Method for calculating and predicting vegetation carbon utilization rate of regional marsh wetland

Abstract

The invention discloses a method for calculating and predicting the vegetation carbon utilization rate of a regional marsh wetland, and relates to a method for calculating and predicting the vegetation carbon utilization rate of the marsh wetland. Belongs to the field of remote sensing, geographic information and ecology. In order to solve the problem that the carbon utilization rate of different spatial and temporal scales cannot be calculated and predicted for marsh wetland vegetation, the method comprises the following steps: acquiring and preprocessing remote sensing image data and meteorological data; interpolating and re-sampling meteorological data in a research time period, extracting an unchanged marsh wetland as a research area, and calculating a marsh wetland vegetation carbon utilization rate result; extracting a marsh wetland vegetation carbon utilization rate and a meteorological element value of each pixel in the research area; constructing a marsh wetland vegetation carbon utilization rate estimation model under the influence of climate change; and predicting the vegetation carbon utilization rate of the marsh wetland in the future in combination with future climate data. The problems that the marsh vegetation carbon utilization rate of regions with different scales cannot be calculated, and the change of the marsh wetland vegetation carbon utilization rate in the region cannot be accurately predicted are solved.

Description

Technical Field

[0001] The invention relates to a method for calculating and predicting carbon utilization rate of swamp wetland vegetation. Background Art

[0002] As a very important type of wetland, swamp wetlands can not only promote water storage and purification, regulate regional climate, but also provide habitats for the reproduction of wild animals. Vegetation, as the core component of swamp wetland ecosystems, can closely link the atmosphere, hydrosphere and soil sphere. Swamp wetlands play an important role in energy exchange and biogeochemical cycles in swamp wetland ecosystems, and have strong carbon storage functions, which help to mitigate greenhouse gas emissions and climate change; and the carbon utilization rate of swamp wetland vegetation is very important in the wetland carbon cycle, which directly affects the vegetation's ability to absorb and fix CO2 in the atmosphere, thereby affecting the carbon balance of the entire swamp wetland ecosystem.

[0003] Gross primary productivity (GPP) of vegetation refers to the total amount of organic matter absorbed and transformed by all vegetation in an ecosystem through photosynthesis. The mass of organic matter remaining after the growth and maintenance metabolism of vegetation itself is called net primary productivity (NPP). GPP and NPP not only directly reflect the production capacity of vegetation under natural environmental conditions, but are also the main indicators for measuring the carbon source / sink function of ecosystems. Vegetation carbon utilization rate (NPP / GPP) reflects the efficiency of ecosystem vegetation in converting productivity into vegetation biomass stored in the ecosystem, and is an important ecological parameter in regional and global carbon cycle research. In terrestrial carbon cycle models, slight changes in NPP / GPP can cause significant changes in carbon budget assessment results. In-depth understanding of the spatiotemporal variation characteristics of vegetation productivity (GPP, NPP) and NPP / GPP and their response to climate change will help understand the carbon allocation status of marsh wetland vegetation under the background of climate change, and is of great significance for evaluating and predicting the carbon budget of regional marsh wetlands. At the same time, it provides a scientific basis for the region to respond to climate change and achieve carbon neutrality. Since traditional observation methods basically estimate photosynthesis and autotrophic respiration to calculate vegetation CUE by simplifying model structures and parameters, most of them focus on changes in vegetation CUE in specific ecosystems at global or continental scales; however, vegetation CUE is highly sensitive to environmental changes, and vegetation CUE at different spatial scales varies greatly, and the driving factors are also inconsistent. There are fewer studies on regional vegetation CUE; current methods cannot accurately calculate the carbon utilization rate of regional marsh wetland vegetation and cannot predict the carbon utilization rate of marsh wetland vegetation under future climate change scenarios, which hinders the accurate assessment and prediction of the carbon cycle of marsh wetland ecosystems. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that it is impossible to accurately calculate and predict the carbon utilization rate of regional swamp wetland vegetation, and to provide a method for calculating and predicting the carbon utilization rate of regional swamp wetland vegetation.

