types in the target area, and calculate the change of soil organic carbon stock in the forest cover change area by multiplying litter productivity and litter turnover rate;

[0082] Calculate soil carbon density: The average carbon density data of Shaoguan soil types (0-20 cm) were obtained from the literature and integrated with the spatial distribution data of the corresponding soil types. The literature data on soil carbon density comes from a paper jointly published by Yan Xiaoyuan of Nanjing Soil Research Institute and Professor Cai Zucong of Nanjing University entitled "Number of soil profiles needed to give areliable overall estimate of soil organic carbon storage using profile carbon density data".

[0083] Table 5 Average soil carbon density of each soil type

[0084]

[0085] Through the litter decomposition process, a fixed first-order decomposition rate is used to multiply the litter productivity and litter turnover rate to calculate the change in soil organic carbon storage in the forest cover change area. The calculation formula for litter productivity and soil organic carbon storage change is as follows:

[0086]

[0087] ΔSOCD=ΔLitter n ×TR L ,

[0088] Where n represents the nth year of each cycle, ΔLitter n Indicates litter productivity in year n. NPP Ai and NPP BiRefers to above-ground and below-ground net primary productivity (NPP) respectively. A and TR B are the turnover rates of aboveground and belowground biomass, respectively. NPP of grassland and impermeable layer Ai With NPP Bi The ratio is estimated to be 1:3, with forest and shrub NPP Ai With NPP Bi The ratio of ΔSOCD to ΔSOCD was estimated to be 4:1. The harvest coefficient for cropland was 0.3. In addition, the root turnover rate for all vegetation types was calculated to be 0.15 per year. For grassland, cropland, and impervious layer, the turnover rate of aboveground biomass was 1.0 per year, while for forest, it was 0.10 per year. Where, ΔSOCD is the change in soil organic carbon stock, TR L is the turnover rate of litter biomass.

[0089] The reliability of the forest vegetation carbon density simulated by the CASA model was verified through data from the 2020 Shaoguan small class survey (i.e. the second category of forest resources survey).

[0090] The 2020 Shaoguan small class data comes from the Guangdong Provincial Forestry Survey and Planning Institute. In order to verify the accuracy of the vegetation carbon density estimated based on the CASA model, the model-estimated Shaoguan forest vegetation carbon density in 2020 was compared with the forest vegetation carbon density data measured in the small class survey in the same year (the sample points of the 2020 small class survey data in Shaoguan area were selected by random sampling, and the forest aboveground biomass at the sample points was extracted and multiplied by the forest vegetation carbon content coefficient of 0.5 to convert it into forest vegetation carbon density data). The results show that there is a significant correlation between the 2020 forest vegetation carbon density estimated by the NPP obtained by the CASA model and the actual forest vegetation field carbon density value measured in the same year, R 2 The value is 0.64 (see Figure 5 ). This shows that the forest vegetation carbon density estimated by NPP based on the CASA model has a good fit and high correlation with the measured forest vegetation carbon density value, confirming that the carbon density value estimated based on the CASA model is suitable for subsequent analysis.

[0091] S5: Estimation and analysis of carbon budget caused by forest cover change based on the improved Bookkeeping model;

[0092] The bookkeeping model is improved and driven by combining the soil carbon density and vegetation carbon density change data of the forest cover change area to obtain the carbon budget caused by forest cover change. The forest cover change types are divided into two parts: afforestation and deforestation, and the carbon budget (carbon loss, carbon sequestration) caused by forest cover change is analyzed, and the impact of afforestation and deforestation on carbon budget is compared.

[0093] The carbon budget caused by forest cover change from 2000 to 2022 is estimated based on the Bookkeeping model. The calculation formula is as follows:

[0094]

[0095] The bookkeeping model is divided into two carbon pools: vegetation and soil. M represents the carbon budget caused by forest cover change; m represents the total number of years studied; ΔSOC n represents the change in soil carbon storage in the nth year; ΔVC n Represents the change in vegetation carbon storage in year n.