[0005] The calculation and prediction methods of regional marsh wetland vegetation carbon utilization rate are as follows:

[0006] Step 1: Obtain the MOD17A2HGF vegetation gross primary productivity (GPP) dataset, MOD17A3HGF vegetation net primary productivity (NPP) dataset, historical meteorological dataset, future meteorological dataset, Normalized Difference Vegetation Index (NDVI) dataset, and two periods of marsh wetland distribution data in the study period, and preprocess the data to obtain long-term high-resolution GPP dataset and NPP dataset;

[0007] Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area;

[0008] Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements;

[0009] Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area;

[0010] Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate CUE value of the marsh wetland vegetation pixel by pixel year during the study period;

[0011] The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows:

[0012] CUE t =NPP t / GPP t (1)

[0013] Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t.

[0014] The data preprocessing method described in step 1 is: splicing and reprojecting the GPP data and NPP data into the same projection and coordinate system to obtain high-resolution GPP data sets and NPP data sets of long time series.

[0015] The prediction method of carbon utilization rate of marsh wetland vegetation in the region is as follows:

[0016] Step 1: Obtain the GPP dataset, NPP dataset, historical meteorological dataset, future meteorological dataset, and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain a long-term high-resolution GPP dataset and NPP dataset;

[0017] Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area;

[0018] Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements;

[0019] Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area;

[0020] Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate CUE value of the marsh wetland vegetation pixel by pixel year during the study period;

[0021] The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows:

[0022] CUE t =NPP t / GPP t (1)

[0023] Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t

[0024] Step 6: Perform stepwise multiple regression analysis on the carbon utilization rate of wetland vegetation per pixel and per year during the study period and the meteorological element values ​​per pixel in the unchanged wetland study area to construct a pixel-by-pixel estimation model for wetland vegetation carbon utilization rate under the influence of climate change;

[0025] A pixel-by-pixel estimation model for carbon utilization rate of wetland vegetation under the influence of climate change was constructed, and the formula is as follows:

[0026] CUE=b+b1Y1+ b2Y2+ b3Y3 +…+ b x Y x (2)

[0027] In the formula, b is a constant term, b1, b2, b3…b x are regression coefficients, Y1, Y2, Y3…Y x for each meteorological element during the study period;

[0028] Step 7: Based on the pixel-by-pixel estimation model of carbon utilization rate of wetland vegetation, combined with future meteorological data sets, predict the future trend of carbon utilization rate of wetland vegetation;

[0029] The calculation formula for predicting the future trend of carbon utilization rate of swamp vegetation is as follows:

[0030]

[0031] Where CUE represents the trend of carbon utilization rate of wetland vegetation in the study period; n is the number of years in the study period; i ranges from 1 to n and is the serial number of the year in the study period; CUE i It represents the average CUE value of the i-th year. CUE>0 means that the carbon utilization rate of wetland vegetation in the time period shows an upward trend. CUE<0 means that the carbon utilization rate of wetland vegetation in the time period shows a downward trend. CUE=0 means that there is no obvious change trend in the carbon utilization rate of wetland vegetation during the study period.

[0032] The present invention proposes a method for estimating the carbon utilization of wetland vegetation by constructing a pixel-by-pixel estimation model of wetland vegetation based on remote sensing data and historical meteorological data, and predicting the carbon utilization of wetland vegetation in the future region by combining meteorological data under future climate change scenarios.

[0033] Based on long-term vegetation GPP, NPP data, meteorological element data sets and marsh wetland distribution data sets, the present invention constructs a marsh wetland vegetation carbon utilization rate estimation model under the influence of climate change through multivariate stepwise regression analysis. The estimation model of marsh wetland vegetation carbon utilization rate constructed according to the present invention and combined with meteorological data under future climate change scenarios can not only estimate the marsh wetland vegetation carbon utilization rate, but also predict the future trend of marsh wetland vegetation carbon utilization rate changes in the study area.

[0034] The advantages of the present invention are:

[0035] The use of remote sensing data and meteorological data to effectively predict future changes in the carbon utilization rate of marsh wetland vegetation in the unchanged marsh wetland study area is conducive to a deeper understanding of the growth and carbon sequestration potential of marsh wetland vegetation, and solves the problem of the inability to continuously monitor the carbon utilization rate of marsh vegetation in large-scale areas and the inability to accurately predict changes in the carbon utilization rate of regional marsh wetland vegetation.

[0036] The present invention realizes the spatial heterogeneity and intuitive and reliable analysis of vegetation carbon utilization rate, can quantitatively evaluate the changes in vegetation carbon utilization rate, and reveal the spatial distribution characteristics of the climate-driven mechanism that affects the changes in vegetation carbon utilization rate, in order to provide theoretical reference and scientific basis for the construction and protection of swamp wetland ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flow chart of the method for predicting carbon utilization rate of regional marsh wetland vegetation of the present invention;

[0038] Figure 2 This is the distribution map of unchanged marsh wetlands in Songnen Plain from 2000 to 2015 in Experiment 1;

[0039] Figure 3 This is the calculated distribution map of carbon utilization rate of marsh wetland vegetation in Songnen Plain in 2015 in Experiment 1;

[0040] Figure 4 This is the predicted distribution map of the changing trend of carbon utilization rate of marsh wetland vegetation in Songnen Plain in 2050 in Experiment 1. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is not limited to the specific implementation modes listed below, but also includes any combination of the specific implementation modes.

[0042] Specific implementation method 1: The calculation method of carbon utilization rate of regional wetland vegetation in this implementation method is as follows:

[0043] Step 1: Obtain the MOD17A2HGF vegetation gross primary productivity (GPP) dataset, MOD17A3HGF vegetation net primary productivity (NPP) dataset, historical meteorological dataset, future meteorological dataset, Normalized Difference Vegetation Index (NDVI) dataset, and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain long-term high-resolution GPP dataset and NPP dataset;

[0044] Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area;

[0045] Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements;

[0046] Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area;

[0047] Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate CUE value of the marsh wetland vegetation pixel by pixel year during the study period;

[0048] The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows:

[0049] CUE t =NPP t / GPP t (1)

[0050] Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t.

[0051] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that the data preprocessing method described in step 1 is: splicing and reprojecting the GPP data and NPP data into the same projection and coordinate system to obtain a high-resolution GPP data set and NPP data set of a long time series. The rest is the same as specific implementation method 1.

[0052] Specific implementation method three: The method for predicting carbon utilization rate of regional marsh wetland vegetation in this implementation method is as follows:

[0053] Step 1: Obtain the GPP dataset, NPP dataset, historical meteorological dataset, future meteorological dataset, and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain a long-term high-resolution GPP dataset and NPP dataset;

[0054] Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area;

[0055] Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements;

[0056] Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area;

[0057] Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate CUE value of the marsh wetland vegetation pixel by pixel year during the study period;

[0058] The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows:

[0059] CUE t =NPP t / GPP t (1)

[0060] Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP tis the GPP value in year t

[0061] Step 6: Perform stepwise multiple regression analysis on the carbon utilization rate of wetland vegetation per pixel and per year during the study period and the meteorological element values ​​per pixel in the unchanged wetland study area to construct a pixel-by-pixel estimation model for wetland vegetation carbon utilization rate under the influence of climate change;

[0062] A pixel-by-pixel estimation model for carbon utilization rate of wetland vegetation under the influence of climate change was constructed, and the formula is as follows:

[0063] CUE=b+b1Y1+ b2Y2+ b3Y3 +…+ b x Y x (2)

[0064] In the formula, b is a constant term, b1, b2, b3…b x are regression coefficients, Y1, Y2, Y3…Y x for each meteorological element during the study period;

[0065] Step 7: Based on the pixel-by-pixel estimation model of carbon utilization rate of wetland vegetation, combined with future meteorological data sets, predict the future trend of carbon utilization rate of wetland vegetation;

[0066] The calculation formula for predicting the future trend of carbon utilization rate of swamp vegetation is as follows:

[0067]

[0068] Where CUE represents the trend of carbon utilization rate of wetland vegetation in the study period; n is the number of years in the study period; i ranges from 1 to n and is the serial number of the year in the study period; CUE i It represents the average CUE value of the i-th year. CUE>0 means that the carbon utilization rate of wetland vegetation in the time period shows an upward trend. CUE<0 means that the carbon utilization rate of wetland vegetation in the time period shows a downward trend. CUE=0 means that there is no obvious change trend in the carbon utilization rate of wetland vegetation during the study period.