[0096] Table 6 Bookkeeping model parameters

[0097] Land Type forest shrub grassland Impermeable layer Carbon content of dead vegetation in soil at the time of removal 33 50 100 49 Proportions of different oxidation rates of vegetation after removal 1 year 40 40 51 10 years 20 10 100 years 7 The proportion of soil organic carbon that increases rapidly after cleaning (%) 16 16 16 23.15 Years required for rapid increase in soil organic carbon after cleanup 15 15 15 5 Maximum reduction in soil organic carbon after cleaning (%) 20 4 4 28.94 Years required to reduce soil organic carbon to maximum value after cleanup 45 45 45 10

[0098] Table 7 Definition of forest cover change types

[0099]

[0100] The calculation formulas for vegetation carbon storage changes and soil carbon storage changes caused by forest cover changes are as follows:

[0101]

[0102] Among them, ΔSOC n represents the change of soil carbon storage in the nth year; i and j represent land cover type i and another land cover type j respectively, and the conversion between the two represents the change of forest cover; A i，j，n is the area of ​​land cover type i transformed into another land cover type j in year n; ΔSOCD i，j，n It represents the change of soil organic carbon density when land cover type i changes to another land cover type j in year n.

[0103] The change in vegetation carbon stock caused by land cover change includes two main processes: the increase in vegetation organic carbon caused by restoration or artificial planting after the removal of native vegetation; the removed vegetation biomass may be used for construction, furniture manufacturing or burned as carbon fuel, and eventually released into the atmosphere as carbon dioxide at different oxidation rates; its calculation formula is as follows:

[0104] ΔVC n =ΔVC RS -ΔVC RM ,

[0105]

[0106] Among them, ΔVC nis the change in vegetation carbon stock converted to other land types in year n; ΔVC RS and ΔVC RM are the changes in vegetation carbon stock due to restoration and removal of biomass in year n, respectively; A i，j，n VCD is the area where land cover type i changes to another land cover type j in year n; i is the vegetation carbon density change rate of land type i, α k is the carbon storage of vegetation and the carbon storage at x k The ratio of total carbon storage at different oxidation rates. k represents the oxidation rate of removed vegetation in k forms; k represents the oxidation form of removed vegetation, including burning of fuelwood (1 year), oxidation of paper (10 years), and oxidation of building materials and furniture (100 years).

[0107] S5: Results and analysis;

[0108] like Figure 2 As shown in (a) of Figure 1, the spatiotemporal dynamics of forest cover change:

[0109] The CLCD data were used to identify the spatial distribution areas of forest cover changes in Shaoguan City, Guangdong Province from 2000 to 2022 through the land use transfer matrix and to count the area.

[0110] The results showed that the forest type in Shaoguan changed by a total of 165,707.6 hectares, accounting for about 9% of the total area. During the period 2000-2022, the forest area in Shaoguan decreased by 59,049.9 hectares. In contrast, the area of ​​farmland and impervious layer increased by 46,946.6 hectares and 13,868.5 hectares, respectively. The analysis of the forest cover change area in Shaoguan showed that during the period 2000-2022, the forest area in Shaoguan decreased (such as Figure 4 The forest area in Shaoguan is mainly driven by farmland expansion, which accounts for 96.6% of the total forest area loss in Shaoguan. Analysis of the impervious layer in Shaoguan shows that the impervious layer area in Shaoguan has increased and the level of urbanization has increased. At the same time, the area of ​​afforestation in Shaoguan has shown an increasing trend, which is confirmed by the Forestry Statistical Yearbook.

[0111] like Figure 2 As shown in (b) of Figure 1, the carbon budget caused by forest cover change is:

[0112] In order to verify the accuracy of the vegetation carbon density estimated by the CASA model, a statistical analysis of the correlation between the forest vegetation carbon density simulated by the CASA model and the forest vegetation carbon density data measured in the small-scale survey in Shaoguan in 2020 was conducted. The results showed that there was a significant relationship between the vegetation carbon density estimated by the NPP results of the CASA model and the carbon density value of the small-scale survey. 2is 0.64 (e.g. Figure 5 This shows that the vegetation carbon density estimated by the model is well-fitted with the measured vegetation carbon density value and has a high correlation, which confirms the reliability of the vegetation carbon density value obtained by the CASA model.

[0113] like Figure 6 , 7 As shown in Figure 2, the carbon balance caused by forest cover change is estimated based on the Bookkeeping model. The results show that from 2000 to 2022, the carbon balance caused by forest cover change in Shaoguan City is generally a net carbon loss, and the forest cover change in Shaoguan has caused a carbon loss of 1.293 TgC. The carbon flux caused by forest cover change in Shaoguan City shows significant spatial differences at the regional scale (see Figure 6 ). The carbon flux of Shaoguan is mainly distributed in the western, central, northeastern and southern regions, and the distribution is relatively dense. Specifically, the central and western regions mainly show carbon loss. The northeastern region presents carbon sequestration. In the southern region, there is both carbon loss and carbon sequestration. Carbon loss and carbon sequestration were monitored in the northwest and southern regions. In order to study the carbon flux caused by different types of forest cover changes, two main land use activity trajectories were defined based on forest changes, including afforestation and deforestation activities (Table 7), and the carbon balance caused by afforestation and deforestation was counted. The results showed that afforestation in Shaoguan led to a net accumulation of carbon in vegetation biomass and soil, resulting in an increase of 1.509 TgC in Shaoguan forest carbon storage. Since the deforestation area in Shaoguan exceeded the afforestation area, the total carbon loss caused by deforestation was 2.802 TgC, which far exceeded the increase in carbon storage brought about by afforestation ( Figure 7 ).

[0114] In summary, during the period 2000-2022, the afforestation area in Shaoguan City was always smaller than the deforestation area. Even though afforestation can offset the carbon loss caused by deforestation to a certain extent, the forests in Shaoguan City still showed a net carbon loss. This shows that in order to achieve the "dual carbon" goal, Shaoguan still needs to carry out sustainable forest management.

[0115] The present invention proposes an improved method for estimating the carbon budget of forest cover change by combining CASA and Bookkeeping models. This method not only obtains the spatial distribution of forest carbon density through the CASA model, but also estimates the response of forest carbon dynamics to forest cover change through the Bookkeeping model, providing a scientific methodology for evaluating the impact of forest cover change on regional forest carbon dynamics. This method can also be applied to estimating the carbon budget caused by land use / cover change.

[0116] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for estimating the carbon budget of forest cover change, characterized by: Through the CASA model and the positive linear relationship between net primary productivity and forest biomass, medium- and high-resolution spatial distribution data of vegetation carbon density in the target area are obtained. Combined with CASA and Bookkeeping models, based on the spatial distribution data of vegetation and soil carbon density, carbon flux estimation and distribution mapping of forest cover changes with spatial details at the regional scale is achieved to simulate more accurate regional carbon budget impact results of forest cover changes.

2. The method for estimating the carbon budget of forest cover change according to claim 1, characterized in that: The specific implementation steps are: S1: Collect and process Landsat and MODIS NDVI remote sensing data, land cover type data, monthly average temperature data, monthly total precipitation data, monthly total solar radiation data and soil spatial distribution data; S2: The net primary productivity of the target area is simulated based on the CASA model and land cover type data; S3: Collect the average carbon density of each land type in the target area, and calculate the forest vegetation carbon density data using the positive linear relationship between net primary productivity and forest biomass; S4: Collect average soil carbon density data in the target area, and use the product of litter productivity and litter turnover rate to calculate the change in soil organic carbon stock in the forest cover change area; S5: Estimate and analyze the carbon budget caused by forest cover change based on the improved Bookkeeping model.

3. The method for estimating the carbon budget of forest cover change according to claim 2, characterized in that: In S1, the specific contents of collecting and processing Landsat and MODIS NDVI remote sensing data, land cover type data, monthly average temperature data, monthly total precipitation data, monthly total solar radiation data and soil spatial distribution data are as follows: The collected NDVI data are synthesized through the high-quality reconstruction algorithm of missing data interpolation of remote sensing time series images to generate monthly NDVI time series data; The monthly precipitation dataset with 1 km resolution and the monthly average temperature dataset with 1 km resolution in China were obtained from the National Tibetan Plateau Data Center, and then interpolated using the Kriging method to ensure that the data had a spatial resolution of 30 meters. The monthly total solar radiation data were generated from the long-sequence, high-density, and high-precision surface daily average solar radiation dataset estimated based on the site, and the monthly total solar radiation data were interpolated using the inverse distance weighted method to ensure that the data had a spatial resolution of 30 meters.

4. The method for estimating the carbon budget of forest cover change according to claim 2, characterized in that: In S2, the specific contents of the net primary productivity of the target area simulated based on the CASA model and land cover type data are as follows: Firstly, the land cover type data were used to detect the forest cover changes in the target area and obtain the spatiotemporal distribution data of forest cover changes. Then, based on the maximum light energy utilization parameters, the model parameters were adjusted to match the forests in the target area. The land cover type data, monthly NDVI time series data, monthly average temperature, monthly total precipitation and monthly total solar radiation data were imported into the CASA model to obtain the spatial distribution data of net primary productivity in the target area over the years.