[0069] The following experiments were used to verify the effect of the present invention:

[0070] Experiment 1:

[0071] The Songnen Plain was selected as the implementation area. Figure 1 , the prediction method of regional marsh wetland vegetation carbon utilization rate is as follows:

[0072] (I) Obtain the MOD17A2HGF vegetation gross primary productivity (GPP) dataset, MOD17A3HGF vegetation net primary productivity (NPP) dataset, historical meteorological dataset, future meteorological dataset, Normalized Difference Vegetation Index (NDVI) dataset and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain long-term high-resolution GPP dataset and NPP dataset;

[0073] The data preprocessing method is: splicing and reprojecting the GPP data and the NPP data into the same projection and coordinate system to obtain a high-resolution GPP data set and NPP data set of a long time series.

[0074] (II) Based on the two periods of marsh wetland distribution data, the distribution area of ​​the marsh wetlands that did not change during the study period was extracted, and the pixel distribution of the NDVI ≥ 0.1 in the marsh wetland vegetation distribution area that did not change during the study period was extracted, and this was used as the unchanged marsh wetland study area. The distribution range of the unchanged marsh wetlands in the Songnen Plain from 2000 to 2015 is as follows: Figure 2 .

[0075] (III) The Kriging interpolation method is used to spatially interpolate the historical meteorological data from 2000 to 2015, and the long-term series meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset is resampled to obtain the numerical spatial distribution dataset of meteorological elements.

[0076] Obtain the 2000-2015 GPP dataset, NPP dataset, 2000 and 2015 marsh wetland distribution data, 2000-2015 meteorological element dataset, and 2021-2050 meteorological element dataset under the RCP4.5 scenario covering the Songnen Plain area. Unify the annual GPP dataset, NPP dataset, and two-period marsh wetland distribution data into the same projection and coordinate system through format conversion and reprojection.

[0077] (iv) Using the unchanged swamp wetland distribution area extracted in step 2, the numerical spatial distribution data set of meteorological elements in step 3 is clipped to extract the values ​​of each meteorological element corresponding to each pixel in the unchanged swamp wetland distribution area.

[0078] (V) Using the unchanged marsh wetland study area extracted in step 2, cut the GPP data and NPP data in step 1, extract the GPP value and NPP value of the study area pixel by pixel and year by year, and calculate the ratio of the NPP value to the GPP value CUE of each pixel and year by year to obtain the CUE of marsh wetland vegetation pixel by pixel and year by year. The calculation formula for the ratio of NPP to GPP is as follows:

[0079] CUEt=NPP t / GPP t (1)

[0080] Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t. Figure 3 This is the distribution map of carbon utilization rate of vegetation in the Songnen Plain marsh wetlands in 2015.

[0081] (VI) Perform stepwise multiple regression analysis on the carbon utilization rate of wetland vegetation per pixel and the meteorological element values ​​from 2000 to 2015, and construct a prediction model for the carbon utilization rate of wetland vegetation per pixel under the influence of climate change. The formula is as follows:

[0082] CUE=b+b1Y1+ b2Y2+ b3Y3 +…+ b x Y x (2)

[0083] In the formula, b is a constant term, b1, b2, b3…b x are regression coefficients, Y1, Y2, Y3…Y x It refers to the various meteorological elements during the study period.

[0084] (VII) Based on the pixel-by-pixel estimation model of carbon utilization rate of wetland vegetation, combined with the climate data set of 2050 under future climate change scenarios, the changing trend of carbon utilization rate of wetland vegetation in 2050 is predicted.

[0085] The calculation formula is as follows:

[0086]

[0087] Where CUE represents the trend of carbon utilization rate of wetland vegetation in the study period; n is the number of years in the study period; i ranges from 1 to n and is the serial number of the year in the study period; CUE i represents the average CUE value of the i-th year, CUE>0 means that the carbon utilization rate of wetland vegetation in the time period shows an upward trend, CUE<0 means that the carbon utilization rate of wetland vegetation in the time period shows a downward trend, and CUE=0 means that there is no obvious change trend in the carbon utilization rate of wetland vegetation during the study period. Figure 4 ).

[0088] In this experiment, remote sensing data and meteorological data were used to effectively predict the future changes in the carbon utilization rate of swamp wetland vegetation from 2021 to 2050, which is conducive to a deeper understanding of the growth and carbon sequestration capacity of swamp wetland vegetation, and solves the problem of the inability to continuously monitor the carbon utilization rate of swamp vegetation in large-scale areas and the inability to accurately predict changes in the carbon utilization rate of regional swamp wetland vegetation.