5. The method for estimating the carbon budget of forest cover change according to claim 2, characterized in that: In S3, the average carbon density of each land type in the target area is collected and processed, and the positive linear relationship between net primary productivity and forest biomass is used to calculate the specific content of forest vegetation carbon density data: The formula for calculating forest vegetation carbon density is as follows: Among them, i represents a pixel; C t represents the average carbon density of forest vegetation; N i represents the net primary productivity of pixel i; C i represents the forest vegetation carbon density of pixel i; n t Indicates the total number of pixels occupied by forests in the target area; ∑ i∈t N i Represents the total net primary productivity of forest pixels.

6. The method for estimating the carbon budget of forest cover change according to claim 2, characterized in that: In S4, the average carbon density data of soil types in the target area are collected, and the changes in soil organic carbon stocks in the forest cover change area are calculated by multiplying litter productivity and litter turnover rate. Through the litter decomposition process, a fixed first-order decomposition rate is used to multiply the litter productivity and litter turnover rate to calculate the change in soil organic carbon storage in the forest cover change area; the calculation formula for litter productivity and soil organic carbon storage change is as follows: △SOCD=△Litter n ×TR L , Where n represents the nth year of each cycle, ΔLitter n represents litter productivity in the nth year; NPP Ai and NPP Bi Refers to above-ground and below-ground net primary productivity, respectively; TR A and TR B are the turnover rates of aboveground and underground biomass, respectively; ΔSOCD is the change in soil organic carbon stock, TR L is the turnover rate of litter biomass.

7. The method for estimating the carbon budget of forest cover change according to claim 6, characterized in that: The reliability of forest vegetation carbon density simulated by the CASA model was verified using field data from the target area in past years. The specific contents are as follows: In order to verify the accuracy of the vegetation carbon density estimated from the CASA model, a statistical analysis was conducted on the correlation between the forest vegetation carbon density of past years estimated by the CASA model and the forest vegetation carbon density data measured in the small-scale survey in the same year. The sample points of the forest small-scale data in the target area were selected by random sampling, and the forest aboveground biomass at the sample points was extracted and converted into forest vegetation carbon density data by multiplying it by the forest vegetation carbon content coefficient of 0.

5.

8. The method for estimating the carbon budget of forest cover change according to claim 2, characterized in that: In S5, the specific contents of estimating and analyzing the carbon budget caused by forest cover change based on the improved Bookkeeping model are as follows: The calculation formula for the carbon budget caused by forest cover change is as follows: Where M represents the carbon budget caused by forest cover change; m represents the total number of years studied; ΔSOC n represents the change in soil carbon storage in the nth year; ΔVC n represents the change in vegetation carbon storage in year n; The calculation formulas for vegetation carbon storage changes and soil carbon storage changes caused by forest cover changes are as follows: Among them, ΔSOC n A represents the change in soil carbon storage in the nth year, i and j represent land cover type i and another land cover type j respectively, and the conversion between the two represents the change in forest cover type; A i，j，n is the area of ​​land cover type i transformed into land cover type j in year n; ΔSOCD i，j，n Represents the change in soil organic carbon density when land cover type i changes to land cover type j in year n.

9. The method for estimating the carbon budget of forest cover change according to claim 8, characterized in that: Changes in vegetation carbon stocks caused by land cover change include two main processes: the increase of vegetation organic carbon through restoration or artificial planting after the removal of native vegetation; the removed vegetation biomass is used for construction, furniture manufacturing or burned as carbon fuel, and is eventually released into the atmosphere as carbon dioxide at different oxidation rates; The calculation formula is as follows: △VC n =△VC RS -△VC RM , Among them, ΔVC n is the change in vegetation carbon storage when a certain land cover type is converted to another land cover type in year n; ΔVC RS and ΔVC RM are the changes in vegetation carbon stock due to restoration and removal of biomass in year n, respectively; A i，j，n is the area of ​​land cover type i transformed into land cover type j in year n; VCD i is the vegetation carbon density change rate of land cover type i, α k is the carbon storage of vegetation and the carbon storage at x k The ratio of total carbon storage under different oxidation rates; x k represents the oxidation rate of removed vegetation in k forms; k represents the oxidation form of the removed vegetation.

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