Claims

1. The calculation and prediction method of carbon utilization rate of regional wetland vegetation is characterized by The calculation method of carbon utilization rate of marsh wetland vegetation in the region is as follows: Step 1: Obtain the vegetation gross primary productivity (GPP) dataset, vegetation net primary productivity (NPP) dataset, historical meteorological dataset, future meteorological dataset, Normalized Difference Vegetation Index (NDVI) dataset, and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain long-term high-resolution GPP dataset and NPP dataset; Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area; Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements; Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area; Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate of the marsh wetland vegetation pixel by pixel year during the study period; The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows: CUE t =NPP t / GPP t (1) Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t.

2. The method for calculating and predicting carbon utilization rate of regional marsh wetland vegetation according to claim 1 is characterized in that The data preprocessing method described in step 1 is: splicing and reprojecting the GPP data and NPP data into the same projection and coordinate system to obtain high-resolution GPP data sets and NPP data sets of long time series.

3. Regional wetland vegetation carbon utilization prediction method, characterized by The prediction method of carbon utilization rate of marsh wetland vegetation in the region is as follows: Step 1: Obtain the GPP dataset, NPP dataset, historical meteorological dataset, future meteorological dataset, and two-period swamp wetland distribution data covering the study area during the study period, and preprocess the data to obtain a long-term high-resolution GPP dataset and NPP dataset; Step 2: Based on the two-period swamp wetland distribution data, extract the swamp wetland distribution area that has not changed during the study period, and extract the pixel distribution of NDVI ≥ 0.1 in each year of the swamp wetland vegetation distribution area that has not changed during the study period, and use this as the unchanged swamp wetland study area; Step 3: Use the Kriging interpolation method to interpolate the historical meteorological data, and resample the long-term meteorological element dataset with the same resolution and projection as the GPP dataset and the NPP dataset to obtain the numerical spatial distribution dataset of meteorological elements; Step 4: using the unchanged swamp wetland distribution area extracted in step 2 to clip the meteorological element numerical spatial distribution data set in step 3, and extracting the meteorological element values ​​corresponding to each pixel in the unchanged swamp wetland distribution area; Step 5: Use the unchanged marsh wetland study area extracted in step 2 to cut the long-term high-resolution GPP dataset and NPP dataset in step 1, extract the GPP value and NPP value of each pixel in the study area year by year, and calculate the ratio of the NPP value to the GPP value of each pixel year by year, and obtain the carbon utilization rate CUE value of the marsh wetland vegetation pixel by pixel year during the study period; The calculation method of the ratio of the NPP value to the GPP value per pixel per year is as follows: CUE t =NPP t / GPP t (1) Among them, t is the year, CUE t refers to the CUE value in year t, NPP t is the NPP value in year t, GPP t is the GPP value in year t Step 6: Perform stepwise multiple regression analysis on the carbon utilization rate of wetland vegetation per pixel and per year during the study period and the meteorological element values ​​per pixel in the unchanged wetland study area to construct a pixel-by-pixel estimation model for wetland vegetation carbon utilization rate under the influence of climate change; A pixel-by-pixel estimation model for carbon utilization rate of wetland vegetation under the influence of climate change was constructed, and the formula is as follows: CUE=b+b1Y1+ b2Y2+ b3Y3 +…+ b x Y x (2) Where CUE is the carbon utilization rate of wetland vegetation, b is a constant term, b1, b2, b3…b x are regression coefficients, Y1, Y2, Y3…Y x for each meteorological element during the study period; Step 7: Based on the pixel-by-pixel estimation model of carbon utilization rate of wetland vegetation, combined with future meteorological data sets, predict the future trend of carbon utilization rate of wetland vegetation; The calculation formula for predicting the future trend of carbon utilization rate of swamp vegetation is as follows: Where Trend CUE Indicates the trend of carbon utilization rate of wetland vegetation in the study period; n is the number of years in the study period; i ranges from 1 to n, which is the serial number of the year in the study period; CUE i It represents the average CUE value of the i-th year. CUE>0 means that the carbon utilization rate of wetland vegetation shows an upward trend during the study period. CUE<0 means that the carbon utilization rate of wetland vegetation shows a downward trend during the study period. CUE=0 means that there is no obvious change trend in the carbon utilization rate of wetland vegetation during the study period.

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